GIS and Agent-Based Modelling

Synthetic population data for the U.S

2012-05-31T11:05:00.000-04:00

When creating agent-based models, one question is how many agents to include and where are they located. Often we create synthetic individuals or households based on census data however, this can be a rather time consuming task. So a recent project from RTI has caught my attention. It is a U.S. wide geospatially explicit synthetic population funded under a grant from NIH/NIGMS.

The dataset provides a synthetic version of each household and person in the U.S. based on 2005-2009 ACS public use microdata and other sources. LandScan 90-meter night-time population distributions were used to place each household across the landscape (a total of 112,383,675).

The basic website location for information and to download the data is at: https://www.epimodels.org/midas/pubsyntdata1.do

Another short write up can be found here: http://www.openabm.org/forum/2852

Call for papers: Intelligent Agents in Urban Simulations and Smart Cities

2012-05-31T10:18:00.000-04:00

Readers of the blog might be interested in the "Intelligent Agents in Urban Simulations and Smart Cities" workshop at the ECAI-2012 Conference in Montpellier, France, August 27 or 28, 2012.

To quote from the call for papers:

In this workshop, we intend to address specific methodological and technological issues raised by the deployment of agents in rich environments such as virtual cities. We will welcome contributions tackling issues related to reactive agents, cognitive architectures, the capacity to scale up to handle thousands or hundreds of thousands of agents, the ability to simulate realistic group behaviors which might be judged non rational, etc., all in the context of urban agents. We will also welcome contributions showcasing original applications of agent and multi-agent technologies within urban simulations, be it for design, planning, education, training, or entertainment.

Workshop Chairs:

Vincent Corruble (contact), Université Pierre et Marie Curie (Paris 6), France
Fabio Carrera, Worcester Polytechnic Institute (WPI), USA
Stephen Guerin, Santa Fe Complex, USA

Important Dates:

*6 June 2012*: Workshop paper submission deadline
28 June 2012: Notifications to authors (subject to modification)
13 July 2012: Submissions of camera-ready copies of selected papers
27 or 28 August 2012: Workshop date

Submission information:

12 page max. submissions, Springer Verlag LNCS conference format Contact: Vincent Corruble (iaussc2012@easychair.org) workshop page: http://www-poleia.lip6.fr/~corruble/IAUSSCws/Home.html

A Semester with OpenSim

2012-05-18T15:39:00.000-04:00

Over the last few months I have been teaching a class in the Department of Computational Social Science entitled "Building Virtual Worlds" where we surveyed the role of virtual worlds for social science research. The emphasis of the class was on tools, software frameworks, and applications of virtual worlds. On the applications side we discussed how virtual worlds are being used for History, Archeology, Healthcare, Tourism, Urban Modeling, Architecture, Agent-based Modeling along with more generally teaching and learning. We explored a variety of tools for building virtual worlds before focusing on OpenSim. The movie below shows some of the final outputs using OpenSim.

We used OpenSim 0.7.3, configured with the Standalone-Hypergrid mode and a SQLite database hosted on a Windows 7 server. The server simultaneously simulated 64 different regions, and at various points during the semester the server hosted well over 15000 primitives (prims) and ran hundreds of scripts across this landscape; one region alone hosted over 8000 prims.

Why so many regions? We were interested in how many the server could cope with but also we wanted to have a virtual world representing the whole of the GMU Fairfax campus (~4km²) and regions in OpenSim are limited to 256m by 256m. We built the terrain for the campus utilizing the National Elevation Dataset (NED) DEM from the United States Geological Survey which was first manipulated in ArcGIS before being processed in L3DT (Large 3D Terrain Generator). Finally, the DEM was imported into OpenSim. The movie below should give a sense of what the basic terrain looks like.

Once the terrain was built, we populated it with buildings, however, we were not just interested in the external appearance of the buildings but also there internal structure for modeling and simulation purposes. Therefore the class focused their attention on building a highly detailed Johnson Center.

Model of Johnson Center taken from Google SketchUp 3D Warehouse

Vector based, 2D CAD files were obtained and imported into Google SketchUp before using SketchLife to build the 3D initial building core, walls, doors and windows.

Constructing a vector-based model of the Johnson Center internal structure

The SketchLife final rendering of the Johnson Center

Once built in SketchUp using SketchLife the model was imported into OpenSim

External view “in world” of what we accomplished in building the Johnson Center

In addition to using SketchLife for the JC, many objects such as chairs, staircases and tables were either built using the tool or those native to OpenSim.

An “in world” shot at ground level, on the 1st floor, viewing the atrium and clock tower
in the Johnson Center

CSS class photo "in-world"

However, our work with OpenSim does not stop here, below is another movie of some ongoing work with one of our PhD students, Chris Rouly who is creating agent-based models embedded in OpenSim to explore past habitats among many other things.

I would like to thank the "Building Virtual Worlds" class and the Department for enabling this blog post.

SimTable and fires

2012-04-25T13:27:00.003-04:00

To quote from the site "The SimTable takes sandtable exercise to the next level by making sandtables real. The SimTable is a 3D interactive fire simulator, bringing sandtable exercises to life." Below is a Los Alamos National Lab video demonstrating their use of the SimTable in their Emergency Operations Center.

The movie below demonstrates more of the functionality of SimTable , specifically how one can simulate rainfall and how it flows over the terrain. Or how it can be used to simulate a wildfire spreading and how residents might evacuate from the area.

