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ISPRS Int. J. Geo-Inf. 2019, 8(1), 26; https://doi.org/10.3390/ijgi8010026

Adaptive Non-Negative Geographically Weighted Regression for Population Density Estimation Based on Nighttime Light

1
Department of Geomatics, National Cheng Kung University, Tainan 70101, Taiwan
2
Graduate Institute of National Development, National Taiwan University, Taipei 10617, Taiwan
*
Author to whom correspondence should be addressed.
Received: 14 November 2018 / Revised: 3 January 2019 / Accepted: 9 January 2019 / Published: 12 January 2019
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Abstract

Nighttime light imagery provides a perspective for studying urbanization and socioeconomic changes. Traditional global regression models have been applied to explore the nonspatial relationship between nighttime lights and population density. In this study, geographically weighted regression (GWR) identifies the spatially varying relationships between population density and nighttime lights in mainland China. However, the rural population does not have a strong relationship with remote-sensing spectral features. The rural population estimation using nighttime light data alone easily identifies meaningless negative population density in the rural area. This study proposes an adaptive non-negative GWR (ANNGWR) to explore the spatial pattern of population density by using nonnegative constraints with an adaptive bandwidth of kernel. The ANNGWR solves the negative value of population density and serious overestimation of the western boundary. The result shows that the ANNGWR provides the best goodness-of-fit compared with linear regression and original GWR. This study applies Moran’s I index to prove that the ANNGWR substantially decreases the spatial autocorrelation of the model residual. The model offers a robust and effective approach for estimating the spatial patterns of regional population density solely on the basis of nighttime light imagery. View Full-Text
Keywords: nighttime light; population density; GWR; nonnegative constraint nighttime light; population density; GWR; nonnegative constraint
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Chu, H.-J.; Yang, C.-H.; Chou, C.C. Adaptive Non-Negative Geographically Weighted Regression for Population Density Estimation Based on Nighttime Light. ISPRS Int. J. Geo-Inf. 2019, 8, 26.

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