Research

Motivation

Broadly speaking I am a geographer but more specifically I could be considered as a geospatial computational social scientist (CSS), who tries to understand people or more specifically on human and environmental interactions with main focus on cities. Why you might ask? This is because the world is constantly changing. For example, cities now provide homes to over 45% (followed by towns 36% and rural areas 19%) of the worlds 8.2 billion people. This has changed substantially from 1950 when only 20% world’s 2.5 billion people lived in cities with the rest living in towns and rural areas (around 40% each)[1]. Thus understanding such changes and the systems within them is of utmost importance, especially that of cities However, building such an understanding is not a trivial task due to the fact that cities are heterogeneous, which makes it challenging to generalize localized dynamics up to the level of city-wide problems [2, 3]. This is because a city is more than the sum of its parts. 

While our understanding of cities has increased throughout the twentieth century and the first part this one, namely by incorporating ideas and theories from a diverse range of subjects including economics, geography, history, philosophy, mathematics and more recently computer science; it is now very clear that there are intrinsic difficulties in applying such understanding to policy analysis and decision-making or addressing societal challenges spanning the spectrum from economics and housing, demographic shifts to that of environmental change and public safety. Exploring these crucial societal challenges are at the core of my research.

Research Areas
Figure 1: Intersection of Research Areas.

In order to understand the connections between people, place and future change, I utilize a diverse toolkit focused around three main research areas: agent-based modeling (ABM), geographical information science (GIS) and social network analysis (SNA) whose intersection can be represented through Geospatial CSS as shown in Figure 1. CSS lies at the foundation of my research as it represents the interdisciplinary science of complex social systems and their investigation through computational modeling and related techniques (e.g., machine learning, artificial intelligence). Initially, my primarily focus was on ABM and GIS. However, over the last decade or so, I have developed an active research interest in SNA, because of its capability to link many different areas of applications (i.e., it allows us to study the connections between people and place [4]). I see this development as a logical progression, not only is ABM and SNA at the core of CSS, but also network analysis has a long history within the geographical sciences [5]. While above I have given a broad overview of my research interests, if you wish to delve deeper into them, click on the links below (or just keep scrolling) to find out more on my work with respect to: 
At the end of this page you can see my potential future research areas (but this could and probably will change) along with those who have supported my research over the years.

Agent-based Modeling

Agent-based modeling (ABM) provides an ideal environment to test ideas and hypotheses that cannot be done in reality [6]. My focus has been, and continues to be, on developing agent-based models specifically relating to geographical systems [7, 8], and linking them to geographical information thus grounding them to place [9], a selection of which are shown in Figure 2. One common theme among the models is trying to understand geographical systems from the bottom-up [2] at a variety of spatial and temporal scales. Specifically, all the models I have developed explore how humans interact with each other and their environment and through such interactions how macro-scale phenomena emerges (especially in the context of urban environments). Examples of such include pedestrian movement [10, 11], riots [12] residential segregation [13], community resource management [14], the growth of slums [15, 16], the migration of displaced people [17], how people react in times of crisis [18, 19], climate adaptation [20, 21, 22], to the spread of diseases [23].

Figure 2: A Selection of Agent-based Modeling Application Areas of Interest (adapted from [24]).

Figure 3: Agent-based models in
Virtual Worlds [39].

These models also range across the spectrum from theoretical to practice (e.g., [9, 25]). Specifically, I have developed theoretical models whose purpose is to explore theory and test hypothesis (e.g., [13, 15, 26]) to more descriptive models based on empirical details of the social phenomena (e.g., [21, 27, 28, 29]), to models which fit between these two extremes (i.e., intermediate models) in essence models which are exploratory in their application of theory but descriptive in their use of empirical data (e.g., [16, 23, 30]). For many of these applications, they require synthetic populations, and rather than creating such populations from scratch we have also developed methods, datasets and pipelines to to utilize synthetic populations in the agent-based modeling workflow (e.g., [31, 32, 33, 34, 35, 36, 37, 38]).

Another area of research with respect to ABM pertains to visualization, sharing and communication of such models utilizing virtual worlds as shown in Figure 3 [39, 40]. I see such worlds as virtual online laboratories for creating, sharing and communicating of models. Moreover, I believe in providing model source code and data, which allows for replication and experimentation of the models (e.g., [10, 12, 13, 14, 15, 19, 23, 26, 28, 29, 39, 41, 42, 43, 44, 45]).

To find out more about my research with respect to ABM, the links below will take you to some of my current and past projects (or watch the YouTube movie below which includes a selection of such models). These models tend to be created either in Repast, NetLogo, MASON [46, 47], MESA [48] as I am a great believer in open-source toolkits.



While the list of application above might suggest that agent-based modeling might be great techniques
to explore the world around us, this is not to say there are not challenges with respect to developing them (see for example [25, 81, 82]) these range from verification, calibration, validation and sensitivity analysis [11, 83, 84]. In addition to this there are also opportunities and challenges with respect to agent-based modeling from artificial intelligence, generative AI and machine learning [53, 85, 86, 87], while at the same time scaling models up to millions of agents remains at issue [47, 88].

Figure 4: Agent-Based Modelling and
Geographical Information Systems: A
Practical Primer [6]

For those who are still reading, and if you wish to find out more, a good introduction into agent-based modeling and how GIS can be integrated within some models, which builds upon much of what was written above, readers are referred to the book I wrote with Nick Malleson, Ed Manley and  Alison Heppenstall entitled "Agent-Based Modelling and Geographical Information Systems: A Practical Primer" (Figuge 4). The book [6] provides a synthesis of the underpinning ideas, techniques and frameworks for integrating agent-based modeling and geographical information systems.

Endorsements:
“A highly original textbook linking complex systems and agent-based modeling with GIS using theoretical and methodological perspectives, software implementations, and real-world applications. A much-needed book for students at all levels to learn about geosimulation and modelling with geographic automata.”
Suzana Dragicevic, Simon Fraser University
“A great introduction for all those interested in learning about agent-based simulation where physical space is an important factor. Importantly this integrates GIS and other common geographic approaches with simulation approaches. Both beginners and more advanced researchers will find a lot of useful information here.”
Bruce Edmonds, Manchester Metropolitan University
“This textbook is a must-have for everybody who wants to learn or know about agent-based models integrated with geographic information systems. It combines in-depth introductions to basic concepts with hands-on advice on technical detail and covers all relevant aspects.”
Volker Grimm, Helmholtz Center for Environmental Research - UFZ 
A review of our book by Zhuge [89] in the Progress in Physical Geography: Earth and Environment, writes:
“Compared with other books on ABM or spatial modelling, this one tends to have much more detailed introductions to ways of developing a spatially explicit Agent-based Model from scratch, particularly using popular open-source software packages for ABM and GIS. Furthermore, readers can also benefit from the interesting and informative discussions on recent challenges and opportunities, as well as useful comparisons between different tools, theories and frameworks for spatial ABM.”
Figure 5: Agent-Based
Models of Geographical Systems [8].

For a more detailed introduction into ABM and how it can be used to study for geographical systems readers are referred to the book I co-edited with Alison Heppenstall, Linda See and Mike Batty entitled: "Agent-Based Models of Geographical Systems" as shown in Figure 5 [8]. The book brings together a comprehensive set of papers on the background, theory, technical issues and applications of ABM within geographical systems. This collection of papers is a useful reference point for experienced agent-based modeler as well those new to the area. Specific geographical issues such as handling scale and space are dealt with as well as practical advice from leading experts about designing and creating agent-based models, handling  complexity, visualizing and validating model outputs. With contributions from many of the world’s leading research institutions, the latest applied research (from micro and macro applications) from around the globe exemplify what can be achieved in geographical context.

