Monday, March 12, 2018

Call for Papers: ABMUS 2018

The ABMUS2018 workshop on Agent-based modelling of urban systems will held on 14/15 July 2018 in Stockholm, Sweden. The workshop is part of the Federated AI Meeting (FAIM2018), which includes the AAMAS2018 conference and IJCAI-ECAI (International Joint Conference on Artificial Intelligence and the European Conference on Artificial Intelligence). It is the follow-up of ABMUS2016, ABMUS2017.

The central goal of this workshop is to bring together the community of researchers and practitioners who use agent-based models and multi-agent systems to understand and manage cities and urban infrastructure systems. Through the exchange of ideas and state-of-the-art within this area, we will pool together current thinking to discuss avenues of fruitful research and methodological challenges we face in building robust, realistic, and trusted models of urban systems.

Agent-based modelling has proven itself to be a useful technique for understanding and predicting changes and impact of urban form and policy on urban systems. However, recognised challenges remain in designing, developing and implementing trusted models that can be used by industry and governments to enhance decision-making. This workshop invites submissions from researchers and practitioners who use agent-based models and agent systems to understand, explore, and manage cities and urban infrastructure systems.

In particular, we invite presentations that describe efforts and challenges in design, development and deployment of urban system models that have balanced the provision of mechanistic insight into complex challenges facing urban systems vs practical challenges of producing 'numbers' for real-world decision support for industry and government.

Workshop topics include, but are not limited to, the following:
  • Large scale urban simulation applications
  • Spatially explicit micro-simulation modelling
  • Agent-based modelling of urban transport, land-use, housing, energy, health, etc.
  • Simulation of household behaviour and technology adoption
  • Localized population synthesis
  • Multi-scale urban systems (temporal and spatial)
  • Social simulation of demographic transitions
  • Model development and co-development processes and protocols
  • Data structures for simulating urban environments
  • (Multi-)agent systems to provide decision support in e.g. transport, energy and air quality
  • Connection of simulation models to social and geographical theory
  • Government and industry engagement in model development and uptake
  • Processes of model co-development to enhance decision-making in urban systems
  • Development in model interfaces and engagement that enhance model uptake
If accepted, each presenter will be given a short time slot (max 10 minutes) to introduce their paper and/or case study, followed by 5-10 minutes in which presenters will share their views on the balancing insight and numbers theme. After three presentations there will be 20-30 minutes of group discussion in which presenters will act as panel members.

Papers should be submitted as an extended abstract (2-4 pages) through the workshop website. Your abstract should include a Title as well as all authors and affiliations. It should articulate the objectives of the paper and provide a brief but thorough description of the research related to the theme of the workshop and the expected gain by those attending the presentation. Accepted authors will be invited to submit a full paper after the workshop to be included in the post-workshop proceedings.

For details on how to submit please see and for more information

Thursday, February 22, 2018

Call for papers: GIScience 2018 Workshop: Rethinking the ABCs: Agent-Based Models and Complexity in the age of Big Data

At the upcoming GIScience 2018 conference, Raja Sengupta and Liliana Perez are organizing a workshop entitled: Rethinking the ABCs: Agent-Based Models and Complexity in the age of Big Data.  To quote from the workshop website:
"The scope of this workshop is to explore novel complexity science approaches to dynamic geographic phenomena and their applications, addressing challenges and enriching research methodologies in geography in a Big Data Era."
For more information, check out the workshop homepage: Note the deadline for papers is May 1st 2018.

Thursday, February 01, 2018

The Future of GEOINT: Data Science Will Not Be Enough

In the 2018 State and Future of GEOINT Report published by the The United States Geospatial Intelligence Foundation (USGIF) we had a paper accepted entitled "The Future of GEOINT: Data Science Will Not Be Enough". In the paper we discuss how there has been a deluge of spatial-temporally enabled data in the last several years with no signs of slowing down (e.g. by the year 2020, many experts predict the global universe of accessible data to be on the order of 44 zettabytes—44 trillion gigabytes). With this growth in data there has been steady uptake of data scientists in the GEOINT community because of their  ability to navigate petabytes of raw and unstructured data, then clean, analyze, and visualize the data. However,  we argue that we must go beyond just statistically analyzing data collected on the world around us to truly gain an understanding of the people who inhabit the world.  In order to do this, we suggest that future GEOINT analysts should not only have skills in data science but also be able to apply advanced computational methods, such as agent-based modeling, social network analysis, geographic information science, and deep learning algorithms (i.e. geospatial computational social science) to explore and test hypotheses based on social and geographic theory to truly achieve an understanding of human interactions.  

