Title
Analyzing Street Crimes In Kobe City Using Prism
Abstract
Purpose In a previous research, the authors proposed a security information service, called Personalized Real-time Information with Security Map (PRISM), which personalizes the incident information based on living area of individual users. The purpose of this paper is to extend PRISM to conduct sophisticated analysis of street crimes. The extended features enable to look back on past incident information and perform statistical analysis. Design/methodology/approach To analyze street crimes around living area in more detail, the authors add three new features to PRISM: showing a past heat map, showing a heat map focused on specified type of incidents and showing statistics of incidents for every type. Using these features, the authors visualize the dynamic transition of street crimes in a specific area and the whole region within Kobe city. They also compare different districts by statistics of street crimes. Findings Dynamical visualization clarifies when, where and what kind of incident occurs frequently. Most incidents occurred along three train lines in Kobe city. Wild boars are only witnessed in a certain region. Statistics shows that the characteristics of street crimes is completely different depending on living area. Originality/value Previously, many studies have been conducted to clarify factors relevant to street crimes. However, these previous studies mainly focus on interesting regions as a whole, but do not consider individual's living area. In this paper, the authors analyze street crimes according to users' living area using personalized security information service PRISM.
Year
DOI
Venue
2019
10.1108/IJWIS-04-2018-0032
INTERNATIONAL JOURNAL OF WEB INFORMATION SYSTEMS
Keywords
Field
DocType
Visualization, Smart city, Web service, Security information service, Street crimes
Information retrieval,Computer science,Visualization,Originality,Smart city,Web service,Statistical analysis
Journal
Volume
Issue
ISSN
15
2
1744-0084
Citations 
PageRank 
References 
0
0.34
1
Authors
3
Name
Order
Citations
PageRank
Takuhiro Kagawa111.64
Sachio Saiki25524.46
Masahide Nakamura352672.51