以統計區分類系統為基礎研討人口重分配之效能

Efficiency of Reconstructing Spatial Population Distribution on a Spatial Statistical Area and Classification System

林美君(Mei-Chun Lin) ； 蘇明道(Ming-Daw Su)

《中國地理學會會刊》 51期 (2013/12) Pp. 53-66

https://doi.org/10.29972/BGSC.201312_(51).0004

摘要

詳細正確的人口資料與人口的空間分布是相關決策的重要依據，但因涉及隱私保護因素，一般多將人口資料加總統計後再以不同的空間單元進行發布，此情況易產生分區單元不夠細緻、空間型態扭曲或因行政邊界變動無法進行跨時間的時序性分析等問題。又災害發生時，因受災範圍與行政單元邊界不一致或橫跨數個行政區，不易得知正確的受災人數，故如何合理重分配加總的人口資料是極為重要的課題。本研究提出多層多類分區密度（multi-layer multi-class dasymetric model, MLMC）的人口重分配模式，利用縣市加總的人口資料為基礎，重建人口空間分布趨勢，MLMCD模式主要是依據輔助資料進行分層與分類，層間具有從屬關係，層內有類別之分類，需要以社會經濟資料決定各類別之人口重分配權重。研究中以臺北市為例，將縣市的總人口重新分配至網格，利用最小統計區之空間單元進行誤差比較，藉此評估MLMCD人口重分配之正確性。

關鍵字

人口 ； 重分配 ； 多層多類分區密度模式 ； 最小統計區 ； 誤差

並列摘要

Detailed and correct spatial distributions of population are the foundation of sound regional planning and management decisions. Population data are usually disseminated in aggregated form for confidentiality concerns. However, this approach to spatial aggregation may distort the original spatial pattern from the modified areal unit problema and the frequent changes in boundaries over time may make cross temporal analysis impossible. It is difficult to estimate the anount of population at risk in a disaster because the boundaries of the disaster area may not coincide with population aggregation units. Therefore, the use of effective algorithms for disaggregating the aggregated population into smaller spatial units has become increasingly important. A multi-layer-multi-class daymetric Daymetric (MLMCD) model was developed in this study to reconstruct spatial distributions from spatially aggregated population data. Ancillary data, such as those frome remote sensing images, census, land use, traffic networks and other infrastructure, were used to disaggregate the aggregated population data into smaller grids. These disaggregated grid data were then summed up to different spatial levels for error comparisons. Mean Mean absolute percentage error was employed to examine the effectiveness of the proposed MLMCD model in this population disaggregation process. Using Taipei metropolitan areas as study area, this determination disaggregates population data which is based on country level into cell units. Finally, error analysis on minimum statistic area can assess the accuracy of reconstructing population distribution in MLMCD.

並列關鍵字

Population ； disaggregate ； multi-layer multi-class dasymetric model (MLMCD) ； error

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