## 5 Conclusions  
In this paper, we conducted an in-depth investigation on the vacancy rate of housing at the residential neighborhood level in Longquanyi District, Chengdu City, and proposed and implemented a method for assessing the vacancy rate of housing based on multi-source big data. The method takes night lighting data as the core, supplemented by POI, AOI, and other data sources, and achieves accurate estimation of the housing vacancy rate by constructing an NP index, kernel density analysis, spatial autocorrelation analysis, and other technical means. The main findings are as follows: 1. Accuracy of the estimation model: the model using night-time lighting data and other multi-source data has high accuracy in estimating the housing vacancy rate at low resolution. The average absolute error of the model is 0.1085, and the root-mean-square error is 0.1448. 2. Situation of the housing vacancy rate: The average housing vacancy rate in the residential community of Longquanyi District is 46%, and the overall vacancy rate is at a high level. 3. Spatial clustering characteristics of housing vacancy rate: The housing vacancy rate in the study area shows certain spatial clustering characteristics, with high vacancy rate areas concentrated at the edge of the area, and low vacancy rate areas concentrated in the core areas of the clusters with convenient traffic and perfect facilities. 4. Relationship between the vacancy rate and housing price and completion time: This study found that the correlation between the vacancy rate and the housing price and completion time in the study area is not significant. Conceptualization, Y.Z. and W.C.; methodology, Y.Z. ; software, J.Z. ; validation, Y.Z. and J.Z. ; formal analysis, Y.Z. ; investigation, D.C.; resources, D.L. ; data curation, W.W.; writing--original draft preparation, Y.Z. ; writing--review and editing, Y.Z. and D.C.; visualization, D.L. ; supervision, W.C.; project administration, Y.Z. ; funding acquisition, W.C. All authors have read and agreed to the published version of the manuscript. This research was funded by the Humanities and Social Sciences Foundation of the Ministry of Education of the People's Republic of China, grant number 18 YJC850004; the Key Project of Sichuan Provincial Science and Technology Program, grant number 2022 YFS0488; and the National Natural Science Foundation of China (NSFC), grant number 32060370. Not applicable. The NPP/VIIRS night-time light data with a spatial resolution of 500 m were obtained from the Earth Observation Group ([[https://eogdata.mines.edu/products/vnl/](https://eogdata.mines.edu/products/vnl/)]([https://eogdata.mines.edu/products/vnl/](https://eogdata.mines.edu/products/vnl/)), accessed on 12 February 2024). The data from February 2024 VIIRS_ycmslcfg provided by the platform were selected and corrected for scattered light to increase the dynamic range. POI (point-of-interest) data were obtained from Gaode Map (Amap) ([[https://lbs.amap.com/](https://lbs.amap.com/)]([https://lbs.amap.com/](https://lbs.amap.com/)), accessed on 12 February 2024). The dataset includes 21 categories and 40,081 points, which were cleaned and filtered to 8 categories and 38,913 points based on their relevance to residents' daily lives. AOI (area-of-interest) data, including residential community boundaries and attributes (e.g., completion year, average unit price), were obtained from Amap ([[https://lbs.amap.com/](https://lbs.amap.com/)]([https://lbs.amap.com/](https://lbs.amap.com/)), accessed on 12 February 2024). After cleaning and validation, a total of 632 residential community surface elements were extracted. Additional residential community information (e.g., price, completion year) was supplemented using data from Shell.com ([[https://cd.ke.com/](https://cd.ke.com/)]([https://cd.ke.com/](https://cd.ke.com/)), accessed on 12 February 2024) and Lianjia.com ([[https://lianjia.com/](https://lianjia.com/)]([https://lianjia.com/](https://lianjia.com/)), accessed on 12 February 2024), two leading online housing platforms in China.