This presentation describes the ongoing work at the Urban Analytics Lab at the National University of Singapore, developing novel methods to assess building completeness at a multi-country scale, as part of a broader project of generating 3D city models on a large-scale using OpenStreetMap.

Non-government organizations and local government units use geographic data from OpenStreetMap (OSM) to target humanitarian aid and public services. As more people start to depend on OSM, it is important to study data completeness in order to identify unmapped regions so that OSM volunteers can focus their attention on these areas. In this study, we propose a method to measure the data completeness of OSM building footprints using human settlements data.

Contributors of OpenStreetMap data for Mozambique, a country in Southern Africa, were classified into four distinct groups. The most active group included 25% of all contributors, most of them long-term contributors, and most features were last edited by members of this group. One can therefore conclude that the quality of the data is likely to be good, however, it lacks in completeness and the number of edits per feature is low. Even though no absolute statements about data quality can be made, the analysis provides valuable insight into the quality and can inform efforts to further improve the quality.

The “localness” of data is often described as a major factor for the authenticity of (geo-) information in OpenStreetMap. However, the exact meaning and relevance of “localness” remain controversial. We compare proposals made for the “measurement”, i.e. for the empirical operationalization, of “localness”. Based on this, two convincing operationalizations were selected and implemented in order to contrast regional differences in “localness”. Our analysis allows the identification of regions in which exceptionally high proportions of data are mapped remotely – mostly regions in the Global South. Bearing this in mind, we discuss how “localness” is negotiated in the OSM community.

Recently, OpenStreetMap (OSM) shows great potentials in providing massive and freely accessible training samples to further empower geospatial machine learning activities. We developed a flexible framework to automatically generate customized training samples from historical OSM data, which in the meantime provide the OSM intrinsic quality measurements as an additional feature. Moreover, different satellite imagery APIs and machine learning tasks are supported within the framework.

The study contributes towards some best practices of carrying out community mapping exercise and, distribution of results freely on OSM and spatial data portal like MASDAP for further studies or decision making. Thus the study focused on preparing for mapping - what to map, how to map and how to record the data; the mapping exercise itself; downloading and digitizing of data in map production; and how to use the maps to aid in decision making.

This study explores the following research questions using OpenStreetMap-based mapping approach and healthcare facility survey from one of seven slums being studied in Africa and Asia. What are the differentials of spatial proximity to health care providers in informal settlements like slum? What are some of the lessons learnt from using OpenStreetMap-based mapping approach for slum health research? Preliminary findings suggest that residents can access four categories of healthcare facilities (Clinics/Maternity Centres; Patent Medicine Stores; Traditional/Faith Healers; Eye Health Centre) within a walking distance (under 1km) where Clinics and Maternity Centres are farthest from most residents.

Since 2010 organized humanitarian mapping has evolved as a constant and growing element of the global OpenStreetMap (OSM) community. We analyse the history of humanitarian mapping using OpenStreetMap History and OSM Tasking Manager (tasks.hotosm.org) data. We conduct a comprehensive quantitative analysis on a global scale and long term perspective to depict more than just snapshots of individual events. Results show that in regard to edits, users, projects, geographic diversity, almost all of these have experienced linear growth. But regarding user commitment and validation efforts we conclude that the humanitarian mapping community still faces huge challenges to achieve sustainability.

We outline methods for a) extracting the geometry of street blocks in urban centres using
OSM and remote sensing data, b) generating approximate cadastral maps of a
block given contained building footprints, and c) quantifying residents’ ability to navigate within
blocks through topological analysis of cadastral maps. This topological metric, termed “spatial
accessibility” and denoted k = 1, 2, 3, ..., determines whether areas of a city are informal
settlements, as blocks where k > 2 contain cadastral parcels without direct access to formal road
networks. We analysed 1 terabyte of OpenStreetMap data for 120 low and middle income (LMIC) countries.

Today, nearly 17% of the global road network was last edited by a corporate data-team member. We further investigate unique editing patterns among three corporations that have specific, localized impacts on the map.