tag:blogger.com,1999:blog-227705022024-03-23T06:15:08.522-04:00GIS and Agent-Based ModelingThis is a research site focused around my interests in Geographical Information Science (GIS) and Agent-Based Modeling (ABM).Unknownnoreply@blogger.comBlogger503125tag:blogger.com,1999:blog-22770502.post-15828872911665241832023-12-19T17:24:00.000-05:002023-12-19T17:24:02.652-05:00Crowdsourcing Dust Storms in the United States Utilizing Flickr<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg_PLBVP6nlFZ6DBImfScjveZhPJ8rV4nDttZEtTb0jUXniD4BX6ikdPhKoeXPJXlQZegin4VYLgd_eCuJVG14ViMWpRjJasH4OSSZ47JeT-rkNI2n7aOX0RbiRWDYV2y5a4yt9rYBUW3QUzWbsEjAU0PTnyDsnVwfak88-wHtCDR5s8os7Pup4/s2995/IMG_6754.jpg" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" data-original-height="2995" data-original-width="2745" height="200" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg_PLBVP6nlFZ6DBImfScjveZhPJ8rV4nDttZEtTb0jUXniD4BX6ikdPhKoeXPJXlQZegin4VYLgd_eCuJVG14ViMWpRjJasH4OSSZ47JeT-rkNI2n7aOX0RbiRWDYV2y5a4yt9rYBUW3QUzWbsEjAU0PTnyDsnVwfak88-wHtCDR5s8os7Pup4/w183-h200/IMG_6754.jpg" width="183" /></a></div><div style="text-align: justify;">In the past on this site we have written about how one can use <a href="https://www.gisagents.org/search/label/Social%20media" target="_blank">social media</a> to study the world around us. Often the focus has been on Twitter but that is not the only social media platform available. Another is <a href="https://www.gisagents.org/search/label/Flickr" target="_blank">Flickr</a>, and while in past posts have show how we can use this platform to explore <a href="https://www.gisagents.org/2020/01/new-paper-insights-into-human-wildlife.html" target="_blank">bird sightings</a>, <a href="https://www.gisagents.org/2016/03/accuracy-of-image-tagging-in-flickr.html">wildfires</a> and <a href="https://www.gisagents.org/2018/09/exodus-20-crowdsourcing-geographical.html" target="_blank">human migration</a> we are now turning our attention to other phenomena. One of which is dust storms. Working with <a href="https://www.buffalo.edu/cas/geography/graduate-program/meet-our-students/festus-adegbola.html" target="_blank">Festus Adegbola</a> and <a href="https://www.buffalo.edu/cas/geography/faculty/faculty_directory/stuart-m-evans.html" target="_blank">Stuart Evans</a> we have just presented a poster at the <a href="https://www.agu.org/fall-meeting" target="_blank">2023 A</a><span style="text-align: justify;"><a href="https://www.agu.org/fall-meeting" target="_blank">merican Geophysical Union Fall Meeting</a> entitled "</span><i>Crowdsourcing Dust Storms in the United States Utilizing Flickr</i>"</div><div style="text-align: justify;"><br /></div><div style="text-align: justify;">In this research we compare Flickr images with <a href="https://mesonet.agron.iastate.edu/" target="_blank">National Weather Service advisories</a> and the <a href="https://www.earthdata.nasa.gov/news/v2-deep-blue-aerosol-products" target="_blank">VIIRS Deep Blue aerosol product data</a> from the Suomi-NPP satellite. Our preliminary findings show that Flickr images of dust storms have a substantial co-occurrence with regions of NWS blowing dust advisories. If this sounds of interest, below you can read our abstract, see our workflow and the poster itself. </div><div><p><b>Abstract</b></p><p></p><blockquote style="text-align: justify;">Dust storms are natural phenomena characterized by strong winds carrying large amounts of fine particles which have significant environmental and human impacts. Previous studies have limitations due to available data, especially regarding short-lived, intense dust storms that are not captured by observing stations and satellite instruments. In recent years, the advent of social media platforms has provided a unique opportunity to access a vast amount of user-generated data. This research explores the utilization of Flickr data to study dust storm occurrences within the United States and their correlation with National Weather Service (NWS) advisories. The work ascertains the reliability of using crowdsourced data as a supplementary tool for dust storm monitoring. Our analysis of Flickr metadata indicates that the Southwest is most susceptible to dust storm events, with Arizona leading in the highest number of occurrences. On the other hand, the Great Plains show a scarcity of Flickr data related to dust storms, which can be attributed to the sparsely populated nature of the region. Furthermore, seasonal analysis reveals that dust storm events are prevalent during the Summer months, specifically from June to August, followed by Spring. These results are consistent with previous studies of dust occurrence in the US, and Flickr-identified images of dust storms show substantial co-occurrence with regions of NWS blowing dust advisories. This research highlights the potential of unconventional user-generated data sources to crowdsource environmental monitoring and research.</blockquote><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhBaxOcensBbK5gGZ6-zy_CgqcrrmEkZSXzt2LAvw9tv87VwtWrVp5i5uKckAvjsQMz7zGAY29zNPFUz7Cc3ZN_E0nFLZln7UECIag8C_BPx_qi_pQn7yN9sG8kyz5sBWaQRtSNXp6cyJeqr_DWSyHEaFPQI4fk-2boABfBEebolb4aJmpgfHJH/s2548/Screenshot%202023-12-19%20at%204.52.48%E2%80%AFPM.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1908" data-original-width="2548" height="480" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhBaxOcensBbK5gGZ6-zy_CgqcrrmEkZSXzt2LAvw9tv87VwtWrVp5i5uKckAvjsQMz7zGAY29zNPFUz7Cc3ZN_E0nFLZln7UECIag8C_BPx_qi_pQn7yN9sG8kyz5sBWaQRtSNXp6cyJeqr_DWSyHEaFPQI4fk-2boABfBEebolb4aJmpgfHJH/w640-h480/Screenshot%202023-12-19%20at%204.52.48%E2%80%AFPM.png" width="640" /></a></div><p></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg6rodHsd3DR5UPCN-NAE5Q1fw3iuse3GhaJUC_8lip42G_2TOacCArOcgdPWMGTNVZlxqPisvdHJ4_vqA7MVKpSH_IR6XammxAJ0VEGQ7CdldLqjPxeceoBFgdq3QqqqVNY4cVCrpeFIb6hR-uExwQ0nr64ptURL7E42-64LPjIgNDnN7G1ZcL/s1626/Screenshot%202023-12-19%20at%204.55.52%E2%80%AFPM.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1122" data-original-width="1626" height="442" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg6rodHsd3DR5UPCN-NAE5Q1fw3iuse3GhaJUC_8lip42G_2TOacCArOcgdPWMGTNVZlxqPisvdHJ4_vqA7MVKpSH_IR6XammxAJ0VEGQ7CdldLqjPxeceoBFgdq3QqqqVNY4cVCrpeFIb6hR-uExwQ0nr64ptURL7E42-64LPjIgNDnN7G1ZcL/w640-h442/Screenshot%202023-12-19%20at%204.55.52%E2%80%AFPM.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Data collection and workflow.</td></tr></tbody></table><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhdp_i-EzwhHCQDYOEmx8J6JnmR356wMbjQngsbf0cxLKDg4GF_Ve0TdddehYSU8mXvhPJdHzw4JqrOjNKQvho6RBdOEgxuYgSStA-02jicts_IPZvQXYqAiRJvvxow9UoDkmInKnnD2n2dS0HE5WD_DfN-NGhftPRbLwK3aYM9T3TW7CWwa2WK/s1398/Screenshot%202023-12-19%20at%204.33.12%E2%80%AFPM.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1000" data-original-width="1398" height="458" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhdp_i-EzwhHCQDYOEmx8J6JnmR356wMbjQngsbf0cxLKDg4GF_Ve0TdddehYSU8mXvhPJdHzw4JqrOjNKQvho6RBdOEgxuYgSStA-02jicts_IPZvQXYqAiRJvvxow9UoDkmInKnnD2n2dS0HE5WD_DfN-NGhftPRbLwK3aYM9T3TW7CWwa2WK/w640-h458/Screenshot%202023-12-19%20at%204.33.12%E2%80%AFPM.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Distribution of Flickr identified dust storm occurrences and NWS dust storm advisories.</td></tr></tbody></table><br /><p><b>Full Reference: </b></p><div style="text-align: justify;"><blockquote><b>Adegbola,</b> F., Crooks, A.T. and Evans, S. (2023), Crowdsourcing Dust Storms in the United States Utilizing Flickr, <i>American Geophysical Union (AGU) Fall Meeting</i>, 11th – 15th December, San Francisco, CA. (<a href="https://www.dropbox.com/scl/fi/nk0veds1elgoufw5j5z2p/DustStorms_AGU.pdf?rlkey=46mvvi2xuc723fbr2ae6rxx0g&dl=0">abstract</a>, <a href="https://www.dropbox.com/scl/fi/7mkpfjd9kr1m12sfvb7wc/AGU2023_poster.pdf?rlkey=m760xtte5t967952drg6teebw&dl=0">poster</a>)</blockquote></div><p></p></div>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-22770502.post-24083039899854725982023-11-14T08:44:00.000-05:002023-11-14T08:44:42.638-05:00Massive Trajectory Data Based on Patterns of Life<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhOnj60UGMI9ZuZhtmiRrcQntf8584RZVj3gSbiF-Vnnk5Qic82k3pF49TgcOgwe_-FMMJNXUjnXqfzW74XsfwzIBWM0jV6mT-MoZ3Ni5RUK2EQq7_m0GJtFPOUZ_eeAKIz_bmALoLUTlXJkbDmSklHxTJigs5r5UApu6pmPG-1DcyTPT_K11Mz/s1380/Screenshot%202023-11-04%20at%205.34.36%E2%80%AFPM.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" data-original-height="512" data-original-width="1380" height="119" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhOnj60UGMI9ZuZhtmiRrcQntf8584RZVj3gSbiF-Vnnk5Qic82k3pF49TgcOgwe_-FMMJNXUjnXqfzW74XsfwzIBWM0jV6mT-MoZ3Ni5RUK2EQq7_m0GJtFPOUZ_eeAKIz_bmALoLUTlXJkbDmSklHxTJigs5r5UApu6pmPG-1DcyTPT_K11Mz/s320/Screenshot%202023-11-04%20at%205.34.36%E2%80%AFPM.png" width="320" /></a></div><p style="text-align: justify;">Following on from the last post, we (<a href="https://scholar.google.com/citations?user=dy14xQMAAAAJ&hl=en" target="_blank">Hossein Amiri</a>, <a href="https://www.linkedin.com/in/shiyang-ruan-7a40b384/" target="_blank">Shiyang Ruan</a>, <a href="https://www.joonseok.org/" target="_blank">Joon-Seok Kim</a>, <a href="https://www.linkedin.com/in/hyunjee-jin-b388175b/" target="_blank">Hyunjee Jin</a>, <a href="https://hamdikavak.com/" target="_blank">Hamdi Kavak</a>, <a href="https://www.dieter.pfoser.org/" target="_blank">Dieter Pfoser</a>, <a href="https://www.cs.tulane.edu/~carola/" target="_blank">Carola Wenk</a> and <a href="https://www.zuefle.org/" target="_blank">Andreas Zufle</a> and myself) have a paper in the Data and Resources track at the <a href="https://sigspatial2023.sigspatial.org/" target="_blank">2023 ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems</a> entitled "<i><b>Massive Trajectory Data Based on Patterns of Life</b></i>". </p><p style="text-align: justify;">This <span style="text-align: left;">Data and Resources paper</span> introduces readers to a <span style="text-align: left;">large sets of simulated individual-level trajectory and location-based social network data</span> we have generated from our<i> Urban Life Model</i> (<a href="https://www.gisagents.org/2021/12/urban-life-model-of-people-and-places.html" target="_blank">click here to find out more about the model</a>). The data comprises of <span style="text-align: left;">4 suburban and urban regions, including 1) the George Mason University Campus area, Fairfax, Virginia, 2) the French Quarter of New Orleans, Louisiana, 3) San Francisco, California, and 4) Atlanta, Georgia. </span><span style="text-align: left;">For each of the 4 study regions, we run the simulation with 1K, 3K, 5K, and 10K agents for 15 months of simulation time.</span><span style="text-align: left;"> </span><span style="text-align: left;">We also provide simulations for 10 years and 20 years, having 1K agents for each of the 4 regions of interest.</span><span style="text-align: left;"> </span><span style="text-align: left;">For each dataset, three items are provided: 1) Check-ins, and 2) social network links and 3) trajectory information per agent per five-minute tick. As such we </span><span style="text-align: left;">argue in the paper that </span><span style="text-align: left;">our datasets are orders of magnitude larger than existing </span><span style="text-align: left;">real-world trajectory and location-based social network (LBSN) data sets. </span></p><p style="text-align: justify;"><span style="text-align: left;">If this sounds of interest we encourage readers to check out the paper (see the bottom of this post), while the </span><span style="text-align: left;">datasets, as well as additional documentation, can be found at OSF (</span><a href="https://osf.io/gbhm8" style="text-align: left;" target="_blank">https://osf.io/gbhm8</a><span style="text-align: left;">/) and the data generator (model) can be found at </span><a href="https://github.com/azufle/pol" style="text-align: left;" target="_blank">https://github.com/azufle/pol</a><span style="text-align: left;">.</span></p><p style="text-align: justify;"><b></b></p><blockquote><p style="text-align: justify;"><b>Abstract</b>: Individual human location trajectory and check-in data have been the driving force for human mobility research in recent years. However, existing human mobility datasets are very limited in size and representativeness. For example, one of the largest and most commonly used datasets of individual human location trajectories, GeoLife, captures fewer than two hundred individuals. To help fill this gap, this Data and Resources paper leverages an existing data generator based on fine-grained simulation of individual human patterns of life to produce large-scale trajectory, check-in, and social network data. In this simulation, individual human agents commute between their home and work locations, visit restaurants to eat, and visit recreational sites to meet friends. We provide large datasets of months of simulated trajectories for two example regions in the United States: San Francisco and New Orleans. In addition to making the datasets available, we also provide instructions on how the simulation can be used to re-generate data, thus allowing researchers to generate the data locally without downloading prohibitively large files.</p><div class="separator" style="clear: both; text-align: justify;"></div></blockquote><div class="separator" style="clear: both; text-align: justify;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg0_kLYmd4b5Q9YjYXFnQAZGuCP9CTDLo_hou25XJ8g93-oDHsm7kClf9nKftNpbLkfJw13gdTwU8f4SSCdxiUnO0v5y-pDKaK5JsTd0PqvO3HnaBcchzxe00b0bv7M-zSkRERQUdEaXKWcDarTuMGNDggKhN6OgCZAGE8PhuX3tl9Yq3luCqVp/s1670/Screenshot%202023-11-04%20at%205.28.41%E2%80%AFPM.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1168" data-original-width="1670" height="448" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg0_kLYmd4b5Q9YjYXFnQAZGuCP9CTDLo_hou25XJ8g93-oDHsm7kClf9nKftNpbLkfJw13gdTwU8f4SSCdxiUnO0v5y-pDKaK5JsTd0PqvO3HnaBcchzxe00b0bv7M-zSkRERQUdEaXKWcDarTuMGNDggKhN6OgCZAGE8PhuX3tl9Yq3luCqVp/w640-h448/Screenshot%202023-11-04%20at%205.28.41%E2%80%AFPM.png" width="640" /></a></div><p><b>Full Referece: </b></p><p></p><blockquote style="text-align: justify;"><b>Amiri, H., Ruan, S., Kim, J., Jin, H., Kavak, H., Crooks, A.T., Pfoser, D., Wenk, C. and Züfle, A.</b> (2023), Massive Trajectory Data Generation using a Patterns of Life Simulation, <i>Proceedings of the 2023 ACM SIGSPATIAL International Conference on Advances in Geographic Information System</i>s, Hamburg, Germany. (<a href="https://www.dropbox.com/scl/fi/x6cfc3zry52vcm33gk0vl/SIGSPATIAL_Data_Paper__Trajectory_Generation.pdf?rlkey=wo1hh21e1k0utqwiwep8tgs8i&dl=0" target="_blank">pdf</a>)</blockquote><p></p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-22770502.post-7930814105913617012023-11-13T10:58:00.001-05:002023-12-21T10:12:42.267-05:00Synthetic Geosocial Network Generation<div class="separator"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh-0sl5wx6mIXqRGNwHDefCFerRaWidA7v291UyhYq9IiNeqYoI-r10iQNfLxUidAcU9O-HvndoK351mlWJYW9N7M0uhHcuHmZa_8jltaEymNglJw7_Xx1CDUF-U5SlaAHAkba9u72eMHCksSQjvTCNRroWXq7omAKt_zCuIFVxPqYEdpz3oh6Y/s613/logo1.jpg" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh-0sl5wx6mIXqRGNwHDefCFerRaWidA7v291UyhYq9IiNeqYoI-r10iQNfLxUidAcU9O-HvndoK351mlWJYW9N7M0uhHcuHmZa_8jltaEymNglJw7_Xx1CDUF-U5SlaAHAkba9u72eMHCksSQjvTCNRroWXq7omAKt_zCuIFVxPqYEdpz3oh6Y/w200-h134/logo1.jpg" /></a></div><div style="text-align: justify;">In the past the blog has explored the creation of social networks for models. Keeping with this vain of research, I was fortunate to work with <a href="https://github.com/KetevanGallagher" target="_blank">Ketevan Gallagher</a>, <a href="https://science.gmu.edu/directory/taylor-anderson">Taylor Anderson</a> and <a href="https://www.zuefle.org/">Andreas Züfle</a> to consider the role of location of individuals when generating social networks. This work has resulted in a new paper entitled "<i>Synthetic Geosocial Network Data Generation</i>" which was presented at the <a href="https://localrec.github.io/2023/">7th ACM SIGSPATIAL Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising (LocalRec 2023)</a>. If this sounds of interest, below you can read the abstract to the paper, see some the generated geosoical networks and find the full reference and link to the paper. In addition to this, the Python code and data used to generate the networks<span style="text-align: left;"> is available at </span><a href="https://github.com/KetevanGallagher/Synthetic-Geosocial-Networks" style="text-align: left;">https://github.com/KetevanGallagher/Synthetic-Geosocial-Networks</a><span style="text-align: left;">.</span></div><div><div><br /><b></b><blockquote style="text-align: justify;"><b>Abstract</b>: Generating synthetic social networks is an important task for many problems that study humans, their behavior, and their interactions. Geosocial networks enrich social networks with location information. Commonly used models to generate synthetic social networks include the classical Erdos-Renyi, Barabasi-Albert, and Watts-Strogatz models. However, these classic social network models do not consider the location of individuals. Real-world geosocial networks do exhibit a strong spatial autocorrelation, thus having a higher likelihood of a social connection between agents that are spatially close. As such, recent variants of the three classical models have been proposed to consider location information. Yet, these existing solutions assume that individuals are located on a uniform lattice and exhibit certain limitations when applied to real-world data that exhibits clusters. In this work, we discuss these limitations and propose new approaches to extend the three classic social network generation models to geosocial networks. Our experiments show that our generated synthetic geosocial networks address the shortcomings of the state-of-the-art models and generate realistic geosocial networks that exhibit high similarity to real-world geosocial networks. </blockquote><blockquote style="text-align: justify;"><b>Keywords</b>: Geosocial Networks, Network Generation, Synthetic Social Networks, Erdos-Renyi, Watts-Strogatz, Barabasi-Albert.</blockquote><p><br /></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjMwxk0J9ri6hvg12OydMq5hNcwVIWjs6gtQaapwG16_gICKzufCbVBCOZ7YffFAWfEYzuxQRQm_j8o8-lcvDl-wOX8FkzCRgjzV8q4Y1mkBlEJReDAkoQ5TA7MxlU0845c4sZmI_KWu3_hRM1Pfl2UDzaj94wikNckCJkiBAkBYku31Hj9ZuLT/s1878/Screenshot%202023-11-04%20at%2012.22.40%E2%80%AFPM.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1546" data-original-width="1878" height="526" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjMwxk0J9ri6hvg12OydMq5hNcwVIWjs6gtQaapwG16_gICKzufCbVBCOZ7YffFAWfEYzuxQRQm_j8o8-lcvDl-wOX8FkzCRgjzV8q4Y1mkBlEJReDAkoQ5TA7MxlU0845c4sZmI_KWu3_hRM1Pfl2UDzaj94wikNckCJkiBAkBYku31Hj9ZuLT/w640-h526/Screenshot%202023-11-04%20at%2012.22.40%E2%80%AFPM.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Real- World Geosocial Network using Facebook Social Connectedness Data between Zone Improvement Plan (ZIP) Region Centroids for the State of Virginia, USA.</td></tr></tbody></table></div></div><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjxVJ29qTxTT6Di8lvytYwYKQBlPD0QHljRbxVXIh_76RLLmXeFvg-gyYLo3KZIuEfJJi0ceSjCz2a9hAkwSML-qujJzxQ0Ai4nm-ghapZITRrz95aLhyphenhyphenPfFtN2MpixyLALGD6GJYb_53_3xqnmND1cMszOX2Mf93P0737X6IdG0dch5907ugrC/s1770/Screenshot%202023-11-04%20at%2012.29.01%E2%80%AFPM.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="520" data-original-width="1770" height="188" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjxVJ29qTxTT6Di8lvytYwYKQBlPD0QHljRbxVXIh_76RLLmXeFvg-gyYLo3KZIuEfJJi0ceSjCz2a9hAkwSML-qujJzxQ0Ai4nm-ghapZITRrz95aLhyphenhyphenPfFtN2MpixyLALGD6GJYb_53_3xqnmND1cMszOX2Mf93P0737X6IdG0dch5907ugrC/w640-h188/Screenshot%202023-11-04%20at%2012.29.01%E2%80%AFPM.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Geosocial graphs using Virginia ZIP code data.</td></tr></tbody></table><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhvStkpztrTEX1YU_WTKPlcCHNlenziQYt9-pOFyP988t-w3G8Y-LCD0fLdtMQbY5QyAnEMzChG13uY1TsNlRGzP3LgVtRnkRSaxNe85Pstm8xdKJunGCOmXuAP2zvBc2BATQALHoSezYcVTk9OHWaehaIduG483aF807Xml2IYW8UG1dypk0mY/s1748/Screenshot%202023-11-04%20at%2012.30.46%E2%80%AFPM.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="506" data-original-width="1748" height="186" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhvStkpztrTEX1YU_WTKPlcCHNlenziQYt9-pOFyP988t-w3G8Y-LCD0fLdtMQbY5QyAnEMzChG13uY1TsNlRGzP3LgVtRnkRSaxNe85Pstm8xdKJunGCOmXuAP2zvBc2BATQALHoSezYcVTk9OHWaehaIduG483aF807Xml2IYW8UG1dypk0mY/w640-h186/Screenshot%202023-11-04%20at%2012.30.46%E2%80%AFPM.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Graphs using Fairfax Census Tract data.</td></tr></tbody></table><br /><div><br /></div><div><b>Full Referece:</b><br /><blockquote><b>Gallagher, K., Anderson, T., Crooks, A.T. and Züfle, A.</b> (2023), Synthetic Geosocial Network Data Generation, <i>Proceedings of the 7th ACM SIGSPATIAL Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising (LocalRec 2023),</i> Hamburg, Germany. (<a href="https://www.dropbox.com/scl/fi/y1rlj0pnvtwelwrwk9jry/SIGSPATIAL_Workshop_23__Spatial_Social_Networks.pdf?rlkey=2xtwq15g10269ifdlygsh7lya&dl=0">pdf</a>) (<a href="https://www.dropbox.com/scl/fi/q6ywzxy0jb6tedvpbmlmu/LocalRec2023_slides.pdf?rlkey=rarqw62bkskxkbemhtm9xfi4m&dl=0" target="_blank">presentation</a>)</blockquote></div>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-22770502.post-62428227209895759182023-11-03T19:06:00.005-04:002023-11-13T10:57:33.045-05:00Geographically Synthetic Populations for ABM: A Gallery of Applications<p><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgj8CZQBXmmisMDy3qNcd0RdgQrk849WlKcyvkFPMZw62w83PeTaXKq-YoB-ZcC8Ff1OLqR_hu4IrpDSa4bb_mrlakBmYGxje5eGNj57wsRtk52kJpAlHKACYVadz_krvTXp-vpTQrTq1G_BqSZXJJgYRkaLSQSThQarqRGUSoP4677QxyfvJ77/s200/cssa.jpeg" style="clear: left; display: inline; float: left; margin-bottom: 1em; margin-right: 1em; text-align: center;"><img border="0" data-original-height="200" data-original-width="200" height="200" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgj8CZQBXmmisMDy3qNcd0RdgQrk849WlKcyvkFPMZw62w83PeTaXKq-YoB-ZcC8Ff1OLqR_hu4IrpDSa4bb_mrlakBmYGxje5eGNj57wsRtk52kJpAlHKACYVadz_krvTXp-vpTQrTq1G_BqSZXJJgYRkaLSQSThQarqRGUSoP4677QxyfvJ77/s1600/cssa.jpeg" width="200" /></a></p><div style="text-align: justify;">Often we are building geographically explicit agent-based models we spend a lot of time creating the synthetic population to instantiate our artificial world. We have tired to overcome this with creating methods to generate such populations (<a href="https://www.gisagents.org/2022/02/new-paper-synthetic-populations-with.html">see this old blog post</a>). Building on this work, <a href="https://www.urbanagentjiang.net/">Na (Richard) Jiang</a>, <a href="https://archplan.buffalo.edu/People/students/phd-students.host.html/content/shared/ap/students-faculty-alumni/phd-candidates/yin.detail.html">Fuzhen Yin</a>, <a href="https://wang-boyu.github.io/">Boyu Wang</a> and myself have a new paper entitled "Geographically-Explicit Synthetic Populations for Agent-based Models: A Gallery of Applications" which was presented at <a href="https://computationalsocialscience.org/conferences/css2023/">2023 Computational Social Science Society of the Americas conference</a>. In the paper we extend the synthetic population to the whole of New York state. While at the same time we introduce a pipeline for using the population datasets for model initialization. To show this pipeline, we present several case studies utilizing Python and <a href="https://github.com/projectmesa/mesa">Mesa</a>. These models range from that of commuting to disease spread and vaccination uptake. If this sounds of interest, below we provide the abstract to the paper along with some of the key figures including our pipeline and example applications. At the bottom of the page we provide the full reference and a link to the paper which has links to the models and data.