Friday, September 25, 2015

Urban Growth Model in NetLogo

Recently Yang Zhou, a PhD student in the Computational Social Program carried out a partial re-implementation of the SLEUTH urban growth model (without Self Modification). The region of study is Santa Fe, New Mexico. The data was obtained from The National Map. The model demonstrates how several raster layers can be used to initialize a NetLogo model. Hopefully others who want to know how to create spatially explicit models in NetLogo will find this useful. The model and data can be downloaded from Yang's GitHub account.

Also you can export the data to at any time to see how the land cover changes over time. For example in the image below we show the land cover at the initialization of the model (t=0, top) and the land cover at t=10 (bottom).
To find out more about CA models, the movie below by Andreas Flache offers a good introduction:

Mesa: An Agent-Based Modeling Framework in Python

Just a short post to say two of our PhD students, David Masad and Jackie Kazil have been developing an agent-based modeling framework in Python called Mesa.

To quote from David's talk abstract:
"Agent-based modeling is currently a hole in in Python’s robust and growing scientific ecosystem. Mesa is a new open-source package meant to fill that gap. It allows users to quickly create agent-based models using built-in core components (such as agent schedulers and spatial grids) or customized implementations; visualize them using an innovative browser-based interface; and analyze their results using Python’s robust data analysis tools. Its goal is to be a Python 3-based alternative to other popular frameworks based in other languages such as NetLogo, Repast, or MASON."

Below is short presentation outlining Mesa from SciPy 2015:

Thursday, September 17, 2015

Call for papers: Symposium on Human Dynamics Research: Urban Analytics at the 2016 AAG

Call for papers: AAG 2016. San Francisco. 29th March – 2nd April

Symposium on Human Dynamics Research: Urban Analytics

A deluge of new data created by people and machines is changing the way that we understand, organise and model urban spaces. New analytics are required to make sense of these data and to usefully apply findings to real systems. This session seeks to bring together quantitative or mixed methods papers that develop or use new analytics in order to better understand the form, function and future of urban systems. We invite methodological, theoretical and empirical papers that engage with any aspect of urban analytics. Topics include, but are not limited to:
  • New methodologies for tackling large, complex or dirty data sets;
  • Case studies involving analysis of novel or unusual data sources;
  • Policy analysis, predictive analytics, other applications of data;
  • Intensive modelling or simulation applied to urban areas or processes; 
  • Individual-level and agent-based models (ABM) of geographical systems; 
  • Validating and calibrating models with novel data sources; 
  • Ethics of data collected en masse and their use in simulation and analytics.

Please e-mail the abstract and key words with your expression of intent to Nick Malleson ( by 22nd October, 2015 (one week before the AAG session deadline). Please make sure that your abstract conforms to the AAG guidelines in relation to title, word limit and key words and as specified at:

An abstract should be no more than 250 words that describe the presentation’s purpose, methods, and  conclusions.

Timeline summary:

  • 22nd October, 2015: Abstract submission deadline. E-mail Nick Malleson by this date if you are interested in being in this session. Please submit an abstract and key words with your expression of intent.
  • 25th October, 2015: Session finalization and author notification
  • 28th October, 2015: Final abstract submission to AAG, via 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 Nick Malleson. Neither the organizers nor the AAG will edit the abstracts.
  • 29th October, 2015: AAG registration deadline. Sessions submitted to AAG for approval.


  • Nick Malleson, School of Geography, University of Leeds  
  • Alex Singleton, School of Environmental Sciences, University of Liverpool  
  • Mark Birkin, Director of the University of Leeds Institute for Data Analytics (LIDA)  
  • Paul Longley, Department of Geography, University College London  
  • Andrew Crooks, Department of Computational and Data Sciences, George Mason University.   
  • Seth Spielman, Geography Department, University of Colorado

Thursday, September 03, 2015

Walk this Way

We recently had published in ISPRS International Journal of Geo-Information a paper entitled "Walk this Way: Improving Pedestrian Agent-Based Models through Scene Activity Analysis". In the paper we explore how new data can help inform our agent-based models. Specifically, pedestrian modeling which has been traditionally faced with the challenge of collecting data to calibrate and validate such models of pedestrian movement. Below is the abstract for the paper:
Pedestrian movement is woven into the fabric of urban regions. With more people living in cities than ever before, there is an increased need to understand and model how pedestrians utilize and move through space for a variety of applications, ranging from urban planning and architecture to security. Pedestrian modeling has been traditionally faced with the challenge of collecting data to calibrate and validate such models of pedestrian movement. With the increased availability of mobility datasets from video surveillance and enhanced geolocation capabilities in consumer mobile devices we are now presented with the opportunity to change the way we build pedestrian models. Within this paper we explore the potential that such information offers for the improvement of agent-based pedestrian models. We introduce a Scene- and Activity-Aware Agent-Based Model (SA 2 -ABM), a method for harvesting scene activity information in the form of spatiotemporal trajectories, and incorporate this information into our models. In order to assess and evaluate the improvement offered by such information, we carry out a range of experiments using real-world datasets. We demonstrate that the use of real scene information allows us to better inform our model and enhance its predictive capabilities.

Keywords: pedestrian modeling; pedestrian tracking; activity monitoring; spatiotemporal trajectories; agent-based modeling
As with many of our models, the source code of the model can be downloaded from here. To give a sense of the model, the movie below shows how agents traverse the scene.

Full Reference: 
Crooks, A.T., Croitoru, A., Lu, X., Wise, S., Irvine, J. and Stefanidis, A. (2015),  Walk this Way: Improving Pedestrian Agent-Based Models through Scene Activity AnalysisISPRS International Journal of Geo-Information, 4(3): 1627-1656. (pdf)