A review of our book by Galán [90] in the Journal of Artificial Societies and Social Simulation writes:
"To sum up, this book is an essential reference for any researcher in the field of ABM and geographical systems. Although a more than 700 pages book can scare everyone, the admirably collective effort to synthesize and provide an up-to-date overview of the most relevant methodological and applied works in the field is worth the challenge. Furthermore, it must be said that it can also be recommended to any reader interested in ABM in general, even if initially unconcerned about geographical applications. Indeed, the first book section covers most of the relevant topics to be considered as a primer in ABM, regardless of the context of application, especially the second ("Principles and Concepts of Agent-Based Modelling") and many chapters of the third part ("Methods, Techniques and Tools for the Design and Construction of Agent-Based Models")."
Another review by Benenson [91] for International Journal of Geographical Information Science writes:
"To conclude, the 37 chapters of this fundamental volume provide a comprehensive perspective of the state of the art in the intensively developing field of modern geographic enquiry to the community of Agent-Based (AB) modelers in geography. I enjoyed reading the book and I am sure it will have an essential influence on the AB modeling community and inspire numerous further developments in the field."
In another review by Dragicevic  [92] for Environment and Planning B writes: 
"Overall, this edited book provides a comprehensive overview of the emerging area of ABM. Together, the chapters provide a rich source of bibliographic references, detailed illustrations to support visual understanding, and a logical presentation of the science behind ABM. This would make the book useful for a variety of target audiences ranging from established professionals who are interested in the current state of ABM to graduate and undergraduate students who need a systematic introduction to ABM. This book will be an essential reference text for academics, students, and decision makers who design and interpret spatial models to understand geographical processes."

Social Network Analysis 

Figure 6: Visualizing Communities: Network Cores
and Retweeting Nodes [115].

While I had explored physical networks in the past [99], as I noted above, more recently I became interested in social networks and their use in studying complex systems. I see this as a logical progression with respect to ABM, as SNA provides a lens to study the relationships among individuals, groups, or organizations as they form complex systems. SNA allows us to explore how different parts of a system are linked together and to define the overall structure of that system and its evolution over time [100] such as that shown in Figure 6. 

Moreover, as we engage in social interactions, our beliefs, behavior, feelings, and actions are deeply influenced by the people we interact with, whether they are our family, our friends, our peers, or society at large. By using SNA we can study this and incorporate such things in our agent-based models. For instance, SNA can be used to study how information spreads during an evacuation, health campaigns, riot, political discourse or disease outbreaks (e.g., [12, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109]). 

Figure 7: Visualizing Communities:
A Social Network of an Interest
Group (A), and its Largest Community
Plotted Geographically (B) [116].

In addition my interest in SNA also ties with my other research interest, that of GIS and the rise in social media and other new forms (e.g., from mobile phone [110]) has drastically changed the medium through which we can engage and study the world around us. For example, in addition to physical interaction, social media in its many forms has opened an entirely new avenue to enable an alternative form of interaction [4, 104, 111, 112, 113, 114].

My initial work with respect to social networks was focused on the geographical visualization of globally distributed communities (e.g., [116, 117]), such as that shown in Figure 7A. However, over time, my work has evolved to more advanced analysis such as viewing the international community as a set of networks, manifested through various transnational activities. The availability of longitudinal datasets, such as international arms trades and United Nations General Assembly votes allows for the study of state driven interactions over time (e.g., who sells arms to who, who votes with who) [100]. In parallel to this top-down approach, the emergence of social media is fostering a bottom-up and citizen-driven avenue for international relations. The comparison of these two network types offers a novel approach to study the alignment between states and their people [100]. I see great potential in fusing SNA with ABM and GIS: with respect to ABM and modeling people more generally, SNA allows us to study these connections directly but the combination of ABM, GIS and SNA is rarely done (see [6, 12] for some representative examples).

Geographical Information Science

Figure 8: Inner-City Function Map [147].
Building on my background in geography and GIS, much of my research is grounded to geographical systems. I see the “S” in GIS as representing both a System and a Science. As a system, GIS allows one to store, organize and access information (i.e., the technology and tools) as shown in Figure 8 but as a science, it allows us to solve problems and discover new knowledge about the world we inhabit (e.g., through spatial analysis). For my agent-based models, GIS allows me to build models related to actual real-world locations (e.g., descriptive models) but also to ground my work in SNA to an actual place as shown in Figure 8. GIS has grown rapidly over the last decade, especially in the amount of geographic data being generated and shared via the crowd through Web 2.0 technologies (e.g [111, 112, 118, 119, 120, 121, 122, 123, 124]). One active research area for me is harvesting crowdsourced data (volunteered and ambient geographic information [115]) from social media (e.g., X (formally Twitter), Flickr) and other platforms (e.g., OpenStreetMap, Mapillary) to increase our situational awareness, understand the linkage of form and function, studying the natural environment or socio-environmental systems by utilizing geographical, social network analysis and natural language processing (e.g., [111, 112, 115, 123, 125–146])

We have termed this GeoSocial analysis, as though the utilization of GIS, SNA and content analysis we can identify the structure of social networks and their distribution in space (e.g., the distribution of a community formed around a specific topic [148]); map the manner in which ideas and information propagate across space in a society (e.g., people reacting to an earthquake [149] or disease outbreaks [101, 150, 151]); map the spatial footprint of people’s opinions and reaction on specific topics and current events at near real-time rates (e.g., observing how fast people react to political or sports events [152]); and identify emerging socio-cultural hot-spots (e.g., popular gathering places [112]).

Figure 9: GeoSocial Analysis - the combination
of geospatial, social network, and content
analysis, to understand the connections between
cyber, social and physical spaces.
My work in this area has already shown that social proximity often overtakes geographical proximity when connections are established [129]. Not only does such research allow for greater situational awareness and understanding of how people perceive and use space but also combines cyber, physical and social spaces as shown in Figure 9, but it also has the potential to be merged with agent-based models [6, 18, 153]. However, such research does pose several challenges [122, 123] such as data quality and accuracy [146, 154, 155, 156, 157], why people contribute such information [158], privacy [115], the role of social bots [102, 159, 160, 161, 162] and the need of new methods for data collection [129]. These crucial issues go back a long way in the GIS community, but nonetheless still need to be addressed. 

More recently, like many disciplines (including ABM, GIS is also changing with the growth of artificial intelligence and generative AI. I see this many opportunities here including mapping the earth service [163], and its impacts on urban analytics at large [164, 165], for example providing new mains to extract information from street-view images [166, 167, 168], text [169] and modeling [85].

Looking Ahead

We have today tremendous amounts of data and sensors to monitor our urban environment, we have tools and techniques to analyze such data which can then be used to inform modeling as shown in Figure 10. However, we often just focus on one aspect, but if we are to address forthcoming societal challenges such as those related to population growth, natural disasters, climate change and migration we need ways to integrate monitoring, analyzing and modeling. For updates on this line of research keep an eye on this website.

Figure 10: The persistent urban morphology concept [47].

Acknowledgments

My research would not have been possible without the support of government, industrial and academic partners as shown in Figure 11. I would like to thank George Mason University, the University at Buffalo, the National Science Foundation (NSF), US Agency for International Development (USAID), U.S. Army Engineer Research and Development Center (ERDC), National Geospatial Intelligence Agency (NGA), Defense Threat Reduction Agency (DTRA), Defense Advanced Research Projects Agency (DARPA), Intelligence Advanced Research projects Activity (IARPA), Draper Labs and EADS North America for supporting my research.

Figure 11: Government and Industrial Partners who have Supported my Research.


