2019-09-22, 17:30–17:50 (Europe/Berlin), Hörsaal West
OpenStreetMap provides a lot of valuable information about urban green spaces, but the numerous and conceptually overlapping OSM tags that describe such features lead to spatially heterogenous representations in OSM. We developed an exploratory data analysis methodology to identify locally relevant OSM tags for mapping green spaces in a specific area and compared the extracted OSM features to administrative data to evaluate the level of completeness in regard to urban green spaces.
Urban green spaces provide a variety of important ecosystem services such as micro-climate regulation, increase of biodiversity and the provision of recreational and cultural services for citizens. Thus, they are an important factor for the quality of life in cities (Bolund and Hunhammar, 1999). However, in order to take advantage of these services citizens need to have sufficient information about the location and qualities of nearby green spaces. Within the project “meinGrün” we are addressing this issue by developing a web-based recommendation service which helps citizens find suitable green spaces that satisfy their personal needs.
OpenStreetMap (OSM) plays an important role in this project, since it provides a lot of valuable information about urban green spaces such as their location and the amenities they provide (playgrounds, benches, toilets etc.) However, its spatially heterogeneous data quality, especially in regard to the level of completeness, provides challenges for its usage in a recommendation system. Therefore, the integration of OSM data for our purposes requires a prior assessment of the completeness of urban green spaces.
The completeness of certain geographic objects is one of the main fields of investigation in regard to OSM data quality. In recent years several studies investigated the completeness of OSM data with respect to the road network (Barrington-Leigh and Millard-Ball, 2017), buildings (Hecht et al., 2013) or land use features (Jokar Arsanjani et al., 2015). Urban green spaces, on the other hand, were rarely the focus of completeness studies. Ali et al. (2016) developed a method to quantify the plausibility of vegetation-related tags being assigned to specific OSM features and Lopes et al. (2017) evaluated the potential of OSM for extracting information about natural local climate zones.
Since both of these studies do not explicitly address the completeness of urban green spaces, we developed a new methodology for this purpose. In contrast to buildings and highways, this poses unique challenges due to the variety of vegetation-related OSM tags and the many different forms of urban vegetation ranging from large parks over private gardens to roadside greenery. OSM tags that describe natural objects are numerous and sometimes conceptually overlapping e.g. some features could be tagged as leisure=park or leisure=garden. This leads to different representations of urban green spaces in OSM across different geographical regions. Defining one set of relevant OSM tags to measure the completeness of urban green spaces that can be applied everywhere is therefore not possible.
To solve this issue, we developed an explorative data analysis methodology based on OSM and satellite imagery to identify locally relevant OSM tags that indicate urban green spaces. The analysis is based on statistical and graphical methods to evaluate the association between a certain OSM tag and the presence of vegetation. After the relevant tags have been identified, features representing green spaces are extracted from OSM and compared to an administrative data set to assess the level of completeness. As a basis for this comparison, the study area is divided into patches of homogenous land use based on natural and human-made barriers such as the road network, rivers or objects that mark changes in land use (fences, walls, etc.). On this basis, features from both data sets are joined and the level of completeness is assessed using different extrinsic data quality measures.
In our talk we will present our methodology along with the results of the completeness assessment for the City of Dresden, which is a pilot city of “meinGrün”, a project funded by the Federal Ministry of Transport and Digital Infrastructure (BMVI).
Ali, A., Sirilertworakul, N., Zipf, A., Mobasheri, A., 2016. Guided classification system for conceptual overlapping classes in OpenStreetMap. ISPRS Int. J. Geo-Inf. 5, 87.
Barrington-Leigh, C., Millard-Ball, A., 2017. The world’s user-generated road map is more than 80% complete. PLOS ONE 12, e0180698. https://doi.org/10.1371/journal.pone.0180698
Bolund, P., Hunhammar, S., 1999. Ecosystem services in urban areas. Ecol. Econ. 29, 293–301. https://doi.org/10.1016/S0921-8009(99)00013-0
Hecht, R., Kunze, C., Hahmann, S., 2013. Measuring completeness of building footprints in OpenStreetMap over space and time. ISPRS Int. J. Geo-Inf. 2, 1066–1091.
Jokar Arsanjani, J., Mooney, P., Zipf, A., Schauss, A., 2015. Quality Assessment of the Contributed Land Use Information from OpenStreetMap Versus Authoritative Datasets, in: Jokar Arsanjani, J., Zipf, A., Mooney, P., Helbich, M. (Eds.), OpenStreetMap in GIScience: Experiences, Research, and Applications, Lecture Notes in Geoinformation and Cartography. Springer International Publishing, Cham, pp. 37–58. https://doi.org/10.1007/978-3-319-14280-7_3
Lopes, P., Fonte, C., See, L., Bechtel, B., 2017. Using OpenStreetMap data to assist in the creation of LCZ maps, in: 2017 Joint Urban Remote Sensing Event (JURSE). IEEE, pp. 1–4.