Natural Disasters and Crowdsourcing: Haiti

2012-04-16T03:20:00.000-04:00

Natural disasters such as earthquakes and tsunamis occur all over the world, altering the physical landscape and often severely disrupting people’s daily lives. Recently researchers’ attention has focused on using crowds of volunteers to help map the infrastructure and devastation caused by natural disasters, such as those in Haiti and Pakistan. For example, in the movie below shows the response to the earthquake by the OpenStreetMap community within 12 hours of the earthquake. The white flashes indicate edits to the map (often by tracing satellite/aerial photography).

While this data is extremely useful, as it is allows us to assess damage and thus aid the distribution of relief, but it tells us little about how the people in such areas will react to the devastation, the supply of food, or the reconstruction. To address this, we are exploring how agent-based modeling can be used to explore peoples reactions. To do this we have created a prototype spatially explicit agent-based model, created using crowdsourced geographic information and other sources of publicly available data, which can be used to study the aftermath of a catastrophic event. The specific case modeled here is the Haiti earthquake of January 2010. Crowdsourced data is used to build the initial populations of people affected by the event, to construct their environment, and to set their needs based on the damage to buildings.

The idea behind the model is to explore how people react to the distribution of aid, as well as how rumors propagating through the population and crowding around aid distribution points might lead to food riots and similar social phenomena. Such a model could potentially provide a link between socio-cultural information of the people affected and relevant humanitarian relief organizations.

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The animation above shows one simulation run where there is the spread of information and agent movement (red dots) around one center (blue dot). While the chart below shows how over time the density of agents around the food station increases over time.

The idea behind such a model is one can take crowdsourced information and fuse it into an agent-based model and see how people will react to the distribution of food centers. For example, the movie below shows how agents find out about four (hypothetical) different food centers and decide whether or not to go to them in a 6 by 8km area of Port-au-Prince.

Spread of information and agent movement (red dots) in a 6 by 8km area of Port-au-Prince.

More details about this model to come......

#Earthquake: Twitter as a Distributed Sensor System

2012-04-13T10:17:00.001-04:00

Our work on using social media continues to develop and we have recently had a paper accepted in Transactions in GIS, entitled "#Earthquake: Twitter as a Distributed Sensor System". Below we present our abstract and some of the results.

Social media feeds are rapidly emerging as a novel avenue for the contribution and dissemination of information that is often geographic. Their content often includes references to events occurring at, or affecting specific locations. Within this paper we analyze the spatial and temporal characteristics of the twitter feed activity responding to a 5.8 magnitude earthquake which occurred on the East Coast of the United States (US) on August 23, 2011. We argue that these feeds represent a hybrid form of a sensor system that allows for the identification and localization of the impact area of the event. By contrasting this to comparable content collected through the dedicated crowdsourcing ‘Did You Feel It?’ (DYFI) website of the US Geological Survey we assess the potential of the use of harvested social media content for event monitoring. The experiments support the notion that people act as sensors to give us comparable results in a timely manner, and can complement other sources of data to enhance our situational awareness and improve our understanding and response to such events.

The movie below show geolocated tweets with references to the earthquake through keyword (earthquake or earth and quake) and hashtag search (#earthquake or #quake) for the first hour after the earthquake.

The following images give a glimpse at some of our analysis.

Response pattern as function of distance from epicenter for the first 400 seconds after the earthquake. At the top we see a plot of (reaction time, distance) of all tweets during that period. At the bottom we show the histogram of the number of tweets as a function of distance.

Locations of the 40 tweets in the shaded area of the figure above overlaid over the USGS CDI scale map. Tweet locations are marked as green circles. Color-coding in the graph is ranging from red (high perceived intensity) to yellow (lower perceived intensity). The dashed line shows a distance of approximately 950 km (8.5 degrees of angular distance) from the epicenter.

The movie below gives you an idea of some of the tweet content:

Full reference to this paper is:

Crooks, A. T., Croitoru, A., Stefanidis, A. and Radzikowski, J. (acepted) "#Earthquake: Twitter as a Distributed Sensor System" Transactions in GIS.

Distributed MASON

2012-03-14T19:12:00.000-04:00

Last week, the Center for Social Complexity at GMU, hosted Prof. Vittorio Scarano and Carmine Spagnuolo from the ISISLab of the Università degli Studi di Salerno who have been working on a distributed version of MASON (DMason). The idea is that one can create an agent-based model in MASON and then use the framework to easily distribute it over many machines. The movie below shows an example of what can be done. More information can be found here.

However, if you don't use MASON, you might also be interested in Repast for High Performance Computing

ElectionGauge

2012-02-19T11:30:00.000-05:00

A project we have been working on at GMU called ElectionGauge has now gone live (although still under development). The idea about the project is tie geo-spatial analysis, linguistic analysis, and social network analysis to analyze Twitter responses to the upcoming US elections in real time with the aim of predicting election results.

One question we are exploring is do the tweets of users match the speech of candidates? For example, as Maksim Tsvetovat, one of the co-founders says “repeal Obamacare” might identify you as Tea Partier, while “legalize marijuana” puts you in Ron Paul’s camp. While still in beta, below is snapshot from the site:

Find out more see: @maksim2042, @JackieKazil & @ElectionGauge or at Tech Cocktail

Social Media and the Emergence of Open-Source Geospatial Intelligence

2012-01-27T15:48:00.000-05:00

Sample of geolocated tweets referring
Occupy Wall Street.

