The perceived wealth and physical disorder scores prediction dataset for urban China

The perceived wealth and physical disorder scores prediction dataset consists of three files, each corresponding to perception data from one of the three analytical levels. Based on the perception image annotation dataset labeled by Chinese urban planners (https://figshare.com/s/a942f102cd07f4a73515), these perception scores are predicted through model training and inference across urban China.The first file (point version) contains perception scores for 36,262,700 street view images. This CSV-formatted data includes the longitude and latitude of each image’s shooting location, the time the image was taken, and the perceived wealth and physical disorder scores. This data can be reprocessed by researchers for their specific analytical need. The second file (grid version) and third file (community version) are stored as GeoTIFF (.tif) files with the Albers conic equal area projection. The mean perceived wealth score and mean perceived physical disorder score from 2013 to 2022 are aggregated using 500m×500m grids and community administrative areas as the analytical units, respectively. They can be processed using GIS software such as ArcGIS and QGIS, as well as Python programming language packages such as Rasterio. We also publish corresponding simplified tables in CSV format showing the mean perceived wealth and physical disorder scores in each grid and community. These tables include the community’s name, the latitude and longitude of the grid (or community) centroid, and the names of the county-, prefecture-, and province-level areas in which the grid (or community) is located.<br><br><b>Source publication</b>: Zhang, Y., You, Y., Chen, S., &amp; Cai, L. (2025). Geospatial dataset on human perceptions of wealth and physical disorder in urban China using street view imagery and deep learning. <i>Data in Brief</i>, 112116.