Abstract
Abstract. Data quality assessment of OpenStreetMap (OSM) data can be carried out by comparing them with a reference spatial data (e.g authoritative data). However, in case of a lack of reference data, the spatial accuracy is unknown. The aim of this work is therefore to propose a framework to infer relative spatial accuracy of OSM data by using machine learning methods. Our approach is based on the hypothesis that there is a relationship between extrinsic and intrinsic quality measures. Thus, starting from a multi-criteria data matching, the process seeks to establish a statistical relationship between measures of extrinsic quality of OSM (i.e. obtained by comparison with reference spatial data) and the measures of intrinsic quality of OSM (i.e. OSM features themselves) in order to estimate extrinsic quality on an unevaluated OSM dataset. The approach was applied on OSM buildings. On our dataset, the resulting regression model predicts the values on the extrinsic quality indicators with 30% less variance than an uninformed predictor.
Highlights
Spatial Data quality is necessary for researchers and practitioner in Geographic Information Science (GIS) (Devillers et al, 2007)
Knowing our aim to infer the dependent variables from the explanatory variables, we are looking for a regression model that highlights the existence of a significant correlation between an extrinsic indicator and a group of intrinsic indicators
With this standard linear regression model, we obtained a share of 31.8% of explained variance relative to the total variance of the dependent variable
Summary
Spatial Data quality is necessary for researchers and practitioner in Geographic Information Science (GIS) (Devillers et al, 2007). The assessment of spatial accuracy becomes crucial and it is part of the quality concept covering the entire process from acquisition to diffusion of geographic information (Devillers et al, 2007). Spatial Data quality assessment is the process of comparing data to their accepted true values, according to fixed specifications. Spatial data quality is assessed by comparison and requires both an external database and specifications. This type of spatial quality evaluation is named extrinsic quality and uses a methodology which involves all parameters mentioned by the ISO as measures of extrinsic quality data
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