Essential Business Visits and Social Vulnerability during New York City's Initial COVID-19 Outbreak.

New York City (NYC) was deeply impacted by COVID-19 in spring 2020, with thousands of new cases daily. However, the pandemic's effects were not evenly distributed across the city, and the specific contributors have not yet been systematically considered. To help investigate that topic, this study analyzed the interaction of people with neighborhood businesses and other points of interest (POIs) in parts of three NYC neighborhoods in the spring of 2020 during the peak of the first COVID-19 wave through anonymized cellphone data and direct the observation of 1313 individuals leaving healthcare facilities. This study considered social vulnerability index (SVI) levels, population density, and POI visit behaviors from both cellphone data and firsthand observations of behavior around select NYC health facilities in different boroughs as various proxies. By considering equivalent businesses or groups of businesses by neighborhood, POI visits better aligned with COVID-19 infection levels than SVI. If tracking POI visit levels proves a reliable direct or relative proxy for disease transmission when checked against larger datasets, this method could be critical in both predictions of future outbreaks and the setting of customer density limits.

Spatial Pattern Analysis of Xinjiang Tourism Resources Based on Electronic Map Points of Interest.

This study considers the Point of Interest data of tourism resources in Xinjiang and studies their spatial distribution by combining geospatial analysis methods, such as the average nearest neighbor index, standard deviation ellipse, kernel density analysis, and hotspot analysis, to explore their spatial distribution characteristics. Based on the analysis results, the following conclusions are made. Different categories of tourism resource sites have different spatial distributions, and all categories of tourism resources in Xinjiang are clustered in Urumqi city. The geological landscape resource sites are widely distributed and have a ring-shaped distribution in the desert area of southern Xinjiang. The biological landscape resources are distributed in a strip along the Tianshan Mountains. The water landscape resources are concentrated in the northern Xinjiang area. The site ruins are mostly distributed in the western region of Xinjiang. The distributions of the architectural landscape and entertainment and shopping resources are highly coupled with the distribution of cities. The distributions of the six categories of tourism resource points are in the northeast-southwest direction. The centripetal force and directional nature of the resource points of the water landscape are not obvious. The remaining five categories of resource points have their own characteristics. The distribution of resources in the site ruins is relatively even, and there are many hotspot areas in the geomantic and architectural landscapes, which are mainly concentrated in Bazhou and other places. The biological landscape has many cold-spot areas, distributed in areas such as Altai in northern Xinjiang and Hotan in southern Xinjiang. The remaining four categories have cold-spot and hotspot areas with different distributions. Tourism is an important thrust for economic development. The study of the distribution of tourism resources on the spatial distribution of tourism resources has clear guidance for later tourism development, can help the tourism industry optimize the layout of resources, and can promote tourism resources to achieve maximum benefits. The government can implement effective control and governance.

LOCATION-BASED SERVICES USING WEB-GIS BY AN ANDROID PLATFORM TO IMPROVE STUDENTS’ NAVIGATION DURING COVID-19

Educational institutions seek to find optimal ways to provide educational services with the need for alternative solutions due to the requirements of the Covid-19 pandemic. The current study proposed a system that aims to identify the most critical new technologies built on Web-GIS for data analysis and associated information retrieval. It presents an algorithm to analyze the spatial information frequented by the user on the campus and determine the services that target the user based on predetermined spatial information. Provide a system based on integrating location-based services (LBS) using Web-GIS through the Android platform to help campus attendees take full advantage of services information granted to them in their whereabouts. The system employs the rule extraction algorithm to give a recommendations list (using extracted rules with confidence=100% and support= 0.7 to achieve high accuracy for the most points of interest (POI) based on the user's preferences. The proposed system evaluates the given recommendation and the application usage to produce satisfactory results. The average overall F-measure and accuracies are 94.8 % and 94.2, respectively. © 2022 Little Lion Scientific

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.