OpenStreetMap’s success continues to grow and contributions are not limited to the collection of spatial data using GPS (Global Positioning System) equipment. A very wide range of software tools developed by, and available to, the OSM community means that at present, anyone can also make a contribution through, for example, tracing aerial imagery, directly importing data, or by adding spatial information retrieved from smartphones. Consequently ‘the map’ has become increasingly rich, but the quality of the data is very often questioned and comes under scrutiny from the GIS and LBS (Location-Based Services) communities. By examining the world map generated from OpenStreetMap, it is relatively easy to identify areas which are more or less well supported in community mapping activities; a very high level of spatial detail in certain areas can indicate the quality of OSM data. MVP OSM is a software tool designed to highlight areas in OpensStreetMap where users (contributors) are dedicated to providing high levels of spatial detail. This usually correlates with the use of a GPS and on-the-ground mapping, or, at the very least, a deep local knowledge of the area and an inherent desire to see it represented in the highest level of detail on OpenStreetMap. The input to MVP OSM is an OSM XML file, which is converted by Python into a file for spatialite (the GIS extension for sqlite). Within spatialite the data is processed to create clusters and using these spatial clusters, the tool can then derive the activity of single or multiple users in that area. Vector layers and heatmaps are generated as output that can be overlaid onto OSM maps. A high level of detail can be considered a good indicator of the quality of OSM data within a given area. The MVP OSM tool hides the details of OSM XML processing, which many researchers find difficult, and processes the data to produce very useful visualizations of contributor activity in any given OSM area

MVP OSM: A tool to identify areas of high quality contributor activity in openstreetmap

Napolitano, Maurizio;
2011

Abstract

OpenStreetMap’s success continues to grow and contributions are not limited to the collection of spatial data using GPS (Global Positioning System) equipment. A very wide range of software tools developed by, and available to, the OSM community means that at present, anyone can also make a contribution through, for example, tracing aerial imagery, directly importing data, or by adding spatial information retrieved from smartphones. Consequently ‘the map’ has become increasingly rich, but the quality of the data is very often questioned and comes under scrutiny from the GIS and LBS (Location-Based Services) communities. By examining the world map generated from OpenStreetMap, it is relatively easy to identify areas which are more or less well supported in community mapping activities; a very high level of spatial detail in certain areas can indicate the quality of OSM data. MVP OSM is a software tool designed to highlight areas in OpensStreetMap where users (contributors) are dedicated to providing high levels of spatial detail. This usually correlates with the use of a GPS and on-the-ground mapping, or, at the very least, a deep local knowledge of the area and an inherent desire to see it represented in the highest level of detail on OpenStreetMap. The input to MVP OSM is an OSM XML file, which is converted by Python into a file for spatialite (the GIS extension for sqlite). Within spatialite the data is processed to create clusters and using these spatial clusters, the tool can then derive the activity of single or multiple users in that area. Vector layers and heatmaps are generated as output that can be overlaid onto OSM maps. A high level of detail can be considered a good indicator of the quality of OSM data within a given area. The MVP OSM tool hides the details of OSM XML processing, which many researchers find difficult, and processes the data to produce very useful visualizations of contributor activity in any given OSM area
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11582/128601
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