臺灣屬於災害易發地區，隨著氣候變遷加劇氣候變遷加災害發生的頻率，提高了地區所面臨的潛在威脅。脆弱度是指「容易受到不利影響的傾向或易受害性，涵蓋了多個概念和元素，涵蓋對傷害的敏感性或易受損程度，以及應對和適應能力的不足」，而社會面的脆弱度是指地區面對自然災害衝擊潛在受到傷害和損失的程度，社會脆弱度是由社會或經濟等相關統計指標所建構，藉由這些組成指標，可進一步了解地區社會面脆弱度的情形。
社會脆弱度會隨時間產生動態變化，回顧過往探討地區社會脆弱度的研究，多從橫切面之單一時間點進行分析，較缺乏討論在時間變化上的縱向分析。不同指標對社會脆弱度的貢獻應有所不同，然過往研究多以專家學者問卷取得權重或設定單一權重，無法體現不同指標的影響性。空間中存在異質性，體現在不同地區的組成以及資源分佈上，了解社會脆弱度在空間中的分佈情形，能指認高值或低值聚集區域，做為資源配置或改善的參考依據。據此，本研究目的有三：（一）了解各行政區社會脆弱度的變化情形及其變化的原因；（二）了解社會脆弱度在空間中的聚集或離散現象；（三）透過綜合分析，就現行地區災害防救計畫給予建議。
本研究以北北基桃四個縣市共61個行政區做為研究範圍，以2011、2014、2017、2020以及2023五個時間點探討社會脆弱度指數，將用於分析之指標進行標準化後，以主成分分析(PCA)將多項指標整合成少數幾個綜合指標，對主成分進行Kaiser Varimax 旋轉，以提高解釋變異的效果，接著使用解釋變異量作為權重以計算社會脆弱度指數，將各行政區五個年份的社會脆弱度指數標準化後再進行比較以分析社會脆弱度在時間上的變化情形。使用Moran's I 和 LISA 進行空間自相關分析，以評估社會脆弱度在地理空間上的聚集情況，更全面的了解時間及空間上的變化。
根據研究結果，社會脆弱度等級在不同年份間變化不大，整體呈現中到中低脆弱度，但高脆弱度地區多於低脆弱度。在被歸類為「惡化」類別的行政區中，影響力較大的指標反而呈現穩定，構成其惡化的原因反映在其他影響力較小的指標上；被歸類到「改善」的行政區中，其變化分別體現在PC1(經濟與醫療因素)及PC2(人口組成因素)，兩種不同變化原因反映在同一類別，說明了解變化原因的必要性。在空間自相關分析中，選用兩種賦權方式一同檢視社會脆弱度指數在空間中的分佈情形，發現高值主要聚集在新北市東側，低值聚集則呈現在臺北市、新北市跟桃園市接鄰地區，桃園市的低值聚集情形在K最近鄰（K-Nearest Neighbors, KNN）的空間權重設定下呈現擴張現象，高值離散的情形在桃園市則隨時間在不同行政區中呈現，此外，臺北市萬華區相較於周邊區域，穩定呈現高值。
透過本研究，探討了各類變化情形社會脆弱度變化的原因，並一定程度上識別出高齡化情形較嚴重或較吸引人口移入的地區，同時也指認出高值、低值聚集區域，以及呈現離散的異常值。這些發現有助於制定或更新地區防災計畫時，做為地區資源配置，或針對特定群體的參考依據，除此之外，高齡城市設計、萎縮城市等議題亦可於都市規劃中著重納入考量。

Taiwan is highly susceptible to natural disasters, and the growing impacts of climate change have intensified the potential risks faced by different regions. Vulnerability refers to the tendency to be adversely affected, encompassing sensitivity to harm as well as limited coping and adaptive capacities. Social vulnerability reflects the degree to which communities are likely to suffer harm from natural hazards.
Social vulnerability is dynamic and changes over time; however, previous studies mostly adopt a cross-sectional approach at a single point in time, lacking longitudinal analysis. Furthermore, indicator weighting often relies on expert judgment or uniform assumptions, failing to capture the actual influence of each factor. Spatial heterogeneity also exists, and understanding the spatial distribution of vulnerability helps identify areas of concern for targeted resource allocation.
This study analyzes 61 administrative districts across Taipei City, New Taipei City, Keelung City, and Taoyuan City using data from 2011, 2014, 2017, 2020, and 2023. Principal Component Analysis (PCA) was applied to consolidate indicators and examine spatiotemporal changes in social vulnerability. Spatial autocorrelation was assessed using Moran's I and LISA to explore clustering patterns.
Results show an overall moderate to low vulnerability, but areas with high vulnerability outnumber low ones. In “deterioration” districts, major indicators remained stable, while changes stemmed from less influential variables. “Improving” districts showed variation across economic/medical and demographic components. High-vulnerability clusters were discovered in eastern New Taipei City, whereas Low-Low clusters were found in Taipei City and New Taipei City adjacent to Taoyuan City. These findings support more informed disaster risk planning and resource allocation.

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