</div><blockquote><div style="text-align: justify;"><b>Abstract</b>: Over the last two decades, there has been a growth in the applications of geographically-explicit agent-based models. One thing such models have in common is the creation of synthetic populations to initialize the artificial worlds in which the agents inhabit. One challenge such models face is that it is often difficult to create reusable geographically-explicit synthetic populations with social networks. In this paper, we introduce a Python based method that generates a reusable geographically-explicit synthetic population dataset along with its social networks. In addition, we present a pipeline for using the population datasets for model initialization. With this pipeline, multiple spatial and temporal scales of geographically-explicit agent-based models are presented focusing on Western New York. Such models not only demonstrate the utility of our synthetic population on commuting patterns but also how social networks can impact the simulation of disease spread and vaccination uptake. By doing so, this pipeline could benefit any modeler wishing to reuse synthetic populations with realistic geographic locations and social networks. </div></blockquote><blockquote><b>Keywords</b>: Agent-Based Model, Geographically-Explicit Agent-Based Models, Synthetic Population, Python, Mesa.</blockquote><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEguAotFWG0xDpB2qOPrOmF7LYALIvDm3cuwsvmvkDc4bFySuoa7biq2atlWCySqO4HrjAYLmcR0b8PlkAZ_-Tc0bluMqjM_GFWG4Is8vYrLhC1JsKycgdd1ViGVvf5fG2KAKDnm3liEix19aBonk0QwOH2FfwfZcYvXYnVXwYGN6cRVSj6bOE01/s1672/Method_Use_Spop.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="856" data-original-width="1672" height="328" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEguAotFWG0xDpB2qOPrOmF7LYALIvDm3cuwsvmvkDc4bFySuoa7biq2atlWCySqO4HrjAYLmcR0b8PlkAZ_-Tc0bluMqjM_GFWG4Is8vYrLhC1JsKycgdd1ViGVvf5fG2KAKDnm3liEix19aBonk0QwOH2FfwfZcYvXYnVXwYGN6cRVSj6bOE01/w640-h328/Method_Use_Spop.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Pipeline of Utilizing Synthetic Population Resulting Datasets in Agent-Based Models.</td></tr></tbody></table><div class="separator" style="clear: both; text-align: center;"><br /></div><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgMIHXtd4vmdBszhgyFVFXMsceTkMh9Vki93W46cefy2BcXFPFQOa96BX5_TKefRCeMNumumLAAeJIzYxfkphK6GaL8YYmoxjyA1isijQZyjdia1GvYm6QJk5dkbN3yVlnKr5Bj5qR_KBA7g2GA8t70vLBY4p8yntSVfAJIJPljiM464KjYa7nA/s1720/SEIR_Struc.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1070" data-original-width="1720" height="398" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgMIHXtd4vmdBszhgyFVFXMsceTkMh9Vki93W46cefy2BcXFPFQOa96BX5_TKefRCeMNumumLAAeJIzYxfkphK6GaL8YYmoxjyA1isijQZyjdia1GvYm6QJk5dkbN3yVlnKr5Bj5qR_KBA7g2GA8t70vLBY4p8yntSVfAJIJPljiM464KjYa7nA/w640-h398/SEIR_Struc.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Large Scale Disease Spread Model Structure.</td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgxP766wtlc0o-TmW8A_rNCaZ6bYJNO8l_g8uPVltYNJIipyNmY5ht4mSklBinR3GqrS7qgmYzZ_uhTT5y52HOoWvX9xY91D4ULUFK2W1pWgPz4Ez0buQXJJyufR0aDLxJ94lMK7kS84zic3d6E8LupCBiMO01PswRTkdxm5n_qteYQ-QdghxNO/s1274/SEIR_dynamics.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1062" data-original-width="1274" height="534" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgxP766wtlc0o-TmW8A_rNCaZ6bYJNO8l_g8uPVltYNJIipyNmY5ht4mSklBinR3GqrS7qgmYzZ_uhTT5y52HOoWvX9xY91D4ULUFK2W1pWgPz4Ez0buQXJJyufR0aDLxJ94lMK7kS84zic3d6E8LupCBiMO01PswRTkdxm5n_qteYQ-QdghxNO/w640-h534/SEIR_dynamics.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Disease Dynamics for Two Diseases.</td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjSka_D1l5giC9k7btm2f-N7TWy7PyGyTE6y0L1Nt8eYe69xHuNpNkmdOyquaHbZEz5q-OMQzvja5purPH9dhDZHXNDFd102SDu54jgabYCwD964VT6khr9RWDcYVcGmX65fnkXp2Xe94STYi5r63bG6wubCS0q9rPieveLz-93c7wPLl2CEsbh/s1730/Vac_Struc.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="820" data-original-width="1730" height="304" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjSka_D1l5giC9k7btm2f-N7TWy7PyGyTE6y0L1Nt8eYe69xHuNpNkmdOyquaHbZEz5q-OMQzvja5purPH9dhDZHXNDFd102SDu54jgabYCwD964VT6khr9RWDcYVcGmX65fnkXp2Xe94STYi5r63bG6wubCS0q9rPieveLz-93c7wPLl2CEsbh/w640-h304/Vac_Struc.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Vaccination Opinion Dynamic Model.</td></tr></tbody></table><p style="text-align: justify;"></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgFK5F_Gdjaw-dA0rYgF1esyr092YyzoY8ZLF3cQL-E9D_r7-xyAiVaHKBp7gX9Th_AuDF4_DWP1yD1i2SlDrgBIL89-_eZlvikZtBWlPDlvkWSMO5MQJToi27nLk41U8_v4qkK1EaTeGGzFP9npZW5aUjjs2xpBU0qKEQCkY7z8mhKX-dm35g-/s1858/Vac_groups.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="554" data-original-width="1858" height="190" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgFK5F_Gdjaw-dA0rYgF1esyr092YyzoY8ZLF3cQL-E9D_r7-xyAiVaHKBp7gX9Th_AuDF4_DWP1yD1i2SlDrgBIL89-_eZlvikZtBWlPDlvkWSMO5MQJToi27nLk41U8_v4qkK1EaTeGGzFP9npZW5aUjjs2xpBU0qKEQCkY7z8mhKX-dm35g-/w640-h190/Vac_groups.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Simulation Vaccination Rate v.s. Real Vaccination Records: (A) All Population; (B) Different Age Groups of Population.</td></tr></tbody></table><p></p><p><b>Full Referece: </b></p><p><b></b></p><blockquote><b>Jiang, N., Crooks, A.T., Yin, F. and Wang B. (2023)</b>, Geographically-Explicit Synthetic Populations for Agent-based Models: A Gallery of Applications, <i>Proceedings of the 2023 Conference of The Computational Social Science Society of the Americas,</i> Santa Fe, NM. (<a href="https://www.dropbox.com/scl/fi/da5wq5iwtxohvbltybaqw/CSSSA_2023.pdf?rlkey=d34vvdhouzi5w9ynjknfbglj2&dl=0" target="_blank">pdf</a>)</blockquote><p></p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-22770502.post-31918328234194601702023-10-23T17:07:00.004-04:002023-10-24T08:32:50.138-04:00Evaluating the incentive for soil organic carbon sequestration from carinata production<p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhhILyUd38o8xqv-iKCM-ZKEg_Z9ejlDgnPSl7NY-onTI1hbgzINTbWMVPeYE4-n3bKk4l7MOaeXqOKkVQ-qSGtypDjaXqto_5QRhM2zAg5bsUitwKB-KqQIm7WzKfXH1JEJvgKjTrTI4MMvv2zlj0Ap7UYh40gBIIdTfdKYxs9egqbQDyOnban/s150/222.gif" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" data-original-height="150" data-original-width="113" height="150" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhhILyUd38o8xqv-iKCM-ZKEg_Z9ejlDgnPSl7NY-onTI1hbgzINTbWMVPeYE4-n3bKk4l7MOaeXqOKkVQ-qSGtypDjaXqto_5QRhM2zAg5bsUitwKB-KqQIm7WzKfXH1JEJvgKjTrTI4MMvv2zlj0Ap7UYh40gBIIdTfdKYxs9egqbQDyOnban/s1600/222.gif" width="113" /></a></div><div style="text-align: justify;">Over the years we have developed several agent-based models that have explored various aspects of <a href="https://www.gisagents.org/search/label/Farming" target="_blank">farming</a>, ranging from farmers selling their land for development to that of water reuse. Keeping with this theme, we have a new paper with <a href="https://scholar.google.com/citations?user=g6_2JhcAAAAJ&hl=en" target="_blank"><span class="given-name">Kazi</span> </a><span class="text surname"><a href="https://scholar.google.com/citations?user=g6_2JhcAAAAJ&hl=en" target="_blank">Ullah </a>and </span><a href="https://www.ornl.gov/staff-profile/gbadebo-oladosu" target="_blank"><span class="given-name">Gbadebo </span></a><span class="text surname"><a href="https://www.ornl.gov/staff-profile/gbadebo-oladosu" target="_blank">Oladosu</a> </span>in the "<a href="https://www.sciencedirect.com/journal/journal-of-environmental-management" target="_blank">Journal of Environmental Management</a>" entitled "<a href="https://www.sciencedirect.com/science/article/pii/S0301479723022065" target="_blank"><i>Evaluating the incentive for soil organic carbon sequestration from carinata production in the Southeast United States</i></a>". </div><div style="text-align: justify;"> </div><div style="text-align: justify;">In the paper we developed an agent-based model to evaluate what incentives might be needed for farmers to sequester soil organic carbon (SOC) when adopting a new bioenergy crop namely carinata. We simulated two carinata management scenarios: business as usual and
climate-smart (no-till). The model finds that SOC sequestration incentives reduce the seed
price needed to reach maximum adoption rates. While incentives lead to
higher adoption rates, SOC sequestration, and profitability with no-till
farming. </div><div style="text-align: justify;"> </div><div style="text-align: justify;">If this sounds of interest, below you can read the abstract to the paper, get a sense of the agent logic and see some of the results. While at the bottom of the page, you can find the full reference and a link to the paper. The model (created in NetLogo) and data needed to run it is available on Kazi's GitHub page: <a href="https://github.com/KaziMaselUllah/Incentive_SOC_Carinata" target="_blank">https://github.com/KaziMaselUllah/Incentive_SOC_Carinata</a>.</div><div style="text-align: justify;"><br /><blockquote><p style="text-align: justify;">
<b>Abstract</b>: Soil organic carbon (SOC) can be increased by cultivating bioenergy crops to produce low-carbon fuels, improving soil quality and agricultural productivity. This study evaluates the incentives for farmers to sequester SOC by adopting a bioenergy crop, carinata. Two agricultural management scenarios – business as usual (BaU) and a climate-smart (no-till) practice – were simulated using an agent-based modeling approach to account for farmers’ carinata adoption rates within their context of traditional crop rotations, the associated profitability, influences of neighboring farmers, as well as their individual attitudes. Using the state of Georgia, US, as a case study, the results show that farmers allocated 1056 × 10<sup>3</sup> acres (23.8%; 2.47 acres is equivalent to 1 ha) of farmlands by 2050 at a contract price of $6.5 per bushel of carinata seeds and with an incentive of $50 Mg<sup>−1</sup> CO2e SOC sequestered under the BaU scenario. In contrast, at the same contract price and SOC incentive rate, farmers allocated 1152 × 10<sup>3</sup> acres (25.9%) of land under the no-till scenario, while the SOC sequestration was 483.83 × 10<sup>3</sup> Mg CO2e, which is nearly four times the amount under the BaU scenario. Thus, this study demonstrated combinations of seed prices and SOC incentives that encourage farmers to adopt carinata with climate-smart practices to attain higher SOC sequestration benefits.</p>
<div style="text-align: justify;"><b>Keywords</b>: Agent-based model, Bioenergy, Climate-smart agriculture, Soil organic carbon, Incentives, Sustainable aviation fuel.</div></blockquote><p> </p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh-8l0thb-aONSRbOUWJY8NS4kAIDV0qbFfImqYIIr4oeUZ-MliWI0iyTNre19r6SNxpCUF2luVriUyQNqY_s1SAqxRJqwCq2DYsy_JQnuSlv0HGFPb2JKyOM0GxMO4EdeLPBQ3ZmotDOUgHYwNXg-ncRhAi9Eoy_j946Lh2V8oYN2AGmXu3uVj/s2680/1-s2.0-S0301479723022065-gr3_lrg.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="2680" data-original-width="2371" height="640" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh-8l0thb-aONSRbOUWJY8NS4kAIDV0qbFfImqYIIr4oeUZ-MliWI0iyTNre19r6SNxpCUF2luVriUyQNqY_s1SAqxRJqwCq2DYsy_JQnuSlv0HGFPb2JKyOM0GxMO4EdeLPBQ3ZmotDOUgHYwNXg-ncRhAi9Eoy_j946Lh2V8oYN2AGmXu3uVj/w566-h640/1-s2.0-S0301479723022065-gr3_lrg.jpg" width="566" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Process, overview and scheduling of the model<br /></td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjVH0NC2bqmwL1jmCrWtUaRzr2oNZveNd22VbuslJSScNJTBtKU5iI1iThE_z9rc4sFWvHCQqKYToPaQzGazBqKKO-1MdJLuhGekco34PnTu4cAra3K765o9j_ryiU7hGqJoKRgSFooCMuEHqw862mHI2Xbg-UnaK59F2NiCd_KrejTRgYKwIUg/s3591/2.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1701" data-original-width="3591" height="304" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjVH0NC2bqmwL1jmCrWtUaRzr2oNZveNd22VbuslJSScNJTBtKU5iI1iThE_z9rc4sFWvHCQqKYToPaQzGazBqKKO-1MdJLuhGekco34PnTu4cAra3K765o9j_ryiU7hGqJoKRgSFooCMuEHqw862mHI2Xbg-UnaK59F2NiCd_KrejTRgYKwIUg/w640-h304/2.jpg" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">An example simulation output of a model run (SOC incentive = $50 Mg<sup>−1</sup> CO2e, Carinata contract price = 6.5, Expanded diffusion, Low initial willingness scenario).</td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgACeo8Z2N4R0w5n7cbZhxgU-UAcN8T8g_by_hNzQ0fCib7yiTyJH0n3MTMbyUYRp2ZU2mhJTbtwBNTKo-pFpZ0mdkYBY7_ObPRdBez3Oucu9DDNDXytPmHR7B4cwaQAHlBz826CWmLzaIO-5y1pffJs77j93LyOK1eK1XEiSdOcAH7i4ca0NYU/s3591/23.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="2835" data-original-width="3591" height="506" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgACeo8Z2N4R0w5n7cbZhxgU-UAcN8T8g_by_hNzQ0fCib7yiTyJH0n3MTMbyUYRp2ZU2mhJTbtwBNTKo-pFpZ0mdkYBY7_ObPRdBez3Oucu9DDNDXytPmHR7B4cwaQAHlBz826CWmLzaIO-5y1pffJs77j93LyOK1eK1XEiSdOcAH7i4ca0NYU/w640-h506/23.jpg" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">The total number of farmers who adopted carinata over the years for two farming scenarios at five levels of incentives for SOC sequestration and at the four price levels.</td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiOHUbNwHfDNCtBIkciZ9-uHM_DRISpTNo4crcGSfJt5D4WwapkYqyG1cMhzSlzgQpQtQHTZBPRhA6_SKtxOwdhGObXhZ4uLQt73D1i1aTXbugNYiDcJ4d3z2jivhU-fn-w3p4GSq5gu2ypNsr3bSBrDn0C_ac8EmWzR5TDIoZIGdpJrqK47mh0/s3591/45.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="2866" data-original-width="3591" height="510" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiOHUbNwHfDNCtBIkciZ9-uHM_DRISpTNo4crcGSfJt5D4WwapkYqyG1cMhzSlzgQpQtQHTZBPRhA6_SKtxOwdhGObXhZ4uLQt73D1i1aTXbugNYiDcJ4d3z2jivhU-fn-w3p4GSq5gu2ypNsr3bSBrDn0C_ac8EmWzR5TDIoZIGdpJrqK47mh0/w640-h510/45.jpg" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">The mean land allocation area for four scenarios and their associated standard deviations (error bar).</td></tr></tbody></table> <br /><p><b>Full Reference: </b></p><blockquote style="text-align: justify;"><b>Ullah, K.M., Gbadebo G.A., and Crooks, A.T. (2023)</b>, Evaluating the Incentive for Soil Organic Carbon Sequestration from Carinata Production in the Southeast United States, <i>Journal of Environmental Management</i>, 348: 119418. Available at <a href="https://doi.org/10.1016/j.jenvman.2023.119418" target="_blank">https://doi.org/10.1016/j.jenvman.2023.119418</a> (<a href="https://www.dropbox.com/scl/fi/a25x8ncbiwyz5e57l3v9n/Kazi_2023.pdf?rlkey=ccusyayl7dlgsaxe8q2tuvq5g&dl=0" target="_blank">pdf</a>)<br /></blockquote></div>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-22770502.post-34587951054781448092023-10-04T16:39:00.003-04:002023-10-04T16:39:35.865-04:00Leveraging newspapers to understand urban issues<p style="text-align: justify;"></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjfTj2kor_huycZ5x336cvTJnQgci0R-Cwel-Pz9v6U1NhTPemsEnwcciRpLsExVG_7h2Q3rq4EgP26JmU-yRaEB10YChHAzt5JfbERfmw2J2uChu-iTpUEozYupgcVrOUHy7g2nzn5D5XckVAdMPkxh5vKmZqx2wmtPEYSbDJGQXoY3BLOrRkf/s600/epb-cover-social.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" data-original-height="314" data-original-width="600" height="104" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjfTj2kor_huycZ5x336cvTJnQgci0R-Cwel-Pz9v6U1NhTPemsEnwcciRpLsExVG_7h2Q3rq4EgP26JmU-yRaEB10YChHAzt5JfbERfmw2J2uChu-iTpUEozYupgcVrOUHy7g2nzn5D5XckVAdMPkxh5vKmZqx2wmtPEYSbDJGQXoY3BLOrRkf/w200-h104/epb-cover-social.jpg" width="200" /></a></div>In the past, this blog has <a href="https://www.gisagents.org/search/label/Detroit" target="_blank">explored several</a> aspects of Detroit, such as how well its covered with <a href="https://www.gisagents.org/2020/05/crowdsourcing-street-view-imagery.html" target="_blank">Volunteered Street View Imagery</a> or how through the use of <a href="https://www.gisagents.org/2021/02/simulating-urban-shrinkage-in-detroit.html" target="_blank">agent-based models one can explore issues with urban shrinkage</a>. Keeping up with the theme of shrinkage and Detroit but at the same time utilizing our growing interest in natural language processing (especially <a href="https://www.gisagents.org/search/label/Topic%20Modeling" target="_blank">topic modeling</a>) we (<a href="https://www.urbanagentjiang.net/" target="_blank">Na (Richard) Jiang</a>, <a href="https://hamdikavak.com/" target="_blank">Hamdi Kavak</a>, Wenjing Wang and myself) have a new paper entitled "<a href="https://journals.sagepub.com/doi/abs/10.1177/23998083231204695" target="_blank"><i><b>Leveraging newspapers to understand urban issues: A longitudinal analysis of urban shrinkage in Detroit</b></i></a>" published in <a href="https://journals.sagepub.com/home/epb" target="_blank">Environment and Planning B</a>. <p></p><p style="text-align: justify;">In the paper, we take 6794 English news articles published by national and local press organizations (e.g., Forbes, The New York Times, Newsweek, The Detroit News) between 1975 to 2021 using the keywords “<i>Detroit</i>”, “<i>shrink</i>” and “<i>decline</i>.” These keywords were selected based on the characteristics of the study area (i.e., Detroit) and the phenomenon of urban shrinkage. With these data we then use <a href="https://maartengr.github.io/BERTopic/index.html" target="_blank">BERTopic </a>to detect and classify all collected news articles into certain topics. We chose BERTopic because it captures the semantic relationship among words converting sentences and words to embedding and automatically generates the topic unlike other NLP topic modeling techniques (e.g., LDA). Our topic modeling results identify several insights with respect to Detroit's shrinkage. For example, we can detect the side effects of the 2007-2009 economic recession on Detroit's automobile industry, local employment status, and the housing market. If sounds of interest and you want to find out more, below we provide the abstract, some figures from the paper including the methodology workflow and an example of the resulting topics over time. Finally, at the bottom of the page you can see the full reference and s link to the paper itself. <br /></p><p style="text-align: justify;"></p><blockquote><p style="text-align: justify;"><b>Abstract</b> </p><p style="text-align: justify;">Today we are awash with data, especially when it comes to studying cities from a diverse data ecosystem ranging from demographic to remotely sensed imagery and social media. This has led to the growth of urban analytics providing new ways to conduct quantitative research within cities. One area that has seen significant growth is using natural language processing techniques on text data from social media to explore various issues relating to urban morphology. However, we would argue that social media only provides limited insights when dealing with longer-term urban phenomena, such as the growth and shrinkage of cities. This relates to the fact that social media is a relatively recent phenomenon compared to longer-term urban problems that take decades to emerge. Concerning longer-term coverage, newspapers, which are increasingly becoming digitized, provide the possibility to overcome the limitations of social media and provide insights over a timeframe that social media does not. To demonstrate the utility of newspapers for urban analytics and to study longer-term urban issues, we utilize an advanced topic modeling technique (i.e., BERTopic) on a large number of newspaper articles from 1975 to 2021 to explore urban shrinkage in Detroit. Our topic modeling results reveal insights related to how Detroit shrinks. For example, side effects of 2007 to 2009 economic recessions on Detroit’s automobile industry, local employment status, and the housing market. </p><p style="text-align: justify;"><b>Key Words:</b> Natural Language Processing, Topic Modeling, Newspapers, Urban Shrinkage, Urban Analytics.<br /></p></blockquote><p style="text-align: justify;"></p><p style="text-align: justify;"> </p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgDh2kxzPc-vzOiPLeSHjUCCjRp6hs3oafWP-XKe5rUg6kdU3cufoPPvr7FsPgsPuHBbSBugGgSqg_KgY3LrrA_AP5X7lj2dzNqPpCxLM1pwyhfvPd-sBLLCAgtRf1-ltwM0TXrBrDIXBjQXCJuQihZhgC9T0oMKhZDZnJY46d0wMo9KLYoRVtx/s3022/Screenshot%202023-10-02%20at%205.31.22%20PM.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1988" data-original-width="3022" height="422" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgDh2kxzPc-vzOiPLeSHjUCCjRp6hs3oafWP-XKe5rUg6kdU3cufoPPvr7FsPgsPuHBbSBugGgSqg_KgY3LrrA_AP5X7lj2dzNqPpCxLM1pwyhfvPd-sBLLCAgtRf1-ltwM0TXrBrDIXBjQXCJuQihZhgC9T0oMKhZDZnJY46d0wMo9KLYoRVtx/w640-h422/Screenshot%202023-10-02%20at%205.31.22%20PM.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;"> Vacancy status change from 1970 to 2010 for city of Detroit and surrounding area.</td></tr></tbody></table><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjN2hqy036xl0tcT4dNQa_uWBDOGb5TrVkLWjCKMEmqlt0TWsV1w0nVkZW_W0c85Nm0aUquLfP2HPzIPPwA3hVwSjWI_rib3KjubNMAg9GxtgAjqXViPw6H8LBEuhM6F1OnaLcFCAmj_Qjph4R4MBB2ZH2ERC0dhUWhcXu08AupoU5iF4Bl-csB/s2995/fig2.jpeg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1486" data-original-width="2995" height="318" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjN2hqy036xl0tcT4dNQa_uWBDOGb5TrVkLWjCKMEmqlt0TWsV1w0nVkZW_W0c85Nm0aUquLfP2HPzIPPwA3hVwSjWI_rib3KjubNMAg9GxtgAjqXViPw6H8LBEuhM6F1OnaLcFCAmj_Qjph4R4MBB2ZH2ERC0dhUWhcXu08AupoU5iF4Bl-csB/w640-h318/fig2.jpeg" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Topic modeling work flow.