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Wu, Z, Li, H., Zipf, A. (2020). From Historical OpenStreetMap data to customized training samples for geospatial machine learning In: Minghini, M., Coetzee, S., Juhász, L., Yeboah, G., Mooney, P., Grinberger, A.Y. (Eds.). Proceedings of the Academic Track at the State of the Map 2020 Online Conference, July 4-5 2020. Available at https://zenodo.org/communities/sotm-2020

The concept of place has recently gained importance in geographical information science (GIScience). One reason for this is the emergence of user-generated geographic information, which partially represents subjective everyday geographical encounters. No consensus, however, on how to deal with place in GIScience has yet been reached. This paper presents a systematic literature review providing an overview of how parts of the GIScience community currently use the concept of place as it is understood in human geography. The results suggest that most place related GIScience scholars refer to the humanistic tradition of geography focusing on the essence of experiences of place. Further, it is found that geotagged data published online are a major driver of place-based research, whereas scientific data (e.g., surveys) are less commonly found in respective papers. Many researchers make use of exploratory approaches, which may reflect the early stage at which place-based GIScience research still sits. We also identify a difference between the approach core members of GIScience take and those working on the edge of the field. Thereby, the former often work more conceptually than the latter. The results of this preliminary review inform the current GIScience discourse on place by important evidence about the intellectual standpoints of GIScience scholars, thus fostering future research into place.

Ludwig et al. (2019). Assessing the Completeness of Urban Green Spaces in OpenStreetMap In: Minghini, M., Grinberger, A.Y., Juhász, L., Yeboah, G., Mooney, P. (Eds.). Proceedings of the Academic Track at the State of the Map 2019, 21-22. Heidelberg, Germany, September 21-23, 2019. Available at https://zenodo.org/communities/sotm-2019 DOI: 10.5281/zenodo.3387701

Lorei et al. (2019). Characterizing player types in gamified geodata acquisition - An exploratory analysis of StreetComplete In: Minghini, M., Grinberger, A.Y., Juhász, L., Yeboah, G., Mooney, P. (Eds.). Proceedings of the Academic Track at the State of the Map 2019, 33-34. Heidelberg, Germany, September 21-23, 2019. Available at https://zenodo.org/communities/sotm-2019 DOI: 10.5281/zenodo.3387723

Grinberger et al. (2019). Analyzing the spatio-temporal patterns and impacts of large-scale data production events in OpenStreetMap In: Minghini, M., Grinberger, A.Y., Juhász, L., Yeboah, G., Mooney, P. (Eds.). Proceedings of the Academic Track at the State of the Map 2019, 9-10. Heidelberg, Germany, September 21-23, 2019. Available at https://zenodo.org/communities/sotm-2019 DOI: 10.5281/zenodo.3387671

Klonner et al(2019). “Ohsome” OpenStreetMap Data Evaluation: Fitness of Field Papers for Participatory Mapping In: Minghini, M., Grinberger, A.Y., Juhász, L., Yeboah, G., Mooney, P. (Eds.). Proceedings of the Academic Track at the State of the Map 2019, 35-36. Heidelberg, Germany, September 21-23, 2019. Available at https://zenodo.org/communities/sotm-2019 DOI: 10.5281/zenodo.3387725

Knowing the location of the main entrance is significant in accurate navigation. Current LBS providers, such as Google Map often guide users to a wrong location that is far away from the main entrance. This is an unpleasant experience especially for the mobility-impaired people. Realizing the importance of the entrance information, many buildings on OpenStreetMap (OSM) have been tagged with the entrance. However, the proportion is still small. To mitigate this gap, this work proposes using machine learning to inferring the location of the main entrance of a building based on its spatial contexts (e.g., main road) and its footprint (e.g., centroid).

Current indoor mapping approaches can detect accurate geometric information but are incapable of detecting the room type or dismiss this issue. This work investigates the feasibility of inferring the room type by using grammars based on geometric maps. Specifically, we take the research buildings at universities as examples and create a constrained attribute grammar to represent the spatial distribution characteristics of different room types as well as the topological relations among them. Based on the grammar, we propose a bottom-up approach to construct a parse forest and to infer the room type. During this process, Bayesian inference method is used to calculate the initial probability of belonging an enclosed room to a certain type given its geometric properties (e.g., area, length, and width) that are extracted from the geometric map. The approach was tested on 15 maps with 408 rooms. In 84% of cases, room types were defined correctly. It, to a certain degree, proves that grammars can benefit semantic enrichment (in particular, room type tagging).

heiMAP is a Virtual Research Environment (VRE) for research in the Humanities that facilitates collaborative work in spatio-temporal contexts. The differentiating characteristic of heiMAP is its holistic approach, representing the entire scientific data lifecycle. This includes the generation of, as well as discourse about, spatio-temporal data, their publication, archiving and sustainable reuse. The VRE consists of an Open Source Content Management System (CMS) that handles data and research projects, and an integrated Web Geographic Information Systems (GIS) application used to contextualize vector and raster data, especially maps. By sticking to international standards (OGC, CIDOC CRM among others), we strive for a maximum of data interoperability and reusability of the data produced within heiMAP. Close cooperation with the Heidelberg University Library and Heidelberg Computing Center allows for the publication, referenced by a Digital Object Identifier (DOI), as well as the long-term archiving of individual research projects and their related outcomes. One major challenge in building an environment for collaborative research is to ensure that users retain control over their data where necessary but are also enabled to share it with others when they wish to. Therefore, heiMAP possesses a sophisticated system for user and rights management as well as fine-grained control of user rights based on the CMS. To allow a broad range of users from different parts of the Humanities (including both University and Citizen Science projects) with varying degrees of expertise in spatial research and GIS, to effectively use heiMAP, the platform focuses on a number of core functions while allowing export of research data into more sophisticated desktop-based GIS programs when necessary.

Finding the shortest path through open spaces is a well-known challenge for pedestrian routing engines. A common solution is routing on the open space boundary, which causes in most cases an unnecessarily long route. A possible alternative is to create a subgraph within the open space. This paper assesses this approach and investigates its implications for routing engines. A number of algorithms (Grid, Spider-Grid, Visibility, Delaunay, Voronoi, Skeleton) have been evaluated by four different criteria: (i) Number of additional created graph edges, (ii) additional graph creation time, (iii) route computation time, (iv) routing quality. We show that each algorithm has advantages and disadvantages depending on the use case. We identify the algorithms Visibility with a reduced number of edges in the subgraph and Spider-Grid with a large grid size to be a good compromise in many scenarios. (VLID)4340075