Full Reference: 
Parrett, C.M., Crooks, A.T. and Pike, T. (2018), The Future of GEOINT: Data Science Will Not Be Enough. The State and Future of GEOINT 2018 Report, The United States Geospatial Intelligence Foundation, Herndon, VA. pp 12-15. (pdf)
As always, any thoughts or comments are welcome. 

Thursday, January 25, 2018

Place-Based Simulation Modelling

Nick Malleson, Alison Heppenstall and myself recently had a chapter published in the Oxford Research Encyclopedia of Criminology and Criminal Justice, entitled "Place-Based Simulation Modelling:  Agent-Based Modelling and Virtual Environments". In the chapter, we discuss how agent-based modeling (ABM) can be used for modeling spatial patterns of crime.

Specifically we discuss the motivation for ABM in criminology. We then introduce the core components of ABM and how one can structure such models and represent human behavior in them (e.g. using Beliefs-Desires-Intentions (BDI) or Physical conditions, Emotional states, Cognitive capabilities and Social status" (PECS) frameworks). Next, we discuss space can be represented in agent-based models, both from an abstract sense but also accurate spatial environments (i.e. by using GIS data). The chapter then moves onto briefly discussing tools for the implementation of agent-based models (e.g. MASON, NetLogo, Repast) before we provide a critique of ABM for modeling spatial crime, including its appeal (e.g. the emergence of crime from the bottom up), the difficulties of using ABM (e.g adequately defining behavior, access to data etc.) and finally the ethical implications of using agent-based models for studying crime. Below you can read the official summary of the chapter along with its full citation.

Chapter Summary: 
Since the earliest geographical explorations of criminal phenomena, scientists have come to the realization that crime occurrences can often be best explained by analysis at local scales. For example, the works of Guerry and Quetelet—which are often credited as being the first spatial studies of crime—analyzed data that had been aggregated to regions approximately similar to US states. The next major seminal work on spatial crime patterns was from the Chicago School in the 20th century and increased the spatial resolution of analysis to the census tract (an American administrative area that is designed to contain approximately 4,000 individual inhabitants). With the availability of higher-quality spatial data, as well as improvements in the computing infrastructure (particularly with respect to spatial analysis and mapping), more recent empirical spatial criminology work can operate at even higher resolutions; the “crime at places” literature regularly highlights the importance of analyzing crime at the street segment or at even finer scales. These empirical realizations—that crime patterns vary substantially at micro places—are well grounded in the core environmental criminology theories of routine activity theory, the geometric theory of crime, and the rational choice perspective. Each theory focuses on the individual-level nature of crime, the behavior and motivations of individual people, and the importance of the immediate surroundings. For example, routine activities theory stipulates that a crime is possible when an offender and a potential victim meet at the same time and place in the absence of a capable guardian. The geometric theory of crime suggests that individuals build up an awareness of their surroundings as they undertake their routine activities, and it is where these areas overlap with crime opportunities that crimes are most likely to occur. Finally, the rational choice perspective suggests that the decision to commit a crime is partially a cost-benefit analysis of the risks and rewards. To properly understand or model these three decisions it is important to capture the motivations, awareness, rationality, immediate surroundings, etc., of the individual and include a highly disaggregate representation of space (i.e. “micro-places”). Unfortunately one of the most common methods for modeling crime, regression, is somewhat poorly suited capturing these dynamics. As with most traditional modeling approaches, regression models represent the underlying system through mathematical aggregations. The resulting models are therefore well suited to systems that behave in a linear fashion (e.g., where a change in model input leads to a predictable change in the model output) and where low-level heterogeneity is not important (i.e., we can assume that everyone in a particular group of people will behave in the same way). However, as alluded to earlier, the crime system does not necessarily meet these assumptions. To really understand the dynamics of crime patterns, and to be able to properly represent the underlying theories, it is necessary to represent the behavior of the individual system components (i.e. people) directly. For this reason, many scientists from a variety of different disciplines are turning to individual-level modeling techniques such as agent-based modeling.