</td></tr></tbody></table><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiv5TBj4kV7vt7r2fVNa9wZeiPpsBDd2zB7AZogX_RO1_rMq65LMZ_V3lpbHIQOZF0tIYbPNKe_AsOVER9IA3jej-qdS66mQ88Wo0dO0gAF-0tVaCOhBM-abpj5PozAtTCSkKnxzt0z5WsDS-KphymwA4utsIVUYul3aZ99dORe46ND_e6kTXBZ/s2999/fig4.jpeg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="2382" data-original-width="2999" height="508" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiv5TBj4kV7vt7r2fVNa9wZeiPpsBDd2zB7AZogX_RO1_rMq65LMZ_V3lpbHIQOZF0tIYbPNKe_AsOVER9IA3jej-qdS66mQ88Wo0dO0gAF-0tVaCOhBM-abpj5PozAtTCSkKnxzt0z5WsDS-KphymwA4utsIVUYul3aZ99dORe46ND_e6kTXBZ/w640-h508/fig4.jpeg" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Topics over time (a) urban, (b) population, (c) shrinkage, (d) economy, (e) job, (f) house.</td></tr></tbody></table><p></p><p><b>Full Reference:</b></p><blockquote><p><b>Jiang, N., Crooks, A.T., Kavak, H. and Wang, W.</b> (2023), Leveraging Newspapers to Understand Urban Issues: A Longitudinal Analysis of Urban Shrinkage in Detroit, <i>Environment and Planning B</i>. Available at <a href="https://doi.org/10.1177/23998083231204695" target="_blank">https://doi.org/10.1177/23998083231204695</a>. (<a href="https://www.dropbox.com/scl/fi/69fnlmr9lxcsfmy4wl6jo/jiang_newspapers_shrinkage.pdf?rlkey=u1uh8d6tzlctwxpgxzijqw90l&dl=0" target="_blank">pdf</a>)<br /></p></blockquote>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-22770502.post-19940596303351822962023-10-02T10:49:00.020-04:002023-12-21T10:56:10.947-05:00Spatial Data Science Symposium<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg7l-9SSvhyphenhyphenoFNqgY_-a702llTDhB1hbpZ7-BCR6fIqVHYoJvOcILfzvH-R6PsQbfirlk27CDK4g_e_6L4mxzDaxqRsGCF0OYpPxY56h9gN5rAFDw1VV9_GPSs6m1WgPLK6pJRgwz181ySzwVu6BflW6R9h2N4XtzzNV0x3AoQJ53jkQNYzfi7b/s2134/Screenshot%202023-12-21%20at%2010.41.18%E2%80%AFAM.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="276" data-original-width="2134" height="82" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg7l-9SSvhyphenhyphenoFNqgY_-a702llTDhB1hbpZ7-BCR6fIqVHYoJvOcILfzvH-R6PsQbfirlk27CDK4g_e_6L4mxzDaxqRsGCF0OYpPxY56h9gN5rAFDw1VV9_GPSs6m1WgPLK6pJRgwz181ySzwVu6BflW6R9h2N4XtzzNV0x3AoQJ53jkQNYzfi7b/w640-h82/Screenshot%202023-12-21%20at%2010.41.18%E2%80%AFAM.png" width="640" /></a></div><div><br /></div><div style="text-align: justify;">The other week <a href="https://geoai.geog.buffalo.edu/" target="_blank">Yingjie Hu</a> and myself co-organized a session entitled "<i>Spatial Data Science for Disaster Resilience</i>" as part for the <a href="http://sdss2023.spatial-data-science.net/" target="_blank">4th Spatial Data Science Symposium (SDSS 2023)</a>. </div><div style="text-align: justify;"><br /></div><div><b>Session Abstract: </b></div><div></div><blockquote><div style="text-align: justify;">Natural disasters, such as hurricanes, floods, tornados, wildfires, earthquakes, and blizzards, pose significant threats to people and society. The availability of various geospatial data sources (e.g., drone-collected images, mobile phone location data, social media data, and sensor network data) combined with the advancement of statistical and machine learning models provide great opportunities for understanding human-environment interactions during these catastrophic events. This session aims to bring together researchers interested in using spatial data science to answer questions and address issues in any aspect related to disaster management.</div><div></div></blockquote><div style="text-align: justify;"><br /></div><div><b>Talks in the session: </b></div><div></div><div><ul style="text-align: left;"><li><a href="https://geography.tamu.edu/people/profiles/faculty/zoulei.html" target="_blank"><b>Lei Zou</b></a> (keynote): </li><ul><li><i>Achieving a Smart and Resilient Future with Spatial Data Science.</i></li></ul><li><a href="https://scdm.geography.wisc.edu/" target="_blank"><b>Qunying Huang</b></a>: </li><ul><li><i>Wildfire Burnt Area Detection with Deep Learning and Sentinel2 Imagery.</i></li></ul><li><a href="https://www.geog.psu.edu/directory/manzhu-yu" target="_blank"><b>Manzhu Yu</b></a>: </li><ul><li><i>Deciphering Wildfire Dynamics: Spatiotemporal Attention-Based Sequence-to-Sequence Models Using ConvLSTM Networks.</i></li></ul><li><a href="https://www.zakariasalim.com/" target="_blank"><b>Md Zakaria Salim</b></a>: </li><ul><li><i>Socio-economic Disparities of Property Damage in Hurricane Ian.</i></li></ul><li><a href="https://qingqingchen.info/" target="_blank"><b>Qingqing Chen</b></a>: </li><ul><li><i>Community Resilience to Wildfire: A Network Analysis Approach by Utilizing Human Mobility Data.</i></li></ul><li><a href="https://kaisun8304.site/" target="_blank"><b>Kai Sun</b></a>: </li><ul><li><i>GALLOC: a GeoAnnotator for Labeling LOCation Descriptions from Disaster-related Text Message</i>s.</li></ul></ul></div><div><div style="text-align: justify;">If these talks sound of interest and as this was a online and distributed event, the main organizers of the Symposium have made all the talks available online. The talks from our session can be seen below and <a href="https://youtube.com/playlist?list=PLLPRl7FLqNFTp6_8fdnNFjDjZb8IxlpwD&si=67UPwshTlKmSpmvG">all the other talks and seasons from the symposium at large can be found here</a>.</div><div><br /></div><div>
<center><iframe allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen="" frameborder="0" height="315" src="https://www.youtube.com/embed/eA6IOb5lJfk?si=ycw-y0kJxbUMxTk6" title="YouTube video player" width="560"></iframe></center></div></div>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-22770502.post-89402217952770042812023-09-29T13:37:00.001-04:002023-10-04T13:36:43.195-04:00Call for Abstracts: Geosimulations for Addressing Societal Challenges<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg1BHntWDm1X7BDfF_31-2Oufn9jUigd7RTuuHt2prGrmz-OSGV7f63ruqLD8GI9f_XqmJ2JpgeExhSENmUiFEuYohnULQeCClh4VmcAR5edbYYrylzPV4MMo4gPq7NWoJV8b-2svfdqGMkuyd6wMqKFuOiPrKY-NFUkQw2tD4zFr6DGwf1KxAv/s1050/aag.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="250" data-original-width="1050" height="152" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg1BHntWDm1X7BDfF_31-2Oufn9jUigd7RTuuHt2prGrmz-OSGV7f63ruqLD8GI9f_XqmJ2JpgeExhSENmUiFEuYohnULQeCClh4VmcAR5edbYYrylzPV4MMo4gPq7NWoJV8b-2svfdqGMkuyd6wMqKFuOiPrKY-NFUkQw2tD4zFr6DGwf1KxAv/w640-h152/aag.jpg" width="640" /></a></div><p></p><p style="text-align: justify;">As part of the The 10th Anniversary Symposium on Human Dynamics Research which will take place at the 2024 American Association of Geographers (AAG) Annual Meeting in Honolulu, Hawaii between Tuesday, April 16 – Saturday, April 20, 2024 we are organizing a session(s) on <b><i>Geosimulations for Addressing Societal Challenges</i></b>. If the session description is of interest, please feel free to submit an abstract (details are below). <br /><br /><b>Session Description: </b><br /><br />There is an urgent need for research that promotes sustainability in an era of societal challenges ranging from climate change, population growth, aging and wellbeing to that of pandemics. These need to be directly fed into policy. We, as a Geosimulation community, have the skills and knowledge to use the latest theory, models and evidence to make a positive and disruptive impact. These include agent-based modeling, microsimulation and increasingly, machine learning methods. However, there are several key questions that we need to address which we seek to cover in this session. For example, What do we need to be able to contribute to policy in a more direct and timely manner? What new or existing research approaches are needed? How can we make sure they are robust enough to be used in decision making? How can geosimulation be used to link across citizens, policy and practice and respond to these societal challenges? What are the cross-scale local trade-offs that will have to be negotiated as we re-configure and transform our urban and rural environments? How can spatial data (and analysis) be used to support the co-production of truly sustainable solutions, achieve social buy-in and social acceptance? And thereby co-produce solutions with citizens and policy makers. <br /><br />We are particularly interested in presentations that will discuss issues relating to: <br /></p><ul style="text-align: left;"><li>Agent-based modeling and microsimulation techniques for responding to societal challenges; Agent-based models used for policy formation; </li><li>Data driven modeling; </li><li>Utilizing machine modeling for geosimulation; </li><li>Creating really big models using exascale computation; </li><li> Model validation and assessment; </li><li>Participatory methods for agent-based modeling; </li><li>Approaches to connect and share (open source) data and models; </li><li>Revealing, quantifying, and reducing socio-economic inequalities with Geosimulation.</li></ul><p style="text-align: left;"><br /><b> Next Steps: </b><br /></p><p style="text-align: justify;">If this sounds of interest, please e-mail the abstract and key words with your expression of intent to Richard Jiang (<a href="mailto: njiang8@buffalo.edu">njiang8@buffalo.edu</a>) by <b>November 9th</b> (one week before the AAG session deadline). Please make sure that your abstract conforms to the AAG guidelines in relation to title, word limit and key words and as specified at: <a href="https://aag.secure-platform.com/aag2024/page/abstracts/abstract-guidelines" target="_blank">https://aag.secure-platform.com/aag2024/page/abstracts/abstract-guidelines</a> <br /><br />An abstract should be no more than 250 words that describe the presentation’s purpose, methods, and conclusions. </p><p style="text-align: justify;"><br /><b>Timeline: </b><br /></p><ul style="text-align: justify;"><li><b>9th November, 2023</b>: Abstract submission deadline. E-mail Richard Jiang by this date if you are interested in being in this session. Please submit an abstract and key words with your expression of intent. </li><li><b>14th November, 2023</b>: Session finalization and author notification </li><li><b>15th November, 2023</b>: Final abstract submission to AAG, via <a href="https://aag.secure-platform.com/aag2024/">https://aag.secure-platform.com/aag2024/</a>. All participants must register individually via this site. Upon registration you will be given a participant number (PIN). Send the PIN and a copy of your final abstract to Richard Jiang. Neither the organizers nor the AAG will edit the abstracts. </li><li><b>16th November, 2023</b>: AAG registration deadline. Sessions submitted to AAG for approval.</li><li><b>16th -20th April 2024</b>: AAG in Honolulu. <br /></li></ul><p style="text-align: left;"><br /><b>Organizers </b></p><ul style="text-align: left;"><li><a href="https://www.gla.ac.uk/schools/socialpolitical/staff/alisonheppenstall/" target="_blank"><b>Alison Heppenstall</b></a>, University of Glasgow, Scotland. <br /></li><li><b><a href="https://www.urbanagentjiang.net/" target="_blank">Na (Richard) Jiang</a></b>, University at Buffalo, USA.</li><li><b><a href="https://www.hutton.ac.uk/staff/gary-polhill" target="_blank">Gary Polhill</a></b>, The James Hutton Institute, Scotland. </li><li><a href="https://www.gisagents.org/" target="_blank"><b>Andrew Crooks</b></a>, University at Buffalo, USA. </li><li><b><a href="https://www.mcgill.ca/geography/people-0/sengupta" target="_blank">Raja Sengupta</a></b>, McGill University, Canada. <br /></li><li><a href="https://www.sfu.ca/dragicevic/" target="_blank"><b>Suzana Dragicevic</b></a>, Simon Fraser University, Canada. </li><li><b><a href="https://profiles.ucl.ac.uk/42176" target="_blank">Sarah Wise</a></b>, University College London, England. </li><li><a href="https://sites.google.com/view/geokang" target="_blank"><b>Jeon-Young Kang</b></a>, Kyung Hee University, South Korea.<br /></li></ul><p></p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-22770502.post-84064766544518494152023-09-07T12:21:00.000-04:002023-09-07T12:21:07.390-04:00Agent-Based Modeling of Consumer Choice <p style="text-align: justify;"></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgMbh_PcD6KLfX5K9Wt4rStfF2blHzYcL4k51gGvYl83d8CnJHftZ-AxWb_sxRWYCaOXwmSji--F9ZxxwaxgPN6Y_nZjWZh00wxxUunUqbfVBFfLRpWEw3RhHbW1As9k-xmOHlxh1sXDOQXF6R-x4Hp_M5m0tG8FYwpO-sHHGy1pwH9E2BTPTHP/s2893/website_logo.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" data-original-height="1275" data-original-width="2893" height="88" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgMbh_PcD6KLfX5K9Wt4rStfF2blHzYcL4k51gGvYl83d8CnJHftZ-AxWb_sxRWYCaOXwmSji--F9ZxxwaxgPN6Y_nZjWZh00wxxUunUqbfVBFfLRpWEw3RhHbW1As9k-xmOHlxh1sXDOQXF6R-x4Hp_M5m0tG8FYwpO-sHHGy1pwH9E2BTPTHP/w200-h88/website_logo.png" width="200" /></a></div>At the upcoming <a href="https://giscience2023.github.io/" target="_blank">International Conference on Geographic Information Science</a> (GIScience 2023) <a href="https://wang-boyu.github.io/" target="_blank">Boyu Wang</a> and myself have a new paper entitled "<i><a href="https://drops.dagstuhl.de/opus/frontdoor.php?source_opus=18976">Agent-Based Modeling of Consumer Choice by Utilizing Crowdsourced Data and Deep Learning</a></i>." In the paper we explore how through mining Yelp reviews can inform an agents choices of restaurants. The model itself was created in <a href="https://github.com/projectmesa/" target="_blank">Mesa</a> and uses <a href="https://www.gisagents.org/2022/11/mesa-geo-abm-and-gis-in-python-update.html" target="_blank">Mesa-Geo</a> and more details about the model can be found at <a href="https://github.com/wang-boyu/yelp-abm">https://github.com/wang-boyu/yelp-abm</a>. If this sounds of interest, below you can see the abstract to the paper, some fugues including the graphical user interface of the model and a link to the paper. <br /><p></p><blockquote style="text-align: left;"><div style="text-align: justify;"><b>Abstract</b>: People’s opinions are one of the defining factors that turn spaces into meaningful places. Online platforms such as Yelp allow users to publish their reviews on businesses. To understand reviewers' opinion formation processes and the emergent patterns of published opinions, we utilize natural language processing (NLP) techniques especially that of aspect-based sentiment analysis methods (a deep learning approach) on a geographically explicit Yelp dataset to extract and categorize reviewers' opinion aspects on places within urban areas. Such data is then used as a basis to inform an agent-based model, where consumers' (i.e., agents') choices are based on their characteristics and preferences. The results show the emergent patterns of reviewers' opinions and the influence of these opinions on others. As such this work demonstrates how using deep learning techniques on geospatial data can help advance our understanding of place and cities more generally. <br /></div><br /><br /><b>Keywords</b>: aspect-category sentiment analysis, consumer choice, agent-based modeling, online restaurant reviews.</blockquote><p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgJq_lyTxs0wlCWR0f0jdiQQoeiyxr9L3lXGSrUTBelZiNvXsGikdijt95WwDJnuh3EBz5U6K9CEfga-jwUiG2ADEbw1iEwNQgQLHkJJlF0ddEs0evodDfcDcrVZ9z7dVNjUgRGnrVerHAgFJbKlvJ9BsmgXXEB-e3BeGaHxJ_G8jAkXJ-LAQ_9/s983/Screen%20Shot%202023-09-07%20at%2012.00.42%20PM.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="313" data-original-width="983" height="204" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgJq_lyTxs0wlCWR0f0jdiQQoeiyxr9L3lXGSrUTBelZiNvXsGikdijt95WwDJnuh3EBz5U6K9CEfga-jwUiG2ADEbw1iEwNQgQLHkJJlF0ddEs0evodDfcDcrVZ9z7dVNjUgRGnrVerHAgFJbKlvJ9BsmgXXEB-e3BeGaHxJ_G8jAkXJ-LAQ_9/w640-h204/Screen%20Shot%202023-09-07%20at%2012.00.42%20PM.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">An overview of proposed agent-based model logic.</td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg7zKGFegx6iKO7mQ85cRS4GDPl3CRILXk8yv7J6-Bw4vw6YcII9w7PlD8lf9SRt5rM05LAnCy3QDaafSSz3KQjHUg9UmpCpCE-zuiPB-M97prCLkNTaKyqMKwF2jAJl4YdydKaiO_9Han_EVF2H9vxgclPAaaRhgoTjNw4SEO76n-HuQOvuc5X/s984/Screen%20Shot%202023-09-07%20at%2012.00.59%20PM.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="328" data-original-width="984" height="214" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg7zKGFegx6iKO7mQ85cRS4GDPl3CRILXk8yv7J6-Bw4vw6YcII9w7PlD8lf9SRt5rM05LAnCy3QDaafSSz3KQjHUg9UmpCpCE-zuiPB-M97prCLkNTaKyqMKwF2jAJl4YdydKaiO_9Han_EVF2H9vxgclPAaaRhgoTjNw4SEO76n-HuQOvuc5X/w640-h214/Screen%20Shot%202023-09-07%20at%2012.00.59%20PM.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Average star rating vs. average sentiment by aspect category for 200 randomly selected restaurants in the City of St. Louis, MO.</td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgfgfqV5obBEJbSTEs4OSQYL45TmT768J2yxolzaVzrMgDE1zwQqqjYA8w-icYSdYz2waJHLIo457cVespSEmPAmYXgDsxUXfZA1_q5ooFTGGQuVBYj3phGGfE5ZGLBXfbG1EX6r3-iEklRFUCsUqkHfmvGBUKxIFk01mzvJARtFZV0J347KNYi/s987/Screen%20Shot%202023-09-07%20at%2012.01.14%20PM.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="941" data-original-width="987" height="610" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgfgfqV5obBEJbSTEs4OSQYL45TmT768J2yxolzaVzrMgDE1zwQqqjYA8w-icYSdYz2waJHLIo457cVespSEmPAmYXgDsxUXfZA1_q5ooFTGGQuVBYj3phGGfE5ZGLBXfbG1EX6r3-iEklRFUCsUqkHfmvGBUKxIFk01mzvJARtFZV0J347KNYi/w640-h610/Screen%20Shot%202023-09-07%20at%2012.01.14%20PM.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">The prototype agent-based model (a) with simulated (b) and actual visiting patterns (c).</td></tr></tbody></table><br /></p><p><b>Full reference:</b></p><blockquote><b>Wang, B. and Crooks, A.T. (2023),</b> Agent-Based Modeling of Consumer Choice by Utilizing Crowdsourced Data and Deep Learning, in Beecham, R., Long, J.A., Smith, D., Zhao, Q., and Wise, S (eds),<i> Proceedings of the 12th International Conference on Geographic Information Science (GIScience 2023)</i>, Dagstuhl Publishing, Dagstuhl, Germany., pp. 81:1-81:6. (<a href="https://www.dropbox.com/scl/fi/u7uzmnhsanemf85bcglu2/LIPIcs-GIScience-2023-81.pdf?rlkey=i6yhovih6aw0y8wrtnu39bo1l&dl=0" target="_blank">pdf</a>)</blockquote><p><br /></p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-22770502.post-30296182138035686452023-08-30T12:21:00.000-04:002023-09-07T12:22:27.470-04:00ABM Online Courses<div style="text-align: justify;">Often I get asked about how to learn about agent-based modeling (ABM). While we have a book on this with respect to <a href="https://www.gisagents.org/p/agent-based-modelling-geographical.html">GIS and ABM</a>, the other day, <a href="https://environment.leeds.ac.uk/geography/staff/2702/jiaqi-ge">Jiaqi Ge</a> posted a question about free <a href="https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=ind2308&L=SIMSOC&O=D&P=13102">ABM online courses on the SIMSOC mailing list</a> and I though it would be worth summarizing the responses here as the resources are quite useful. <br /><br /><a href="https://environment.leeds.ac.uk/geography/staff/2702/jiaqi-ge">Jiaqi</a> shared some really good resources like the <a href="https://www.santafe.edu/">Santa Fe Institutes</a> "<i><a href="https://www.complexityexplorer.org/courses/146-introduction-to-agent-based-modeling-summer-2022">Introduction to Agent-Based Modeling</a></i>" and "<i><a href="https://www.complexityexplorer.org/courses/84-fundamentals-of-netlogo">Fundamentals of NetLogo</a></i>" along with the University of Geneva's Coursera course "<a href="https://www.coursera.org/learn/modeling-simulation-natural-processes"><i>Simulation and modeling of natural processes</i></a>". </div><div style="text-align: justify;"> </div><div style="text-align: justify;">Others also responded to the question. For example, <a href="https://www.rug.nl/staff/w.jager/">Wander Jager</a><a href="https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=ind2308&L=SIMSOC&D=0&O=D&P=14218"> responded</a> with online modules developed from the <a href="https://actiss-edu.eu/">Action for Computational Thinking in Social Sciences (A</a><a href="https://actiss-edu.eu/">CTiSS)</a> team. <a href="https://jbadham.biz/" target="_blank">Jen Badham</a><a href="https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=ind2308&L=SIMSOC&D=0&O=D&P=18715"> responded</a> with an <a href="https://jbadham.biz/Research/ABMBook/">extended tutorial about model design and creating models in Netlogo</a> while <a href="https://www.dinocarp.com/">Dino Carpentras </a><a href="https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=ind2308&L=SIMSOC&D=0&O=D&P=19277">responded</a> with <a href="https://www.youtube.com/watch?v=eo-BIKAu8Bc&list=PLsv8cj_Tu8Ks_JKH9ZshfA6C6DO3MAG_q&ab_channel=SocialComplexity%2FComputationalSocialScience">several general videos on YouTube</a> on ABM which he has created. Hopefully readers will find these useful and also you might want to see our <a href="https://github.com/abmgis/abmgis" target="_blank">Github pages on GIS and ABM</a>. <br /></div> Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-22770502.post-51932271318706673372023-06-28T15:29:00.003-04:002023-06-28T15:29:54.168-04:00Editorial: Urban analytical approaches to combating the Covid-19 pandemic<div class="separator" style="clear: both; text-align: left;"><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh7QvChU2OMDeYNUodEGg_07jCzHR4kRajZyZODivLnnTPvEbh2oLwzC_S7yICUl0Lb_6q2pbW0oSd3yLd-sxG33Od_HIEQLVL6FQXrHxSuBPik2TqoQ34zbe024YE_FRAJYye2zsahf5hpEYVoPFoncPfiGTZnLfdUT1_U9Ytzi3BlXYDkZQ/s600/epb-cover-social.jpg" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" data-original-height="314" data-original-width="600" height="80" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh7QvChU2OMDeYNUodEGg_07jCzHR4kRajZyZODivLnnTPvEbh2oLwzC_S7yICUl0Lb_6q2pbW0oSd3yLd-sxG33Od_HIEQLVL6FQXrHxSuBPik2TqoQ34zbe024YE_FRAJYye2zsahf5hpEYVoPFoncPfiGTZnLfdUT1_U9Ytzi3BlXYDkZQ/w154-h80/epb-cover-social.jpg" width="154" /></a></div><div style="text-align: justify;">While there has been a lot written about COVID-19 <a href="https://geography.uga.edu/directory/people/xiaobai-angela-yao" target="_blank">Angela Yao</a>, <a href="http://giscience.hig.se/binjiang/" target="_blank">Bin Jiang</a>, <a href="https://www.uni-augsburg.de/en/fakultaet/fai/geo/prof/geoagi/geoagi-team/j-krisp/" target="_blank">Jukka Krisp</a>, <a href="https://geoxlab.github.io/" target="_blank">Xintao Liu</a>, and <a href="https://users.ugent.be/~haohuang/" target="_blank">Haosheng Huang</a> and myself have just recently wrapped up a <a href="https://journals.sagepub.com/toc/epbb/50/5" target="_blank">special issue</a> in <a href="https://journals.sagepub.com/home/EPB" target="_blank"><i>Environment and Planning B</i></a> and how it can be studied through the lens of urban analytics. After a <a href="https://journals.sagepub.com/pb-assets/cmscontent/EPB/CallforSubmissionsUrbanAnalyticalApproachestoCombatingCovid-19-1626071395.pdf" rel="nofollow" target="_blank">call for papers</a> for the <a href="https://journals.sagepub.com/toc/epbb/50/5" target="_blank">special issue</a>, we published 10 papers that cover a wide spectrum of analytical methods have been used to study the pandemic. These ranged from how policies impacted pedestrian patterns to how data could on the disease could be visualized along with many things in between. Below you can see papers: </div></div><p></p><ul style="text-align: justify;"><li>Angel, A., A. Cohen, S. Dalyot and P. Plaut.</li><ul><li><a href="https://journals.sagepub.com/doi/10.1177/23998083221113332" target="_blank">Impact of COVID-19 policies on pedestrian traffic and walking patterns</a>. </li></ul><li>Dass, S., D. T. O'Brien, A. Ristea. (2023):</li><ul><li><a href="https://doi.org/10.1177/23998083221114645" target="_blank">Strategies and inequities in balancing recreation and COVID exposure when visiting green spaces</a>. </li></ul><li>Li, R. and Huang, Y. (2023):</li><ul><li><a href="https://doi.org/10.1177/239980832211265" target="_blank">COVID-19 Pandemic and minority health disparities in New York city: A spatial and temporal perspective</a>. </li></ul><li>Li,Y., Z. Ran, L. Tsai, and S. Williams. (2023):</li><ul><li><a href="https://doi.org/10.1177/23998083231158377" target="_blank">Using CDR (Call Detail Records) to determine mobility patterns of different socio-demographic groups in the western area of Sierra Leone during early COVID-19 crisis.</a> </li></ul><li>Praharaj, S., Solis, P., and Wentz, E. A. (2023):</li><ul><li><a href="https://doi.org/10.1177/239980832211428" target="_blank">Deploying geospatial visualization dashboards to combat the socioeconomic impacts of COVID-19</a>. </li></ul><li>Tong C, Shi W, Zhang A, et al. (2023):</li><ul><li><a href="https://journals.sagepub.com/doi/full/10.1177/23998083221127703" target="_blank">Predicting
