Investigating the spatiotemporal characteristics and medical response during the initial COVID-19 epidemic in six Chinese cities.

In the future, novel and highly pathogenic viruses may re-emerge, leading to a surge in healthcare demand. It is essential for urban epidemic control to investigate different cities' spatiotemporal spread characteristics and medical carrying capacity during the early stages of COVID-19. This study employed textual analysis, mathematical statistics, and spatial analysis methods to examine the situation in six highly affected Chinese cities. The findings reveal that these cities experienced three phases during the initial outbreak of COVID-19: "unknown-origin incubation", "Wuhan-related outbreak", and "local exposure outbreak". Cities with a high number of confirmed cases exhibited a multicore pattern, while those with fewer cases displayed a single-core pattern. The cores were distributed hierarchically in the central built-up areas of cities' economic, political, or transportation centers. The radii of these cores shrank as the central built-up area's level decreased, indicating a hierarchical decay and a core-edge structure. It suggests that decentralized built environments (non-clustered economies and populations) are less likely to facilitate large-scale epidemic clusters. Additionally, the deployment of designated hospitals in these cities was consistent with the spatial distribution of the epidemic; however, their carrying capacity requires urgent improvement. Ultimately, the essence of prevention and control is the governance of human activities and the efficient management of limited resources about individuals, places, and materials through leveraging IT and GIS technologies to address supply-demand contradictions.

River system changes and locust breeding area evolution in the Qin-Jin region of the Yellow River Basin during the Ming and Qing dynasties and their disaster effects.

Based on the locust outbreak records in historical documents, we extracted 654 locust outbreak events in the Qin-Jin region of the Yellow River Basin during the Ming and Qing dynasties (1368-1911 CE), reconstructed the locust disaster index series according to the severity of locust plagues, and compared them with the flood, drought, famine and river disasters in the same period. The objective was to investigate the process of river system changes in the Qin-Jin region of the Yellow River Basin, their relationship with the evolution of the locust breeding area and disaster effects. The results indicate that locust outbreaks in the Qin-Jin region of the Yellow River basin during the Ming and Qing dynasties were concentrated in the summer and autumn, with disaster grades 2 and 3 predominating. The interannual series of locust outbreaks showed "one peak (1644-1650 CE) and four mounds (1527-1537 CE, 1613-1620 CE, 1690-1704 CE, and 1854-1864 CE)". On the 10-year scale, locust outbreaks were positively correlated with famine and moderately associated with drought and river clearing. The spatial distribution of locust-prone areas corresponded well with drought and famine. The locust breeding areas in the Qin-Jin region were dominated by river flooding locust breeding areas, where topographic factors and river changes more influenced locust distribution. The DPSIR model revealed that potential climatic, locust, and demographic "drivers" exerted "pressure" on the Qin-Jin region of the Yellow River Basin, causing changes in the social, economic and environmental "state" of the locust-prone areas, which in turn "impact" people's livelihoods and ultimately led to a series of central-local-populace "responses".

Post-pandemic shared mobility and active travel in Alabama: A machine learning analysis of COVID-19 survey data.

The COVID-19 pandemic has had unprecedented impacts on the way we get around, which has increased the need for physical and social distancing while traveling. Shared mobility, as an emerging travel mode that allows travelers to share vehicles or rides has been confronted with social distancing measures during the pandemic. On the contrary, the interest in active travel (e.g., walking and cycling) has been renewed in the context of pandemic-driven social distancing. Although extensive efforts have been made to show the changes in travel behavior during the pandemic, people's post-pandemic attitudes toward shared mobility and active travel are under-explored. This study examined Alabamians' post-pandemic travel preferences regarding shared mobility and active travel. An online survey was conducted among residents in the State of Alabama to collect Alabamians' perspectives on post-pandemic travel behavior changes, e.g., whether they will avoid ride-hailing services and walk or cycle more after the pandemic. Machine learning algorithms were used to model the survey data (N = 481) to identify the contributing factors of post-pandemic travel preferences. To reduce the bias of any single model, this study explored multiple machine learning methods, including Random Forest, Adaptive Boosting, Support Vector Machine, K-Nearest Neighbors, and Artificial Neural Network. Marginal effects of variables from multiple models were combined to show the quantified relationships between contributing factors and future travel intentions due to the pandemic. Modeling results showed that the interest in shared mobility would decrease among people whose one-way commuting time by driving is 30-45 min. The interest in shared mobility would increase for households with an annual income of $100,000 or more and people who reduced their commuting trips by over 50% during the pandemic. In terms of active travel, people who want to work from home more seemed to be interested in increasing active travel. This study provides an understanding of future travel preferences among Alabamians due to COVID-19. The information can be incorporated into local transportation plans that consider the impacts of the pandemic on future travel intentions.

