Application of Land-Use Inventory Data and Random Forest Models for Estimating Population Densities in Rural Areas

In regions frequently affected by natural disasters, risk assessment and identification of vulnerable areas are critical to the management and security of the population. However, it is difficult for traditional choropleth maps to meet the requirements of fine-scale and actual population estimates. This study developed 2 random forest models named the Town-Village model and Forest model to explore correlations between population densities and land-use patterns in Taiwan for the development of dasymetric mapping approaches. A simple linear regression of observations versus predictions was performed, and the mean squared error between observations and predictions was calculated to evaluate the performance of the 2 models. In addition, prediction error rates were calculated to evaluate the prediction accuracy of the models ranging from inhabitant-sparse areas to densely populated cities. Model evaluations revealed that both the Town-Village and Forest models had sufficient predictive abilities with mean absolute error rates of ＜ 10% in areas with population densities ranging 51~25,000 people km^(-2). The models were applied to estimate population densities for all 261 villages in Nantou County, central Taiwan. Results revealed that in Nantou County, the Forest model exhibited a lower prediction error in inhabitant-sparse areas (with densities of ＜ 100 people km^(-2)) and could precisely estimate population densities under error rates of 0.34~7.31% in villages with an actual density of fewer than 5000 people km^(-2). The Forest model could be a more-applicable and better-suited approach for socioeconomic and policy initiative studies in mountain villages.

關鍵字

population ； random forest ； risk assessment ； vulnerability

並列摘要

陡峻地形與劇烈的夏季降雨，導致臺灣山區天然災害頻繁，並使山村聚落之脆弱度評估及災害風險管理成為森林經營及土地管理最重要的議題。其中，又以「人口」為最關鍵的脆弱度指標。然而人口統計通常以行政區或最小地理區進行，導致人口資料的空間細節極不均勻，尤其在面積廣袤但人口分布稀疏的山區，鄰里資料往往難以描述人口的精確空間位置，更遑論與點狀或線狀的災害圖資、醫療資源及交通運輸圖層的進一步疊合與交叉統計。為改善此問題，本研究以隨機森林方法為基礎，探索及統計全臺灣地理單元內人口密度與土地利用型態之關係，並依據不同的訓練樣本，建立名為Town-Village model與Forest model的兩種人口密度預測模型，進一步產製以網格為基礎（grid-based）的人口密度分布預測圖。研究結果除了揭示與人口密度高相關的土地利用型態，亦利用總樣本數的1/3資料進行交叉驗證，並以南投縣261村里為案例，評估兩模型的實際應用情形。結果顯示，就全台土地利用與人口分布型態而言，兩模型在人口密度51～25,000 people km^(-2)地區應用性佳，平均絕對誤差可在10%以內；此外，在人口稀疏（密度低於100 people km^(-2)）的山區，Forest model的預測能力顯著較佳，對南投縣人口密度5000 people km^(-2)以下的村里，其預測誤差約介於0.34～7.31%，是適合山村地區應用的人口分布研究工具。

並列關鍵字

人口；隨機森林；風險評估；脆弱度

國際替代計量

Application of Land-Use Inventory Data and Random Forest Models for Estimating Population Densities in Rural Areas

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