Spatial Statistical Methods Research Articles

Spatiotemporally comparative analysis of three common infectious diseases in China during 2013–2015

BackgroundDengue fever (DF), influenza, and hand, foot, and mouth disease (HFMD) have had several various degrees of outbreaks in China since the 1900s, posing a serious threat to public health. Previous studies have found that these infectious diseases were often prevalent in the same areas and during the same periods in China.MethodsThis study combined traditional descriptive statistics and spatial scan statistic methods to analyze the spatiotemporal features of the epidemics of DF, influenza, and HFMD during 2013–2015 in mainland China at the provincial level.ResultsDF got an intensive outbreak in 2014, while influenza and HFMD were stable from 2013 to 2015. DF mostly occurred during August–November, influenza appeared during November–next March, and HFMD happened during April–November. The peaks of these diseases form a year-round sequence; Spatially, HFMD generally has a much higher incidence than influenza and DF and covers larger high-risk areas. The hotspots of influenza tend to move from North China to the southeast coast. The southeastern coastal regions are the high-incidence areas and the most significant hotspots of all three diseases.ConclusionsThis study suggested that the three diseases can form a year-round sequence in southern China, and the southeast coast of China is a particularly high-risk area for these diseases. These findings may have important implications for the local public health agency to allocate the prevention and control resources.

Regionalization and Analysis of PM2.5 and O3 Synergetic Prevention and Control Areas Based on Remote Sensing Data

Site-based air pollution monitoring data cannot support the regionalization of air pollution prevention and control areas. Faced with this problem, this study proposed a method of regionalizing synergetic prevention and control areas based on multi-source remote sensing data and GIS spatial statistical analysis methods and carried out quantitative analyses of PM2.5 and O3 air pollution in China from 2015 to 2020. The results showed that there was an obvious decrease in PM2.5 concentrations, and O3concentrations remained stable; PM2.5 pollution mostly occurred in autumn and winter, and O3 pollution occurred in spring and summer. A significant spatial inconsistency was shown between the change rate of PM2.5 and O3 concentrations, in which the proportions of PM2.5 decreasing and O3 increasing, PM2.5 and O3 both decreasing, PM2.5 and O3 both increasing, and PM2.5 increasing and O3 decreasing accounted for 38.34%, 35.12%, 15.24%, and 10.89%, respectively. The results also showed that the boundary of PM2.5 and O3 synergetic prevention and control areas was dynamic during 2015 and 2020, showing a trend of expanding from 2015 to 2018 and then becoming smaller after 2019. Generally, the scope of PM2.5 and O3 synergetic prevention and control areas was concentrated in "2+26" cities, Fenwei plain, north of the Yangtze River Delta, and Shandong. In contrast, the regional scopes of "PM2.5 first" and "O3 first" were relatively stable. Areas of "PM2.5 first" were mainly carried out in Liaoning-Jilin, Hubei-Hunan-Jiangxi, Chengdu-Chongqing, and Taklimakan-Hexi Corridor, whereas "O3 first" areas were mainly in specific regions of the Pearl River Delta, Yangtze River Delta, and surrounding areas of Bohai Bay. Remote sensing-based PM2.5 and O3 mapping has the advantages of full-coverage and fine spatial simulation, which can support the regionalization of synergetic prevention and control areas and implementation of policies.

Comparative Validation of Spatial Interpolation Methods for Traffic Density for Data-driven Travel-time Prediction

In data-driven travel-time prediction, previous studies have mainly used speed as the input. However, from a traffic engineering perspective, given that speed varies little in the free-flow regime, traffic density, which can accurately represent traffic conditions from the free-flow regime to the congested-flow regime, is preferable as an input. In this study, we compared the accuracy of traffic densities spatially interpolated using spatial statistical and machine learning methods, and validated their effectiveness as inputs for travel-time prediction. The results show that even traffic density interpolated by simple spatial interpolation contributes to the accuracy of travel-time prediction and is superior to speed for early detection of traffic congestion.

