Abstract: In response to the challenge of inadequately explaining the spatial layout of the secondary industry using nighttime light data, this paper proposes a novel method suitable for spatializing the added value of the secondary industry. The approach involves combining selected points of interest (POI) data with land surface temperature data to construct the secondary industry surface temperature-POI index (STPI Index). This index is then coupled with nighttime light data from rural residential areas, and the study is conducted in the core urban cluster of the Huaihai economic zone. Results show that, compared to methods coupling land use data with nighttime light remote sensing data, the proposed spatialization model for the secondary industry consistently demonstrates superior goodness of fit in the years 2014, 2016, 2018, and 2020 (0.926, 0.882, 0.907, 0.896, respectively) compared to the former (0.859, 0.805, 0.880, 0.849, respectively). The average relative error each year is lower than the former, maintaining around 10%. Using Xuzhou city as an example, a local comparison of the spatialized results for the added value of the secondary industry reveals that the proposed method significantly enhances modeling accuracy and spatialization effectiveness. The spatial distribution pattern is more aligned with reality. The results of this study can provide valuable references for relevant departments in formulating regional economic development plans.

Key words: secondary industry space, remote sensing of night light, POI data, surface temperature data, STPI index