We have just finished a paper entitled 'Social Media and the Emergence of Open-Source Geospatial Intelligence' for Socio-Cultural Dynamics and Global Security. For those interested below is the abstract:

The emergence of social media has provided the public with an effective and irrepressible real-time mechanism to broadcast information. The great popularity of platforms such as twitter and YouTube, and the substantial amount of content that is communicated through them are making social media an essential component of open-source intelligence. The information communicated through such feeds conveys the interests and opinions of individuals, and reveals links and the complex structure of social networks. However, this information is only partially exploited if one does not consider its geographical aspect. Indeed, social media feeds more often than not have some sort of geographic content, as they may communicate the location from where a particular report is contributed, the geolocation of an image, or they may refer to a specific sociocultural hotspot. By harvesting this geographic content from social media feeds we can transfer the extracted knowledge from the amorphous cyberspace to the geographic space, and gain a unique understanding of the human lansdscape, its structure and organization, and its evolution over time. This new-found opportunity signals the emergence of open-source geospatial intelligence, whereby social media contributions can be analyzed and mined to gain unparalleled situational awareness. In this paper we showcase a number of sample applications that highlight the capabilities of harvesting geospatial intelligence from social media feeds, focusing particularly on twitter as a representative data source.

Selection of geolocated pairs of tweeters and retweeters in Tokyo at the time immediately
following the Sendai earthquake

Full reference:

Stefanidis, A., Crooks, A. T., Radzikowski, J., Croitoru, A. and Rice, M. (in press), Social Media and the Emergence of Open-Source Geospatial Intelligence, in Tucker, C. and Tomes, R. (eds.), Socio-Cultural Dynamics and Global Security: Interdisciplinary Perspectives on Human Geography in an Era of Persistent Conflict, US Geospatial Intelligence Foundation (USGIF). Click here to see a draft of the paper.

Agent-based Models and Geographical Systems at the AAG

2012-01-18T10:24:00.003-05:00

As Agent-based modeling (ABM) within geographical systems is starting to mature as a methodology in geography and across the social sciences. We (Alison Heppenstall, Mike Batty, Mark Birkin, Christopher Bone and Andrew Crooks) have organized several sessions under the topic of "Agent-based Models and Geographical Systems"at the forthcoming AAG Annual meeting in NY. These start nice and early on Saturday the 25th of February and go until Sunday the 26th.

The aim of the sessions is to bring together researchers utilizing agent-based models (and associated methodologies) to discuss topics relating to: theory, technical issues and applications domains of ABM within geographical systems.

If you plan on attending the AAG, feel free to pass by and say hello. For Saturday, all talks will be in the Carnegie Suite West, Third Floor, Sheraton Hotel starting at 8am. For Sunday, all talks will be in the Carnegie Suite East, Third Floor, Sheraton Hotel

Agent-based Models and Geographical Systems: Applications (1)

8:00 AM - 9:40 AM, Chair: Mark Birkin

*Neeraj G Baruah, Robert P. Haining & Mike Bithell,

Using Dynamic Agent Models In Understanding Socio-spatial Patterning Of Health Behaviours

*Ateen Patel

A Crowd Simulation converging Macro and Micro-scopic models

*Elenna R. Dugundji & László Gulyás

Sociodynamic Discrete Choice on Networks in Space: The Role of Utility Parameters and Connectivity in Emergent Outcomes

*Alison Heppenstall & Kirk Harland

Using Agent-Based Models for Education Planning: is the UK education system agent based?

*René Janelle Jordan, Mark Birkin & Andrew Evans

A Social Simulation of Housing Choice and Housing Policy in the EASEL Regeneration District, Leeds UK

Agent-Based Models and Geographical Systems: Applications (2)

10:00 AM - 11:40 AM, Chair: Alison Heppenstall

*Handi Chandra Putra & Clinton J Andrews

Agent based model for efficient operation of HVAC systems in commercial buildings

*Regina Ryan

A Climatic Rendering of Agent-based GIS

*Sebastien Rey Coyrehourcq & *Clara Schmitt

Guided and automated exploration for the calibration of an agent-based model

Agent-Based Models and Geographical Systems: Decision Making

12:40 PM - 2:20 PM, Chair: Andrew Crooks

*David Bennett, Jerry Schnoo, Marian Muste, Silvia Secchi, Deng Ding, Sudipta Mishra & Umashanker Rapol

Modelling Land Use/Land Cover Change in Response to Changing Economic and Environmental Drivers

*Christopher Bone

Integrating multi-objective decision making theory and agent-based modeling for enhancing spatial decision support systems

*Dawn Parker, Tianyi Yang, Qingxu Huang, Shipeng Sun, & Raymond Cabrera

A web-based model output query and visualization tool for the Agent-Based Land Market Model

Tyler Bonnell, *Raja Sengupta, Colin Chapman & Tony Goldberg

Linking Landscapes to Disease: An Agent-Based Model simulating the impact of forest composition on spread of disease in red colobus populations

*Roger White

Predicting the Dynamics of Population and Land Use at High Resolution by Means of a Cellular Automaton Based Model: Problems of Validation and Verification

Agent-Based Models and Geographical Systems: GIS and Geocomputation

2:40 PM - 4:20 PM, Chair: Alison Heppenstall

*Majeed Pooyandeh & Danielle Marceau

A web-based agent based model to simulate the stakeholders' evaluation of different scenarios of land-use change in the Elbow River watershed in southern Alberta

*Gabriel Wainer & Mariano Zapatero

A CELLULAR MODELING ENVIRONMENT FOR BEHAVIOR ANALYSIS IN GIS

*Andrew Crooks, Mark Coletti, Sarah Wise & Keith Sullivan

GeoMason: Integrating GIS and Agent-based modeling - A Gallery of Applications

Christopher McCreadie, David Blackwood, Mark Rounsevell, & *Ruth E Falconer

Rural Sustainability Visualisation Tool (RS-VT): An interactive 3D Agent Based Model using XNA and Protocol Buffers

Agent-Based Models and Geographical Systems: Land Use Change

4:40 PM - 6:20 PM, Chair: Mike Batty

*Shipeng Sun, Dawn Parker, Dan Brown, Qingxu Huang, Tatiana Filatova, Derek Robinson, Meghan Hutchins & Rick Riolo

Explicitly Representing Heterogeneous Land Developers in Agent-based Modeling of Land Use Change: A Preliminary Experiment

*Peter Deadman & Raymond Cabrera

Comparison of Decision-Making Methods in an Agent-based Model of Land Use Change.