Natural Regeneration Dynamics of Longleaf Pine Under Frequent, Low-Intensity Prescribed Fires in Southern Alabama, USA

Abstract Frequent (≤3 y) prescribed burn has been conducted in the Escambia Experimental Forest in southern Alabama, USA, since 2001.The change in the density of new germinants (≤1 y) and older seedlings and saplings (>1 y) of longleaf pine (Pinus palustris Mill.) from stands of varying conditions was quantified using the locally estimated scatterplot smoothing and spatial statistical methods. A relatively longer fire interval or time since last fire (TSLF) > 600 days favored the recruitment of young seedlings. However, a short fire return interval or TSLF≤ 600 days promoted the recruitment of older seedlings and saplings. Large basal area (BA) and site index (SI) tended to diminish natural regeneration, whereas large quadratic mean diameter (QMD) and age were facilitative. The interactive effect of these factors indicated that a ratio of TSLF/BA of 60 to 120 and QMD/SI of 1.8 to 2.2 would maximize the recruitment of young seedlings. The recruitment of older seedlings and saplings increased linearly with QMD/SI but had no relationship with TSLF/BA. Recruitment was largely absent when QMD/SI was <2.0. As a result, we recommend that QMD/SI be no greater than 2.0 and TSLF/BA range from 60 to 120 to regenerate longleaf pine naturally.

Analysis of Spatiotemporal Characteristics and Influencing Factors for the Aid Events of COVID-19 Based on GDELT

The uncertainty of COVID-19 and the spatial inequality of anti-pandemic materials have made international aid an important means for many countries to cope with this global public health crisis. It is of far-reaching significance to analyze the spatiotemporal characteristics and influencing factors of international aid events for the global joint fight against COVID-19 and the sustainability of global public health business. The data on aid events from 23 January 2020 to 31 October 2021, were from the GDELT database. China, the United States, the United Kingdom, and Canada were selected as the study objects because they provided more aid. Their spatiotemporal characteristics of main aid flows, the response characteristics of the aid requests, and the characteristics of verbal aid to cash in were studied using spatial statistical analysis methods. The influencing factors of aid allocation also were studied by regression analysis. The results found that: the international aid flow of each country was consistent in spatial distribution, mainly to countries with severe pandemics and neighboring countries. However, there were differences in the recipients. China mainly aided developing countries, while the United States, the United Kingdom, and Canada mainly aided developed countries. Relatively speaking, China was more responsive to aid requests and more aggressive in cashing in on verbal aid. The countries considered the impact of their economic interests when they planned to aid. At the same time, there were obvious “bandwagon effect” and “small country tendency” on the aid events.

Impact of Land Use and Transport Interaction on Transmition of COVID-19 in Jakarta

Land use and transportation are well known to interact and influence one another. The COVID-19 pandemic is the global health crisis. The world faces difficulties managing disasters while making efforts to slowing the spread of COVID-19. The COVID-19 pandemic spreads quickly through physical contact, making it easy to spread in public places. The purpose of this study to determine the effects of land use and transportation interactions on COVID-19 transmission. The quantitative descriptive analysis and spatial statistical methods in Geographic Information Systems (GIS) have been used in this study. Descriptive analysis to describe data related to land use characteristics in the research area. Spatial Statistical Method in GIS to analyze data from both structured and spatial observations. Cross-checking the land use from Google Street View around the bus stop within a 400 meter radius with the existing land use directly yielded the observations. The findings revealed that the type of land use demonstrated an intensity of activity that had a high potential for spreading COVID-19 because at the bus stop with land use around the trading center, shopping centers, and office buildings that have a high activity intensity, it appears that they have a high positive number of COVID-19

A comprehensive data set of physical and human-dimensional attributes for China’s lake basins

Lakes provide water-related ecosystem services that support human life and production. Nevertheless, climate changes and anthropogenic interventions remarkably altered lake and basin hydrology in recent decades, which pose a significant threat to lacustrine ecosystems. Therefore, assessments of lacustrine ecosystems require the spatial and temporal characteristics of key physical and human-dimensional attributes for lakes and lake basins. To facilitate stakeholders obtaining comprehensive data of lake basins in China, we compiled the comprehensive data set for China’s lake basins (CODCLAB) mostly from publicly available data sources based on spatial analysis and mathematical statistics methods in this study. The CODCLAB is available in three data formats, including raster layers (Level 1) in “tiff” format, vector shapefiles (Level 2), and attribute tables (Level 3). It covers 767 lakes (>10 km2) in China and their basin extent associating with 34 variables organized into five categories: Hydrology, Topography, Climate, Anthropogenic, and Soils. This unique database will provide basic data for research on the physical processes and socioeconomic activities related to these lakes and their basins in China and expect to feed a broad user community for their application in different areas.

