Moran's I statistic-based nonparametric test with spatio-temporal observations

A Study on the Online Attention of Emergency Events of Torrential Rain in Shanxi and Henan

To analyze the differences in the spatial and temporal distribution patterns of online attention to sudden torrential rain public events in Shanxi and Henan provinces in China, and their influencing factors, the Baidu index search platform was used to obtain the online attention of 31 provinces (autonomous regions and municipalities directly under the central government) nationwide for the 20 July 2021 to 9 August 2021 Henan torrential rain and the 5 October 2021 to 25 October 2021 Shanxi torrential rain. The geographical concentration index, the coefficient of variation, the Moran index, and the Pearson correlation were used to analyse the spatial and temporal distribution characteristics of the online attention of the floods in Shanxi and Henan and their influencing factors. The study shows that: (1) from a temporal perspective, the daily internet attention of both the Shanxi and Henan torrential rain reached its highest value on the third day, showing a sharp rise followed by a sharp fall in an inverted “V” pattern, and compared to the prolonged and continuous torrential rain in Shanxi, the short-lived precipitation and broken ring of the Henan torrential rain were seriously urgent, showing a higher internet attention. For example, the highest daily attention in Shanxi was only 73,643, while the highest daily attention in Henan was 56,054, which is 7.6 times higher than the former. The Geographical Concentration (G) index of internet attention for torrential rain in Shanxi increased from 1.55 to 8.25 within three weeks, with attention shifting from scattered to concentrated, while the G-index for torrential rain in Henan showed an inverted “V” shape; the Coefficient of Variation (CV) index for torrential rain in both provinces showed a downward trend within three weeks. (2) In terms of spatial distribution, the regional distribution of the network concern about the torrential rain in Shanxi and Henan is uneven. The provinces and cities with a higher concern about the torrential rain in Shanxi are mainly some eastern provinces and cities, such as Jiangsu, Beijing, Zhejiang, Fujian, Hainan, etc., who have signed coal protection agreements with Shanxi. The provinces and cities with a higher concern about the torrential rain in Henan are mainly the economically developed coastal provinces or provinces adjacent to Henan, such as Beijing, Zhejiang, Shandong, and Shaanxi, etc. The concern of each city for the torrential rain in the two provinces is in line with the law of distance decay. (3) In terms of influencing factors, the level of regional economic development, demographic factors, and Internet penetration and distance all have a certain influence on the level of attention to sudden torrential rain, and the study found that the level of economic development and demographic factors have the most significant influence on the level of internet attention.

Rediscovering the Scaling Law of Urban Land from a Multi-Scale Perspective—A Case Study of Wuhan

The law of urban scaling implies that there is a universally applicable nonlinear scaling relationship between population size and urban indicators, which is a method of quantitative analysis that can reflect the growth law and internal logic of the urban system. However, most present research is conducted at the municipal scale, and studies of scaling law in the inner-city system are scarce, especially from the perspective of compact urban form development. The goal of this paper is to discover the scaling law within urban systems from a multi-scale perspective. Through the empirical analysis of Wuhan, this paper examines the internal scale law of the urban system from the municipal and district scales. Moreover, we use the landscape expansion index to perform spatial autocorrelation analysis. In this way, we assess the relationship between the compactness of urban morphological development and the urban scaling law. The results indicate that the temporal scaling law on the city scale has a more significant linear law than the single-year scaling law. The analysis also shows the scaling law relationship within the inner-city system. Nevertheless, there is a deviation between the temporal scaling law and the cross-section scaling law. Namely, the time series development of a district does not follow the section scaling law of the urban system. Furthermore, the urban scaling law shows a negative correlation with the compactness of the urban form development. It is crucial to understand the current economic development and resource endowment of an urban system in the urbanization process, as it significantly contributes to urban development and regional coordinated planning.

Spatial Characteristics and Regional Transmission Analysis of PM2.5 Pollution in Northeast China, 2016-2020

Northeast China is an essential industrial development base in China and the regional air quality is severely affected by PM_2.5 pollution. In this paper, spatial autocorrelation, trajectory clustering, hotspot analysis, PSCF and CWT analysis are used to explore the spatial pollution characteristics of PM_2.5 and determine the atmospheric regional transmission pattern for 40 cities in Northeast China from 2016 to 2020. Analysis of PM_2.5 concentration characteristics in the northeast indicates that the annual average value and total exceedance days of PM_2.5 concentration in Northeast China showed a U-shaped change, with the lowest annual average PM_2.5 concentration (31 μg/m³) in 2018, decreasing by 12.1% year-on-year, and the hourly PM_2.5 concentration exploding during the epidemic lockdown period in 2020. A stable PM_2.5 pollution band emerges spatially from the southwest to Northeast China. Spatially, the PM_2.5 in Northeast China has a high degree of autocorrelation and a south-hot-north-cool characteristic, with all hotspots concentrated in the most polluted Liaoning province, which exhibits the H-H cluster pattern and hotspot per year. Analysis of the air mass trajectories, potential source contributions and concentration weight trajectories in Northeast China indicates that more than 74% of the air mass trajectories were transmitted to each other between the three heavily polluted cities, with the highest mean value of PM_2.5 pollution trajectories reaching 222.4 μg/m³, and the contribution of daily average PM_2.5 concentrations exceeding 60 μg/m³ within Northeast China. Pollution of PM_2.5 throughout the Northeast is mainly influenced by short-range intra-regional transport_, with long-range transport between regions also being an essential factor; organized integration is the only fundamental solution to air pollution.

Effects of Environmental Regulations on Technological Innovation Efficiency in China’s Industrial Enterprises: A Spatial Analysis

China’s traditional industrial model is characterized by high energy consumption and high pollution, which results in many environmental problems that cannot be ignored. To achieve sustainable development, the Chinese government has proposed five development concepts of “innovation, coordination, green, openness, and sharing”. This initiative highlights the urgency of China’s efforts to strengthen environmental regulation. Based on the panel data of industrial enterprises in China from 2006 to 2015, this study not only investigates the spatial features of technological innovation efficiency, but also examines the relationship between technology innovation efficiency and environmental regulations from a spatial perspective. The results indicate that first, China’s provincial-level technological innovation efficiencies are uneven in space. Second, voluntary regulation positively affects the technological innovation efficiency of industrial enterprises at the provincial level, while mandatory regulation has no significant impact. Third, there is a spatial spillover effect in voluntary regulation at the provincial level. One highlight implication is that the government should promulgate environmental regulations based on each province’s technological innovation potential, due to the spatial differences in technological innovation activities.