Tuesday, May 10, 2022

Analyzing the vaccination debate in social media data Pre- and Post-COVID-19 pandemic

In the past we have written about how vaccination is discussed on social media but such studies were often just focused on short study periods (i.e. a month). However, with the current COVID-19 pandemic we thought we would revisit the vaccination  debate and see if it has changed. So in a new paper with Qingqing Chen entitled "Analyzing the vaccination debate in social media data Pre- and Post-COVID-19 pandemic," we did just that. We explored approximately 11.7 million tweets posted between January 2015 to July 2021 and measured and mapped vaccine sentiments (Pro-vaccine, Anti-vaccine, and Neutral) across the US. Not to ruin the surprise of what we found but also to encourage you to read the paper we will not write about the results here. Only show the abstract of the paper, a few of the figures and a link to the paper itself.

Abstract

The COVID-19 virus has caused and continues to cause unprecedented impacts on the life trajectories of millions of people globally. Recently, to combat the transmission of the virus, vaccination campaigns around the world have become prevalent. However, while many see such campaigns as positive (e.g., protecting lives), others see them as negative (e.g., the side effects that are not fully understood scientifically), resulting in diverse sentiments towards vaccination campaigns. In addition, the diverse sentiments have seldom been systematically quantified let alone their dynamic changes over space and time. To shed light on this issue, we propose an approach to analyze vaccine sentiments in space and time by using supervised machine learning combined with word embedding techniques. Taking the United States as a test case, we utilize a Twitter dataset (approximately 11.7 million tweets) from January 2015 to July 2021 and measure and map vaccine sentiments (Pro-vaccine, Anti-vaccine, and Neutral) across the nation. In doing so, we can capture the heterogeneous public opinions within social media discussions regarding vaccination among states. Results show how positive sentiment in social media has a strong correlation with the actual vaccinated population. Furthermore, we introduce a simple ratio between Anti and Pro-vaccine as a proxy to quantify vaccine hesitancy and show how our results align with other traditional survey approaches. The proposed approach illustrates the potential to monitor the dynamics of vaccine opinion distribution online, which we hope, can be helpful to explain vaccination rates for the ongoing COVID-19 pandemic.

Keywords: COVID-19, Pandemic, Vaccination Sentiment Analysis, Time and Space, Social Media, United States.

Overview of our research pipeline.

Comparison between Twitter data and Google Trends. (a) Distribution of keywords search and tweets over time; (b) Correlation between Google Trends and Twitter data of keywords search; (c) Important news or announcements catched on Twitter activity (Note: period is the shaded area in (a)).

Correlation between Pro-vaccine users and actual vaccination records (a) Spatial distribution of odds ratio of Pro-vaccine users; (b) Spatial distribution of odds ratio of actual vaccination records; (c) Correlation between the Pro-vaccine users and the actual vaccination records.

Full Reference: 

Chen, Q. and Crooks, A.T. (2022), Analyzing the Vaccination Debate in Social Media Data Pre- and Post-COVID-19 Pandemic, International Journal of Applied Earth Observation and Geoinformation, 110: 102783.  Available at https://doi.org/10.1016/j.jag.2022.102783 (pdf)


Wednesday, March 16, 2022

Leveraging Street Level Imagery for Urban Planning

Just a short post that say that  Linda See and myself have a new editorial in Environment and Planning B: Urban Analytics and City Science entitled " Leveraging Street Level Imagery for Urban Planning." While in the in the past we have written about street view imagery and how there are initiatives like KartaView (previously named OpenStreetView and OpenStreetCam) and Mapillary which allow for the collection of volunteered street view imagery (VSVI) using just smartphones. But we have not really delved much into how such initiatives could be used to assist assist urban planning (e.,g. change detection, augmented reality (AR) and urban navigation).  If this sounds of interest,  please feel free to check out our editorial here

Exploring urban change in Buffalo, New York with Google Street View in October 2020 and the same location in the 2007 inset.