Spatiotemporal Coupling Effect of Regional Economic Development and De-Carbonisation of Energy Use in China: Empirical Analysis Based on Panel and Spatial Durbin Models

The synergistic development of economic construction and low-carbon transformation of energy systems must be promoted for building a green, low-carbon, and cyclic economic system and achieving the “double carbon” goal in China. Based on the panel data of 30 provincial-level administrative regions in China from 2015 to 2019, the global entropy method, coupling coordination degree model, and spatial statistical analysis methods are applied to analyse the factors affecting the coupling effect. The coordination degree increased in the study period, with Beijing, Shanghai, Tianjin, Jiangsu, and Zhejiang being the regions with the highest values. The spatial distribution of the coupling coordination degree is strongly positively correlated with the eastern provincial-level administrative regions located in the high–high concentration area of the Moran scatterplot and western provincial-level administrative regions concentrated in the low–low concentration area. The spatial association pattern is stable in the study period, with only two provinces exhibiting a transition: Shandong province made the transition to high–high agglomeration areas, and Liaoning province made the transition to low–low agglomeration areas. The level of regional economy, urbanization process, energy consumption structure, and level of investment in science and innovation enhance the coupling coordination degree, whereas the industrial structure deteriorates this degree.

A deep reinforced learning spatiotemporal energy demand estimation system using deep learning and electricity demand monitoring data

Tracking decarbonization effects requires a model for the identification of spatial energy demands on city facilities. However, most developing countries lack detailed discrete time and device-specific energy demand data. In this study, we installed multiple energy demand monitoring systems that could observe electricity demands at the device level in some residences in Bogor, Indonesia. The study aimed to estimate the time-series and equipment ratio of electricity consumption by households in the entire city based on the monitoring data. However, the number of households monitored was small, and therefore unlikely to be regarded as having a representative system. Therefore, we used questionnaire data to create monitored mimic data and increased the number of samples to estimate the energy demand characteristics of the entire city via spatial interpolation. In addition, we developed a reinforcement learning system for discrete time–electricity demand estimation systems for unmonitored households using a 5-step procedure; 1) Analyzing energy demand and its patterns from monitoring data, 2) Questionnaire-based surveying of households, 3) Estimation of energy demand and its patterns based on questionnaire responses in monitored households, 4) Development of a deep learning model that extends the results from (3) to unmonitored households using data fusion, and 5) Spatial interpolation of energy demand characteristics for all households in Bogor using a spatial statistics method. The spatial electricity demand of households was interpolated from GIS and high-resolution satellite data matching procedures. Based on this analysis, we developed an hourly energy demand prediction system that could be automatically improved by adding new data from the reinforced learning framework.

Spatial structure characteristics and effects of self-driving tourism flows before and after the new crown epidemic: Taking Yunnan Province as an example

<p>To obtain the impact of the new crown epidemic on the spatial structure of self-driving tourism flow during the Spring Festival Golden Week, social network and spatial statistical analysis methods are used to integrate road traffic flow big data and travelogue data to analyze the spatial structure characteristics of self-driving tourism flow in Yunnan Province during the Spring Festival Golden Week in 2018 and 2021. The results show that: 1) The self-driving tourism flow in Yunnan Province during the Spring Festival Golden Week in 2021 shows the “dragonfly” spatial clustering characteristics of “two centers, one axis and two wings”, and the new sub-core area of Qujing is added to the core area of Kunming in 2018, and the self-driving tourists are affected by the epidemic. 2) During the Spring Festival Golden Week in 2021, compared with 2018, there is no significant change in the spatial structure of tourism flow into Yunnan from outside the province, but the degree of intermediary centrality of provincial boundary nodes is significantly weakened, and the self-driving tourism flow at Wenshan junction and Lijiang junction decreased by 72.54% and 87.26%. The New Crown epidemic hindered the development of self-drive tours into Yunnan from Guangxi and Sichuan. 3) During the New Crown epidemic, tourists were less willing to visit hotspot cities, and showed overall behavioral preference characteristics of avoiding crowd gathering and focusing on health and safety. Under the New Crown epidemic, the tendency of long-distance self-driving shifts to close distance self-driving between neighboring cities centered on Kunming.</p><div><p> </p></div>

