Friday, December 16, 2011

Occupy Wall Street movement via Twitter

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...

Monday, December 12, 2011

New GeoMason Models

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.




Tuesday, December 06, 2011

Harvesting ambient geospatial information from social media feeds

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

Wednesday, November 30, 2011

Project Geppetto

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.









Tuesday, November 29, 2011

Book: Agent-Based Models of Geographical Systems

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.

To see a sample of the book click here.

Book Contents:

Part 1: Computational Modelling: Techniques for Simulating Geographical Systems
  1. Perspectives on Agent-Based Models and Geographical Systems.
  2. A Generic Framework for Computational Spatial Modelling.
  3. A Review of Microsimulation and Hybrid Agent Based Approach.
  4. Cellular Automata in Urban Spatial Modelling.
  5. Introduction to Agent-Based Modelling.
Part 2: Principles and Concepts of Agent-Based Modelling.
  1. Agent-Based Models - Because they're Worth it?
  2. Agent-Based Modelling and Complexity.
  3. Designing and Building an Agent-Based Model.
  4. Modelling Human Behaviour in Agent-Based Models.
  5. Calibration and Validation of Agent-Based Models of Land Cover Change.
  6. Networks in Agent-Based Social Simulation.
Part 3: Methods, Techniques and Tools for the Design and Construction of Agent-Based Models:
  1. The Integration of Agent-Based Modelling and Geographical Information for Geospatial Simulation.
  2. Space in Agent-Based Models.-
  3. Large Scale Agent-Based Modelling: A Review and Guidelines for Model Scaling.
  4. Uncertainty and Error.-
  5. Agent-Based Extensions to a Spatial Microsimulation Model of Demographic Change.
  6. Designing, Formulating, and Communicating Agent-Based Models.-
  7. Agent Tools Techniques and Methods for Macro and Microscopic Simulation.
Part 4: Fine-Scale, Micro Applications of Agent-Based Models:
  1. Using Agent-Based Models to Simulate Crime.
  2. Urban Geosimulation.
  3. Applied Pedestrian Modelling.
  4. Business Applications and Research Questions using Spatial Agent-Based Models.
  5. Using Agent-Based Models for Education Planning. Is the UK Education System Agent Based?
  6. Simulating Spatial Health Inequalities.
  7. ABM of Residential Mobility, Housing Choice and Regeneration.-
  8. 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.
  9. Exploring Coupled Housing and Land Market Interactions Through an Economic Agent-Based Model (CHALMS).
Part 5: Linking Agent-Based Models to Aggregate Applications Macro:
  1. Exploring Urban Dynamics in Latin American Cities using an Agent-Based Simulation Approach.
  2. An Agent-Based/Network Approach to Spatial Epidemics.
  3. An Agent-Based Modelling Application of Shifting Cultivation.
  4. Towards New Metrics for Urban Road Networks. Some Preliminary Evidence from Agent-Based Simulations.
  5. A Logistic Based Cellular Automata Model for Continuous Urban Growth Simulation: A Case Study of the Gold Coast City, Australia.
  6. Exploring Demographic and Lot Effects in an ABM/LUCC of Agriculture in the Brazilian Amazon.
  7. Beyond Zipf: An Agent Based Understanding of City Size Distributions.
  8. The Relationship of Dynamic Entropy Maximising and Agent Based Approaches in Urban Modelling.
  9. Multi-Agent System Modelling for Urban Systems: The Series of SIMPOP Models.
  10. Reflections and Conclusions: Geographical Models to Address Grand Challenges

Reviews of the book:

By José Manuel Galán for Journal of Artificial Societies and Social Simulation:
"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")."
By Itzhak Benenson for International Journal of Geographical Information Science:
"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."
By Suzana Dragićević for Environment and Planning B
"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."


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
 
 

To see a sample of the book click here.

Thursday, November 10, 2011

1st International Workshop on Advances in Computational Social Science

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




Friday, October 21, 2011

GeoMason Examples

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.

Thursday, October 13, 2011

Alice: 3D programming environment

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.






Wednesday, October 12, 2011

Complex Adaptive Systems

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

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

Monday, October 10, 2011

FuturICT

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

Sunday, October 02, 2011

Virtual Geographic Environments

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."