onset risk of COVID-19 symptom to support healthy travel route planning
in the new normal of long-term coexistence with SARS-CoV-2</a>. <br /></li></ul><li>Venerandi, A., Aiello, L.M., and Porta, S. (2023): </li><ul><li><a href="https://doi.org/10.1177/239980832211333" target="_blank">Urban form and COVID-19 cases and deaths in Greater London: an urban morphometric approach</a>. </li></ul><li>Wolday, F. Bocker, L. (2023):</li><ul><li><a href="https://journals.sagepub.com/doi/10.1177/23998083231164398" target="_blank">Exploring changes in residential preference during COVID-19</a>.</li></ul><li>Yu, Z. and Liu, X. (2023):</li><ul><li><a href="https://journals.sagepub.com/doi/10.1177/23998083221107019" target="_blank">Spatial
variations of the third and fourth Covid-19 waves in Hong Kong: a
comparative study using built environment and socio-demographic
characteristics</a>. </li></ul><li>Zhang, W., Barchers, C. and Smith-Colin, J. (2023): </li><ul><li><a href="https://journals.sagepub.com/doi/10.1177/23998083221135609" target="_blank">Transit communication via Twitter during the COVID-19 pandemic</a>. </li></ul></ul><p style="text-align: justify;">Accompanying these papers is an editorial entitled "<i><a href="https://journals.sagepub.com/doi/10.1177/23998083231174748" target="_blank">An overview of urban analytical approaches to combating the Covid-19 pandemic,</a></i>" In this editorial we situate these papers in the larger literature of urban analytics and Covid-19. Also in the editorial, we explore what can be learned from the current research on Covid-19 and finally we identify gaps and future research opportunities for urban analytics in combating epidemic outbreaks.<br /></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhIIAKrknVFlb1rfyGtqlios5CqF0Y9AwWrRn3ORCNDVQv0LP3kD1mmgb8I3mUW82kr_6By9TG8CyZPXDKame1DHj0ezpvO7JUcwZwmMTICLVI9s7Kf2kMRn875HIJJdqRGYxWsKfGQSH6sqbo1Qhbl02gQdf7XT7LaBhku4jqVrVEeh1IAeg/s2900/Covid.jpeg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1900" data-original-width="2900" height="420" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhIIAKrknVFlb1rfyGtqlios5CqF0Y9AwWrRn3ORCNDVQv0LP3kD1mmgb8I3mUW82kr_6By9TG8CyZPXDKame1DHj0ezpvO7JUcwZwmMTICLVI9s7Kf2kMRn875HIJJdqRGYxWsKfGQSH6sqbo1Qhbl02gQdf7XT7LaBhku4jqVrVEeh1IAeg/w640-h420/Covid.jpeg" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">A framework of the Covid-19 pandemic dynamics in urban systems.</td></tr></tbody></table><p></p><p></p><p><b><br /></b></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhpzix_zAxTXUR2K2I0EOEqqaMNOepgL8RkNf3Zjp5stQYFnwK8KCqVAuPtLSZ1CCFshTdD9lJ-3O_Bo_iOVIqTgDCpMDIOxiadCllax4kxAlC_HY7--BBFaiVVw83HH5sjPIBFYrIP3gJF1KWioGcq3fvnwMa8xoFco2k4RC4LDTYFYD3B8w/s3464/Screen%20Shot%202023-05-30%20at%203.18.50%20PM.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1264" data-original-width="3464" height="234" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhpzix_zAxTXUR2K2I0EOEqqaMNOepgL8RkNf3Zjp5stQYFnwK8KCqVAuPtLSZ1CCFshTdD9lJ-3O_Bo_iOVIqTgDCpMDIOxiadCllax4kxAlC_HY7--BBFaiVVw83HH5sjPIBFYrIP3gJF1KWioGcq3fvnwMa8xoFco2k4RC4LDTYFYD3B8w/w640-h234/Screen%20Shot%202023-05-30%20at%203.18.50%20PM.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Covid-19 research themes and topics through the lens of geography and urban analytics.</td></tr></tbody></table><b><br /></b><p></p><p><b>Full Reference:</b><br /></p><blockquote><b>Yao, X.A, Crooks, A.T., Jiang, B., Krisp, J., Liu, X. and Huang, H.</b> (2023), <a href="https://doi.org/10.1177/23998083231174748" target="_blank">An overview of urban analytical approaches to combating the Covid-19 pandemic</a>, <i>Environment and Planning B, </i>50 (5), pp. 1133–1143. (<a href="https://www.dropbox.com/s/syhq47ao3rk1cnn/Covid_editorial.pdf?dl=0" target="_blank">pdf</a>) </blockquote><p></p>
<br />Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-22770502.post-63894628444749251642023-05-20T10:39:00.002-04:002023-07-05T08:57:41.678-04:00Simulation & Optimization Techniques for the Mitigation of Disruptions to Supply Chains<div style="text-align: justify;"><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjC1Km-_520tuAg7EJLORHqSzNCRvuJBk9PdN979xIh-K0y5DPRisPx5Z-o5N_lkoYI-TlffpJQhbuu-6FsIyxqwhRvKVzCkA2sK7LUuC85Xpt0AMKw0C6qU-zy8xW2Q8kK54bjgEFs0dozmZRX5IaPVZ-K1rqR5J_Rs1Iz-io46KtF054glw/s2340/Screen%20Shot%202023-05-02%20at%201.28.46%20PM.png" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" data-original-height="752" data-original-width="2340" height="103" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjC1Km-_520tuAg7EJLORHqSzNCRvuJBk9PdN979xIh-K0y5DPRisPx5Z-o5N_lkoYI-TlffpJQhbuu-6FsIyxqwhRvKVzCkA2sK7LUuC85Xpt0AMKw0C6qU-zy8xW2Q8kK54bjgEFs0dozmZRX5IaPVZ-K1rqR5J_Rs1Iz-io46KtF054glw/s320/Screen%20Shot%202023-05-02%20at%201.28.46%20PM.png" width="320" /></a></div>Our last paper at the <a href="https://scs.org/annsim/" target="_blank">Annual Modeling and Simulation Conference</a> (ANNSIM) is entitled "<a href="https://www.dropbox.com/s/bzfvdkj51iqztkk/ANNSIM_Supply_Chain.pdf?dl=0" target="_blank"><i>Simulation and Optimization Techniques for the Mitigation of Disruptions to Supply Chains</i></a>" where we (<a href="https://www.linkedin.com/in/raj-patel-b2488792/" target="_blank">Raj Patel</a>, <a href="https://www.linkedin.com/in/abhisekh-rana-9622b469/" target="_blank">Abhisekh Rana</a>, <a href="https://cs.gmu.edu/~sean/" target="_blank">Sean Luke</a>, <a href="https://cs.gmu.edu/~carlotta/" target="_blank">Carlotta Domeniconi</a>, <a href="https://hamdikavak.com/" target="_blank">Hamdi Kavak</a>, <a href="https://volgenau.gmu.edu/profiles/jjonesu" target="_blank">Jim Jones</a> and myself) build upon <a href="https://www.gisagents.org/search/label/Supply%20Chains" target="_blank">our previous work</a> which explored how the actions of criminal networks and agents might impact supply chains. </div><div style="text-align: justify;"> </div><div style="text-align: justify;">This paper extends this research to incorporate both disruption and mitigation modeling into the same simulation. By using evolutionary computation optimization techniques (e.g., <a href="https://en.wikipedia.org/wiki/CMA-ES" target="_blank">Covariance Matrix Adaptation Evolution Strategy</a>) we demonstrate how we can optimize both the disruption and mitigation scenarios in a pharmaceutical supply chain (which we call PharmaSIM). Our results demonstrate how evolutionary computation techniques could be used to not only identify worst-case disruption scenarios but to also optimize the allocation of the mitigations to counter their effects. If this sounds of interest, below we provide the abstract to the paper,
some of the figures we use to support our discussion and results. While at the bottom of
the post we provide the full reference to the paper along with a link to
a preprint of it. </div><p style="text-align: left;"></p><p style="text-align: left;"></p><blockquote style="text-align: left;"><b>Abstract</b><br /><div style="text-align: justify;">The COVID-19 pandemic has clearly highlighted the importance of supply chains to the function of the world economy. Moreover, the global nature of most modern supply chains along with their complexity has left them vulnerable to a wide-ranging set of disruptive scenarios. This increase in complexity has also led to a corresponding increase in disruptions to supply chains from criminal networks. In this paper, we demonstrate how a generic pharmaceutical supply chain network can be successfully modeled using discrete event simulation. We outline how disruptions by criminal networks and mitigation strategies to counter them can be effectively incorporated into the same model. Finally, we show how optimization techniques, such as evolutionary computation, can be used to not only identify worst-case disruptions and find mitigations for them, but also be used to identify mitigation strategies that are effective against a diverse set of damaging disruption scenarios.<br /></div><br /><div style="text-align: justify;"><b>Keywords</b>: Simulation, Optimization, Supply Chains, Disruptions, Mitigation. </div></blockquote><p style="text-align: left;"></p><p style="text-align: left;"><br /></p><table cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: left;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjfzMF_8Zepz8KeasQbnpjDMSGuPILTXAjAUdj5lIYz8F5sj0BHFXZh_MPfOXE6NxzoiwcXeLEec67n6jP7KQ8EtkBkn1KT841dosO4xcMc3C6znn_DoDJPeeJ7OYFyebSCpHxcCesNRamCSe64PAMDtiDUdhiZ8dHPZDuI0tNIRPAq9--HKA/s2410/Supply%20Chain%20Model.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1350" data-original-width="2410" height="359" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjfzMF_8Zepz8KeasQbnpjDMSGuPILTXAjAUdj5lIYz8F5sj0BHFXZh_MPfOXE6NxzoiwcXeLEec67n6jP7KQ8EtkBkn1KT841dosO4xcMc3C6znn_DoDJPeeJ7OYFyebSCpHxcCesNRamCSe64PAMDtiDUdhiZ8dHPZDuI0tNIRPAq9--HKA/w640-h359/Supply%20Chain%20Model.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Topology of the generic pharmaceutical supply chain (PharmaSIM) model.</td></tr></tbody></table><br /><table cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: left;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEijljAEbpJojk0hpktjpttTU4o0LYl8n90VmNImt66aUhg41R4_7MHNoE3XN5FqmcExyVBXqztpCZKPlsXDCFI4CoVOiEiHCWCadVY2d798QB2yNBquVk2QePaY2boCFTBF_XNK-78i6MJLHh184VxWFqeXE91bxBOig7l-ynEqIKyxVnhXdA/s1501/Screen%20Shot%202023-05-09%20at%201.21.48%20PM.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="799" data-original-width="1501" height="340" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEijljAEbpJojk0hpktjpttTU4o0LYl8n90VmNImt66aUhg41R4_7MHNoE3XN5FqmcExyVBXqztpCZKPlsXDCFI4CoVOiEiHCWCadVY2d798QB2yNBquVk2QePaY2boCFTBF_XNK-78i6MJLHh184VxWFqeXE91bxBOig7l-ynEqIKyxVnhXdA/w640-h340/Screen%20Shot%202023-05-09%20at%201.21.48%20PM.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Fitness after evolutionary optimization of attack configurations and corresponding safety stock allocation for different budgets.</td></tr></tbody></table><br /><table cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: left;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj4vcako8uACzp21zyCNBNOYumjAT1_Da-Atw7EXsPYhuY5nJXKPAeDc_ZBQMHP2bq6d7iveWKJ-37D72kWUs1rTbnESQe8LtHf5O4N2E4Qfej6zISfT8yWeUTIX84sHTvHapnxieFvHPRHak2AqM7lvI7eDrkfJNeSXUwdyriMJ6Ct9eDqKw/s800/Fig4.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="600" data-original-width="800" height="480" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj4vcako8uACzp21zyCNBNOYumjAT1_Da-Atw7EXsPYhuY5nJXKPAeDc_ZBQMHP2bq6d7iveWKJ-37D72kWUs1rTbnESQe8LtHf5O4N2E4Qfej6zISfT8yWeUTIX84sHTvHapnxieFvHPRHak2AqM7lvI7eDrkfJNeSXUwdyriMJ6Ct9eDqKw/w640-h480/Fig4.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Fitness by generation for the coevolution of attack vectors and mitigation configurations.</td></tr></tbody></table><br /><p style="text-align: left;"><b>Full Reference</b>:</p><blockquote style="text-align: left;"><p><b>Rana, R., Patel, R., Luke, S., Domeniconi, C., Kavak, H., Jones, J. and Crooks, A.T. </b> (2023), Simulation And Optimization Techniques for the Mitigation of Disruptions to Supply Chains, <i>The Annual Modeling and Simulation Conference (ANNSIM)</i>, Hamilton, ON. (<a href="https://www.dropbox.com/s/bzfvdkj51iqztkk/ANNSIM_Supply_Chain.pdf?dl=0" target="_blank">pdf</a>)<br /></p></blockquote>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-22770502.post-28295674940569878322023-05-20T10:38:00.001-04:002023-05-23T11:33:42.431-04:00Spiral Software Development Process for ABM<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgja5hu0Qtfqk9IE7FJRt12TnzdPULQKC5sd11ESVk7-QrAw4vAdjHCuw3mCihyGhhujWWKhiY6PV0TrgKdlo4e8NqAE-lTQKoGQNoUQCF-9kFAta3KHFFwkRsJtakFxXaxkyFpFbYVQApHSFIJb6ijP-QAR5YwBmuPh8vM7_2ty75_T2wcSA/s2340/Screen%20Shot%202023-05-02%20at%201.28.46%20PM.png" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" data-original-height="752" data-original-width="2340" height="103" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgja5hu0Qtfqk9IE7FJRt12TnzdPULQKC5sd11ESVk7-QrAw4vAdjHCuw3mCihyGhhujWWKhiY6PV0TrgKdlo4e8NqAE-lTQKoGQNoUQCF-9kFAta3KHFFwkRsJtakFxXaxkyFpFbYVQApHSFIJb6ijP-QAR5YwBmuPh8vM7_2ty75_T2wcSA/s320/Screen%20Shot%202023-05-02%20at%201.28.46%20PM.png" width="320" /></a></div><p></p><p style="text-align: justify;">Readers of this blog might gather that we are constantly developing <a href="https://www.gisagents.org/search/label/Agent%20Based%20Models" target="_blank">agent-based models</a> to study and better understand a wide range of problems but unlike in say the software industry, agent-based model development is rather ad hoc in terms of a standardized software development process. To this end <a href="https://www.linkedin.com/in/max-malikov/" target="_blank">Maxim Malikov</a>, <a href="https://www.linkedin.com/in/fahad-aloraini-773753142/" target="_blank">Fahad Aloraini</a>, <a href="https://hamdikavak.com/" target="_blank">Hamdi Kavak</a> and <a href="http://www.mllab.com/" target="_blank">William Kennedy</a> from George Mason University and myself have a paper entitled "<i><a href="https://www.dropbox.com/s/etywvns9zc3z71q/ANNSIM_2023_SIND.pdf?dl=0" target="_blank">Developing a Large-Scale Agent-Based Model Using the Spiral Software Development Process</a></i>" which we will be presenting at the upcoming <a href="https://scs.org/annsim/" target="_blank">Annual Modeling and Simulation Conference</a>
(ANNSIM).<br /></p><p style="text-align: justify;">In the paper, we review the unique aspects of agent-based models and discuss the challenges faced in the development of our own large-scale agent-based model, which simulates the impact of a <a href="https://www.gisagents.org/search/label/Disasters" target="_blank">disaster</a> on the infrastructure and the population of a city. This project combines the expertise of teams with multiple disciplines, and therefore must be able to adjust to novel input from these teams over the life of the project. Furthermore, we describe our solution to these challenges in the form of a variation of the <a href="https://en.wikipedia.org/wiki/Spiral_model" target="_blank">Spiral model of software development</a> and the ways this approach helped us address the exploratory nature of agent-based modeling. </p><p style="text-align: justify;">If this sounds of interest, below we provide the abstract to the paper, some of the figures we use to support our discussion. At the bottom of the post we provide the full reference to the paper along with a link to a preprint of it. <br /></p><b>Abstract:</b><br /><p></p><blockquote style="text-align: left;"><div style="text-align: justify;">As the level of complexity of agent-based models grows, so does the complexity of their development. At the time of writing, the discipline of agent-based modeling does not have an established standard for the software development process to support this increasing complexity. We hope to address this need by introducing our variation of the Spiral model of software development and demonstrating an application of this process through a simple use case. We argue that the Spiral model of software development is a flexible approach that can be tailored to fit the needs of almost any project type. Further, our agent-based modeling variation of the Spiral model is an effective approach that is capable of guiding and supporting large interdisciplinary teams participating in a project, while providing sufficient flexibility to account for the uncertainty in the requirements that may arise during the development period. <br /></div><br /><div style="text-align: justify;"><b>Keywords</b>: Software development, Agent-based Modeling, Spiral Development, Disaster.</div></blockquote><p></p><p> </p><p></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhRVEWu4lHnuwq9NR8xI5qlK895k3L9oFGJsoC_hdVDCZUzQ1lyg5RvfJhvuoR7l3RG9uU1nFWTDKZIcQOo4CzifQccNbFF0e1s-cLBoiH_rAPvDEh9imxZr4DTioejmhqenPJYwGNeMK7uTPtxnKC9UTe58OIcJN9nDXEgHfLG9CAiqQR7pA/s1553/Screen%20Shot%202023-05-09%20at%2011.51.01%20AM.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1245" data-original-width="1553" height="514" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhRVEWu4lHnuwq9NR8xI5qlK895k3L9oFGJsoC_hdVDCZUzQ1lyg5RvfJhvuoR7l3RG9uU1nFWTDKZIcQOo4CzifQccNbFF0e1s-cLBoiH_rAPvDEh9imxZr4DTioejmhqenPJYwGNeMK7uTPtxnKC9UTe58OIcJN9nDXEgHfLG9CAiqQR7pA/w640-h514/Screen%20Shot%202023-05-09%20at%2011.51.01%20AM.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;"><p>Spiral model with adjustments made to account for the specifics of complex agent-based models. Adopted from <a href="https://apps.dtic.mil/sti/citations/ADA382590" target="_blank">Boehm (2000)</a>.<br /></p></td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgObW6oi4iYCi221sgs6plJUntpH2YpQ6TFhJg4wlCjq6J1D97j5BHMPO3FtqM0gUkPNMoIFrf4CWDMtjJ6bcG-72m9SNzlt4LrpgmKcevyY_gV_4r76ZGEq7OeIAPadvoUGUrj8bMykXP5O51POt7JzmMfFR4pV64lT0vO7T_9XuxI_fiZfA/s2646/MASON_UI.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1260" data-original-width="2646" height="304" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgObW6oi4iYCi221sgs6plJUntpH2YpQ6TFhJg4wlCjq6J1D97j5BHMPO3FtqM0gUkPNMoIFrf4CWDMtjJ6bcG-72m9SNzlt4LrpgmKcevyY_gV_4r76ZGEq7OeIAPadvoUGUrj8bMykXP5O51POt7JzmMfFR4pV64lT0vO7T_9XuxI_fiZfA/w640-h304/MASON_UI.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Prototype 1 of the city infrastructure simulation. This graphical user