Keywords: agent-based modelling, crime simulation, travel to crime, virtual environment, NetLogo, virtual laboratory.

Figure 1: The process of initializing, running, and analyzing an agent-based model.

Full Reference:
Malleson, N., Heppenstall, A. and Crooks, A.T. (2018). Place-Based Simulation Modelling: Agent-Based Modelling and Virtual Environments, Oxford Research Encyclopedia of Criminology and Criminal Justice, Oxford University Press. DOI: 10.1093/acrefore/9780190264079.013.319 (pdf)

Wednesday, January 24, 2018

A Review of High and Very High Resolution Remote Sensing Approaches for Detecting and Mapping Slums

Regular readers of this site might of noticed that we have an interest in slums. In the past this has focused on modeling them from an agent-based perspective, comparing volunteered geographical information to more authoritative data on slums, to that of attempting to come up with a Slum Severity Index. However, more recently we have taken to looking at how remote sensing approaches have been and can be used to detect and map slums.

To this end we recently had a review paper accepted in Urban Systems entitled "A Critical Review of High and Very High Resolution Remote Sensing Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging Opportunities". In this paper we carry out a comprehensive review of studies that have used high and very high resolution (H/VH-R) remote sensing techniques to detect and map slums (along with their global footprint). We discuss approaches used (e.g. multi-scale, image texture analysis, landscape analysis, object-based image analysis, building feature extraction, data mining, socio-economic measures) using H/VH-R imagery for identifying and mapping slums, listing what are the limitations and advantages of each. After this, we  discuss emerging sources of geospatial data that should we thing should be considered (e.g., volunteer geographic information, VGI, social media) in conjunction with growing trends and advancements in technology (e.g., geosensor networks, unmanned aerial vehicles (UAVs) or “drones) when trying to map and monitor slums. We argue that it is only through such data integration and analysis that we can then create a benchmark for determining the most suitable methods for mapping slums in a given locality. Below you can read the abstract of the paper and see some of the figures we use to support our discussion, along with the full reference.

Abstract: Slums are a global urban challenge, with less developed countries being particularly impacted. To adequately detect and map them, data is needed on their location, spatial extent and evolution. High- and very high-resolution remote sensing imagery has emerged as an important source of data in this regard. The purpose of this paper is to critically review studies that have used such data to detect and map slums. Our analysis shows that while such studies have been increasing over time, they tend to be concentrated to a few geographical areas and often focus on the use of a single approach (e.g., image texture and object-based image analysis), thus limiting generalizability to understand slums, their population, and evolution within the global context. We argue that to develop a more comprehensive framework that can be used to detect and map slums, other emerging sourcing of geospatial data should be considered (e.g., volunteer geographic information) in conjunction with growing trends and advancements in technology (e.g., geosensor networks). Through such data integration and analysis we can then create a benchmark for determining the most suitable methods for mapping slums in a given locality, thus fostering the creation of new approaches to address this challenge.
Keywords: high and very high resolution imagery; remote sensing, slums; geosensor networks; image analysis.

Global distribution of urban and slum populations.

Country level distribution of H/VH-R studies (studies published between 1997-2016).

OSM and Google Maps views of Kibera slum (a) Top:Left OSM and right Google Maps (b) Bottom:Left OSM and right Google Maps.

Full Reference:
Mahabir, R., Croitoru, A., Crooks, A.T., Agouris, P. and Stefanidis, A. (2018), A Critical Review of High and Very High Resolution Remote Sensing  Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging Opportunities, Urban Science. 2(1), 8; doi:10.3390/urbansci2010008 (pdf)
As always, any thoughts or comments are most welcome.