*Nicholas Magliocca, Erle Ellis & Daniel Brown

Using an agent-based virtual laboratory to explore smallholder land-use decisions and implications for 'induced intensification' theory

*Fang Wang & Danielle Marceau

A Combined-Patch-Based Cellular Automata Model for Simulating Land-Use Changes Using High-Spatial Resolution Data

*Patrick Harrison & *Seth Spielman

The Co-Evolution of the Built and Social Environment

161 Agent-Based Models and Geographical Systems: Methodological Advances

Sunday, 8:00 AM - 9:40 AM in Carnegie Suite East, Third Floor, Sheraton Hotel, Chair: Andrew Crooks

*Sanna Iltanen

Morphologically structured agent based model of urban retail system

*Kenneth Steif

Simulating Classroom Evacuation: A Pedagogical Exercise in Agent Based Modeling

*David O'Sullivan

Agent-based models: what are they good for? Or: did Schelling really need an ABM?

*Paul Torrens

Modeling human movement with machines, muscles, and morsels of data

*Hyeyoung Kim & Chulmin Jun

Integrating an Agent-based Model with Spatial Databases for Indoor Crowds Simulation

Agent-Based Models and Geographical Systems: Policy Modelling

10:00 AM - 11:40 AM, Chair: Mark Birki

*Claudio Cioffi-Revilla, Andrew Crooks, Kenneth De Jong, Timothy Gulden, William Kennedy, Sean Luke & Mark Coletti

MASON RiftLand: An Agent-Based Model for Analyzing Conflict, Disasters, and Humanitarian Crises in East Africa

*Johannes Flacke

An agent-based model of informal settlement growth in Dar es Salaam, Tanzania

*Atesmachew Hailegiorgis

An Agent-Based Modeling of Climate Change, Land Acquisition, and Household Dynamics in Southern Ethiopia

*Amit Patel, Andrew Crooks & Naoru Koizumi

Integrating GIS and ABM to Explore Spatio-temporal Dynamics of Slum Formation in Mumbai, India

*Elfie Swerts

Simulating the evolution of urban systems in developing countries: the Indian case

Agent-Based Models and Geographical Systems: Urban Networks

12:40 PM - 2:20 PM, Chair: Mike Batty

*Einar Holm & Lena Sanders

Agent-based spatial microsimulation for modeling emergence and long term dynamics of labor markets and settlement systems.

*Clémentine Cottineau

A simulation tool for assessing the specificity of the evolutionary path of Russian cities

Timothy Gulden & *Ross Hammond

Beyond Zipf: An Agent-Based Understanding of City Size Distributions

*Thomas LOUAIL

Consequences of streets networks and accessibility patterns on cities' spatial organization : a computational study

*Marie-Noelle Comin

Analyzing and modelling interactions of actors involved in scientific networks

Agent-based Models and Geographical Systems: Urban Simulation

2:40 PM - 4:20 PM, Chair: Andrew Crooks

*Ed Manley, Tao Cheng, Alan Penn & Andy Emmonds

Integrating Agent-based Models with Macroscopic Traffic Simulation

*Huiye Ma, Theo A. Arentze & Harry Timmermans

Agent-Based Population Generation, Activity Generation, and Scheduling for Dynamic Activity-Travel Scheduling Implementation

*Mark Birkin & Nick Malleson

The calibration of metropolitan dynamics with microsimulation, agent-based models and social network data

*Mike Batty & Erez Hatna

Simulating Residential Dynamics in London's Housing Market

*Denise Pumain

The Simpop family: from an evolutionary urban theory to a computable, agent-based geographical ontology

*Denotes speaker(s)

Occupy Wall Street movement via Twitter

2011-12-16T09:56:00.001-05:00

Following on from our work on harvesting ambient geospatial information (AGI) from social media feeds we have started to explore the Occupy Wall Street movement. The movie below shows just one part of this work, specifically the movement of the protesters in New York during the Action Day (November 17) from Wall Street to Brooklyn Bridge. The red dots denote locations of the tweets. Selected tweets are displayed at the bottom of the screen. Active tweets are marked with a white star.

More ananylsis to follow...

New GeoMason Models

2011-12-12T13:20:00.001-05:00

We been working on adding more spatial agent based models examples to GeoMason, the GIS extension for MASON. These include a vegetation growth model utilizing raster data and a simple disease outbreak model utilizing vector data. See below for more details.

Vegetation Growth Model:

Eastern Africa has undergone sustained drought for over a decade placing a great strain on the local population. This demo introduces an agent-based model of grazing called Turkana South. The model makes use of NDVI data and monthly rainfall data to drive vegetation growth. After describing the model, the paper investigates the effect rainfall has on carrying capacity and how carrying capacity varies based on initial starting conditions. I conclude that carrying capacity is independent of initial population size.