References
  1. United Nations (2025). World Urbanization Prospects 2025: Summary of results. Department of Economic and Social Affairs, New York, NY.Available at https://population.un.org/wup/
  2. Crooks, A. T. (2012), The Use of Agent-Based Modelling for Studying the Social and Physical Environment of Cities, in De Roo, G, Hiller, J. and Van Wezemael, J. (eds.), Complexity and Planning: Systems, Assemblages and Simulations, Ashgate, Burlington, VT, pp. 385-408. (pdf).
  3. Crooks, A.T. (2024), Environment and Planning B: Its Shaping of Urban Modeling and Me, Environment and Planning B, 51(5) 1020-1022. (pdf)
  4. Crooks, A.T., Croitoru, A., Jenkins, A., Mahabir, R., Agouris, P. and Stefanidis A. (2016), User-Generated Big Data and Urban Morphology, Built Environment, 42 (3): 396-414. (pdf)
  5. Haggett, P. and Chorley, R.J. (1969), Network Analysis in Geography, Edward Arnold, London, UK.
  6. Crooks, A.T., Malleson, N., Manley, E. and Heppenstall, A.J. (2019), Agent-based Modelling and Geographical Information Systems: A Practical Primer, Sage, London, UK.
  7. Crooks, A.T. and Heppenstall, A.J. (2012), Introduction to Agent-based Modelling, in Heppenstall, A.J., Crooks, A.T., See, L.M. and Batty, M. (eds.), Agent-based Models of Geographical Systems, Springer, New York, NY, pp. 85-108. (pdf)
  8. Heppenstall, A., Crooks, A. T., See, L.M., and Batty, M. (eds.)(2012) Agent-based Models of Geographical Systems. Springer, New York, NY.
  9. Crooks, A.T. and Castle, C. (2012), The Integration of Agent-Based Modelling and Geographical Information for Geospatial Simulation, in Heppenstall, A.J., Crooks, A.T., See, L.M. and Batty, M. (eds.), Agent-based Models of Geographical Systems, Springer, New York, NY, pp. 219-252.(pdf)
  10. Crooks, A.T., Croitoru, A., Lu, X., Wise, S., Irvine, J. and Stefanidis, A. (2015), Walk this Way: Improving Pedestrian Agent-Based Models through Scene Activity Analysis, ISPRS International Journal of Geo-Information, 4(3): 1627-1656. (pdf)
  11. Choi, M., Crooks, A.T., Wan, N., Brewer, S., Cova, T.J. and Hohl, A. (2024), Addressing Equifinality in Agent-based Modeling: A Sequential Parameter Space Search Method Based on Sensitivity Analysis, International Journal of Geographical Information Science, 38(6): 1007-1034. Available at: https://doi.org/10.1080/13658816.2024.2331536. (pdf)
  12. Pires, B. and Crooks, A.T. (2017), Modeling the Emergence of Riots: A Geosimulation Approach, Computers, Environment and Urban Systems, 61: 66-80. (pdf)
  13. Crooks, A.T. (2010), Constructing and Implementing an Agent-Based Model of Residential Segregation through Vector GIS, International Journal of Geographical Information Science, 24(5): 661-675. (pdf).
  14. Wise, S. and Crooks, A.T. (2012), Agent Based Modelling and GIS for Community Resource Management: Acequia-based Agriculture, Computers, Environment and Urban Systems, 36(6): 562-572. (pdf)
  15. Patel, A., Crooks, A.T. and Koizumi, N. (2012), Slumulation: an Agent-based Modeling Approach to Slum Formations, Journal of Artificial Societies and Social Simulation, 15 (4): 12. Available at http://jasss.soc.surrey.ac.uk/15/4/2.html
  16. Patel, A., Crooks, A.T. and Koizumi, N. (2018), Spatial Agent-based Modeling to Explore Slum Formation Dynamics in Ahmedabad, India, in Thill J.C. and Drajicavic, S. (eds.), Geocomputational Analysis and Modeling of Regional Systems, Springer, New York, NY, pp 121-141. (pdf)
  17. Gulden, T., Harrison, J. F. and Crooks, A.T. (2011), Modeling Cities and Displacement through an Agent-based Spatial Interaction Model. The Computational Social Science Society of America Conference (2011), Santa Fe, NM. (pdf)
  18. Crooks, A.T. and Wise, S. (2013), GIS and Agent-Based models for Humanitarian Assistance, Computers, Environment and Urban Systems, 41: 100-111. (pdf)
  19. Zhou, Z. and Crooks, A.T. (2025), Modeling Wildfire Evacuation with Embedded Fuzzy Cognitive Maps: An Agent-Based Simulation of Emotion and Social Contagion, Proceedings of the 2025 International Conference of the Computational Social Science Society of the Americas, Santa Fe, NM. (pdf)
  20. Hailegiorgis, A.B., Crooks, A.T. and Cioffi-Revilla, C. (2018), An Agent-Based Model of Rural Households’ Adaptation to Climate Change, Journal of Artificial Societies and Social Simulation, 21 (4): 4. Available at http://jasss.soc.surrey.ac.uk/21/4/4.html.
  21. Hemmati, M., Hussam N., Ellingwood B.R. and Crooks, A.T. (2021), Unraveling the Complexity of Human Behavior and Urbanization on Community Vulnerability to Floods, Scientific Reports, 11, 20085. Available at: https://doi.org/10.1038/s41598-021-99587-0
  22. Hemmati, M., Ellingwood, B.R., Mahmoud, H. and Crooks, A.T. (2022), Life-Cycle Risk-Informed Decisions for Future Community Development in Regions Prone to Riverine Flooding, ICOSSAR 2022: 13th International Conference on Structural Safety and Reliability, Shanghai, China. (pdf)
  23. Crooks, A.T. and Hailegiorgis, A.B. (2014), An Agent-based Modeling Approach Applied to the Spread of Cholera, Environmental Modelling and Software, 62: 164-177 (pdf)
  24. Crooks, A.T. (2015), Agent-based Models and Geographical Information Systems, in Brunsdon, C. and Singleton, A. (eds.), Geocomputation: A Practical Primer, Sage, London, UK, pp. 63-77. (pdf)
  25. Crooks, A.T., Castle, C.J.E. and Batty, M. (2008), Key Challenges in Agent-Based Modelling for Geo-spatial Simulation, Computers, Environment and Urban Systems, 32(6): 417-430. (pdf)
  26. Malik, A., Crooks, A.T., Root, H. and Swartz, M. (2015), Exploring Creativity and Urban Development with Agent-Based Modeling, Journal of Artificial Societies and Social Simulation, 18(2): 12, Available at http://jasss.soc.surrey.ac.uk/18/2/12.html.
  27. Łatek, M.M., Mussavi Rizi, S.M., Crooks, A.T. and Fraser, M. (2012), Social Simulations for Border Security, Workshop on Innovation in Border Control 2012, Co-located with the European Intelligence and Security Informatics Conference (EISIC 2012), Odense, Denmark, pp. 340-345. (pdf)
  28. Oldham, M. and Crooks, A.T. (2019), Drafting Agent-Based Modeling into Basketball Analytics, 2019 Spring Simulation Conference (SpringSim’19), Tucson, AZ. (pdf)
  29. Yin, F., Jiang, Na., Crooks, A.T., Laurian, L. (2024), Agent-based Modeling of Covid-19 Vaccine Uptake in New York State: Information Diffusion in Hybrid Spaces, Proceedings of the 7th ACM SIGSPATIAL International Workshop on Geospatial Simulation (GeoSim 2024), Atlanta, GA., pp. 11-20. (pdf)
  30. Pires, B. and Crooks, A.T. (2016), The Geography of Conflict Diamonds: The Case of Sierra Leone, Proceedings of the 2016 International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction and Behavior Representation in Modeling and Simulation, Springer, Washington, DC. pp. 335-345. (pdf)
  31. Burger, A., Oz, T., Crooks, A.T. and Kennedy, W.G. (2017), Generation of Realistic Mega-City Populations and Social Networks for Agent-Based Modeling. The Computational Social Science Society of Americas Conference, Santa Fe, NM. (pdf)
