Exploring the spatio-temporal clusters of closed restaurants after the COVID-19 outbreak in Seoul using relative risk surfaces.

This study explores the clusters of closed restaurants in Seoul in response to the COVID-19 pandemic using the relative risk surface (RRS). The RRS developed based on kernel density estimation provides alternative perspectives for finding the cluster by combining different control and case events. Specifically, the varying impacts on diverse types of restaurants are examined by comparing the densities of closed casual restaurants and cafes. The clusters of closed businesses following the COVID-19 outbreak are subsequently explored through a comparison of the densities of the closed businesses preceding the outbreak. Furthermore, this analysis estimates the clusters of declined commercial areas after the pandemic outbreak based on the comparison between the densities of opened and closed restaurants. Finally, the specific time and region of the clusters are explored using space-time RRS. The analysis results effectively demonstrate various aspects of the closed restaurant clusters. For example, in the central business areas, the densities of closed cafes have decreased after the pandemic outbreak, and the density of closed cafes is significantly higher than that of opened cafes. This study would contribute to the literature on spatial data analysis and urban policy support in response to future epidemics.

Spatiotemporal trends in discarded needle reports near schools in Boston, Massachusetts, between 2016-2019.

BACKGROUND: Childhood exposures to discarded needles pose a direct risk for infection with blood-borne pathogens and psychological trauma for caregivers and children. Little is known about environmental predictors of discarded needles relative to areas where children are frequent, such as schools. OBJECTIVE: We investigated spatiotemporal trends in discarded needle reports and the density near schools in Boston, Massachusetts, between 2016 and 2019. METHODS: We used the kernel density estimation (KDE) and a relative risk function (RRF) to explore their spatial distribution and temporal changes of discarded needles reported through the 311 service request system in Boston. The density of needle pick-up requests around schools was investigated by using Thiessen polygons. RESULTS: Between January 2016 and December 2019, 18,272 discarded needle reports were made. Publicly reported discarded needles in Boston sharply increased over the 4 years and the highest density of needles was found in 2 central neighborhoods. The density of reports of discarded needles near schools increased among the majority of schools. About 30% of schools demonstrated an increase of 100% or more in reports of discarded needles. CONCLUSION: This analysis provides insight into potential risk of exposure to needle stick injuries for children based on utilizing publicly available crowd-sourced data. Monitoring the density of discarded needles near schools may be a novel approach to improve public health efforts to distribute safe needle disposal locations and reduce injection drug use in public.

Measuring global spatial autocorrelation with data reliability information.

Assessing spatial autocorrelation (SA) of statistical estimates such as means is a common practice in spatial analysis and statistics. Popular spatial autocorrelation statistics implicitly assume that the reliability of the estimates is irrelevant. Users of these SA statistics also ignore the reliability of the estimates. Using empirical and simulated data, we demonstrate that current SA statistics tend to overestimate SA when errors of the estimates are not considered. We argue that when assessing SA of estimates with error, it is essentially comparing distributions in terms of their means and standard errors. Using the concept of the Bhattacharyya coefficient, we proposed the Spatial Bhattacharyya coefficient (SBC) and suggested that it should be used to evaluate the SA of estimates together with their errors. A permutation test is proposed to evaluate its significance. We concluded that the SBC more accurately and robustly reflects the magnitude of SA than traditional SA measures by incorporating errors of estimates in the evaluation.

Geovisualizing attribute uncertainty of interval and ratio variables: a framework and an implementation for vector data.

Geovisualization of attribute uncertainty helps users to recognize underlying processes of spatial data. However, it still lacks an availability of uncertainty visualization tools in a standard GIS environment. This paper proposes a framework for attribute uncertainty visualization by extending bivariate mapping techniques. Specifically, this framework utilizes two cartographic techniques, choropleth mapping and proportional symbol mapping based on the types of attributes. This framework is implemented as an extension of ArcGIS in which three types of visualization tools are available: overlaid symbols on a choropleth map, coloring properties to a proportional symbol map, and composite symbols.

Integrating spatial data analysis functionalities in a GIS environment: Spatial Analysis using ArcGIS Engine and R (SAAR).

Spatial data analysis (SDA) tools to efficiently handle and explore spatial data have become readily available. Although these SDA tools have their own strengths and purposes, they suffer from limited support in terms of a development environment offering easy customization and high extensibility, a strength of open source software. This paper presents a stand-alone software package for SDA in a geographic information systems (GIS) environment, called Spatial Analysis using ArcGIS Engine and R (SAAR), which provides an integrated GIS and SDA environment. A set of SDA tools in SAAR utilize functions in R using R.NET, while other tools were developed in .NET independent of R. SAAR provides an efficient working environment for both general and advanced GIS users. For general GIS users with limited programming skills, SAAR furnishes advanced SDA tools in a popular ArcGIS environment with graphical user interfaces. For advanced GIS users, SAAR offers an extensible GIS platform to help them customize and implement SDA functions with relatively little development effort. This paper demonstrates some functionalities of SAAR using census data for Texas counties.