Monday, September 26, 2011

Advanced GeoSimulation Models

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.

Full Reference:
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

Monday, September 19, 2011

An agent-based model of the housing market

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"



Wednesday, June 22, 2011

Agent-Based Models and Geographical Systems Session at AAG

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

Friday, May 06, 2011

5th Annual French Complex Systems Summer School

This might be of interest to some.


5th Annual French Complex Systems Summer School

"Complex Systems and Complex Networks"

Paris, July 4th to 16th, 2011


Website: http://iscpif.fr/CSSS2011

The school will provide in-depth reference courses to a multi-disciplinary audience of researchers and students. The level of lectures will range from introductory to advanced, as attendees are not expected to be familiar with all the fields covered. Lecture topics will address specific complex systems methods and tools and their relevance to various disciplines (physics, biology, computer science, geography, sociology, linguistic, etc.). An emphasis will be given to complex networks both as objects of study and as a framework for modeling social and natural phenomena.

Group projects
During the school participants will have to conduct a group project to which about 50% of their time will be dedicated. Small size groups will be constituted on the basis of personal motivations. Groups will have to present their project collectively at the end of school. According to group preferences, projects will be oriented towards some particular aspects of complex networks and particular objects: dynamics reconstruction from data, network analysis and visualization (GEPHI), modeling (NetLogo). Distributed computing facilities will be made available for projects (OpenMole), so that projects requiring intensive simulations and processing can be led.

Tutorials
Specific tutorials (GEPHI, NetLogo and OpenMole) will be given, so that attendees could quickly converge towards the required knowledge on these shared platforms. Each work group will be followed daily by a dedicated teacher, to make sure methodological and technical gaps are filled in. Therefore, no specific knowledge, either in GEPHI, NetLogo or OpenMole is required to attend this school.

This new series of international Complex Systems Summer School (CSSS2011) is organized by the Complex Systems Institute Paris Île-de-France (ISC-PIF), in coordination with the overarching National Network of Complex Systems (RNSC) and & the Complex Systems Institute Rhône-Alpes (IXXI). Our Summer School is also one of the "Thematic School" supported by the CNRS.

The summer school will take place in Paris at the ISC-PIF: 57-59 rue Lhomond,75005, Paris, France

Invited Teachers

Lectures
  • Marc barthelemy, CEA (IPhT)/EHESS (CAMS), France
  • Nathalie Corson, Laboratoire de Mathématiques Appliquées du Havre, France
  • René Doursat, ISC-PIF, France
  • Sebastian Grauwin, ENS Lyon/IXXI, France
  • Jean-Loup Guillaume, LIP6, France
  • Hidde de Jong, INRIA, France
  • Luciano Pietronero, Physics Department, Rome University "La Sapienza", Italy
  • Camille Roth, CAMS/ISC-PIF, France

Tutorials and/or group projects following
Netlogo Arnaud Banos | Nathalie Corson | Jeremy Fiegel | Sebastian Grauwin |Nicolas Marilleau | Clara Schmitt
GEPHI Julian Bilcke | David Chavalarias
Open Mole Mathieu Leclaire | Romain Reuillon

Applying to the Summer School

The application tuition rate is €500 for the whole school. Tuition rate includes:

Important:
From the first announcement day until the registration deadline (31 May), each application will be studied as soon as we receive it (first-come, first-served). If the applicant is selected, a registration confirmation will be quickly sent. We expect each selected applicant to confirm its registration in the week after reception of our email - and to pay the school fees when the dedicated web site will be open (15 May).

Why these rules? Because the school is organized for 25 people only, due to the importance of group projects during this summer school (50% of the total time). Moreover, the sooner you are confirmed, the sooner you can book your flight tickets!

Overview of important dates:
  • Application deadline: May 31
  • Notification of acceptance of applications: after reception of each application (first-come, first-served)
  • Payment website opening: May 15

WE STRONGLY RECOMMEND YOU TO APPLY AS SOON AS YOU CAN (the school is limited to 25!).

Tuesday, May 03, 2011

Final Geospatial Revolution Episode

Over the last few months the Geospatial Revolution Project from Penn State has created some great short documentaries about the use of GIS in our daily lives.

