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

Abstract

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.