張康聰臺灣大學：地理環境資源學研究所賴致瑜Lai, Chih-YuChih-YuLai2007-11-262018-06-282007-11-262018-06-282006http://ntur.lib.ntu.edu.tw//handle/246246/54905住宅竊盜與犯罪發生地點息息相關，竊賊必須至某住宅行竊，犯罪行為具有空間相依性。本研究以台北市為研究地區，製作住宅竊盜犯罪地圖，辨識犯罪熱點，探討住宅竊盜的區位特性。由核密度推估圖與Getis-Ord G值犯罪地圖顯示，住宅竊盜犯罪熱點有從市中心向外擴展的趨勢。犯罪區位分析發現高教育程度人口比率、20∼60歲人口比率、相對地價殘差、建地密度、人口密度等預測因子與住宅竊盜率有統計上的顯著相關，其R2為0.316。由於高教育程度人口和相對地價殘差高可提升目標吸引性，建地密度和人口密度高會增加犯罪機會，台北市的住宅竊盜區位較著重於目標吸引性高和犯罪機會多。地理加權迴歸能反應空間變異情形，R2值自0.316提升至0.568，殘差總和從28.1下降至17.8。Burglary is the most frequent crime in Taiwan nowadays and is closely related to geographical locations. This study is aimed to identify the hotspots of residential burglary in Taipei in 2000 and 2004 and to determine the locational characteristics of these hotspots. The locations of residential burglaries in Taipei were converted into a point map by matching the addresses reported to the police. The residential burglaries were not randomly distributed in Taipei, but concentrated in certain areas. Both burglary hotspots of Taipei City obtained by using kernel density and Getis-Ord G are concentrated around the city center and spread to the city outskirt. To understand the locational characteristics of high-intensity residential burglary areas, this study presented two kinds of regression models. Stepwise multivariate regression analysis showed that population with college or higher degrees, relative housing price, population of age 20-60, density of built-up area, and population density were significantly correlated with intensity of residential burglaries (R2 = 0.316). The results support the routine activity theory, which suggests burglaries occur in areas of higher daily activities, areas with more opportunities for burglaries and have less chance of being arrested. The other model is the geographically weighted regression model that had a R2 value of 0.568, higher than the global regression model.目 錄 第一章 緒論 1 第一節 研究背景與動機 1 第二節 研究目的 3 第二章 文獻回顧 4 第一節 犯罪理論 4 第二節 犯罪地圖製作 11 第三節 犯罪區位分析 23 第四節 小結 25 第三章 研究設計與方法 26 第一節 研究設計 26 第二節 研究方法 30 第四章 犯罪地圖製作 34 第一節 資料處理 34 第二節 分析結果 39 第五章 犯罪區位分析 44 第一節 資料處理 44 第二節 分析結果 48 第六章 結論與建議 56 第一節 研究結論 56 第二節 未來研究建議 58 參考文獻 60 中文參考文獻 60 英文參考文獻 614071830 bytesapplication/pdfen-US住宅竊盜犯罪地圖犯罪熱點分析犯罪區位分析地理加權迴歸Residential BurglaryCrime mapHot Spot AnalysisCrime Location AnalysisGeographical Weighted Regression[SDGs]SDG11[SDGs]SDG16thesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/54905/1/ntu-95-R92228026-1.pdf