As a geospatial computational social scientist (CSS), 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 (4.2 billion people or 55% of the worlds population). This number is expected to grow in the coming decades: a recent prediction by the United Nations [1] indicates that between 2018 and 2050 the world’s urban population will grow by another 2.5 billion. According to estimates by the 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, 4]. 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 [5]. 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 [6], 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; 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 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). Initially, my primarily focus was on ABM and GIS. However, over the last several years, I have developed an active research interest in SNA (and “big data”), because of its capability to link many different areas of applications (i.e. it allows us to study the connections between people and place [7]). I see this development as a logical progression, not only is ABM and SNA at the core of CSS [8], but also network analysis has a long history within the geographical sciences [9].

Agent-based Modeling 

ABM provides an ideal environment to test ideas and hypotheses that cannot be done in reality [10]. My focus has been, and continues to be, on developing agent-based models specifically relating to geographical systems [11, 12], and linking them to geographical information thus grounding them to place [13], 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 [6] 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 [14], riots [15] residential segregation [16], community resource management [17], the growth of slums [18, 19], the migration of displaced people [20], how people react in times of crisis [21], climate adaptation [22], to the spread of diseases [23].

Figure 2: A Selection of Agent-based Modeling Application Areas of Interest (adapted from [34]).

Figure 3: Agent-based models in
Virtual Worlds [29].
These models also range across the spectrum from theoretical to practice (e.g. [13, 24]). Specifically, I have developed theoretical models whose purpose is to explore theory and test hypothesis (e.g. [16, 18, 25]) to more descriptive models based on empirical details of the social phenomena (e.g. [26, 27]), 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. [23, 28, 19]). 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 [29, 30]. 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. [14, 15, 16, 17, 18, 19, 21, 22, 23, 25, 27, 28, 29, 31, 32, 33])

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).

Figure 4: Agent-Based Modelling and
Geographical Information Systems: A
Practical Primer [10]
For 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 Nicolas Malleson, Ed Manley and  Alison Heppenstall entitled "Agent-Based Modelling and Geographical Information Systems: A Practical Primer (Figure 4)." The book [10] provides a synthesis of the underpinning ideas, techniques and frameworks for integrating agent-based modelling and geographical information systems.

“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 (2019) 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 [12].
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 [12]. 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 (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 6: Visualizing Communities: Network Cores
and Retweeting Nodes [41].
While I had explored physical networks in the past [42], more recently 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 [43] 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. [15, 43, 44, 45, 46]). Moreover, my interest in SNA ties with my other research interest, that of GIS and the rise in social media (see below). New forms of data (e.g. from mobile phone) and social media in particular 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 [7, 46, 47, 48, 49].

My initial work with respect to social networks was focused on the geographical visualization of globally distributed communities [50, 51], 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 allows for the study of state-driven interactions over time (e.g. who sells arms to who, who votes with who) [43]. 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 [43]. 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 [10, 15] for some representative examples).

Figure 7: Visualizing Communities: A Social Network of an Interest Group (A),
and its Largest Community Plotted Geographically (B) [50].

Geographical Information Science

Figure 8: Inner-City Function Map [52].
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. [47, 48, 53, 54, 55, 56]). One active research area for me is harvesting crowdsourced data (volunteered and ambient geographic information [41]) from social media (e.g. Twitter, Flickr) and other platforms (e.g. OpenStreetMap) to increase our situational awareness, understand the linkage of form and function, by utilizing both geographical and social network analysis (e.g. [41, 47, 48, 56, 57, 58, 59]).

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 [60]); map the manner in which ideas and information propagate across space in a society (e.g. people reacting to an earthquake [61] or disease outbreaks [62, 63]); 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 [64]); and identify emerging socio-cultural hot-spots (e.g. popular gathering places [48]).

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 [58]. 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 [10, 21, 65]. However, such research does pose several challenges [55, 56] such as data quality and accuracy [66, 67, 68, 69], why people contribute such information [70], privacy [41], the role of social bots [44, 71, 72, 73] and the need of new methods for data collection [58]. 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 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 blog.

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


The above and continuing research would not have been possible without the support of government and industrial partners as shown in Figure 11. 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 11: Government and Industrial Partners who have Supported my Research.