Landscape Preference of Rural Cultural Tourism Tourists Based on GIS Spatial Statistical Technology and Emotion Analysis

How to combine GIS spatial statistics technology to realize the intelligent and efficient classification of tourists’ landscape preferences is a hot spot in the direction of rural cultural tourism. Based on this, this paper studies the emotion analysis strategy based on GIS spatial statistical method in the field of landscape preference of rural cultural tourism tourists. Firstly, an intelligent and efficient classification model based on remote sensing discrete expansion algorithm is proposed. Combined with the observation strategy of common emotional rules in the landscape preference analysis of rural cultural tourism tourists, the landscape preference of rural cultural tourism tourists is processed with high quantitative emotional information, so as to realize the intelligent separation of preference, and then the error of discrete remote sensing data expansion model is corrected. Secondly, the judgment function of emotional rules is constructed, and the commonly used data layer-driven decentralized method is used in preference classification to eliminate the secondary factors in the landscape preference analysis of rural cultural tourism tourists. At the same time, the remote sensing discrete expansion algorithm is used to optimize and improve the emotional rules of high-volume emotional information based on GIS spatial statistical method. Finally, through the design of confirmatory experiments, the results show that the landscape preference analysis model based on GIS spatial statistics method and remote sensing discrete expansion algorithm is more efficient than the conventional preference classification method.

Emerging hot spot analysis to indicate forest conservation priorities and efficacy on regional to continental scales: a study of forest change in Selva Maya 2000–2020

Despite the importance of preserving contiguous tropical forest areas to maintain biodiversity and terrestrial carbon stocks, methodological challenges continue to hinder broad-scale analysis of threats to these forests. Emerging Hot Spot Analysis (EHSA) is a spatial-statistical method that conveys complex information about the temporal dynamics of deforestation across a range of moderate to coarse spatial scales. Using Global Forest Change (GFC) data as inputs, EHSA produces spatially comprehensive, gridded outputs that represent a standardized, reproduceable way to instantiate contiguous forest tracts as spatial objects. Doing so allows aggregation of other GFC-derived values and analysis of alternative geographic configurations besides sub-national jurisdictions or protected areas, which can limit observation of finer scale variations. This paper illustrates the method’s utility to comprehensively characterize the magnitude and temporality of pressures facing the Selva Maya, a transboundary forest region with extensive areas under conservation that covers portions of Mexico, Guatemala, and Belize.

Spatial Regression Analysis of 2020 Pulmonary TB Cases in North Sumatra Province

Tuberculosis is a disease caused by a contagious infection of the bacterium Mycobacterium tuberculosis which is transmitted through droplets in the air. Based on information from the Indonesian Ministry of Health, there has been an increase in cases since 2017 by 254 cases per 100,000 population, in 2018 250 cases per 100,000 population and in 2019 to 254 cases per 100,000 population in Indonesia. Based on a report from the Central Statistics Agency of North Sumatra, in 2018 cases of pulmonary TB in North Sumatra Province as a whole reached 23,237 cases, while in 2020 there were around 33,779 cases of the spread of this disease. This indicates an increase in cases of 10,542 cases in the span of two years. This study aims to provide an overview related to the distribution pattern of the number of pulmonary TB patients in the North Sumatra Province area by utilizing technology based on geographic information systems using spatial statistical regression method. The applications used in this processing include ArcGIS in processing data and GeoDa in regression analysis. The results showed that there was a correlation between the population density variable between regions and the level of pulmonary TB sufferers in North Sumatra. Based on the test statistic value in the Spatial Autoregressive Model is 2.5634 with a p-value of 0.473466 indicating the decision to reject H0 which states that there is a spatial dependence between regions with population density variables and the high and low cases of pulmonary TB that occur in districts and cities in North Sumatra Province and supported with the results of the autocorrelation Moran's I index obtained is 0.268999 with a p-value of 0.000001 so that it shows that there is a positive spatial autocorrelation.

