A comprehensive assessment approach for multiscale regional economic development: Fusion modeling of nighttime lights and OpenStreetMap data

Abstract

Assessing regional economic development is key for advancing towards the Sustainable Development Goals and ensuring sustainable societal progress. Traditional evaluation methods focus on basic economic metrics like population and capital, which may not fully reflect the complexities of economic activities. Nighttime light (NTL) has been validated as an alternative indicator for regional economic development, yet limitations persist in its evaluation. This study integrates OpenStreetMap (OSM) data and NTL data, providing a novel data integration approach for evaluating economic development. The study uses mainland of China as a case, applying ordinary least squares (OLS) and geographically weighted regression (GWR) to evaluate OSM and NTL data across provincial, municipal, and county levels. It compares OSM, NTL, and their combined use, providing key empirical insights for enhancing data fusion models. The study results reveal: (1) NTL data is more accurate for provincial-level economic activity, while OSM data excels at the county level. (2) GWR demonstrates superior capability over OLS in revealing the spatial dynamics of economic development across scales. (3) Through the integration of both datasets, it is observed that, compared to single-data modeling, the performance is enhanced at the city scale and county scale. The study demonstrates that combining OSM and NTL data effectively assesses economic development in both developed and underdeveloped areas at provincial, municipal, and county levels. The study offers a straightforward and efficient approach to data integration. The findings offer new research perspectives and scientific support for sustainable regional economic growth, particularly valuable in data-scarce, underdeveloped areas.

Keywords

Volunteered geographic information (VGI) / Nighttime light (NTL) / Geographically weighted regression (GWR) / Regional economy / Multiscale

CRediT authorship contribution statement

Zhe Wang: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft. Jianghua Zheng: Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing – review & editing. Chuqiao Han: Methodology, Writing – review & editing. Binbin Lu: Methodology, Software, Writing – review & editing. Danlin Yu: Writing – review & editing. Juan Yang: Writing – review & editing. Linzhi Han: Writing – review & editing.

Declaration of competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Funding

This research was funded by The Third Comprehensive Scientific Investigation in Xinjiang (Grant No. 2021xjkk1001), Program of National Social Science Foundation of China (Grant No. 22BJL061), Major Project of Xinjiang Social Science Foundation (Grant No. 21AZD008) and The National Natural Science Foundation of China (Grant No. 41461035).

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