Spatial Analysis Of The Effective Coverage Of Land-Based Weather Stations For Traffic Crashes

Keywords

Cohen's κ statistics; Geospatial crash risk analysis model; Multiple buffering techniques; Quality Controlled Local Climatological Data (QCLCD); Spatial coverage analysis; Weather data; Weather-related fatal crashes

Abstract

This paper investigates the effective spatial coverage of nationwide land-based weather stations of the National Oceanic Atmospheric Administration (NOAA) for traffic crash analysis. The weather data were collected from the Quality Controlled Local Climatological Data (QCLCD) and the fatal crashes were obtained from the Fatality Analysis Reporting Systems (FARS) during the year of 2007–2014. Both QCLCD and FARS contain geographic coordinates for locations and weather condition information as a categorical variable. The spatial coverage of weather stations for the analysis was made by geoprocessing, which uses multiple buffers (i.e. radii 5, 10, 15, and 20 miles), and then was evaluated via Cohen's κ statistics, which is used to determine an agreement of weather between QCLCD and FARS within the buffer. The applicability of the weather station's data by nine climate regions was assessed by developing a series of negative binomial models. According to the estimated Cohen's κ statistics, the rain and snow weather conditions have a moderate agreement up to 20 miles. However, in the case of fog weather condition, it has a slight agreement. The statistical modeling results showed that weather stations data can be a good exposure measure for weather-related fatal crashes along with the vehicle-miles-traveled. Considering one geographical feature that approximately more than 75% of all fatal crashes are located within 20-miles radius of the weather stations in the USA, it is evident that the data from the existing weather stations can be cost-effective to develop geospatial crash risk analysis model.

Publication Date

1-1-2018

Publication Title

Applied Geography

Volume

90

Number of Pages

17-27

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.apgeog.2017.11.010

Socpus ID

85036593103 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85036593103

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