Sustainable Development of Ice and Snow Tourism—Theory & Empirical Studies: Preface

The Beijing Winter Olympics brought a historical miracle to the development of China's ice and snow tourism. It is both an opportunity and a challenge to reap the extra dividends of the times and to develop China's ice and snow tourism. By selecting 18 academic papers, this special issue deeply discusses the status quo and the future measures of ice and snow tourism in China from three aspects: ice and snow tourism development and management, the ice and snow tourism experience and its influencing factors, and ice and snow tourism impact assessment. Moreover, one part entitled “Tourism Culture and Ecotourism” has been set up to emphasize those cutting-edge research topics. It is worth mentioning that this issue is in-depth and has profound connotations. First of all, the research area ranges from national to regional to the city-level, including both longitudinal and horizontal research. In additon, the research methods are practical, innovative, and at the frontier of scientific research, and include industrial correlation analysis methods, kernel density, spatial autocorrelation and other geospatial analysis methods, content analysis methods, sentiment analysis methods, structural equation models, Delphi methods, spatial statistics methods, geographic detector method and many others. In general, this special issue combines qualitative research with quantitative research, provides a variety of theoretical perspectives such as perceived value theory, institutional evolution theory, etc., and builds various useful models like the pre-competition evaluation model for ice and snow tourism heritage, the consumption skill-travel radius decision-making influence model under the moderating effect of the ski resort comprehensive leisure environment, the evaluation model of the suitability of glacier tourism resource development, the model of the relationship between tourists' experience value and loyalty in the context of ice and snow tourism, among others. These studies are expected to provide theoretical and practical guidance for the development of ice and snow tourism in the post-Winter Olympics era, and help ice and snow tourism develop in a sustainable and high-quality direction.

Spatial and Temporal Distribution Characteristics of Flower-Viewing Tourism and Its Influencing Factors in China

Despite the rapid development of flower-viewing tourism in China in recent years, there is almost no systematic research on it. Therefore, this study analyzes the spatial and temporal distribution characteristics of flower-viewing tourism and its influencing factors in China using the spatial statistical analysis methods and the geographic detector method. The study uses the Point-of-Interest data of flower-viewing tourist attractions from networks such as Qunar and Ctrip, the flower observation data from China Phenological Observation Network, Chinese network news, and Weibo, and the statistical data from yearbooks. The results are as follows: 1) The spatial attribution type of flower-viewing tourism in China is aggregated into areas, including two high-density aggregated areas, three medium-density aggregated areas, and one general-density aggregated area. Furthermore, five major types of flower-viewing tourist attractions have formed several aggregated areas. 2) The time of flower viewing in China starts from about February and lasts about eight months till October each year. Florescence and flowering time of different ornamental flowers in different regions are different. 3) The spatial and temporal distribution characteristics of flower-viewing tourism in China are mainly affected by ornamental flower phenology, spatial distribution characteristics of flower-viewing resources, regional permanent population size, youth population size, female population size, regional GDP, and added value of the tertiary sector. These conclusions clarify the spatial and temporal distribution characteristics of flower-viewing tourism and its influencing factors in China. They could provide a scientific basis and useful reference for the coordination and sustainable development of regional flower-viewing tourism in China.

