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題名 1999臺北市民當家熱線受理違規停車處理之空間資料探勘:以核密度分析為工具
Spatial Data Mining on Parking Violation Cases in 1999 Citizen Hotline of Taipei City Government: Kernel Density Estimation as the Tool
作者 廖興中
Liao , Hsin-Chung
廖洲棚
Liao , Zhou-Peng
陳敦源
Chen, Don-Yun
貢獻者 公行系
關鍵詞 空間資料探勘 ; 地理資訊系統 ; 平均最近鄰分析 ; 核密度估計 ; 1999 市民熱線
 spatial data mining ;  geographic information system  ;  average nearest neighbor ;  Kernel Density Estimation ; 1999 citizen hotline
日期 2015-12
上傳時間 20-八月-2019 14:49:23 (UTC+8)
摘要 本研究利用空間資料探勘的方法,分析臺北市政府 1999 市民熱線在 2010 年 4 月、5 月與 6 月的違規停車紀錄,並試著瞭解其發生位置與整體趨勢。平均最近鄰 分析與核密度估計的結果顯示,違規停車紀錄確實呈現顯著的空間群聚現象,最主 要的高密度區域在中正區的北部、中正區的西部、松山區的中部、信義區的西北部 與大安區的中南部。總之,本研究呈現空間資料探勘方法如何協助政府發掘資料背 後所隱藏的民意知識,以作為未來擬定或支援決策的參考。
Using spatial data mining method, this study analyzed parking violation cases from April to June in 2010, which were provided by 1999 citizen hotline of Taipei City Government, and tried to understand the locations of incidents and the trend. The results of Average Nearest Neighbor and Kernel Density Estimation revealed that parking violation cases did exhibit significant spatial dependence in Taipei City, and the areas with higher density of parking violation are included northern and western Zhongzheng, middle Songshan, northwestern Xinyi, and middle southern Da`an. In general, this study presented how Spatial Data mining can help the government to find the public opinion knowledge hidden in the data in order to further develop strategies and support policies.
關聯 行政暨政策學報, No.61, pp.51-77
資料類型 期刊論文
dc.contributor 公行系
dc.creator (作者) 廖興中
dc.creator (作者) Liao , Hsin-Chung
dc.creator (作者) 廖洲棚
dc.creator (作者) Liao , Zhou-Peng
dc.creator (作者) 陳敦源
dc.creator (作者) Chen, Don-Yun
dc.date (日期) 2015-12
dc.date.accessioned 20-八月-2019 14:49:23 (UTC+8)-
dc.date.available 20-八月-2019 14:49:23 (UTC+8)-
dc.date.issued (上傳時間) 20-八月-2019 14:49:23 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/125326-
dc.description.abstract (摘要) 本研究利用空間資料探勘的方法,分析臺北市政府 1999 市民熱線在 2010 年 4 月、5 月與 6 月的違規停車紀錄,並試著瞭解其發生位置與整體趨勢。平均最近鄰 分析與核密度估計的結果顯示,違規停車紀錄確實呈現顯著的空間群聚現象,最主 要的高密度區域在中正區的北部、中正區的西部、松山區的中部、信義區的西北部 與大安區的中南部。總之,本研究呈現空間資料探勘方法如何協助政府發掘資料背 後所隱藏的民意知識,以作為未來擬定或支援決策的參考。
dc.description.abstract (摘要) Using spatial data mining method, this study analyzed parking violation cases from April to June in 2010, which were provided by 1999 citizen hotline of Taipei City Government, and tried to understand the locations of incidents and the trend. The results of Average Nearest Neighbor and Kernel Density Estimation revealed that parking violation cases did exhibit significant spatial dependence in Taipei City, and the areas with higher density of parking violation are included northern and western Zhongzheng, middle Songshan, northwestern Xinyi, and middle southern Da`an. In general, this study presented how Spatial Data mining can help the government to find the public opinion knowledge hidden in the data in order to further develop strategies and support policies.
dc.format.extent 4444344 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) 行政暨政策學報, No.61, pp.51-77
dc.subject (關鍵詞) 空間資料探勘 ; 地理資訊系統 ; 平均最近鄰分析 ; 核密度估計 ; 1999 市民熱線
dc.subject (關鍵詞)  spatial data mining ;  geographic information system  ;  average nearest neighbor ;  Kernel Density Estimation ; 1999 citizen hotline
dc.title (題名) 1999臺北市民當家熱線受理違規停車處理之空間資料探勘:以核密度分析為工具
dc.title (題名) Spatial Data Mining on Parking Violation Cases in 1999 Citizen Hotline of Taipei City Government: Kernel Density Estimation as the Tool
dc.type (資料類型) 期刊論文