interface shows agent and infrastructure changes after a disaster.</td></tr></tbody></table><p><b>Full reference</b>:</p><div style="text-align: justify;"><blockquote><b>Malikov, M., Aloraini, F., Crooks, A.T., Kavak, H. and Kennedy, W.G. </b>(2023), Developing a Large-Scale Agent-Based Model Using the Spiral Software Development Process, <i>The Annual Modeling and Simulation Conference (ANNSIM)</i>, Hamilton, ON. (<a href="https://www.dropbox.com/s/etywvns9zc3z71q/ANNSIM_2023_SIND.pdf?dl=0" target="_blank">pdf</a>) </blockquote></div><p></p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-22770502.post-66045767450505487342023-05-16T14:55:00.000-04:002023-05-16T14:55:20.135-04:00Modeling Forced Migration<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiS8lBhV23bzooWBR_Jx3dGg0K1JPUytcOuwrfGe3DhYkGAqGl5eTSfJWBfePqWCG26Bya7fJsTiWI7A8u1ZI52Uf4lZ2qp0k-65QtSxzGyP_EtJWa0R7B0reLDyNP0mzIpUS1nhjsefVCYnTTEqkdZIRCwnapbmlw_-rJ5zYRO_L9sx28fMA/s2340/Screen%20Shot%202023-05-02%20at%201.28.46%20PM.png" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" data-original-height="752" data-original-width="2340" height="103" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiS8lBhV23bzooWBR_Jx3dGg0K1JPUytcOuwrfGe3DhYkGAqGl5eTSfJWBfePqWCG26Bya7fJsTiWI7A8u1ZI52Uf4lZ2qp0k-65QtSxzGyP_EtJWa0R7B0reLDyNP0mzIpUS1nhjsefVCYnTTEqkdZIRCwnapbmlw_-rJ5zYRO_L9sx28fMA/s320/Screen%20Shot%202023-05-02%20at%201.28.46%20PM.png" width="320" /></a></div><div style="text-align: justify;">At the upcoming <a href="https://scs.org/annsim/" target="_blank">Annual Modeling and Simulation Conference (ANNSIM)</a> we have several papers being presented. One of which is with Troy Curry and <a href="https://scholar.google.com/citations?user=t2kPvicAAAAJ&hl=en" target="_blank">Arie Croitoru</a> entitled "<a href="https://www.dropbox.com/s/lzv78q9it632vex/ANNSIM_forced_Migration.pdf?dl=0" target="_blank"><i>Modeling Forced Migration: A System Dynamic Approach</i></a>." In this paper we study how forced migration can be modeled through a systems dynamics perspective. </div><div style="text-align: justify;"> </div><div style="text-align: justify;">To some extent this paper builds upon our previous work on <a href="https://www.gisagents.org/search/label/refugee" target="_blank">refugees</a> especially making use new open data sources that allow us to study forced migration. Using ideas from systems thinking which incorporates notions non-linearity, interconnectedness, relationships, causality and feedbacks we build a systems dynamics model of the Syrian refugee crisis from January 2012 until December 2018. The model itself explores refugee-producing variables that have been linked as determinants of forced migration including human rights violations, political violence, generalized violence, and civil war. We use these refugee-producing variables to simulate the flow of refugees from Syria to Greece, Turkey, Lebanon and Jordan. </div><div style="text-align: justify;"> </div><div style="text-align: justify;">If this sounds of interest, below you can read the abstract of the model, see a high-level causal loop diagram for our forced migration model along with our validation attempts such as comparing predicted system dynamics model refugee counts vs. reference United Nations High Commissioner for Refugees (UNHCR) refugee counts. We also have included a movie of one such model scenario however, readers can also run the model <a href="https://cloud.anylogic.com/model/15bd8b52-d5a2-4b90-9fae-2a958cc41464?mode=SETTINGS&tab=GENERAL" target="_blank">here</a>. Finally at the bottom of the page you can find the full reference to paper along with a link to a pre-print. <br /></div><p style="text-align: left;"><b>Abstract: </b></p><p style="text-align: left;"></p><div style="text-align: justify;"><blockquote><p>Forced migration of populations is a topic of increasingly national and international importance due to security, international relations, and humanitarian considerations. Despite its importance, there has been a dearth of quantitative research to support modeling and simulation of this topic, thus hindering our ability to better understand this phenomenon. Motivated by this gap, this research leverages the recent availability of diverse set of data related to forced migration, including regime legitimacy, violence, human rights violations, conflict, socio-political mobilization, intervening opportunities, and social media. The purpose of this article is to explore the applicability and utility of open-source data in a system dynamics model to forecast population displacement, and to illustrate the benefits of using a system dynamics approach to modeling displaced population on a national and international scale. Our results suggest that this proposed approach can be used to understand such migration processes and simulate possible scenarios. <br /></p></blockquote></div><blockquote style="text-align: left;"><p style="text-align: left;"><b>Keywords</b>: forced migration, refugee, system dynamics, prediction model, Middle East.</p></blockquote><p style="text-align: left;"><br /></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEio0ns5reJ9lTRsOOa-UCgXd0RyO1yH10hUsEQLze7noBg_dMokDCha9ZaAW19zIxdghMIJXoMY7-aM00ZkuvJcDuimjIQKeC8L4MGCADafOKV_nvsQn2jCWsiNjpe6E5w0CkDPhjfAzzG1Wk4XjVXe1XlwaN877MUjvc6veCgrm6HHgpDkGw/s737/Fig1.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="474" data-original-width="737" height="412" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEio0ns5reJ9lTRsOOa-UCgXd0RyO1yH10hUsEQLze7noBg_dMokDCha9ZaAW19zIxdghMIJXoMY7-aM00ZkuvJcDuimjIQKeC8L4MGCADafOKV_nvsQn2jCWsiNjpe6E5w0CkDPhjfAzzG1Wk4XjVXe1XlwaN877MUjvc6veCgrm6HHgpDkGw/w640-h412/Fig1.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">High-level causal loop diagram for forced migration. <br /></td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjozTpxirWzAdedClpUUFFnlEMNzDCBVKGFfiB9pu-z-DlPrXKV9u0LJVsYU3McClkXPzq16Q3PTsVy_JuwwxT7yuLkfVKZzqjS2LwVDGGAnxBoOqexNS9_gG5rAbSyVOZZuEXW7BXHGhuQwcZujkBtjApayxtlocd2m2rpsLVxR1nTAE1ipw/s722/Figure4.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="556" data-original-width="722" height="492" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjozTpxirWzAdedClpUUFFnlEMNzDCBVKGFfiB9pu-z-DlPrXKV9u0LJVsYU3McClkXPzq16Q3PTsVy_JuwwxT7yuLkfVKZzqjS2LwVDGGAnxBoOqexNS9_gG5rAbSyVOZZuEXW7BXHGhuQwcZujkBtjApayxtlocd2m2rpsLVxR1nTAE1ipw/w640-h492/Figure4.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Migration routes in simulation (i.e., Greece, Turkey, Lebanon, Jordan).</td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi9g0RszhbuQZBXb4vLWJWDOig1Gp__gJsPTZmVAwIkIqT2hdOq0EUGb5suPf25ZOkR-S9A6K6u2tAnwOcpGTMGoZI2mbEdvVR2rOXVEqmUzXGSeAhjJEbGV07Y0btDojYlDrpb-sFRTM9i1dC3GkUC6wCkSt2CHdtUZKO1C-yB7ejP-Is2Wg/s722/Figure5.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="429" data-original-width="722" height="380" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi9g0RszhbuQZBXb4vLWJWDOig1Gp__gJsPTZmVAwIkIqT2hdOq0EUGb5suPf25ZOkR-S9A6K6u2tAnwOcpGTMGoZI2mbEdvVR2rOXVEqmUzXGSeAhjJEbGV07Y0btDojYlDrpb-sFRTM9i1dC3GkUC6wCkSt2CHdtUZKO1C-yB7ejP-Is2Wg/w640-h380/Figure5.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Simulation refugee counts for paths to different countries (i.e., Greece, Turkey, Lebanon, Jordan).</td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgxDxtNI-Vdaq8sXpSzemPjA-DXNwn9Y8KDnl3sUOSt1WtqrD1MnmPbTEtYUy1whDFtqUw7ZQCPwNuJoEmNQzsbv4eX2EiK-P_-MJtbuhCJ1h8b6s3VRwpxQImq7r_NjITCE5adYVwJE8Z2ouPCWnqOzQUh9hdFyvhQCwaFPQYq-dL5DzEOcQ/s681/Figure6.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="389" data-original-width="681" height="366" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgxDxtNI-Vdaq8sXpSzemPjA-DXNwn9Y8KDnl3sUOSt1WtqrD1MnmPbTEtYUy1whDFtqUw7ZQCPwNuJoEmNQzsbv4eX2EiK-P_-MJtbuhCJ1h8b6s3VRwpxQImq7r_NjITCE5adYVwJE8Z2ouPCWnqOzQUh9hdFyvhQCwaFPQYq-dL5DzEOcQ/w640-h366/Figure6.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Model validation - comparing predicted system dynamics model refugee counts vs. reference UNHCR refugee counts.</td></tr></tbody></table><p style="text-align: left;"></p><center><iframe allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen="" frameborder="0" height="315" src="https://www.youtube.com/embed/SvEejgC0o3U" title="YouTube video player" width="560"></iframe></center><br /><p></p><p style="text-align: left;"><br /></p><p style="text-align: left;"><b>Full reference: </b><br /></p><blockquote><p style="text-align: left;"><b>Curry, T., Croitoru, A. and Crooks, A.T.</b> (2023), Modeling Forced Migration: A System Dynamic Approach, The Annual Modeling and Simulation Conference (ANNSIM), Hamilton, ON. (<a href="https://www.dropbox.com/s/lzv78q9it632vex/ANNSIM_forced_Migration.pdf?dl=0" target="_blank">pdf</a>)<br /></p></blockquote>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-22770502.post-66805467960484839992023-04-29T17:16:00.034-04:002023-05-08T08:33:53.806-04:00GAMA: (Gis & Agent-based Modelling Architecture) Platform<p style="text-align: justify;">Reader of the blog know we use predominantly <a href="https://www.gisagents.org/search/label/MASON">MASON</a>, <a href="https://www.gisagents.org/search/label/NetLogo">NetLogo</a> and <a href="https://www.gisagents.org/search/label/MESA">MESA</a> for our modeling projects but there are others (which we have <a href="https://www.gisagents.org/search/label/ABM%20Platforms">discussed</a><a href="https://www.gisagents.org/search/label/ABM%20Platforms"> in other posts</a>). To this end, recently over on the <a href="https://www.jiscmail.ac.uk/cgi-bin/webadmin?A0=simsoc">SIMSOC@JISCMAIL.AC.UK</a> mailing list, <a href="https://www.researchgate.net/profile/Alexis-Drogoul">Alexis Drogoul</a> <a href="https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=ind2304&L=SIMSOC&O=D&P=35777">announced the release of </a><a href="https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=ind2304&L=SIMSOC&O=D&P=35777">GAMA 1.9.1</a> and if the YouTube movie of the release is anything to go by it looks really interesting. More information about <a href="https://gama-platform.org/">GAMA</a> can be seen on their project page: <a href="https://gama-platform.org/">https://gama-platform.org/</a><br /></p><p></p>
<center><iframe allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen="" frameborder="0" height="315" src="https://www.youtube.com/embed/LvmNtsB1ytY" title="YouTube video player" width="560"></iframe></center>
<p style="text-align: justify;">On a side note, if you are interested in using computer simulation in the social sciences the <a href="https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A0=SIMSOC" target="_blank">SIMSOC mailing list is worth signing up for</a>. </p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-22770502.post-62473736417635591952023-03-22T14:55:00.002-04:002023-03-22T14:55:57.984-04:00AAG 2023 Presentations<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhlD-AhgR76jSaT60s-6nTRce1cPsBR9DbW8Bd5ibuig8fNnGDYtFHtcsPmRXnjUb_8ihnhKHE94YMF6u5XLVTXQ2GVq7wTil8pXL9twBhYtIumXkz3G9uTOHQVPN2k2x-YAnXBAA18jbygl5f7plGoLbGw-yR6Gwl-TR1AlcXQxhOwhDLz0Q/s318/Screen%20Shot%202023-03-22%20at%2011.47.23%20AM.png" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" data-original-height="190" data-original-width="318" height="119" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhlD-AhgR76jSaT60s-6nTRce1cPsBR9DbW8Bd5ibuig8fNnGDYtFHtcsPmRXnjUb_8ihnhKHE94YMF6u5XLVTXQ2GVq7wTil8pXL9twBhYtIumXkz3G9uTOHQVPN2k2x-YAnXBAA18jbygl5f7plGoLbGw-yR6Gwl-TR1AlcXQxhOwhDLz0Q/w200-h119/Screen%20Shot%202023-03-22%20at%2011.47.23%20AM.png" width="200" /></a></div><p style="text-align: justify;">At this years <i><a href="https://www.aag.org/events/aag2023/" target="_blank">Association of American Geographers (AAG) Annual Meeting</a></i> we have a number of presentations ranging from how one can leverage newspaper articles to study cities over time, to that of how people may chose to become vaccinated. These presentations build on the great work of students and postdocs here at the <a href="https://www.buffalo.edu/cas/geography.html" target="_blank">University at Buffalo</a> and link to our interests in urban analytics, machine learning and agent-based modeling. Below we just give a glimpse at these topics (along with their abstracts) and if you are interested in finding out more <a href="https://www.gisagents.org/p/contact.html" target="_blank">please reach out to us</a>. <br /></p><p style="text-align: justify;">First up is a presentation with <a href="https://urbansenses.org/" target="_blank">Qingqing Chen</a> and <a href="http://wang-boyu.github.io/" target="_blank">Boyu Wang</a> entitled "<a href="https://aag.secure-platform.com/aag2023/organizations/main/gallery/rounds/54/details/34353"><i>Community resilience to wildfires: A network analysis approach utilizing human mobility data</i></a>." In this presentation we explore how we can quantify a communities resilience to wildfires utilizing human mobility through network analysis methods. </p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiTczlVBpiNLRBjbeHDQuy1hmDxXsuxUHWzUEQAlsSCAFnEpg5mTACL_oprwqsn7AIJHhJzAoSsgdMJVdxlihGNNNCn1l2l2qFRnIvTRpHvIJLnShY9tH4jWBek5i9o84Wtmd9bUz780wNQ10Yf9SflzThglLtRKj8TKMezUWXevG4r2wuz6w/s1674/Screen%20Shot%202023-03-22%20at%2012.43.20%20PM.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="954" data-original-width="1674" height="364" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiTczlVBpiNLRBjbeHDQuy1hmDxXsuxUHWzUEQAlsSCAFnEpg5mTACL_oprwqsn7AIJHhJzAoSsgdMJVdxlihGNNNCn1l2l2qFRnIvTRpHvIJLnShY9tH4jWBek5i9o84Wtmd9bUz780wNQ10Yf9SflzThglLtRKj8TKMezUWXevG4r2wuz6w/w640-h364/Screen%20Shot%202023-03-22%20at%2012.43.20%20PM.png" width="640" /></a></div><p style="text-align: justify;"><b>Abstract</b> </p><p style="text-align: justify;"></p><blockquote><p style="text-align: justify;">Natural disasters, such as earthquakes, floods, and wildfires, have been a long-standing concern to societies at large. With growing attention being paid to sustainable and resilient communities, such concern has been brought to the forefront of resilience studies. However, the definition of disaster resilience is intricate and can vary across the diverse disciplines that study them (e.g., geography, sociology and political science), making its definition and quantification elusive. Moreover, the vast majority of studies often focus on the immediate response to an event, not the long-term recovery of the area impacted by disasters. Thus to date investigating the resilience of an area or a society over a prolonged period of time has remained largely unexplored. To overcome these issues, we propose a novel approach from a social perspective utilizing network analysis and concepts from disaster science (e.g., the resilience triangle) to quantify the long-term impacts of wildfires, especially on collective human behavior. Taking the Camp and Mendocino Complex wildfires - the most deadly and the largest complex wildfires in California to date, respectively - as case studies, we capture the features of resilience, such as robustness and vulnerability, of communities based on human mobility data from 2018 to 2020. The results show that demographic and socioeconomic characteristics alone only partially capture community resilience, however, by leveraging human mobility data and network analysis techniques, we can enhance our understanding of resilience over space and time, which can provide a new lens to study natural disasters and their long-term impacts on society.</p><p><b>Keywords</b>: Community Resilience, Natural Disasters, Wildfires, Social Network Analysis, Human Mobility, Space and Time. </p></blockquote><p></p><div style="text-align: justify;"><p><b>Full Reference </b></p></div><div style="text-align: justify;"><blockquote><b>Chen, Q., Wang, B. and Crooks, A.T. </b>(2023), Community Resilience to Wildfires: A Network Analysis Approach Utilizing Human Mobility Data, <i>The Association of American Geographers (AAG) Annual Meeting</i>, 23rd –27th March, Denver, CO. (<a href="https://www.dropbox.com/s/v4oss8rqk47q9p4/AAG_2023_Qingqing.pdf?dl=0" target="_blank">pdf</a>)</blockquote></div><p style="text-align: justify;">Next up, moving from mobility to textural data, specifically that of newspapers <a href="https://github.com/njiang8/" target="_blank">Na (Richard) Jiang</a> and myself have a presentation entitled "<i><a href="https://aag.secure-platform.com/aag2023/organizations/main/gallery/rounds/54/details/33451" target="_blank">Leveraging Newspapers to Understand Urban Issues: A Longitudinal Analysis of Urban Shrinkage in Detroit</a></i>". In this work we explore how can leverage <a href="https://arxiv.org/abs/2203.05794" target="_blank">Bertopic</a> (a topic modeling technique) on newspaper articles spanning the years 1975 to 2021 to explore urban
shrinkage in Detroit. </p><p style="text-align: justify;"></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjhwmypYSgmzGnO0_8bLoCRmdP31eMKAcYJFTftIr9CvqgFEpM-8_A4e8v8gYF0qwDcoUOHz6LyTuKn7RB6IqG86hW0l41TLMmSl14bAGjbQ1eLhx42R1YIGuSYC4sffnReDIQINm142JOnVFCPgREO0ZF5a4du_p5ygSjIUb7tQW-UjgE3tg/s1350/Screen%20Shot%202023-03-22%20at%2012.52.52%20PM.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="688" data-original-width="1350" height="326" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjhwmypYSgmzGnO0_8bLoCRmdP31eMKAcYJFTftIr9CvqgFEpM-8_A4e8v8gYF0qwDcoUOHz6LyTuKn7RB6IqG86hW0l41TLMmSl14bAGjbQ1eLhx42R1YIGuSYC4sffnReDIQINm142JOnVFCPgREO0ZF5a4du_p5ygSjIUb7tQW-UjgE3tg/w640-h326/Screen%20Shot%202023-03-22%20at%2012.52.52%20PM.png" width="640" /></a></div><p></p><p><b> </b></p><p><b>Abstract</b> </p><p></p><blockquote><p style="text-align: justify;">Today we are awash with data especially when it comes to studying cities from a diverse data ecosystem ranging from demographic to that of remotely sensed imagery and social media. This has led to the growth of geographical data science and urban analytics providing new ways to conduct quantitative research within cities. One area that has seen significant growth is that of using natural language processing techniques on text data from social media to explore various issues relating to urban morphology. However, social media only provides limited insights when dealing with longer-term urban phenomena, such as the growth and shrinkage of cities. This relates to the fact that social media is a relatively recent phenomenon compared to more longer-term urban problems that take decades to emerge. With respect to the longer-term coverage, newspapers which are increasingly becoming digitized provide the possibility to overcome the limitations of social media and provide insights over a timeframe that social media does not. To demonstrate the utilization of newspapers within urban analytics and to study longer-term urban issues, we present an advanced topic modeling technique (i.e., Bertopic) on a large number of newspaper articles spanning the years 1975 to 2021 to explore urban shrinkage in Detroit. Our topic modeling results reveal the insights related to Detroit's shrinkage can be linked to the side effects of economic recessions on Detroit's automobile industry, local employment status, and the housing market. As such, this work demonstrates the potential of utilizing newspaper articles to study long-term issues <br /></p><p style="text-align: justify;"><b>Keywords</b>: Natural Language Processing, Topic Modeling, Newspapers, Text Data, Urban Shrinkage, Urban Analytics. </p></blockquote><p> <b>Full Reference</b></p><p></p><div style="text-align: justify;"><blockquote><b>Jiang, N., and Crooks A.T.</b> (2023), Leveraging Newspapers to Understand Urban Issues: A Longitudinal Analysis of Urban Shrinkage in Detroit, <i>The Association of American Geographers (AAG) Annual Meeting</i>, 23rd –27th March, Denver, CO. (<a href="https://www.dropbox.com/s/7zy6jshgkrlxf0k/AAG_2023_Richard.pdf?dl=0" target="_blank">pdf</a>)</blockquote></div><p style="text-align: justify;">Switching gears slightly, we have another presentation that leverages text data, in this case Yelp reviews to help inform decision making within an agent-based model. This presentation with <a href="http://wang-boyu.github.io/" target="_blank">Boyu Wang</a> is entitled "<a href="https://aag.secure-platform.com/aag2023/organizations/main/gallery/rounds/54/details/34669" target="_blank">Do people care about others' opinions of places? Utilizing crowdsourced data and deep learning to model peoples’ review patterns</a>." We use a geospatial artificial intelligence (GeoAI) technique called aspect-based sentiment analysis to extract and categorize reviewers' opinion aspects on places within urban areas and then use this information to inform an agent-based model of peoples choices to which restaurants to go to. </p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhH8oStaNPcgI0PCa6a4L6Cf73d4Wh4CsWcSMtGd8SSkYqcmCUu6iWwiEotC_IGAfS2XffrdI_HrdsaQJHDSBC4ylThGaCmljMh17WNDpbiVD3h7j11GrlLuLhjIFt68VT5gI5AbtueZ3jpucKvyVasO4u29rN2xy-que5z8H4wLIKYocOROg/s2206/Screen%20Shot%202023-03-22%20at%201.00.04%20PM.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="644" data-original-width="2206" height="186" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhH8oStaNPcgI0PCa6a4L6Cf73d4Wh4CsWcSMtGd8SSkYqcmCUu6iWwiEotC_IGAfS2XffrdI_HrdsaQJHDSBC4ylThGaCmljMh17WNDpbiVD3h7j11GrlLuLhjIFt68VT5gI5AbtueZ3jpucKvyVasO4u29rN2xy-que5z8H4wLIKYocOROg/w640-h186/Screen%20Shot%202023-03-22%20at%201.00.04%20PM.png" width="640" /></a></div><br /><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgKbtDorfal4cXUek6xrPwPjhXQowRcNszR_Y2nitRGar5P9klvxulATmWfW2I_JcJPAY56UWTlgpgwJojGlWdZpNDM-kQ3CKvVlz7GxpeFA--jiZd6_FOPpe6ou_k_YSm8343Rd7u9pwtCD9KLwBuyTyuHjx0dinNNkJqH49eKtwhR2erX-g/s2252/Screen%20Shot%202023-03-22%20at%201.00.34%20PM.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="888" data-original-width="2252" height="252" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgKbtDorfal4cXUek6xrPwPjhXQowRcNszR_Y2nitRGar5P9klvxulATmWfW2I_JcJPAY56UWTlgpgwJojGlWdZpNDM-kQ3CKvVlz7GxpeFA--jiZd6_FOPpe6ou_k_YSm8343Rd7u9pwtCD9KLwBuyTyuHjx0dinNNkJqH49eKtwhR2erX-g/w640-h252/Screen%20Shot%202023-03-22%20at%201.00.34%20PM.png" width="640" /></a></div><p></p><p><b>Abstract </b></p><p style="text-align: justify;"></p><blockquote><p style="text-align: justify;">People's opinions are one of the defining factors that turn spaces into meaningful places. While these opinions are subject to individual differences, they can also be influenced by the opinions from others. Online platforms such as Yelp allow users to publish their reviews on businesses. To understand reviewers' opinion formation processes and the emergent patterns of published opinions, we utilize geospatial artificial intelligence (GeoAI) techniques especially that of aspect-based sentiment analysis methods (a deep learning approach) on a geographically explicit Yelp dataset to extract and categorize reviewers' opinion aspects on places within urban areas. Such data is then used as a basis to inform an agent-based model, where reviewers' (i.e., agents') opinions are characterized by opinion dynamics. The parameters of these models are calibrated using extracted opinion aspects from the Yelp dataset. Such a method moves opinion dynamics models away from theoretical concepts to a more data-driven approach, with a specific emphasis being made on place. Focusing on 10 US metropolitan areas which are spread out across the country, we examine the calibrated influence coefficients for each opinion aspect category (e.g., location, experience, service), to compare reviewers' opinion formation processes across different categories. The results show the emergent patterns of reviewers' opinions and the influence of these opinions on others. As such this work demonstrates how using deep learning techniques on geospatial data can help advance our understanding of place and cities more generally.<br /></p><p style="text-align: justify;"><b>Keywords</b>: Agent-Based Modeling, Crowdsourcing, Deep Learning, GeoAI, Opinion Dynamics, Urban Analytics</p></blockquote><p style="text-align: justify;"></p><p><b>Full Reference </b></p><div style="text-align: justify;"><blockquote><b>Wang, B. and Crooks, A.T. </b>(2023),
Do People Care About Others' Opinions of Places? Utilizing Crowdsourced
