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.

Tuesday, January 09, 2018

New Paper: Cancer and Social Media

Continuing our work on geosocial analysis we recently had a paper entitled "Cancer and Social Media: A Comparison of Traffic about Breast Cancer, Prostate Cancer, and Other Reproductive Cancers on Twitter and Instagram" published in the Journal of Health Communication. In the paper we  present a comparative study of differences in messaging for women’s and men’s cancer campaigns on social media through three discrete approaches. 
  1. we directly compare the incident rates of women’s and men’s cancers in the United States to the corresponding levels of traffic that these cancers elicited during World Cancer Day across two social media platforms, Twitter and Instagram. 
  2. we examine social media activity for breast cancer versus prostate cancer on both Twitter and Instagram during the dedicated month-long campaigns (October and November, respectively). 
  3. we compare the top terms associated with each campaign on these two social media platforms to discover whether there are differences in the terms associated with these online discussions.
Below you can read the abstract to our paper, see some of our results and at the bottom of the post have the full citation and link to the paper.

Social media are often heralded as offering cancer campaigns new opportunities to reach the public. However, these campaigns may not be equally successful, depending on the nature of the campaign itself, the type of cancer being addressed, and the social media platform being examined. This study is the first to compare social media activity on Twitter and Instagram across three time periods: #WorldCancerDay in February, the annual month-long campaigns of National Breast Cancer Awareness Month (NBCAM) in October and Movember in November, and during the full year outside of these campaigns. Our results suggest that women’s reproductive cancers – especially breast cancer – tend to outperform men’s reproductive cancer – especially prostate cancer – across campaigns and social media platforms. Twitter overall generates substantially more activity than Instagram for both cancer campaigns, suggesting Instagram may be an untapped resource. However, the messaging for both campaigns tends to focus on awareness and support rather than on concrete actions and behaviors. We suggest health communication efforts need to focus on effective messaging and building engaged communities for cancer communication across social media platforms.

A comparison of percentages of cancer cases (green bars) and references to corresponding cancers in Twitter (blue bar) and Instagram (orange bar) during World Cancer Day 2016.

 References to breast cancer (green line), prostate cancer (orange line), and Movember (blue line) over the full year 2015 in Instagram.

Full Reference: 

Vraga, E., Stefanidis, A., Lamprianidis, G., Croitoru, A., Crooks, A.T. Delamater, P.L., Pfoser, D., Radzikowski, J. and Jacobsen, K.H. (2018), Cancer and Social Media: A Comparison of Traffic about Breast Cancer, Prostate Cancer, and Other Reproductive Cancers on Twitter and Instagram, Journal of Health Communication. 3(2), 181-189. (pdf)