Spatio-Temporal Dynamics of Urban Expansion in Japan Using Gridded Land Use Data, Population Census Data and DMSP Data

Wednesday, 17 December 2014
Hasi Bagan and Yoshiki Yamagata, National Institute for Environmental Studies, Japan, Tsukuba City, Ibarak, Japan
Integration of population data, land-use data, and satellite images can be used to identify and characterize the spatio-temporal extent and expansion trends of urban growth. We provided an idea to investigate the spatio-temporal urban growth using satellite images with population data.

We analyze the urban expansion in Japan from 1990 to 2005 by using gridded land-use data, population census data, and DMSP satellite images of nighttime lights. First, we mapped the DMSP nighttime lights and land-use data onto a grid based on the standard 1 km2grid cell system of Japan to determine the proportional areas of DMSP nighttime lights and urban land use within each grid cell. Then, we investigated the relationships among population density, DMSP nighttime lights area, and urban area. A rapid expansion of the urban/built-up area around megacities was associated with population increases; in contrast, population density dropped steeply in rural areas and in small towns.

Spatial correlation analysis showed a strong positive correlation between population density and urban land use (r= 0.59). In addition, correlation coefficients between population density and DMSP data increased as the DMSP nighttime lights brightness value increased. We then used census population data as the base population input, and performed a linear multiple regression analysis to predict population density from the combination of urban land-use area and DMSP data in Hokkaido, Japan. Visual and numerical evaluation of the results showed that the combination of urban land-use data and DMSP data could be used to predict the spatial distribution of population density.

The results from this study indicated the high correlation between these data and suggested the potentials of population density prediction using DMSP data and land use data.


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