To quote from the site:
"The mission of the Geospatial Revolution Project is to expand public knowledge about the history, applications, related privacy and legal issues, and the potential future of location-based technologies"

The final episode focuses on monitoring global climate change, preventing famine, tracking disease and mapping communities never before seen on a map.


The other three episodes are:
  1. The introduction of the geospatial revolution
  2. Explore local governments and business use geospatial technology
  3. Explores geospatial technology in the world of security
If you not seen any of these, they really are worth checking out.

Friday, April 29, 2011

Using agents to explore traffic: Part Two-Micro to Macro

Following on from a previous post on traffic modeling with agent-based models, I have been thinking of other work in this area and came across the following movies on Youtube. The first is a traffic simulator from Martin Treiber. What is interesting is the "coffeemeter" that gives an impression of the accelerations and jerks in the traffic. You can investigate this model further here: http://www.traffic-simulation.de/ or watch the movie below.




The question you might be asking in yourselves is, do such models work in reality? The mathematical theory behind these so-called "shockwave" jams was developed more than 15 years ago using models that show jams appear from nowhere on roads carrying their maximum capacity of free-flowing traffic – typically triggered by a single driver slowing down. Below is a movie of the NetLogo Traffic Basic Model exploring this principle.


Hopefully the movie above helps add something to your question. But if not check out the next movie (make sure the sound is on). In which a team of Japanese researchers recreated the phenomenon on a test-track by putting 22 vehicles on a 230-meter single-lane circuit. Drivers were asked to cruise steadily at 30 kilometers per hour, and at first the traffic moved freely. But small fluctuations soon appeared in distances between cars, breaking down the free flow, until finally a cluster of several vehicles was forced to stop completely for a moment. That cluster spread backwards through the traffic like a shockwave. Every time a vehicle at the front of the cluster was able to escape at up to 40 km/h, another vehicle joined the back of the jam. The full article can be read in New Scientist (click here).


Moving away from traffic jams, as the previous post highlighted we can also use agent-based models to look at traffic intersections. The movie shows a more complicated intersection than in the last post and shows how different intersections can be visualized and modeled.



But while the above movie is rather simplistic, agent-based models can be developed from such simple situations to more complex one. For example, if you can model one type of intersection what is stopping you for modeling more? The movie below shows a more complex set of intersections using Paramics (however, this is noted to be a microsimulation model, if you are interested in finding out the difference between microsimulation and ABM see here).



From a local scene we can also turn to exploring more larger scenes such as entire metropolitan regions. The movie below is of that of TRANSIMS microsimulation-agent based model applied to downtown Chicago:



What I find so interesting about such traffic models is how one can go from basic models at the micro level and scale up such models (and of adding more complexity) to explore more macro phenomena such as traffic jams at metropolitan scales.

Tuesday, March 29, 2011

Using agents to explore traffic

After spending time in the US, I am amazed how much one has to drive and this got me thinking about using agent-based models for traffic simulations (which is a large body of literature accompanying it). It also relates to my interests in urban systems and the fact that as cities have grown, transportation technologies have evolved (from walking, to trains etc), and now the automobile has become the dominant mode of transport for moving within and between cities. Trips range from journeys to work to shopping trips. The wide spread adoption and use of automobile is also one of the contributors to sprawl (in its many shapes and forms) as the car is not restrained by frequent stops or set routes, for instance such as trains are. Thus, if one can understand the relationships between land use and transportation one can investigate issues relating to urban sustainability. This is where agent-based models come in, in the sense they allow one to focus on the behavior of people. For example, how people decide to go to work.


ABM also allows us to explore simple thought experiments and how more aggregate results emerge from individual interactions such as: what is more effective, a four way stop or a traffic lights at a road intersection? The simple agent-based model presented below utilizes MASON and was created by Omar Guerrero of the CSS department at GMU. The rules of the model are simple, in the sense that at a four way stop, the vehicle that is first to arrive, it is first to move, unless two vehicles arrive at the intersection at the same time and then the vehicle has to give way to the car on the right. While at traffic lights vehicles must stop at red lights. The movie below shows part of the graphical user interface for a particular model run of both a four way stop and traffic light.


