Tuesday, March 31, 2020

A Simple Locational Model

While there are many sophisticated urban growth and planning models (e.g. the SLEUTH model and UrbanSim), there are also many more theoretical ones which say explore the evolution of land markets. Take for example Alonso’s (1964) urban land market theory. In this theory firms or residents desire a certain amount of space and this desire for space leads to competition for land and specific locations and thus driving up the price in the most accessible areas.

To this we have created a simple model which replicates what is postulated in Alonso’s  (1964) urban land market theory. The original model (Crooks, 2007) was created in Repast J (more details and source code can be found here) and now it has been replicated in NetLogo (and can be downloaded from https://github.com/acrooks2/Bid-Rent-Model). The basic model logic is presented in the figure below and a movie of simulation is also provided. However, unlike the original Alonso (1964) model, by using agents we can  incorporate issues such as time, therefore allowing the system to adapt and evolve to changes in the environment, for example infrastructure investment or population growth. The NetLogo model "Info" tab has several suggestions on  extending the basic model if you so desire. While for interested readers more complex land market models are also available such as Filatova et al (2009) land market model and Magliocca et al (2011) model of coupled housing and land markets


Basic Model Logic: Searching for the "best" location.



References: 
Alonso, W. (1964), Location and Land Use: Toward a General Theory of Land Rent, Harvard University Press, Cambridge, MA.
Crooks, A.T. (2007), Experimenting with Cities: Utilizing Agent-Based Models and GIS to Explore Urban Dynamics, PhD Thesis, University College London, London, England.
Filatova, T., Parker, D. and van der Veen, A. (2009), 'Agent-Based Urban Land Markets: Agent's Pricing Behavior, Land Prices and Urban Land Use Change', Journal of Artificial Societies and Social Simulation, 12(1), Available at http://jasss.soc.surrey.ac.uk/12/1/3.html.
Magliocca, N., Safirova, E., McConnell, V. and Walls, M. (2011), 'An Economic Agent-based Model of Coupled Housing and Land Markets (CHALMS)', Computers, Environment and Urban Systems, 35(3): 183-191.

Wednesday, February 26, 2020

Class Model Examples

Avid readers of this blog (if there are any) might have noticed at the end of each semester I do a post pertaining to class models from the various courses I teach. This often involves a short movie of some of these models like the one below.


Often I get asked about these models are, so finally I have complied a selection of them on GitHub: https://github.com/acrooks2/ClassModels. These are only NetLogo  models (for the time being) as I use it as a way of introducing students to agent-based modeling and programing.  As noted on the readme of the repository these models come as is. What explanations there are is given in the readme file for each model (these mainly come in the form of abstracts from the papers that were submitted with the models). No further explanations, support etc. will be given and are only provided to show the range of problems agent-based models can be used to explore. I also need to acknowledge all the students who submitted the models, you know who you are! This project would not be possible without you!

Maybe one day I will also get around to containerizing some of these models. For those interested containerization and how to do this for NetLogo models, https://www.comses.net/ has a great tutorial on this (click here for further details).

Examples of the types of GIS and agent-based modeling projects.

Friday, January 31, 2020

The Interplay Between the Media and the Public in Mass Shootings

Continuing our work on shootings we recently had a paper published in Criminology and Public Policy entitled: "Responses to Mass Shooting Events: The Interplay Between the Media and the Public." However, here we do not look at bots but instead explore the how the public responds to mass shooting events (e.g. Las Vegas, Sutherland Springs, Marshall County, Parkland, Santa Fe), by seeking additional information or exchanging opinions about them in media coverage (e.g. newspaper articles via LexisNexis) and through online sources of information (e.g. Google Trends, Wikipedia and Online Social Networks (i.e. Twitter)). 

Overall, our results show discernible patterns in both time and space in the public’s online information seeking activities after a mass shooting. In addition we find discernible online information seeking patterns in geographic space, with a focal area of interest in the state in which the shooting event occurs, surrounded by a region of reduced interest. This finding further suggests that online information seeking activities are driven, at least in part, by geographic proximity to mass shooting events.

If you wish to find out more about this research, below we provide the summary and policy implication to the paper along with some figures from our methodology (e.g., how we go about analyzing temporal and geographical trends) and some of the results. Finally at the bottom of the post we provide the full reference and a link to the paper.

