Análisis exploratorio espacial de los accidentes de tránsito en Ciudad Juárez, México

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Abstract

OBJETIVO: Elaborar una herramienta para el estudio exploratorio de los accidentes viales en Ciudad Juárez, Chihuahua, México, que aplique de forma exclusiva la variable geográfica espacial (ubicación). MÉTODO: Estudio observacional y transversal que utiliza un sistema de información geográfica para explorar la naturaleza espacial de 13 305 accidentes viales registrados durante 2008 y 2009 en Ciudad Juárez. Se construyeron indicadores aproximados al flujo de tránsito y se incluyeron dos variables: índice de jerarquía urbana y densidad de población. RESULTADOS: El valor de la autocorrelación espacial global fue positivo, indicando la presencia de agrupamientos que fueron identificados a través de los indicadores de asociación espacial. Existen clústeres de riesgo vial localizados en las zonas con una alta jerarquía intraurbana, baja densidad de población y un elevado flujo de tránsito. CONCLUSIONES: El análisis exploratorio de datos espaciales es una fase previa al uso de técnicas multivariadas de mayor alcance. La aplicación de las técnicas del análisis exploratorio por sí misma permite tipificar agrupamientos espaciales, identificar autocorrelación global e indicar la dirección de las variables en estudio.

Translated abstract

OBJECTIVE: Prepare a tool for the exploratory study of road accidents in Ciudad Juarez, Chihuahua, Mexico, that exclusively applies the spatial geographical variable (location). METHODS: Observational and cross-sectional study that uses a Geographic Information System to explore the spatial nature of 13 305 road accidents recorded during 2008 and 2009 in Ciudad Juarez. Indicators were constructed that approximated the transit flow and included two variables: indices of the level of urbanization and population density. RESULTS: The value of the global spatial autocorrelation was positive, indicating the presence of groupings that were identified through the spatial association indicators. There are road risk clusters located in areas with a high level of urbanization, low population density, and a high transit flow level. CONCLUSIONS: The exploratory analysis of spatial data is a phase that precedes the use of multivariate techniques with a broader scope. The application of exploratory analysis techniques in itself makes it possible to standardize spatial groupings, identify global autocorrelation, and indicate the direction of the variables under study.

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An area-level model of vehicle-pedestrian injury collisions with implications for land use and transportation planning.

Megan Wier, June M. Weintraub, Elizabeth Humphreys … (2009)

There is growing awareness among urban planning, public health, and transportation professionals that design decisions and investments that promote walking can be beneficial for human and ecological health. Planners need practical tools to consider the impact of development on pedestrian safety, a key requirement for the promotion of walking. Simple bivariate models have been used to predict changes in vehicle-pedestrian injury collisions based on changes in traffic volume. We describe the development of a multivariate, area-level regression model of vehicle-pedestrian injury collisions based on environmental and population data in 176 San Francisco, California census tracts. Predictor variables examined included street, land use, and population characteristics, including commute behaviors. The final model explained approximately 72% of the systematic variation in census-tract vehicle-pedestrian injury collisions and included measures of traffic volume, arterial streets without transit, land area, proportion of land area zoned for neighborhood commercial and residential-neighborhood commercial uses, employee and resident populations, proportion of people living in poverty and proportion aged 65 and older. We have begun to apply this model to predict area-level change in vehicle-pedestrian injury collisions associated with land use development and transportation planning decisions.