Spatiotemporal Evolution and Formation Mechanism of Missing-Person Incidents in the United States

The problem of missing persons is a major global challenge, which causes serious harm to their families and societies. For this study, we collected 9,193 U.S. missing-persons records for the years 1996-2021 from the Doe Network platform. We used mathematical statistics and Moran's I index to analyze the sociodemographic characteristics, spatio-temporal distribution and its evolution patterns. Then the geodetector was applied to conduct an in-depth analysis of the influencing factors in socio-cultural, economic and demographic aspects. Based on above findings, a sociological theory of the formation mechanism of the missing-person phenomenon in the United States was proposed. Major findings included: (1) With age increasing, the number of missing persons initially increased and then dropped gradually. The highest missing rate was found among adolescents (13-18 years old) and adults (19-59 years old). Although more males than females were reported missing, the high-incidence period of males lagged slightly behind that of females. The high missing rate among adolescent females was linked to sexual crimes, including sex trafficking and rape, while that of adult men tended to be caused by family discord or debt problems. Among racial groups, black people faced the greatest risk of going missing. (2) From 1996, the number of missing-person incidents initially showed a wave upward trended and then fell sharply, after peaking in 2017, because of a series of immigration regulations. In 2020, it declined dramatically again, due to COVID-19. Influenced by the temperature, school holidays, and festivals, most people were reported missing during the months of June, August, and December. Only few missing incidents happened between February and April. (3) Spatially, at the state level, the missing population distribution decreased from the coastal border area to the inland area;over time, areas with a great number of missing-person incidents advanced simultaneously from the eastern and western coastal areas and the southern US-Mexico border to US inland areas. At the county level, they were concentrated on the edge and scattered internally. (4) Missing-person incidents were caused by the interaction of multiple factors;regional population mobility, fertility rate, and the number of vulnerable people had a positive impact on numbers of missing people, while per capital GDP had a negative impact. The power of population-based environmental factors was significantly enhanced after be interacted with social and economic factors, on explaining the missing-person spatial distribution, all of which were above 80%. (5) The underlying mechanism of missing-person incidents could be understood from the perspective of "social anomie". In other words, the disconnect between social goals and means led to social anomie, which then induced deviant behavior, including abduction, murder, and running away from home, increasing the likelihood of missing-person incidents. Finally, we offered suggestions for disappearance prevention and further study directions. The findings provided a basic understanding of the missing-person phenomenon, contributing to global scientific information, which could aid in preventing missing-person incidents. © Starobulgarska Literatura 2022.

Evolution and control of the COVID-19 pandemic: A global perspective.

We investigated the factors influencing the progression of the pandemic from a global perspective by using the Geodetector and Correlation methods and explored the pandemic response policies and effects in different countries. The results yielded three notable findings. First, empirical results show the COVID-19 pandemic is influenced by various factors, including demographic and economic parameters, international travelers, urbanization ratio, urban population, etc. Among them, the correlation between urban population and confirmed cases is strongest. Cities become the key factor affecting the COVID-19 pandemic, with high urbanization levels and population mobility increases the risk of large-scale outbreaks. Second, among control measures, School-closures, International-travel-restrictions, and Public-gathering-restriction have the best control effect on the epidemic. In addition, the combination of different types of control measures is more effective in controlling the outbreak, especially for Public-gathering-restrictions ∩ School-closures, International-travel-restrictions ∩ Workplace-closures, Public-transport-restrictions ∩ International-travel-restrictions. Third, implementing appropriate control measures in the first month of an outbreak played a critical role in future pandemic trends. Since there are few local cases in this period and the control measures have an obvious effect.

Spatiotemporal evolution of online attention to vaccines since 2011: An empirical study in China.