Spatial analysis and optimization of self-pickup points of a new retail model in the Post-Epidemic Era: the case of Community-Group-Buying in Xi'an City.

The Community-Group-Buying Points (CGBPs) flourished during COVID-19, safeguarding the daily lives of community residents in community lockdowns, and continuing to serve as a popular daily shopping channel in the Post-Epidemic Era with its advantages of low price, convenience and neighborhood trust. These CGBPs are allocated on location preferences however spatial distribution is not equal. Therefore, in this study, we used point of interest (POI) data of 2,433 CGBPs to analyze spatial distribution, operation mode and accessibility of CGBPs in Xi'an city, China as well as proposed the location optimization model. The results showed that the CGBPs were spatially distributed as clusters at α = 0.01 (Moran's I = 0.44). The CGBPs operation mode was divided into preparation, marketing, transportation, and self-pickup. Further CGBPs were mainly operating in the form of joint ventures, and the relying targets presented the characteristic of 'convenience store-based and multi-type coexistence'. Influenced by urban planning, land use, and cultural relics protection regulations, they showed an elliptic distribution pattern with a small oblateness, and the density showed a low-high-low circular distribution pattern from the Palace of Tang Dynasty outwards. Furthermore, the number of communities, population density, GDP, and housing type were important driving factors of the spatial pattern of CGBPs. Finally, to maximize attendance, it was suggested to add 248 new CGBPs, retain 394 existing CGBPs, and replace the remaining CGBPs with farmers' markets, mobile vendors, and supermarkets. The findings of this study would be beneficial to CGB companies in increasing the efficiency of self-pick-up facilities, to city planners in improving urban community-life cycle planning, and to policymakers in formulating relevant policies to balance the interests of stakeholders: CGB enterprises, residents, and vendors.

Exploring variations and influencing factors of illegal adoption: A comparison between child trafficking and informal adoption.

BACKGROUND: Illegal adoption, which mainly includes child trafficking and informal adoption, has long been a prevalent social issue in China. However, the processes and patterns of illegal adoption are not well understood due to the scarcity of data. OBJECTIVE: The findings are expected to provide insightful clues for the government and the public to better understand the two categories of illegal adoption. PARTICIPANTS AND SETTING: This study included 4296 trafficking cases and 4499 informal adoption cases between 1949 and 2018. The data came from the 'Baby Coming Back Home' (https://www.baobeihuijia.com) website, which is the most comprehensive commonweal forum established by nongovernmental volunteers for finding missing persons in China. METHODS: Mathematical statistics and hot spot analysis were used to visualize the spatiotemporal pattern of illegal adoption. RESULTS: Child trafficking and informal adoption show opposite gender preferences and different age gradients. The numbers of both cases peaked in the early 1990s and then dropped. More than 50 % of all trafficked children were male, whereas approximately 83 % cases of informal adoption were female between 1980 and 2000. Hot spots of illegal adoption have shifted from the cities of the Huai River Basin to the southeastern coastal cities over time, 39.40 % of trafficking cases occurred in rural residential areas, and 52.45 % of informal adoption cases were observed in hospitals. CONCLUSIONS: Child trafficking and informal adoption are two different ways of adopting children in China. The combination of the one-child policy and the traditional culture of son preference shaped the different characteristics of the illegal adoption of children during a critical period.

The effect of environmental changes on locust outbreak dynamics in the downstream area of the Yellow River during the Ming and Qing Dynasties.

Locust outbreaks were one of the primary biological disasters in ancient China. Using historical data from the Ming and Qing Dynasties, the temporal and spatial relationships between the changes in the aquatic environment and the locust dynamics in the downstream areas of the Yellow River were investigated via quantitative statistics, and other factors affecting locust outbreaks were also studied. This study demonstrated that locust, drought and flood outbreaks were spatiotemporally correlated. Locusts and droughts were synchronous for long-term series, but locust outbreaks were weakly correlated with floods. In drought years, the probability of a locust outbreak occurring in the same month as a drought was higher than that in other years and months. The probability of a locust outbreak was higher in the one to two years following a flood than in other years, but locusts were not easily triggered by extreme flooding. In the waterlogged and riverine locust breeding areas, locust outbreaks were more closely related to flooding and drought than in other breeding areas. Affected by the diversion of the Yellow River, the areas of frequent locust outbreaks were around riverine areas. In addition, climate change affects the hydrothermal conditions in which locusts occur, and human activities influence the occurrence of locusts by changing their habitats. Analyzing the relationship between historical locust outbreaks and water system changes provides valuable information for formulating and implementing disaster prevention and mitigation policies in this region.

From zero to hero: the spatio-temporal patterns and migration influence mechanism of world-class athletes in China.

