Thursday, August 17, 2017

Big Data, Agents and the City

In the recently published book "Big Data for Regional Science" edited by Laurie Schintler and  Zhenhua Chen, Nick Malleson, Sarah Wise, and Alison Heppenstall and myself have a chapter entitled: Big Data, Agents and the City. In the chapter we discuss how big data can be used with respect to building more powerful agent-based models. Specifically how data from say social media could be used to inform agents behaviors and their dynamics; along with helping with the calibration and validation of such models with a emphasis on urban systems. 

Below you can read the abstract of the chapter, see some of the figures we used to support our discussion, along with the full reference and a pdf proof of the chapter. As always any thoughts or comments are welcome.

Big Data (BD) offers researchers the scope to simulate population behavior through vastly more powerful Agent Based Models (ABMs), presenting exciting opportunities in the design and appraisal of policies and plans. Agent-based simulations capture system richness by representing micro-level agent choices and their dynamic interactions. They aid analysis of the processes which drive emergent population level phenomena, their change in the future, and their response to interventions. The potential of ABMs has led to a major increase in applications, yet models are limited in that the individual-level data required for robust, reliable calibration are often only available in aggregate form. New (‘big’) sources of data offer a wealth of information about the behavior (e.g. movements, actions, decisions) of individuals. By building ABMs with BD, it is possible to simulate society across many application areas, providing insight into the behavior, interactions, and wider social processes that drive urban systems. This chapter will discuss, in context of urban simulation, how BD can unlock the potential of ABMs, and how ABMs can leverage real value from BD.  In particular, we will focus on how BD can improve an agent’s abstract behavioral representation and suggest how combining these approaches can both reveal new insights into urban simulation, and also address some of the most pressing issues in agent-based modeling; particularly those of calibration and validation.

Keywords: Agent-based models, Big Data, Emergence, Cities.

The growth in Agent-based modeling -from search results of Web of Science and Google Scholar.

Hotspots of activity of Tweeter Users: Tweet locations and associated densities for a selection of prolific users.

Full Reference:
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)

Sunday, August 13, 2017

Predicting the Evolution of Narratives in Social Media

Building on our work on narratives and social media at the 15th International Symposium on Spatial and Temporal Databases (SSTD'17) we have a paper entitled: "Predicting the Evolution of Narratives in Social Media." In the paper we discuss briefly the challenges that social media poses with respect to understanding narratives and propose a framework that could be used to develop simulation models to predict the spread and evolution of narratives by blending the social, spatial and contextual dimensions of online narratives that are contextually informed by past events. Below you can read the abstract to our paper along with a link to the paper itself.
Abstract. The emergence of global networking capabilities (e.g. social media) has provided newfound mechanisms and avenues for information to be generated, disseminated, shaped, and consumed. The spread and evolution of online information represents a unique narrative ecosystem that is facilitated by cyberspace but operates at the nexus of three dimensions: the social network, the contextual, and the spatial. Current approaches to predict patterns of information spread across social media primarily focus on the social network dimension of the problem. The novel challenge formulated in this work is to blend the social, spatial, and contextual dimensions of online narratives in order to support high fidelity simulations that are contextually informed by past events, and support the multi-granular, reconfigural and dynamic prediction of the dissemination of a new narrative.

Full Reference:
Schmid, K. A. Zufle, A., Pfoser, D., Crooks, A.T., Croitoru, A. and Stefanidis, A. (2017), Predicting the Evolution of Narratives in Social Media, in Gertz, M., Renz, M., Zhou, X., Hoel, E., Ku, W.-S., Voisard, A., Zhang, C., Chen, H., Tang, L., Huang, Y., Lu, C.-T. and Ravada, S. (eds.) Advances in Spatial and Temporal Databases: Proceedings of the 2017 International Symposium on Spatial and Temporal Databases, Springer, New York, NY., pp. 388-392 (pdf)

Saturday, August 05, 2017

Spatial Agent-based Modeling to Explore Slum Formation Dynamics

In the newly published book edited by Jean-Claude Thill  and Suzana Drajicavic entitled:  "Geocomputational Analysis and Modeling of Regional Systems" Amit Patel, Naoru Koizumi and myslef have a chapter which explores some of our work with respect to modeling slums in India. The chapter is titled:  "Spatial Agent-based Modeling to Explore Slum Formation Dynamics in Ahmedabad, India." In which we report some of the work we did with pertaining to our sponsored NSF Project: "An Integrated Simulation Framework to Explore Spatio-temporal Dynamics of Slum Formation in Ahmedabad, India". Below you can see the abstract for the chapter along with some of the figures and a link to the project page.

 "More than 900 million people or one third of the world’s urban population lives in either slum or squatter settlements. Urbanization rates in developing countries are often so rapid that formal housing development cannot meet the demand. In the past decades, international, national and local development communities have taken several policy actions in an attempt to improve the living conditions of people within slums or to eradicate them completely. However, such policies have largely failed and slum-free cities have remained a distant goal for many developing countries. This chapter argues that for informed policymaking, it is important to investigate questions related to slum formation such as: (1) How do slums form and expand? (2) Where and when are they formed? (3) What types of structural changes and/or policy interventions could improve housing conditions for the urban poor? In order to address these questions, this chapter develops a geosimulation model that is capable of exploring the spatio-temporal dynamics of slum formation and simulating future formation and expansion of slums within cities of the developing world. Our geosimulation model integrates agent-based modeling (ABM) and Geographic Information System (GIS), methods that are often applied separately to explore slums. In our model, ABM simulates human behavior and GIS provides a spatial environment for the housing market. GIS is also used to analyze empirical data using spatial analyses techniques, which is in turn used to validate the model outputs. The core of this framework is a linked dynamic model operating at both micro and macro geographic and demographic scales. The model explores the collective effect of many interacting inhabitants of slums as well as non-slum actors (e.g. local government) and how their interactions within the spatial environment of the city generate the emergent structure of slums at the macro scale. We argue that when empirical data is absent, geosimulation provides useful insights to study implications of various policies. The goal of this framework is to develop a decision support tool that could allow urban planners and policymakers to experiment with new policy ideas ex-ante in a simulated environment. We calibrate and validate the model using data from Ahmedabad, the sixth largest city of India, where 41% of its population lives in slums. This is one of the first attempts to develop an integrated and multi-scalar analytical framework to tackle slum issues in the developing world at multiple spatial scales."
Keywords: Slums Agent-based modeling India Geosimulation

Integrated Simulation Framework
Slum Locations and Slum Sizes in Ahmedabad, 2001

Spatial Sprawl Experiment

Full Reference:
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)

Further details of the model and project can be found here. As normal any thoughts and comments are most welcome.