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Vehicle crashes on roads are caused by many factors. However, the influence of these factors is not necessarily homogenous across locations, which is a challenge for non-stationary modeling approaches. To address this problem, this paper adopts two types of methods allowing parameters to fluctuate among observations, that is, the random parameter approach and the geographically weighted regression (GWR) approach. With road curvature, curve length, pavement friction, and traffic volume as independent variables, vehicle crash frequencies are modeled by two non-spatial methods, including the negative binomial (NB) model and random parameter negative binomial (RPNB), as well as three spatial methods (GWR approach). These models are calibrated in microlevel using a dataset of 9415 horizontal curve segments with a total length of 1545 kilometers for a period of three years (2016–2018) over the State of Indiana. The results revealed that the GWR approach can capture spatial heterogeneity and therefore significantly outperforms the conventional non-spatial approach. Based on the Akaike Information Criterion (AICc), geographically weighted negative binomial regression (GWNBR) was proved to be a superior approach for statewide microlevel crash analysis. [ABSTRACT FROM AUTHOR]

The identification of urban functional areas is essential for urban planning and sustainable development. Spatial grids are the basic units for the implementation of urban plans and management by cities or development zones. The emergence of internet "big data" provides new ideas for the identification of urban functional areas. Based on point of interest (POI) data from Baidu Maps, the Xicheng District of Beijing was divided into grids with side lengths of 200, 500, and 1000 m in this study. The kernel density method was used to analyze the spatial structure of POI data. Two indicators, that is, the frequency density and category ratio, were then used to identify single- and mixed-functional areas. The results show that (1) commercial and financial areas are concentrated in the city center and multiple business centers have not developed; (2) scenic areas account for the largest proportion of single-functional areas in the Xicheng District of Beijing, followed by education and training, residence, and party and government organizations areas; and (3) the 200 × 200 m and 500 × 500 m grids are the most suitable for the identification of single- and mixed-functional areas, respectively. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) pandemic has had tremendous and extensive impacts on the people's daily activities. In Chicago, the numbers of crime fell considerably. This work aims to investigate the impacts that COVID-19 has had on the spatial and temporal patterns of crime in Chicago through spatial and temporal crime analyses approaches. The Seasonal-Trend decomposition procedure based on Loess (STL) was used to identify the temporal trends of different crimes, detect the outliers of crime events, and examine the periodic variations of crime distributions. The results showed a certain phase pattern in the trend components of assault, battery, fraud, and theft. The largest outlier occurred on 31 May 2020 in the remainder components of burglary, criminal damage, and robbery. The spatial point pattern test (SPPT) was used to detect the similarity between the spatial distribution patterns of crime in 2020 and those in 2019, 2018, 2017, and 2016, and to analyze the local changes in crime on a micro scale. It was found that the distributions of crime significantly changed in 2020 and local changes in theft, battery, burglary, and fraud displayed an aggregative cluster downtown. The results all claim that spatial and temporal patterns of crime changed significantly affected by COVID-19 in Chicago, and they offer constructive suggestions for local police departments or authorities to allocate their available resources in response to crime. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease (COVID-19) has become a public health problem at a global scale because of its high infection and mortality rate. It has affected most countries in the world, and the number of confirmed cases and death toll is still growing rapidly. Susceptibility studies have been conducted in specific countries, where COVID-19 infection and mortality rates were highly related to demographics and air pollution, especially PM2.5, but there are few studies on a global scale. This paper is an exploratory study of the relationship between confirmed COVID-19 cases and death toll per million population, population density, and PM2.5 concentration on a worldwide basis. A multivariate linear regression based on Moran eigenvector spatial filtering model and Geographically weighted regression model were undertaken to analyze the relationship between population density, PM2.5 concentration, and COVID-19 infection and mortality rate, and a geostatistical method with bivariate local spatial association analysis was adopted to explore their spatial correlations. The results show that there is a statistically significant positive relationship between COVID-19 confirmed cases and death toll per million population, population density, and PM2.5 concentration, but the relationship displays obvious spatial heterogeneity. While some adjacent countries are likely to have similar characteristics, it suggests that the countries with close contacts/sharing borders and similar spatial pattern of population density and PM2.5 concentration tend to have similar patterns of COVID-19 risk. The analysis provides an interpretation of the statistical and spatial association of COVID-19 with population density and PM2.5 concentration, which has implications for the control and abatement of COVID-19 in terms of both infection and mortality. [ABSTRACT FROM AUTHOR]

COVID-19 containment policies are not only curbing the spread of COVID-19 but also changing human behavior. According to the routine activity theory, owing to lockdown, the closure of entertainment sites (e.g., pubs and bars), an increase in stay-at-home time, and an increase in police patrols are likely to influence chance of committing a crime. In this study, we aimed to further examine the spatial association of COVID-19 infection rate and crime rate. Particularly, we empirically validated the speculation that increase in COVID-19 cases is likely to reduce crime rate. In the empirical study, we investigated whether and how COVID-19 infection rate is spatially associated with crime rate in London. As the spatial data used are mainly areal data, we adopted a spatial regression mode (i.e., the "random effects eigenvector spatial filtering model") to investigate the spatial associations after controlling for the socioeconomic factors. More specifically, we investigated the associations for all the four crime categories in three consequent months (March, April, and May of 2020). The empirical results indicate that 1) crime rates of the four categories have no statistically significant associations with COVID-19 infection rate in March; 2) violence-against-the-person rate has no statistically significant association with COVID-19 infection rate; and 3) robbery rate, burglary rate, and theft and handling rate have a statistically significant and negative association with COVID-19 infection rate in both April and May. [ABSTRACT FROM AUTHOR]

To overcome the difficulties of quantitatively optimizing and evaluating evacuation guidance systems, we proposed a perception model based on virtual reality (VR) experiments and the social force model (SFM). We used VR and eye tracking devices to carry out experiments. The VR experiment data was mainly used for three purposes: to determine the parameter values of the perception model, to optimize the evacuation guidance system by quantitative analysis, and to validate the perception model. Additionally, we compared the VR experimental and model simulation results before and after the optimization to quantitatively assess the improvement in the optimized evacuation guidance system. The results showed that our model can effectively simulate the perception behaviors of evacuees on the evacuation guidance system and it can quantitatively evaluate different evacuation guidance system schemes. The model simulations showed that the optimized evacuation guidance system improved the evacuation efficiency, with the average escape time and distance of the two starting positions reduced by 37% and 28%, respectively. [ABSTRACT FROM AUTHOR]