  32. Jiang, N., Burger, A., Crooks, A.T. and Kennedy, W.G. (2020), Integrating Social Networks into Large-scale Urban Simulations for Disaster Responses. Geosim ’20: 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, Seattle, WA., pp. 52-55. (pdf)
  33. 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 (pdf)
  34. Jiang, N., Yin, F., Wang., B. and Crooks, A.T. (2024), A Large-Scale Geographically Explicit Synthetic Population with Social Networks for the United States, Scientific Data, 11, 1204. Available at: https://doi.org/10.1038/s41597-024-03970-1 (pdf)
  35. Gallagher, K., Anderson, T., Crooks, A.T. and Züfle, A. (2023), Synthetic Geosocial Network Data Generation, Proceedings of the 7th ACM SIGSPATIAL Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising (LocalRec 2023), Hamburg, Germany, pp. 15–24. (pdf)
  36. Jiang, N., Crooks, A.T., Yin, F. and Wang B. (2023), Geographically-Explicit Synthetic Populations for Agent-based Models: A Gallery of Applications, in Yang, Z. and Krejci C. (eds.) Proceedings of the 2023 Conference of The Computational Social Science Society of the Americas, Santa Fe, NM. pp 158- 172. (pdf)
  37. Han, D., Islam, S., Anderson, T., Crooks, A.T. and Kavak, H. (2025), Quantitative Comparison of Population Synthesis Techniques, in Azar, E., Djanatliev, A., Harper, A., Kogler, C., Ramamohan, V., Anagnostou, A. and Taylor, S.J.E. (eds.), Proceedings of the 2025 Winter Simulation Conference, Seattle, WA, IEEE. pp. 151-162. (pdf)
  38. Jiang, N., Crooks, A.T., Kennedy, W.G., and Kavak, H. (2021), Generation of Reusable Synthetic Population And Social Networks for Agent-Based Modeling, in  Martin, C.R.,  Blas, M.J. and Psijas, A.I (eds.), Proceedings of the 2021 Annual Modeling and Simulation Conference (ANNSIM), Online, pp. 1-12. (pdf)
  39. Crooks, A.T., Hudson-Smith, A. and Dearden, J. (2009), Agent Street: An Environment for Exploring Agent-Based Models in Second Life, Journal of Artificial Societies and Social Simulation 12(4): 10, Available at http://jasss.soc.surrey.ac.uk/12/4/10.html.
  40. Crooks, A.T., Hudson-Smith, A. and Patel, A. (2011), Advances and Techniques for Building 3D Agent-Based Models for Urban Systems, in Marceau, D. and Benenson, I. (eds.), Advanced Geosimulation Models, Bentham Science Publishers, Hilversum, The Netherlands, pp. 49-65. (pdf)
  41. Yuan, X. and Crooks, A.T. (2017), From Cyber Space Opinion Leaders and the Spread of Anti-Vaccine Extremism to Physical Space Disease Outbreaks, in Lee, D., Lin, Y., Osgood, N. and Thomson, R. (eds.), Proceedings of the 2017 International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, Springer, Arlington, VA, pp. 114-119. (pdf)
  42. Bagheri-Jebelli, N., Crooks, A.T. and Kennedy, W.G. (2019), Capturing the Effects of Gentrification on Property Values: An Agent-Based Modeling Approach, In Z Yang and E von Briesen (eds), The 2019 Computational Social Science Society of Americas Conference, Santa Fe, NM, 2019. Springer, pp. 245–264. (pdf)
  43. McEligot, K., Brouse, P. and Crooks, A.T. (2019), Sea Bright, New Jersey Reconstructed: Agent-Based Protection Theory Model Responses to Hurricane Sandy, in Mustafee, N., Bae, K.-H.G., Lazarova-Molnar, S., Rabe, M., Szabo, C., Haas, P. and Son, Y.-J. (eds.), Proceedings of the 2019 Winter Simulation Conference, IEEE, National Harbor, MD, pp. 251-262. (pdf)
  44. Lee, J. and Crooks A.T. (2021), Youth and their Artificial Social Environmental Risk and Promotive Scores (Ya-TASERPS): An Agent-Based Model of Interactional Theory of Delinquency, Journal of Artificial Societies and Social Simulation. 24 (4) 2. Available at: https://www.jasss.org/24/4/2.html (pdf)
  45. Jiang, N., Crooks, A.T., Wang, W. and Xie, Y. (2021), Simulating Urban Shrinkage in Detroit via Agent-Based Modeling, Sustainability, 13, 2283. Available at https://doi.org/10.3390/su13042283. (pdf).
  46. Luke, S., Simon, R., Crooks, A.T., Wang, H., Wei, E., Freelan, D., Spagnuolo, C., Scarano, V., Cordasco, G. and Cioffi-Revilla, C. (2018), The MASON Simulation Toolkit: Past, Present, and Future, in Davidsson P. and Verhagen H. (eds.), Proceedings of the 19th International Workshop on Multi-Agent-Based Simulation, Stockholm, Sweden, pp. 75-87. (pdf)
  47. Wang, H., Wei, E., Simon, R., Luke, S., Crooks, A.T., Freelan, D. and Spagnuolo, C. (2018), Scalability in the MASON multi-agent simulation system, in Besada, E., Polo, Ó.R., De Grande, R. and Risco J.L (eds.). Proceedings of the 22nd International Symposium on Distributed Simulation and Real Time Applications, Madrid, Spain, pp. 135-144. (pdf)
  48. Kazil, J., Masad, D. and Crooks, A.T. (2020), Utilizing Python for Agent-based Modeling: The Mesa Framework, in Thomson, R., Bisgin, H., Dancy, C., Hyder, A. and Hussain, M. (eds), 2020 International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation, Washington DC., pp. 308-317. (pdf)
  49. Bard, J.E., Jiang, N., Emerson, J., Bartz, M., Lamb, N.A., Marzullo, B.J., Pohlman, A., Boccolucci, A., Nowak, N.J., Yergeau, D.A., Crooks, A.T. and Surtees, J. (2024), Genomic Profiling and Spatial SEIR Modeling of COVID-19 Transmission in Western New York, Frontiers in Microbiology, 15. Available at https://doi.org/10.3389/fmicb.2024.1416580 (pdf).
  50. Jiang N. and Crooks, A.T. (2024), Studying Contagious Disease Spread Utilizing Synthetic Populations Inspired by COVID-19: An Agent-based Modeling Framework, Proceedings of the 7th ACM SIGSPATIAL International Workshop on Geospatial Simulation (GeoSim 2024), Atlanta, GA., pp. 29-32. (pdf).
  51. Yin, F., Crooks, A.T. and Yin, L. (2024), How Information Propagation in Hybrid Spaces Affects Decision-making: Using ABM to Simulate Covid-19 Vaccine Uptake, International Journal of Geographical Information Science, 38(6):1109–1135. (pdf)
  52. Wang, B. and Crooks, A.T. (2023), 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), Proceedings of the 12th International Conference on Geographic Information Science (GIScience 2023), Dagstuhl Publishing, Dagstuhl, Germany., pp. 81:1-81:6. (pdf)
  53. Brearcliffe, D.K. and Crooks, A.T. (2020), Creating Intelligent Agents: Combining Agent-Based Modeling with Machine Learning, in Yang, Z. and von Briesen, E. (eds.), Proceedings of the 2020 Conference of The Computational Social Science Society of the Americas, Online., pp 31-58. (pdf)
  54. Łatek, M.M., Mussavi Rizi, S.M., Crooks, A.T. and Fraser, M. (2012), A Spatial Multi-agent Model of Border Security for the Arizona-Sonora Borderland, The 2012 Computational Social Science Society of America Conference, Santa Fe, NM. (pdf)
  55. Züfle, A., Pfoser, D., Wenk, C., Crooks, A.T., Kavak, H., Anderson, T., Kim, J-S., Holt, N. and Diantonio, A. (2024), In Silico Human Mobility Data Science: Leveraging Massive Simulated Mobility Data, Transactions on Spatial Algorithms and Systems, 10(2): 13. Available at https://doi.org/10.1145/3672557 (pdf).
  56. Züfle, A., Wenk, C., Pfoser, D., Crooks, A.T., Kavak, H., Kim, J-S. and Jin, H. (2023), Urban Life: A Model of People and Places, Computational and Mathematical Organization Theory, 29: 20-51. (pdf)
