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題名 空間異質性檢測方法的比較與應用
A Study of Methods for Detecting Spatial Inhomogeneity作者 梁舒涵
Liang, Shu-Han貢獻者 余清祥<br>楊曉文
Yue, Ching-Syang<br>Yang, Sheau-Wen
梁舒涵
Liang, Shu-Han關鍵詞 空間異質性
群聚偵測
空間自相關
電腦模擬
檢定力
Spatial heterogeneity
Cluster detection
Spatial autocorrelation
Computer simulation
Power日期 2022 上傳時間 2-Sep-2022 14:45:33 (UTC+8) 摘要 空間異質性(Spatial Inhomogeneity)是空間統計中的重要議題。空間異質性的檢定可分為三種類型：總體檢定(Global test)、局部檢定(Local test)、焦點檢定(Focused test)，總體檢定可用於檢定全區域的資料是否為空間同質，局部檢定多用於偵測高風險地區（或稱為群聚，Cluster），焦點檢定可用於確認特定地區周圍是否有較高的發生率。本文選擇常見的三種異質性檢定：Moran’s I（總體檢定）、SaTScan（局部檢定）以及Tango Score Test（焦點檢定），透過模擬及實證分析評估這些方法在不同空間特性之下，像是存在空間自相關(Spatial Autocorrelation)及群聚時的偵測效果，以提供實務分析的參考。本文電腦模擬的實驗區間為二度空間，大小為5×5、7×7、9×9、…、21×21的格子點，檢測各方法在空間同質、空間自相關、群聚的效果。研究發現三種方法在空間同質性的結果大致相同，Moran’s I對於空間自相關的最為敏感，而對於群聚存在則以SaTScan效果最佳，Tango Score Test次之。模擬結果亦發現，在風力影響之下會導致Tango Score Test以及SaTScan的偵測能力（偽陰性，False Negative）大幅度下降，但Moran’s I的偽陽性（False Positive）偏高，使用時需特別注意。本文也將這些方法套用至臺灣鄉鎮市區歷年前三大死因（惡性腫瘤、心臟疾病、肺炎），發現主要死因的死亡率具有空間異質性，熱區大多落在東南部山區，且位置並未隨時間有明顯改變，可能與醫療資源分配不均有關；肺炎死亡率在資源充足的西半部逐年上升，推測與都市化的空氣品質惡化有關。
Judging spatial heterogeneity has always been an important topic in spatial statistics. There are three types of tests for checking spatial heterogeneity: Global test, Local test and Focused test. Global test can be used to test whether the data are spatially homogeneous; Local test is usually used to detect the location of high-risk areas (i.e., clusters); Focused test can be used to confirm whether there are high incident rates around a specific area. In this study, we select three common heterogeneity tests: Moran’s I (Global test), SaTScan (Local test) and Tango Score Test (Focused test), and evaluate these methods can detect spatial autocorrelation and/or clusters via simulation and empirical analysis.The simulation study is performed on a two-dimensional space, with lattice data of size 5×5, 7×7, 9×9, …, 21×21, under the assumption that the data satisfying spatial homogeneity, spatial autocorrelation and clustering. The empirical data considered are the township-level overall and major cause mortality rates in Taiwan. We found that these methods have similar results in checking spatial homogeneity. Moran’s I is the most sensitive test to spatial autocorrelation, and SaTScan is the best for testing the existence of clusters, followed by Tango Score Test. On the other hand, the simulation results show that under the influence of wind, the testing powers of Tango Score Test and SaTScan will be greatly reduced, while the False Positive rates of Moran’s I are misleadingly high. Thus, the spatial methods need to be used carefully under the influenced of wind. We also apply these methods to the mortality rates of top three major death causes (cancer, heart disease and pneumonia) in Taiwan. It seem they change steadily, or the difference of mortality rates between two consecutive years satisfying spatially heterogeneity, and most clusters of mortality rates are located in the southeastern mountain areas.參考文獻 一、中文部分江博煌、謝顯堂、陳筱蕙、詹大千、劉德明、溫啟邦、陳筱蕾、毛義方（2006），「利用地理資訊系統評估台南安順污染廠址周圍之土壤污染」，《台灣公共衛生雜誌》，25卷5期，頁363-371。.呂宗學（2020），「原死因選擇準則改變對死因別死亡率趨勢分析的影響」，《台灣公共衛生雜誌》，39卷5期，頁469-477。李宗儒、陳昭榮、李妙純（2021），「台灣大腸癌死亡率之空間分析」，《台灣公共衛生雜誌》，40卷2期，頁225-240。余清祥、梁穎誼、林佩柔（2022），「健康、醫療利用與人口移動的關聯」，to appear in《地理學報》。林志銘、林文苑（2012），「台灣酒精性疾病死亡率之空間聚集分析」，《台灣公共衛生雜誌》，31卷2期，頁195-204。