藉空間自相關與路網分析探討災害弱勢族群與防災避難圈域之區位關係-以淹水災害為例

Abstract

鑒於人口老化與M型社會趨勢，導致弱勢族群人口數逐年上升，而現今研究不乏針對弱勢族群進行探討，雖掌握弱勢族群名冊，但卻無法明確得知弱勢族群之空間分佈聚集狀態。因此本研究藉由內政部統計處公布之最小統計區資料，包含：老人、小孩、身心障礙與中低收入戶等4類弱勢人口，以新北市板橋區、中和區與永和區作為研究區域，運用空間自相關方法進行弱勢族群於空間上之聚集地點（即熱區）分析，並套疊24小時累積雨量450毫米淹水潛勢，掌握災害弱勢族群分布位置與最小統計區之數量。 另外，於評估避難收容處所之防災避難圈域，現今常用方法係以環域分析與徐昇多邊形方式進行推估，但其卻未考慮空間上交通阻礙等問題，爰本研究利用路網分析中之服務範圍區位分析，並以500公尺與1,000公尺作為防災避難圈域之劃設標準。 其後，先以適用淹水災害之49處避難收容處所進行防災避難圈域分析並檢視其是否皆涵蓋災害弱勢族群，若否，則進一步檢討新增避難收容處所，以及防災避難圈域與災害弱勢族群間之適配性。希冀藉由本研究研提收容所資源分布之改善建議，可供地方政府參考，以強化災害弱勢族群的防救災能力與及時避災能力，進而降低傷亡損失程度。

The disadvantaged people has increased annually due to the population aging and M-shaped society. Although the names of the disadvantaged were tabulated and their conditions were studied, their cluster distribution remained unclear. Therefore, this research collected the minimal statistical zone (MSZ) data from Department of Statistics, Ministry of Interior, which involves 4 groups of the disadvantaged such as the elders, children, disabled, and low-income households. Banqiao District, Zhonghe District, and Yonghe District in New Taipei City, were chosen as study area and the above data were analyzed via spatial autocorrelation to identify the cluster distribution, i.e. spatial hot spots, and overlay with 24hr-450mm flooding potential map to understand the distribution of the disadvantaged minority and number of MSZs affected by inundation.Disaster prevention was usually assessed by buffer zones analysis and Thiessen polygons method without considering the traffic congestion. This study employed the service area analysis of network analyst and set the standard radius of 500m and 1000m for the disaster prevention. Whether the service area of 49 shelters for flooding fully covers the cluster of the disadvantaged minority is investigated via service area analysis and discussion given upon the necessity of adding new shelters if coverage is not complete and the appropriateness of disaster prevention to the disadvantaged minority. In general, this research expected to provides suggestions of improving shelters distribution for the local government in order to not only enhance the capability of preventing and avoiding disaster but also reduce casualties and losses.