Disease Outbreak:

This demo introduces a new agent-based model (ABM) for studying the spread of influenza through the schools and households of Fairfax County, VA. It is intended to explore the following questions. How does an epidemic outbreak spread through a school system? What containment approaches might be most effective at stopping an outbreak?

To find out more about GeoMason (including the data and source code for these models) click here.

Thanks to Joseph Harrison for sharing these models which where developed for class projects at the Department of Computational Social Science at George Mason University.

Harvesting ambient geospatial information from social media feeds

2011-12-06T09:47:00.000-05:00

A paper I recently co-authored with Anthony Stefanidis and Jacek Radzikowski from George Mason University entitled "Harvesting ambient geospatial information from social media feeds" is now available in GeoJournal.

The abstract for the paper reads as follows: "Social media generated from many individuals is playing a greater role in our daily lives and provides a unique opportunity to gain valuable insight on information flow and social networking within a society. Through data collection and analysis of its content, it supports a greater mapping and understanding of the evolving human landscape. The information disseminated through such media represents a deviation from volunteered geography, in the sense that it is not geographic information per se. Nevertheless, the message often has geographic footprints, for example, in the form of locations from where the tweets originate, or references in their content to geographic entities. We argue that such data conveys ambient geospatial information, capturing for example, people’s references to locations that represent momentary social hotspots. In this paper we address a framework to harvest such ambient geospatial information, and resulting hybrid capabilities to analyze it to support situational awareness as it relates to human activities. We argue that this emergence of ambient geospatial analysis represents a second step in the evolution of geospatial data availability, following on the heels of volunteered geographical information."

Geolocating pairs of tweeters and retweeters

Project Geppetto

2011-11-30T14:04:00.001-05:00

Project Geppetto from Autodesk attempts to make it it easy, fast, and fun to add crowds to 3ds Max scenes. It is part of Autodesk's "People Power" concept, where the basic idea is to try to assemble all the components one needs to create, manage, and control large crowds of characters. Specificcally it attempts to create believable motion, allow for cultural influences (Evolver) and to create a framework for thousands of characters to interact in. Below are some examples of the project.

New Book: Agent-Based Models of Geographical Systems

2011-11-29T13:20:00.003-05:00

Agent-Based Models of Geographical Systems, is editied by Alison Heppenstall, Andrew Crooks, Linda See and Mike Batty; and brings together a comprehensive set of papers on the background, theory, technical issues and applications of agent-based modelling (ABM) within geographical systems. This collection of papers (see below) is an invaluable reference point for the experienced agent-based modeller 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, visualising and validating model outputs. With contributions from many of the world’s leading research institutions (see map below), the latest applied research (micro and macro applications) from around the globe exemplify what can be achieved in geographical context.

This book is relevant to researchers, postgraduate and advanced undergraduate students, and professionals in the areas of quantitative geography, spatial analysis, spatial modelling, social simulation modelling and geographical information sciences.

Book Contents:

Part 1: Computational Modelling: Techniques for Simulating Geographical Systems

Perspectives on Agent-Based Models and Geographical Systems.
A Generic Framework for Computational Spatial Modelling.
A Review of Microsimulation and Hybrid Agent Based Approach.
Cellular Automata in Urban Spatial Modelling.
Introduction to Agent-Based Modelling.

Part 2: Principles and Concepts of Agent-Based Modelling.

Agent-Based Models - Because they're Worth it?
Agent-Based Modelling and Complexity.
Designing and Building an Agent-Based Model.
Modelling Human Behaviour in Agent-Based Models.
Calibration and Validation of Agent-Based Models of Land Cover Change.
Networks in Agent-Based Social Simulation.

Part 3: Methods, Techniques and Tools for the Design and Construction of Agent-Based Models:

The Integration of Agent-Based Modelling and Geographical Information for Geospatial Simulation.
Space in Agent-Based Models.-
Large Scale Agent-Based Modelling: A Review and Guidelines for Model Scaling.
Uncertainty and Error.-
Agent-Based Extensions to a Spatial Microsimulation Model of Demographic Change.
Designing, Formulating, and Communicating Agent-Based Models.-
Agent Tools Techniques and Methods for Macro and Microscopic Simulation.

Part 4: Fine-Scale, Micro Applications of Agent-Based Models:

Using Agent-Based Models to Simulate Crime.
Urban Geosimulation.
Applied Pedestrian Modelling.
Business Applications and Research Questions using Spatial Agent-Based Models.
Using Agent-Based Models for Education Planning. Is the UK Education System Agent Based?
Simulating Spatial Health Inequalities.
ABM of Residential Mobility, Housing Choice and Regeneration.-
Do Land Markets Matter? A Modelling Ontology and Experimental Design to Test the Effects of Land Markets for an Agent-Based Model of Ex-urban Residential Land-Use Change.
Exploring Coupled Housing and Land Market Interactions Through an Economic Agent-Based Model (CHALMS).

