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論文名稱(外文):Spatial and Temporal Analysis of Travel Distance and Step Distance of Serial Sexual Offenders with Different Characteristics
指導教授(外文):Pei-Fen Kuo
外文關鍵詞:serial sexual offendertravel distancestep distanceKnox testspatio-temporal clusterLévy walk
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為了比較國外與國內連續性侵犯之特性,本研究收集了三個不同區域的數據。數據集A引用 LeBeau(1992)所收集之4名美國聖地牙哥連續性侵犯之犯罪資料,4名犯人合計共犯下52起案件。數據集B引用 Hsu(2017) 所收集,24名連續性侵犯合計於台灣北部犯下100起案件。數據集C則是包含台灣中部地區78名連續性侵犯合計犯下之246起性侵案紀錄。
本研究主要方法有三:1. 利用卡方檢定 (Chi-squared test) 檢視不同變數與犯人移動距離 (travel distance) 之關係,進而找出可能影響犯人行為模式的因子。 2. 利用Knox test 分析連續性侵案件之時空特性。3. 利用配適度檢定 (goodness-of-fit test) 檢視犯人移動距離 (travel distance & step distance) 是否符合 Lévy walk 或其他重尾機率分布。其結果能幫助我們進一步了解連續性侵犯之犯罪行為模式。首先,根據Knox test之結果顯示,連續性侵案件間呈現短距離、短時間間隔之分布,其距離間隔在500公尺內,而時間間隔則在七天內。第二,根據卡方檢定之結果顯示,當犯人年齡大於40歲或未使用交通工具時,犯人多選擇在住家或附近犯案。而若犯人與被害者熟識或被害者年齡小於14歲,則會有較高的機率在犯人住家或附近發生性侵案件。
此外,本研究亦發現不同數據集的配飾度檢定結果不一致。台灣中部之性侵犯,其旅行距離(travel distance)與犯案距離(step distance)皆符合 Lévy walk分布,而聖地牙哥與台灣北部之性侵犯,其旅行距離及犯案距離則分別符合對數常態分佈及韋伯分布。表示連續性侵犯之移動特性,隨著當地資源或被害人空間分佈之不同,可能會有不同之行為模式,並非所有犯罪行為皆符合 Lévy walk分布。本研究為不同類型之性侵犯找到新的犯罪行為模式特徵,並發現可能影響其犯罪模式之相關因素。該結果能協助政府單位與警方加強部屬,以達到降低或預防犯罪之目的。
Many criminologists have pointed out that the occurrence of crime is not randomly distributed, but has specific behavior patterns according to the habits and characteristics of different offenders. In the existing studies of Taiwan, most of them focused on property crimes, such as residential burglary, vehicle theft, and robbery. However, relatively few studies concentrate on sexual crimes, especially serial sexual crimes. In addition, these scholars didn’t compare the criminal behavior pattern of different types offenders and find out the possible related factors. As the result, this study explored the spatial and temporal characteristics of serial sexual crimes and found out the crime pattern of sexual offender with different characteristics.
In order to compare the characteristics of serial sexual offenders abroad and domestically, this study collected three datasets in different regions. The first dataset includes four serial sexual offenders and a total of 52 cases in San Diego, CA, United States (LeBeau (1992). The second includes 24 serial sexual offenders and a total of 100 cases in northern Taiwan (Hsu, 2017). The third dataset includes 78 serial sexual offender and 246 cases in central Taiwan.
There are three statistical methods in this study: 1. Chi-squared test was used to examine the relationship between different variables and the travel distance of the offender, and then define important factors that may affect the behavior pattern of the offender. 2. Knox test was used to define if there are spatiotemporal cluster of serial sexual crimes. 3 the goodness-of-fit test was used to define which model fit the observed data best.

The results show that: (1) the serial sexual crimes were clustered in 500 meters and 7 days; (2) offender who is older than 40 years older who doesn’t use any transportation, tend to commit crime at home or short distance. And if victim is familiar with offender or victim’s age is younger than 14 years old, sexual crime has higher probability happened at offender’s home or short distance. In addition, this study also found inconsistent results from different datasets. The travel distance and step distance in central Taiwan follow the Lévy walk distribution, while other two datasets (San Diego and northern Taiwan) follow the log-normal distribution and Weibull distribution respectively. In other words, criminals in different regions may have different behavior patterns, not all criminal’s behaviors follow Lévy walk distribution. This result can assist government units and the police to strengthen their subordinates to achieve the goal of reducing or preventing crime.
1.1 Background-----1
1.2 Study Goal-----5
1.3 Organization of Thesis-----6
2.1 Crime Theory-----8
2.1 Analysis of Offender’s Temporal and Spatial Characteristics-----10
2.1 Crime Factors-----12
2.2 Victim Vulnerability Factors-----15
2.3 Application of Spatial Analysis to Crimes-----16
3.1 Study Area-----20
3.2 Data Collection-----21
3.3 Chi-squared Test of Independence-----23
3.4 Knox Test-----26
3.5 Goodness-of-fit Test-----28
4.1 Chi-squared Test of Independence----35
4.2 Knox Test-----39
4.3 Goodness-of-fit Test-----41
4.3.1 Descriptive Statistics-----42
4.3.2 Goodness-of-fit Test of Travel Distance-----43
4.3.3 Goodness-of-fit Test of Step Distance-----49
5.1 Conclusions and Discussions-----55
5.2 Limitations and Future Works-----63
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