在犯罪事件中透過潛在關聯性推論缺失的空間地點資訊

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研究生：	伊斯梅爾 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 Inferring Missing Spatial Locations Based on Implicit Relationships in Crime Incidents
指導教授：	陳宜欣 Chen, Yi-Shin
口試委員:	彭文志 Peng, Wen-Chih 陳朝欽 Chen, Chaur-Chin
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 資訊系統與應用研究所 Institute of Information Systems and Applications
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：	Geocoding, Clustering, Fuzzy set theory
相關次數：	點閱：3 下載：0
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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
1 Criminal Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 Crime Hotspots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
3 Proximal Relationship Generation . . . . . . . . . . . . . . . . . . . . 6
4 Fuzzy Sets and Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Crime as a Set of Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2 Feature Representations . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.1 Temporal Information . . . . . . . . . . . . . . . . . . . . . . 9
2.2 Topographic Information . . . . . . . . . . . . . . . . . . . . . 10
2.3 Responding Officer Information . . . . . . . . . . . . . . . . . 11
Identifying Relationships  . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1 Officer Patterns from Temporal Features . . . . . . . . . . . . . . . . . 14
2 Sequence Fuzzyfication . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.1 Edit Distance . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3 Expanding Officer Search Space . . . . . . . . . . . . . . . . . . . . . 17
4 Linking Inconsistent Topographic Features . . . . . . . . . . . . . . . . 18
Implicitly Inferring a Location  . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1 First Stage Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . .22
2Inferring Approximate Spatial Locations . . . . . . . . . . . . . . . . . 23
2.1 Officer Shift Association (OSA) . . . . . . . . . . . . . . . . . 23
2.2 Area Based Association (ABA) . . . . . . . . . . . . . . . . . 24
2.3 Officer Patrol Association (OPA) . . . . . . . . . . . . . . . . . 26
3 Implicit Incident Association . . . . . . . . . . . . . . . . . . . . . . . 29
4 Final Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
Experiments  . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
1 Data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3 Baseline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4 Experiment Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.1 Cluster Comparisons . . . . . . . . . . . . . . . . . . . . . . . 35
4.2 Experiment Hyper-parameters . . . . . . . . . . . . . . . . . . 38
Results  . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
1 ABA Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
2 OSA Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3 OPA Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4 Combined Performance . . . . . . . . . . . . . . . . . . . . . . . . . . 45
5 Final Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
List of Tables
Summary of Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Summary of Functions . . . . . . . . . . . . . . . . . . . . . . . . . . 20
                                

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