Abstract:
Research Summary: Public mass shootings tend to capture the public’s attention and receive substantial coverage in both traditional media and online social networks (OSNs) and have become a salient topic in them. Motivated by this, the overarching objective of this paper is to advance our understanding of how the public responds to mass shooting events in such media outlets. Specifically, it aims to examine whether distinct information seeking patterns emerge over time and space, and whether associations between public mass shooting events emerge in online activities and discourse. Towards this objective, we study a sequence of five public mass shooting events that have occurred in the United States between October 2017 and May 2018 across three major dimensions: the public’s online information seeking activities, the media coverage, and the discourse that emerges in a prominent OSN. To capture these dimensions, respectively, data was collected and analyzed from Google Trends, LexisNexis, Wikipedia Page views, and Twitter. The results of our analysis suggest that distinct temporal patterns emerge in the public’s information seeking activities across different platforms, and that associations between an event and its preceding events emerge both in the media coverage and in OSNs.
Policy Implication: Studying the evolution of discourse in OSNs provides a valuable lens to observe how society’s views on public mass shooting events are formed and evolved over time and space. The ability to analyze such data allows tapping into the dynamics of reshaping and reframing public mass shooting events in the public sphere and enable it to be closely studied and modeled. A deeper understanding of this process, along with the emerging associations drawn between such events, can then provide policy and decision-makers with opportunities to better design policies and communicate the significance of their goals and objectives to the public.
A framework for the analysis of temporal and geographical trends .

The analysis processes of Twitter and LexisNexis data.

Geographic patterns in online search activity in Google Trends for the five events in our study.

Chronologically ordered Google Trends search activity (a, left) and Wikipedia page views (b, right). Each vertical solid black line marks the occurrence of one of four shooting events examined in the analysis (as indicated by the line label).

Mentions of prior events during the first approximately 1-month period following each event in each of the events studied. (a) Sutherland Springs, (b) Marshall County, (c) Parkland, (d) Santa Fe.

Full Reference:
Croitoru, A., Kien, S., Mahabir, R., Radzikowski, J., Crooks, A.T., Schuchard, R., Begay, T., Lee, A., Bettios, A. and Stefanidis, A. (2020), Responses to Mass Shooting Events: The Interplay Between the Media and the Public, Criminology and Public Policy, 19(2): 335–360. (pdf)

Thursday, January 30, 2020

Comparison of Emoji Use in Names, Profiles, and Tweets

In most of our work to date with respect to exploring social media, we have only looked at the text or images from online social media platforms (e.g. Twitter and Flickr) and excluded  emojis from the analysis. However, this has now changed with a new paper co-authored with  Melanie Swartz entitled "Comparison of Emoji Use in Names, Profiles, and Tweets" which will be presented at he Eighth IEEE International Workshop on Semantic Computing for Social Networks and Organization Sciences in conjunction with 14th IEEE International Conference on Semantic Computing

In the paper we discuss how emoji use is becoming more and more popular by users of online social networking sites as they can be an effective way to express sentiment, sarcasm or feelings which are not easily conveyed as text. However, limited research has focused on analysis of the behavior of emoji use or how to compare emoji use across users or documents. To overcome this limitation, in this paper: (1) we present a methodology to extract, aggregate, and compare emoji use across a collection of documents based on Unicode emoji category and subcategories, (2) we present a baseline of statistics of emoji use in user names, profile descriptions, and tweets, and (3) we compare emoji use as categories and subcategories between users and content a user shares in the user name, profile description, retweets and non-retweets.

By considering this semantic grouping of emojis, we move the research on emojis beyond just comparing individual emojis and broad aggregations. In applying our methodology to a set of 44 million tweets and over 3 million user profiles, we find that differences in emoji use emerged based on document type (i.e., user names, profile descriptions, retweets, and non-retweets). As such, our work offers a new lens to study and compare forms of self expression across a variety of digital media content types. If you wish to find out more about this work, below we present the abstract to the paper, our workflow that allows for emoji comparison and some results. Finally at the bottom of the page we provide the full reference and a link to the paper.

