To support organizations that use OpenStreetMap data for disaster response, the HOT Data Team is strengthening our data quality and fitness measures.

Several teams at HOT, including the Data Team, Technology & Innovation Team, and the Regional Hubs, are collaborating to develop resources, tools, skill sharing, and community feedback mechanisms that will be avenues for data creators and data users to collaborate to improve OpenStreetMap data quality.

Data Team:

The HOT Data Team presented the top 10 data quality issues in a lightning talk at State of the Map 2022 in Florence. We categorize these data quality issues into three main categories:

Semantic Accuracy

• Tagging
• Tasking Manager project consistencies

Positional Accuracy

• Spatial offsets
• Feature tracing inconsistencies
• Logical consistencies of map features

Completeness

• Temporal inconsistencies
• Road network inconsistencies
• Completeness of health facilities
• Completeness of public service data for sustainable communities
• Administrative boundaries

The Data Team is also defining use cases and data quality metrics. Measuring data quality starts with identifying core datasets for each of our impact areas. Examples include highways and health facilities for Public Health, water & sanitation, transportation, and education for Sustainable Cities & Communities, and waterways, buildings, and highways for Disasters & Climate Resilience.

We then evaluated the use cases and the metrics for assessing the quality of each dataset, enabling us to identify ways of improving data quality.

Technology & Innovation Team:

Technology & Innovation Team is implementing automated tools for measuring OpenStreetMap data quality.

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Background.

Even as OSM is rapidly growing in content and contributors, its credibility has been one of the main concerns for authoritative users. The belief that it is made by volunteers can limit the trust in the value of this free data source within traditional GIS communities. At HOT, we have prioritized the top 10 data quality aspects that we want to minimize. These aspects have been categorized under 3 categories the are Positional Accuracy, Semantic Accuracy, and Completeness.

We came to reach the top 10 list through a number of consultations with the Data Quality Working Group, representative from open mapping communities, and associates from HOT regional Hubs.

This information has been shared for reference by all OpenStreetMap data contributors, and users across the open mapping ecosystem, data quality associates at the HOT Regional Hubs, HOT partners and other communities that engage with OpenStreetMap.

HOT is focusing on prioritizing these aspects and implementation on how to minimize/eliminate them through HUB centered community engagements in form of trainings, collaborating with partners and developing tools that can be used to improve the quality of mapping.

There are many other issues affecting the quality of OSM data, however, our top 10 data quality aspects are;

1. Spatial offsets.

2. Temporal consistencies.

3. Feature tracing inconsistencies.

4. Road network consistency.

5. Completeness of health facilities.

6. Completeness of public service data for sustainable communities.

7. Administrative boundary inconsistencies.

8. Tagging.

9. Logical consistencies of map features.

10. Tasking Manager project consistencies.

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1. Spatial Offsets:

An offset is the degree of deviation of an object from its intended position.

Category: Positional Accuracy

Possible Sources:

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My 214 Mapping Days of 2021

At the beginning of the year 2021, one of my targets was to upload a changeset at least every day until the end of the year. However, I have not managed to hit my target. Nevertheless, this year still emerges the year with my highest number of contribution days.

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The unforgettable experience at 2018 FOSS4G in Dar es salaam, Tanzania.

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