Case Study of Crash Severity Spatial Pattern Identification in Hot Spot Analysis

Lee, Myungwoo; Khattak, Aemal J.

doi:10.1177/0361198119845367

Cited by 24 publications

(18 citation statements)

References 33 publications

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“…The hot spot analysis tool in ArcMap identifies a significant hot/cold spot based on the attribute values of its neighbors. A significant hotspot is a feature with a high attribute value surrounded by other adjacent features that also have high values [ 51 ]. When implementing these methods in ArcGIS, an appropriate spatial relationship between features is needed to reflect the spatial and distributional circumstances of actual target features.…”

Section: Methodsmentioning

confidence: 99%

Investigating the Association between Streetscapes and Mental Health in Zhanjiang, China: Using Baidu Street View Images and Deep Learning

Zhang

Zhai

Liu

et al. 2022

IJERPH

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Mental health is one of the main factors that significantly affect one’s life. Previous studies suggest that streets are the main activity space for urban residents and have important impacts on human mental health. Existing studies, however, have not fully examined the relationships between streetscape characteristics and people’s mental health on a street level. This study thus aims to explore the spatial patterns of urban streetscape features and their associations with residents’ mental health by age and sex in Zhanjiang, China. Using Baidu Street View (BSV) images and deep learning, we extracted the Green View Index (GVI) and the street enclosure to represent two physical features of the streetscapes. Global Moran’s I and hotspot analysis methods were used to examine the spatial distributions of streetscape features. We find that both GVI and street enclosure tend to cluster, but show almost opposite spatial distributions. The Results of Pearson’s correlation analysis show that residents’ mental health does not correlate with GVI, but it has a significant positive correlation with the street enclosure, especially for men aged 31 to 70 and women over 70-year-old. These findings emphasize the important effects of streetscapes on human health and provide useful information for urban planning.

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Section: Methodsmentioning

confidence: 99%

Investigating the Association between Streetscapes and Mental Health in Zhanjiang, China: Using Baidu Street View Images and Deep Learning

Zhang

Zhai

Liu

et al. 2022

IJERPH

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“…Table 1 presents studies that used this approach to identify hotspots of different crash types on roadways. Most studies focused on identifying patterns of all crashes (13)(14)(15)(16)(17), whereas others concentrated on pedestrian crashes (9,18,19), one on bicycle crashes (20), and the other on crash injury severity (21).…”

Section: Roadway Crash Hotspotsmentioning

confidence: 99%

Influence of Built Environment on Pedestrian Crashes: A Case Study of Miami-Dade County

Soto¹,

Kitali

Raihan

et al. 2022

Transportation Research Record: Journal of the Transportation R

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The objective of this study was to identify pedestrian crash hotspots and assess the built environment to identify factors that increase the probability of a roadway corridor becoming a hotspot. Therefore, this research attempted to identify the pedestrian hotspots and then answer the question, do locations with a “specific built environment” have a higher probability of becoming a pedestrian crash hotspot? The study used 4 years of pedestrian crash data (2011 to 2014) from Miami-Dade County, one of the top three leading counties in the United States with the highest pedestrian fatalities. Pedestrian crash hotspots were first identified in the ArcGIS environment, integrating spatial analysis. The Bayesian complementary log-log (cloglog) model was then used to develop a hotspot risk prediction model, in which the likelihood of a corridor becoming a hotspot was linked to the built environment, and to demographic and socioeconomic factors. The density of bus stops, shopping centers, healthcare facilities, hotels, alcohol sales establishments, households without vehicles, traffic volume, presence of sidewalks, and presence of medians were found to significantly increase the likelihood of a corridor becoming a pedestrian crash hotspot. The methodological framework and findings of this study could be used while developing site-specific proactive measures to improve pedestrian safety.

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“…The outcome of KDE, based on Euclidean distance over planer space, was found to be biased for location datasets over a network space, due to the over-estimation of crash clusters [24]. To overcome this limitation, a network-based KDE was developed and adopted in road network spaces to discern the crash patterns involving different age groups and severity levels [25,26].…”

Section: Spatial Analysis Of Crash Patternsmentioning

confidence: 99%

Evaluation of Spatial Resilience of Highway Networks in Response to Adverse Weather Conditions

Han

Zhang

2020

IJGI

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Adverse weather poses a significant threat to the serviceability of highway infrastructure, as it causes more frequent and severe crash incidents. This study focuses on evaluating the resilience of highway networks by examining the crash-induced safety impact in response to extreme weather events. Unlike traditional service drop-based methods for resilience evaluation, this study endeavors to evaluate highway resilience in a spatial context. Three spatial metrics, including K-nearest neighbors, proximity to highways, and Kernel density hot spot, are introduced and employed to compare and analyze the spatial patterns (magnitude and distribution) of crashes in pre- and post-weather conditions. An illustrative example is also provided to showcase the applications of the proposed spatial resilience metrics for two study areas in the State of Illinois, U.S. The contribution of this study is to provide transportation practitioners with a tool to evaluate highway spatial resilience both visually and quantitatively, and ultimately improve highway safety and operation.

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Case Study of Crash Severity Spatial Pattern Identification in Hot Spot Analysis

Cited by 24 publications

References 33 publications

Investigating the Association between Streetscapes and Mental Health in Zhanjiang, China: Using Baidu Street View Images and Deep Learning

Investigating the Association between Streetscapes and Mental Health in Zhanjiang, China: Using Baidu Street View Images and Deep Learning

Influence of Built Environment on Pedestrian Crashes: A Case Study of Miami-Dade County

Evaluation of Spatial Resilience of Highway Networks in Response to Adverse Weather Conditions

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