Part 5: Linking Agent-Based Models to Aggregate Applications Macro:

Exploring Urban Dynamics in Latin American Cities using an Agent-Based Simulation Approach.
An Agent-Based/Network Approach to Spatial Epidemics.
An Agent-Based Modelling Application of Shifting Cultivation.
Towards New Metrics for Urban Road Networks. Some Preliminary Evidence from Agent-Based Simulations.
A Logistic Based Cellular Automata Model for Continuous Urban Growth Simulation: A Case Study of the Gold Coast City, Australia.
Exploring Demographic and Lot Effects in an ABM/LUCC of Agriculture in the Brazilian Amazon.
Beyond Zipf: An Agent Based Understanding of City Size Distributions.
The Relationship of Dynamic Entropy Maximising and Agent Based Approaches in Urban Modelling.
Multi-Agent System Modelling for Urban Systems: The Series of SIMPOP Models.
Reflections and Conclusions: Geographical Models to Address Grand Challenges

World Map of authors who contributed to Agent-Based Models of Geographical Systems

View Contributors to Agent-Based Models of Geographical Systems in a larger map

1st International Workshop on Advances in Computational Social Science

2011-11-10T12:53:00.003-05:00

Call for papers for the 1st International Workshop on Advances in Computational Social Science in conjunction with 12th International Conference on Computational Science, June 4–6, 2012, Omaha, Nebraska, USA.

The workshop webpage is at http://www1.spms.ntu.edu.sg/~cheongsa/acss.html

Advances in computational systems and methods (parallel, distributed, cloud; agents, networks) are revolutionizing how social science research is done. It is now possible to simulate entire cities, for example, in tremendous detail, not only in terms of technical infrastructures like traffic, but also in terms of the social choices of individuals and how these interact with each other to produce complex phenomena. At the same time, advances in informatics infrastructures mean that more data and more detailed data are collected. These data are not just on our physical environment, but are also along social dimensions. The confluence of these two developments open up many possibilities, and social scientists are now probing questions that they could never ask before. Frequently, asking these questions generate even more inquiry into the interfaces between social science, computer science, information science, and engineering.

In this workshop, we aim to provide a forum for computational social scientists to share advances made in their respective fields, and the innovations they have developed across disciplinary boundaries: on models, methods, data integration and analysis, as well as interpretation of diverse social phenomena. We also hope to foster an environment for earnest dialogue between social scientists keen to employ sophisticated computational models and methods in their research, and computer/information scientists and engineers interested in understanding social science problems.

We invite original research papers on the following topics:

Modeling methodologies
Simulation strategies and algorithms
Organization of heterogeneous social data
Data-mining and machine learning on social, behavioral, and economic data
Integration of social data into simulations
Computational studies of specific social science problems

Computational social science papers that are relevant to this workshop, but cannot be easily classified based on the topics above will also be considered.

Papers should be written in English, up to a page limit of 10 pages. The papers should follow the Procedia format, and be submitted electronically through the ICCS submission engine.

Please remember to select the workshop ADVANCES IN COMPUTATIONAL SOCIAL SCIENCES in the last field of the submissions page.

We ask authors to also send a note to cheongsa@ntu.edu.sg after their submission.

All papers will be peer reviewed. Accepted papers will be published by Elsevier in the open-access Procedia Computer Science series. The proceedings will be available at the conference.

At least one author of an accepted paper must register for the ICCS 2012 conference to present the paper at the workshop.

A selected number of papers will be invited to be extended for inclusion in a special issue of the Journal of Computational Science.

Important Dates

Full paper submission: January 9, 2012
Notification of acceptance: February 9, 2012
Camera-ready papers: March 1, 2012
Early registration ends: April 15, 2012
Conference: June 4–6, 2012

Organizing Committee
Heiko AYDT Nanyang Technological University, Singapore
Tibor BOSSE Vrije Universiteit Amsterdam, the Netherlands
Siew Ann CHEONG Nanyang Technological University, Singapore
Andrew CROOKS George Mason University, USA
Nicolas MALLESON University of Leeds, UK
Paul TORRENS University of Maryland, College Park, USA

GeoMason Examples

2011-10-21T17:00:00.005-04:00

GeoMason has recently been updated to support changes to MASON itself and I have contributed a few models to highlight the basic functionality of GeoMason and act as examples for how geographically explicit models can be built. Below are some of the new models that now come with GeoMason.

Sillypeds

This model demonstrates how one can use GeoMason to explore evacuations from a building. The simulation starts by reading raster data describing a building layout (converted from CAD files). The simulation randomly places a number of agents on walkable areas within side of the building. Once the agents have been placed on the ground, they follow the lowest cost path to the exit (in this example there is only one). The movie below demonstrates how the agents (red dots) move through the space, and through this movement congestion emerges around the exit. The yellow paths are traces of pedestrian moment.

Water World

Inspired by NetLogo's Grand Canyon Model. The aim of the model is to show how data in the form of a elevation, can be used as a foundation of a simple spatial agent-based model. Similar to the Netlogo model, the elevation data comes from the National Elevation Dataset. It was converted from an ESRI Grid into an ASCII grid file using ArcGIS.

Similar to Sillypeds, the elevation data acts as our terrain, in this case its Crater Lake in Oregon. Agents within the model (in this case water) fall at random over the terrain and then flows downhill over the terrain. If the water cannot flow downhill, it pools up and once the gradient is sufficient, the water flows. For example, water falling in Crater Lake, initially has to pool up until the water level is sufficient to breach the caldera. Once this occurs water flows out of the lake as highlighted in the movie below.

In the second movie (below) highlights the testing of the inner logic of the model, in the sense are the raindrops doing as they are expected to do. If you want to test this, uncomment out (e.g. remove '//') from either one of the two lines below:

//landscape = setupLandscape(); // uniform landscape, completely flat

//landscape = setupLandscapeGradientIn(); // landscape that slopes in

These lines can be found in the start method of WaterWorld.java file but ensure you comment out the (e.g. add '//' ) to the following line:

landscape = setupLandscapeReadIn("elevation.txt"); // read landscape from file

GridLock

This basic traffic model explores how agents travel to Tyson's Corner, Virginia for work. The idea is that if you increased the number of agents (people) more congestion will arise. To some extent this is similar to the GeoMason sim.app.geo.campusworld example.The model demonstrates how you can make agents move along networks (in this case road lines in the form of ESRI shapefiles) from their origin to their destination via a shortest path algorithm.

