Spatial autocorrelation analysis of cargo trucks on highway crashes in Chile

A spatial autocorrelation analysis of road traffic crash by severity using Moran's I spatial statistics: A comparative study of Addis Ababa and Berlin cities

Methodological advancements in road safety research reveal an increasing inclination toward integrating spatial approaches in hot spot identification, spatial pattern analysis, and developing spatially lagged models. Previous studies on hot spot identification and spatial pattern analysis have overlooked crash severities and the spatial autocorrelation of crashes by severity, missing valuable insights into crash patterns and underlying factors. This study investigates the spatial autocorrelation of crash severity by taking two capital cities, Addis Ababa and Berlin, as a case study and compares patterns in low and high-income countries. The study used three-year crash data from each city. It employed the average nearest neighbor distance (ANND) method to determine the significance of spatial clustering of crash data by severity, Global Moran's I to examine the statistical significance of spatial autocorrelation, and Local Moran's I to identify significant cluster locations with High-High (HH) and Low-Low (LL) crash severity values. The ANND analysis reveals a significant clustering of crashes by severity in both cities, except in Berlin's fatal crashes. However, different Global Moran's I results were obtained for the two cities, with a strong and statistically significant value for Addis Ababa compared to Berlin. The Local Moran's I result indicates that the central business district and residential areas have LL values, while the city's outskirts exhibit HH values in Addis Ababa. With some persistent HH value locations, Berlin's HH and LL grid clusters are intermingled on the city's periphery. Socio-economic factors, road user behavior and roadway factors contribute to the difference in the result. Nevertheless, it is interesting to note the similarity of significant HH value locations on the outskirts of both cities. Finally, the results are consistent with previous studies and indicate the need for further investigation in other locations.

Spatiotemporal distribution of schistosomiasis transmission risk in Jiangling County, Hubei Province, P.R. China

OBJECTIVE

This study aims to explore the spatiotemporal distribution of schistosomiasis in Jiangling County, and provide insights into the precise schistosomiasis control.

METHODS

The descriptive epidemiological method and Joinpoint regression model were used to analyze the changes in infection rates of humans, livestock, snails, average density of living snails and occurrence rate of frames with snails in Jiangling County from 2005 to 2021. Spatial epidemiology methods were used to detect the spatiotemporal clustering of schistosomiasis transmission risk in Jiangling county.

RESULTS

The infection rates in humans, livestock, snails, average density of living snails and occurrence rate of frames with snails in Jiangling County decreased from 2005 to 2021 with statistically significant. The average density of living snails in Jiangling County was spatially clustered in each year, and the Moran's I varied from 0.10 to 0.26. The hot spots were mainly concentrated in some villages of Xionghe Town, Baimasi Town and Shagang Town. The mean center of the distribution of average density of living snails in Jiangling County first moved from northwest to southeast, and then returned from southeast to northwest after 2014. SDE azimuth fluctuated in the range of 111.68°-124.42°. Kernal density analysis showed that the high and medium-high risk areas of Jiangling County from 2005 to 2021 were mainly concentrated in the central and eastern of Jiangling County, and the medium-low and low risk areas were mainly distributed in the periphery of Jiangling County.

CONCLUSIONS

The epidemic situation of schistosomiasis decreased significantly in Jiangling County from 2005 to 2021, but the schistosomiasis transmission risk still had spatial clustering in some areas. After transmission interruption, targeted transmission risk intervention strategies can be adopted according to different types of schistosomiasis risk areas.

