Spatial patterns of off-the-system traffic crashes in Miami-Dade County, Florida, during 2005-2010

Spatial and temporal analysis of road traffic crashes and ambulance responses in Lagos state, Nigeria

BACKGROUND

Sub-Saharan African countries, Nigeria inclusive, are constrained by grossly limited access to quality pre-hospital trauma care services (PTCS). Findings from pragmatic approaches that explore spatial and temporal trends of past road crashes can inform novel interventions. To improve access to PTCS and reduce burden of road traffic injuries we explored geospatial trends of past emergency responses to road traffic crashes (RTCs) by Lagos State Ambulance Service (LASAMBUS), assessed efficiency of responses, and outcomes of interventions by local government areas (LGAs) of crash.

METHODS

Using descriptive cross-sectional design and REDcap we explored pre-hospital care data of 1220 crash victims documented on LASAMBUS intervention forms from December 2017 to May 2018. We analyzed trends in days and times of calls, demographics of victims, locations of crashes and causes of delayed emergency responses. Assisted with STATA 16 and ArcGIS pro we conducted descriptive statistics and mapping of crash metrics including spatial and temporal relationships between times of the day, seasons of year, and crash LGA population density versus RTCs incidence. Descriptive analysis and mapping were used to assess relationships between 'Causes of Delayed response' and respective crash LGAs, and between Response Times and crash LGAs.

RESULTS

Incidences of RTCs were highest across peak commuting hours (07:00-12:59 and 13:00-18:59), rainy season and harmattan (foggy) months, and densely populated LGAs. Five urban LGAs accounted for over half of RTCs distributions: Eti-Osa (14.7%), Ikeja (14.4%), Kosofe (9.9%), Ikorodu (9.7%), and Alimosho (6.6%). On intervention forms with a Cause of Delay, Traffic Congestion (60%), and Poor Description (17.8%), had associations with LGA distribution. Two densely populated urban LGAs, Agege and Apapa were significantly associated with Traffic Congestion as a Cause of Delay. LASAMBUS was able to address crash in only 502 (36.8%) of the 1220 interventions. Other notable outcomes include: No Crash (false calls) (26.6%), and Crash Already Addressed (22.17%).

CONCLUSIONS

Geospatial analysis of past road crashes in Lagos state offered key insights into spatial and temporal trends of RTCs across LGAs, and identified operational constraints of state-organized PTCS and factors associated with delayed emergency responses. Findings can inform programmatic interventions to improve trauma care outcomes.

Assessing the extent and impacts of linear infrastructure on Sri Lanka's natural and protected areas: Implications for future development planning

Linear infrastructure (LI) has varying effects on landscapes depending on different ecosystems' sensitivity and threat levels. Economically developing tropical countries are particularly at risk from LI. Therefore, understanding a country's current LI network and planning future developments to avoid further fragmentation and disturbance is crucial. This study aimed to assess the extent of Sri Lanka's LI network (i.e., roads, railroads, and powerlines), given that it is both a biodiversity hotspot and an economically developing country in the tropics. First, we calculated the average normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) indices and examined their spatial autocorrelation per divisional secretariat division. Then a multivariate cluster analysis was used to determine clusters of natural and protected areas that may receive similar impacts from different LI and their combinations. Results indicated that roads are the most widespread LI type in Sri Lanka, followed by powerlines and railroads. Over 80% of Sri Lanka's total land area falls within 1 km of either a national or a provincial/local road. Areas with high NDVI were primarily manmade habitats, with less than 20% contribution from protected areas. Over 50% of the total protected area of Sri Lanka is being impacted by all three types of LI. Powerlines were the most common LI type in protected areas in proportion to their total network lengths. To minimize environmental impact while achieving development goals, future LI development activities should use a landscape approach to identify development needs and strategies informed by these findings.

Spatial pattern identification and crash severity analysis of road traffic crash hot spots in Ohio

Safety experts and transportation departments are focused on reducing road accidents and their societal and economic effects. The most crucial step in establishing a successful road safety practice is identifying dangerous highway zones through the study of crashes and looking at how the location of accidents relates to surrounding geography and other factors. Using the latest cutting-edge GIS analytical methods, this study aims to map the locations of accident hot spots and evaluate the severity and spatial extent of crash occurrences in Ohio. Road traffic crash (RTC) data has been analyzed using sophisticated GIS-based hot spot analysis for decades by safety researchers. Using four years' worth of crash data from the state of Ohio and spatial autocorrelation analysis, this study aims to show how a GIS technique can be used to find places where accidents are likely to happen (2017-2020). The study analyzed and ranked crash hotspot areas using the matching severity levels of RTCs. Cluster zones of high and low crash severity were discovered using the spatial autocorrelation tool and the Getis Ord Gi* statistics tool to evaluate the distribution of RTCs. The analysis used Getis Ord Gi*, the crash severity index, and Moran's I spatial autocorrelation of accident events. The findings indicated that these techniques were useful for identifying and rating crash hotspot locations. Since the sites of the identified accident hotspots are located in significant cities in the state of Ohio, such as Cleveland, Cincinnati, Toledo, and Columbus, the organizations in charge of traffic management should make it their top priority to minimize the negative socioeconomic impact that RTCs have and should also conduct a thorough investigation. This study's contribution is the incorporation of crash severity into hot spot analysis using GIS, which could lead to better-informed decision-making in the realm of highway safety.