Even though this is a simple model, one can explore a number of issues such as how these different intersection configurations impact on the flow of traffic under different volumes of traffic. For example, at low traffic volumes in general, the stop sign is more effective (i.e. allows more cars to cross) than the traffic light. However, at greater traffic volumes the traffic lights out performs the four way stop (in the sense there are more cars in queues) but also with high traffic volumes, one can see oscillations in traffic waiting at the lights while the four way stops create long queues of traffic as shown in the figure below:



Moving away from micro patterns of traffic flows one can use ABM to explore daily commuting. For example, traffic models such as TRANSIMS or MATSim allow for the study of entire metropolitan regions and how traffic jams etc. form. To give a simple example of such a movement, the model presented below illustrates how many individuals can cause traffic jams. The model (using GeoMason) is based on commuters working within the Tyson’s Corner area of Virginia which boarders Washington DC. We take road and travel to work data from the US census and use this as the basis for our model.


The road data acts as a basis for our agents (red) to move from their homes (areas shaded green) to Tyson’s corner and the census data provides us with the number of agents who travel to the area on a daily basis. The agents attempt to find the shortest path from their home to the destination with preferential attachment to highways and freeways over smaller country roads. By running the model, cars start at homes and travel towards Tyson’s Corner and as more cars join certain sections of roads, traffic jams start to form (as speed is a function of the number of cars on a specific section of road). For example in the movie above, individual cars can be distinguished when they are not clustered but when traffic density increases, larger clusters develop.

Monday, February 28, 2011

The Business Assessment Model

The Business Assessment Model (BAM) evaluates trajectories of People, Performance and Planet (3P) of a single company as well as of the whole market system as a function of business decision of actors, exogenous events in the broader socioeconomic environment or both.

BAM computes the cumulative di difference between predictions of the perturbed and original 3P trajectories in order to conduct analysis of decisions within the medium run planning horizon.





Figure: Integrated 3P trajectories comparing system-level dynamics of both Baseline and Variant scenarios

Who is behind BAM?


This project has been developed by Robert Axtell and Maciej Latek from the Department of Computational Social Science, at George Mason University and Francesco Cordaro from Mars Corporation. Funding has been provided by Mars Inc. through it's Economics of Mutality initiative.

Where can I see BAM?

The project website https://www.assembla.com/wiki/show/sweetmutuality/ on which you can:
  • Read an early working paper (pdf, 13 pages);
  • Download self contained, ready to run version of the BAM simulation used in the "Comprehensive Assessment of Businesss Decisions" working paper (zip file) and associated presentation (ppt file) on running and interpreting outputs;
  • See Validation Verification experiments we have performed with the current revision of the BAM, not included in the working paper;
BAM is implemented in MASON simulation framework.

Wednesday, February 09, 2011

Summer Course - Decision Maker Short Course - Computational Social Science & Policy

The Krasnow Institute at George Mason University is offering a Computational Social Science & Policy short course from June 19 - Jun 24, 2011. Click here to see more details.

The course description is as follows:

This six-day, non-credit, course is a unique opportunity to work with a team of experienced computational social scientists to explore and understand the application of new interdisciplinary approaches to modeling and making decisions involving the operations of social systems. Participants will imerse themselves in an intensive tour of the field of Computational Social Science, a broad set of efforts that seek to explain and predict how large-scale human systems from organizations to urban systems, from economies to society as a whole, evolve, react to stresses and stimuli, and cooperate and compete. Participants will hear presentations from experts in the field and engage in intensive dialoguing, demonstrations, and policy scenarios.

For further information and details see: http://krasnow.gmu.edu/DMSC/dmsc-css.html

Computational Social Science Concentration in the Master of Arts in Interdisciplinary Studies

We have recently received approval for a Master of Arts in Interdisciplinary Studies MAIS with a concentration in Computational Social Science starting in the Fall of 2011. Click here to to see the full details or read below.

Computational Social Science (CSS) is a relatively new interdisciplinary science in which social science questions are investigated with modern computational tools. Computational social scientists investigate complex social phenomenon such as economic markets, traffic control, and political systems by simulating the interactions of the many actors in such systems, on computers. They hope to gain insights which will lead to better management of the behavior of the larger social systems, i.e., prevention of market crashes, smoothed traffic flow, or maintenance of political stability. The intractability of many social problems calls for the new approaches provided by computational social science.