Epidemiological Profile and Spatial Patterns of Enterobiasis in Children Aged 3-9 Years in China from 2016 to 2020

(1) Background: Enterobius vermicularis infection causes a significant health burden in children. The infection occurs throughout the country and remains a serious public concern in China. Therefore, it is necessary to know the situation of E. vermicularis infection, to provide a scientific basis for the disease control and the optimum conditions for children's growth. (2) Methods: Descriptive epidemiological analysis was implemented to demonstrate the status and changing trend of E. vermicularis infection from 2016 to 2020, while the spatial distribution characteristics and spatial-temporal clustering were illuminated by spatial autocorrelation analysis and spatio-temporal scanning analysis. (3) Results: The infection of E. vermicularis showed a fluctuating downward trend with a decline of 32.00% in 2020 compared to that in 2016 and was concentrated in central and southern China. There was no significant difference in infection rate between boys and girls, while the high infection rate was presented in 4-, 5- and 6- year-old children. The hotspots and spatial clustering areas were mainly concentrated in southern China. (4) Conclusions: From 2016 to 2020, the infection rate of E. vermicularis in children aged 3 to 9 years in China demonstrated a declining trend, and its distribution showed spatial clustering, mainly in southern China. Therefore, it is necessary to strengthen surveillance and implement control measures in combination with health education and environmental improvement.

Epidemiological features and spatial-temporal clustering of visceral leishmaniasis in mainland China from 2019 to 2021

Background

Visceral leishmaniasis (VL) is a serious vector-borne disease in central and western China. In recent years, the number of VL cases increased gradually, particularly the mountain-type zoonotic visceral leishmaniasis (MT-ZVL). This study clarified the epidemiological features and spatial-temporal clustering of VL in China between 2019 and 2021, identified the risk areas for VL transmission, and provided scientific evidence for the prevention and control of VL.

Materials and methods

The information on VL cases in 2019–2021 was collected from the Infectious Disease Reporting Information Management System of the Chinese Center for Disease Control and Prevention. The epidemiological characteristics of VL cases were analyzed. The global Moran’s I and Getis-ORD Gi* statistical data were processed for spatial autocorrelation and hotspot analysis in ESRI ArcGIS software. Also, spatial-temporal clustering analysis was conducted with the retrospective space–time permutation scan statistics.

Results

A total of 608 VL cases were reported from 2019 to 2021, with 158, 213, and 237 cases reported each year, respectively. Of the 608 cases, there were 10 cases of anthroponotic visceral leishmaniasis (AVL), 20 cases of desert-type zoonotic visceral leishmaniasis (DT-ZVL), and 578 cases of MT-ZVL. The age of VL cases was mainly distributed in the group of subjects aged ≥ 15 years. Peasants and infants were the dominant high-risk population. The incidence peak season of VL occurred between March and May. The cases were mainly distributed in Shanxi (299 cases), Shaanxi (118 cases), and Gansu (106 cases) Provinces, accounting for 86.02% (523/608) of the total reported cases in China. Spatial analysis revealed that clustering of infection is mainly located in eastern Shanxi Province and Shaanxi–Shanxi border areas, as well as southern Gansu and northern Sichuan Province. In addition, new reemergence hotspots in Shanxi, Henan, and Hebei Provinces have been detected since 2020. Spatio-temporal clustering analysis revealed an increase in the degree of infection aggregation in eastern Shanxi Province and Shaanxi–Shanxi border areas.

Conclusion

The AVL and DT-ZVL were endemic at a lower level in western China, whereas MT-ZVL rebounded rapidly and showed a resurgence in historically endemic counties. The spatial-temporal clustering analysis displayed that the high-incidence areas of VL have shifted to central China, particularly in Shanxi and Shaanxi Provinces. Integrated mitigation strategies targeting high-risk populations are needed to control VL transmission in high-risk areas.

Differential impacts of ridesharing on alcohol-related crashes by socioeconomic municipalities: rate of technology adoption matters

BACKGROUND

An emergent group of studies have examined the extent under which ridesharing may decrease alcohol-related crashes in countries such as United States, United Kingdom, Brazil, and Chile. Virtually all existent studies have assumed that ridesharing is equally distributed across socioeconomic groups, potentially masking differences across them. We contribute to this literature by studying how socioeconomic status at the municipal level impacts Uber's effect on alcohol-related crashes.

