2020
DOI: 10.3390/ijgi9040229
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GIS-Based Statistical Analysis of Detecting Fear of Crime with Digital Sketch Maps: A Hungarian Multicity Study

Abstract: This study evaluates fear of crime perception and official crime statistics in a spatial context, by applying digital sketch maps and statistical GIS methods. The study aims to determine explanatory motives of fear of crime by comparing results of selected large, medium and small sized Hungarian cities. Fear of crime information of residents were collected by using a web application, which gave the possibility to mark regions on a map, where respondents have a sense of safety or feel fear. These digital sketch… Show more

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Cited by 21 publications
(14 citation statements)
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“…In particular, in line with other studies [ 11 , 15 ], it would be relevant to examine other variables, such as a comparison between day and night or the relationship between targets and offenders. Similarly to the studies developed by authors such Jakobi and Põdör [ 62 ] or Fuhrmann, Huynh, and Scholz [ 63 ] that related fear of crime maps and official crime statistics, it would be interesting to compare whether those areas where students felt more unsafe match the areas were more criminal incidents happens [ 62 ]. Future work should also focus on the impact of the perception of (in)security on several domains (lifestyle, academic performance, interpersonal relationships, etc.)…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…In particular, in line with other studies [ 11 , 15 ], it would be relevant to examine other variables, such as a comparison between day and night or the relationship between targets and offenders. Similarly to the studies developed by authors such Jakobi and Põdör [ 62 ] or Fuhrmann, Huynh, and Scholz [ 63 ] that related fear of crime maps and official crime statistics, it would be interesting to compare whether those areas where students felt more unsafe match the areas were more criminal incidents happens [ 62 ]. Future work should also focus on the impact of the perception of (in)security on several domains (lifestyle, academic performance, interpersonal relationships, etc.)…”
Section: Discussionmentioning
confidence: 99%
“…Moreover, lighting, communication about safety, and crime-related services were identified as issues needing improvement. To our best knowledge, none of these measures have been assessed or applied in a Portuguese case; consequently, they should be the focus of careful analysis, discussion, prioritization, and implementation for this particular community [ 34 , 62 ]. This was a descriptive exploratory study; therefore, it seems to be reasonable to establish a work group or task force involving students, police, administrators, faculty, professors, parents, politicians, and researchers in an effort to further analyze and prevent perceptions of insecurity and crime on campus.…”
Section: Discussionmentioning
confidence: 99%
“…If appropriate data are available, it is worth using the ambient population to estimate the expected number of crimes in the Bayesian model and making a comparison with the results of this study. In addition, if incident-based crime data with detailed locational information were obtained in the future, it might be promising to conduct Bayesian spatiotemporal analysis to better explore the relationships between crime and influential factors at smaller units, such as regular grids that can be readily created using GIS tools [64]. Finally, different types of crimes may have specific spatial and temporal patterns as well as influential factors.…”
Section: Discussionmentioning
confidence: 99%
“…Geographic information systems (GIS) were the first and most common analytical tools for spatial data. GIS is useful for mapping and retrospectively finding links between criminal structures and various spatial and social conditions [24,46,59,67], but itself does not provide much predictive power. With the increasing availability of fine-grained urban data, such as public service data, meteorological data, POI data, and human mobility data, data-driven crime prediction problems have received extensive attention from researchers for decades.…”
Section: Traditional Methodsmentioning
confidence: 99%