At present, China is in an important stage of transition into a global sports power where "outstanding competitive sports talents" are required to play an important role. Therefore, it is of great theoretical and practical significance to conduct in-depth research on domestic "outstanding competitive sports talents" to promote the sustainable development of China's competitive sports and enhance its comprehensive strength. In this study, WCA refers to "world-class athletes," indicating a group of talents who won medals in international sports events such as the Olympic Games during 2009-2019. In this regard, this study uses statistical and spatial analysis methods to reveal the spatial and temporal characteristics, evolutionary process, and migration mechanism of Chinese WCA. The conclusion shows that: In terms of temporal characteristics, the population numbers in general and among different genders are characterized by a "three peaks and two troughs" pattern. In contrast, the individual temporal pattern is characterized by an "inverted U″ and "inverted V″, with an average age of 23.18 years. In terms of space, a positive correlation is shown on the whole (Moran's I > 0), however, characteristics of geographical proximity and spatial heterogeneity are not prominent, illustrating the spatial form of random distribution with low aggregation which is primarily concentrated in the southeast of China and demonstrates a "northeast-southwest" trend. There are apparent differences between areas of origin and immigration areas: Liaoning and Shandong are the main areas of origin while destination areas are frequently located in the southeast and "People's Liberation Army of China" (PLA for short). Lastly, this paper discusses the causes and influences of the migration groups from three aspects: the migrating talents, the areas of origin and immigration areas, and Chinese sports, revealing the formation and influence mechanisms.

Establishment of hospital resilience framework in urban China: insight from Wuhan City.

Since the Corona Virus Disease 2019 (COVID-19) swept the world, many countries face a problem that is a shortage of medical resources. The role of emergency medical facilities in response to the epidemic is beginning to arouse public attention, and the construction of the urban resilient emergency response framework has become the critical way to resist the epidemic. Today, China has controlled the domestically transmitted COVID-19 cases through multiple emergency medical facilities and inclusive patient admission criteria. Most of the existing literature focuses on case studies or characterizations of individual facilities. This paper constructs an evaluation system to measure urban hospital resilience from the spatial perspective and deciphered the layout patterns and regularities of emergency medical facilities in Wuhan, the city most affected by the epidemic in China. Findings indicate that the pattern of one center and two circles are a more compelling layout structure for urban emergency medical facilities in terms of accessibility and service coverage for residents. Meanwhile, the Fangcang shelter hospital has an extraordinary performance in terms of emergency response time, and it is a sustainable facility utilization approach in the post-epidemic era. This study bolsters areas of the research on the urban resilient emergency response framework. Moreover, the paper summarizes new medical facilities' planning and location characteristics and hopes to provide policy-makers and urban planners with valuable empirical evidence.

Modeling of emergency support capacity and optimization of delivery service system for urban living materials under uncertain situations: a case study of Xi'an City during COVID-19 epidemic.

The severe acute respiratory syndrome coronavirus 2 (COVID-19) pandemic has brought a heavy burden and severe challenges to the global economy and society, forcing different countries and regions to take various preventive and control measures ranging from normal operations to partial or complete lockdowns. Taking Xi'an city as an example, based on multisource POI data for the government's vegetable storage delivery points, logistics terminal outlets, designated medical institutions, communities, etc., this paper uses the Gaussian two-step floating catchment area method (2SFCA) and other spatial analysis methods to analyze the spatial pattern of emergency support points (ESPs) and express logistics terminals in different situations. It then discusses construction and optimization strategies for urban emergency support and delivery service systems. The conclusions are as follows. (1) The ESPs are supported by large-scale chain supermarkets and fresh supermarkets, which are positively related to the population distribution.The spatial distribution of express logistics terminals is imbalanced, dense in the middle while sparse at the edges. 90% of express terminals are located within a 500 m distance of communities, however, some terminals are shared, which restrict their ability to provide emergency support to surrounding residents. (2) In general, accessibility increases as the number of ESPs increases; under normal traffic, as the distance threshold increases, the available ESPs increase but accessibility slightly decreases; with a traffic lockdown, the travel distance of residents is limited, and as ESPs increase, accessibility and the number of POIs covered significantly increase. (3) The spatial accessibility of the ESPs has a "dumbbell-shaped" distribution, with highest accessibility in the north and south, higher around the second ring road, slightly lower in the center, and lowest near the third ring road at east and west. (4) With the goal of "opening up the logistics artery and unblocking the distribution microcirculation", based on "ESPs + couriers + express logistics terminals + residents", this paper proposes to build and optimize the urban emergency support and delivery service system to improve the comprehensive ability of the city to cope with uncertain risks.

Spatio-temporal evolution and influencing mechanism of the COVID-19 epidemic in Shandong province, China.