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.

Spatio-temporal patterns in pedestrian crashes and their determining factors: Application of a space-time cube analysis model

The number of vehicle accidents involving pedestrians in Korea has decreased gradually since the Pedestrian Safety and Convenience Enhancement Act was enacted in 2012, but the number of serious pedestrian-related crashes per capital remains near the top of a list of such rates for member countries of the Organization of Economic Cooperation and Development. Previous studies of pedestrian safety have been conducted based on various built environments. However, few have analyzed spatio-temporal changes and influential factors over more than 10 years, despite dramatic changes in the built environment during such time spans. Here, we examine big data on pedestrian-related crashes in Seoul from 2009 to 2018 using a space-time cube methodology and binary logistic regression analysis. The results show that the trend in pedestrians killed or severely injured is decreasing with pedestrian environment enhancement projects and pedestrian safety measures in Seoul. Also, the analysis reveals a need to pay more attention to pedestrian safety in areas with a large older population. Pedestrian safety measures should be reinforced in areas of concentrated wholesale and retail businesses. This study also indicates that illegal parking poses a threat to pedestrian safety. Lastly, this study confirms some positive impacts of redeveloped or newly developed areas and pedestrian environment enhancement projects on pedestrian safety.

A Spatial Web Application to Explore the Interactions between Human Mobility, Government Policies, and COVID-19 Cases

Reports of coronavirus disease 2019 (COVID-19) cases began in December 2019. Soon after, the virus had spread around the world and became a pandemic. Social restrictions, quarantines, and other governmental policies in response to the pandemic altered normal operations across the world. One area significantly affected is human mobility. Typical movement patterns have been hindered by the pandemic. But inversely, mobility patterns can influence patterns of the virus. With this in mind, we created an interactive web application to visualize in near-real time the relationship between the COVID-19 pandemic and human mobility, as well as the impact of governmental policies at different spatial scales. The web application allows users to select a country at the global scale or a state or county for the USA and then displays a corresponding plot that compares human mobility to COVID-19 cases across time for the location, as well as to policy data. The application is useful for quickly revealing insightful patterns. First, the initial impact of the COVID-19 pandemic was a rather sudden decrease in mobility. Second, a relationship exists between mobility and COVID-19 offset by a lag, but that lag is not consistent over space or time. Third, spatial autocorrelation of relationship is apparent, meaning locations near each other share similar patterns. Overall, the application is a useful data visualization tool that helps uncover patterns that might otherwise go unnoticed. The application is available at this link: https://chrischapin7.shinyapps.io/covid19_vs_humanmobility/

Towards Deriving Freight Traffic Measures from Truck Movement Data for State Road Planning: A Proposed System Framework

To make the right decisions on investments, operations, and policies in the public road sector, decision makers need knowledge about traffic measures of trucks, such as average travel time and the frequency of trips among geographical zones. Private logistics companies daily collect a large amount of freight global positioning system (GPS) and shipment data. Processing such data can provide public decision makers with detailed freight traffic measures, which are necessary for making different planning decisions. The present paper proposes a system framework to be used in the research project “A new system for sharing data between logistics companies and public infrastructure authorities: improving infrastructure while maintaining competitive advantage”. Previous studies ignored the fact that the primary step for delivering valuable and usable data processing systems is to consider the final user’s needs when developing the system framework. Unlike existing studies, this paper develops the system framework through applying a user-centred design approach combining three main steps. The first step is to identify the specific traffic measures that satisfy the public decision makers’ planning needs. The second step aims to identify the different types of freight data required as inputs to the data processing system, while the third step illustrates the procedures needed to process the shared freight data. To do so, the current work employs methods of literature review and users’ need identification in applying a user-centralized approach. In addition, we develop a systematic assessment of the coverage and sufficiency of the currently acquired data. Finally, we illustrate the detailed functionality of the data processing system and provide an application case to illustrate its procedures.

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.

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.