The number of agents is based census tract information i.e. the number of people who work in Tyson's Corner and their corresponding home locations which is restricted to Washington DC, Virginia and Maryland. The movie below shows the fully functional model.

Schelling Polygon

In this model we demonstrate how one can use polygons (such as census tracks) to create an abstract Schelling model stylized on Washington DC. The model reads in a ESRI Polygon shapefile and uses attributes of the shapefile to create Red and Blue agents and a number of Unoccupied areas. As with the traditional Schelling model, Red and Blue agents want to be located in neighborhoods were a certain percentage of their neighbors are of the same type. However, instead of using a Moore or Von Neumann which is common practice in cell based models. Here neighborhoods are calculated using the neighbors that share a common edge to the agent in question. If an agent is dissatisfied with its current neighborhood, it will move to a random Unoccupied polygon, regardless of whether or not this new location meets its preference. The movie below shows this movement.

Point Schelling Model

This model in a sense extends the Schelling Polygon model, however, instead of the polygon being the agent we take attribute data from the polygon model and create individual agents (see Crooks, 2010). This is based on the notion that much of the data we have comes at an aggregate level and often in some sort of vector representation of space such as census data. However, if we want to model the individuals or groups of individuals, we need to disaggregate the data.

To do this we create a number of Red and Blue agents based on population counts held within the polygon shapefile. As with the previous model, all agents want to be located in neighborhoods were a certain percentage of their neighbors are of the same type. However, instead of using a Moore or Von Neumann which is common practice in cell based models. Here neighborhoods are calculated using buffer distance from the agent in question. If an agent is dissatisfied with its current neighborhood, it will move to a random location, regardless of whether or not this new location meets its preference. Moreover, the model demonstrates how to link points (agents) to polygons along with some other basic geographical operations (such as union, point in polygon, buffer). The movie below shows this movement both at the individual level and at the aggregate (census track level).

SLEUTH: Urban Growth Model

This model shows a basic urban growth model based loosely on the SLEUTH model. In the sense, that we have only implemented the four growth rules (spontaneous, new spreading centers, edge and road-influenced growth) and not the self modification element of the SLUETH model. The model demonstrates how different layers (e.g. slope, land use, exclusion, urban extent - urbanized or non-urbanized, transportation, hillshade) can be read into a model to provide cells with multiple values. The movie below shows a specific growth scenario under specific coefficients (parameters) for Santa Fe, New Mexico.

More information about GeoMason can be found here along with the source code and data for all the models presented in this post.

Alice: 3D programming environment

2011-10-13T17:18:00.002-04:00

Alice is a easy to use 3D programming environment where one can create an animation for telling a story or be used for playing an interactive game. It is designed to teach the fundamentals of object-oriented programming.

In Alice, people, animals, and vehicles etc are 3D objects that populate a virtual world which one can then animate. What is nice about Alice is its interactive interface, where one can drag and drop graphic tiles to create a program (similar in a way to StarLogo TNG). Below is our (Ernesto Carrella and myself) first brief attempt of modeling agents exiting a room (we quite like the funny walk which reminds us of a John Cleese's silly walks sketch).

Another nice feature of Alice is one can import models from Sketchup to Alice, opening up many possibilities, as shown in the movie below.

Complex Adaptive Systems

2011-10-12T10:32:00.001-04:00

The other day I was teaching a class in the Introduction of Computational Social Science at GMU on complex adaptive systems and I came across the talk below by John Holland and thought it was worth sharing.

Fluid Dynamics and ABM used for the evacuation of a city

2011-10-12T09:30:00.000-04:00

Emergencies are times of great uncertainty and while GIS has been used for a long time for planning evacuations, it has only been during the last few years that agent-based modeling (ABM) has been used to study peoples behavior in such situations. In a recent article by Epstein et al. (2011), they combine Computational Fluid Dynamics (CFD) and ABM to study urban evacuation planning.

CFD is used to model the airborne transport of contaminants, while the ABM models the social dynamics of the population. Coupling of the two allows for simulating how populations might respond to a physically realistic contaminant plume.

The movie below shows a hypothetical aerosol release in Los Angeles.

More information can be found at:

Epstein JM, Pankajakshan R, Hammond RA, 2011 Combining Computational Fluid Dynamics and Agent-Based Modeling: A New Approach to Evacuation Planning. PLoS ONE 6(5): e20139. doi:10.1371/journal.pone.0020139

FuturICT

2011-10-10T09:54:00.000-04:00

What a great idea:

"The FuturICT flagship proposal intends to unify hundreds of the best scientists in Europe in a 10 year 1 billion EUR program to explore social life on earth and everything it relates to."

The movie below gives a nice overview of its aim:

More movies about the project can be found here or follow them on twitter

Virtual Geographic Environments

2011-10-02T18:56:00.001-04:00

A quick note for a new book entitled "Virtual Geographic Environments" from ESRI Press who write:

"Virtual Geographic Environments, edited by Hui Lin and Michael Batty, collects key papers that define the current momentum in GIS and "virtual geographies." Contributions by leading members of the geospatial community to Virtual Geographic Environments illustrate the cutting edge of GIScience, as well as new applications of GIS with the processing and delivery of geographic information through the Web and handheld devices, forming two major directions to these developments. The four-part organization leads from a primer on VGEs to virtual cities and landscapes, interface design and public participation, and finally mobile and networked VGEs. Current topics, such as crowd sourcing and related services, point to the development of new business models that merge proprietary and nonproprietary systems."