Data and Deep Learning to Model Peoples’ Review Patterns, <i>The Association of American Geographers (AAG) Annual Meeting</i>, 23rd –27th March, Denver, CO. (<a href="https://www.dropbox.com/s/mhji11bq1goltuj/AAG_2023_Boyu.pdf?dl=0" target="_blank">pdf</a>)</blockquote></div><div style="text-align: justify;">Following with the agent-based modeling theme, our final presentation with <a href="https://archplan.buffalo.edu/People/students/phd-students.host.html/content/shared/ap/students-faculty-alumni/phd-candidates/yin.detail.html" target="_blank">Fuzhen Yin</a> and <a href="https://archplan.buffalo.edu/People/faculty.host.html/content/shared/ap/students-faculty-alumni/faculty/Yin.detail.html" target="_blank"> Li Yin</a> is entitled "<i><a href="https://aag.secure-platform.com/aag2023/organizations/main/gallery/rounds/54/details/40088" target="_blank">How Information Propagation in Physical, Relational and Cyber Spaces Affects Covid-19 Vaccine Uptake: Evidence from Rural Areas</a></i>." In this work we explore how people may or not be influenced by others (in <a href="https://www.gisagents.org/2022/09/information-propagation-on-cyber.html" target="_blank">physical, relational and cyber spaces</a>) with respect to vaccination uptake. </div><div style="text-align: justify;"><br /></div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh0YHVVJU8yArGW1bmf9csvlc6JgORW4PwLSIatc0C5zayZkf9r5D9Q8UY0Bb2JSByNH3qZ9AxDvcwbd_WEzrLdf5KFaEbAoKzPpsQ5jOQ6dyFKEXwIN30bMBmpCdkJSEi9jZyD1oQYMIJY1KdIn5jSFWEAV93vDMNgN2R9NXfYvu8Du-uKTw/s2358/Screen%20Shot%202023-03-22%20at%201.10.07%20PM.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1318" data-original-width="2358" height="358" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh0YHVVJU8yArGW1bmf9csvlc6JgORW4PwLSIatc0C5zayZkf9r5D9Q8UY0Bb2JSByNH3qZ9AxDvcwbd_WEzrLdf5KFaEbAoKzPpsQ5jOQ6dyFKEXwIN30bMBmpCdkJSEi9jZyD1oQYMIJY1KdIn5jSFWEAV93vDMNgN2R9NXfYvu8Du-uKTw/w640-h358/Screen%20Shot%202023-03-22%20at%201.10.07%20PM.png" width="640" /></a> <br /></div><div style="text-align: justify;"><b> </b></div><div style="text-align: justify;"><b>Abstract</b> </div><div style="text-align: justify;"></div><blockquote><div style="text-align: justify;">With the advent of information and communication technologies, human dynamics studied in a purely physical space increasingly shift to a cyber and relational context. While researchers increasingly recognize the shift and call for attention to the multi-dimensionality of human dynamics (e.g., Splatial framework). Rarely have studies investigated how the information propagated in hybrid spaces affects people’s decision-making process, such as Covid-19 vaccine uptake. Meanwhile, compared to the urban population, the rural population faces greater digital barriers and has been further left out in human dynamics research. To fill this gap, our study investigates Covid-19 vaccine uptake in a rural county (i.e., Chautauqua) in New York State through agent-based modeling. We first generated a synthetic population to match the demographic characteristics of the census data. Then we created home, work, school, and social media networks to represent hybrid spaces. We defined the opinion dynamics of agents based on the social influence network theory. Next, we calibrated and validated our agent-based model based on real-world vaccine update records. Our research helps to elucidate the information propagation mechanism in hybrid spaces and clarify the decision-making process in the digital age. Furthermore, our method can also shed light on how to overcome data limitations for under-represented populations such as those who live in rural areas.</div><div style="text-align: justify;"><br /></div><div style="text-align: justify;"><b>Keywords</b>: Agent-based modeling, Covid-19, Vaccination, Opinion dynamics, Urban informatics, Rural geography</div></blockquote><div style="text-align: justify;"></div><p></p><div style="text-align: justify;"><b>Full Reference </b> <br /></div><blockquote><div style="text-align: justify;"><b>Yin, F., Crooks, A.T. and Yin,
L.</b> (2023), How Information Propagation in Physical, Relational and Cyber
Spaces Affects Covid-19 Vaccine Uptake: Evidence from Rural County, <i>The Association of American Geographers (AAG) Annual Meeting</i>, 23rd –27th March, Denver, CO. (<a href="https://www.dropbox.com/s/0dl11tbe59hck9h/AAG_2023_Fuzhen.pdf?dl=0" target="_blank">pdf</a>) </div></blockquote><div style="text-align: justify;"><br /></div><p></p><p></p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-22770502.post-67706665001654906132023-02-09T16:31:00.004-05:002023-02-09T16:36:06.652-05:00Comparison between Online Social Media Discussions and Vaccination Rates<p style="text-align: justify;"></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiZEesTVH3psziF51LXgjUkq_SgBJvrHmtWrrs-4mfoIHW94qsiLdrjuroeoTHYPZyHQaGBJxv1DXhS13MSXx2EFzJzFkKPv3FfMArc38lQHKdCDnyG0FVqb-4xYFBfwYhQw2CvWHX6wBzRyLCBp6RKuOJmnOomOpux7javDWZ-Wn8TWdItkQ/s1648/Screen%20Shot%202023-02-08%20at%204.13.04%20PM.png" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" data-original-height="1648" data-original-width="1156" height="191" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiZEesTVH3psziF51LXgjUkq_SgBJvrHmtWrrs-4mfoIHW94qsiLdrjuroeoTHYPZyHQaGBJxv1DXhS13MSXx2EFzJzFkKPv3FfMArc38lQHKdCDnyG0FVqb-4xYFBfwYhQw2CvWHX6wBzRyLCBp6RKuOJmnOomOpux7javDWZ-Wn8TWdItkQ/w134-h191/Screen%20Shot%202023-02-08%20at%204.13.04%20PM.png" width="134" /></a></div>Continuing our work on <a href="https://www.gisagents.org/search/label/Social%20media" target="_blank">social media</a> and <a href="https://www.gisagents.org/search/label/Vaccination" target="_blank">vaccinations</a>, <a href="https://urbansenses.org/" target="_blank">Qingqing Chen</a>, <a href="https://science.gmu.edu/directory/arie-croitoru" target="_blank">Arie Croitoru</a>, and myself have a new paper entitled "<i><a href="https://journals.sagepub.com/doi/10.1177/20552076231155682" target="_blank">A comparison between online social media discussions and vaccination rates: A tale of four vaccines</a></i>" published in <i><a href="https://journals.sagepub.com/home/DHJ" target="_blank">DIGITAL HEALTH</a>.</i> In the paper we explore online debates among four prominent vaccines
(i.e., COVID-19, Influenza, MMR, and HPV) as captured on Twitter in the United States (US) from 2015 to 2021.<p></p><p style="text-align: justify;">By using machine learning models (e.g., Naive Bayes, support vector machine (SVM), logistic regression, and extreme gradient boosting (XGBoost)) on over 11.7 million Twitter messages sent by approximately 2.6 million distinct users we found that while the COVID-19, it has come to dominate the vaccination discussion, there was an apparent discrepancy between the online debates and the actual vaccination rates in the US. </p><p style="text-align: justify;">If this sounds of interest and you wish to find out more, below we provide the abstract to to the paper, some figures which captures our workflow and a sample of the results such as a comparison between different vaccine discussions on Twitter and the actual vaccination rate. Finally at the bottom of the page you can find the full reference and a link to the paper.<br /></p><p style="text-align: left;"></p><p style="text-align: left;"></p><p style="text-align: left;"></p><p style="text-align: left;"></p><p style="text-align: left;"></p><p style="text-align: left;"></p><p style="text-align: left;"></p><p style="text-align: left;"></p><p style="text-align: left;"></p><p style="text-align: left;"><b></b></p><blockquote style="text-align: left;"><p><b>Abstract</b>: </p><p></p><p style="text-align: justify;">The recent COVID-19 pandemic has brought the debate around vaccinations to the forefront of public discussion. In this discussion, various social media platforms have a key role. While this has long been recognized, the way by which the public assigns attention to such topics remains largely unknown. Furthermore, the question of whether there is a discrepancy between people's opinions as expressed online and their actual decision to vaccinate remains open. To shed light on this issue, in this paper we examine the dynamics of online debates among four prominent vaccines (i.e., COVID-19, Influenza, MMR, and HPV) through the lens of public attention as captured on Twitter in the United States from 2015 to 2021. We then compare this to actual vaccination rates from governmental reports, which we argue serve as a proxy for real-world vaccination behaviors. Our results demonstrate that since the outbreak of COVID-19, it has come to dominate the vaccination discussion, which has led to a redistribution of attention from the other three vaccination themes. The results also show an apparent discrepancy between the online debates and the actual vaccination rates. These findings are in line with existing theories, that of agenda-setting and zero-sum theory. Furthermore, our approach could be extended to assess the public's attention toward other health-related issues, and provide a basis for quantifying the effectiveness of health promotion policies.</p><p><b>Keywords</b>: COVID-19, Influenza, MMR, HPV, Social media, Vaccination.</p></blockquote><p> </p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhuCTTYX4fG85O6mcbXkz782JnuLVldPJ63L1V7XTo0_WdyJtkJTrKkcN6KyBsrH9wMqN6GyBS3Mad7SI7N0HONwC0pMYhwAobkxCW9iR44ZQDQU0Xt4gCVP8NfXyXSOfzgK9NELC3cSw2-CfwVilyDVkDjrKqMm0iDaPBUty2vg_NOexNLfA/s3850/images_large_10.1177_20552076231155682-fig1.jpeg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1386" data-original-width="3850" height="230" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhuCTTYX4fG85O6mcbXkz782JnuLVldPJ63L1V7XTo0_WdyJtkJTrKkcN6KyBsrH9wMqN6GyBS3Mad7SI7N0HONwC0pMYhwAobkxCW9iR44ZQDQU0Xt4gCVP8NfXyXSOfzgK9NELC3cSw2-CfwVilyDVkDjrKqMm0iDaPBUty2vg_NOexNLfA/w640-h230/images_large_10.1177_20552076231155682-fig1.jpeg" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">The workflow for comparing between online social media discussion and vaccination rates.</td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhMin1d5EYFWFoktmrllJhbpKPLb-TNiTtWs-Vlw-DT-WL-j54mc03h5dp2OeGDTVcIZjvvZf84OYgKA-Iep2uHPT8jfdvYXo_P0maiGET9djdUVy2c-Gw3CDdBOZyu_6T-IPl9zEC77eafdmvLDm74X2Y3TRJ094DLAsDhKwm6hi9VOa0pEQ/s3850/images_large_10.1177_20552076231155682-fig2.jpeg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1854" data-original-width="3850" height="308" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhMin1d5EYFWFoktmrllJhbpKPLb-TNiTtWs-Vlw-DT-WL-j54mc03h5dp2OeGDTVcIZjvvZf84OYgKA-Iep2uHPT8jfdvYXo_P0maiGET9djdUVy2c-Gw3CDdBOZyu_6T-IPl9zEC77eafdmvLDm74X2Y3TRJ094DLAsDhKwm6hi9VOa0pEQ/w640-h308/images_large_10.1177_20552076231155682-fig2.jpeg" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">The quarterly distribution of percentage of users by different vaccine discussion from 2015 to 2021.</td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhpW1KXhMXLzlkn0RD0tCNz3Jmr525DkrhjV8YUjlg--FmTwMh6YRkaULC2sruxuZE5BEkKmgfsmCh9jwGsSEKbNkCWs88NNaxDH3hl7S3fQozlyOGJu-iWvH7PMVZX2pGyBtLUaJKFa9QZ1tBQIMFWhB5p6Cyz5C1b63td6N-GGyoG1XrpDQ/s3850/images_large_10.1177_20552076231155682-fig3.jpeg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="2191" data-original-width="3850" height="364" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhpW1KXhMXLzlkn0RD0tCNz3Jmr525DkrhjV8YUjlg--FmTwMh6YRkaULC2sruxuZE5BEkKmgfsmCh9jwGsSEKbNkCWs88NNaxDH3hl7S3fQozlyOGJu-iWvH7PMVZX2pGyBtLUaJKFa9QZ1tBQIMFWhB5p6Cyz5C1b63td6N-GGyoG1XrpDQ/w640-h364/images_large_10.1177_20552076231155682-fig3.jpeg" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;"> The comparison between different vaccine discussions on Twitter and growth rate of the actual vaccination rate collected from the CDC (a) COVID-19; (b) Influenza; (c) HPV; (d) MMR.</td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhmDmy-hzW79A5HdptvDLfUdxXeGHcBHtdYCKR2klxOGVHWyWqEPGnSBYL5ULi5mx0Zq0AzwoU7BS5s8sK75oTUtIoQhmNpe7qyB_0ChGH1ARvMUpzdZ4aucxrF3oQblz36Ru2nP2MhJmq2-_CWgzz4rPrXdQsAuZm31VDzb6_4mY_21wwilw/s5100/images_large_10.1177_20552076231155682-fig6.jpeg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="5100" data-original-width="2581" height="640" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhmDmy-hzW79A5HdptvDLfUdxXeGHcBHtdYCKR2klxOGVHWyWqEPGnSBYL5ULi5mx0Zq0AzwoU7BS5s8sK75oTUtIoQhmNpe7qyB_0ChGH1ARvMUpzdZ4aucxrF3oQblz36Ru2nP2MhJmq2-_CWgzz4rPrXdQsAuZm31VDzb6_4mY_21wwilw/w324-h640/images_large_10.1177_20552076231155682-fig6.jpeg" width="324" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">The changes of emotion over time for different vaccines.</td></tr></tbody></table><p style="text-align: left;"><b>Full reference: </b><br /></p><p style="text-align: left;"></p><div style="text-align: justify;"><blockquote><b>Chen Q, Croitoru A. and Crooks A.T (2023)</b>, A Comparison between Online Social Media Discussions and Vaccination Rates: A tale of four vaccines. <i>DIGITAL HEALTH</i>: 9. doi:<a href="https://doi.org/10.1177/20552076231155682" target="_blank">10.1177/20552076231155682</a>. (<a href="https://www.dropbox.com/s/7cespibtd1q7vpb/Digital_Health_vaccines.pdf?dl=0" target="_blank">pdf</a>)<br /></blockquote></div>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-22770502.post-40246377625307944052023-01-26T10:34:00.041-05:002023-05-24T11:02:37.429-04:00Repast4Py<p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEifYq6nLWU3mAWnWlkCLRxdBVCzaHfbTE2-j3zlrt1xlBusMO-ZtVSFTLwXGe-nLTJpM4v5Fb15WOO5715kNGboUHAhLzTfIBDDnqY3UHu_TVbFLiQLyoZ1C3ogofjin4Y3u-fKilOoiIam1geM8x5AgX7yaK9vcy8KO_qTVHWVtak2nividg/s888/Screen%20Shot%202023-05-24%20at%2010.48.34%20AM.png" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" data-original-height="256" data-original-width="888" height="83" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEifYq6nLWU3mAWnWlkCLRxdBVCzaHfbTE2-j3zlrt1xlBusMO-ZtVSFTLwXGe-nLTJpM4v5Fb15WOO5715kNGboUHAhLzTfIBDDnqY3UHu_TVbFLiQLyoZ1C3ogofjin4Y3u-fKilOoiIam1geM8x5AgX7yaK9vcy8KO_qTVHWVtak2nividg/w289-h83/Screen%20Shot%202023-05-24%20at%2010.48.34%20AM.png" width="289" /></a></div><p style="text-align: justify;">In the past we have blogged about how we are using the <a href="https://www.gisagents.org/search/label/MESA">Python agent-based modeling framework Mesa</a>. But we would be remiss if we did not also mention other Python toolkits. One of which is <a href="https://repast.github.io/repast4py.site/index.html" target="_blank">Repast4Py</a> from the people we created <a href="https://repast.github.io/" target="_blank">Repast</a> suite. To quote from their <a href="https://repast.github.io/repast4py.site/guide/user_guide.html" target="_blank">user guide website</a>:</p><p></p><p></p><div style="text-align: justify;"><blockquote><i>"<a href="https://repast.github.io/repast4py.site/guide/user_guide.html" target="_blank">Repast4Py is a Python package and is designed to provide an easier on-ramp for researchers from diverse scientific communities to apply large-scale distributed ABM methods</a>."</i></blockquote></div> <p></p><p style="text-align: justify;">To find out more about Repast4Py I highly recommend readers to look at the following publications or their webpage: <a href="https://repast.github.io/repast4py.site/" target="_blank">https://repast.github.io/repast4py.site/</a>.</p><p style="text-align: justify;"><b>References: </b><br /></p><ul style="text-align: justify;"><li class="gs_citr" tabindex="0">Collier, N.T., Ozik, J. and Tatara,
E.R. (2020). <a href="https://ieeexplore.ieee.org/document/9307948" target="_blank">Experiences in Developing a Distributed Agent-based Modeling Toolkit with Python</a>. In <i>2020 IEEE/ACM 9th Workshop on Python for High-Performance and Scientific Computing (PyHPC)</i> (pp. 1-12). IEEE.</li><li class="gs_citr" tabindex="0">Collier, N. and Ozik, J. (2022). <a href="https://ieeexplore.ieee.org/abstract/document/10015389?casa_token=Gyvd1WiuLYcAAAAA:emsj5SF_Vb0oXRgQC9rtdzZdouyQYKzsbBeEydnlxphoYpW7zJfueOlTj2YS7JS5IZdNk4L_jg" target="_blank">Distributed Agent-Based Simulation with Repast4Py</a>. In <i><a href="https://ieeexplore.ieee.org/abstract/document/10015389?casa_token=Gyvd1WiuLYcAAAAA:emsj5SF_Vb0oXRgQC9rtdzZdouyQYKzsbBeEydnlxphoYpW7zJfueOlTj2YS7JS5IZdNk4L_jg" target="_blank">2022 Winter Simulation Conference</a> (WSC)</i> (pp. 192-206). IEEE.</li></ul><p> </p><p> </p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-22770502.post-90082522725686760442022-12-08T16:53:00.003-05:002023-01-05T13:42:16.941-05:00Simulating Geographical Systems using CA and ABMs<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgVcJYz6MEY2keRWOtkgSjEQfFuo27GCArdjUpGGifEJBpSmSrxyn4ZIOQBYEROGUl5SALykpWEk2qJpvTV_MGpvVWXyVFurbSSgBWGvx9qq3oG2H1lHU8nPTd_H9SW2-hFOPavDGfZjw3O0oJA-IkYwpujNefd8G3BwOrSXC4uvm82B-4eQA/s305/9781789903935.jpg" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" data-original-height="305" data-original-width="200" height="143" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgVcJYz6MEY2keRWOtkgSjEQfFuo27GCArdjUpGGifEJBpSmSrxyn4ZIOQBYEROGUl5SALykpWEk2qJpvTV_MGpvVWXyVFurbSSgBWGvx9qq3oG2H1lHU8nPTd_H9SW2-hFOPavDGfZjw3O0oJA-IkYwpujNefd8G3BwOrSXC4uvm82B-4eQA/w94-h143/9781789903935.jpg" width="94" /></a></div><div style="text-align: justify;">In the recently released "<a href="https://www.e-elgar.com/shop/usd/handbook-of-spatial-analysis-in-the-social-sciences-9781789903935.html" rel="nofollow" target="_blank"><i>Handbook of Spatial Analysis in the Social Sciences</i></a>" edited by <a href="https://sergerey.org/" target="_blank">Sergio Rey</a> and <a href="https://www.ncl.ac.uk/curds/people/staffprofile/rachelfranklin.html#background" target="_blank">Rachel Franklin</a>, we (<a href="https://alisonheppenstall.co.uk/" target="_blank">Alison Heppenstall</a>, <a href="http://urbanmovements.co.uk/" target="_blank">Ed Manley</a>, <a href="http://www.nickmalleson.co.uk/" target="_blank">Nicolas Malleson</a> and myself) have a chapter entitled "<i><a href="https://www.elgaronline.com/view/book/9781789903942/book-part-9781789903942-16.xml" target="_blank">Simulating Geographical Systems using Cellular Automata and Agent-based Models</a></i>." </div><div style="text-align: justify;"> </div><div style="text-align: left;"><div style="text-align: justify;">In the chapter we discuss how thinking and studying of geographical systems like cities has changed over time from top down aggregate analysis to more bottom up approaches which captures the complex nature of such systems. We then discuss how we can model such systems from a cellular automata and agent-based perspectives. and how these styles of models have evolved and how they can be used to model future systems. If this sounds of interest below we provide the abstract to the chapter, some of the figures that accompany it and at the bottom of the page we provide the full reference to the paper along with a link to the chapter itself. <br /></div><div style="text-align: justify;"><blockquote>"<b>Abstract</b>: How we view and understand the processes driving and shaping geographical systems is constantly evolving. This is due to the appearance of new rich data sources, increased computing power and storage, and the development of individual-level approaches. This allows us to explore geographical systems (from the bottom up) at scales not possible in the past. In this chapter, we examine the utility of two of the most commonly used individual-level modelling approaches, cellular automata and agent-based modelling. We outline their key differences and how these models are being used to further our understanding of geographical systems through simulation. We conclude with a discussion about the challenges that both approaches need to meet to continue developing into the future. </blockquote></div><blockquote><b>Keywords</b>: Cellular automata; Agent-based models; Geographical systems; Machine learning <br /> </blockquote><p><b></b></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg4B0yqRiAoXtXCEnB_2ZCe-hGeOBLyTdDSr3UMxyr8WctoMXDZfFseNwRaUUXgctZR7aINifXmFTNT5h9ccXU_wR4JsiT48912So7WZLsfTHQgtNJb025wZA-byCaSzKSLd6Uouia4W4mVaqBwb2HBcrMdblIrMgdwJROh-XPlGkWiwwXxYg/s1462/Screen%20Shot%202022-12-08%20at%204.50.13%20PM.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="440" data-original-width="1462" height="192" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg4B0yqRiAoXtXCEnB_2ZCe-hGeOBLyTdDSr3UMxyr8WctoMXDZfFseNwRaUUXgctZR7aINifXmFTNT5h9ccXU_wR4JsiT48912So7WZLsfTHQgtNJb025wZA-byCaSzKSLd6Uouia4W4mVaqBwb2HBcrMdblIrMgdwJROh-XPlGkWiwwXxYg/w640-h192/Screen%20Shot%202022-12-08%20at%204.50.13%20PM.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">A SLEUTH like model stylized on Santa Fe, New Mexico denoting how land use charges over time from undeveloped (grey) to urban (red).</td></tr></tbody></table><b><br /></b><p></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiS3V46JPHnWK6_RTAHHv1Bos-1ONY6ZPQonPXbIDVhwpf33bPAiIKA-Xo-yc-hwkyOP2zeNZwWNKkYucej638YW_VHHBagVzB8iSG7Np3wxZ5VpuHea7IB6kPpyzdtVpRR8K_F-7nOUf-hE_4KVzbTiy74EWkRwbQlqagSP1ILU2pQRjaSMA/s1552/Screen%20Shot%202022-12-08%20at%204.51.21%20PM.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="960" data-original-width="1552" height="396" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiS3V46JPHnWK6_RTAHHv1Bos-1ONY6ZPQonPXbIDVhwpf33bPAiIKA-Xo-yc-hwkyOP2zeNZwWNKkYucej638YW_VHHBagVzB8iSG7Np3wxZ5VpuHea7IB6kPpyzdtVpRR8K_F-7nOUf-hE_4KVzbTiy74EWkRwbQlqagSP1ILU2pQRjaSMA/w640-h396/Screen%20Shot%202022-12-08%20at%204.51.21%20PM.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Example applications of agent-based models at different spatial and temporal scales</td></tr></tbody></table><div class="separator" style="clear: both; text-align: center;"><b></b></div><b><br /></b><p><b>Full reference: </b><br /></p><div style="text-align: justify;"><blockquote><b>Heppenstall, A., Crooks, A.T., Manley, E. and Malleson, N. </b>(2022) Simulating Geographical Systems using Cellular Automata and Agent-based Models, in Rey S. and Franklin, R. (eds.), Handbook of Spatial Analysis in the Social Sciences, Edward Elgar Publishing, Cheltenham, UK, pp. 142-157. (<a href="https://www.dropbox.com/s/zctku56fkczrltr/Simulating_Geographical_Systems_CA_and_ABMs.pdf?dl=0">pdf</a>)</blockquote></div><p></p></div>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-22770502.post-31748369950147381452022-11-11T15:17:00.002-05:002022-11-15T09:05:27.830-05:00Announcing MASON 21, Geomason 1.7 & Distributed MASON 1<div class="separator" style="clear: both; text-align: justify;"><div class="separator" style="clear: both;"><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgDG6NwhXdNF1wQesfhoqRe_2ZN5RtEiNGLpMsLotxglvi-ofk6JYK0MkhRrFUdtgSCRT1kf_0Ytw7HfHYFDFSg61rjE2pz4YyXNYUH-zYI4yrqiwoLyRAK6YQjYPixmFQW4tDfGc_r25mENmfbgy7gfhaG2QKhaVWLWkGIyfelS1CaLdkGtw/s1686/Screen%20Shot%202022-11-11%20at%202.57.43%20PM.png" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" data-original-height="1212" data-original-width="1686" height="144" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgDG6NwhXdNF1wQesfhoqRe_2ZN5RtEiNGLpMsLotxglvi-ofk6JYK0MkhRrFUdtgSCRT1kf_0Ytw7HfHYFDFSg61rjE2pz4YyXNYUH-zYI4yrqiwoLyRAK6YQjYPixmFQW4tDfGc_r25mENmfbgy7gfhaG2QKhaVWLWkGIyfelS1CaLdkGtw/w200-h144/Screen%20Shot%202022-11-11%20at%202.57.43%20PM.png" width="200" /></a></div>Many visitors and readers to this site know that for a long time I have been involved with and developing agent-based models utilizing <a href="https://www.gisagents.org/search/label/MASON" target="_blank">MASON</a>. To this end, the other day <a href="https://cs.gmu.edu/~sean/" target="_blank">Sean Luke</a> p<a href="https://listserv.gmu.edu/cgi-bin/wa?A2=ind2211&L=MASON-INTEREST-L&P=790" target="_blank">osted a message to the MASON list-serve</a> regarding new releases of MASON, GeoMASON and the first release of Distributed MASON which is part of our <a href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=1727303&HistoricalAwards=false" target="_blank">NSF </a><span class="pageheadline"><span class="pageheadline"><a href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=1727303&HistoricalAwards=false" target="_blank">CI-EN: Enhancement of a Large-scale Multiagent Simulation Tool</a> project</span></span>. </div></div><p></p><p>To quote from the email: <br /></p><p></p><p></p><p>
