.png){kind=icon}
.png){kind=icon}
Return
A novel socioeconomic downscaling method by integrating multi-dimensional spatial proximity with multi-source remote sensing data
.png){kind=icon}
PRE
AI
W
T
L
Y
W
DOI：10.1080/01431161.2026.2697507.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
With the elimination of absolute poverty in China, promoting coordinated regional development has become a key agenda. However, the county-level resolution of the socioeconomic development dataset released by the statistics yearbook has limited its use at the local level. Thus, benefiting from fine-grained observation of remote sensing data, we propose a novel socioeconomic downscaling framework accounting for integrated spatial-attribute proximity, namely the Multi-Distance Geographically Neural Network Weighted Regression (MD-GNNWR). Taking the Yangtze River Delta (YRD) as a case study, we first construct a Multi-dimensional Relative Development Index (MRDI) to reflect comprehensive development, by integrating living standards, education, and health dimensions. Subsequently, based on multi-source remote sensing data, i.e. night-time light (NTL), land cover, road networks, points of interest (POI), and terrain data, we develop the first estimates of MRDI for the township-level and for a 1-km grid. Results show that the MD-GNNWR model with spatial proximity achieves R2 = 0.851, over 0.1 higher than the classical Random Forest (RF) model. The township-scale MRDI is significantly correlated with a survey-derived wealth index (Pearson’s r=0.60), while comparison with an external Human Development Index (HDI) product further supports the consistency of the grid-scale MRDI, with Pearson’s r>0.70. We also illustrate how these data can improve decision-making. The grid-scale MRDI Gini coefficient of Anhui Province reaches 0.429, highlighting pronounced inequality and the urgent action. Geographical detector analysis shows bivariate enhancement among variables, with average NTL intensity (ANTL) contributing most (q-value = 0.630). This framework enables fine-scale monitoring of regional development, supporting the identification of spatial disparities and informing coordinated, sustainable strategies.
Keywords:
Socioeconomic downscaling
spatial proximity
multi-source remote sensing
inequality
regional development
Journal
IF:
2.6
Papers:
1.2W
Citations:
2.7W
Organization
Cited Papers