Geospatial-based data and knowledge driven approaches for burglary crime susceptibility mapping in urban areas

The Damansara-Penchala region in Malaysia, is well-known for its high frequency of burglary crime and monetary loss based on the 2011-2016 geospatial burglary data provided by the Polis Diraja Malaysia (PDRM). As such, in order to have a better understanding of the components which influenced the burglary crime incidences in this area, this research aims at developing a geospatial-based burglary crime susceptibility mapping in this urban area. The spatial indicator maps was developed from the burglary data, census data and building footprint data. The initial phase of research focused on the development of the spatial indicators that influence the susceptibility of building towards the burglary crime. The indicators that formed the variable of susceptibility were first enlisted from the literature review. They were later narrowed down to the 18 indicators that were marked as important via the interview sessions with police officers and burglars. The burglary susceptibility mapping was done based on data-driven and knowledge-driven approaches. The data-driven burglary susceptibility maps were developed using bivariate statistics approach of Information Value Modelling (IVM), machine learning approach of Support Vector Machine (SVM) and Artificial Neural Network (ANN). Meanwhile, the knowledge-driven burglary susceptibility maps were developed using Relative Vulnerability Index (RVI) based on the input from experts. In order to obtain the best results, different parameter settings and indicators manipulation were established in the susceptibility modelling process. Both susceptibility modelling approaches were compared and validated with the same independent validation dataset using several accuracy assessment approaches of Area Under Curve - Receiver Operator Characteristic (AUC-ROC curve) and correlation matrix of True Positive and True Negative. The matrix is used to calculate the sensitivity, specificity and accuracy of the models. The performance of ANN and SVM were found to be close to one another with a sensitivity of 91.74% and 88.46%, respectively. However, in terms of specificity, SVM had a higher percentage than ANN at 57.59% and 40.46% respectively. In addition, the error term in classifying high frequency burglary building was also included as part of the measurements in order to decide on the best method. By comparing both classification results with the validation data, it was found that the ANN method has successfully classified buildings with high frequency of burglary cases to the high susceptibility class with no error at all, thus, proving it to be the best method. Meanwhile, the output from IVM had a very moderate percentage of sensitivity and specificity at 54.56% and 46.42% respectively. On the contrary, the knowledge-driven susceptibility map had a high percentage of sensitivity (86.51%) but a very low percentage of specificity (16.4%) which making it the least accurate model as it was not able to classify the high susceptible area correctly as compared to other modelling approaches. In conclusion, the results have indicated that the 18 indicators used in this research could be employed to successfully map the burglary susceptibility in the study area. Furthermore, it was also found that residential areas within the vicinity of Brickfields, Bangsar Baru, Hartamas and Bukit Pantai are consistent to be classified as high susceptible areas, meanwhile areas of Jalan Duta and Taman Tunku are both identified as the least susceptible areas across the modelling methods

Proceedings, MSVSCC 2017

Proceedings of the 11th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 20, 2017 at VMASC in Suffolk, Virginia. 211 pp

Proceedings of the GIS Research UK 18th Annual Conference GISRUK 2010

This volume holds the papers from the 18th annual GIS Research UK (GISRUK). This year the conference, hosted at University College London (UCL), from Wednesday 14 to Friday 16 April 2010. The conference covered the areas of core geographic information science research as well as applications domains such as crime and health and technological developments in LBS and the geoweb. UCL’s research mission as a global university is based around a series of Grand Challenges that affect us all, and these were accommodated in GISRUK 2010. The overarching theme this year was “Global Challenges”, with specific focus on the following themes: * Crime and Place * Environmental Change * Intelligent Transport * Public Health and Epidemiology * Simulation and Modelling * London as a global city * The geoweb and neo-geography * Open GIS and Volunteered Geographic Information * Human-Computer Interaction and GIS Traditionally, GISRUK has provided a platform for early career researchers as well as those with a significant track record of achievement in the area. As such, the conference provides a welcome blend of innovative thinking and mature reflection. GISRUK is the premier academic GIS conference in the UK and we are keen to maintain its outstanding record of achievement in developing GIS in the UK and beyond

Could social medias reflect acquisitive crime patterns in London?

Embraced within the framework of crime opportunities integrated with Social Disorganization theory and Broken Windows theory, this paper intends to explore the patterns of four types of acquisitive crimes, using social media data i.e. Twitter, Foursquare and cross-sectional data acquired through text analysis technique. With Greater London as the study area, models like negative binominal regression (NBR) and geographically weighted regression (GWR) are performed to illustrate the aggregated relationships between acquisitive crimes and crime opportunities at London-wide and sub-regional MSOAs levels respectively. The results work towards to hypotheses that: the tweets sentiment could reflect property-related crime rates positively in light of Broken Windows Theory; more tweets with negative sentiment may incur increases of acquisitive crimes. It contributed to existing studies in (1) providing empirical evidence for integrating these three theories; (2) complementing current research on local discrepancies of acquisitive crimes by utilising both GWR and NBR models; (3) challenging the traditional stereotypes about racial disparities with the finding that ethnic heterogeneity and instrumental crimes have counterintuitive association, especially taking education factor into consideration; (4) implicating some localised acquisitive crime prevention strategies to policy makers in light of the reality that the relationship between local variations and different crime types may vary by place

Proceedings, MSVSCC 2016

Proceedings of the 10th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 14, 2016 at VMASC in Suffolk, Virginia

A review of spatial econometric models for count data

Despite the increasing availability of spatial count data in research areas like technology spillovers, patenting activities, insurance payments, and crime forecasting, specialized models for analysing such data have received little attention in econometric literature so far. The few existing approaches can be broadly classified into observation-driven models, where the random spatial effects enter the moments of the dependent variable directly, and parameterdriven models, where the random spatial effects are unobservable and induced via a latent process. Moreover, within these groups the modelling approaches (and therefore the interpretation) of spatial effects are quite heterogeneous, stemming in part from the nonlinear structure of count data models. The purpose of this survey is to compare and contrast the various approaches for econometric modelling of spatial counts discussed in the literature

The Impact of Community Cohesion on Crime

Community cohesion generally acts to increase the safety of communities by increasing informal guardianship, and enhancing the work of formal crime prevention organisations. Understanding the dynamics of local social interactions is essential for community building. However, community cohesion is difficult to empirically quantify, because there are no obvious and direct indicators of community cohesion collected at population levels within official datasets. A potentially more promising alternative for estimating community cohesion is through the use of data from social media. Social media offers an opportunity for exploring networks of social interactions in a local community. This research will use social media data to explore the impact of community cohesion on crime. Sentiment analysis of tweets can help to uncover patterns of community mood in different areas. Modelling of community engagement on Facebook is useful for understanding patterns of social interactions and the strength of social networks in local communities. The central contribution of this thesis is the use of new metrics that estimate popularity, commitment and virality known as the PCV indicators for quantifying community cohesion on social media. These metrics, combined with diversity statistics constructed from “traditional” Census data, provide a better correlate of community cohesion and crime. To demonstrate the viability of this novel method for estimating the impact of community cohesion, a model of community engagement and burglary rates is constructed using Leeds community areas as an example. By examining the diversity of different community areas and strength of their social networks, from traditional and new data sources; it was found that stability and strong social media engagement in a local area are associated with lower burglary rates. The proposed new method can provide a better alternative for estimating community cohesion and its impact on crime. It is recommended that policy planning for resource allocation and community building needs to consider social structure and social networks in different communities

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity