跳到主要內容

臺灣博碩士論文加值系統

(100.28.0.143) 您好!臺灣時間:2024/07/18 07:09
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:伊斯梅爾
研究生(外文):Noble, Ismael Augusto
論文名稱:在犯罪事件中透過潛在關聯性推論缺失的空間地點資訊
論文名稱(外文):Inferring Missing Spatial Locations Based on Implicit Relationships in Crime Incidents
指導教授:陳宜欣陳宜欣引用關係
指導教授(外文):Chen, Yi-Shin
口試委員:彭文志陳朝欽
口試委員(外文):Peng, Wen-ChihChen, Chaur-Chin
口試日期:2018-06-19
學位類別:碩士
校院名稱:國立清華大學
系所名稱:資訊系統與應用研究所
學門:電算機學門
學類:系統設計學類
論文種類:學術論文
論文出版年:2018
畢業學年度:106
語文別:英文
論文頁數:53
中文關鍵詞:地理編碼分群模糊集合論
外文關鍵詞:GeocodingClusteringFuzzy set theory
相關次數:
  • 被引用被引用:0
  • 點閱點閱:140
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
在發展中國家城市中警察工作是一項艱鉅的任務,而社會中普遍人民仍對警寄 與高度期望即使社會資源如此匱乏,這些壓力加深了社會中許多的零星犯罪, 導致警察們經歷一段密集的工作時期,這也讓他們沒有多餘的時間來建立精準 毫無錯誤的報告。而報告數據中的錯誤以及資料不一致也讓在對這些數據進行 地理編碼時更加困難,這也進一步造成大多數事件報告仍然無法進行地理編 碼。然而我們可以透過模糊集合理論找出每個事件報告的關係來緩解這個問 題。我們常利用地理編碼數據與無法做編碼的數據之間的關係來生成相近的事 件位置。在本篇論文中,針對不可進行地理數據編碼的資料藉由找出地形特 徵,時間特徵和警察局的位置資訊,來近似估出犯罪事件的所在位置,並可以 讓以群集方式產生的地理編碼事件更為豐富。
Police work can be a difficult task in the urban cities of developing nations, high expectations combined with a lack of resources are common occurrences. These stresses are further compounded by the sporadic nature of crime, causing officers to experience periods of intense work activity. As a result officers spend a very small amount of their available time to ensure flawless report creation. Errors in report data coupled with inconsistent representations make geocoding this data very difficult. These difficulties causes the majority of incident reports to remain ungeocodable, and by extension unusable for clustering. However, this problem is mitigated through the application of fuzzy set theory, relationships between incident reports can be formed. Relationships between geocodable data and ungeocodable data are used to generate an approximation of the ungeocodable incident’s location. In this thesis the relationships found in topographic features, temporal features and the modeling of police officer information are used to generate approximate location information for ungeocodable crime incidents. Which can then be used to enrich geocoded incidents in crime cluster generation.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1 Criminal Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.2 Crime Hotspots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.3 Proximal Relationship Generation . . . . . . . . . . . . . . . . . . . . 6
2.4 Fuzzy Sets and Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Crime as a Set of Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.2 Feature Representations . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.2.1 Temporal Information . . . . . . . . . . . . . . . . . . . . . . 9
3.2.2 Topographic Information . . . . . . . . . . . . . . . . . . . . . 10
3.2.3 Responding Officer Information . . . . . . . . . . . . . . . . . 11
Identifying Relationships . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.1 Officer Patterns from Temporal Features . . . . . . . . . . . . . . . . . 14
4.2 Sequence Fuzzyfication . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4.2.1 Edit Distance . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4.3 Expanding Officer Search Space . . . . . . . . . . . . . . . . . . . . . 17
4.4 Linking Inconsistent Topographic Features . . . . . . . . . . . . . . . . 18
Implicitly Inferring a Location . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
5.1 First Stage Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . .22
5.2Inferring Approximate Spatial Locations . . . . . . . . . . . . . . . . . 23
5.2.1 Officer Shift Association (OSA) . . . . . . . . . . . . . . . . . 23
5.2.2 Area Based Association (ABA) . . . . . . . . . . . . . . . . . 24
5.2.3 Officer Patrol Association (OPA) . . . . . . . . . . . . . . . . . 26
5.3 Implicit Incident Association . . . . . . . . . . . . . . . . . . . . . . . 29
5.4 Final Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
6.1 Data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
6.2 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
6.3 Baseline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
6.4 Experiment Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
6.4.1 Cluster Comparisons . . . . . . . . . . . . . . . . . . . . . . . 35
6.4.2 Experiment Hyper-parameters . . . . . . . . . . . . . . . . . . 38
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
7.1 ABA Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
7.2 OSA Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
7.3 OPA Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
7.4 Combined Performance . . . . . . . . . . . . . . . . . . . . . . . . . . 45
7.5 Final Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
List of Tables
1 Summary of Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2 Summary of Functions . . . . . . . . . . . . . . . . . . . . . . . . . . 20
[1] Tony H. Grubesic and Alan T. Murray. Detecting hot spots using cluster analysis and gis. In Proceedings from the fifth annual international crime mapping research conference, volume 26, 2001.
[2] Mark Alex Noble. Two fold clustering approach to placing ungeocodable points in a cluster. , pages 1–24, 2016.
[3] Lawrence W. Sherman and David Weisburd. General deterrent effects of police patrol in crime “hot spots”: A randomized, controlled trial. Justice quarterly, 12(4):625–648, 1995.
[4] André-Michel Guerry. Essai sur la statistique morale de la france. 01 1833.
[5] Ernest W. Burgess. The growth of the city: an introduction to a research project. In Urban ecology, pages 71–78. Springer, 2008.
[6] Frederic Milton Thrasher. The gang: A study of 1,313 gangs in Chicago. University of Chicago Press, 2013.
[7] Clifford R. Shaw and Henry D. McKay. Juvenile delinquency and urban areas. Chicago, Ill, 1942.
[8] Sanjoy Chakravorty. Identifying crime clusters: The spatial principles. Middle States Geographer, 28:53–58, 1995.
[9] Patricia Brantingham and Paul Brantingham. Crime pattern theory. In Environmental criminology and crime analysis, pages 100–116. Willan, 2013.
[10] John E. Eck and David L. Weisburd. Crime places in crime theory. 2015.
[11] Patricia Brantingham and Paul Brantingham. Crime pattern theory. In Environmental criminology and crime analysis, pages 100–116. Willan, 2013.
[12] Lawrence W. Sherman, Patrick R. Gartin, and Michael E. Buerger. Hot spots of predatory crime: Routine activities and the criminology of place. Criminology, 27(1):27–56, 1989.
[13] Ralph Taylor, Stephen D. Gottfredson, and Sidney Brower. Block crime and fear: Defensible space, local social ties, and territorial functioning. 21:303–331, 11 1984.
[14] David Weisburd and Lorraine Green. Policing drug hot spots: The jersey city drug market analysis experiment. 12:711–735, 12 1995.
[15] Richard L. Block and Carolyn Rebecca Block. Space, place and crime: Hot spot areas and hot places of liquor-related crime. Crime and place, 4(2):145–184, 1995.
[16] John Eck, Spencer Chainey, James Cameron, and Ronald Wilson. Mapping crime: Understanding hotspots. 2005.
[17] James Q. Wilson and George L. Kelling. Broken windows. Atlantic monthly, 249(3):29–38, 1982.
[18] Ralph Taylor. Breaking away from broken windows: Baltimore neighborhoods and the nationwide fight against crime, grime, fear, and decline. Routledge, 2018.
[19] Timothy C. Hart and Paul A. Zandbergen. Effects of data quality on predictive hotspot mapping. National Criminal Justice Reference Service, 2012.
[20] E. Limoges. Improvement of decennial census small-area employment data: New methods to allocate ungeocodable workers. In Transportation Research Board Conference Proceedings, volume 2, 1997.
[21] Lotfi A. Zadeh. Fuzzy logic. Computer, 21(4):83–93, 1988.
[22] Lotfi A. Zadeh. Similarity relations and fuzzy orderings. Information sciences, 3(2):177–200, 1971.
[23] Lotfi A. Zadeh. Fuzzy sets. In Fuzzy Sets, Fuzzy Logic, And Fuzzy Systems: Selected Papers by Lotfi A Zadeh, pages 394–432. World Scientific, 1996.
[24] Lotfi A. Zadeh. Fuzzy logic= computing with words. IEEE transactions on fuzzy systems, 4(2):103–111, 1996.
[25] Didier Dubois and Henri Prade. A review of fuzzy set aggregation connectives. Information sciences, 36(1-2):85–121, 1985.
[26] Gonzalo Navarro. A guided tour to approximate string matching. ACM computing surveys (CSUR), 33(1):31–88, 2001.
[27] Vladimir I Levenshtein. Binary codes capable of correcting deletions, insertions, and reversals. In Soviet physics doklady, volume 10, pages 707–710, 1966.
[28] josegonzalez acslater00, medecau. fuzzywuzzy - fuzzy string matching using levenshtein distance. https://github.com/seatgeek/fuzzywuzzy, 2018.
[29] Luc Anselin. Local indicators of spatial association—lisa. Geographical analysis, 27(2):93–115, 1995.
[30] Jared Aldstadt and Arthur Getis. Using amoeba to create a spatial weights matrix and identify spatial clusters. Geographical Analysis, 38(4):327–343, 2006.
[31] Eric Jones, Travis Oliphant, Pearu Peterson, et al. SciPy: Open source scientific tools for Python, 2001–. [Online; accessed ¡today¿].
[32] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830, 2011.
[33] Joseph C Dunn. A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters. 1973.
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top