Since the outbreak of Coronavirus Disease 2019 (COVID-19), the Chinese government has taken a number of measures to effectively control the pandemic. By the end of 2021, China achieved a full vaccination rate higher than 85%. The Chinese Plan provides an important model for the global fight against COVID-19. Internet search reflects the public's attention toward and potential demand for a particular thing. Research on the spatiotemporal characteristics of online attention to vaccines can determine the spatiotemporal distribution of vaccine demand in China and provides a basis for global public health policy making. This study analyzes the spatiotemporal characteristics of online attention to vaccines and their influencing factors in 31 provinces/municipalities in mainland China with Baidu Index as the data source by using geographic concentration index, coefficient of variation, GeoDetector, and other methods. The following findings are presented. First, online attention to vaccines showed an overall upward trend in China since 2011, especially after 2016. Significant seasonal differences and an unbalanced monthly distribution were observed. Second, there was an obvious geographical imbalance in online attention to vaccines among the provinces/municipalities, generally exhibiting a spatial pattern of "high in the east and low in the west." Low aggregation and obvious spatial dispersion among the provinces/municipalities were also observed. The geographic distribution of hot and cold spots of online attention to vaccines has clear boundaries. The hot spots are mainly distributed in the central-eastern provinces and the cold spots are in the western provinces. Third, the spatiotemporal differences in online attention to vaccines are the combined result of socioeconomic level, socio-demographic characteristics, and disease control level.

Effects of Meteorological Factors and Air Pollutants on COVID-19 Transmission under the Action of Control Measures.

At present, COVID-19 is still spreading, and its transmission patterns and the main factors that affect transmission behavior still need to be thoroughly explored. To this end, this study collected the cumulative confirmed cases of COVID-19 in China by 8 April 2020. Firstly, the spatial characteristics of the COVID-19 transmission were investigated by the spatial autocorrelation method. Then, the factors affecting the COVID-19 incidence rates were analyzed by the generalized linear mixed effect model (GLMMs) and geographically weighted regression model (GWR). Finally, the geological detector (GeoDetector) was introduced to explore the influence of interactive effects between factors on the COVID-19 incidence rates. The results showed that: (1) COVID-19 had obvious spatial aggregation. (2) The control measures had the largest impact on the COVID-19 incidence rates, which can explain the difference of 34.2% in the COVID-19 incidence rates, while meteorological factors and pollutant factors can only explain the difference of 1% in the COVID-19 incidence rates. It explains that some of the literature overestimates the impact of meteorological factors on the spread of the epidemic. (3) The influence of meteorological factors was stronger than that of air pollution factors, and the interactive effects between factors were stronger than their individual effects. The interaction between relative humidity and NO2 was stronger. The results of this study will provide a reference for further prevention and control of COVID-19.

Equilibrium of Tiered Healthcare Resources during the COVID-19 Pandemic in China: A Case Study of Taiyuan, Shanxi Province.

COVID-19 has caused more than 500 million infections and 6 million deaths. Due to a continuous shortage of medical resources, COVID-19 has raised alarm about medical and health resource allocation in China. A balanced spatial distribution of medical and health resources is a key livelihood issue in promoting the equalization of health services. This paper explores the spatial allocation equilibrium of two-tier medical and health resources and its influencing factors in Taiyuan. Using extracted POIs of medical and health resources of AMAP, we evaluated the spatial quantitative characteristics through the Health Resources Density Index, researched the spatial distribution pattern by kernel density analysis, hot spot analysis, and service area analysis, and identified the influencing factors of the spatial distribution equilibrium by the Geodetector model. The findings are as follows. The overall allocation level of medical and health resources in Taiyuan is low. There are tiered and regional differences; the response degree of primary care facilities to external factors is greater than that of hospitals; and the comprehensive influence of economic and topographic systems is crucial compared with other factors. Therefore, in order to promote the rational spatial distribution of medical and health resources in Taiyuan and to improve the construction of basic medical services within a 15 min radius, it is important to continuously improve the tiered healthcare system, uniformly deploy municipal medical and health resources, and increase the resource allocation to surrounding counties and remote mountainous areas. Future research should focus on collecting complete data, refining the research scale, analyzing qualitative differences, and proposing more accurate resource allocation strategies.

