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| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 唐代彪(De-Piao Tang) | |
| dc.contributor.author | Chih -kuo Chang | en |
| dc.contributor.author | 張致國 | zh_TW |
| dc.date.accessioned | 2023-03-19T23:54:56Z | - |
| dc.date.copyright | 2022-08-23 | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-08-19 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/86423 | - |
| dc.description.abstract | 在資通訊技術的進步下,警方現行執行勤務所使用的警用行動載具M-Police,可透過各項警政APP輔助員警各項警察任務,由於M-Police是員警執勤時的必要裝備,故其查詢之日誌紀錄即為員警的攔查或臨檢的紀錄歷程,且其後台查詢紀錄亦包含時間及座標位置,資料具有深入分析的價值。 本研究利用員警的盤查紀錄建立犯罪者的社會接觸網絡,再以故意殺人、竊盜、毒品、詐欺、賭博、槍砲刀械及妨害性自主案件等犯罪類型,建立各犯罪類型之社會接觸網絡,以社會網絡分析(Social Network Analysis,SNA)對不同犯罪類型之網絡進行驗證分析,透過分析網絡的密度(Density)及群聚係數(Clustering coefficient)探討網絡的凝聚程度(Cohesion),以及分析不同犯罪角色的連線中心性(Degree Centrality)、介數中心性(Betweenness Centrality)及接近中心性(Closeness Centrality)等中心性指標,探討不同犯罪角色在網絡中的呈現特性。 研究結果發現,不同犯罪類型所形成之網絡型態大相逕庭,在傳統暴力犯罪類型中,犯罪者之間並未發現接觸紀錄,故社會網絡分析針對此犯罪類型,可提供刑案偵查或犯罪預防之效果有限。但在毒品及詐欺犯罪中,不同犯罪者之間接觸的情形複雜。毒品犯罪角色不同,則網絡中呈現的樣貌也有所差異,其中以毒品施用者最積極與他人建立關係,毒品犯罪角色位階越高,其網絡的連線中心性及介數中心性呈現越低,接近中心性越高,代表高階毒品犯罪者雖然盡量減少與其他節點接觸,但卻身處網絡的中心,具備獨立性;而詐欺犯罪者呈現恰好與毒品犯罪者相反,犯罪角色位階越高,連線中心性及介數中心性呈現越高,接近中心性越低,代表低階詐欺犯罪者大多各自為政,反倒是高階詐欺犯罪者基於犯罪需要,必須與其他犯罪者建立關係。 | zh_TW |
| dc.description.abstract | Due to the advancement of information and communication technology, M-Police, currently used by the police to perform everyday duties, can assist police officers in numerous police tasks through various apps. Since the M-Police is necessary for police officers on duty, the log records of the query are the records of the police's interception or inspection, and its background include time and coordinate position. Therefore In-depth analysis to this data are highly valued. This research used the police investigation records to establish the social contact network of criminals, and then establishes social contacts for each type of crimes, such as intentional homicide, theft, drugs, fraud, gambling, guns and knives, and cases of obstructing sexual autonomy. IT used Social Network Analysis to verify and analyze the network of different types of crimes, analyzed the density and the clustering coefficient of the network to explore the degree of network cohesion, and analyzed Centrality indicators such as Degree Centrality, Betweenness Centrality and Closeness Centrality of different criminal roles to explore the revealing characteristics of different criminal roles in the network. The results of the study found that the style of networks form by different types of crimes are so different. In the types of traditional violent crimes, no contact records were found between offenders. Social network analysis can neither provide information on criminal investigation nor crime prevention for this type of crime, the effect is limited. However, in the case of drug and fraud crimes, the interacting between different offenders are complex. The appearances of different roles in the drug crime performance differently. Drug users prefer to establish relationships most proactively. The higher the rank of the drug crime role is, the lower Degree Centrality and Betweenness Centrality it shows. The higher Closeness Centrality presents that although the higher-level drug criminals try to minimize contact with other nodes, they are in the center of the network with independence. Fraud offenders, however, appear to be the exact opposite of drug offenders. The higher the rank of the drug crime role is, the higher Degree Centrality and Betweenness Centrality it shows. The lower Closeness Centrality presents that Low-level fraud offenders mostly act on their own, but high-level fraud offenders must establish relationships with other offenders based on criminal needs. | en |