METHODS

We use data provided by Chile's Road Safety Commission considering all alcohol-related crashes, and fatal and severe alcohol-related injuries that occurred between January 2013 and September 2013 (before Uber) and January and September 2014 (with Uber) in Santiago. We first apply spatial autocorrelation techniques to examine the level of spatial dependence between the location of alcohol-related crashes with and without Uber. We then apply random-effects meta-analysis to obtain risk ratios of alcohol-related crashes by considering socioeconomic municipality differences before and after the introduction of Uber.

RESULTS

In both analyses, we find that the first 9 months of Uber in Santiago is associated with significant rate ratio decreases (RR = 0.71 [95% Confidence Interval (C.I.) 0.56, 0.89]) in high socioeconomic municipalities in all alcohol-related crashes and null (RR = 1.10 [95% C.I. 0.97, 1.23]) increases in low socioeconomic municipalities. No concomitant associations were observed in fatal alcohol-related crashes regardless of the socioeconomic municipality group.

CONCLUSIONS

One interpretation for the decline in alcohol-related crashes in high socioeconomic municipalities is that Uber may be a substitute form of transport for those individuals who have access to credit cards, and thus, could afford to pay for this service at the time they have consumed alcohol. Slight increases of alcohol-related crashes in low socioeconomic municipalities should be studied further since this could be related to different phenomena such as increases in alcohol sales and consumption, less access to the provision of public transport services in these jurisdictions, or biases in police reports.

Epidemiological features and spatial-temporal distribution of visceral leishmaniasis in mainland China: a population-based surveillance study from 2004 to 2019

BACKGROUND

Although visceral leishmaniasis (VL) was largely brought under control in most regions of China during the previous century, VL cases have rebounded in western and central China in recent decades. The aim of this study was to investigate the epidemiological features and spatial-temporal distribution of VL in mainland China from 2004 to 2019.

METHODS

Incidence and mortality data for VL during the period 2004-2019 were collected from the Public Health Sciences Data Center of China and annual national epidemic reports of VL, whose data source was the National Diseases Reporting Information System. Joinpoint regression analysis was performed to explore the trends of VL. Spatial autocorrelation and spatial-temporal clustering analysis were conducted to identify the distribution and risk areas of VL transmission.

RESULTS

A total of 4877 VL cases were reported in mainland China during 2004-2019, with mean annual incidence of 0.0228/100,000. VL incidence showed a decreasing trend in general during our study period (annual percentage change [APC] = -4.2564, 95% confidence interval [CI]: -8.0856 to -0.2677). Among mainly endemic provinces, VL was initially heavily epidemic in Gansu, Sichuan, and especially Xinjiang, but subsequently decreased considerably. In contrast, Shaanxi and Shanxi witnessed significantly increasing trends, especially in 2017-2019. The first-level spatial-temporal aggregation area covered two endemic provinces in northwestern China, including Gansu and Xinjiang, with the gathering time from 2004 to 2011 (relative risk [RR] = 13.91, log-likelihood ratio [LLR] = 3308.87, P < 0.001). The secondary aggregation area was detected in Shanxi province of central China, with the gathering time of 2019 (RR = 1.61, LLR = 4.88, P = 0.041). The epidemic peak of October to November disappeared in 2018-2019, leaving only one peak in March to May.

CONCLUSIONS

Our findings suggest that VL is still an important endemic infectious disease in China. Epidemic trends in different provinces changed significantly and spatial-temporal aggregation areas shifted from northwestern to central China during our study period. Mitigation strategies, including large-scale screening, insecticide spraying, and health education encouraging behavioral change, in combination with other integrated approaches, are needed to decrease transmission risk in areas at risk, especially in Shanxi, Shaanxi, and Gansu provinces.

