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| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 賴進貴(Jinn-Guey Lay) | |
| dc.contributor.author | Po-Hui Yu | en |
| dc.contributor.author | 游柏輝 | zh_TW |
| dc.date.accessioned | 2021-06-13T03:13:20Z | - |
| dc.date.available | 2016-08-04 | |
| dc.date.copyright | 2011-08-04 | |
| dc.date.issued | 2011 | |
| dc.date.submitted | 2011-07-29 | |
| dc.identifier.citation | REFERENCE
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/31463 | - |
| dc.description.abstract | 為了更有效地理解地域建成環境中犯罪影響因子與犯罪熱點的時空動態，本研究嘗試以多維度核密度估計與時空加權迴歸模型為工具，試圖在時間地理學的脈絡下討論利用此方法偵測犯罪熱點與機制移轉的可能性與實務上意義。
由於空間資料本身具有空間自相關與誤差異質性，直至今日地理加權迴歸模型已被驗證為處理空間資料變數不穩定性的有效取徑。然而地理加權僅能從時間剖面分析的本質，亦限制其探索多維度社會現象的可能性。從而本研究試圖發展其時間延伸模型，以探索時空資料之變數不穩定性。 基於過往研究證實住宅竊盜與環境因子具有深度的互動關係。本研究利用臺北市大安區1999至2008年之住宅竊盜空間對位資料，驗證該延伸模型之分析結果不僅足以在三維時空中探索犯罪群聚的轉移，更可以深入地檢視其誘發機制的動態過程，並為後續犯罪機制時空動態移轉之研究提供基礎。 | zh_TW |
| dc.description.abstract | For a more effective understanding of spatial-temporal dynamic of criminal factors and hotspots in local-scale built environment, this study employs multi-dimensional kernel density estimation (KDE) and extended weighted regression (STWR) to uncover future possibility of detecting the displacement of hotspots and factors in a context of time geography.
Due to the spatial autocorrelation and heterogeneity of spatial data, geographically weighted regression (GWR) has been proven as a significant approach to address the spatial heterogeneity. However, the cross-sectional nature of GWR constrains it to explore the multi-dimensional phenomena simultaneously. Thus, this study develops a temporal variant of GWR to detect the spatial-temporal heterogeneity of structural measures in space-time cube. Using a geocoded database of residential burglary in Da-an district of Taipei City from 1999 to 2008, this study examine that proposed framework allowing interactively 3-D geovisualization of hotspots by volume rendering. This thesis also represents the spatial heterogeneity of estimations of social structural measures by spatial-temporal weighted regression. Emphasizing the supplementary aspect of this embedded framework, the author concludes that interactive spatial-temporal data analysis and weighted regression could extract and interpret the spatial-temporal heterogeneity of residential burglary as well as uncover the possibility of detecting criminal displacement in future study. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T03:13:20Z (GMT). No. of bitstreams: 1 ntu-100-R99228017-1.pdf: 2836316 bytes, checksum: 58e39285006b013d9bb93dfc72b260c5 (MD5) Previous issue date: 2011 | en |
| dc.description.tableofcontents | CONTENTS
ACKNOWLEDGEMENT i 摘要 iii ABSTRACT v CONTENTS vii LIST OF FIGURES ix LIST OF TABLES xi Chapter 1 INTRODUCTION 1 1.1 Research Background 1 1.2 Research Objectives 3 1.3 Organization of the Thesis 5 Chapter 2 LITERATURE REVIEW 7 2.1 Environmental Criminology and Theories 7 2.1.1 Social Disorganization Theory 7 2.1.2 Routine Activity Theory 10 2.1.3 New Opportunity Theory 11 2.2 Methods for Addressing the Spatial-Temporal Issues 14 2.2.1 Time Geography 14 2.2.2 Geographically Weighted Regression 19 2.3 Geovisualization of Multi-dimensional Phenomena 23 Chapter 3 RESEARCH DESIGN 27 3.1 Methods 27 3.1.1 Spatial-Temporal Kernel Density Estimation (STKDE) 27 3.1.2 Spatial-Temporal Weighted Regression (STWR) 32 3.2 Data 36 3.2.1 Criminal Data: Dependent Variable 36 3.2.2 Environmental Factors: Independent Variables 40 3.3 Study Area 44 3.4 Matlab Programming 47 Chapter 4 EMPIRICAL STUDY 50 4.1 Diagnosis of Spatial-Temporal Heterogeneity 50 4.2 Selection of Structural Measures 52 4.3 Hypotheses Testing 54 4.3.1 F-Test: exam the adequacy of variables selection 54 4.3.2 T-Test: exam the efficiency and influence of variables 55 4.4 Models Comparison 55 4.5 Results and Discussion 56 4.5.1 3-D Geovisualization results 56 4.5.2 Discussion: STWR in Criminology and Political Practices 60 Chapter 5 CONCLUSIONS 63 REFERENCE 65 | |
| dc.language.iso | en | |
| dc.subject | 犯罪轉移 | zh_TW |
| dc.subject | 時空動態 | zh_TW |
| dc.subject | 加權迴歸模型 | zh_TW |
| dc.subject | 空間異質性 | zh_TW |
| dc.subject | 住宅竊盜 | zh_TW |
| dc.subject | Criminal displacement | en |
| dc.subject | Spatial-Temporal | en |
| dc.subject | Weighted regression | en |
| dc.subject | Spatial Heterogeneity | en |
| dc.subject | Residential burglary | en |
| dc.title | 以時空加權迴歸模型探索地域犯罪機制的時空變異性 | zh_TW |
| dc.title | Exploring Spatial Heterogeneity of Local Factors of Crime Events with Spatial-Temporal Weighted Regression | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 99-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 郭迺峰,溫在弘(Tzai-Hung Wen) | |
| dc.subject.keyword | 時空動態,加權迴歸模型,空間異質性,住宅竊盜,犯罪轉移, | zh_TW |
| dc.subject.keyword | Spatial-Temporal,Weighted regression,Spatial Heterogeneity,Residential burglary,Criminal displacement, | en |
| dc.relation.page | 71 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2011-08-01 | |
| dc.contributor.author-college | 理學院 | zh_TW |
| dc.contributor.author-dept | 地理環境資源學研究所 | zh_TW |
| Appears in Collections: | 地理環境資源學系 | |
Files in This Item:
| File | Size | Format | |
|---|---|---|---|
| ntu-100-1.pdf Restricted Access | 2.77 MB | Adobe PDF |
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