A study on longitudinal changes in white matter microstructure of parents who have lost their only child based on diffusion tensor imaging and its relationship with symptoms of posttraumatic stress disorder

Objective: To investigate the longitudinal changes of white matter microstructural based on diffusion tensor imaging in parents who lost their only child without psychiatric disorders and its relationship with symptoms of posttraumatic stress disorder (PTSD). Methods: Parents who had who lost their only child and without psychiatric disorders in Jiangsu Province, from September 2016 to March 2017, were retrospectively collected (TENP group, 32). MRI scans were performed at baseline and at the end of 5-year follow-up, and the Clinician Administered PTSD Scales (CAPS) were used for assessing the severity of symptoms. Additionally, sex, age and education level matched healthy subjects were recruited as healthy controls (control group, 27) and underwent MRI scanning using the same protocol. The differences of fractional anisotropy (FA) values between TENP group and control group at baseline were analyzed by using Tract-based spatial statistics method, and the brain areas of lateral differences were used as the regions of interest for longitudinal follow-up analysis of TENP group. Partial correlation analysis was used to evaluate the relationship between FA values changes in longitudinal differences in brain regions and CAPS scores. Results: Compared with the control group, FA values of the right cingulate gyrus, Uncinate fasciculus, superior longitudinal fasciculus, corticospinal tract, Inferior fronto-occipital fasciculus, Inferior longitudinal fasciculus and forceps major in TENP group were decreased at baseline ((0.613±0.032) vs (0.631±0.034), (0.539±0.048) vs (0.563±0.045), (0.534±0.033) vs (0.558±0.039), (0.560±0.038) vs (0.580±0.030), (0.519±0.023) vs(0.549±0.024), (0.489±0.038) vs (0.518±0.027), (0.499±0.027) vs (0.533±0.032); all P<0.05). From baseline to follow-up, scores of trauma reexperience symptoms and avoidance/numbness symptoms were decreased ((5.2±2.8) vs (8.1±4.9), (4.0±3.2) vs (6.6±5.4); all P<0.05); FA values of the right corticospinal tract, Inferior fronto-occipital fasciculus, Inferior longitudinal fasciculus and forceps major were decreased ((0.523±0.049) vs (0.537±0.049), (0.568±0.052) vs (0.590±0.050), (0.540±0.063) vs (0.559±0.059), (0.520±0.059) vs (0.547±0.059); all P<0.05); The decrease of FA values of the right Inferior fronto-occipital fasciculus and right Inferior longitudinal fasciculus was negatively correlated with the decrease of avoidance/numbness symptoms scores (r=-0.458, -0.374, respectively, all P<0.05). Conclusions: The trauma of parents who lost their only child can result in impaired microstructural integrity of white matter. As the post-traumatic time goes by, parents who have lost their only child do not develop to PTSD and other psychiatric disorders, and the clinical symptoms are alleviated, the damage of the white matter microstructure continued to progress.

Spatial analysis of the impact of transport accessibility on regional performance: A study for Europe

Several works have addressed the impact of transport accessibility on regional development. Nevertheless, it is not consensual that new levels of accessibility lead to the improvement of regional development. This article proposes a methodological approach to assess whether significant relationship exist between these two variables. The methodology uses Data Envelopment Analysis to evaluate regional performance and spatial statistics methods (local bivariate relationships) to evaluate the type of relationships between accessibility and performance for each region, along with its significance. The approach was applied to 186 NUTS that cover 19 European countries, extending previous works which focus on global results, in the sense that only one (usually large) area is considered. Results indicate that approximately half the regions have a significant relationship between transport accessibility and regional performance. Logit regression analyses show that central regions are more likely to exhibit significance, as well as regions with high (low) levels of performance surrounded by regions with high (low) levels of accessibility, hinting at a synergy between the two variables. An important policy implication is that regions of high performance surrounded by regions of low accessibility may benefit from investments in the transport infrastructure.