胡立諄、賴進貴（2006），「臺灣女性癌症的空間分析」，《臺灣地理資訊學刊》，4期，頁39-55。凃明蕙（2020），「臺灣居民健康與壽命之空間分析」，政治大學統計學系碩士論文。黃信誠（2000），「空間統計簡介」，《自然科學簡訊》，12卷3期，頁101-104。黃柏誠（2012），「多重群集的偵測研究」，政治大學統計學系碩士論文。蔡承庭（2011），「焦點檢定方法比較」，政治大學統計學系碩士論文。二、英文部分Anselin, L. (1995). “Local Indicators of Spatial Association—LISA,” Geographical Analysis, vol. 27, issue 2, pp. 93-115.Anselin, L. and Kelejian, H.H. (1997). “Testing for Spatial Error Autocorrelation in the Presence of Endogenous Regressors,” International Regional Science Review, vol. 20, issue 1-2, pp. 153-182.Escamilla, V., Hampton, K.H., Gesink, D.C., Serre, M.L., Emch, M., Leone, P.A., Samoff, E. and Miller, W.C. (2016), “Influence of Detection Method and Study Area Scale on Syphilis Cluster Identification in North Carolina,” Sexually Transmitted Diseases, vol. 43, no. 4, pp. 216-221.Geary, R.C. (1954). “The Contiguity Ratio and Statistical Mapping,” The Incorporated Statistician, vol. 5, no. 3, pp. 115-127, 129-146.Kulldorff, M. and Nagarwalla, N. (1995). “Spatial Disease Clusters: Detection and Inference,” Statistics in Medicine, vol. 14, issue 8, pp. 799-810.Kulldorff, M. (2021). SaTScan User Guide, version 10. (Cambridge MA: Harvard.)Lawson, A.B. (1993). “On the Analysis of Mortality Events Associated with a Prespecified Fixed Point,” Journal of Royal Statistical Society, Series A (Statistics in Society), vol. 156, no. 3, pp. 363-377.Moran, P. (1950). “A Test for the Serial Independence of Residuals,” Biometrika, vol. 37, no. 1/2, pp. 178-181.Oden, N. (1955). “Adjusting Moran`s I for population density,” Statistics in Medicine, vol. 14, issue 1, pp. 17-26.Perry, G.L.W., Miller, B.P. and Enright, N.J. (2006). “A Comparison of Methods for the Statistical Analysis of Spatial Point Patterns in Plant Ecology,” Plant Ecology , vol. 187, issue 1, pp. 59-82.Puett, R.C., Lawson, A.B., Clark, A.B., Hebert, J.R. and Kulldorff, M. (2010), “Power Evaluation of Focused Cluster Tests,” Environmental and Ecological Statistics, vol. 17, pp. 303-316.Song, C. and Kulldorff, M. (2003), “Power Evaluation of Disease Clustering Tests,” International Journal of Health Geographics, vol. 2, no. 9, pp. 1-8.Stone, R.A. (1988), “Investigations of Excess Environmental Risks around Putative Sources: Statistical Problems and a Proposed Test,” Statistics in Medicine, vol. 7, issue 6, pp. 649-660.Sun, Y. (2002), “Determining the Size of Spatial Clusters in Focused tests: Comparing Two Methods by Means of Simulation in a GIS,” Journal of Geographical Systems, vol. 4, pp. 359-370.Tango, T. (1995), “A Class of Tests for Detecting ‘General’ and ‘Focused’ Clustering of Rare Diseases,” Statistics in Medicine, vol. 14, issue 21-22, pp. 2323-2334.Tango, T. (2002), “Score Tests for Detecting Excess Risks around Putative Sources,” Statistics in Medicine, vol. 21, issue 4, pp. 497-514.Tango, T. and Takahashi, K. (2005). “A Flexibly Shaped Spatial Scan Statistic for Detecting Clusters,” International Journal of Health Geographics, vol. 4, no. 11, pp. 799-810.Tobler, W.R. (1970). “A Computer Movie Simulating Urban Growth in the Detroit Region,” Economic Geography, vol. 46, issue 1, pp. 234-240.Turcke, M.A. and Kueper, B.H. (1996). “Geostatistical Analysis of the Borden Aquifer Hydraulic Conductivity Field,” Journal of Hydrology, vol. 178, issue 1-4, pp. 223-240.Waldhör, T. (1996). “The Spatial Autocorrelation Coefficient Moran`s I under Heteroscedasticity,” Statistics in Medicine, vol. 15, issue 