Thursday, February 12, 2015

Village Model
What now seems like a very long long time ago, when I was getting up to speed with Agent-based modeling and GIS, I came across a great edited book entitled "Dynamics in Human and Primate Societies: Agent-Based Modeling of Social and Spatial Processes". 

One chapter in particular that I really enjoyed because of its clarity and use of data was by Kohler et al. (2000) entitled "Be There Then: A Modeling Approach to Settlement Determinants and Spatial Efficiency Among Late Ancestral Pueblo Populations of the Mesa Verde Region, U.S. Southwest". 

The chapter explored the question of why did Pueblo people vary their living arrangements between  compact villages and dispersed hamlets between 901-1287AD? To this day, I use this chapter when I am teaching about early agent-based models. While the initial model was implemented in Swarm, it has now been ported to Repast and developed further by an NSF supported program called Village Ecodynamics Project.

Full Reference:
Kohler, T.A., Kresl, J., Van Wes, Q., Carr, E. and Wilshusen, R.H. (2000), 'Be There Then: A Modeling Approach to Settlement Determinants and Spatial Efficiency Among Late Ancestral Pueblo Populations of the Mesa Verde Region, U.S. Southwest', in Kohler, T.A. and Gumerman, G.J. (eds.), Dynamics in Human and Primate Societies: Agent-Based Modeling of Social and Spatial Processes, Oxford University Press, Oxford, UK, pp. 145-178.

Monday, February 09, 2015

Geosimulation and Big Data: A Marriage made in Heaven or Hell? Schedule

Do you like big data and geosimulation and wondering when to book flights or which sessions to attend at the forthcoming AAG Annual Meeting,  If so, you might like our sessions entitled "Geosimulation and Big Data: A Marriage made in Heaven or Hell? " taking place on Wednesday the 22nd of April 2015.

Abstract of the Sessions:

In recent years, human emotions, intentions, moods and behaviors have been digitised to an extent previously unimagined in the social sciences. This has been in the main due to the rise of a vast array of new data, termed 'Big Data'.  These new forms of data have the potential to reshape the future directions of social science research, in particular the methods that scientists use to model and simulate spatially explicit social systems. Given the novelty of this potential "revolution" and the surprising lack of reliable behavioral insight to arise from Big Data research, it is an opportune time to assess the progress that has been made and consider the future directions of socio-spatial modelling in a world that is becoming increasingly well described by Big Data sources.

In these sessions we will have methodological, theoretical and empirical papers that that engage with any aspect of geospatial modelling and the use of Big Data. We are particularly interested in the ways that insight into individual or group behavior can be elucidated from new data sources - including social media contributions, volunteered geographical information, mobile telephone transactions, individually-sensed data, crowd-sourced information, etc. -  and used to improve models or simulations.  Topics include, but are not limited to:
  • Using Big Data to inform individual-based models of geographical systems;
  • Translating Big Data into agent rules;
  • Elucidating behavioral information from diverse data;
  • Improving simulated agent behavior;
  • Validating agent-based models (ABM) with Big Data;
  • Ethics of data collected en masse and their use in simulation.
2192 Geosimulation and Big Data: A Marriage made in Heaven or Hell? (1)

Wednesday, 4/22/2015.
8:00 AM - 9:40 AM.
600a Classroom, University of Chicago Gleacher Center, 6th Floor.

Chair: Nick Malleson 


*Atsushi Nara:
A GPGPU approach for simulating and analyzing human dynamics
*Kira KowalskaJohn Shawe-Taylor and Paul Longley:
 Data-driven modelling of police patrol activity 
*Martin Zaltz Austwick, Gustavo Romanillos Arroyo and Borka Moya-Gomez:
Simulating Rush Hour Bicycle Traffic in Madrid 
*Hai Lan  and Paul Torrens:
Voxel based Cellular Automata with massive cells for Geo-simulation: Ice dynamics simulation in Antarctic locations as example
*Philippe J. Giabbanelli, Thomas Burgoine, Pablo Monsivais and James Woodcock:
Using big data to develop individual-centric models of food behaviours

2292 Geosimulation and Big Data: A Marriage made in Heaven or Hell? (2) 

Wednesday, 4/22/2015.
10:00 AM - 11:40 AM.
600a Classroom, University of Chicago Gleacher Center, 6th Floor.

