集成多源地理大数据感知城市空间分异格局

Incorporating Multi-source Big Geo-data to Sense Spatial Heterogeneity Patterns in an Urban Space

多源地理大数据为地理现象的分布格局、相互作用及动态演化提供了前所未有的社会感知手段。城市是人类活动最为集中的区域,产生了多种地理大数据,并支持对于城市空间的理解。城市内部的分异格局是城市研究和规划所要面对的重要议题,社会感知数据提供了从'人-地-静-动'4个维度刻画城市分异格局的途径。梳理了不同类型大数据对于表达这4个维度特征的支持,并借鉴'生态位'模型,通过一个实例研究展示了集成多源数据量化城市空间分异特征的应用,最后讨论了相关的理论问题。

Multi-source big geo-data provides us an unprecedented opportunity to investigate geographic phenomena from perspective of their spatial distribution patterns,spatial interactions and dynamic evolution.Cities are the most concentrated areas of human activities and thus massive amount of geographic big data have been produced to improve our understanding of urban spaces.The spatial heterogeneity patterns in cities is an essential topic in geographic research and urban planning.Social sensing offers an analytical framework to characterize urban spatial heterogeneity from four dimensions:human,environment,statics and dynamics.This paper summarizes the contributions of different types of big geo-data in characterizing urban features.Borrowing the concept of "niche model" from ecological studies,a case study is introduced to demonstrate the quantification of spatial heterogeneity patterns in urban space incorporating multi-source big geo-data.Theoretical issues such as unit selection are also discussed to address some related problems.