7-9, pp. 887-892.Waller, L.A., Turnbull, B.W., Clark, L.C. and Nasca, P. (1992). “Chronic Disease Surveillance and Testing of Clustering of Disease and Exposure: Application to Leukemia Incidence and TCE-Contaminated Dumpsites in Upstate New York,” Environmetrics, vol. 3, issue 3, pp. 281-300.Waller, L.A and Lawson, A.B. (1995). “The Power of Focused tests to Detect Disease Clustering,” Statistics in Medicine, vol. 14, issue 21-22, pp. 2291-2308.Wang, T.C. and Yue, C.J. (2013). “Spatial Clusters in a Global-Dependence Model,” Spatial and Spatio-temporal Epidemiology, vol. 5, pp. 39-50.Wu, C.C. and Shete, S. (2002). “Differentiating Anomalous Disease Intensity with Confounding Variables in Space,” International Journal of Health Geographics, vol. 19, no. 37, pp. 19-37. 描述 碩士
國立政治大學
統計學系
109354010資料來源 http://thesis.lib.nccu.edu.tw/record/#G0109354010 資料類型 thesis dc.contributor.advisor 余清祥<br>楊曉文 zh_TW dc.contributor.advisor Yue, Ching-Syang<br>Yang, Sheau-Wen en_US dc.contributor.author (Authors) 梁舒涵 zh_TW dc.contributor.author (Authors) Liang, Shu-Han en_US dc.creator (作者) 梁舒涵 zh_TW dc.creator (作者) Liang, Shu-Han en_US dc.date (日期) 2022 en_US dc.date.accessioned 2-Sep-2022 14:45:33 (UTC+8) - dc.date.available 2-Sep-2022 14:45:33 (UTC+8) - dc.date.issued (上傳時間) 2-Sep-2022 14:45:33 (UTC+8) - dc.identifier (Other Identifiers) G0109354010 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/141546 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 統計學系 zh_TW dc.description (描述) 109354010 zh_TW dc.description.abstract (摘要) 空間異質性(Spatial Inhomogeneity)是空間統計中的重要議題。空間異質性的檢定可分為三種類型：總體檢定(Global test)、局部檢定(Local test)、焦點檢定(Focused test)，總體檢定可用於檢定全區域的資料是否為空間同質，局部檢定多用於偵測高風險地區（或稱為群聚，Cluster），焦點檢定可用於確認特定地區周圍是否有較高的發生率。本文選擇常見的三種異質性檢定：Moran’s I（總體檢定）、SaTScan（局部檢定）以及Tango Score Test（焦點檢定），透過模擬及實證分析評估這些方法在不同空間特性之下，像是存在空間自相關(Spatial Autocorrelation)及群聚時的偵測效果，以提供實務分析的參考。本文電腦模擬的實驗區間為二度空間，大小為5×5、7×7、9×9、…、21×21的格子點，檢測各方法在空間同質、空間自相關、群聚的效果。研究發現三種方法在空間同質性的結果大致相同，Moran’s I對於空間自相關的最為敏感，而對於群聚存在則以SaTScan效果最佳，Tango Score Test次之。模擬結果亦發現，在風力影響之下會導致Tango Score Test以及SaTScan的偵測能力（偽陰性，False Negative）大幅度下降，但Moran’s I的偽陽性（False Positive）偏高，使用時需特別注意。本文也將這些方法套用至臺灣鄉鎮市區歷年前三大死因（惡性腫瘤、心臟疾病、肺炎），發現主要死因的死亡率具有空間異質性，熱區大多落在東南部山區，且位置並未隨時間有明顯改變，可能與醫療資源分配不均有關；肺炎死亡率在資源充足的西半部逐年上升，推測與都市化的空氣品質惡化有關。 zh_TW dc.description.abstract (摘要) Judging spatial heterogeneity has always been an important topic in spatial statistics. There are three types of tests for checking spatial heterogeneity: Global test, Local test and Focused test. Global test can be used to test whether the data are spatially homogeneous; Local test is usually used to detect the location of high-risk areas (i.e., clusters); Focused test can be used to confirm whether there are high incident rates around a specific area. In this study, we select three common heterogeneity tests: Moran’s I (Global test), SaTScan (Local test) and Tango Score Test (Focused test), and evaluate these methods can detect spatial autocorrelation and/or clusters via simulation and empirical analysis.The simulation study is performed on a two-dimensional space, with lattice data of size 5×5, 7×7, 9×9, …, 21×21, under the assumption that the data satisfying spatial homogeneity, spatial autocorrelation and clustering. The empirical data considered are the township-level overall and major cause mortality rates in Taiwan. We found that these methods have similar results