Tea Cultivation Suitability Evaluation and Driving Force Analysis Based on AHP and Geodetector Results: A Case Study of Yingde in Guangdong, China

Tea is an economically important crop. Evaluating the suitability of tea can better optimize the regional layout of the tea industry and provide a scientific basis for tea planting plans, which is also conducive to the sustainable development of the tea industry in the long run. Driving force analysis can be carried out to better understand the main influencing factors of tea growth. The main purpose of this study was to evaluate the suitability of tea planting in the study area, determine the prioritization of tea industry development in this area, and provide support for the government’s planning and decision making. This study used Sentinel image data to obtain the current land use data of the study area. The results show that the accuracy of tea plantation classification based on Sentinel images reached 86%, and the total accuracy reached 92%. Then, we selected 14 factors, including climate, soil, terrain, and human-related factors, using the analytic hierarchy process and spatial analysis technology to evaluate the suitability of tea cultivation in the study area and obtain a comprehensive potential distribution map of tea cultivation. The results show that the moderately suitable area (36.81%) accounted for the largest proportion of the tea plantation suitability evaluation, followed by the generally suitable area (31.40%), the highly suitable area (16.91%), and the unsuitable area (16.23%). Among these areas, the highly suitable area is in line with the distribution of tea cultivation at the Yingde municipal level. Finally, to better analyze the contribution of each factor to the suitability of tea, the factors were quantitatively evaluated by the Geodetector model. The most important factors affecting the tea cultivation suitability evaluation were temperature (0.492), precipitation (0.367), slope (0.302), and elevation (0.255). Natural factors influence the evaluation of the suitability of tea cultivation, and the influence of human factors is relatively minor. This study provides an important scientific basis for tea yield policy formulation, tea plantation site selection, and adaptation measures.

Exploring the Relationship between the Spatial Distribution of Different Age Populations and Points of Interest (POI) in China

Population spatialization data is crucial to conducting scientific studies of coupled human–environment systems. Although significant progress has been made in population spatialization, the spatialization of different age populations is still weak. POI data with rich information have great potential to simulate the spatial distribution of different age populations, but the relationship between spatial distributions of POI and different age populations is still unclear, and whether it can be used as an auxiliary variable for the different age population spatialization remains to be explored. Therefore, this study collected and sorted out the number of different age populations and POIs in 2846 county-level administrative units of the Chinese mainland in 2010, divided the research data by region and city size, and explored the relationship between the different age populations and POIs. We found that there is a complex relationship between POI and different age populations. Firstly, there are positive, moderate-to-strong linear correlations between POI and population indicators. Secondly, POI has a different explanatory power for different age populations, and it has a higher explanatory power for the young and middle-aged population than the child and old population. Thirdly, the explanatory power of POI to different age populations is positively correlated with the urban economic development level. Finally, a small number of a certain kinds of POIs can be used to effectively simulate the spatial distributions of different age populations, which can improve the efficiency of obtaining spatialization data of different age populations and greatly save on costs. The study can provide data support for the precise spatialization of different age populations and inspire the spatialization of the other population attributes by POI in the future.

Coupling Coordination and Spatiotemporal Dynamic Evolution Between Medical Services and Tourism Development in China.

This work constructs an evaluation index system and quantitatively explores the coupling coordination relationship between the tourism development system and the medical services system in China. Results show that the degree of coupling coordination between the tourism development system and the medical services system showed a good upward trend in China during the period 2012-2019. However, the relationship was barely balanced, with tourism development lagging. The overall layout shows a spatial pattern of "high in the north and low in the south, high in the east and low in the west." The degree of coupling coordination tends to be randomly distributed from clustered distribution, and the cold-hot spots show a spatial development pattern of "cold in the northwest and hot in the southeast" as time passes. The power of government to regulate has always been an important mechanism affecting the degree of coupling coordination. The study aims to provide reference for the rationalization of medical tourism layout and sustainable development.

