Based on nighttime light data and statistical data, this study calculated the level of urban–rural integration (URI) of Shandong province, researched spatial heterogeneity of URI levels by local spatial autocorrelation analysis, Geodetector, and geographically weighted regression, and analyzed its influencing factors and spatial heterogeneity. The results concluded that: (1) The spatial pattern of urban–rural integrated level is consistent with the level of regional economic development in Shandong province. The level of URI is higher along the Qingdao–Jinan railway and along the coast, whereas the level is lower in southwest Shandong and northwest Shandong. (2) The cities of Yantai and Weifang are High–High cluster areas of urban integration, and Jining is a Low–Low cluster area. The spatial agglomeration characteristics are not significant in other cities. (3) Among the main factors affecting URI, the explanatory power of the rural population with high school or technical secondary school education or above, the area of urban construction land, and the secondary and tertiary industry GDP to the spatial pattern of URI in Shandong province are 73.58%, 62.08%, and 58.66%, respectively. As the key factors, spatial heterogeneity, such as north–south differences, southwest-to-northeast differences, and east–west differences, is evident.
The spatial distribution pattern of buildings is an entry point for controlling the diffusion of pollution particles at an urban spatial structure scale. In this study, we adopted ordinary kriging interpolation and other methods to study the spatial distribution pattern of PM2.5 and constructed urban spatial structure indexes based on building distribution patterns to reveal the in uence of building spatial distribution patterns on PM2.5 concentration across the study area and at different elevations. The present study suggests that: (1) Topographic elevation is an important factor in uencing the distribution of PM2.5; the correlation coe cient reaches −0.761 and exceeds the 0.001 con dence level. As the elevation increases, the urban spatial structure indexes show signi cant correlations with PM2.5, and the regularity becomes stronger. (2) The PM2.5 concentration is negatively correlated with the mean and standard deviation of the DEM, the mean and maximum absolute building height, the outdoor activity area, and the average distance between adjacent buildings; and is positively correlated with the sum of the building base area, the building coverage ratio, the space area, the building coverage ratio, the space occupation ratio, and the sum of the building volume. These urban spatial structure indexes are important factors affecting PM2.5 concentration and distribution and should be considered in urban planning. (3) Spatio-temporal differences in PM2.5 concentration and distribution were found at different elevation and time ranges. Indexes, such as the average building height, the average building base area, the sum of the building volume, and the standard deviation of building volume experienced signi cant changes. Higher PM2.5 concentration yielded a more signi cant in uence of urban spatial structure indexes on PM2.5 distribution. More discrete spatial distributions of PM2.5 yielded weaker correlations between PM2.5 concentrations and the urban spatial structure indexes.
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