in checking spatial homogeneity. Moran’s I is the most sensitive test to spatial autocorrelation, and SaTScan is the best for testing the existence of clusters, followed by Tango Score Test. On the other hand, the simulation results show that under the influence of wind, the testing powers of Tango Score Test and SaTScan will be greatly reduced, while the False Positive rates of Moran’s I are misleadingly high. Thus, the spatial methods need to be used carefully under the influenced of wind. We also apply these methods to the mortality rates of top three major death causes (cancer, heart disease and pneumonia) in Taiwan. It seem they change steadily, or the difference of mortality rates between two consecutive years satisfying spatially heterogeneity, and most clusters of mortality rates are located in the southeastern mountain areas. en_US dc.description.tableofcontents 第一章 緒論..........................1第一節 研究動機...................1第二節 研究目的...................3第二章 文獻探討與研究方法.............5第一節 空間統計概論...............5第二節 空間異質性檢測方法..........7第三節 研究方法...................13第三章 電腦模擬......................20第一節 空間同質...................20第二節 空間異質...................26第三節 位置檢測...................40第四節 風向模擬...................47第四章 臺灣死因空間特性的實證分析......51第一節 臺灣全死因死亡率探討........52第二節 主要死因的空間特性..........58第三節 空間異質性偵測.............68第四節 死亡率變化的空間特性........75第五章 結論與建議.....................82第一節 結論......................82第二節 未來研究建議...............84參考文獻..............................85附錄一、群聚模擬結果...................89附錄二、空間自相關模擬結果..............91附錄三、SaTScan位置檢測的型I型II錯誤發生率....93附錄四、LISA位置檢測的型I型II錯誤發生率.......95 zh_TW dc.format.extent 7524145 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0109354010 en_US dc.subject (關鍵詞) 空間異質性 zh_TW dc.subject (關鍵詞) 群聚偵測 zh_TW dc.subject (關鍵詞) 空間自相關 zh_TW dc.subject (關鍵詞) 電腦模擬 zh_TW dc.subject (關鍵詞) 檢定力 zh_TW dc.subject (關鍵詞) Spatial heterogeneity en_US dc.subject (關鍵詞) Cluster detection en_US dc.subject (關鍵詞) Spatial autocorrelation en_US dc.subject (關鍵詞) Computer simulation en_US dc.subject (關鍵詞) Power en_US dc.title (題名) 空間異質性檢測方法的比較與應用 zh_TW dc.title (題名) A Study of Methods for Detecting Spatial Inhomogeneity en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 一、中文部分江博煌、謝顯堂、陳筱蕙、詹大千、劉德明、溫啟邦、陳筱蕾、毛義方（2006），「利用地理資訊系統評估台南安順污染廠址周圍之土壤污染」，《台灣公共衛生雜誌》，25卷5期，頁363-371。.呂宗學（2020），「原死因選擇準則改變對死因別死亡率趨勢分析的影響」，《台灣公共衛生雜誌》，39卷5期，頁469-477。李宗儒、陳昭榮、李妙純（2021），「台灣大腸癌死亡率之空間分析」，《台灣公共衛生雜誌》，40卷2期，頁225-240。余清祥、梁穎誼、林佩柔（2022），「健康、醫療利用與人口移動的關聯」，to appear in《地理學報》。林志銘、林文苑（2012），「台灣酒精性疾病死亡率之空間聚集分析」，《台灣公共衛生雜誌》，31卷2期，頁195-204。胡立諄、賴進貴（2006），「臺灣女性癌症的空間分析」，《臺灣地理資訊學刊》，4期，頁39-55。凃明蕙（2020），「臺灣居民健康與壽命之空間分析」，政治大學統計學系碩士論文。黃信誠（2000），「空間統計簡介」，《自然科學簡訊》，12卷3期，頁101-104。黃柏誠（2012），「多重群集的偵測研究」，政治大學統計學系碩士論文。蔡承庭（2011），「焦點檢定方法比較」，政治大學統計學系碩士論文。二、英文部分Anselin, L. (1995). “Local Indicators of Spatial Association—LISA,” Geographical Analysis, vol. 27, issue 2, pp. 93-115.Anselin, L. and Kelejian, H.H. (1997). “Testing for Spatial Error Autocorrelation in the Presence of Endogenous Regressors,” International Regional Science Review, vol. 20, issue 1-2, pp. 153-182.Escamilla, V., Hampton, K.H., Gesink, D.C., Serre, M.L., Emch, M., Leone, P.A., Samoff, E. and Miller, W.C. (2016), “Influence of Detection Method and Study Area Scale on Syphilis Cluster Identification in North Carolina,” Sexually Transmitted Diseases, vol. 43, no. 4, pp. 216-221.Geary, R.C. (1954). “The Contiguity Ratio and Statistical