The quality of OpenStreetMap food-related point-of-interest data for use in epidemiological research

Maria Gabriela M. Pinho; Benjamin Flueckiger; Antonia Valentin; Maria Iosifina Kasdagli; Kalliopi Kyriakou; Jeroen Lakerveld; Joreintje D. Mackenbach; Joline W.J. Beulens; Kees de Hoogh

doi:10.1016/j.healthplace.2023.103075

The quality of OpenStreetMap food-related point-of-interest data for use in epidemiological research

Maria Gabriela M. Pinho^*, Benjamin Flueckiger, Antonia Valentin, Maria Iosifina Kasdagli, Kalliopi Kyriakou, Jeroen Lakerveld, Joreintje D. Mackenbach, Joline W.J. Beulens, Kees de Hoogh

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

We assessed the quality of food-related OpenStreetMap (OSM) data in urban areas of five European countries. We calculated agreement statistics between point-of-interests (POIs) from OSM and from Google Street View (GSV) in five European regions. We furthermore assessed correlations between exposure measures (distance and counts) from OSM data and administrative data from local data sources on food environment data in three European countries. Agreement between POI data in OSM compared to GSV was poor, but correlations were moderate to high between exposures from OSM and local data sources. OSM data downloaded in 2020 seems to be an acceptable source of data for generating count-based food exposure measures for research in selected European regions.

Original language	English
Article number	103075
Journal	Health and Place
Volume	83
DOIs	https://doi.org/10.1016/j.healthplace.2023.103075
Publication status	Published - Sept 2023

Keywords

Exposome
Food environment
Google street view
OpenStreetMap
Quality assessment

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10.1016/j.healthplace.2023.103075Licence: CC BY

1-s2.0-S1353829223001120-mainFinal published version, 370 KBLicence: CC BY

Cite this

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title = "The quality of OpenStreetMap food-related point-of-interest data for use in epidemiological research",

abstract = "We assessed the quality of food-related OpenStreetMap (OSM) data in urban areas of five European countries. We calculated agreement statistics between point-of-interests (POIs) from OSM and from Google Street View (GSV) in five European regions. We furthermore assessed correlations between exposure measures (distance and counts) from OSM data and administrative data from local data sources on food environment data in three European countries. Agreement between POI data in OSM compared to GSV was poor, but correlations were moderate to high between exposures from OSM and local data sources. OSM data downloaded in 2020 seems to be an acceptable source of data for generating count-based food exposure measures for research in selected European regions.",

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AU - Pinho, Maria Gabriela M.

AU - Flueckiger, Benjamin

AU - Valentin, Antonia

AU - Kasdagli, Maria Iosifina

AU - Kyriakou, Kalliopi

AU - Lakerveld, Jeroen

AU - Mackenbach, Joreintje D.

AU - Beulens, Joline W.J.

AU - de Hoogh, Kees

PY - 2023/9

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N2 - We assessed the quality of food-related OpenStreetMap (OSM) data in urban areas of five European countries. We calculated agreement statistics between point-of-interests (POIs) from OSM and from Google Street View (GSV) in five European regions. We furthermore assessed correlations between exposure measures (distance and counts) from OSM data and administrative data from local data sources on food environment data in three European countries. Agreement between POI data in OSM compared to GSV was poor, but correlations were moderate to high between exposures from OSM and local data sources. OSM data downloaded in 2020 seems to be an acceptable source of data for generating count-based food exposure measures for research in selected European regions.

AB - We assessed the quality of food-related OpenStreetMap (OSM) data in urban areas of five European countries. We calculated agreement statistics between point-of-interests (POIs) from OSM and from Google Street View (GSV) in five European regions. We furthermore assessed correlations between exposure measures (distance and counts) from OSM data and administrative data from local data sources on food environment data in three European countries. Agreement between POI data in OSM compared to GSV was poor, but correlations were moderate to high between exposures from OSM and local data sources. OSM data downloaded in 2020 seems to be an acceptable source of data for generating count-based food exposure measures for research in selected European regions.

KW - Exposome

KW - Food environment

KW - Google street view

KW - OpenStreetMap

KW - Quality assessment

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DO - 10.1016/j.healthplace.2023.103075

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SN - 1353-8292

VL - 83

JO - Health and Place

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ER -

The quality of OpenStreetMap food-related point-of-interest data for use in epidemiological research

Abstract

Keywords

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