  57. Amiri, H., Kohn, W., Ruan, S., Kim, J-S., Kavak, H., Crooks, A.T., Pfoser, D., Wenk, C. and Züfle, A. (2024) The Pattern of Life Human Mobility Simulation,  in  Nascimento, A., Xiong, M.L., Züfle, A., Chiang, Y., Eldawy, A. and Kroger, P (eds),  Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems, Atlanta, GA., pp. 653–656. (pdf)
  58. Hossein, A., Yang, R., Ruan, S., Kim, J-S., Kavak, H., Crooks, A.T., Pfoser, D., Wenk, C. and Züfle, A., (2025). HD-GEN: A Software System for Large-Scale Human Mobility Data Generation Based on Patterns of Life. In Proceedings of the  33rd ACM International Conference on Advances in Geographic Information Systems (SIGSPATIAL ’25), Minneapolis, MN. pp. 407-410. (pdf) 
  59. Amiri, H., Ruan, S., Kim, J., Jin, H., Kavak, H., Crooks, A.T., Pfoser, D., Wenk, C. and Züfle, A. (2023), Massive Trajectory Data Generation using a Patterns of Life Simulation, Proceedings of the 2023 ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Hamburg, Germany. (pdf)
  60. Kim, J-S., Kavak, H., Manzoor, U., Crooks, A.T., Pfoser, D., Wenk C. and Züfle, A (2019), Simulating Urban Patterns of Life: A Geo-Social Data Generation Framework, in Banaei-Kashani, F., Trajcevski, G., Güting, R.H., Kulik, L. and Newsam, S. (eds.), Proceedings of the 27th International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2019), Chicago, IL., pp. 576–579 (pdf)
  61. Crooks, A. T. and Wise, S. (2011), Modelling the Humanitarian Relief through Crowdsourcing, Volunteered Geographical Information and Agent-based Modelling: A test Case - Haiti, Proceedings of the 11th International Conference on Geocomputation, University College London, London, England, pp 183-187. (pdf)
  62. Iasiello, C., Crooks, A.T. and Wittman, S. (2020), The Human Resource Management Parameter Experimentation Toolhe Human Resource Management Parameter Experimentation Tool, in Thomson, R., Bisgin, H., Dancy, C., Hyder, A. and Hussain, M. (eds), 2020 International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation, Washington DC., pp. 298-307. (pdf)
  63. Crooks, A.T. (2007), Experimenting with Cities: Utilizing Agent-Based Models and GIS to Explore Urban Dynamics, PhD Thesis, University College London, London, UK.
  64. Alizadeh, M., Cioffi-Revilla, C. and Crooks, A.T. (2015), The Effect of In-group Favoritism on the Collective Behavior of Individuals' Opinions, Advances in Complex Systems, 18 (1): 02. (pdf)
  65. Briggs, T. and Crooks, A.T. (2016), Close, But Not Close Enough: A Spatial Agent-Based Model of Manager-Subordinate Proximity, The 2016 Computational Social Science Society of America Conference, Santa Fe, NM. (pdf)
  66. Chen, Q and Crooks, A.T. (2021). Delineating a ‘15-Minute City’: An Agent-based Modeling Approach to Estimate the Size of Local Communities. In Proceedings of the 4th ACM SIGSPATIAL International Workshop on Geospatial Simulation (GeoSim 2021), November 2, 2021, Beijing, China, pp. 29-37. (pdf)
  67. Pint, B., Crooks, A.T. and Geller, A. (2010), Exploring the Emergence of Organized Crime in Rio de Janeiro: An Agent-Based Modeling Approach, in Dimuro, G.P., da Rocha Costa, A.C., Sichman, J.S., Tedesco, P., Adamatti, D.F., Balsa, J. and Antunes, L. (eds.), Advances in Social Simulation: Proceedings of the 2nd Brazilian Workshop on Social Simulation, IEEE, Sao Bernardo do Campo, Brazil, pp. 7-14. (pdf)
  68. Rouly, C. and Crooks, A.T. (2010), A Prototype Multi-agent System for the Study of the Peopling of the Western Hemisphere, 3rd World Congress on Social Simulation: Scientific Advances in Understanding Societal Processes and Dynamics, Kassel, Germany. (pdf)
  69. Wang, B., Hess, V. and Crooks A.T. (2022), Mesa-Geo: A GIS Extension for the Mesa Agent-Based Modeling Framework in Python, Proceedings of the 5th ACM SIGSPATIAL International Workshop on Geospatial Simulation (GeoSim 2022), Seattle, WA., pp 1-10. (pdf)
  70. Crooks, A.T. and Chopra, P. (2014), An Agent-based Model for the Spread and Containment of Tuberculosis, The Association of American Geographers (AAG) Annual Meeting, Tampa, FL. (pdf)
  71. Ullah, K.M., Gbadebo G.A., and Crooks, A.T. (2023), Evaluating the Incentive for Soil Organic Carbon Sequestration from Carinata Production in the Southeast United States, Journal of Environmental Management, 348: 119418. Available at https://doi.org/10.1016/j.jenvman.2023.119418 (pdf)
  72. Ullah, K. and Crooks A.T., (2022), Modelling Farmers’ Adoption Potential to New Bioenergy Crops: An Agent-based Approach, In: Yang, Z., Núñez-Corrales, S. (eds) Proceedings of the 2022 Conference of The Computational Social Science Society of the Americas, Santa Fe, NM., pp 63–75. https://doi.org/10.1007/978-3-031-37553-8_5 (pdf)
  73. Shoushtarian, F., Negahban-Azar, M. and Crooks A.T. (2022), Investigating the Micro-level Dynamics of Water Reuse Adoption by Farmers and the Impacts on Local Water Resources using an Agent-based Model, Socio-Environmental Systems Modelling, 4: 18148. Available at https://doi.org/10.18174/sesmo.18148. (pdf)
  74. Hailegiorgis, A.B., Crooks, A.T. and Cioffi-Revilla, C. (2018), An Agent-Based Model of Rural Households’ Adaptation to Climate Change, Journal of Artificial Societies and Social Simulation, 21 (4): 4. Available at http://jasss.soc.surrey.ac.uk/21/4/4.html.
  75. Crooks, A.T. and Hailegiorgis, A. (2013), Disease Modeling Within Refugee Camps: A Multi-agent Systems Approach, in Pasupathy, R., Kim, S.-H., Tolk, A., Hill, R. and Kuhl, M. E. (eds.), Proceedings of the 2013 Winter Simulation Conference, Washington, DC, pp 1697-1706. (pdf)
  76. Jiang, N. and Crooks, A.T. (2020), Utilizing Agents to Explore Urban Shrinkage: A Case Study of Detroit, in Barros, F.J., Hu, X., Kavak, H. and Del Barrio, A.A. (eds.), Proceedings of 2020 Spring Simulation Conference (SpringSim), Fairfax, VA. (pdf)
  77. Sibley, C. and Crooks, A.T. (2020), Exploring the Effects of Link Recommendations on Social Networks: An Agent-Based Modeling Approach, in Barros, F.J., Hu, X., Kavak, H. and Del Barrio, A.A. (eds.), Proceedings of 2020 Spring Simulation Conference (SpringSim),2020 Spring Simulation Conference (SpringSim’20), Fairfax, VA. (pdf)
  78. Shapiro, B. and Crooks, A.T. (2021), Kinetic Action and Radicalization: A Case Study of Pakistan, in Thomson, R., Hussain, M.N., Dancy, C.L. and Pyke, A. (eds), Proceedings of 2021 International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation, Washington DC., pp 321-330. (pdf)
  79. Shapiro, B. and Crooks, A.T. (2023) Drone Strikes and Radicalization: An Exploration Utilizing Agent-Based Modeling and Data Applied to Pakistan, Computational and Mathematical Organization Theory, 29: 415-433. (pdf)
  80. Stine, A.A. and Crooks, A.T. (2024), Retention in Higher Education: An Agent-Based Model of Social Interactions and Motivated Agent Behavior, Proceedings of the 2024 International Conference of the Computational Social Science Society of the Americas, Santa Fe, NM., pp. 23–38. (pdf)
  81. Heppenstall, A., Crooks, A.T., Malleson, N., Manley, E., Ge, J. and Batty, M. (2021), Future Developments in Geographical Agent-Based Models: Challenges and Opportunities, Geographical Analysis. 53(1): 76-91.
