OQT for Food Environments

HeiGIT and the Amsterdam University Medical Centers (UMC) partnered up to investigate food environments through the lens of OpenStreetMap (OSM) data. UMC analyses how exposure to different categories of food related retailers affects health matters. The assumption is that the distance to retailers of a certain category, as well as the number of these retailers, can influence the health of people living in an area. This analysis of the is part of the European EXPANSE project, a five-year research project which focuses on the urban exposome, which encompasses all environmental drivers of health and diseases (more information). 20 academic and non-academic partners located in14 European Countries and the USA are involved in the EXPANSE Project.

In order to be able to do such an analysis reliable and up-to-date information on food retailers is required. OSM holds the potential to be a source of such information, as it usually shows high completeness and up-to-date data in Europe. However, those assumptions need to be tested for the area of interest of the food environment analysis. This was the task of the HeiGIT team.

OSM Quality Analysis Results

We calculated the completeness and currentness for eight different categories of food or beverage retailers mapped in OSM across 17 areas of interest (AoIs) in Europe. Each AoI consists of multiple NUTS-3 polygons for which the two OSM quality indicators are calculated for each category of food or beverage retailer, resulting in 16 indicators per polygon. These indicator results are combined into a single report that represents the average quality indication across all retailer categories. However, if one of the indicators shows low completeness or low currentness, the overall result will also be marked as “low quality”. Following this approach, OSM cannot serve as a data source for the analysis in the respective area if a single quality aspect of data is unreliable.

Figure 1: Overall Results for OSM Quality of Food-Related PoIs in Switzerland

The overall result and the completeness indicator result were added as attributes to each polygon per AoI. These generated GeoJSON files were made available to the project partners, allowing them to use GIS to visualise the results and assess which parts of their areas of interest OSM is suitable as a data source. It is also possible to visualise the completeness for each retailer category. Visualising the results on maps provides a quick overview of the quality of the OSM data and allows comparisons to be made between different regions. A guide on how to visualise the results of our ohsome quality API in QGIS has been published in this blog post.

For in-depth analysis of a region, a quality report per polygon was generated and submitted. These reports show the overall quality assessment as well as the 16 indicator results. The indicator results are accompanied by a description and graphs that further explain the respective score. Below are the data quality indicator figures for the category ‘Restaurants’ from the quality report of one of the regions in Switzerland.

Figure 2 shows the number of local food shops mapped in OSM and a fitted curve to this data. If the growth of the fitted curve is minimal, it can be assumed that no new features can be added to OSM as all features are already mapped. Mapping is therefore saturated and OSM is almost complete in regard of the respective features.

Figure 2: The saturation of the last 3 years is 99.07%.High saturation has been reached (97% < Saturation < 100%).
Figure 3: In the last 3 years 73.28 % of the elements were edited the last time. In the period from 4 to 7 years ago 11.45 % of the elements were edited the last time. The remaining 15.27 % were last edited at least 8 years ago. The median currentness of the 131 features (Restaurants) is 0 years. Note that only contributions up to 2023-05-14 are taken into account. It is likely that most features are up-to-date.

Figure 3 shows when the respective features within the area of interest were last edited or created. Rather than assuming the completeness of features, this indicator refers to data quality in respect to currentness. It tells us whether features were once created but not kept up-to-date or if the data is being maintained.

These results form the basis on which project partners can decide if and for which areas OSM can be used as a data source for their project. Although we can assess the quality of the OSM data, it is important to determine which levels of completeness and currentness are “good enough” for the OSM data to be used in the analysis. Oftentimes, this can be complicated as each project requires a different level of quality depending on the research and its scale.

Background Information

To find out more about the quality assessment of OSM data, visit OQT. If you want to try out the completeness and currentness indicators yourself, check out the ohsome dashboard. See also this blog post about the integration of the ohsome quality API (formerly ohsome quality analyst) into the ohsome dashboard.

Do you have any questions or would you like to find out more about our tools? Then feel free to contact us at ohsome@heigit.org.

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