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:

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 (n.s.malleson@leeds.ac.uk) 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:

http://www.aag.org/cs/annualmeeting/call_for_papers

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 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 Nick Malleson. Neither the organizers nor the AAG will edit the abstracts.
• 29th October, 2015: AAG registration deadline. Sessions submitted to AAG for approval.

Organizers

• 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

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 Analysis,  ISPRS International Journal of Geo-Information, 4(3): 1627-1656. (pdf)

Summer Research Projects

Over the summer, Arie Croitoru and myself took part for the first time in the George Mason University Aspiring Scientists Summer Internship Program (ASSIP). We worked with three very talented high-school students who over the course of the seven and a half week program produced some excellent research around the areas of agent-based modeling (ABM), volunteered geographical analysis (VGI), social media and text analysis. An overview of their work can be seen below in the posters and abstracts that the students produced at the end of the internship.

End of Summer Research Poster Display

In the first project, Kevin Geng and Varun Talwar explored how online news stories propagate around the world in a project entitled: "MediaPulse: A System Prototype for News Media Aggregation and Analytics". We would also like to thank Trevor Thrall for his assistance and guidance in this project. Below you can read the abstract and see the poster from this work.

"The news media play a major role in shaping the opinions and beliefs of people around the globe. Alongside more traditional media distribution modes, such as the printed press, in recent years the Internet has begun to play a more important role in news distribution. With the emergence of online news, information can now be disseminated across the globe in real-time. By monitoring such online sources, we can, for the first time, obtain valuable insight into the propagation of news around the world over time, and understand how such media is both produced and consumed. However, obtaining and analyzing this data on a massive scale has proven to be challenging. To address this challenge, we present a novel system for the collection and analysis of news articles. Our system has the capability to extract both metadata and text from a large array of online news sources, and analyze it with respect to themes, locations, time, and language. In order to showcase the utility of our system, we selected 440 prominent news sources across the globe, and monitored their RSS feeds every hour using our system. Through this process we collected roughly 30,000 articles per day over the duration of the study period. To demonstrate the analytical capabilities of our system, we present a case study analysis of coverage of the Islamic State of Iraq and Syria (ISIS) using the system that we developed. In particular, we analyze ISIS-related news using both their content and metadata to show how news propagates over time and space, and explore how the sentiment of the coverage varies."

In a second project Rohan Suri developed an agent-based model to explore the spread and  containment of Ebola in a project entitled "A novel computational agent based model for the spread and containment of Ebola Virus Disease". Below is the abstract, poster and a example model run from this project. More information about the model can be found here.

"During the Summer of 2014, the countries of Sierra Leone, Guinea, and Liberia were devastated by an Ebola Virus Disease (EVD) epidemic.  Although it killed more than 10,000 people, little is known about EVD dynamics in a macro population. While various attempts have been made to better understand EVD dynamics, such past attempts at modeling EVD exclude an explicit spatial scale, implied general mixing, and did not consider human-to-human interactions. In view of these limitations, this research aims to develop a novel computational agent based model (ABM) to investigate spatial and temporal EVD spread, and to study the effectiveness of control and prevention measures for EVD. In this model, OpenStreetMap (OSM) data was used to construct the physical environment (e.g., road networks), and a realistic population for the three countries was generated from Landscan data and previous surveys. EVD spread was modeled through explicit agent-to-agent interaction and the use of a Suspected-Infected-Exposed-Recovered (SEIR) model"

It was a great learning experience from our side by participating in the ASSIP Program.

Slides from Lipari: GIS and Agent-Based

I have just come back from the Lipari School on Computational Social Science which was focusing on Algorithms, Data, and Models for Social and Urban Systems. While there I gave a class entitled "GIS and Agent-based modeling" split over two parts. Below you can see the abstract for my class and the slides.

Abstract: Understanding and modeling human behavior is not as simple as it sounds. This is because humans do not just make random decisions, but base actions upon their knowledge and abilities. Moreover, one might think that human behavior is rational, but this is not always the case: decisions can also be based on emotions (e.g. happiness, anger, fear). Emotions can influence decision-making by altering our perceptions about the environment and future evaluations. The question therefore is how we model human behavior? Over the last decade, one of the dominant ways of modeling human behavior in its many shapes and forms has been through agent-based modeling (ABM). In this workshop I will provide a general overview of what agents are, why there is a need for agent-based models for studying cities, and how it links to how we believe societies operate through ideas of complexity theory. I will sketch out how geographical information systems (GIS) can be used to create spatially explicit agent-based models, before reviewing a range of applications where agent-based models have been developed using geographical data. The workshop concludes with an overview of challenges modelers face when using agent-based models to study geographical problems with a special emphasis on cities, and identify future avenues of research relating to big data and social network analysis.

   

In addition to giving my own class I was also fortunate to attend a diverse set of classes by several other people including  Alessandro Mei, Carlo Ratti, Claudio Cioffi Revilla, Paolo Ferragina, Wulf Daseking and Daniele Quercia. All of which explored how urban systems can be studied from a variety of disciplines. One of the highlights of the summer school for me was interacting with the students and learning about their own research. The school was excellent and the location was perfect for learning and relaxing.