The Spatial Patterns of Red Beds and Danxia Landforms: Implication for the formation factors-China

This research examined the distribution features of red beds and 1,100 Danxia landform sites across China, while probing the relationship between these spatial patterns and geological elements. This study is based on geological and tectonic maps of China. ArcGIS software was used to process the adjacent index, then perform a spatial analysis of Danxia landforms and red beds, and a coupling analysis of Danxia landforms and red beds with tectonics. Based on a point pattern analysis of Danxia landforms, the adjacent index is 0.31, and the coefficient of variation verified by Thiessen polygon reaches 449%. These figures reflect the clustered distribution pattern of the Danxia landforms. Across the country, Danxia landforms are concentrated into three areas, namely, the Southeast China region, the Sichuan Basin region and the Qilian-Liupan region. The exposure of red beds covers 9.16 × 10⁵ km², which accounts for 9.5% of the total land area of China. With this research background, the geological elements of tectonics and their effects on the distribution, number, and spatial pattern of Danxia landforms and red beds were analyzed.

Spatial autocorrelation analysis of cargo trucks on highway crashes in Chile

The growing number of cargo trucks on highway crashes in recent years due to the increase in freight movement in Chile motivates this study to identify the formation of persistent crash clusters on highway Ruta 5 (R5). Two spatial statistical methods (Moran's I and Getis-Ord Gi*) were used to determine whether crashes on this highway showed spatial clustering over time from a global and local perspective. Globally, recurrent crash clusters are spatially correlated on vertical curves and straight highway sections on northern R5 with different truck types and with the tractor-trailer units during rainy days on southern R5. The local spatial autocorrelation results suggest that the contributing causes related to the loss of control of the vehicle, the fatigue and imprudence of the driver, and crashes involving tractor units with trailer tend to cause persistent rollover crash clusters throughout R5. Overall, clustering of crash attributes with high values (i.e., hot spots) occurring on highway locations with vertical curves and on cloudy days predominated in the northern R5, and the largest number of recurrent hot spots occurred on sunny days along southern R5. A hot spot spatial co-occurrence analysis was further performed to identify the strong relationships between the studied crash attributes, and the crash and injury types as outcomes. The indication of high risk for the clustering of cargo trucks on highways crashes provides a basis for improving highway safety and reduce the associated social and economic costs.

A new approach for calibrating safety performance functions

Safety performance functions (SPFs) are statistical regression models used for estimating crash counts by roadway facility classification. They are required for identifying high crash risk locations, assessing the effectiveness of safety countermeasures and comparing road designs in terms of safety. Roadway agencies may opt to develop local SPFs or adopt them from elsewhere such as the national Highway Safety Manual (HSM), provided by the American Association of State Highway and Transportation Officials. The HSM offers a simple technique to calibrate its SPFs to conditions of specific jurisdictions. A more recent calibration technique, also known as the calibration function, is similar to that of the HSM. In this research, we develop SPFs of total crashes for rural divided multilane highway segments in four states. The states are Florida, Ohio, California and Washington. We also calibrate each SPF to each state using the HSM calibration method and the calibration function. Furthermore, we propose the use of the K nearest neighbor data mining method for calibrating SPFs. According to the goodness of fit (GOF) results, our proposed calibration method performs better than the other two methods.

Quantifying the Spatiotemporal Dynamics of Industrial Land Uses through Mining Free Access Social Datasets in the Mega Hangzhou Bay Region, China

China has experienced rapid industrial growth over the last three decades, leading to diverse social and environmental issues. In the new industrialization era, it is urgent to quantify industrial land use (ILU) dynamics for sustainable industrial management, yet there have been limited attempts to systematically quantify these changes, especially in large-scale areas. Through points-of-interest (POIs), a free access geospatial big data, we developed a new framework for exploring ILU dynamics in the mega Hangzhou Bay region (MHBR). The ILU was identified by using natural language processing to mine the semantic information of industrial POIs from 2005, 2011, and 2016. Then, a two-step approach that integrated statistical analysis and hotspots detection was introduced to quantify the changes. The results revealed that traditional industries such as textile products and apparel manufacturing, unspecific equipment manufacturing, and electrical machinery and components manufacturing were dominant types across MHBR, with the enterprise number reaching 14,543, 9412, and 4374, respectively, in 2016. The growth rates of these traditional industries dropped during 2011–2016, while the growth rates of new industries such as Internet information industry and logistics industry increased remarkably, particularly in Hangzhou and Ningbo. Additionally, traditional industrial factories mainly expanded in the urban periphery and coastal zones, whereas new industries mainly grew in the urban center. Shrinkages in the hotspots of traditional industries between 2011 and 2016 were also observed. Our study provides a detailed spatial view of ILU, indicating that MHBR has undergone an industrial transition from traditional industry to new industry.