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

Scott, Mackenzie Chance; Roy, Shouraseni Sen; Prasad, Shivangi

doi:10.1080/15389588.2016.1144878

Cited by 27 publications

(11 citation statements)

References 24 publications

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“…The pattern of distribution was considered to be clustered if the value of Moran’s I was > 0 and P < 0.05; otherwise, the pattern was considered to be nonclustered [ 18 ]). Local spatial autocorrelation and the Getis-Ord Gi* statistic were used to identify the hot spots of CHD birth prevalence within the study area (the Gi* statistics is actually a Z score, and Z score values equal or greater than 1.96 were used to indicate the significance of observed hot spots with a P value of less than 0.05) [ 19 , 20 ].…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2020

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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. Electronic supplementary material The online version of this article (10.1007/s10654-020-00653-0) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

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

et al. 2020

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“…There have been many studies of crashes undertaken internationally using techniques such as the Moran's I statistic, the Getis-Ord Gi* statistic and KDE individually and in combination. In some studies the crashes were spatially and/or temporally partitioned (Chance Scott et al, 2016;Prasannakumar et al, 2011). Some studies used severity indices, as well as actual counts, with more severe crashes being given a higher weighting (Choudhary, Ohri & Kumar, 2015;Soltani & Askari, 2017).…”

Section: Findings From Previous Studies Using Spatial Analysismentioning

confidence: 99%

“…Some studies used severity indices, as well as actual counts, with more severe crashes being given a higher weighting (Choudhary, Ohri & Kumar, 2015;Soltani & Askari, 2017). The results of the studies showed significant clustering of crashes, especially on arterial roads and near urban activity centres, but found that in certain locations the distribution of crashes was random (Chance Scott, Sen Roy & Prasad, 2016;Prasannakumar et al, 2011;Soltani & Askari, 2017). Benedek, Ciobanu and Man (2016) used network-based analysis to identify crash hot spots and found that the number of crashes was directly proportional to the traffic volume, and that the hot spots were in locations where traffic was high.…”

Section: Findings From Previous Studies Using Spatial Analysismentioning

confidence: 99%

Use of Spatial Analysis Techniques to Identify Statistically Significant Crash Hot Spots in Metropolitan Melbourne

Hovenden¹,

Liu

2020

JRS

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Understanding where, when, what type and why crashes are occurring can help determine the most appropriate initiatives to reduce road trauma. Spatial statistical analysis techniques are better suited to analysing crashes than traditional statistical techniques as they allow for spatial dependency and non-stationarity. For example, crashes tend to cluster at specific locations (spatial dependency) and vary from one location to another (non-stationarity). Several spatial statistical methods were used to examine crash clustering in metropolitan Melbourne, including Global Moran’s I statistic, Kernel Density Estimation and Getis-Ord Gi* statistic. The Global Moran’s I statistic identified statistically significant clustering on a global level. The Kernel Density Estimation method showed clustering but could not identify the statistical significance. The Getis-Ord Gi* method identified local crash clustering and found that 15.7 per cent of casualty crash locations in metropolitan Melbourne were statistically significant hot spots at the 95 per cent confidence level. The degree, location and extent of clustering was found to vary for different crash categories, with fatal crashes exhibiting the lowest level of clustering and bicycle crashes exhibiting the highest level of clustering. Temporal variations in clustering were also observed. Overlaying the results with land use and road classification data found that hot spot clusters were in areas with a higher proportion of commercial land use and with a higher proportion of arterial and sub-arterial roads. Further work should investigate network based hot spot analysis and explore the relationship between crash clusters and influencing factors using spatial techniques such as Geographically Weighted Regression.

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“…In traffic crash analysis, many empirical findings indicate that the spatial pattern of traffic collisions can vary according to a wide range of factors. The distribution of hot spots or hot zones can, for example, vary based on temporal change [20], road types (e.g., highways and urban roads) [21], land uses [13] and pedestrians or other vulnerable groups [22]. Putting the spatial context into the perspectives of hotspot and hot zone identification is therefore conducive to formulating geographical-specific and effective road safety measures.…”

Section: Literature Reviewmentioning

confidence: 99%

A Combined Approach to Address Road Traffic Crashes beyond Cities: Hot Zone Identification and Countermeasures in Indonesia

et al. 2020

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Addressing fatalities on road is a major concern in most countries in the world. South-East Asian countries are no exception. In Indonesia, three persons die on road every hour. Understanding where and how road traffic crashes happen is imperative before the most efficient countermeasures can be devised and implemented. In this paper, three tools-hot spots, hot zones and hot clusters-are used to identify sections of two main highways in the Province of Aceh that require most urgent action. Many countermeasures have been developed to address the problem of black sites (hot spots). Examples of implementation often come from Australia, Europe or North America. Less research exists on countermeasures in hot zones, even less so in the Global South (less developed countries from Southeast Asia, Africa and Latin America). This research applies quantitative spatial analysis that builds on existing works using the hot zone methodology and goes a step further by suggesting relevant countermeasures. More precisely, by taking into consideration the global urban-rural divide, this paper attempts to identify the most dangerous highway sections, in Indonesia, and to suggest appropriate hot zone countermeasures based on the characteristics of these hot zones. The results showed that urban highways, when compared to rural highways, were characterized by higher crash rates and a larger number of hot zones. Formulating hot zone countermeasures in urban environments should therefore consider their associated dangerousness and environmental features. Proposed countermeasures in urban roads include a stricter monitoring of the use of helmet, seat belt and cellphone, and the development of periodic communication and awareness campaigns.

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

Abstract: This understanding of patterns can help the county target high-risk areas and help to reduce crash fatalities to create a safer environment for motorists and pedestrians.

Cited by 27 publications

References 24 publications

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

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

Use of Spatial Analysis Techniques to Identify Statistically Significant Crash Hot Spots in Metropolitan Melbourne

A Combined Approach to Address Road Traffic Crashes beyond Cities: Hot Zone Identification and Countermeasures in Indonesia

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