By analyzing the research of domestic and foreign scholars on the spatial pattern of logistics industry, from the perspective of the concept of logistics and the research area, we use the literature research method to systematically sort out the relevant research progress about the spatial distribution of logistics. The progress of research on logistics industry is reviewed. The results show that: the research on the spatial pattern of logistics industry in foreign countries has started earlier and is relatively mature, and has basically formed a perfect theory, and can clearly understand that logistics industry has a great impact on economy and society; the reasonable optimization of the spatial pattern of logistics industry can realize the optimal allocation of resource factors, and different regions have shown their own uniqueness in the process of evolution. The research method has changed from qualitative description and simple quantitative evaluation to the use of qualitative and quantitative combination, cluster analysis, GIS spatial analysis, factor analysis and other technical methods; with the advent of the era of scientific and technological information, big data or new data began to be applied to the study of logistics industry space. Compared with the developed regions in the east, the research results on the spatial pattern of logistics industry in Xinjiang are not very rich. With the development and evolution of the spatial pattern of logistics industry, most of the previous researches are based on gravity model and vector autoregressive Granger causality test; and the study of the distribution of the spatial pattern of logistics industry in terms of time or geographical space is relatively single.

PM2.5 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 PM2.5 pollution in Henan Province in terms of spatial and temporal distribution characteristics and analyze its causes. The results show that: (1) The annual average PM2.5 concentration in Henan Province fluctuates, but decreases from 2017 to 2020, and is higher in the north and lower in the south. (2) The PM2.5 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 PM2.5 concentration were construction output value > industrial electricity consumption > energy intensity; those with negative effects were: environmental regulation > green space coverage ratio > population density. Lastly, PM2.5 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.

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.