The novel coronavirus pneumonia (COVID-19) outbreak that emerged in late 2019 has posed a severe threat to human health and social and economic development, and thus has become a major public health crisis affecting the world. The spread of COVID-19 in population and regions is a typical geographical process, which is worth discussing from the geographical perspective. This paper focuses on Shandong province, which has a high incidence, though the first Chinese confirmed case was reported from Hubei province. Based on the data of reported confirmed cases and the detailed information of cases collected manually, we used text analysis, mathematical statistics and spatial analysis to reveal the demographic characteristics of confirmed cases and the spatio-temporal evolution process of the epidemic, and to explore the comprehensive mechanism of epidemic evolution and prevention and control. The results show that: (1) the incidence rate of COVID-19 in Shandong is 0.76/100,000. The majority of confirmed cases are old and middle-aged people who are infected by the intra-province diffusion, followed by young and middle-aged people who are infected outside the province. (2) Up to February 5, the number of daily confirmed cases shows a trend of "rapid increase before slowing down", among which, the changes of age and gender are closely related to population migration, epidemic characteristics and intervention measures. (3) Affected by the regional economy and population, the spatial distribution of the confirmed cases is obviously unbalanced, with the cluster pattern of "high-low" and "low-high". (4) The evolution of the migration pattern, affected by the geographical location of Wuhan and Chinese traditional culture, is dominated by "cross-provincial" and "intra-provincial" direct flow, and generally shows the trend of "southwest → northeast". Finally, combined with the targeted countermeasures of "source-flow-sink", the comprehensive mechanism of COVID-19 epidemic evolution and prevention and control in Shandong is revealed. External and internal prevention and control measures are also figured out.

Bicyclist injury severity in traffic crashes: A spatial approach for geo-referenced crash data to uncover non-stationary correlates.

INTRODUCTION: Bicyclists are among vulnerable road users with their safety a key concern. This study generates new knowledge about their safety by applying a spatial modeling approach to uncover non-stationary correlates of bicyclist injury severity in traffic crashes. METHOD: The approach is Geographically Weighted Ordinal Logistic Regression (GWOLR), extended from the regular Ordered Logistic Regression (OLR) by incorporating the spatial perspective of traffic crashes. The GWOLR modeling approach allows the relationships between injury severity and its contributing factors to vary across the spatial domain, to account for the spatial heterogeneity. This approach makes use of geo-referenced data. This study explored more than 7,000 geo-referenced bicycle--motor-vehicle crashes in North Carolina. RESULTS: This study performed a series of non-stationarity tests to identify local relationships that vary substantially across the spatial domain. These local relationships are related to the bicyclist (bicyclist age, bicyclist behavior, bicyclist intoxication, bicycle direction, bicycle position), motorist (driver age, driver intoxication, driver behavior, vehicle speed, vehicle type) and traffic (traffic volume). CONCLUSIONS: Results from the regular OLR are in general consistent with previous findings. For example, an increased bicyclist injury severity is associated with older bicyclists, bicyclist being intoxicated, and higher motor-vehicle speeds. Results from the GWOLR show local (rather than global) relationships between contributing factors and bicyclist injury severity. Practical Applications: Researchers and practitioners may use GWOLR to prioritize cycling safety countermeasures for specific regions. For example, GWOLR modeling estimates in the study highlighted the west part (from Charlotte to Asheville) of North Carolina for increased bicyclist injury severity due to the intoxication of road users including both bicyclists and drivers. Therefore, if a countermeasure is concerned with the road user intoxication, there may be a priority for the region from Charlotte to Asheville (relative to other areas in North Carolina).

Pedestrian injury severity in motor vehicle crashes: An integrated spatio-temporal modeling approach.

Traffic crashes are outcomes of human activities interacting with the diverse cultural, socio-economic and geographic contexts, presenting a spatial and temporal nature. This study employs an integrated spatio-temporal modeling approach to untangle the crashed injury correlates that may vary across the space and time domain. Specifically, this study employs Geographically and Temporally Weighted Ordinal Logistic Regression (GTWOLR) to examine the correlates of pedestrian injury severity in motor vehicle crashes. The method leverages the space- and time-referenced crash data and powerful computational tools. This study performed non-stationarity tests to verify whether the local correlates of pedestrian injury severity have a significant spatio-temporal variation. Results showed that some variables passed the tests, indicating they have a significantly varying spatio-temporal relationship with the pedestrian injury severity. These factors include the pedestrian age, pedestrian position, crash location, motorist age and gender, driving under the influence (DUI), motor vehicle type and crash time in a day. The spatio-temporally varying correlates of pedestrian injury severity are valuable for researchers and practitioners to localize pedestrian safety improvement solutions in North Carolina. For example, in near future, special attention may be paid to DUI crashes in the city of Charlotte and Asheville, because in such areas DUI-involved crashes are even more likely to cause severe pedestrian injuries that in other areas. More implications are discussed in the paper.