Mapping,” The Incorporated Statistician, vol. 5, no. 3, pp. 115-127, 129-146.Kulldorff, M. and Nagarwalla, N. (1995). “Spatial Disease Clusters: Detection and Inference,” Statistics in Medicine, vol. 14, issue 8, pp. 799-810.Kulldorff, M. (2021). SaTScan User Guide, version 10. (Cambridge MA: Harvard.)Lawson, A.B. (1993). “On the Analysis of Mortality Events Associated with a Prespecified Fixed Point,” Journal of Royal Statistical Society, Series A (Statistics in Society), vol. 156, no. 3, pp. 363-377.Moran, P. (1950). “A Test for the Serial Independence of Residuals,” Biometrika, vol. 37, no. 1/2, pp. 178-181.Oden, N. (1955). “Adjusting Moran`s I for population density,” Statistics in Medicine, vol. 14, issue 1, pp. 17-26.Perry, G.L.W., Miller, B.P. and Enright, N.J. (2006). “A Comparison of Methods for the Statistical Analysis of Spatial Point Patterns in Plant Ecology,” Plant Ecology , vol. 187, issue 1, pp. 59-82.Puett, R.C., Lawson, A.B., Clark, A.B., Hebert, J.R. and Kulldorff, M. (2010), “Power Evaluation of Focused Cluster Tests,” Environmental and Ecological Statistics, vol. 17, pp. 303-316.Song, C. and Kulldorff, M. (2003), “Power Evaluation of Disease Clustering Tests,” International Journal of Health Geographics, vol. 2, no. 9, pp. 1-8.Stone, R.A. (1988), “Investigations of Excess Environmental Risks around Putative Sources: Statistical Problems and a Proposed Test,” Statistics in Medicine, vol. 7, issue 6, pp. 649-660.Sun, Y. (2002), “Determining the Size of Spatial Clusters in Focused tests: Comparing Two Methods by Means of Simulation in a GIS,” Journal of Geographical Systems, vol. 4, pp. 359-370.Tango, T. (1995), “A Class of Tests for Detecting ‘General’ and ‘Focused’ Clustering of Rare Diseases,” Statistics in Medicine, vol. 14, issue 21-22, pp. 2323-2334.Tango, T. (2002), “Score Tests for Detecting Excess Risks around Putative Sources,” Statistics in Medicine, vol. 21, issue 4, pp. 497-514.Tango, T. and Takahashi, K. (2005). “A Flexibly Shaped Spatial Scan Statistic for Detecting Clusters,” International Journal of Health Geographics, vol. 4, no. 11, pp. 799-810.Tobler, W.R. (1970). “A Computer Movie Simulating Urban Growth in the Detroit Region,” Economic Geography, vol. 46, issue 1, pp. 234-240.Turcke, M.A. and Kueper, B.H. (1996). “Geostatistical Analysis of the Borden Aquifer Hydraulic Conductivity Field,” Journal of Hydrology, vol. 178, issue 1-4, pp. 223-240.Waldhör, T. (1996). “The Spatial Autocorrelation Coefficient Moran`s I under Heteroscedasticity,” Statistics in Medicine, vol. 15, issue 7-9, pp. 887-892.Waller, L.A., Turnbull, B.W., Clark, L.C. and Nasca, P. (1992). “Chronic Disease Surveillance and Testing of Clustering of Disease and Exposure: Application to Leukemia Incidence and TCE-Contaminated Dumpsites in Upstate New York,” Environmetrics, vol. 3, issue 3, pp. 281-300.Waller, L.A and Lawson, A.B. (1995). “The Power of Focused tests to Detect Disease Clustering,” Statistics in Medicine, vol. 14, issue 21-22, pp. 2291-2308.Wang, T.C. and Yue, C.J. (2013). “Spatial Clusters in a Global-Dependence Model,” Spatial and Spatio-temporal Epidemiology, vol. 5, pp. 39-50.Wu, C.C. and Shete, S. (2002). “Differentiating Anomalous Disease Intensity with Confounding Variables in Space,” International Journal of Health Geographics, vol. 19, no. 37, pp. 19-37. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202201267 en_US