  82. Heppenstall, A., Malleson, N. and Crooks A.T. (2016). “Space, the Final Frontier”: How Good are Agent-based Models at Simulating Individuals and Space in Cities?, Systems, 4(1): 9; doi: 10.3390/systems4010009 (pdf) 
  83. Kang, J-Y., Michels, A., Crooks, A.T., Aldstradt, J. and Wang, S. (2022), An Integrated Framework of Global Sensitivity Analysis and Calibration for Spatially Explicit Agent-Based Models, Transactions in GIS, 26(1): 100-128. (pdf)
  84. Malikov, M., Aloraini, F., Crooks, A.T., Kavak, H. and Kennedy, W.G. (2023), Developing a Large-Scale Agent-Based Model Using the Spiral Software Development Process, Proceedings of the Annual Modeling and Simulation Conference (ANNSIM), Hamilton, ON., pp. 282-293. (pdf)
  85. Crooks, A.T., Jiang, N. and Wang, B. (2025), Generative AI and Urban Modeling, Environment and Planning B, 52(6), 1277-1281. (pdf)
  86. Malleson, N., Crooks, A.T., Heppenstall, A. and Manley, E. (2025), Enhancing Spatial Reasoning and Behavior in Urban ABMs with Large-Language Models and Geospatial Foundation Models. In Cramer-Greenbaum, S., Dennett, A., and Zhong, C (eds.), Proceedings of the 19th International Conference on Computational Urban Planning and Urban Management (CUPUM), London, UK. (pdf)
  87. Cummings, P. and Crooks, A.T. (2020), Development of a Hybrid Machine Learning Agent Based Model for Optimization and Interpretability, in Thomson, R., Bisgin, H., Dancy, C., Hyder, A. and Hussain, M. (eds), 2020 International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, Washington DC., pp 151-160. (pdf)
  88. Manzoor, U., Kavak, H., Kim, J-S., Crooks, A.T., Pfoser, D., Zufle, A. and Wenk, C. (2021), Towards Large-Scale Agent-Based Geospatial Simulation, 2021 International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, Washington DC. (pdf)
  89. Zhuge, C. (2019), Review of Agent-based Modelling and Geographical Information Systems: A Practical Primer. Progress in Physical Geography: Earth and Environment, 43(4):601–603.
  90. Galen, G.M. (2012), Review of Agent-based Models of Geographical Systems. Journal of Artificial Societies and Social Simulation, 15(2):2, 2012.
  91. Benenson. I. (2013), Review of Agent-based Models of Geographical Systems. International Journal of Geographical Information Science, 27(5):1047–1053, 2013.
  92. Dragicevic, S. (2013), Review: Agent-based models of Geographical Systems, Environment and Planning B, 40(5):945–946, 2013.
  93. Curry, T., Croitoru, A. and Crooks, A.T. (2023), Modeling Forced Migration: A System Dynamic Approach, Proceedings of the Annual Modeling and Simulation Conference (ANNSIM), Hamilton, ON., pp. 110-121. (pdf)
  94. Hemmati, M., Hussam N., Ellingwood B.R. and Crooks, A.T. (2021), Shaping Urbanization to Achieve Sustainable Communities Resilient to Floods, Environmental Research Letters, 16, 094033 https://doi.org/10.1088/1748-9326/ac1e3c. (pdf)
  95. Zhang, R., Tian, Q., Jiang, L., Crooks, A.T., Qi, S. and Yang, R. (2018), Projecting Cropping Patterns around Poyang Lake and Prioritizing Areas for Policy Intervention to Promote Rice: A Cellular Automata Model, Land Use Policy, 74: 248-260. (pdf)
  96. Heppenstall, A., Crooks, A.T., Manley, E. and Malleson, N. (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. (pdf)
  97. Rana, R., Patel, R., Luke, S., Domeniconi, C., Kavak, H., Jones, J. and Crooks, A.T. (2023), Simulation And Optimization Techniques for the Mitigation of Disruptions to Supply Chains, Proceedings of the Annual Modeling and Simulation Conference (ANNSIM), Hamilton, ON., pp. 134-145. (pdf)
  98. Rana, R., Kavak, H., Crooks, A.T., Domeniconi, C., Luke, S. and Jones, J. (2022), Mitigation of Optimized Pharmaceutical Supply Chain Disruptions by Criminal Agents, in Thomson, R., Dancy, C. and Pyke, P. (eds), Proceedings of the 2022 International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction and Behavior Representation in Modeling and Simulation, Pittsburgh, PA., pp 13-23. (pdf)
  99. Masucci, A.P., Smith, D., Crooks, A.T. and Batty, M. (2009), Random Planar Graphs and the London Street Network, The European Physical Journal B, 71(2): 259–271. (pdf)
  100. Crooks, A.T., Masad, D., Croitoru, A., Cotnoir, A., Stefanidis, A. and Radzikowski, J. (2014), International Relations: State-Driven and Citizen-Driven Networks, Social Science Computer Review, 32(2): 205-220. (pdf)
  101. Jacobsen, K.H., Aguirre, A.A., Bailey, C.L., Baranova, A.V., Crooks, A.T., Croitoru, A., Delamater, P.L., Gupta, J., Kehn-Hall, K., Narayanan, A., Pierobon, M., Rowan K.E., Schwebach, J.R., Seshaiyer, P., Sklarew, D.M., Stefanidis, A. and Agouris, P. (2016), Lessons from the Ebola Outbreak: Action Items for Emerging Infectious Disease Preparedness and Response, EcoHealth, 13(1): 200-212 (pdf)
  102. Schuchard, R., Crooks, A.T., Stefanidis, A. and Croitoru, A. (2018), Bots in Nets: Empirical Comparative Analysis of Bot Evidence in Social Networks, in Aiello, L.M., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P. and Rocha, L.M. (eds.), Proceedings of the 7th International Conference on Complex Networks and Their Applications, Springer, Cambridge, United Kingdom, pp. 424-436. (pdf)
  103. Yuan, X. and Crooks, A.T. (2018), Examining Online Vaccination Discussion and Communities in Twitter, Proceedings of the 9th International Conference on Social Media and Society, Copenhagen, Denmark, pp. 197-206. (pdf)
  104. Vraga, E., Stefanidis, A., Lamprianidis, G., Croitoru, A., Crooks, A.T., Delamater, P.L., Pfoser, D., Radzikowski, J. and Jacobsen, K.H. (2018), Cancer and Social Media: A Comparison of Traffic about Breast Cancer, Prostate Cancer, and Other Reproductive Cancers on Twitter and Instagram, Journal of Health Communication, 3(2): 181-189. (pdf)
  105. Vraga, E. K., Radzikowski, J., Stefanidis, A., Croitoru, A., Crooks, A.T., Delamater, P., Pfoser, D. and Jacobsen, K. H. (2017). Social Media Engagement with Cancer Awareness Campaigns Declined During the 2016 U.S. Presidential Election. World Medical and Health Policy, 9(4): 456–465. (pdf)
  106. Yin, F., Crooks, A.T. and Yin, L. (2022), Information Propagation on Cyber, Relational and Physical Spaces about Covid-19 Vaccine: Using Social Media and the Splatial Framework, Computers, Environment and Urban Systems, 98, 01887. (pdf)
  107. 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)
  108. Chen Q, Croitoru A, and Crooks A.T. (2023), A Comparison between Online Social Media Discussions and Vaccination Rates: A Tale of Four Vaccines. DIGITAL HEALTH, 9: 1–16. doi: 10.1177/20552076231155682. (pdf)
  109. Sasse, K., Mahabir, R., Gkountouna. O,. Crooks, A.T. and Croitoru, A. (2024), Understanding the Determinants of Vaccine Hesitancy in the United States: A Comparison of Social Surveys and Social Media, PLoS ONE, 19(6): e0301488. Available at: https://doi.org/10.1371/journal.pone.0301488 (pdf)
  110. Chen, Q., Wang, B. and Crooks, A.T. (2024), Community Resilience to Wildfires: A Network Analysis Approach by Utilizing Human Mobility Data, Computers, Environment and Urban Systems, 110: 102110. (pdf)
  111. Crooks, A.T., Pfoser, D., Jenkins, A., Croitoru, A., Stefanidis, A., Smith, D.A., Karagiorgou, S., Efentakis, A. and Lamprianidis, G. (2015), Crowdsourcing Urban Form and Function, International Journal of Geographical Information Science, 29(5): 720-741. (pdf)
  112. Jenkins, A., Croitoru, A., Crooks, A.T. and Stefanidis, A. (2016), Crowdsourcing A Collective Sense of Place, PLoS ONE, 11(4): e0152932. (pdf)
  113. Stefanidis, A., Jenkins, A., Croitoru, A. and Crooks, A.T. (2016), Megacities Through the Lens of Social Media, Journal of the Homeland Defense and Security Information Analysis Center, 3(1): 24-29. (pdf)
  114. Burger, A., Kennedy, W.G. and Crooks A.T. (2021), Organizing Theories for Disasters into a Complex Adaptive System Framework, Urban Science, 5(3), 61. Available at https://doi.org/10.3390/urbansci5030061 (pdf)
  115. Stefanidis, T., Crooks, A.T. and Radzikowski, J. (2013), Harvesting Ambient Geospatial Information from Social Media Feeds, GeoJournal, 78(2): 319-338. (pdf)
  116. Croitoru, A., Crooks, A.T., Radzikowski, J. and Stefanidis, A. (2017), Geovisualization of Social Media, in Richardson, D., Castree, N., Goodchild, M.F., Kobayashi, A.L., Liu, W. and Marston, R. (eds.), The International Encyclopedia of Geography: People, the Earth, Environment, and Technology, Wiley Blackwell. (pdf)
  117. Stefanidis, A., Cotnoir, A., Croitoru, A., Crooks, A.T., Radzikowski, J. and Rice, M. (2013), Demarcating New Boundaries: Mapping Virtual Polycentric Communities Through Social Media Content, Cartography and Geographic Information Science, 40(2): 116-129. (pdf)
  118. Hudson-Smith, A., Batty, M., Crooks, A.T., and Milton R. (2009), Mapping Tools for the Masses: Web 2.0 and Crowdsourcing, Social Science Computer Review, 27 (4): 524-538. (pdf)
  119. Hudson-Smith, A., Crooks, A.T., Gibin, M., Milton, R., and Batty, M. (2009), Neogeography and Web 2.0: Concepts, Tools and Applications, Journal of Location Based Services, 3(2) 118 - 145. (pdf)
  120. Anand, S., Batty, M., Crooks, A.T., Hudson-Smith, A., Jackson, M., Milton, R. and Morley, J. (2010), Data Mash-ups and the Future of Mapping, Joint Information Systems Committee (JISC) Technology and Standards Watch (TechWatch) Horizon Scanning Report 10_01, Bristol, UK. (pdf)
  121. Batty, M., Hudson-Smith, A., Milton, R. and Crooks, A.T. (2010), Map MashUps, Web 2.0 and the GIS Revolution, Annals of GIS, 16(1): 1-13. (pdf)
  122. Crooks, A.T., Hudson-Smith, A., Croitoru, A. and Stefanidis, A. (2014), The Evolving GeoWeb, in Abrahart, R.J. and See, L.M. (eds.), Geocomputation (2nd Edition), CRC Press, Boca Raton, FL, pp. 67-94. (pdf)
  123. Croitoru, A., Crooks, A.T., Radzikowski, J., Stefanidis, A., Vatsavai, R.R. and Wayant, N. (2014), Geoinformatics and Social Media: A New Big Data Challenge, in Karimi, H.A. (ed.) Big Data Techniques and Technologies in Geoinformatics, CRC Press, Boca Raton, FL, pp. 207-232. (pdf)
  124. Crooks, A.T., Schechtner, K., Day, A.K and Hudson-Smith, A (2017) Creating Smart Buildings and Cities, IEEE Pervasive Computing, 16 (2): 23-25. (pdf)
  125. Panteras, G., Wise, S., Lu, X., Croitoru, A., Crooks, A.T. and Stefanidis, A. (2015), Triangulating Social Multimedia Content for Event Localization using Flickr and Twitter, Transactions in GIS, 19(5): 694–715. (pdf)
  126. Panteras, G., Lu, X., Croitoru, A., Crooks, A.T. and Stefanidis, A. (2016), Accuracy Of User-Contributed Image Tagging In Flickr: A Natural Disaster Case Study, in Gruzd, A., Jacobson, J., Mai, P., Ruppert, E. and Murthy, D. (eds), Proceedings of the 7th International Conference on Social Media and Society, London, UK. (pdf)
  127. Curry, T., Croitoru, A., Crooks, A.T. and Stefanidis, A. (2019), Exodus 2.0: Crowdsourcing Geographical and Social Trails of Mass Migration, Journal of Geographical Systems, 21 (1): 161-187. (pdf)
  128. Stefanidis, A., Crooks, A.T., Radzikowski, J., Croitoru, A. and Rice, M. (2014), Social Media and the Emergence of Open-Source Geospatial Intelligence, in Murdock, D.G., Tomes, R. and Tucker, C. (eds.), Human Geography: Socio-Cultural Dynamics and Global Security, US Geospatial Intelligence Foundation (USGIF), Herndon, VA, pp. 109-123. (pdf)
  129. Croitoru, A., Crooks, A.T., Radzikowski, J. and Stefanidis, A. (2013), GeoSocial Gauge: A System Prototype for Knowledge Discovery from Geosocial Media, International Journal of Geographical Information Science, 27(12): 2483-2508. (pdf)
  130. Yuan, X. and Crooks, A.T.  (2019), Assessing the Placeness of Locations through User-contributed Content, in Gao, S., Newsam, S., Zhao, L., Lunga, D., Hu, Y., Martins, B., Zhou, X. and Chen, F. (eds.), Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (GeoAI), ACM, Chicago, IL,, pp. 15-23. (pdf)
  131. Yuan, X., Mahabir, R., Crooks, A.T. and Croitoru, A. (2022), Achieving Situational Awareness of Drug Cartels with Geolocated Social Media, GeoJournal, 87(5):3453–3471. (pdf)
  132. Yuan X., Crooks, A.T. and Züfle, A. (2020), A Thematic Similarity Network Approach for Analysis of Places Using Volunteered Geographic Information, ISPRS International Journal of Geo-Information, 9(6), 385. Available at https://www.mdpi.com/2220-9964/9/6/385/htm. (pdf)
  133. Croitoru, A., Stefanidis, A., Radzikowski, J., Crooks, A. T., Stahl, J. and Wayant, N. (2012), Towards a Collaborative GeoSocial Analysis Workbench. COM-Geo, Washington, DC., pp. 1-9. (pdf)
  134. Adegbola, F., Crooks, A.T. and Evans, S.M. (2025), Crowdsourcing Dust Storms in the United States Utilizing Social Media Data, GeoJournal, 90(3): 1-18. Available at https://doi.org/10.1007/s10708-025-11359-9 (pdf)
  135. Chen, Q., Crooks, A.T., Sullivan, A.J., Surtees, J.A. and Tumiel-Berhalter, L. (2025). From Print to Perspective: A Mixed-method Analysis of the Convergence and Divergence of COVID-19 Topics in Newspapers and Interviews, PLOS Digital Health. Available at https://doi.org/10.1371/journal.pdig.0000736. (pdf)
  136. Jiang, N., Crooks, A.T., Kavak, H. and Wang, W. (2024), Leveraging Newspapers to Understand Urban Issues: A Longitudinal Analysis of Urban Shrinkage in Detroit, Environment and Planning B, 51(5): 1089-1103. (pdf)
  137. Mahabir, R., Schuchard, R., Crooks, A.T., Croitoru, A. and Stefanidis, A. (2020), Crowdsourcing Street View Imagery: A Comparison of Mapillary and OpenStreetCam, ISPRS International Journal of Geo-Information, 9(6), 341. Available at https://doi.org/10.3390/ijgi9060341. (pdf)
  138. Swartz, M., Crooks, A.T. and Croitoru, A. (2020), Beyond Words: Comparing Structure, Emoji Use, and Consistency Across Social Media Posts, in Thomson, R., Bisgin, H., Dancy, C., Hyder, A. and Hussain, M. (eds), 2020 International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation, Washington DC., pp 1-11. (pdf)
  139. Swartz, M., Crooks, A.T. and Kennedy, W.G. (2020), Diversity from Emojis and Keywords in Social Media, in Gruzd, A., Mai, P., Recuero, R., Hernández-García, A., Lee, C.S., Cook, J., Hodson, J., McEwan, B and Hopke, J. (eds.), Proceedings of the 11th International Conference on Social Media and Society, Toronto, Canada, pp 92-100. (pdf)
