Analyzing the non-stationary space relationship of a city’s degree of vegetation and social economic conditions in Shanghai, China using OLS and GWR models

Kejing Wang; Yuan Zhang; Youzhi An; Zhuoxin Jing; Chao Wang

doi:10.1117/12.2023476

24 September 2013 Analyzing the non-stationary space relationship of a city’s degree of vegetation and social economic conditions in Shanghai, China using OLS and GWR models

Kejing Wang, Yuan Zhang, Youzhi An, Zhuoxin Jing, Chao Wang

Author Affiliations +

Proceedings Volume 8869, Remote Sensing and Modeling of Ecosystems for Sustainability X; 88690O (2013) https://doi.org/10.1117/12.2023476
Event: SPIE Optical Engineering + Applications, 2013, San Diego, California, United States

Abstract

With the fast urbanization process, how does the vegetation environment change in one of the most economically developed metropolis, Shanghai in East China? To answer this question, there is a pressing demand to explore the non-stationary relationship between socio-economic conditions and vegetation across Shanghai. In this study, environmental data on vegetation cover, the Normalized Difference Vegetation Index (NDVI) derived from MODIS imagery in 2003 were integrated with socio-economic data to reflect the city’s vegetative conditions at the census block group level. To explore regional variations in the relationship of vegetation and socio-economic conditions, Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models were applied to characterize mean NDVI against three independent socio-economic variables, an urban land use ratio, Gross Domestic Product (GDP) and population density. The study results show that a considerable distinctive spatial variation exists in the relationship for each model. The GWR model has superior effects and higher precision than the OLS model at the census block group scale. So, it is more suitable to account for local effects and geographical variations. This study also indicates that unreasonable excessive urbanization, together with non-sustainable economic development, has a negative influence of vegetation vigor for some neighborhoods in Shanghai.

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Kejing Wang, Yuan Zhang, Youzhi An, Zhuoxin Jing, and Chao Wang "Analyzing the non-stationary space relationship of a city’s degree of vegetation and social economic conditions in Shanghai, China using OLS and GWR models", Proc. SPIE 8869, Remote Sensing and Modeling of Ecosystems for Sustainability X, 88690O (24 September 2013); https://doi.org/10.1117/12.2023476

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KEYWORDS

Vegetation

MODIS

Data modeling

Analytical research

Electromagnetism

Remote sensing

Data integration

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