Clustering Effect of crime:Spatial Analysis of Taiwan's Theft Crime Data,2001~2008
Date Issued
2012
Date
2012
Author(s)
Hsu, Yi-Fang
Abstract
In this paper, we had the routine activity theory as the basic ground to built up a suitable analysis framework, and adopted the Spatial Analysis as the main tool to make it a standard and to study the crime rate of theft from 2001 to 2008 in Taiwan. Then, we put the result of the Spatial Analysis and the information of areas of specific precinct together to get the difference between them. In this way, we would know whether this method works out or not, and become the useful data for improving crime rate in the future.
There are three conclusions in this paper. First, based on analysis data of Exploratory Spatial, it showed that the crime rate of theft clustered in specific region, like Taichung City, Hsinchu city and so on. Besides, the crime rate of theft in Xitun District of Taichung Sixth Precinct had rapidly increased since 2002. In addition, after ranking years of the crime rate of theft in every precinct, the phenomona showed out that it increased rapidly in the Taichung Sixth Precinct while the Tainan Second Precinct went up gradually year by year. And why Taichung Sixth Precinct was on the top？It’s because Taiching has been famous for its pornographic place,and it’s located in the center of Taiwan.And because of it’s the transport hub, potential criminals would have much better pipeline to dispose stolen goods after commiting crimes.As for hotspots in the Lisa map,there are several cities with high crime rate like the Forth ( Fitfh, Sixth) Precinct of Taichung, the Third(Sixth) Precinct of Tainan, the First Precinct of Hsinchu. While the coldspots are Yilan Sansing Precinct, Taichung Heping Precinct, Kaohsiung Liou-Guei Precinct and Taitung Guanshan Precinct. The result conveyed that the low-low crime rate happened in those rural area.
Second, according to the Empirical Regress Model, among the variables of economy and society, there were some reached significant effect like the divorce rate, popularity density , average income, the index of landprice and other variables. Above all, the behavior of crime rate could not be explained by only one factor.
Finally, after putting each kind of social and economic factors into control, the crime rate of theft had neighborhood effect which meant it was influenced by spatial neighborhood variables. And its coefficient was 0.4159 in the SLM regress model. In other word,as for precint unit, we found that the crime rate of theft of neighborhood would affect native’s.
Subjects
the crime rate of theft
spatial analasis
the Lisa map
hotspot
neighborhood effect
SDGs
Type
thesis
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