Research

Motivation

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].

Click on the links below to find out more on my work with respect to: 

• Agent-based Modeling
• Social Network Analysis
• Geographical Information Science

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 2.  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 2: The persistent urban morphology concept.

Acknowledgments

My research would not have been possible without the support of government and industrial partners as shown in Figure 3. 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 3: Government and Industrial Partners who have Supported my Research.

References

1. United Nations (2018), 2018 Revision of World Urbanization Prospects, Department of Economic and Social Affairs, New York, NY, Available at https://population.un.org/wup/.
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.