Literature DB >> 25551356

Spatiotemporal approaches to analyzing pedestrian fatalities: the case of Cali, Colombia.

Lani Fox1, Marc L Serre, Steven J Lippmann, Daniel A Rodríguez, Shrikant I Bangdiwala, María Isabel Gutiérrez, Guido Escobar, Andrés Villaveces.   

Abstract

OBJECTIVE: Injuries among pedestrians are a major public health concern in Colombian cities such as Cali. This is one of the first studies in Latin America to apply Bayesian maximum entropy (BME) methods to visualize and produce fine-scale, highly accurate estimates of citywide pedestrian fatalities. The purpose of this study is to determine the BME method that best estimates pedestrian mortality rates and reduces statistical noise. We further utilized BME methods to identify and differentiate spatial patterns and persistent versus transient pedestrian mortality hotspots.
METHODS: In this multiyear study, geocoded pedestrian mortality data from the Cali Injury Surveillance System (2008 to 2010) and census data were utilized to accurately visualize and estimate pedestrian fatalities. We investigated the effects of temporal and spatial scales, addressing issues arising from the rarity of pedestrian fatality events using 3 BME methods (simple kriging, Poisson kriging, and uniform model Bayesian maximum entropy). To reduce statistical noise while retaining a fine spatial and temporal scale, data were aggregated over 9-month incidence periods and censal sectors. Based on a cross-validation of BME methods, Poisson kriging was selected as the best BME method. Finally, the spatiotemporal and urban built environment characteristics of Cali pedestrian mortality hotspots were linked to intervention measures provided in Mead et al.'s (2014) pedestrian mortality review.
RESULTS: The BME space-time analysis in Cali resulted in maps displaying hotspots of high pedestrian fatalities extending over small areas with radii of 0.25 to 1.1 km and temporal durations of 1 month to 3 years. Mapping the spatiotemporal distribution of pedestrian mortality rates identified high-priority areas for prevention strategies. The BME results allow us to identify possible intervention strategies according to the persistence and built environment of the hotspot; for example, through enforcement or long-term environmental modifications.
CONCLUSIONS: BME methods provide useful information on the time and place of injuries and can inform policy strategies by isolating priority areas for interventions, contributing to intervention evaluation, and helping to generate hypotheses and identify the preventative strategies that may be suitable to those areas (e.g., street-level methods: pedestrian crossings, enforcement interventions; or citywide approaches: limiting vehicle speeds). This specific information is highly relevant for public health interventions because it provides the ability to target precise locations.

Entities:  

Keywords:  Bayesian maximum entropy; Colombia; geographic analysis; pedestrian injuries; road safety

Mesh:

Year:  2014        PMID: 25551356     DOI: 10.1080/15389588.2014.976336

Source DB:  PubMed          Journal:  Traffic Inj Prev        ISSN: 1538-9588            Impact factor:   1.491


  3 in total

1.  The Spatial Distribution of Adult Obesity Prevalence in Denver County, Colorado: An Empirical Bayes Approach to Adjust EHR-Derived Small Area Estimates.

Authors:  David C Tabano; Kirk Bol; Sophia R Newcomer; Jennifer C Barrow; Matthew F Daley
Journal:  EGEMS (Wash DC)       Date:  2017-12-06

2.  Urban landscape and street-design factors associated with road-traffic mortality in Latin America between 2010 and 2016 (SALURBAL): an ecological study.

Authors:  D Alex Quistberg; Philipp Hessel; Daniel A Rodriguez; Olga L Sarmiento; Usama Bilal; Waleska Teixeira Caiaffa; J Jaime Miranda; Maria de Fatima de Pina; Akram Hernández-Vásquez; Ana V Diez Roux
Journal:  Lancet Planet Health       Date:  2022-02

3.  Global Incidence and Mortality Patterns of Pedestrian Road Traffic Injuries by Sociodemographic Index, with Forecasting: Findings from the Global Burden of Diseases, Injuries, and Risk Factors 2017 Study.

Authors:  Moien A B Khan; Michal Grivna; Javaid Nauman; Elpidoforos S Soteriades; Arif Alper Cevik; Muhammad Jawad Hashim; Romona Govender; Salma Rashid Al Azeezi
Journal:  Int J Environ Res Public Health       Date:  2020-03-23       Impact factor: 3.390

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.