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

LIU Yu; ZHAN Zhaohui; ZHU Di; CHAI Yanwei; MA Xiujun; WU Lun

doi:10.13203/j.whugis20170383

Volume 43 Issue 3

Mar. 2018

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Geomatics and Information Science of Wuhan University > 2018 > 43(3): 327-335. > DOI: 10.13203/j.whugis20170383

LIU Yu, ZHAN Zhaohui, ZHU Di, CHAI Yanwei, MA Xiujun, WU Lun. Incorporating Multi-source Big Geo-data to Sense Spatial Heterogeneity Patterns in an Urban Space[J]. Geomatics and Information Science of Wuhan University, 2018, 43(3): 327-335. DOI: 10.13203/j.whugis20170383

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

LIU Yu^{1, 2,},
ZHAN Zhaohui²,
ZHU Di²,
CHAI Yanwei³,
MA Xiujun^{4, 5},
WU Lun^2, ,

1.
Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources, Shenzhen 518034, China
2.
Institute of Remote Sensing and Geographical Information Systems, Peking University, Beijing 100871, China
3.
College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
4.
School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China
5.
Key Laboratory of Machine Perception, Peking University, Beijing 100871, China

Funds:

The Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources KF-2016-02-023

the National Natural Science Foundation of China 41625003

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Author Bio:
LIU Yu, PhD, professor, specializes in GIScience and big geo-data. E-mail: liuyu@urban.pku.edu.cn
Corresponding author:
WU Lun, PhD, professor.E-mail: lwu@urban.pku.edu.cn
Received Date: November 22, 2017
Published Date: March 04, 2018

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Abstract

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.
- big geo-data,
- spatial heterogeneity,
- social sensing,
- urbanspace

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References

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

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