臺北市住宅竊盜犯罪群聚及區位因素之研究

A Study of the Clustering and Ecological Factors of Burglary in Taipei City

隨著犯罪地理學與環境犯罪學的理論軌跡匯合，以地理資訊系統製作犯罪地圖日臻普及與成熟，但如何將時間因素同時納入分析卻是棘手難題。為此，本研究使用掃描統計與地理資訊系統，針對臺北市2015至2017年住宅竊盜犯罪的空間、時間及時空分布進行分析。空間分析方面，臺北市住宅竊盜熱區以中山區、萬華區、大同區及士林區較為顯著；在時間群聚部分，歲末年終與跨年假期為住宅竊盜高發生時段；在時空掃描方面，「回顧性」掃描顯示犯罪群聚的空間分布仍集中在中山區、萬華區及士林區等；「時間趨勢的空間變化」與「前瞻性」掃描則發現在南港、內湖部分地區有異常群聚現象，應特別予以關注。
本研究依據社會解組與新機會理論，選取社經人文變項進行區位分析。首先，運用群聚共變項分析，確認選取之變項與群聚具有相關；其次，採用資料探勘技術，篩選重要變項計8項，顯示高所得及房價較高地區有較低住宅竊盜；空屋率與單獨住戶比例較高，易導致住宅竊盜犯罪；而監控方面，犯罪熱區中有較多的警力配置。
最後，本研究建議應針對不同熱區特性擬具特色警務規劃，運用前瞻性掃描統計建構及時監測預警系統，並根據犯罪群聚最大概率與被害風險數據合理配置警政資源，以提升犯罪預防之成效。

With the integration of the theory of criminal geography and environmental criminology, using geographic information systems to make crime maps has become increasingly popular and mature, but how to analyze time and space at the same time is a difficult problem. For this reason, this study used scan statistics and geographic information system to analyze the spatial, temporal and Spatiotemporal distribution of burglary in Taipei city from 2015 to 2017.The results show that, in terms of spatial analysis, the hot spots of burglary are Zhongshan District, Wanhua District, Datong District and Shilin Distric. In the time cluster, the time series scanning shows that the year-end and New Year holidays were hot time of burglary. And in terms of space-time scanning, in the“retrospective”scanning, the spatial distribution of burglary hotspots was still concentrated in the Zhongshan district, Wanhua District and Shilin District.“Spatial variation in the time trend”and“prospective”scanning found that some areas such as Nangang District, Neihu District have abnormal clustering phenomenon, which should be paid special attention.
This study selects various socioeconomic variables for ecological analysis based on criminology theory and relevant literature, so as to clarify the formation factors of crime clustering. Firstly, cluster covariant analysis was used to confirm the correlation between the selected variables and the clustering in this study. Secondly, data mining were used to screen out a total of 8 important ecological variations of crime cluster. The results show that the characteristics of burglary coldpots in Taipei city are in hight-income areas. The vacancy rate and the proportion of individual households are relatively high, resulting in reduced surveillance and easy to become burglary hotspots. There are more police forces in crime hotspots, while street lamps and monitors have no obvious effect on burglary prevention.
To sum up the above research results, this study suggests that special police planning should be developed for different crime hotspots, then prospective scan statistics should be used to construct a burglary timely monitoring and early warning system, and based on the maximum probability of crime and victim risk data to allocate police resources to enhance the effectiveness of crime prevention.