Analysis of the spatial characteristics of inhalable particulate matter concentrations under the influence of a three-dimensional landscape pattern in Xi'an, China

Atmospheric inhalable particulate matter increases the risks of damage to the respiratory system and cardiovascular diseases among residents, and threatens urban health and sustainable development. In this study, a geographically weighted regression model was constructed using spatial statistical quantification methods to explore the spatial variability characteristics of different influencing factors of the distribution of the atmospheric inhalable particulate matter concentration. Based on a visual simulation and redundancy analysis, the spatial distribution characteristics of the atmospheric inhalable particulate matter concentration in the main urban area of Xi'an were revealed. The results of the study are illustrated as daily average situations, and the selected set of urban landscape metrics performed well at explaining the variation in the atmospheric inhalable particulate matter concentration. Furthermore, the degree of building height variability, the connectivity of green space patches, the number of air-polluting enterprises, and the area of residential land had greater impacts than other factors on the PM2.5 concentration in winter. The degree of building clustering, the shape complexity of the green space patches, the elevation and the distance from a motorway, on the other hand, had some influence on the PM10 concentration in winter. From the perspective of the landscape pattern, it was found that the higher the density and area of green space patches were, the more complex the shape and the higher the connectivity were; additionally, the higher the spatial contiguity was, the lower the concentration of inhalable particulate matter in the atmosphere in winter. The concentrations of inhalable particulate matter in winter increased with the average heights, densities, and volumes of the architectural landscape; and increased with the roughness of the underlying surface, the height variation and building clustering. To the best of our knowledge, this study is the first to apply a geographically weighted regression model to the Guanzhong Basin, a heavily polluted region in China, and provides a scientific basis for urban planning, air pollution control, and public health policy formulation, thus promoting the sustainability of the urban environment.

Assessment of Intelligent Compaction Quality Evaluation Index and Uniformity

Compaction Measurement Value (CMV) is the most widely used quality evaluation index for intelligent compaction. However, with the growth of compaction degree, CMV becomes less accurate due to increased reaction force from densified geomaterials. To solve this problem, this paper proposes a more accurate intelligent compaction quality evaluation index, acceleration intelligent compaction value (AICV). Based on field measurement data analysis, AICV turned out to be effective to elevate quality evaluation accuracy. Additionally, the singularities were detected by 3σ normal distribution in order to assess the uniformity of subgrade compaction quality. Based on the semivariogram analysis method of spatial statistics, CMV and AICV were analyzed for spatial correlation, and the influence range of intelligent compaction technology employed for subgrade was evaluated. The results from this study provide a basis for further research on precision control and uniformity analysis of intelligent compaction technology.

Spatiotemporal Distribution of Continuous Air Pollution and Its Relationship with Socioeconomic and Natural Factors in China.

Continuous air pollution (CAP) incidents last even longer and generate greater health hazards relative to conventional air pollution episodes. However, few studies have focused on the spatiotemporal distribution characteristics and driving factors of CAP in China. Drawing on the daily reported ground monitoring data on the ambient air quality in 2019 in China, this paper identifies the spatiotemporal distribution characteristics of CAP across 337 Chinese cities above the prefecture level using descriptive statistics and spatial statistical analysis methods, and further examines the spatial heterogeneity effects of both socioeconomic factors and natural factors on CAP with a Multiscale Geographically Weighted Regression (MGWR) model. The results show that the average proportion of CAP days in 2019 reached 11.50% of the whole year across Chinese cities, a figure equaling to about 65 days, while the average frequency, the maximum amount of days and the average amount of days of CAP were 8.02 times, 7.85 days and 4.20 days, respectively. Furthermore, there was a distinct spatiotemporal distribution disparity in CAP in China. Spatially, the areas with high proportions of CAP days were concentrated in the North China Plain and the Southwestern Xinjiang Autonomous Region in terms of the spatial pattern, while the proportion of CAP days showed a monthly W-shaped change in terms of the temporal pattern. In addition, the types of regions containing major pollutants during the CAP period could be divided into four types, including “Composite pollution”, “O3 + NO2 pollution”, “PM10 + PM2.5 pollution” and “O3 + PM2.5 pollution”, while the region type “PM10 + PM2.5 pollution” covered the highest number of cities. The MGWR model, characterized by multiple spatial scale impacts among the driving factors, outperformed the traditional OLS and GWR model, and both socioeconomic factors and natural factors were found to have a spatial non-stationary relationship with CAP in China. Our findings provide new policy insights for understanding the spatiotemporal distribution characteristics of CAP in urban China and can help the Chinese government make prevention and control measures of CAP incidents.