Andrew Hudson-Smith and myself have contributed a chapter entitled "The Renaissance of Geographic Information: Neogeography, Gaming and Second Life". The abstract for our paper is:

"Web 2.0, specifically The Cloud, GeoWeb and Wikitecture are revolutionising the way in which we present, share and analyse geographic data. In this paper we outline and provide working examples a suite of tools which are detailed below, aimed at developing new applications of GIS and related technologies. GeoVUE is one of seven nodes in the National Centre for e-Social Science whose mission it is to develop web-based technologies for the social and geographical sciences. The Node, based at the Centre for Advanced Spatial Analysis, University College London has developed a suite of free software allowing quick and easy visualisation of geographic data in systems such as Google Maps, Google Earth, Crysis and Second Life. These tools address two issues, firstly that spatial data is still inherently difficult to share and visualise for the non-GIS trained academic or professional and secondly that a geographic data social network has the potential to dramatically open up data sources for both the public and professional geographer. With our applications of GMap Creator, and MapTube to name but two, we detail ways to intelligently visualise and share spatial data. This paper concludes with detailing usage and outreach as well as an insight into how such tools are already providing a significant impact to the outreach of geographic information."

Advanced GeoSimulation Models

2011-09-26T09:19:00.000-04:00

Advanced GeoSimulation Models edited by Danielle Marceau and Itzhak Benenson brings together a number of authors that highlight the the frontier in geosimulation in particular, and in cellular automata and agent-based modelling in general.

Click here to see the forward by Mike Batty.

The author of this blog also has a chapter in the book entitled "Advances and Techniques for Building 3D Agent-Based Models for Urban Systems" with Andrew Hudson-Smith and Ateen Patel

An agent-based model of the housing market

2011-09-19T17:48:00.001-04:00

Why agent-based modeling? In the interview below Doyne Farmer discuses his work with Rob Axtell and John Geanakoplos, who aim to create an agent-based model of the U.S. economy that will people make better understand past, and future, financial crises.

But going back to the question above, why agents? to quote from the SFI website: "Whereas a traditional economic model makes future predictions based on past market behavior and thus fails in unprecedented situations, their agent-based model takes into account the actions of individual decision makers, assigning behavioral rules to each agent in the model"

Agent-Based Models and Geographical Systems Session at AAG

2011-06-22T10:19:00.001-04:00

AAG 2012 - CALL FOR PAPERS

SPECIAL SESSION(S): Agent-Based Models and Geographical Systems

LOCATION AND DATES
Association of American Geographers Annual Meeting
February, 24-28th, 2012, New York, USA

DESCRIPTION
Agent-based modeling (ABM) within geographical systems is starting to mature as a methodology in geography and across the social sciences. The aim of this session(s) is to bring together researchers utilizing agent-based models (and associated methodologies) to discuss topics relating to: theory, technical issues and applications domains of ABM within geographical systems.

We would particularly welcome papers relating to:

Validation, verification and calibration of Agent-based models
Hybrid modeling approaches (e.g. utilizing Cellular Automata, Spatial Interaction, Microsimulation, etc.)
Handling scale and space issues
Visualization of agent-based models (along with their outputs)
Ways of representing behavior within models of geographical systems
Participatory modeling and simulation
Applications: Ranging from the micro to macro scale

Please e-mail the abstract and key words with your expression of intent to Alison Heppenstall <A.J.Heppenstall@leeds.ac.uk> by September 15th, 2011. Please make sure that your abstract conforms to the AAG guidelines in relation to title, word limit and key words and as specified at <http://www.aag.org/cs/annualmeeting/call_for_papers/abstract_guidelines>. An abstract should be no more than 250 words that describes the presentation's purpose, methods, and conclusions as well as to include keywords. Full submissions will be given priority over submissions with just a paper title.

We are currently investigating journals (e.g. Environment and Planning B) in order to widely disseminate the ideas emerging from this session(s). Authors will have the opportunity to suitably revise their presentations for publication.

ORGANIZERS:

Alison Heppenstall, School of Geography, University of Leeds, Leeds, UK .

Andrew Crooks, Krasnow Institute for Advanced Study, George Mason University, USA,

Linda See, International Institute of Applied Systems Analysis (IIASA), Laxenburg, Austria

Mark Birkin, School of Geography, University of Leeds, Leeds, UK .

Michael Batty, Centre for Advanced Spatial Analysis (CASA), University College London, London, UK

TIMELINE:

September 15th, 2011: Abstract submission and expression of intent to session organizers. E-mail Alison Heppenstall <A.J.Heppenstall@leeds.ac.uk> by this date if you are interested in being in this session. Please submit an abstract and key words with your expression of intent. Full submissions will be given priority over submissions with just a paper title.

September 22th, 2011: Session finalization. Session organizers determine session order and content and notify authors.

September 26th, 2011: Final abstract submission to AAG, via www.aag.org. All participants must register individually via this site. Upon registration you will be given a participant number (PIN). Send the PIN and a copy of your final abstract to Alison Heppenstall . Neither the organizers nor the AAG will edit the abstracts.

September 28th, 2011: AAG registration deadline. Sessions submitted to AAG for approval.

February 24-28th, 2012: AAG meeting, New York, USA