2022:  Inequalities in the completeness of OpenStreetMap buildings in urban centers
Channel: State of the Map
Date: 2022-10-11

https://media.ccc.de/v/state-of-the-map-2022-academic-track-18598-inequalities-in-the-completeness-of-openstreetmap-buildings-in-urban-centers



Albeit the manifold usage of OSM building footprints an adequate investigation into their completeness on the global scale has not been conducted so far. This talk investigates OSM building completeness within all 13,135 urban centers covering about 50% of the global population.

The collaborative maps of OpenStreetMap (OSM) have become a major source of geospatial baseline data for humanitarian organisations, companies and public authorities. Describing the elements of spatial data quality (e.g. positional accuracy, completeness, temporal quality) for the OSM dataset is a key prerequisite to provide the potential stakeholders with the necessary information to decide on the fitness for use of a data set for their particular application [1]. Without information on spatial data quality there are serious barriers to the adoption and usage of new sources such as OSM.

A large community of researchers has analyzed the quality of OSM data in comparison to authoritative reference data sets, by means of remote sensing and using intrinsic measures [2–4]. It has been acknowledged that the OSM data in general is strongly biased, in part due to a much larger contributor basis in countries in the global North as a consequence of socio-economic inequalities and the digital divide [5, 6]. Albeit the manifold usage of OSM building footprints an adequate investigation into their completeness on the global scale has not been conducted so far. This talk investigates OSM building completeness in regions home to a population of 3.5 billion people (about 50% of the global population). First, we propose a machine learning regression method based on generalized additive models (GAMs) to assess OSM building completeness within all 13,135 urban centers (as defined by the European Commission [7]). The analysis utilizes an extensive collection of open building data from commercial and authoritative sources as training data and builds upon very recent technological advances to utilize OSM full-history data for spatio-temporal data analysis on the global scale [8]. This allow us – for the first time – to present a comprehensive assessment of the evolution of urban OSM building completeness which encompasses all data contributed to OSM since 2008.

For each urban center we calculated the OSM building completeness using the area ratio method which has been applied55 by several other researchers in the context of urban areas [9–11] . Several measures have been adopted to describe the temporal evolution of inequality in urban OSM building mapping on the global scale and per World Bank region. First, we analyzed the share of population living in urban centers with low completeness (&lt;20%) and high completeness (&gt;80%). Gini coefficient has been utilized to derive the degree of evenness of urban OSM building completeness following an approach proposed by Massey & Denton (1988) to study residential segregation 12 . Moran’s I has been selected as a measure of global spatial autocorrelation of urban OSM building completeness. Spatial autocorrelation has been proposed as an explicitly spatial indicator of segregation covering the dimension of clustering [12, 13]. These analyses has been conducted for annual snapshots from 2008-01-01 up until 2022-01-01.

Overall, urban OSM building completeness is estimated at 38% globally. Our results emphasize that although the well-examined Global North - Global South bias in OSM still exists, over the past years mapping has spread substantially across the globe and within regions. The analysis of the spatial clustering of high completeness values and low completeness values disclosed that global spatial inequality in OSM building completeness has sharply increased between 2008 and 2014. This shows that although overall OSM building completeness became more even in the same period, mapping activity in that time favoured cities which were located close to other cities which were mapped already. One might interprete this as a reinforcing effect. Ongoing mapping in one area triggered even more mapping in surrounded areas. At the same time this also indicates that up until 2014 the expansion of OSM mapping to distant and un-mapped regions (likely to be located in the Global South) didn’t happen at a significant scale.

Nevertheless, since 2014 Moran’s I global spatial autocorrelation declined and was measured at 0.55 as of 2022. Combined with the decrease of the Gini coefficient in the same time, this suggests that OSM building completeness has become more even because mapping activity has been expanded to regions which were previously mapped much less. In that regard, OSM building data as of today was much less segregated in terms of both dimensions (evenness and clustering) compared to the state-of-the-map in 2014. This process was to a limited extend positively in