Based on the POI data of logistics enterprises in Xinjiang in 2012, 2016, and 2020, the ArcGIS spatial analysis technique, geographic detector, and other methods were used for the quantitative analysis of the spatial and temporal distributions of logistics enterprises in Xinjiang during 2012–2020 and the influencing factors. The following findings were obtained in the present study: (1) there was a significant difference in the distributions of logistics enterprises in Xinjiang at different development stages, with unbalance among areas; further, there was a higher number of logistics enterprises in Northern Xinjiang compared with Southern Xinjiang; (2) the spatial distribution of logistics enterprises in Xinjiang was generally characterized by a “northeast–southwest” trend; there was a periodic shift in the distribution center from northeast to southwest; the distribution center remained in Bayingolin Mongol Autonomous Prefecture in 2012 and 2020, and shifted to Changji Hui Autonomous Prefecture in 2016, close to the junction of the two areas; (3) the agglomeration of logistics enterprises in Xinjiang was positively correlated with the scale; the kernel density analysis results revealed that there was obvious spatial differentiation characterized by “multi-center development with core agglomeration and patch distribution at the edge”, and the hotspot areas of logistics enterprises were distributed in major cities, with small variations; the Tianshan Mountain North Slope Economic Belt was the main agglomeration area of logistics enterprises; (4) the results from the geographic detector show that the regional GDP, regional total retail sales of consumer goods, regional utilization of foreign direct investment, and regional fixed assets investment were factors that influenced the spatial distribution of logistics enterprises in Xinjiang, thereby significantly promoting the stable and rapid development of logistics enterprises.

PM_2.5 is the main cause of haze pollution, and studying its spatio-temporal distribution and driving factors can provide a scientific basis for prevention and control policies. Therefore, this study uses air quality monitoring information and socioeconomic data before and during the COVID-19 outbreak in 18 prefecture-level cities in Henan Province from 2017 to 2020, using spatial autocorrelation analysis, ArcGIS mapping, and the spatial autocorrelation analysis. ArcGIS mapping and the Durbin model were used to reveal the characteristics of PM_2.5 pollution in Henan Province in terms of spatial and temporal distribution characteristics and analyze its causes. The results show that: (1) The annual average PM_2.5 concentration in Henan Province fluctuates, but decreases from 2017 to 2020, and is higher in the north and lower in the south. (2) The PM_2.5 concentrations in Henan Province in 2017–2020 are positively autocorrelated spatially, with an obvious spatial spillover effect. Areas characterized by a high concentration saw an increase between 2017 and 2019, and a decrease in 2020; values in low-concentration areas remained stable, and the spatial range showed a decreasing trend. (3) The coefficients of socio-economic factors that increased the PM_2.5 concentration were construction output value > industrial electricity consumption > energy intensity; those with negative effects were: environmental regulation > green space coverage ratio > population density. Lastly, PM_2.5 concentrations were negatively correlated with precipitation and temperature, and positively correlated with humidity. Traffic and production restrictions during the COVID-19 epidemic also improved air quality.