publications and feeds directly into my teaching activities.
For a good introduction into ABM, how it can be integrated with GIS and its application for geographical systems readers are referred to the recently book I co-edited with Alison Heppenstall, Linda See and Mike Batty entitled: "Agent-Based Models of Geographical Systems" The book brings together a comprehensive set of papers on the background, theory, technical issues and applications of ABM within geographical systems. This collection of papers is a useful reference point for experienced agent-based modeler 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, visualizing and validating model outputs. With contributions from many of the world’s leading research institutions, the latest applied research (from micro and macro applications) from around the globe exemplify what can be achieved in geographical context.
A review of our book by Galán (2012) in the Journal of Artificial Societies and Social Simulation writes:
"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")."Another review by Benenson (2013) 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."
GIS and ABM
Much of my research and teaching relates to creating agent-based models coupled with GIS. The reason I utilize these methodologies is that many geographical systems are characterized by continual change and evolution through time and space. The impacts of interactions between individual agents (humans, cities or more abstract representations), or an individual agent and the environment (physical, social, information etc) can be felt at multiple scales as well as over differing timescales. Previous approaches to modeling the complexity of geographical systems have focused on representing these systems as static aggregations of populations, rational aggregate behavior and flows of information. Examples of these “traditional approaches” include multiple regression, location-allocation and spatial interaction models.
While the utility of these approaches are exemplified within the academic literature, one of the central criticisms that can be leveled at them is treatment of all geographical components as largely homogeneous entities, for example, populations modeled with the same characteristics. Over the course of the 20th century geography has incorporated ideas and theories from other disciplines including economics, mathematics and computer science. These ideas have strengthened the significance of both modeling and understanding the impact of individual agents and the heterogeneity of geographical systems at different spatial and temporal scales. Simulating these processes and their impacts ‘realistically’ presents a significant challenge for the 21st century geographer.
The integration of ABM and GIS provides the ability to have agents that are related to actual geographic locations. This is of crucial importance with regard to urban modeling for example, as everything within a city or region is connected to a place. Furthermore, it allows modelers to think about how objects or agents and their aggregations, interact and change in space and time. For GIS users, it provides the ability to model the emergence of phenomena through individual interactions of features on a GIS over time and space. Moreover, while GIS provides us with the ability to monitor the world it provides no mechanism to discover new decision making frameworks such as why people have moved to a new areas.
This is ongoing research, the links below are some of my current and past projects. Click on the links below to find out more.
- Constructing and Implementing an Agent-Based Model of Residential Segregation through Vector GIS
- Slumulation: An Agent-based Modeling Approach to Slum Formations
- Experimenting with Cities: Utilizing Agent-Based Models and GIS to Explore Urban Dynamics
- Agent Based Modeling and GIS for Community Resource Management
- An Agent-based Model of Organized Crime: Favelas and the Drug Trade
- A Prototype, Multi-agent System for the Study of the Peopling of the Western Hemisphere
- GeoMason Examples
Agent-based Modeling in Virtual Worlds
Within the field of urban modeling, iconic and symbolic models were traditionally developed by different disciplines and professions. However, the gap between the two has narrowed with the growth of digital computing and the rise of 3D environments. Yet applications linking the iconic and symbolic worlds still tend to be based on single users' desktop environments. The growth of virtual worlds such as Second Life, Active Worlds and OpenSim allows for symbolic models to be incorporated into 3D iconic environments which are accessible to multiple users in near real time. This moves the environment from an isolated laboratory on the desktop into a more collaborative 3D digital laboratory.
My research in virtual worlds explores how symbolic agent-based models can be incorporated into 3D virtual worlds which are open to anyone with an internet connection. Furthermore, I am interested in how 3D symbolic and iconic models can be merged together and how people can not only interact with such models but also become part of the model and affect simulation outcomes.
Such a highly visual and immersive medium offered by virtual worlds has the potential to greatly aid in the dissemination of such models. The visualization and communication options provided by virtual worlds such as Second Life allows us to address the challenge modelers face on how we might communicate and share agent-based models with all those we seek to influence (Crooks et al 2008). In the past, this was mainly done through discussion of model results. Through Second Life, it is possible to share modeling processes and outcomes with various non-expert participants in a way unimaginable ten years ago. Multiple users and modelers can be in the same virtual space but spread out over the world communicating by text or voice over IP in real time which is not easily achievable in other modeling environments.
Such a medium potentially allows for a 'meeting' point for interested parties such as academics without the need to travel to workshops or classes. Furthermore, such an environment may allow non-experts to participate in actual model construction. It could offer an environment for rapid prototyping of ideas in near real time, engaging both modelers and multiple stakeholders under which key policy initiatives could be tested on large scale populations simulated at the individual level.
Web 2.0, GIS, Crowdsourcing and Neogeography
The world of geographic information and analysis is undergoing a transformation. Mapping services, hacks and task-specific software have emerged that are changing the way we share, communicate and distribute data. These changes are to such deep extent that we stand on the edge of a new geography based on a digitally connected world at whose core lies citizen-created data organized at an increasingly fine geographic scale. Central to these changes and the explosion in available data organized by its geography is the concept of Web 2.0, a term adapted by O'Reilly Media in 2004 to summarize the rise of services from web-based communities focusing on technologies of social networking, social booking marking, blogging, Wikis and RSS/XML feeds.
I have an interest in exploring the concepts and applications of Web 2.0 through the new media of NeoGeography and its impact on how we collect, interact, search for and use spatial information.
Social media have drastically altered the concept of information contribution and dissemination by empowering the general public to publish and distribute user-generated content. The information conveyed through such media is thematically diverse, ranging from important (e.g. reporting an earthquake) to mundane (e.g. pop culture references). However, this information often contains a geospatial component: a tweet on a specific topic may have a set of precise coordinates associated with it, so that we know where the author was when she posted it. Another tweet may include a reference to a specific location. Similarly, a flickr image or a YouTube video often have geolocation information associated with them, offering us the same insight on their point of origin. All this represents a brand new type of geospatial information: while traditional geospatial information was available for example as maps or satellite imagery we now have geospatial information embedded in text or in the trends of images posted in social media sites. These are manifestations of people acting as sensors: something is picking their interest and they post about it.
While their format ranges from SMS-like messages limited to 140 characters (twitter) to images (flickr) and video (YouTube), these social media feeds share a common nature: they are real-time published expressions of a society's cultural and societal interests. Thus harvesting and analyzing their content can offer unparalleled insight on sociocultural structure and its dynamics. For example, it allows us to:
This represents an evolution of the manner in which geospatial information is collected and analyzed. For the longest time we had been focusing on buildings, roads, the terrain, and infrastructure, ignoring the people who are living in this area. Now we are presented with a unique opportunity to observe the human landscape as the living, breathing organism that it is: we can witness the explosion-like dissemination of information within a society, or the clusters of individuals who share common opinions or attitudes, and map the locations of these clusters. This is an unprecedented development that broadens drastically our understanding of the way that people act, react, and interact with each other and with their environment, hence the term GeoSocial Analysis.
The Built Environment
The built environment is a significant factor in many urban processes, yet direct measures of built form are seldom used in geographical studies. Representation and analysis of urban form and function could provide new insights and improve the evidence base for research.