銀行區位選址決策支援系統之研發 －以台北市為例

Abstract

過去的銀行區位研究缺少空間資料分析，而進行區位研究若不考慮空間資料不僅是不足，而且是缺少說服力的。本研究使用台北市普查資料並與空間資料結合，計算出各銀行商圈內分派到的普查資料量與空間關係，再透過地理資訊系統結合關聯規則的方式，發掘台北市銀行設立時共同的區位現象，以至少19%支持度及90%信賴度條件下發現約176條規則，這些規則90%以上會出現銀行，本研究建立一套決策支援系統，可將銀行商圈外尚未有銀行的點位，且符合探勘後的規則條件者選出，即成為適合設立新銀行的候選位置，提供決策者擇址時之參考依據。

Previous studies of bank location in Taipei area have neglected using spatial data to analyze. If a study does not use spatial data in analyzing bank location, it would be insufficient and unpersuasive. This study integrated census data and spatial data in Taipei City to analyze the spatial relationship between census data and bank trade area. Through GIS and association rule, one of data mining methods, common characteristics, e.g. spatial and non-spatial factors, of bank locations would be discovered. The result showed that 176 rules (situations) related to bank location were discovered under the condition of supporting level equal and greater than 19% , and confidence interval equal or greater than 90% . Moreover, these rules would be used build a spatial decision support system for decision makers to evaluate potential locations of bank in Taipei City.