The impact of meteorological factors on COVID-19 of California and its lag effect

As of March 30, 2021, COVID-19 has been circulating globally for more than 1?year, posing a huge threat to the safety of human life and property. Understanding the relationship between meteorological factors and the COVID-19 can provide positive help for the prevention and control of the global epidemic. We take California as the research object, use Geodetector to screen out the meteorological factors with the strongest explanatory power for the epidemic, then use partial correlation analysis to study the correlation between the two, and finally construct a distributed lag non-linear model (DLNM) to further explore the relationship between the dominant factor and COVID-19 and its lag effect. It turns out that temperature has a greater impact on COVID-19 and the two have a significant negative correlation. When the temperature is lower than 50°F, it has a significant promotion effect on the epidemic, and the relative risk (RR) increases approximately exponentially as the temperature decreases. The delayed effect of the cold effect on the epidemic can be as long as 15?days. This study has shown that more attention should be paid to epidemic prevention and control when the temperature is low, and the delay effect of temperature on the spread of the epidemic cannot be ignored.

Global COVID-19 pandemic trends and their relationship with meteorological variables, air pollutants and socioeconomic aspects.

Meteorological variables, air pollutants, and socioeconomic factors are associated with COVID-19 transmission. However, it is unclear what impact their interactions have on COVID-19 transmission, whether their impact on COVID-19 transmission is linear or non-linear, and where the inflexion points are. This study examined 1) the spatial and temporal trends in COVID-19 monthly infection rate of new confirmed cases per 100,000 people (Rn) in 188 countries/regions worldwide from March to November 2020; 2) the linear correlation between meteorological variables (temperature (T), rainfall (R), wind speed (WS), relative humidity (RH), air pressure (AP)), air pollutants (nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), ozone (O3)) and socioeconomic aspects (population density (PD), gross domestic product per capita (GDP), domestic general government health expenditure per capita (GHE)) and Rn, and 3) the interaction and non-linear effects of the different variables on Rn, based on GeoDetector and Boosted regression tree. The results showed that the global Rn had was spatially clustered, and the average Rn increased From March to November 2020. Global Rn was negatively correlated with meteorological variables (T, R, WS, AP) and positively correlated with air pollutants (NO2, SO2, O3) and socioeconomic aspects (GDP, GHE). The interaction of SO2 and O3, SO2 and RH, and O3 and T strongly affected Rn. The variables effect on COVID-19 transmission was non-linear, with one or more inflexion points. The findings of this work can provide a basis for developing a global response to COVID-19 for global sustainable development.

Spatial and temporal differentiation of COVID-19 epidemic spread in mainland China and its influencing factors.

This paper uses the exploratory spatial data analysis and the geodetector method to analyze the spatial and temporal differentiation characteristics and the influencing factors of the COVID-19 (corona virus disease 2019) epidemic spread in mainland China based on the cumulative confirmed cases, average temperature, and socio-economic data. The results show that: (1) the epidemic spread rapidly from January 24 to February 20, 2020, and the distribution of the epidemic areas tended to be stable over time. The epidemic spread rate in Hubei province, in its surrounding, and in some economically developed cities was higher, while that in western part of China and in remote areas of central and eastern China was lower. (2) The global and local spatial correlation characteristics of the epidemic distribution present a positive correlation. Specifically, the global spatial correlation characteristics experienced a change process from agglomeration to decentralization. The local spatial correlation characteristics were mainly composed of the'high-high' and 'low-low' clustering types, and the situation of the contiguous layout was very significant. (3) The population inflow from Wuhan and the strength of economic connection were the main factors affecting the epidemic spread, together with the population distribution, transport accessibility, average temperature, and medical facilities, which affected the epidemic spread to varying degrees. (4) The detection factors interacted mainly through the mutual enhancement and nonlinear enhancement, and their influence on the epidemic spread rate exceeded that of single factors. Besides, each detection factor has an interval range that is conducive to the epidemic spread.