Abstract 
Online social networking applications are popular venues for self-expression, communication, and building connections between users. One method of expression is that of emojis, which is becoming more prevalent in online social networking platforms. As emoji use has grown over the last decade, differences in emoji usage by individuals and the way they are used in communication is still relatively unknown. This paper fills this gap by comparing emoji use across users and collectively in user names, profiles, and in original and re-shared content. We present a methodology that enables comparison of semantically similar emojis based on Unicode emoji categories and subcategories. We apply this methodology to a corpus of over 44 million tweets and associated user names and profiles to establish a baseline which reveals differences in emoji use in user names, profile descriptions, non-retweets, and retweets. In addition, our analysis reveals emoji super users who have a significantly higher proportion and diversity of emoji use. Our methodology offers a novel approach for summarizing emoji use and enables systematic comparison of emojis across individual user profiles and communication patterns, thus expanding methods for semantic analysis of social media data beyond just text.
Keywords: emoji; social media analytics; content analysis; online social networks.

Workflow for emoji comparison.

Proportion of emoji use in profiles, names, retweets, and non-retweets, ordered by category.

Proportion of emoji use by subcategory.

Top emoji for each communication type.

Reference:
Swartz, M. and Crooks, A.T. (2020), Comparison of Emoji Use in Names, Profiles, and Tweets, The Eighth IEEE International Workshop on Semantic Computing for Social Networks and Organization Sciences: From User Information to Social Knowledge, San Diego, CA. (pdf)

Tuesday, January 14, 2020

New Paper: Insights into Human-wildlife Interactions in Cities from Bird Sightings Recorded Online

In the past we have explored how social media can be used to delineate earthquakes, locate wildfires or be used to understand urban morphology. However, recently we have also started to explore how social media and crowdsourced data can be utilized to to study socio-environmental systems. Keeping with this them, Bianca Lopez, Emily Minor, and myself have recently had a paper published in Landscape and Urban Planning entitled "Insights into Human-wildlife Interactions in Cities from Bird Sightings Recorded Online."  

In the paper we explore where do people observe birds, using the city of Chicago as our case study. By utilizing urban bird observations collected from eBird, iNaturalist, and Flickr we find that most bird observations occurred in open space zoned for recreational use. Further analysis revealed that the number of bird observations varied with income, population size, and proximity to Lake Michigan. If you want to find out more, below is the abstract to the paper, along with some figures of the results and at the bottom of the post, the full reference and a link to the paper. 

Abstract:
Interactions with nature can improve the wellbeing of urban residents and increase their interest in biodiversity. Many places within cities offer opportunities for people to interact with wildlife, including open space and residential yards and gardens, but little is known about which places within a city people use to observe wildlife. In this study, we used publicly available spatial data on people’s observations of birds from three online platforms—eBird, iNaturalist, and Flickr—to determine where people observe birds within the city of Chicago, Illinois (USA). Specifically, we investigated whether land use or neighborhood demographics explained where people observe birds. We expected that more observations would occur in open spaces, and especially conservation areas, than land uses where people tend to spend more time, but biodiversity is often lower (e.g., residential land). We also expected that more populated neighborhoods and those with higher median age and income of residents would have more bird observations recorded online. We found that bird observations occurred more often in open spaces than in residential areas, with high proportions of observations in recreation areas. In addition, a linear regression model showed that neighborhoods with higher median incomes, those with larger populations, and those located closer to Lake Michigan had more bird observations recorded online. These results have implications for conservation and environmental education efforts in Chicago and demonstrate the potential for social media and citizen science data to provide insight into urban human-wildlife interactions.
Keywords: Urban biodiversity, human-nature interaction, open space, residential, spatial analysis, birdwatching.

Map of bird observations from the three web platforms (Flickr, eBird, and iNaturalist) across the city of Chicago, in relation to mean median income of community areas (left panel) and open space, residential land use, highways, and waterways (right panel).

Proportions of observations recorded in different land uses on the three different online platforms (n = 7944 eBird; n = 474 iNaturalist; n = 561 Flickr). There was a significant difference between the three distributions (simulated p-value less than 0.001), including in the proportions of observations in conservation, recreation, and residential land uses.

Full Reference:
Lopez, B.E., Minor, E.S. and Crooks, A.T. (2020), Insights into Human-wildlife Interactions in Cities from Bird Sightings Recorded Online, Landscape and Urban Planning. 196: 103742. (pdf)