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The approaches to measuring the potential spatial access to urban health services revisited: distance types and aggregation-error issues

BACKGROUND: The potential spatial access to urban health services is an important issue in health geography, spatial epidemiology and public health. Computing geographical accessibility measures for residential areas (e.g. census tracts) depends on a type of distance, a method of aggregation, and a...

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Autores principales: Apparicio, Philippe, Gelb, Jérémy, Dubé, Anne-Sophie, Kingham, Simon, Gauvin, Lise, Robitaille, Éric
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5568316/
https://www.ncbi.nlm.nih.gov/pubmed/28830461
http://dx.doi.org/10.1186/s12942-017-0105-9
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author Apparicio, Philippe
Gelb, Jérémy
Dubé, Anne-Sophie
Kingham, Simon
Gauvin, Lise
Robitaille, Éric
author_facet Apparicio, Philippe
Gelb, Jérémy
Dubé, Anne-Sophie
Kingham, Simon
Gauvin, Lise
Robitaille, Éric
author_sort Apparicio, Philippe
collection PubMed
description BACKGROUND: The potential spatial access to urban health services is an important issue in health geography, spatial epidemiology and public health. Computing geographical accessibility measures for residential areas (e.g. census tracts) depends on a type of distance, a method of aggregation, and a measure of accessibility. The aim of this paper is to compare discrepancies in results for the geographical accessibility of health services computed using six distance types (Euclidean and Manhattan distances; shortest network time on foot, by bicycle, by public transit, and by car), four aggregation methods, and fourteen accessibility measures. METHODS: To explore variations in results according to the six types of distance and the aggregation methods, correlation analyses are performed. To measure how the assessment of potential spatial access varies according to three parameters (type of distance, aggregation method, and accessibility measure), sensitivity analysis (SA) and uncertainty analysis (UA) are conducted. RESULTS: First, independently of the type of distance used except for shortest network time by public transit, the results are globally similar (correlation >0.90). However, important local variations in correlation between Cartesian and the four shortest network time distances are observed, notably in suburban areas where Cartesian distances are less precise. Second, the choice of the aggregation method is also important: compared with the most accurate aggregation method, accessibility measures computed from census tract centroids, though not inaccurate, yield important measurement errors for 10% of census tracts. Third, the SA results show that the evaluation of potential geographic access may vary a great deal depending on the accessibility measure and, to a lesser degree, the type of distance and aggregation method. Fourth, the UA results clearly indicate areas of strong uncertainty in suburban areas, whereas central neighbourhoods show lower levels of uncertainty. CONCLUSION: In order to accurately assess potential geographic access to health services in urban areas, it is particularly important to choose a precise type of distance and aggregation method. Then, depending on the research objectives, the choices of the type of network distance (according to the mode of transportation) and of a number of accessibility measures should be carefully considered and adequately justified.
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spelling pubmed-55683162017-08-29 The approaches to measuring the potential spatial access to urban health services revisited: distance types and aggregation-error issues Apparicio, Philippe Gelb, Jérémy Dubé, Anne-Sophie Kingham, Simon Gauvin, Lise Robitaille, Éric Int J Health Geogr Methodology BACKGROUND: The potential spatial access to urban health services is an important issue in health geography, spatial epidemiology and public health. Computing geographical accessibility measures for residential areas (e.g. census tracts) depends on a type of distance, a method of aggregation, and a measure of accessibility. The aim of this paper is to compare discrepancies in results for the geographical accessibility of health services computed using six distance types (Euclidean and Manhattan distances; shortest network time on foot, by bicycle, by public transit, and by car), four aggregation methods, and fourteen accessibility measures. METHODS: To explore variations in results according to the six types of distance and the aggregation methods, correlation analyses are performed. To measure how the assessment of potential spatial access varies according to three parameters (type of distance, aggregation method, and accessibility measure), sensitivity analysis (SA) and uncertainty analysis (UA) are conducted. RESULTS: First, independently of the type of distance used except for shortest network time by public transit, the results are globally similar (correlation >0.90). However, important local variations in correlation between Cartesian and the four shortest network time distances are observed, notably in suburban areas where Cartesian distances are less precise. Second, the choice of the aggregation method is also important: compared with the most accurate aggregation method, accessibility measures computed from census tract centroids, though not inaccurate, yield important measurement errors for 10% of census tracts. Third, the SA results show that the evaluation of potential geographic access may vary a great deal depending on the accessibility measure and, to a lesser degree, the type of distance and aggregation method. Fourth, the UA results clearly indicate areas of strong uncertainty in suburban areas, whereas central neighbourhoods show lower levels of uncertainty. CONCLUSION: In order to accurately assess potential geographic access to health services in urban areas, it is particularly important to choose a precise type of distance and aggregation method. Then, depending on the research objectives, the choices of the type of network distance (according to the mode of transportation) and of a number of accessibility measures should be carefully considered and adequately justified. BioMed Central 2017-08-23 /pmc/articles/PMC5568316/ /pubmed/28830461 http://dx.doi.org/10.1186/s12942-017-0105-9 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology
Apparicio, Philippe
Gelb, Jérémy
Dubé, Anne-Sophie
Kingham, Simon
Gauvin, Lise
Robitaille, Éric
The approaches to measuring the potential spatial access to urban health services revisited: distance types and aggregation-error issues
title The approaches to measuring the potential spatial access to urban health services revisited: distance types and aggregation-error issues
title_full The approaches to measuring the potential spatial access to urban health services revisited: distance types and aggregation-error issues
title_fullStr The approaches to measuring the potential spatial access to urban health services revisited: distance types and aggregation-error issues
title_full_unstemmed The approaches to measuring the potential spatial access to urban health services revisited: distance types and aggregation-error issues
title_short The approaches to measuring the potential spatial access to urban health services revisited: distance types and aggregation-error issues
title_sort approaches to measuring the potential spatial access to urban health services revisited: distance types and aggregation-error issues
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5568316/
https://www.ncbi.nlm.nih.gov/pubmed/28830461
http://dx.doi.org/10.1186/s12942-017-0105-9
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