</p><blockquote style="text-align: left;"><div style="text-align: justify;">
"<a href="https://cs.gmu.edu/~eclab/projects/mason/" target="_blank"><b>MASON</b></a> is a high performance open-source modeling toolkit in pure Java,
designed to be fast, highly hackable and modifiable, and to guarantee
repeatable results, among many other capabilities. MASON comes with
extensive visualization capabilities and regularly runs on everything
from laptops to back-end supercomputers".<br /></div><br /><div style="text-align: justify;">"<b>Distributed MASON</b> is an
open-source, massively distributed version of MASON meant for
server/farm and cloud computing deployment using a combination of MPI
and RMI. It runs MASON over a large number of collective machines. "<br /></div><br /><div style="text-align: justify;">"<a href="https://cs.gmu.edu/~eclab/projects/mason/extensions/geomason//" target="_blank"><b>GeoMASON</b></a>
is an open source set of extensions to MASON which add GIS
capabilities, including reading and writing standard formats, embodying
agents in GIS environments, and visualization." <br /></div>
<br /><div style="text-align: justify;">"<b>Distributed
GeoMASON</b> is an open source set of extensions to GeoMASON to enable it to
run over Distributed MASON in both server/farm and cloud computing
environments."<br /></div>
</blockquote><p>For those interested in GIS and agent-based models, we have added many more application examples (a sample of which is shown below), along with fixing a number of bugs, and adding new code for compatibility with Distributed MASON. For more details check out the MASON webpage: <a href="http://cs.gmu.edu/~eclab/projects/mason/" target="_blank">http://cs.gmu.edu/~eclab/projects/mason/.</a><br /></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjKiImIwgJOOvI5-Xd0z8azCqLv2a5uEPIPaSTJp8hfhKXr-4VihWttPPcXEq5_wfPj0jMA_lYwMJOlgOwN0e81NppbcIPKzXEMH7AiC6MsaxZG-Sxw4rq0iY9R23I2lAYx6zwWOD5mmyO8OoQj2zmiY8HfHbEBwZPNiHI4V9b2YbtouLBcPA/s2540/Screen%20Shot%202022-11-10%20at%201.55.30%20PM.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1766" data-original-width="2540" height="445" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjKiImIwgJOOvI5-Xd0z8azCqLv2a5uEPIPaSTJp8hfhKXr-4VihWttPPcXEq5_wfPj0jMA_lYwMJOlgOwN0e81NppbcIPKzXEMH7AiC6MsaxZG-Sxw4rq0iY9R23I2lAYx6zwWOD5mmyO8OoQj2zmiY8HfHbEBwZPNiHI4V9b2YbtouLBcPA/w640-h445/Screen%20Shot%202022-11-10%20at%201.55.30%20PM.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Examples of some of the GeoMason Models<br /></td></tr></tbody></table><br /><p></p><p>If you have questions regarding MASON, GeoMason, or
their distributed versions, <a href="https://cs.gmu.edu/~eclab/projects/mason/#mailinglist." target="_blank">join the MASON mailing list and ask</a>. </p><p> </p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-22770502.post-35657071248562681442022-11-01T19:17:00.001-04:002022-11-02T17:22:55.180-04:00 Mesa-Geo: ABM and GIS in Python (A Update)<p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhdoOZBNrrzW1OPX2QlRqGa9AVl7ezzDY51DO9uRdMsXOdXJV9ccbtI4RY468Eo7dHzyaTLxkosFxuymbYLAjjnS56q3koa-T1GRVwfLHwH51A78g9mL7JBA5OvMVnlpVClRZ7M8TCJuzNQgDBYPylqLyI0hKkY9O77dVs1DM4G1fxuBvaUOQ/s319/GeoSimLogo2022.png" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" data-original-height="161" data-original-width="319" height="101" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhdoOZBNrrzW1OPX2QlRqGa9AVl7ezzDY51DO9uRdMsXOdXJV9ccbtI4RY468Eo7dHzyaTLxkosFxuymbYLAjjnS56q3koa-T1GRVwfLHwH51A78g9mL7JBA5OvMVnlpVClRZ7M8TCJuzNQgDBYPylqLyI0hKkY9O77dVs1DM4G1fxuBvaUOQ/w200-h101/GeoSimLogo2022.png" width="200" /></a></div><p style="text-align: justify;">A couple of months ago we had a <a href="https://www.gisagents.org/2022/08/mesa-geo-abm-and-gis-in-python.html">post</a> about Mesa-Geo but only a short one. Now we want to go into more detail as we (<a href="https://github.com/wang-boyu" target="_blank">Boyu Wang</a>, <a href="https://orcid.org/0000-0002-9242-8500" target="_blank">Vincent Hess</a> and myself) just presented a paper about it at the <i><a href="http://www.geosim.org/" target="_blank">5th ACM SIGSPATIAL International Workshop on Geospatial Simulation</a></i> (GeoSim 2022). The paper itself was entitled "<a href="https://dl.acm.org/doi/10.1145/3557989.3566157" target="_blank"><i>Mesa-Geo: A GIS Extension for the Mesa Agent-Based Modeling Framework in Python</i></a>" in which we discuss in detail the need for a python library for creating geographically explicit agents (or GeoAgents) and introduce its architecture. </p><p style="text-align: justify;">In the paper we detail how we have designed Mesa-Geo to handle spatial data (both in terms of raster and vector via GeoSpace), how we have enabled visualization of geographical data and such models along with creating features to export geographical data from the simulations (using <a href="https://rasterio.readthedocs.io/en/latest/" target="_blank">Rasterio</a> and <a href="https://geopandas.org/en/stable/" target="_blank">GeoPandas</a>). To support this discussion we also provide some explicit examples on how the pieces fit together range from rainfall flowing over a digital terrain model (DEM)
to <a href="https://en.wikipedia.org/wiki/Schelling%27s_model_of_segregation" target="_blank">Schelling</a> types of models using points and polygons as agents, to
that of agents using road networks to navigate over an area. <a href="https://github.com/wang-boyu">Boyu</a> has also put together more details about the examples at: <a href="https://mesa-geo.readthedocs.io/en/latest/examples/overview.html">https://mesa-geo.readthedocs.io/en/latest/examples/overview.html</a> (which includes movies of them running). The actual code for the models and Mesa-Geo can be found at <a href="https://github.com/projectmesa/mesa-geo" target="_blank">https://github.com/projectmesa/mesa-geo</a>. Just to give you a sense of the paper and what Mesa-Geo can do, below we provide the abstract to the paper, some figures showing the architecture, along with some example applications. While at the bottom of the post you can see the full reference and a link to the paper itself. </p><p style="text-align: justify;"> </p><p style="text-align: left;"><b></b></p><div style="text-align: justify;"><blockquote><b>Abstract</b>: Mesa is an open-source agent-based modeling (ABM) framework implemented in the Python programming language, allowing users to build and visualize agent-based models. It has been used in a diverse range of application areas over the years ranging from biology to workforce dynamics. However, there has been no direct support for integrating geographical data from geographical information systems (GIS) into models created with Mesa. Users have had to rely on their own implementations to meet such needs. In this paper we present Mesa-Geo, a GIS extension for Mesa, which allows users to import, manipulate, visualize and export geographical data for ABM. We introduce the main components and functionalities of Mesa-Geo, followed by example applications utilizing geographical data which demonstrates Mesa-Geo's core functionalities and features common to agent-based models. Finally, we conclude with a discussion and outlook on future directions for Mesa-Geo. </blockquote></div><div><p></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiQlvpchzqfZRBn9G4NMfmhM8AW_h22PXQh7OdV8vGLxfUi3cqlnzSzz0MjMbNzP56tqHoqFY5qS3fO3pb8I8kUosLkLBjBIpouao05HBfv26JirhP26Df3mBB3tol_svyUPT4HeB_8oH7IlHLyiSg9xTdrqoY5blrD5jRgXN-LWI_jqP2TbQ/s1364/Screen%20Shot%202022-10-14%20at%209.36.40%20AM.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="606" data-original-width="1364" height="284" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiQlvpchzqfZRBn9G4NMfmhM8AW_h22PXQh7OdV8vGLxfUi3cqlnzSzz0MjMbNzP56tqHoqFY5qS3fO3pb8I8kUosLkLBjBIpouao05HBfv26JirhP26Df3mBB3tol_svyUPT4HeB_8oH7IlHLyiSg9xTdrqoY5blrD5jRgXN-LWI_jqP2TbQ/w640-h284/Screen%20Shot%202022-10-14%20at%209.36.40%20AM.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Class diagram of the Agent, GeoAgent, and Cell classes</td></tr></tbody></table><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiPtHJW02EQRAkyXt8YsVbTHD0a3ej8T9iopGttnvDAPILSAjKp2crgrWl0P_VThbAEb10q614HrepD-6JbY4vx_KAtcqJ9XIV_pzyEv9q0F3fNo3k3sza_4NAFcH8izDBfc5WYRgy0ZB5JtBhOMzz7UObz7bgE-osAYeMyCZ4ef1HW2fyT9A/s1308/Screen%20Shot%202022-10-14%20at%209.36.43%20AM.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="898" data-original-width="1308" height="440" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiPtHJW02EQRAkyXt8YsVbTHD0a3ej8T9iopGttnvDAPILSAjKp2crgrWl0P_VThbAEb10q614HrepD-6JbY4vx_KAtcqJ9XIV_pzyEv9q0F3fNo3k3sza_4NAFcH8izDBfc5WYRgy0ZB5JtBhOMzz7UObz7bgE-osAYeMyCZ4ef1HW2fyT9A/w640-h440/Screen%20Shot%202022-10-14%20at%209.36.43%20AM.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Component diagram of GeoSpace and its related classes</td></tr></tbody></table><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh02X3TItwledjMagy3H5cRuejFJn2SF8ousDHCv-B-d0vU_G2koRU2dA0DA7ZVreBta4fcq-0lSyVnfSoQrfkvuq8c3eLEgXoTvfryWEVmgXpcQuAEZK0ozscwdXkfRuwcYMsDRKeJSIoXx4Hx7H68oqDFubN2YbkQ-Zwkf2TdyGjCrj2meA/s2724/Screen%20Shot%202022-10-14%20at%209.37.02%20AM.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1800" data-original-width="2724" height="422" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh02X3TItwledjMagy3H5cRuejFJn2SF8ousDHCv-B-d0vU_G2koRU2dA0DA7ZVreBta4fcq-0lSyVnfSoQrfkvuq8c3eLEgXoTvfryWEVmgXpcQuAEZK0ozscwdXkfRuwcYMsDRKeJSIoXx4Hx7H68oqDFubN2YbkQ-Zwkf2TdyGjCrj2meA/w640-h422/Screen%20Shot%202022-10-14%20at%209.37.02%20AM.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Example applications using Mesa and Mesa-Geo: (a) Rainfall model, (b) Population model, (c) GeoSchelling (polygons) model, (d) GeoSchelling (points \& polygons) model, and (e) Agents and networks model.</td></tr></tbody></table><p></p><center><iframe allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen="" frameborder="0" height="315" src="https://www.youtube.com/embed/iLMU6jfmir8" title="YouTube video player" width="560"></iframe></center> <br /><p></p><p> If you have any thoughts or comments about <a href="https://github.com/projectmesa/mesa-geo" target="_blank">Mesa-Geo</a> please let us know. </p><p><b>Full reference: </b><br /></p><p><b></b></p><blockquote><b>Wang, B., Hess, V. and Crooks A.T.</b> (2023), Mesa-Geo: A GIS Extension for the Mesa Agent-Based Modeling Framework in Python, <i>Proceedings of the 5th ACM SIGSPATIAL International Workshop on Geospatial Simulation (GeoSim 2022)</i>, Seattle, WA. pp 1-10. (<a href="https://www.dropbox.com/s/6ocgaojo437ktl7/Mesa_Geo_GEOSIM_2022.pdf?dl=0" target="_blank">PDF</a>)<br /></blockquote><p></p></div>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-22770502.post-91101024617765488052022-10-28T16:19:00.003-04:002022-11-03T15:07:45.402-04:00Modeling Farmers’ Adoption Potential to New Bioenergy Crops<div style="text-align: justify;"><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhy4YLQoaE-fzIyslCwuhQRYXjBChCMyMFkt2EolkxdLvf4JIHNsVCDCHMa2U6Ln_r2vBFBvXJtxcpqvx-HtfUVnaJXlmpQJHVcv4gX6fwzD-IYp7zFdyKMe1YWwziIyLTIpPaw3FdosyA9XF93l2BPeEeYMoIVlt1PucNpxH7i_lxlHeL7Aw/s642/ScreenM.png" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" data-original-height="642" data-original-width="626" height="138" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhy4YLQoaE-fzIyslCwuhQRYXjBChCMyMFkt2EolkxdLvf4JIHNsVCDCHMa2U6Ln_r2vBFBvXJtxcpqvx-HtfUVnaJXlmpQJHVcv4gX6fwzD-IYp7zFdyKMe1YWwziIyLTIpPaw3FdosyA9XF93l2BPeEeYMoIVlt1PucNpxH7i_lxlHeL7Aw/w134-h138/ScreenM.png" width="134" /></a></div>Close on the heals of the last post on farming, we have a new paper co-authored with <a href="https://scholar.google.com/citations?user=g6_2JhcAAAAJ&hl=en">Kazi Masel</a> entitled "<a href="https://www.dropbox.com/s/ecxavsvf9s5n9is/BioenergyCrops_ABM_CSSSA2022.pdf?dl=0">Modelling Farmers’ Adoption Potential to New Bioenergy Crops: An Agent-based Approach</a>" which was presented at the <a href="https://computationalsocialscience.org/conferences/css2022/">2022 Computational Social Science Society of the Americas (CSS 2022) Annual Conference</a>. In the paper we explore the potential of farmers to adopt carinata in the state of Georgia. Carinata in an oilseed crop which could be used as a sustainable aviation fuel. Through our agent-based model our results suggest that a viable contract price made by investors could persuade farmers to adopt carinata. If this sounds of interest, below we provide the abstract to the paper along with a movie showing the model running along with some figures of the model logic and an example of one of the results. At the bottom of the post you can find the full reference to the paper and a link to a pdf of it. Similar to our other papers a detailed Overview, Design concepts and Details (ODD) protocol along with the model and the data needed to run the model has been made available at <a href="https://www.comses.net/codebase-release/5c2c06f0-3f6d-4f8d-b198-ce24b55feb2f/">https://www.comses.net/codebase-release/5c2c06f0-3f6d-4f8d-b198-ce24b55feb2f/</a>. This additional material allows for a more in-depth description of the model, as well as facilitates the replication of results or extension of the model. </div><div style="text-align: justify;"><br /></div>
<div style="text-align: justify;"><blockquote><b>Abstract</b>: The use of fossil fuels is the primary source of greenhouse gas emissions but there are alternatives to these especially in the form of biofuels, fuels derived from bioenergy crops. This paper aims to determine farmers’ potential adoption rates of newly introduced bioenergy crops with a specific example of carinata in the state of Georgia. The determination is done using an agent-based modeling technique with two principal assumptions – farmers are profit maximizer and they are influenced by neighboring farmers. Two diffusion parameters (traditional and expansion) are followed along with two willingness (high and low) scenarios to switch at varying production economics to carinata and other prominent traditional field crops (cotton, peanuts, corn) in the study region. The paper finds that a contract prices around $9, $8 and $7 can be a viable option for encouraging farmers to adopt carinata in low, average, and high profit conditions, respectively. Expansion diffusion (that diffuses all over the geographical area), rather than centered to the few places like traditional diffusion at the early stage of adoption in conjunction with higher willingness conditions influences higher adoption rates in the short-term. As such, the model can be used to understand the behavioral economics of carinata in Georgia and beyond, as well as offering a potential tool to study similar bioenergy crops.
<br /></blockquote></div><blockquote style="text-align: left;"><b>Keywords</b>: Adoption, Agent-based modeling, Bioenergy Crops, Farming.</blockquote>
<center>
<iframe allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen="" frameborder="0" height="315" src="https://www.youtube.com/embed/4etJyCzA8ck" title="YouTube video player" width="560"></iframe>
</center>
<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: left;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiyHo43hLjQtOAugkl94qrw8q5lAuHwTwZ_51MIMkEUzRb5W8al9Zrk6ccqhOm9mKcfaF3eJGYaMvqvN_c-LMXOoFhERmIZrUaIIV-NhxLJ4sGS5znVJw6glGYwqC8bk_bLvALXYY-R2RO5UO9S3DNcghRkmdRw7IDjA7BQ6eNHfr1XbQQeqw/s10800/StudyArea.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="9000" data-original-width="10800" height="534" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiyHo43hLjQtOAugkl94qrw8q5lAuHwTwZ_51MIMkEUzRb5W8al9Zrk6ccqhOm9mKcfaF3eJGYaMvqvN_c-LMXOoFhERmIZrUaIIV-NhxLJ4sGS5znVJw6glGYwqC8bk_bLvALXYY-R2RO5UO9S3DNcghRkmdRw7IDjA7BQ6eNHfr1XbQQeqw/w640-h534/StudyArea.jpg" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">
<span style="font-family: "Times New Roman",serif; font-size: 12pt; line-height: 107%; mso-ansi-language: EN-US; mso-bidi-language: AR-SA; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">County-wise land availability for carinata production</span>
</td></tr></tbody></table><table cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: left;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiK79u1DjpG4HWNeqHkw4QBcH7dkO5mL636m2WsJK-m514Zmwy6l9SSmvtjSCK8vcDEvcRtExjZCWtlu-c2cDcLJF_ezQWLA7nG8pSzYXDssmbNOxzSKJ1xq0D6iS6079GYORpCxUt0hjwNQZOUXzrWaXyeFeMSmvrhAu7hTUhtbScnqT2koA/s1738/Screen%20Shot%202022-10-28%20at%202.01.56%20PM.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1738" data-original-width="1164" height="640" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiK79u1DjpG4HWNeqHkw4QBcH7dkO5mL636m2WsJK-m514Zmwy6l9SSmvtjSCK8vcDEvcRtExjZCWtlu-c2cDcLJF_ezQWLA7nG8pSzYXDssmbNOxzSKJ1xq0D6iS6079GYORpCxUt0hjwNQZOUXzrWaXyeFeMSmvrhAu7hTUhtbScnqT2koA/w428-h640/Screen%20Shot%202022-10-28%20at%202.01.56%20PM.png" width="428" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">
<span style="font-family: "Times New Roman",serif; font-size: 12pt; line-height: 107%; mso-ansi-language: EN-US; mso-bidi-language: AR-SA; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">Process, overview and scheduling of the model</span></td></tr></tbody></table><table cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: left;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjnDBbeqpFeuatgekftLJr2vh_mm868ejw6RI81m8V6-Pfqb7xzByW9Ug1CMMrVUAIrhRX1yLP5DThR46eEEOML91ZY1favcN8dn-gKHJvUijlZUNoam0FI6dqgaYMIMR-1J9z2LjzhMQjzJ9cP-lqZ78KRfizB7aoWz281GkSDrRJ2P1Yd6Q/s977/Picture3.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="489" data-original-width="977" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjnDBbeqpFeuatgekftLJr2vh_mm868ejw6RI81m8V6-Pfqb7xzByW9Ug1CMMrVUAIrhRX1yLP5DThR46eEEOML91ZY1favcN8dn-gKHJvUijlZUNoam0FI6dqgaYMIMR-1J9z2LjzhMQjzJ9cP-lqZ78KRfizB7aoWz281GkSDrRJ2P1Yd6Q/w640-h320/Picture3.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Number of farmers who adopt carinata in the rotation years with high profit condition (carinata yield = 60 bu/acre, carinata production cost = $260/acre) <br /></td></tr></tbody></table><p style="text-align: left;"><b>Full Reference:</b><br /></p>
<blockquote style="text-align: left;"><b>Ullah, K. and Crooks A.T., (2022),</b> Modelling Farmers’ Adoption Potential to New Bioenergy Crops: An Agent-based Approach, <i>The 2022 Computational Social Science Society of Americas Conference</i>, Santa Fe, NM. (<a href="https://www.dropbox.com/s/ecxavsvf9s5n9is/BioenergyCrops_ABM_CSSSA2022.pdf?dl=0">PDF</a>)</blockquote>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-22770502.post-53423089717584191722022-10-27T13:23:00.000-04:002022-10-28T13:24:25.589-04:00Water reuse adoption by farmers & the impacts on local water resources using an ABM<p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjFK3oa23jdz9vAKzOWqUrJlw8S6eCrGKqFO-b-AKkSLxPv7uhZ1HsAb977Mr0CoftTa1NhpelaxsbV6KQAtl1lP6FMeGK_PTvwCKTzcKwNROXsSulbvZ47qzmGPpg6b8ijb3vhHNOoDfXJ7tqYoToKBOkZzLV0N4kFr4ne93II9aA_eP2s9A/s152/Screen%20Shot%202022-10-28%20at%201.15.54%20PM.png" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" data-original-height="152" data-original-width="144" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjFK3oa23jdz9vAKzOWqUrJlw8S6eCrGKqFO-b-AKkSLxPv7uhZ1HsAb977Mr0CoftTa1NhpelaxsbV6KQAtl1lP6FMeGK_PTvwCKTzcKwNROXsSulbvZ47qzmGPpg6b8ijb3vhHNOoDfXJ7tqYoToKBOkZzLV0N4kFr4ne93II9aA_eP2s9A/s16000/Screen%20Shot%202022-10-28%20at%201.15.54%20PM.png" /></a></div><div style="text-align: justify;">In the past we heave explored a how farmers might <a href="https://www.gisagents.org/2012/09/new-paper-agent-based-modeling-for.html" target="_blank">sell their land</a> but not how they might adapt new technologies or farming practices such as water reuse. But this has now changed with a new paper co-authored with <a href="https://scholar.google.com/citations?user=U32I1GEAAAAJ&hl=en" target="_blank">Farshid Shoushtarian</a> and <a href="https://watersustainabilitycom.wordpress.com/people/dr-masoud-negahban-azar/" target="_blank">Masoud Negahban-Azar</a> entitled "<i><a href="https://sesmo.org/article/view/18148" target="_blank">Investigating the micro-level dynamics of water reuse adoption by farmers and the impacts on local water resources using an agent-based model</a></i>" which was recently published in the journal <a href="https://sesmo.org/index" target="_blank">Socio-Environmental Systems Modelling</a>. In the paper we introduce the <b>WRAF</b> (water reuse adoption by farmers) model which explores how farmers might adopt water recycled water (reuse) practices. Using the model, results suggest that it might be possible through freshwater shortage or groundwater
withdrawal regulations could increase recycled water use by farmers. If this sounds of interest, below we provide an abstract to the model, some figures from the agent logic (i.e., decision making), an overview of simulation results and the full reference to the paper. Along with the paper, we have also provided more details about the WRAF model following the Overview, Design concepts, Details, and Decision-making (ODD) protocol along with the NetLogo source code which can be found at <a href="https://www.comses.net/codebase-release/cc6d551e-cf0f-472e-a54b-28591cd39b4d/" target="_blank">https://www.comses.net/codebase-release/cc6d551e-cf0f-472e-a54b-28591cd39b4d/</a>.</div><p><br /></p><div style="text-align: justify;"><blockquote><b>Abstract</b>: Agricultural water reuse is gaining momentum to address freshwater scarcity worldwide. The main objective of this paper was to investigate the micro-level dynamics of water reuse adoption by farmers at the watershed scale. An agent-based model was developed to simulate agricultural water consumption and socio-hydrological dynamics. Using a case study in California, the developed model was tested, and the results showed that agricultural water reuse adoption by farmers is a gradual and time-consuming process. In addition, results also showed that agricultural water reuse could significantly decrease the water shortage (by 57.7%) and groundwater withdrawal (by 74.1%). Furthermore, our results suggest that recycled water price was the most influential factor in total recycled water consumption by farmers. Results also showed how possible freshwater shortage or groundwater withdrawal regulations could increase recycled water use by farmers. The developed model can significantly help assess how the current water reuse management practices and strategies would affect the sustainability of agricultural water resources.