 Full Reference:

Crooks, A.T. and See, L. (2022), Leveraging Street Level Imagery for Urban Planning, Environment and Planning B, https://doi.org/10.1177/23998083221083364

Thursday, February 17, 2022

New Paper: Synthetic Populations with Social Networks

When developing geographically explicit agent-based models, one thing we spend a lot of time on is building synthetic populations and then linking the agents in the synthetic population to each other.  To overcome this issue we have a new paper published in "Computational Urban Science " entitled "A method to create a synthetic population with social networks for geographically-explicit agent-based models" In this paper  Na (Richard) Jiang, Hamdi Kavak, Annetta Burger, William Kennedy and myself present a synthetic population generation method that also includes social networks and use the New York Metro as a study site, which covers an area of 262 x 234 km and is home to over 23 million people. 

To show the utility of this method we also present three simple applications (e.g., a disease , a disaster  and a traffic model) which utilize different parts of this synthetic population but are all geographically explicit and use networks in some shape or form. If this sounds of interest, below you can read the abstract from the paper, along with seeing some of the figures from our methodology and example applications. While at the bottom of the post we provide the full citation and a link to the paper. The paper itself also has links to actual code that generates the synthetic population and the resulting datasets and models  (code: https://bit.ly/SynPopABM; source and resulting synthetic population data: https://osf.io/3vsaj/) .  

Abstract

Geographically-explicit simulations have become crucial in understanding cities and are playing an important role in urban science. One such approach is that of agent-based modeling which allows us to explore how agents interact with the environment and each other (e.g., social networks), and how through such interactions aggregate patterns emerge (e.g., disease outbreaks, traffic jams). While the use of agent-based modeling has grown, one challenge remains, that of creating realistic, geographically-explicit, synthetic populations which incorporate social networks. To address this challenge, this paper presents a novel method to create a synthetic population which incorporates social networks using the New York Metro Area as a test area. To demonstrate the generalizability of our synthetic population method and data to initialize models, three different types of agent-based models are introduced to explore a variety of urban problems: traffic, disaster response, and the spread of disease. These use cases not only demonstrate how our geographically-explicit synthetic population can be easily utilized for initializing agent populations which can explore a variety of urban problems, but also show how social networks can be integrated into such populations and large-scale simulations.

Keywords: Synthetic Population Generation, Agent-Based Modeling, New York, Traffic Dynamics, Disease, Disaster

Study Area

Workflow for Generation of Synthetic Population and Networks

Creation of Social Networks: (a) Selected Population; (b) Creation of a Household Network; (c) Creation of Work and Educational Networks for each Member of the Household; (d) The Household, its Networks within the Full Census Tract

Model Component Structure of Population Respond to Disaster event

Agents’ Health Status After 1 Minute of the Disaster Event

Full Reference:

Jiang, N., Crooks, A.T., Kavak, H., Burger, A. and Kennedy, W.G. (2022), A Method to Create a Synthetic Population with Social Networks for Geographically Explicit Agent-Based Models, Computational Urban Science, 2:7. Available at https://doi.org/10.1007/s43762-022-00034-1

Monday, January 24, 2022

Using GIS data to Build a Segregation Model in NetLogo

While our book "Agent-based Modelling and Geographical Information Systems" has a lot of details and examples about how to use GIS data within NetLogo (see https://github.com/abmgis/abmgis), as I was  preparing for my course this semester entitled "Spatial Simulation" I thought I would develop a more detailed tutorial on how to use vector data to build a segregation model in NetLogo. The model itself is inspired by Schelling's model of segregation, but unlike the regular cell versions which are commonly used as examples here the cells are polygons which are based on census boundaries of Washington DC. The movie below gives a sense of what it looks like when completed. 


If you are interested in how this was built and want relativity step by step instructions click here to download a zip file of the completed model, the shapefile and a pdf.  Alternatively just go to https://github.com/abmgis/abmgis/tree/master/Chapter06-IntegratingABMandGIS and download the Segregation Tutorial. I hope you find this useful.