  1. United Nations (2018), 2018 Revision of World Urbanization Prospects, Department of Economic and Social Affairs, New York, NY, Available at
  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. Mahabir, R., Crooks, A.T., Croitoru, A. and Agouris, P. (2016), 'The Study of Slums as Social and Physical Constructs: Challenges and Emerging Research Opportunities', Regional Studies, Regional Science, 3(1): 737-757. (pdf)
  5. 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)
  6. 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)
  7. 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)
  8. Cioffi-Revilla, C. (2017), Introduction to Computational Social Science: Principles and Applications (2nd edition), Springer, New York, NY.
  9. Haggett, P. and Chorley, R.J. (1969), Network Analysis in Geography, Edward Arnold, London, UK.
  10. 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
  11. 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)
  12. Heppenstall, A.J., Crooks, A.T., Batty, M. and See, L.M. (eds.) (2012), Agent-based Models of Geographical Systems, Springer, New York, NY.
  13. 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)
  14. Crooks, A.T., Croitoru, A., Lu, X., Wise, S., Irvine , J.M. 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)
  15. Pires, B. and Crooks, A.T. (2017), 'Modeling the Emergence of Riots: A Geosimulation Approach', Computers, Environment and Urban Systems, 61: 66-80. (pdf)
  16. 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)
  17. 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)
  18. 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): 2, Available at
  19. 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 Dragicevic, S. (eds.), GeoComputational Analysis and Modeling of Regional Systems, Springer, New York, NY, pp. 121-141. (pdf)
  20. Gulden, T., Harrison, J.F. and Crooks, A.T. (2011), 'Modeling Cities and Displacement through an Agent-based Spatial Interaction Model', The 2011 Computational Social Science Society of America Conference, Santa Fe, NM. (pdf)
  21. Crooks, A.T. and Wise, S. (2013), 'GIS and Agent-Based models for Humanitarian Assistance', Computers, Environment and Urban Systems, 41: 100-111. (pdf)
  22. Hailegiorgis, A.B., Crooks, A.T. and Cioff-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
  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., 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)
  25. Malik, A., Crooks, A., 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
  26.  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)
  27. Oldham, M. and Crooks, A.T. (2019), 'Drafting Agent-Based Modeling into Basketball Analytics', 2019 Spring Simulation Conference (SpringSim’19), Tucson, AZ. (pdf)
  28. 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)
  29. 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
  30. 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)
  31. 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)
  32. Bagheri-Jebelli, N., Crooks, A.T. and Kennedy, W.G. (2019), 'Capturing the Effects of Gentrification on Property Values: An Agent-Based Modeling Approach', The 2019 Computational Social Science Society of Americas Conference, Santa Fe, NM. (pdf)
  33. McEligot, K., Brouse, P. and A.T., C. (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)
  34. 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)
  35. Łatek, M.M., Mussavi Rizi, S.M., Crooks, A.T. and Fraser, M. (2012), 'A Spatial Multiagent Model of Border Security for the Arizona-Sonora Borderland', The 2012 Computational Social Science Society of America Conference, Santa Fe, NM. (pdf)
  36. Crooks, A.T. (2007), Experimenting with Cities: Utilizing Agent-Based Models and GIS to Explore Urban Dynamics, PhD Thesis, University College London, London, UK.
  37. 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)
  38. 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)
  39. 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)
  40. 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)
  41. Stefanidis, T., Crooks, A.T. and Radzikowski, J. (2013), 'Harvesting Ambient Geospatial Information from Social Media Feeds', GeoJournal, 78(2): 319-338. (pdf)
  42. 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)
  43. 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)
  44. 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)
  45. 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)
  46. 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)
  47. 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)
  48. Jenkins, A., Croitoru, A., Crooks, A.T. and Stefanidis, A. (2016), 'Crowdsourcing A Collective Sense of Place', PLoS ONE, 11(4): e0152932. (pdf)
  49. 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)
  50. 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)
  51. 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)
  52. 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)
  53. 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)
  54. 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)
  55. 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)
  56. 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)
  57. 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)
  58. 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)
  59. Yuan, X. and A.T., C. (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)
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