Putting Fear Of Crime On The Map

Putting Fear of Crime on the Map

Since first emerging as an issue of concern in the late 1960s, fear of crime has become one of the most researched topics in contemporary criminology and receives considerable attention in a range of other disciplines including social ecology, social psychology and geography. Researchers looking the subject have consistently uncovered alarming characteristics, primarily relating to the behavioural responses that people adopt in relation to their fear of crime. This book reports on research conducted over the past eight years, in which efforts have been made to pioneer the combination of techniques from behavioural geography with Geographic Information Systems (GIS) in order to map the fear of crime. The first part of the book outlines the history of research into fear of crime, with an emphasis on the many approaches that have been used to investigate the problem and the need for a spatially-explicit approach. The second part provides a technical break down of the GIS-based techniques used to map fear of crime and summarises key findings from two separate study sites. The authors describe collective avoidance behaviour in relation to disorder decline models such as the Broken Windows Thesis, the potential to integrate fear mapping with police-community partnerships and emerging avenues for further research. Issues discussed include fear of crime in relation to housing prices and disorder, the use of fear mapping as a means with which to monitor the impact of Closed Circuit Television (CCTV) and fear mapping in transit environments.

Putting Fear of Crime on the Map

The Rise of Big Spatial Data

This edited volume gathers the proceedings of the Symposium GIS Ostrava 2016, the Rise of Big Spatial Data, held at the Technical University of Ostrava, Czech Republic, March 16–18, 2016. Combining theoretical papers and applications by authors from around the globe, it summarises the latest research findings in the area of big spatial data and key problems related to its utilisation. Welcome to dawn of the big data era: though it’s in sight, it isn’t quite here yet. Big spatial data is characterised by three main features: volume beyond the limit of usual geo-processing, velocity higher than that available using conventional processes, and variety, combining more diverse geodata sources than usual. The popular term denotes a situation in which one or more of these key properties reaches a point at which traditional methods for geodata collection, storage, processing, control, analysis, modelling, validation and visualisation fail to provide effective solutions. >Entering the era of big spatial data calls for finding solutions that address all “small data” issues that soon create “big data” troubles. Resilience for big spatial data means solving the heterogeneity of spatial data sources (in topics, purpose, completeness, guarantee, licensing, coverage etc.), large volumes (from gigabytes to terabytes and more), undue complexity of geo-applications and systems (i.e. combination of standalone applications with web services, mobile platforms and sensor networks), neglected automation of geodata preparation (i.e. harmonisation, fusion), insufficient control of geodata collection and distribution processes (i.e. scarcity and poor quality of metadata and metadata systems), limited analytical tool capacity (i.e. domination of traditional causal-driven analysis), low visual system performance, inefficient knowledge-discovery techniques (for transformation of vast amounts of information into tiny and essential outputs) and much more. These trends are accelerating as sensors become more ubiquitous around the world.