As a geospatial computational social scientist, I focus on understanding people and more specifically on human and environmental interactions. This arises from the fact that for the first time in human history, more people are living in urban areas. This number is expected to grow in the coming decades: a recent prediction by the World Health Organization [1] indicates that between 2009 and 2050 the world’s urban population will almost double, from approximately 3.4 billion to 6.4 billion. According to estimates by UN-Habitat [2], this urbanization process is expected to occur at an unprecedented high rate of approximately 180,000 people per day, in particular in developing countries where already 1 in 3 residents live in slums [3]. Given the sheer volume and high rate of urban population growth, cities around the world will undoubtedly experience increasing pressures on their infrastructure, including housing, transportation, communication, energy and water [4]. However, understanding how such population growth will impact upon urban systems is extremely difficult. This is due to the fact that the heterogeneous nature of cities makes it challenging to generalize localized dynamics up to the level of city-wide problems [5], in the sense that a city is more than the sum of its parts. While our understanding of cities has increased throughout the twentieth century, by incorporating ideas and theories from a diverse range of subjects including economics, geography, history, philosophy, mathematics and more recently computer science [6]; however, it is now very clear that there are intrinsic difficulties in applying such understanding to policy analysis and decision-making. 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 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. Prior to arriving at George Mason University (Mason), my primarily focus was on ABM and GIS. Since coming to Mason 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). I see this development as a logical progression, not only is ABM and SNA at the core of CSS [7] but also network analysis has a long history within the geographical sciences [8].

Agent-based Modeling

ABM provides an ideal environment to test ideas and hypotheses that cannot be done in reality [9]. My focus has been, and continues to be, on developing ABMs specifically relating to geographical systems [10], and linking them to geographical information thus grounding them to place [11], 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 [5]. 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. Examples of such include pedestrian movement [12], residential segregation [13], community resource management [14], the growth of slums [15], the migration of displaced people [16], how people react in times of crisis [17], to the spread of diseases [18].

Figure 2: ABM Application Areas of Interest [12].

Figure 3: Agent-based models in
Virtual Worlds [23].

These models also range across the spectrum from theoretical to practice [e.g. 19, 11]. Specifically, I have developed theoretical models whose purpose is to explore theory and test hypothesis [e.g. 15, 16] to more descriptive models based on empirical details of the social phenomena [e.g. 20], 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. 21, 22]. 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 [23, 24]. 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. 17, 14,15,13, 21, 25].

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).

Figure 4: Agent-Based
Models of Geographical Systems [10].
For a good introduction into ABM, how it can be integrated with GIS and its application 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 4. 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 ABMs, 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 (2012) 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 (2013) 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  (2013) 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 5: Visualizing Communities: Network Cores
and Retweeting Nodes [26].
While I had explored physical networks [27] in the past, it was not until arriving at Mason that I became interested in social networks and their use in studying complex systems. This is 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 [28], such as that shown in Figure 5. 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 ABMs. For instance, SNA can be used to study how information spreads during an evacuation or riot (both of which are topics which have been explored by my PhD. students). Moreover, my interest in SNA ties with my other research interest, that of GIS and the rise in social media (see below). Social media has drastically changed the medium through which we can engage others: in addition to physical interaction, social media in its many forms has opened an entirely new avenue to enable an alternative form of interaction.

While my initial work with respect to social networks has focused on the geographical visualization of globally distributed communities [29, 30] such as that shown below, 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 allows for the study of state-driven interactions over time (e.g. who sells arms to who, who votes with who). In parallel to this top-down approach, the recent 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 [28]. 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.

Geographical Information Science

Figure 6: Inner-City Function Map [31].
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 6 but as a science, it allows us to solve problems and discover new knowledge about the world we inhabit (e.g. through spatial analysis) as shown in Figure 6. For my ABMs, 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 7. 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. 32, 33, 34, 35].  One active research area for me is harvesting crowdsourced data (volunteered and ambient geographic information) from social media (e.g. Twitter, Flickr, OpenStreetMap) to increase our situational awareness, utilizing both geographical and social network analysis [e.g. 36, 26].

Figure 7: Visualizing Communities: A Social Network of an Interest Group (A),
and its Largest Community Plotted Geographically (B) [30].

We have termed this GeoSocial analysis, as though the utilization of GIS and SNA 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 [37]); map the manner in which ideas and information propagate across space in a society (e.g. people reacting to an earthquake [38]); 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); and identify emerging socio-cultural hotspots (e.g. popular gathering places [39]). My work in this area has already shown that social proximity often overtakes geographical proximity when connections are established [36]. Not only does such research allow for greater situational awareness, but it also has the potential to be merged with ABMs [17]. However, such research does pose several challenges [34] such as data quality and accuracy [40, 41], privacy [26] and the need of new methods for data collection [36]. These crucial issues go back a long way in the GIS community, but nonetheless still need to be addressed.

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 8.  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.

Figure 8: The persistent urban morphology concept [42].


The above and continuing research would not have been possible without the support of government and industrial partners as shown in Figure 9. I would like to thank 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), Draper Labs and EADS North America for supporting my research.

Figure 9: Government and Industrial Partners who have Supported my Research.