<p><b>Keywords</b>: Water reuse; agent-based modelling; agricultural water management; recycled water for irrigation <br /></p></blockquote></div>
<br /><div><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg9QEdtYJaohpTm5mdDCpYKCgh6ukZ27vgKYm-Ru4Ym0keNWIOZEmRK4eJCWnKW9LI0qt9C-17alCBpF8ql_bgfGhPGyUvRZXWMQbFYnxHKUKx5RTLncj3Ea3InBQDbOwxRjna5YFy9yNOe95QqGSB6OPuFqq3IRTur_zk1_tUQ80BS0r7MBw/s1964/Screen%20Shot%202022-10-28%20at%201.08.13%20PM.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="678" data-original-width="1964" height="221" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg9QEdtYJaohpTm5mdDCpYKCgh6ukZ27vgKYm-Ru4Ym0keNWIOZEmRK4eJCWnKW9LI0qt9C-17alCBpF8ql_bgfGhPGyUvRZXWMQbFYnxHKUKx5RTLncj3Ea3InBQDbOwxRjna5YFy9yNOe95QqGSB6OPuFqq3IRTur_zk1_tUQ80BS0r7MBw/w640-h221/Screen%20Shot%202022-10-28%20at%201.08.13%20PM.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">(a) WRAF framework; (b) Farmers' decision-making flowchart</td></tr></tbody></table><p><b></b></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhQ7-gPaDpDMiTixogPqCcSw5PnMlfGYoQ-C7BYafHVx5BBc9v9bJ4WUWNzeaxvnznv4ZMNKudcSKNKYJGjLRknJusKfulvLTvTRLYH2DtDJ25yiGoB8DAgrs1YfwsPeSQG3SFKTFSvfSBLPON6Ozk5uTuiHWGYB8hT7YmApI9l6_KXu6_hNg/s2736/Screen%20Shot%202022-10-28%20at%201.09.45%20PM.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1262" data-original-width="2736" height="296" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhQ7-gPaDpDMiTixogPqCcSw5PnMlfGYoQ-C7BYafHVx5BBc9v9bJ4WUWNzeaxvnznv4ZMNKudcSKNKYJGjLRknJusKfulvLTvTRLYH2DtDJ25yiGoB8DAgrs1YfwsPeSQG3SFKTFSvfSBLPON6Ozk5uTuiHWGYB8hT7YmApI9l6_KXu6_hNg/w640-h296/Screen%20Shot%202022-10-28%20at%201.09.45%20PM.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">(a) Water reuse adoption sub-model framework; (b) Wastewater treatment plants flowchart</td></tr></tbody></table><b><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh-mCK7TFq7sKF8uPyBZQudDz0EKj-oBhbchzHaYEKNutHMKtfzKdyBukrgyfa408EItOIq3gsVoLuJbYC2PMyFvS7pFYrzXu4WCYJN9dWbnOh4MUckA5n-UlLjVrpr8DvAkGXQpf-cJ2HsBRrr3GDxzhEHrEHanHa7PW9AurR-iSxBQfcGLw/s1602/Screen%20Shot%202022-10-28%20at%201.11.38%20PM.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1344" data-original-width="1602" height="536" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh-mCK7TFq7sKF8uPyBZQudDz0EKj-oBhbchzHaYEKNutHMKtfzKdyBukrgyfa408EItOIq3gsVoLuJbYC2PMyFvS7pFYrzXu4WCYJN9dWbnOh4MUckA5n-UlLjVrpr8DvAkGXQpf-cJ2HsBRrr3GDxzhEHrEHanHa7PW9AurR-iSxBQfcGLw/w640-h536/Screen%20Shot%202022-10-28%20at%201.11.38%20PM.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Representative simulation results: farmers’ water resources distribution in year one (a) andyear84(b); available recycled water in the storage ponds of Modesto (c) and Turlock (d)wastewater treatment plants; total recycled water used by farmers in year two (e) and year 84(f)</td></tr></tbody></table><br /></b><p></p><p><b>Full Reference: </b></p><blockquote><p style="text-align: justify;"><b>Shoushtarian, F., Negahban-Azar, M. and Crooks A.T. </b>(2022), Investigating the Micro-level Dynamics of Water Reuse Adoption by Farmers and the Impacts on Local Water Resources using an Agent-based Model, <i>Socio-Environmental Systems Modelling</i>, 4: 18148. Available at <a href="https://doi.org/10.18174/sesmo.18148" target="_blank">https://doi.org/10.18174/sesmo.18148</a>. (<a href="https://www.dropbox.com/s/iruf3zve0fbug2u/WRAF_Model_2022.pdf?dl=0" target="_blank">pdf</a>)<br /></p></blockquote></div><br />Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-22770502.post-25306517805060855742022-09-21T13:04:00.001-04:002022-09-21T13:04:30.769-04:00Mitigation of Supply Chain Disruptions by Criminal Agents<div class="separator" style="clear: both; text-align: left;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgnhbYAxudi2cPb7dlxk0JCGvqwJKSKiiFXkLqaxxfpLFpZyEXbAfbZZTfbGy2Tr6pTV69CHbcPSNeWHGKRZaMWOSriFmFPQURYYCdTdtS2bqRq06r91wqcfre9GmP5FkkRuV_ae_YO2_Iq8EigMr7KfB0QnXpEBrSltEKRXRp8v7Fd9Eu41g/s604/Screen%20Shot%202022-09-19%20at%209.48.16%20AM.png" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" data-original-height="104" data-original-width="604" height="34" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgnhbYAxudi2cPb7dlxk0JCGvqwJKSKiiFXkLqaxxfpLFpZyEXbAfbZZTfbGy2Tr6pTV69CHbcPSNeWHGKRZaMWOSriFmFPQURYYCdTdtS2bqRq06r91wqcfre9GmP5FkkRuV_ae_YO2_Iq8EigMr7KfB0QnXpEBrSltEKRXRp8v7Fd9Eu41g/w200-h34/Screen%20Shot%202022-09-19%20at%209.48.16%20AM.png" width="200" /></a></div><p style="text-align: justify;">Since the outbreak of COVID, the role of supply chains has been brought front and center in many aspects of our daily lives. For example, the disruption to supply chains can significantly influence the operation of
the world economy and this has been shown to permeate and affect a
large majority of countries and their citizens. However, it is not just diseases outbreaks that can affect them, but also criminal agents. To this end at the<a href="http://sbp-brims.org/2022/" target="_blank"> 15th International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation</a> (or SBP-BRiMs for short), <a href="https://www.linkedin.com/in/abhisekh-rana-9622b469" target="_blank">Abhisekh Rana</a>, <a href="https://hamdikavak.com/" target="_blank">Hamdi Kavak</a>, <a href="https://cs.gmu.edu/~sean/" target="_blank">Sean Luke</a>, <a href="https://cs.gmu.edu/~carlotta/" target="_blank">Carlotta Domeniconi</a>, <a href="https://volgenau.gmu.edu/profiles/jjonesu" target="_blank">Jim Jones</a> and myself have a paper entitled "<i><a href="https://link.springer.com/chapter/10.1007/978-3-031-17114-7_2" target="_blank">Mitigation of Optimized Pharmaceutical Supply Chain Disruptions by Criminal Agents.</a></i>" </p><p style="text-align: justify;">The paper presents some initial results from a model that explores the disruptions to supply chains by a criminal agent and possible mitigation strategies. We construct a model of a typical pharmaceutical manufacturing supply chain, which is implemented via discrete event simulation. The criminal agent optimizes its resource allocation using a <a href="https://en.wikipedia.org/wiki/CMA-ES" target="_blank">CMA-ES</a> algorithm to maximize disruption to the supply chain. CMA-ES is part of a family of sample-based optimization techniques
collectively known as evolutionary algorithms. Broadly speaking, CMA-ES starts with a
sample of random candidate solutions to optimize. It then iteratively
assesses the quality of each candidate solution, then performs
resampling based on their quality to produce a new sample of
candidates. By combining our supply chain model with our
criminal agent, and by leveraging CMA-ES, we attempt to
identify the main bottlenecks and the most vulnerable points in the
pharmaceutical supply chain. Our findings show criminal agents can cause cascading damage and exploit vulnerabilities, which inherently exist within the supply chain itself. We also demonstrate how basic mitigation strategies can efficaciously alleviate this potential damage. If this sounds of interest, below we provide the abstract to the paper, along with some of the key figures and at the bottom of the post the full reference and a link to the paper. </p><p style="text-align: left;"><b>Abstract: </b></p><p style="text-align: left;"></p><div style="text-align: justify;"><blockquote><p>Disruption to supply chains can significantly influence the operation of the world economy and this has been shown to permeate and affect a large majority of countries and their citizens. We present initial results from a model that explores the disruptions to supply chains by a criminal agent and possible mitigation strategies. We construct a model of a typical pharmaceutical manufacturing supply chain, which is implemented via discrete event simulation. The criminal agent optimizes its resource allocation to maximize disruption to the supply chain. Our findings show criminal agents can cause cascading damage and exploit vulnerabilities, which inherently exist within the supply chain itself. We also demonstrate how basic mitigation strategies can efficaciously alleviate this potential damage. </p><p><b>Keywords</b>:
Pharmaceutical supply chains, Criminal agents,
Evolutionary computation, Mitigation. </p></blockquote></div><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjjL_UzM31Q7u3cYelFCu0CDxE74d-RbCapXIE9pvj5AFbWjt_JXydGOGfiXaJQYrJVCQlMMv2fE0C4Rik6uEAGTulSj2DEDGZt9D6QcyG2lD8VbJlt9-M6rxGNcdMKycbFuuW_3lX6l816LZht63oPIQQEQBQUyzj-FJ-OMwUdYa7f3PFUQw/s975/Picture1.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="500" data-original-width="975" height="328" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjjL_UzM31Q7u3cYelFCu0CDxE74d-RbCapXIE9pvj5AFbWjt_JXydGOGfiXaJQYrJVCQlMMv2fE0C4Rik6uEAGTulSj2DEDGZt9D6QcyG2lD8VbJlt9-M6rxGNcdMKycbFuuW_3lX6l816LZht63oPIQQEQBQUyzj-FJ-OMwUdYa7f3PFUQw/w640-h328/Picture1.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">A simplified version of a typical pharmaceutical supply chain.</td></tr></tbody></table><p></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg49eF7qhQFhpwD5iEWmIGfaMmSm12Wz3TdeaOWfy7vKtQhjUjKGlDomq4KTlqE_HAkU-jTfrhVULICrnPNFGu9fkww_GiiENFryOBzVi31C3FdMOvZEKkdtR3n8dFrm8d_a-R0Qd-Y3Wcifh-Q4EzwS0RX2DDH3MKydBGcQvS0ZE7oWASjXg/s1574/Screen%20Shot%202022-09-21%20at%2012.31.21%20PM.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="486" data-original-width="1574" height="198" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg49eF7qhQFhpwD5iEWmIGfaMmSm12Wz3TdeaOWfy7vKtQhjUjKGlDomq4KTlqE_HAkU-jTfrhVULICrnPNFGu9fkww_GiiENFryOBzVi31C3FdMOvZEKkdtR3n8dFrm8d_a-R0Qd-Y3Wcifh-Q4EzwS0RX2DDH3MKydBGcQvS0ZE7oWASjXg/w640-h198/Screen%20Shot%202022-09-21%20at%2012.31.21%20PM.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Design of the criminal agent.</td></tr></tbody></table><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhaY7bNkyOKSofl076o_VRmI8lgQPssYpr29PjVsEgkl1jVageJFvKFkoLlYnyFthizGmR6K17M0NB3OTzDBkRK7vA7PrXjNQlfoiM4OC6d7LI54xAJVwyLAnHwnhO5-5Hrwlw8wRmWEOhwNAYwWTQODMCMjZdIjzhxCxkyXJMvv3LrAFEudw/s975/fig3.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="497" data-original-width="975" height="326" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhaY7bNkyOKSofl076o_VRmI8lgQPssYpr29PjVsEgkl1jVageJFvKFkoLlYnyFthizGmR6K17M0NB3OTzDBkRK7vA7PrXjNQlfoiM4OC6d7LI54xAJVwyLAnHwnhO5-5Hrwlw8wRmWEOhwNAYwWTQODMCMjZdIjzhxCxkyXJMvv3LrAFEudw/w640-h326/fig3.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Sample simulations for the baseline model, without any disruption, and attacks at the five main disruption points in the supply chain.</td></tr></tbody></table><p style="text-align: left;"></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhTPGuNT_vpBzmekr-Xu8pr6Z-p9NiJ6EdWW6Jq80XJB7ri-rlu0nEC_cMue3KGdo6c7K8Fbs4KFPtXP87usi01ZpLaQa_DtYyk8JU5cXpLRgqhXNIMpXNYzgTGAtrnGnb1gginlLEcUg1vMZbSxSAgP7tWUirSHf7HZTBgZONt0Z1196RXlg/s1914/Mitigation2.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="866" data-original-width="1914" height="290" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhTPGuNT_vpBzmekr-Xu8pr6Z-p9NiJ6EdWW6Jq80XJB7ri-rlu0nEC_cMue3KGdo6c7K8Fbs4KFPtXP87usi01ZpLaQa_DtYyk8JU5cXpLRgqhXNIMpXNYzgTGAtrnGnb1gginlLEcUg1vMZbSxSAgP7tWUirSHf7HZTBgZONt0Z1196RXlg/w640-h290/Mitigation2.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Summary statistics and sample simulations for CMAES optimized disruptions with and without mitigation in place.</td></tr></tbody></table><br /><b>Full Reference: </b><br /><p></p><p style="text-align: left;"><b></b></p><blockquote style="text-align: justify;"><b>Rana, R., Kavak, H., Crooks, A.T., Domeniconi, C., Luke, S. and
Jones, J. </b>(2022), Mitigation of Optimized Pharmaceutical Supply Chain
Disruptions by Criminal Agents, in Thomson, R., Dancy, C. and Pyke, P.
(eds), <i>Proceedings of the 2022 International Conference on Social
Computing, Behavioral-Cultural Modeling, & Prediction and Behavior
Representation in Modeling and Simulation</i>, Pittsburgh, PA., pp 13-23. (<a href="https://www.dropbox.com/s/rjec7q7xcba17u2/SupplyChains_SBP2022.pdf?dl=0" rel="nofollow" target="_blank">pdf</a>)</blockquote><p></p><p style="text-align: justify;"> </p><p style="text-align: justify;"></p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-22770502.post-51935935946588537332022-09-19T09:47:00.001-04:002022-09-19T09:47:26.093-04:00Information propagation on cyber, relational and physical spaces about covid-19 vaccine<p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg38OBbpoTsDkW0g8eXsexaVJ1oggsO0sHbzFje-LMTwgqm-KDxoKq1hlyoP6Lggo64uusNxNDVsrD3ntZz7hChxmIfBSwDCe5sG8Y5Ir6ZoZQaOUIiSoaKfHp6QwxvuKSJBDhQReJeHEa4ndbzkA3tX6IUjgiU8LeWCAXDCKHppA39tDp9yQ/s768/X01989715.jpg" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" data-original-height="768" data-original-width="576" height="200" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg38OBbpoTsDkW0g8eXsexaVJ1oggsO0sHbzFje-LMTwgqm-KDxoKq1hlyoP6Lggo64uusNxNDVsrD3ntZz7hChxmIfBSwDCe5sG8Y5Ir6ZoZQaOUIiSoaKfHp6QwxvuKSJBDhQReJeHEa4ndbzkA3tX6IUjgiU8LeWCAXDCKHppA39tDp9yQ/w150-h200/X01989715.jpg" width="150" /></a></div><p style="text-align: justify;">It seems that its been a quite some time that we posted about <a href="https://www.gisagents.org/search/label/GeoSocial" target="_blank">geosocial analysis</a> but in a recent paper with <a href="https://archplan.buffalo.edu/People/students/phd-students.host.html/content/shared/ap/students-faculty-alumni/phd-candidates/yin.detail.html" target="_blank">Fuzhen Yin</a> and <a href="https://archplan.buffalo.edu/People/faculty.host.html/content/shared/ap/students-faculty-alumni/faculty/Yin.html" target="_blank">Li Yin</a> entitled "<a href="https://www.sciencedirect.com/science/article/pii/S0198971522001314" target="_blank">Information Propagation on Cyber, Relational and Physical Spaces about Covid-19 Vaccine: Using Social Media and the Splatial Framework</a>" published in <a href="https://www.sciencedirect.com/journal/computers-environment-and-urban-systems" target="_blank"><i>Computers, Environment and Urban Systems</i></a> we revisit this line of work while at the same time linking it to Covid and vaccination debates. </p><p style="text-align: justify;">Specifically we examine the interaction between cyber, relational (i.e, networks between objects), and physical spaces using the <a href="https://www.tandfonline.com/doi/abs/10.1080/24694452.2019.1631145" target="_blank">Splatial framework</a>. Through our analysis focused on New York State, we find that non-polarized vaccination debates were observed in cyber, relational, and physical spaces. Furthermore, we found that while physical space users had less anti-vaccine stance than relational and cyber space users there were strong interactions are observed between physical–relational, and relational-cyber spaces.If this sort of thing interests you. Below we provide the abstract to the paper along with some figures which show the study area, our methodology and some of the results. While at the bottom of the post we provide the full reference and the link to the paper. <br /></p><div id="as010"><p id="sp010"><b>Abstract:</b></p><div style="text-align: justify;"><blockquote>With the advent of social media, human dynamics studied in purely physical space have been extended to that of a cyber and relational context. However, connections and interactions between these hybrid spaces have not been sufficiently investigated. The “space-place (Splatial)” framework proposed in recent years allows capturing human activities in the hybrid of spaces. This study applies the Splatial framework to examine the information propagation between cyber, relational, and physical spaces through a case study of Covid-19 vaccine debates in New York State (NYS). Whereby the physical space represents the regional boundaries and locations of social media (i.e., Twitter) users in NYS, the relational space indicates the social networks of these NYS users, and the cyber space captures the larger conversational context of the vaccination debate. Our results suggest that the Covid-19 vaccine debate is not polarized across all three spaces as compared to that of other vaccines. However, the rate of users with a pro-vaccine stance decreases from physical to relational and cyber spaces. We also found that while users from different spaces interact with each other, they also engage in local communications with users from the same region or same space, and distance-based and boundary-confined clusters exist in cyber and relational space communities. These results based on the Splatial framework not only shed light on the vaccination debates but also help to define and elucidate the relationships between the three spaces. The intense interactions between spaces suggest incorporating people’s relational network and cyber presence in physical place-making.<br /><br /><b>Keywords</b>: Covid-19, Vaccination, Social media, Social network analysis, Community detection, Urban informatics <br /></blockquote><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg64KOViwK3SiMkDbpWNFyEfmz0mzVOz_53uH4b5mS8_X5Lhnma5lx3Cw1Q3v0YDTqsyKGF33KMyzTJc_W5zdFgzGR1-KwKJMNeWa49rTRmIilGFqOQxuMLo9iXVBnI4FUwjPkLES5MX1TW04reXsYz-qXWhqH2RpKIb6B85HHFjpEyJCbbww/s2372/fig1.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1849" data-original-width="2372" height="498" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg64KOViwK3SiMkDbpWNFyEfmz0mzVOz_53uH4b5mS8_X5Lhnma5lx3Cw1Q3v0YDTqsyKGF33KMyzTJc_W5zdFgzGR1-KwKJMNeWa49rTRmIilGFqOQxuMLo9iXVBnI4FUwjPkLES5MX1TW04reXsYz-qXWhqH2RpKIb6B85HHFjpEyJCbbww/w640-h498/fig1.jpg" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Schematic representation of the three spaces: cyber, relational and physical spaces.</td></tr></tbody></table><p></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh94XoV_C702eAe5c7wsSwZvXHwFiERqIqAmvnXTuBCZKjMIPHvwB_8I5K4UzUWKKL1qiwGwu-Db7nUY04bR8qSId04WfbCtwsb1FOpbLjOsZljFkGkUO7ejAihZKWEH3Xb3fMIWxKH_xXM1z8N8EwmqiSCs3_yuvl4VZdVGfS0f9saHcx5hA/s2963/2.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="2242" data-original-width="2963" height="484" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh94XoV_C702eAe5c7wsSwZvXHwFiERqIqAmvnXTuBCZKjMIPHvwB_8I5K4UzUWKKL1qiwGwu-Db7nUY04bR8qSId04WfbCtwsb1FOpbLjOsZljFkGkUO7ejAihZKWEH3Xb3fMIWxKH_xXM1z8N8EwmqiSCs3_yuvl4VZdVGfS0f9saHcx5hA/w640-h484/2.jpg" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Map of study area (NYS) with the primary road system. Red dots denote collected vaccine-related tweets in NYS.</td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjspMVBbLzIwTCVjZeTbYi8Ao_rxYtYaF5kedenBcjLTIza6fMhX1980TnIpoOvlk_YycwmJAF5Wv8clSjKQM8TCMoU-U9Xo7_wcwMveJmylyfn60ITqtX5-7a-Uw7GmPn7ZkhaaEfbUX24w0z6-KxiHUQ6ly_9kg7cS_ZjQLmEDiVWuc2wqw/s2768/3.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="2477" data-original-width="2768" height="572" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjspMVBbLzIwTCVjZeTbYi8Ao_rxYtYaF5kedenBcjLTIza6fMhX1980TnIpoOvlk_YycwmJAF5Wv8clSjKQM8TCMoU-U9Xo7_wcwMveJmylyfn60ITqtX5-7a-Uw7GmPn7ZkhaaEfbUX24w0z6-KxiHUQ6ly_9kg7cS_ZjQLmEDiVWuc2wqw/w640-h572/3.jpg" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Research workflow to investigate the propagation of different opinions between three spaces: cyber, relational and physical spaces.</td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjDcBey6MZ5-Idq-599hZ_AfbYFTY0H2EfddDsXQCWkqIFdzkrSyM4Jb5Iy8y0hNogZt0SppC7tyaCWwK0jNEecfiyCUmEB3ZYaJmpCNdoMgVdXoygtFg0VJ7MF2oWERfkR4_g3SQKhcMzxu-5MINsMsrppsY1PB0RmXpfckgRUE95BuiwPzA/s3553/12.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="2177" data-original-width="3553" height="392" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjDcBey6MZ5-Idq-599hZ_AfbYFTY0H2EfddDsXQCWkqIFdzkrSyM4Jb5Iy8y0hNogZt0SppC7tyaCWwK0jNEecfiyCUmEB3ZYaJmpCNdoMgVdXoygtFg0VJ7MF2oWERfkR4_g3SQKhcMzxu-5MINsMsrppsY1PB0RmXpfckgRUE95BuiwPzA/w640-h392/12.jpg" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Network visualization of the eight top large communities in relational space. (A) Visualization of communities using ForceAtlas layout. (B) Project communities into physical space. Nodes without location information are placed outside of NYS.</td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj3oO-NBC5hVsVICdccPye2Et6ou6V71NhxCFPCmO8k7h49Ro2HkuV0RNmZ-EeceV7xyLzOhIapEjsZj3vFpAV_X2WR8bz3YZsfjlUKO0scgaFBUNJ1t39G5iQB6PKsVb35GWZK8mR2eFIyQViz74k6QuTZhfvukVAYrKssO4s0gxGEEpbjkA/s2790/r11.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="2790" data-original-width="2766" height="640" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj3oO-NBC5hVsVICdccPye2Et6ou6V71NhxCFPCmO8k7h49Ro2HkuV0RNmZ-EeceV7xyLzOhIapEjsZj3vFpAV_X2WR8bz3YZsfjlUKO0scgaFBUNJ1t39G5iQB6PKsVb35GWZK8mR2eFIyQViz74k6QuTZhfvukVAYrKssO4s0gxGEEpbjkA/w634-h640/r11.jpg" width="634" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">The hybrid space network shows the information propagation between physical and relational spaces. (A) shows the network of all tweets, (B) shows the pro-vaccine tweets, and (C) shows the anti-vaccine tweets.</td></tr></tbody></table><b> </b></div><div style="text-align: justify;"><b>Full Reference: </b><br /><p></p><p></p><blockquote><b>Yin, F., Crooks, A.T. and Yin, L. </b>(2022), Information Propagation on Cyber, Relational and Physical Spaces about Covid-19 Vaccine: Using Social Media and the Splatial Framework, <i>Computers, Environment and Urban Systems</i>. Available at: <a href="https://doi.org/10.1016/j.compenvurbsys.2022.101887" target="_blank">https://doi.org/10.1016/j.compenvurbsys.2022.101887</a>. (<a href="https://www.dropbox.com/s/2f5nztr044cpx6q/splatial_covid.pdf?dl=0" target="_blank">pdf</a>)<br /></blockquote><p></p></div><div style="text-align: justify;"><p></p><p></p></div></div>Unknownnoreply@blogger.com0