CSS is a highly interdisciplinary field that requires teams to plan and complete projects, be they undertaken by government, industry, or non-profit entities. Project managers of such teams, overseeing all elements of project design and execution, tend to hold PhDs. The MAIS concentration will train students to be members of these project teams, able to meaningfully contribute to background research and to project design, execution, and communication.

Prior background should include a bachelor’s degree in one of the social sciences, in computer science, in engineering, or in a relevant discipline, as well as undergraduate courses in these and related areas. Bachelor’s degrees in other areas are also eligible, but the student may be required to take additional courses in social science, mathematics, or computer science as prerequisites to admission.

This concentration will be available in fall 2011.

Concentration Requirements (Catalog Year 2010-2011)

  • Six core courses (18 credits)

  • Three required courses (9 credits): CSS 600, 605, 610

    The required CSS courses provide an understanding of the conceptual, technical, and practical foundations of computational social science.

  • Three elective courses (9 credits) chosen from: CSS 620, 625, 645, 692, 739

    The electives provide an understanding of the technical foundations and current work in at least two subfields of computational social science.

  • One research courses (3 credits) chosen from: CSS 796, 898, 899

    The research course provides students with exposure to the most current ongoing research in the field and allows them to further develop their computational research expertise.

  • Three-four elective courses (9-12 credits)

    The electives allow students to acquire a substantive specialization as well as additional training in social and computational science. Because of the broad spectrum of social science phenomena, methodologies, and student backgrounds, there is a large pool of potential courses. Electives may include any Mason master’s-level course in computational social science, social science, computer science, statistics, or other quantitative methods such as data visualization, information technology, and geographic information science. Electives should be selected in conjunction with and approval of the student’s advisor and the Director of CSS Graduate Studies. If the student does not have prior coursework in multivariate statistical analysis, the electives should include at least one such course relevant for the student’s chosen specialization.

    Students who elect to do a 5-credit project or a thesis take 9 elective credits. Students who do a 2-credit project take 12 credits.

  • Proposal (1 credit): MAIS 797

  • Project (2-5 credits) or thesis (5 credits): MAIS 798 or MAIS 799.

Total: 36 Credits

Requirements may be different for earlier catalog years. See the University Catalog archives.

Director
Claire Snyder-Hall
mais@gmu.edu

Contact information
Robert Axtell
Head of the Concentration in Computational Social Science

Contact: Karen Underwood
Academic Department Coordinator
Department of Computational Social Science
Research 1, Room 373, MSN 6B2
Fairfax, VA 22030
703-993-9298
cssgrad@gmu.edu

Monday, January 31, 2011

Urban Growth and Change


As many of us know more people are now living in cities than ever before. Over half (3.3 billion people) of the world’s population are located in urban areas and this proportion is predicted to increase to over 75 percent by the year 2100 (United Nations, 2007). What I find most interesting is that while cities are growing they are also constantly changing.

The reason I bring this up are twofold. First I recently stumbled upon a site from the NY Times entitled "Mapping America: Every City, Every Block" which shows data from the US Census Bureau's American Community Survey from 2005 to 2009. What is interesting is that there are several maps showing the distribution of racial and ethnic groups throughout the US along with the percentage of foreign born and how neighborhood have changed over time (similar work is also being done at CASA for the UK's population). Such data could come in handy for residential segregation models.

Secondly, as cities grow, the question is where will such growth occur or in some instances where will urban areas decline (e.g. urban shrinkage). Recently we started using GeoMason to explore urban growth using the SLEUTH model as our basis. In the movie below we explore a simple growth scenario around Santa Fe, New Mexico. The model, like the original has the 5 growth coefficients (Dispersion, Breed Spread, Slope, Road) which affect how the growth rules are applied. We also implement the same growth rules (that of Spontaneous Growth, New Spreading Centers, Edge Growth,Road-Influenced Growth) as described on the SLEUTH website. The only thing missing from the model is the self modification procedures during the run time of the model. Data for the model comes a variety of sources including the National Map, the New Mexico Atlas and ESRI.