Chair: Alison Heppenstall


*Kostas Cheliotis:
Coupling Public Space Simulations with Real-Time Data Streams 
*Andrew Crooks and Sarah Wise:
Leveraging Crowdsourced data for Agent-based modeling: Opportunities, Examples and Challenges 
*Ed Manley, Chen Zhong and Michael Batty:
Towards Real-Time Simulation of Transportation Disruption - Building Agent Populations from Big Mobility Data 
*Alison Heppenstall, *Nick Malleson and Andrew Evans:
Evaluating Big Data demographics for population modelling 
Muhammad Adnan, Alistair Leak and *Paul Longley:
Exploring the geo-temporal patterns of Twitter messages

2492 Geosimulation and Big Data: A Marriage made in Heaven or Hell? (3) Discussion Session

Wednesday, 4/22/2015.
1:20 PM - 3:00 PM.
600a Classroom, University of Chicago Gleacher Center, 6th Floor.

Chair: Nick Malleson

*Paul M Torrens and Hai Lan:
Micro big data and geosimulation 
*Mark Birkin:
The Ten Commandments of Big Data 
 2:00 PM to 3:00PM: Discussion


  • Alison Heppenstall, School of Geography, University of Leeds
  • Nick Malleson, School of Geography, University of Leeds
  • Andrew Crooks, Department of Computational Social Science, George Mason University
  • Paul Torrens, Department of Geographical Sciences, University of Maryland
  • Ed Manley, Centre for Advanced Spatial Analysis, University College London

Wednesday, February 04, 2015

Agent-based models in a web browser

Sharing agent-based models over the web is never very easy. You can do it with NetLogo but it requires that your web browser supports  Java 5 (but this is not recommended). One could create a jar file for your model if you are using MASON, for example. But this still requires a number of steps before you see the model running. One way to bypass this is to build the model directly into the web page. While we have highlighted the use of JavaScript for Agent-based modeling in a previous post. Ernesto Carrella, a PhD  candidate from the Department of Computational Social Science has just created some proof of concept agent-based models exploring fishing using Dart which are overlaid on top of Google maps. If you want to find out more, checkout his models over on GitHub.

Saturday, January 31, 2015

New Book Chapter: Agent-based modelling and geographical information systems

Chris Brunsdon and Alex Singleton recently edited a book entitled "Geocomputation: A Practical Primer". The book covers a plethora of topics relating to geocomputation, and to quote from the website: "Chapters provide highly applied and practical discussions of:
      • Visualization and exploratory spatial data analysis
      • Space time modelling
      • Spatial algorithms
      • Spatial regression and statistics
      • Enabling interactions through the use of neogeography "
In the book I contributed a chapter on "Agent-based Models and Geographical Information Systems". Such a topic might not come as a surprise to readers of this blog but I essentially wanted to showcase a series of applications that we have been working on here at George Mason University and moreover, provide a general introduction to agent-based modeling (ABM) and how to link it to geographical information.

The main argument of the chapter is that the ABM paradigm provides a mechanism for understanding the effects of interactions of individuals and through such interactions emergent structures develop, both in the social and physical environment. By coupling agent-based models to geographical information systems (GIS), spatially explicit agent-based models can be created exploring the complexities of our world from the bottom-up. 

Representing the world as a series of layers of fixed and
non-fixed objects (adapted from Benenson and Torrens, 2004).

The chapter therefore introduces agent-based models to those interested in geocomputational methods, argues why such models should be used to study geographical problems before discussing how one can use GIS to create geographical explicit agent-based models. Through a series of examples we demonstrate how raster or vector spatial data can be used to model various aspects of our daily lives from that of the micro movement of pedestrians over seconds and minutes, to that of the macro patterns of urban growth over years and decades. By integrating spatial data and agent-based models at different spatial and temporal scales, such a modeling approach provides the flexibility to aid social scientists to explore the complex world that we inhabit.

