DBPapers
DOI: 10.5593/SGEM2016/B21/S08.087

IDENTIFICATION OF CRIME ENVIRONMENTAL FACTORS BASED ON SPATIAL HUMAN DATA INTEGRATION

I.Ivan, D. Kocich, J. Horak
Friday 9 September 2016 by Libadmin2016

References: 16th International Multidisciplinary Scientific GeoConference SGEM 2016, www.sgem.org, SGEM2016 Conference Proceedings, ISBN 978-619-7105-58-2 / ISSN 1314-2704, June 28 - July 6, 2016, Book2 Vol. 1, 697-704 pp

ABSTRACT
Among various socio-pathological events, the crime is perceived as the most serious issue. Understanding of factors influencing the intensity, structure and dynamic of crime is essential for appropriate targeting of preventive efforts. Due to the complexity of crime, it is necessary to integrate various sources of data which are usually complicated by differences in spatial referencing, scale, temporal referencing, and data semantic. The study demonstrates data integration based on multidimensional modelling using Online Analytical Processing (OLAP) for analysis. Data from selected sources (e.g. population, crime, dwelling) were aggregated into 1 km grid with an appropriate temporal interval, transformed into relative indicators describing the local demographic and environmental features, and intensities of selected types of crime. The analysis includes evaluation of pairwise correlation and regression analysis (using backward method). The results in the Ostrava pilot area show the highest explanation of variability in the case of thefts (R2 0.60; important descriptors are several other types of crime but also higher share of retired population and unemployed with basic education), burglaries (R2 0.54, share of young population, long-term unemployed), violent crime and property offences (both R2 0.5). To the opposite, the overall index of crime can be explained by the set of independent factors from only 11%. It supports the idea of the selective influence of explored factors which differently affect particular types of crime. It was confirmed that the spatial distribution of selected types of crime intensity depends on the demographic features. The findings emphasize the role of social factors in crime preventive efforts and the necessity to utilize operative registers of public administration for data integration and monitoring of the current state of social conditions.

Keywords: data integration, GIS, OLAP, crime, regression analysis

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