【摘要】：先前的研究已經(jīng)證明從DMSP/OLS(Defense Meteorological Satellite Program/Operational Linescan System)穩(wěn)定夜間燈光數(shù)據(jù)中可以提取城市建成區(qū),并且以此估算了人口、GDP等其他各項(xiàng)指標(biāo),但對提取出的城市建成區(qū)空間分布模式研究較少。本文提出了一種改進(jìn)的基于密度的聚類(Density-based Spatial Clustering of Qpplications with Boise,DBSCAN)算法,將對于點(diǎn)要素的聚類擴(kuò)展到面要素,以面狀對象的邊緣作為界定包含關(guān)系的準(zhǔn)則;確定了聚類參數(shù),以要素間距離的突變點(diǎn)作為距離參數(shù)。按此方法對夜間燈光數(shù)據(jù)提取的中國城市對象按照密度分布的特點(diǎn)進(jìn)行聚類,通過改變距離參數(shù)得到在不同尺度下中國城市的集聚形態(tài)。通過與實(shí)證資料的對比驗(yàn)證了該算法的有效性,為研究中國城市的空間分布及其演變提供了有力的研究方法。
[Abstract]:Previous studies have shown that urban built-up areas can be extracted from DMSP/OLS (Defense Meteorological Satellite Program/Operational Linescan System) stable night light data, and other indicators such as population, GDP and so on have been estimated. But there is little research on the spatial distribution model of urban built-up area. In this paper, an improved density-based clustering (Density-based Spatial Clustering of Qpplications with Boise,DBSCAN) algorithm is proposed, which extends the clustering of point elements to surface elements and uses the edge of the plane object as the criterion to define the inclusion relationship. The clustering parameters are determined, and the abrupt point of the distance between the elements is taken as the distance parameter. According to this method, the Chinese urban objects extracted by night lighting data are clustered according to the characteristics of density distribution, and the gathering patterns of Chinese cities at different scales are obtained by changing the distance parameters. The validity of the algorithm is verified by comparing with the empirical data, which provides a powerful research method for studying the spatial distribution and evolution of Chinese cities.
【作者單位】： 華東師范大學(xué)地理科學(xué)學(xué)院;華東師范大學(xué)地理信息科學(xué)教育部重點(diǎn)實(shí)驗(yàn)室;
【基金】：國家自然科學(xué)基金人才培養(yǎng)項(xiàng)目(J1310028) 上海市教育委員會科研創(chuàng)新項(xiàng)目(15ZZ026) 中央高�；究蒲袠I(yè)務(wù)費(fèi)專項(xiàng)資金項(xiàng)目
【分類號】：TP311.13;TP751

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