Many of the models discussed in the chapter were either created in NetLogo or MASON. If you look at my research page you can find the source code to most of these models.  

A sample of application domains for spatial agent-based models discussed in the chapter.

A simple example using MASON on how Agent-based models can be to used explore rush hour congestion:
(A): Road and census data used for model inputs. (B) Zoomed in section of A with agents (red circles)
moving towards Tyson’s Corner and causing traffic jams.

Reference Cited:
Benenson, I. and Torrens, P.M. (2004), Geosimulation: Automata-Based Modelling of Urban Phenomena, John Wiley & Sons, London, UK.
Full Reference to Chapter:
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)
Happy reading, and if you cannot find the models discussed in the chapter, feel free to drop me an email and I can point you in the right direction.

Thursday, January 08, 2015

Crowdsourcing Urban Form and Function

We have just had published a new paper entitled: "Crowdsourcing Urban Form and Function" in International Journal of Geographical Information Science which showcases some of our recent work with respect to cities and how new sources of information can be used to study urban morphology at a variety of spatial and temporal scales. Below is the abstract for the paper: 

"Urban form and function have been studied extensively in urban planning and geographic information science. However, gaining a greater understanding of how they merge to define the urban morphology remains a substantial scientific challenge. Towards this goal, this paper addresses the opportunities presented by the emergence of crowdsourced data to gain novel insights into form and function in urban spaces. We are focusing in particular on information harvested from social media and other open-source and volunteered datasets (e.g. trajectory and OpenStreetMap data). These data provide a first-hand account of form and function from the people who define urban space through their activities. This novel bottom-up approach to study these concepts complements traditional urban studies work to provide a new lens for studying urban activity. By synthesizing recent advancements in the analysis of open-source data we provide a new typology for characterizing the role of crowdsourcing in the study of urban morphology. We illustrate this new perspective by showing how social media, trajectory, and traffic data can be analyzed to capture the evolving nature of a city’s form and function. While these crowd contributions may be explicit or implicit in nature, they are giving rise to an emerging research agenda for monitoring, analyzing and modeling form and function for urban design and analysis."
This paper builds and extends considerably our prior work, with respect to crowdsourcing, volunteered and ambient geographic information. In the scope of this paper we use the term ‘urban form’ to refer to the aggregate of the physical shape of the city, its buildings, streets, and all other elements that make up the urban space. In essence, the geometry of the city. In contrast, we use the term ‘urban function’ to refer to the activities that are taking place within this space. To this end we contrast how crowdsourced data can related to more traditional sources of such information both explicitly and implicitly as shown in the table below. 

A typology of implicit and explicit form and function content

In addition, we also discuss in the paper how these new sources of data, which are often at finer resolutions than more authoritative data are allowing us to to customize the we we aggregate the data  at various geographical levels as shown below. Such aggregations can range from building footprints and addresses to street blocks (e.g. for density analysis), or street networks (e.g. for accessibility analysis). For large-scale urban analysis we can revert to the use of zonal geographies or grid systems.  
Aggregation methods for varied scales of built environment analysis

In the application section of the paper we highlight how we can extract implicit form and function from crowdsourced data. The image below for example, shows how we can take information from Twitter, and differentiate different neighborhoods over space and time.

Neighborhood map and topic modeling results showing the mixture of social functions in each area.

Finally in the paper, we outline an emerging research agenda related to the "persistent urban morphology concept" as shown below. Specifically how crowdsourcing is changing how we collect, analyze and model urban morphology. Moreover, how this new paradigm provides a new lens for studying the conceptualization of how cities operate, at much finer temporal, spatial, and social scales than we had been able to study so far.

The persistent urban morphology concept.

We hope you enjoy the paper.

Full Reference:  
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. DOI: 10.1080/13658816.2014.977905 (pdf)