  1. World Health Organization (2010), Hidden Cities: Unmasking and Overcoming Health Inequities in Urban Settings, The WHO Centre for Health Development, Kobe, Japan.
  2. UN-Habitat (2006), State of the World's Cities 2006/7, UN-Habitat, Nairobi, Kenya.
  3. Patel, A., Koizumi, N. and Crooks, A.T. (2014), 'Measuring Slum Severity in Mumbai and Kolkata: A Household-based Approach', Habitat International, 41(300-306). (pdf)
  4. Crooks, A.T., Patel, A. and Wise, S. (2014), 'Multi-agent Systems for Urban Planning', in Pinto, N.N., Tenedório, J., P., A.A. and Roca, J. (eds.), Urban and Spatial Planning: Virtual Cities and Territories, IGI Global, Hershey, PA, pp. 29-56. (pdf)
  5. 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)
  6. Wilson, A.G. (2000), Complex Spatial Systems: The Modelling Foundations of Urban and Regional Analysis, Pearson Education, Harlow, UK.
  7. Cioffi-Revilla, C. (2014), Introduction to Computational Social Science: Principles and Applications, Springer, New York, NY.
  8. Haggett, P. and Chorley, R.J. (1969), Network Analysis in Geography, Edward Arnold, London, England.
  9. Crooks, A.T. and Heppenstall, A. (2012), 'Introduction to Agent-Based Modelling', in Heppenstall, A., Crooks, A.T., See, L.M. and Batty, M. (eds.), Agent-based Models of Geographical Systems, Springer, New York, NY, pp. 85-108. (pdf)
  10. Heppenstall, A.J., Crooks, A.T., Batty, M. and See, L.M. (eds.) (2012), Agent-based Models of Geographical Systems, Springer, New York, NY.
  11. Crooks, A.T. and Castle, C. (2012), 'The Integration of Agent-Based Modelling and Geographical Information for Geospatial Simulation', in Heppenstall, A., Crooks, A.T., See, L.M. and Batty, M. (eds.), Agent-based Models of Geographical Systems, Springer, New York, NY, pp. 219-252. (pdf)
  12. Crooks, A.T. (2015), 'Agent-based Models and Geographical Information Systems', in Brunsdon, C. and Singleton, A. (eds.), Geocomputation: A Practical Primer, Sage, London, England, pp. 63-77. (pdf).
  13. Crooks, A.T. (2010), 'Constructing and Implementing an Agent-Based Model of Residential Segregation through Vector GIS', International Journal of GIS, 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), Available at
  16. 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)
  17. Crooks, A.T. and Wise, S. (2013), 'GIS and Agent-Based models for Humanitarian Assistance', Computers, Environment and Urban Systems, 41: 100-111. (pdf)
  18. 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)
  19. 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)
  20. Latek, 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)
  21. 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)
  22. Patel, A., Crooks, A.T. and Koizumi, N. (2012), 'Simulating Spatio-Temporal Dynamics of Slum Formation in Ahmedabad, India', 6th Urban Research and Knowledge Symposium - Rethinking Cities: Framing the Future, Barcelona, Spain. (pdf)
  23. 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), Available at
  24. Crooks, A.T., Hudson-Smith, A. and Patel, A. (2011), 'Advances and Techniques for Building 3D Agent-Based Models for Urban Systems', in D., M. and Benenson, I. (eds.), Advanced Geosimulation Models, Bentham Science Publishers, Hilversum, The Netherlands, pp. 49-65. (pdf)
  25. Malik, A.A., Crooks, A.T., Root, H.L. and Swartz, M. (2015), Exploring Creativity and Urban Development through Agent-Based Modeling, Journal of Artificial Societies and Social Simulation. 18 (2): 12. Available at
  26. Stefanidis, T., Crooks, A.T. and Radzikowski, J. (2013), 'Harvesting Ambient Geospatial Information from Social Media Feeds', GeoJournal, 78(2): 319-338. (pdf)
  27. 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)
  28. 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)
  29. 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)
  30. 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. DOI: 10.1002/9781118786352.wbieg0605 (pdf)
  31. Smith, D. A. and Crooks, A. T. (2010), From Buildings to Cities: Enabling the Multi-Scale Analysis of Urban Form and Function through the integration of Geographical and Geometric Methods. Centre for Advanced Spatial Analysis (University College London): Working Paper 155, London, England. (pdf)
  32. 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)
  33. Crooks, A.T., Hudson-Smith, A., Croitoru, A. and Stefanidis, A. (2014), 'The Evolving GeoWeb', in J., A.R. and See, L.M. (eds.), Geocomputation (2nd Edition), CRC Press, Boca Raton, FL, pp. 67-94. (pdf)
  34. 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)
  35. 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 & Standards Watch (TechWatch) Horizon Scanning report 10_01, Bristol, England.  (pdf)
  36. 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)
  37. 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)
  38. 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)
  39. Wayant, N., Crooks, A.T., Stefanidis, A., Croitoru, A., Radzikowski, J., Stahl, J. and Shine, J. (2012), 'Spatiotemporal Clustering of Social Media Feeds for Activity Summarization', GI Science (7th International Conference for Geographical Information Science), Columbus, OH. (pdf)
  40. 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)
  41. 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)
  42. 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)

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