| dc.description.provenance | Made available in DSpace on 2023-03-19T23:54:56Z (GMT). No. of bitstreams: 1 U0001-1508202210293500.pdf: 2560376 bytes, checksum: a1471330d935e31611cd3cb075e031fa (MD5) Previous issue date: 2022 | en |
| dc.description.tableofcontents | 第一章 緒論 1 第一節 研究緣起與問題意識 1 第二節 研究目的與主要研究問題 3 第二章 相關理論回顧與文獻探討 8 第一節 整體犯罪社會網絡分析研究 9 第二節 個體犯罪社會網絡分析研究 10 第三節 基於位置的社交網絡應用 11 第四節 相關犯罪學理論研究 12 第五節 文獻綜評 14 第三章 研究設計 17 第一節 研究架構與研究假設 17 第二節 研究途徑、工具與流程 20 第三節 研究限制 30 第四節 研究對象與資料來源 31 第四章 資料分析 33 第一節 犯罪類型凝聚程度 33 第二節 犯罪者角色中心性指標 42 第三節 綜合分析結果 44 第五章 結論 53 第一節 主要研究發現 53 第二節 後續研究建議 56 第三節 政策建議 56 參考文獻 59 附錄 65 圖表目錄 圖1 歷年全般刑案發生數 4 圖2各類案件佔全般刑案比例 5 圖3詐欺案件被害總金額 5 圖4毒品查獲純質淨重 6 圖5研究架構圖 19 圖6研究流程圖 26 圖7各類犯罪網絡孤點比例圖 33 圖8竊盜案類整體網絡結構圖 34 圖9賭博案類整體網絡結構圖 35 圖10槍砲刀械案類整體網絡結構圖 37 圖11詐欺案類整體網絡結構圖 38 圖12故意殺人案類整體網絡結構圖 39 圖13 妨害性自主案類整體網絡結構圖 40 圖14 毒品案類整體網絡結構圖 41 圖15各階級犯罪角色連線中心性指標 47 圖16各階級犯罪角色介數中心性指標 48 圖17各階級犯罪角色接近中心性指標 48 圖18毒品案類角色分階級網絡圖 50 圖19毒品案類角色分階級網絡圖 51 表1毒品犯罪角色分級表 29 表2詐欺犯罪角色分級表 29 表3竊盜案類凝聚程度相關數據彙整表 35 表4賭博案類凝聚程度相關數據彙整表 36 表5槍砲刀械案類凝聚程度相關數據彙整表 37 表6詐欺案類凝聚力相關數據彙整表 38 表7故意殺人案類凝聚力相關數據彙整表 40 表8妨害性自主案類凝聚力相關數據彙整表 41 表9毒品案類凝聚力相關數據彙整表 42 表10毒品犯罪角色中心性指標分析表 43 表11詐欺犯罪角色中心性指標分析表 44 表12各犯罪類型凝聚程度比較分析 45 表13毒品犯罪角色分區密度表 49 表14詐欺犯罪角色分區密度表 50 表15犯罪網絡被害人數表 52 | |
| dc.language.iso | zh-TW | |
| dc.subject | 警用行動載具 | zh_TW |
| dc.subject | 中心性指標 | zh_TW |
| dc.subject | 凝聚程度 | zh_TW |
| dc.subject | 警用行動載具 | zh_TW |
| dc.subject | 社會網絡分析 | zh_TW |
| dc.subject | 犯罪網絡 | zh_TW |
| dc.subject | 中心性指標 | zh_TW |
| dc.subject | 犯罪網絡 | zh_TW |
| dc.subject | 社會網絡分析 | zh_TW |
| dc.subject | 凝聚程度 | zh_TW |
| dc.subject | criminal network | en |
| dc.subject | M-Police | en |
| dc.subject | social network analysis | en |
| dc.subject | social network analysis | en |
| dc.subject | M-Police | en |
| dc.subject | Cohesion | en |
| dc.subject | criminal network | en |
| dc.subject | Centrality | en |
| dc.subject | Cohesion | en |
| dc.subject | Centrality | en |
| dc.title | M-Police查詢紀錄之應用-犯罪者社會網絡之研究 | zh_TW |
| dc.title | The Application of M-Police Log Recording: Research on the Social Network of Criminals | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 110-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 鄧志松(Chih-Sung Teng) | |
| dc.contributor.oralexamcommittee | 周嘉辰(Chia-Chen Chou),馬財專(Tsai-Chuan Ma) | |
| dc.subject.keyword | 犯罪網絡,社會網絡分析,警用行動載具,凝聚程度,中心性指標, | zh_TW |
| dc.subject.keyword | criminal network,social network analysis,M-Police,Cohesion,Centrality, | en |
| dc.relation.page | 80 | |
| dc.identifier.doi | 10.6342/NTU202202389 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2022-08-19 | |
| dc.contributor.author-college | 社會科學院 | zh_TW |
| dc.contributor.author-dept | 國家發展研究所 | zh_TW |
| dc.date.embargo-lift | 2022-08-23 | - |
| Appears in Collections: | 國家發展研究所 | |
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| File | Size | Format | |
|---|---|---|---|
| U0001-1508202210293500.pdf | 2.5 MB | Adobe PDF | View/Open |
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