An analytic framework using deep learning for prediction of traffic accident injury severity based on contributing factors

Vulnerable road users (VRUs) are exposed to the highest risk in the road traffic environment. Analyzing contributing factors that affect injury severity facilitates injury severity prediction and further application in developing countermeasures to guarantee VRUs safety. Recently, machine learning approaches have been introduced, in which analyses tend to be one-sided and may ignore important information. To solve this problem, this paper proposes a comprehensive analytic framework that employs a deep learning model referred to as the stacked sparse autoencoder (SSAE) to predict the injury severity of traffic accidents based on contributing factors. The essential idea of the method is to integrate various analyses into an analytical framework that performs corresponding data processing and analysis by different machine learning approaches. In the proposed method, first, we utilize a machine learning approach (i.e., Catboost) to analyze the importance and dependence of the contributing factors to injury severity and remove low correlation factors; second, according to the geographical information, we classify the data into different classes by utilizing a machine learning approach (i.e., k-means clustering); third, by employing high correlation factors, we employ an SSAE-based deep learning model to perform injury severity prediction in each data class. By experiments with a real-world traffic accident dataset, we demonstrated the effectiveness and applicability of the framework. Specifically, (1) the importance and dependence of contributing factors were obtained by CatBoost and the Shapley value, and (2) the SSAE-based deep learning model achieved the best performance compared to other baseline models. The proposed analytic framework can also be utilized for other accident data for severity or other risk indicator analyses involving VRUs safety.

Measuring the spatiotemporal evolution of accident hot spots

One reality of transportation systems is that vehicular accidents can happen practically anywhere and at any time. An increasing body of research suggests though that spatial and/or temporal dependencies (i.e., clusters or hot spots) among accidents likely exist. Along with understanding where and when such spatiotemporal dependencies may occur, another important facet to consider is the geographic extent or area associated with the hot spots. For example, an accident hot spot may involve a small, isolated portion of the transportation system or a much more expansive geographic area. Better delineation and quantification of the morphological characteristics of accident hotspots can provide valuable decision support for planning for accident hot spot mitigation and prevention. As the size and shape of accident hot spots may evolve over time, the capability to track such dynamics is vital, especially with respect to the identification of processes effecting hot spot occurrence as well as assessments of the efficacy of efforts to mitigate factors underlying hot spot development. To this end, a Geographical Information Systems (GIS) based framework is outlined to facilitate the analysis of the morphological characteristics of hot spots over time. The analysis framework is applied to a case study of vehicular accidents reported over a two-year period to demonstrate its practical utility. The application results indicate that patterns of change in hot spot morphology can be effectively quantified and a variety of informative spatial and temporal patterns can be detected.

Exploring pedestrian injury severities at pedestrian-vehicle crash hotspots with an annual upward trend: A spatiotemporal analysis with latent class random parameter approach

INTRODUCTION

With the increasing trend of pedestrian deaths among all traffic fatalities in the past decade, there is an urgent need for identifying and investigating hotspots of pedestrian-vehicle crashes with an upward trend.

METHOD

To identify pedestrian-vehicle crash locations with aggregated spatial pattern and upward temporal pattern (i.e., hotspots with an upward trend), this paper first uses the average nearest neighbor and the spatial autocorrelation tests to determine the grid distance and the neighborhood distance for hotspots, respectively. Then, the spatiotemporal analyses with the Getis-Ord Gi* index and the Mann-Kendall trend test are utilized to identify the pedestrian-vehicle crash hotspots with an annual upward trend in North Carolina from 2007 to 2018. Considering the unobserved heterogeneity of the crash data, a latent class model with random parameters within class is proposed to identify specific contributing factors for each class and explore the heterogeneity within classes. Significant factors of the pedestrian, vehicle, crash type, locality, roadway, environment, time, and traffic control characteristics are detected and analyzed based on the marginal effects.

RESULTS

The heterogeneous results between classes and the random parameter variables detected within classes further indicate the superiority of latent class random parameter model. Practical Applications: This paper provides a framework for researchers and engineers to identify crash hotspots considering spatiotemporal patterns and contribution factors to crashes considering unobserved heterogeneity. Also, the result provides specific guidance to developing countermeasures for mitigating pedestrian-injury at pedestrian-vehicle crash hotspots with an upward trend.

Predictors and geographic analysis of road traffic accidents in Leon, Nicaragua

OBJECTIVE

To identify environmental factors present in areas with high density of road traffic accidents (RTA) in Leon, Nicaragua.