  140. 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., pp. 375–380 (pdf)
  141. Burger, A., Oz, T., Kennedy, W.G. and Crooks, A.T. (2019), Computational Social Science of Disasters: Opportunities and Challenges, Future Internet, 11(5): 103. Available at https://doi.org/10.3390/fi11050103. (pdf)
  142. Lopez, B., Magliocca, N. and Crooks, A.T. (2019), Challenges and Opportunities of Social Media Data for Socio-environmental Systems Research, Land, 8(7), 107. Available at https://doi.org/10.3390/land8070107. (pdf)
  143. 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)
  144. Chen, Q., Poorthuis A. and Crooks, A.T. (2025), Mapping the Invisible: Decoding Perceived Urban Smells through Geosocial Media in New York City, Annals of the American Association of Geographers, 115(6), 1444-1464. Available at https://doi.org/10.1080/24694452.2025.2485233. (pdf)
  145. 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)
  146. Mahabir, R., Agouris, P., Stefanidis, A., Croitoru, A. and Crooks, A.T. (2018), Detecting and Mapping Slums using Open Data: A Case Study in Kenya, International Journal of Digital Earth: DOI: https://doi.org/10.1080/17538947.2018.1554010. (pdf)
  147. Smith, D.A. and Crooks, A.T. (2010), From Buildings to Cities: Techniques for the Multi-Scale Analysis of Urban Form and Function, Centre for Advanced Spatial Analysis (University College London): Working Paper 155, London, UK. (pdf)
  148. Lu, X., Croitoru, A., Radzikowski, J., Crooks, A.T. and Stefanidis, A. (2013), Comparing the Spatial Characteristics of Corresponding Cyber and Physical Communities: A Case Study, Proceedings of the 6th ACM SIGSPATIAL International Workshop on Location-Based Social Networks, Orlando, FL, pp. 11-14. (pdf)
  149. Crooks, A.T., Croitoru, A., Stefanidis, A. and Radzikowski, J. (2013), #Earthquake: Twitter as a Distributed Sensor System, Transactions in GIS, 17(1): 124-147. (pdf)
  150. Radzikowski, J., Stefanidis, A., Jacobsen, K.H., Croitoru, A., Crooks, A.T. and Delamater, P.L. (2016), The Measles Vaccination Narrative in Twitter: A Quantitative Analysis, JMIR Public Health and Surveillance, 2(1): e1. (pdf)
  151. Stefanidis, A., Vraga, E., Lamprianidis, G., Radzikowski, J., Delamater, P.L., Jacobsen, K.H., Pfoser, D., Croitoru, A. and Crooks, A.T. (2017), Zika in Twitter: Temporal Variations of Locations, Actors, and Concepts, JMIR Public Health and Surveillance, 3(2): e22. (pdf)
  152. Croitoru, A., Wayant, N., Crooks, A.T., Radzikowski, J. and Stefanidis, A. (2015), Linking Cyber and Physical Spaces Through Community Detection and Clustering in Social Media Feeds, Computers, Environment and Urban Systems, 53: 47–64. (pdf)
  153. Crooks, A.T., Malleson, N., Wise, S. and Heppenstall, A. (2018), Big Data, Agents and the City, in Schintler, L.A. and Chen, Z. (eds.), Big Data for Urban and Regional Science, Routledge, New York, NY, pp. 204-213. (pdf)
  154. Mullen, W., Jackson, S.P., Croitoru, A., Crooks, A.T., Stefanidis, A. and Agouris, P. (2015), Assessing the Impact of Demographic Characteristics on Spatial Error in Volunteered Geographic Information Features, GeoJournal, 80(4): 587-605. (pdf)
  155. Jackson, S.P., Mullen, W., Agouris, P., Crooks, A.T., Croitoru, A. and Stefanidis, A. (2013), 'Assessing Completeness and Spatial Error of Features in Volunteered Geographic Information', ISPRS International Journal of Geo-Information, 2(2): 507-530. (pdf)
  156. Mahabir, R., Croitoru, A., Crooks, A.T., Agouris, P. and Stefanidis, A. (2018), A Critical Review of High and Very High Resolution Remote Sensing Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging Opportunities, Urban Science, 2(1), 8; DOI:10.3390/urbansci2010008 (pdf)
  157. Mahabir, R., Stefanidis, A., Croitoru, A., Crooks, A.T. and Agouris, P. (2017), Authoritative and Volunteered Geographical Information in a Developing Country: A Comparative Case Study of Road Datasets in Nairobi, Kenya, ISPRS International Journal of Geo-Information, 6(1): 24. (pdf)
  158. Mahabir, R., Croitoru, A., Crooks, A.T., Agouris, P. and Stefanidis, A. (2018), News Coverage, Digital Activism, and Geographical Saliency: A Case Study of Refugee Camps and Volunteered Geographical Information, PLoS ONE, 13(11): e0206825. (pdf)
  159. Schuchard, R.J. and Crooks, A.T. (2021), Insights into Elections: An Ensemble Bot Detection Coverage Framework Applied to the 2018 U.S. Midterm Elections, PLoS ONE, 16(1): e0244309. Available at https://doi.org/10.1371/journal.pone.0244309. (pdf)
  160. Schuchard, R., Crooks, A.T., Stefanidis, A. and Croitoru, A. (2019), Bot Stamina: Examining the Influence and Staying Power of Bots in Online Social Networks, Applied Network Science, 4(55), Available at https://doi.org/10.1007/s41109-019-0164-x. (pdf)
  161. Yuan, X., Schuchard, R. and Crooks, A.T. (2019), Examining Emergent Communities and Detecting Social Bots within the Polarized Online Vaccination Debate in Twitter, Social Media and Society, Available at https://doi.org/10.1177/2056305119865465. (pdf)
  162. Schuchard, R., Crooks, A.T., Croitoru, A. and Stefanidis, A. (2019), Bots Fired: Examining Social Bot Evidence in Online Mass Shooting Conversations, Palgrave Communications, 5: , 5(158), Available at https://doi.org/10.1057/s41599-019-0359-x. (pdf)
  163. See, L., Chen, Q., Crooks, A.T., Bayas, J.C.L., Fraisl, D., Fritz, S., Georgieva, I., Hager, G., Hofer, M., and Lesiv, M., Malek, Ž., Milenković, M., Moorthy, I., Orduña-Cabrera, F., Pérez-Guzmán, K., Schepaschenko, D., Shchepashchenko, M., Steinhauser, J.and McCallum, I. (2025), New Directions in Mapping the Earth’s Surface with Citizen Science and Generative AI, iScience, 28(3): 111919. Available at: https://doi.org/10.1016/j.isci.2025.111919 (pdf)
  164. Crooks, A.T., Jiang, N., See, L. Alvanides, S., Arribas-Bel. D., Wolf, L.J. and Batty, M. (2024), EPB Turns 50 Years Old: An Analytical Tour of the Last Five Decades, Environment and Planning B, 51(5): 1028-1037. (pdf)
  165. Crooks, A.T. (2024), Cities and Disasters: What can Urban Analytics Do?, Environment and Planning B, 52(3): 523-526. (pdf)
  166. Crooks, A.T. and See, L. (2022), Leveraging Street Level Imagery for Urban Planning, Environment and Planning B, 49(3): 773-776. (pdf)
  167. Crooks A.T. and Chen, Q (2024), Exploring the New Frontier of Information Extraction through Large Language Models in Urban Analytics, Environment and Planning B, 51(3) 565-569. (pdf)
  168. Chen, Q., See, L. and Crooks, A.T. (2025), Using New Sources of Data for Urban Climate Modeling Generated through MLLMs on Street View Imagery. In Cramer-Greenbaum, S., Dennett, A., and Zhong, C (eds.), Proceedings of the 19th International Conference on Computational Urban Planning and Urban Management (CUPUM), London, UK. (pdf)
  169. Fu, X., Brinkley, C., Sanchez, T.W., Li, C. and Crooks, A.T. (2026), Not Just Numbers: Understanding Cities through their Words, Environment and Planning B, 53(1): 3-10. (pdf)

No comments:

Post a Comment