METHODS

The analysis included all accidents recorded by the Police Department in León City, from January to June 2017. All crashes were georeferenced, and data were collected from the environment elements within a perimeter of 20 meters from the site in which accidents occurred with a pre-tested data collection instrument. We specified a Poisson regression model to estimate incidence rate ratios (IRR) and 95% confidence intervals (CI) to determine environmental factors associated with the event incidence. For the identification areas with high, medium, and low occurrences of crashes, kernel density around points in which RTA occurred were estimated.

RESULTS

Out of 667 recorded crashes, 90% involved men aged 15-40, and motorcycle accidents represented 60% of injuries or deaths. Environmental factors that were positively associated with RTA included good road conditions (adjusted IRR = 1.36, 95%CI 1.13 - 1.63) and the existence of bicycle lanes (adjusted IRR = 1.64, 95%CI 1.29 - 2.10). Environmental characteristics associated with higher speeds and heavier accidents can increase their incidence.

CONCLUSION

We found that high-foot-traffic commercial or touristic centers are three areas with high density of crashes. Local authorities can use these findings to promote road safety measures in high-incidence areas in León City.

Quantitative Evaluation of Spatial Differentiation for Public Open Spaces in Urban Built-Up Areas by Assessing SDG 11.7: A Case of Deqing County

Urban public open spaces refer to open space between architectural structures in a city or urban agglomeration that is open for urban residents to conduct public exchanges and hold various activities. Sustainable Development Goal (SDG) 11.7 in the 2030 UN Agenda for Sustainable Development clearly states that the distribution characteristics of public open spaces are important indicators to measure the sustainable development of urban ecological society. In 2018, in order to implement the sustainable development agenda, China offered the example of Deqing to the world. Therefore, taking Deqing as an example, this paper uses geographic statistics and spatial analysis methods to quantitatively evaluate and visualize public open spaces in the built area in 2016 and analyzes the spatial pattern and relationship of the population. The results show that the public open spaces in the built-up area of Deqing have typical global and local spatial autocorrelation. The spatial pattern shows obvious differences in different parts of the built area and attributes of public open spaces. According to the results of correlation analysis, it can be seen that the decentralized characteristics of public open spaces have a significant relationship with the population agglomeration, and this correlation is also related to the types of public open spaces. The assessment results by SDG 11.7.1 indicate that the public open spaces in the built-up area of Deqing conform to the living needs of residents on the whole and have a humanized space design and good accessibility. However, the per capita public open spaces of towns and villages outside the built area are relatively low, and there is an imbalance in public open spaces. Therefore, more attention should be paid to constructing urban public open spaces fairly.

Influential Factors Associated with Consecutive Crash Severity: A Two-Level Logistic Modeling Approach

A consecutive crash series is composed by a primary crash and one or more subsequent secondary crashes that occur immediately within a certain distance. The crash mechanism of a consecutive crash series is distinctive, as it is different from common primary and secondary crashes mainly caused by queuing effects and chain-reaction crashes that involve multiple collisions in one crash. It commonly affects a large area of road space and possibly causes congestions and significant delays in evacuation and clearance. This study identified the influential factors determining the severity of primary and secondary crashes in a consecutive crash series. Basic, random-effects, random-parameters, and two-level binary logistic regression models were established based on crash data collected on the freeway network of Guizhou Province, China in 2018, of which 349 were identified as consecutive crashes. According to the model performance metrics, the two-level logistic model outperformed the other three models. On the crash level, double-vehicle primary crash had a negative association with the severity of secondary consecutive crashes, and the involvement of trucks in the secondary consecutive crash had a positive contribution to its crash severity. On a road segment level, speed limit, traffic volume, tunnel, and extreme weather conditions such as rainy and cloudy days had positive effects on consecutive crash severity, while the number of lanes was negatively associated with consecutive crash severity. Policy suggestions are made to alleviate the severity of consecutive crashes by reminding the drivers with real-time potential hazards of severe consecutive crashes and providing educative programs to specific groups of drivers.

Birth prevalence of congenital heart disease in China, 1980-2019: a systematic review and meta-analysis of 617 studies

To assess the birth prevalence and spatial distribution of congenital heart disease (CHD) in China by conducting a complete overview and using spatial epidemiological methods. Unrestricted searches were conducted on seven electronic databases, with an end-date parameter of May 2019. Data on the birth prevalence of CHD and its subtypes were collected and combined using either the random-effect model or fixed-effect model. Subgroup sensitivity analyses were performed to explore potential heterogeneity moderators. The three-dimensional trend analysis and a visualization of CHD birth prevalence among different provinces were performed to describe the spatial distribution characteristics. Total 617 studies involving 76,961,354 births and 201,934 CHD individuals were included. Overall, total CHD birth prevalence increased continuously over time, from 0.201‰ in 1980-1984 to 4.905‰ in 2015-2019. The study on the high-income provinces, population-based monitoring model, male births, and urban regions reported a significantly higher prevalence of total CHD compared with upper-middle-income provinces, hospital-based monitoring model, female births, and rural regions, respectively. National CHD birth prevalence increased gradually from western region to eastern region, but decreased gradually from southern to northern region. Relevant heterogeneity moderators including gender, geographic region, income levels, and monitoring models have been identified by subgroup analyses. Sensitivity analysis yielded consistent results. Total CHD birth prevalence in China increases continuously in the past 40 years. Significant differences in gender, geographical regions, income levels, and monitoring models were found. In the future, population wide prospective birth defect registries covering the entire Chinese population need to determine the exact birth prevalence.

Epidemiological features and trends of influenza incidence in mainland China: A population-based surveillance study from 2005 to 2015

OBJECTIVES

To investigate epidemiological features and trends of influenza incidence with 1,173,640 cases in mainland China from 2005 to 2015.

METHODS

Incidence and mortality data for influenza from 2005 to 2015 were provided by the data-center of China public health science and covered a population of about 1.3 billion people from 31 provinces and regions in mainland China. Joinpoint regression and exploratory spatial data analyses were used to examine the incidence trends from 2005 to 2015.

RESULTS

The first upsurge in influenza cases occurred in 2009, and the highest incidence of influenza occurred in 2014 (15.9045 cases/100,000 people). The average incidence per year from 2009 to 2015 was threefold higher than that from 2005 to 2008 (10.5308 vs 3.4589 cases/100,000 people; incidence rate ratio=3.0446). The joinpoint regression results showed that there was an increasing influenza incidence trend from 2005 to 2015 (annual change in percentage=13.6%, 95%CI 2.2-26.3, p=0.0236). The seasonal pattern analysis showed that influenza typically occurred in winter and spring during each monitoring year, peaking from November to March the next year.

CONCLUSIONS

This study will help governments to make valuable decisions in allocating scarce resources and providing strategies to limit the spread of influenza.

Pattern of road traffic crash hot zones versus probable hot zones in Tunisia: A geospatial analysis

Focusing on how hot zones mapping can predict spatial patterns of crashes and how different mapping approaches compare can help to better inform their application in practice. This study examines the stability of the performance of two spatial autocorrelation measures on the basis of a Road Safety Risk Index (RSRI) through the comparison of the results for three regions (North-West, Center-East, and Center-West) and for three time periods (2002-2005, 2006-2009 and 2010-2013) in Tunisia. Our study differs from others in that it discusses the identification of probable hot zones and enhances the capability to examine a given highway by determining "dangerous probable lengths", which aims to anticipate the traffic crashes in the future. The identified hot zones and probable hot zones exhibit different regional and temporal characteristics. There are clearly some outstanding spatial clusters of crashes covering specific locations. In both Northwest and Center-West regions, the majority of the identified hot zones and probable hot zones predominantly occur along mainly highways characterized by a dominant rural character. In the Center-East region, both hot zones and probable hot zones are mostly spread northeast and south-west more precisely in NH1 and NH2 where many urban activities are taking place. Spatial autocorrelation indices per region address the diversity within the regions and provide us with useful insights that can be translated into safety policies in Tunisia.