Apple, Microsoft and Facebook are contributing to OpenStreetMap with data validation and AI-assisted image tracing to improve its quality and completeness. These projects were presented at the State of the map conference in Milan last month, according to the Open Data Institute.
OpenStreetMap (OSM) is created by over three million licenced contributors. Besides the stereotype hobby cartographers digitising their local area from the bedroom, an increase in contributions from multi-national commercial organisations will increase the level of professionalism and quality.
So will this affect the suitability of OSM for city wayfinding mapping? At T-Kartor we have developed city wayfinding systems based on both city-owned / licenced databases (Legible London; WalkNYC; Toronto TO 360) and royalty-free sources, including OSM (Interconnect West Midlands; NTA Ireland). We asked our technical lead, Matthew Archer, to explain some of the issues.
“There is no out-of-the-box dataset which is ready for use as a city wayfinding basemap. Our city wayfinding basemaps are crafted from many sources in a UCD (User Centric Design) process. Usability, legibility and the city’s identity are at the centre of this process.”
“Cities almost always have their own GIS data or a license to use publically financed source data. Despite this, many of our customers are interested in the possibility of building a royalty-free wayfinding basemap. This can future-proof their system by enabling city maps to be freely distributed, for example digitally and commercially. For this reason OSM is always worth consideration.”
A look into the separate parts of the basemap will explain how we evaluate source data.
The first stage of building up a wayfinding basemap is to create a detailed ’topographic’ base, which defines land use such as building, road, footpath, green areas or water.
Brooklyn Bridge access road, NYC, is a complex area for pedestrians to navigate. OSM data (left) and the WalkNYC basemap (right)
Waterloo Station, London, is complex due to multiple entrances on many levels. OSM data (left) and the Legible London basemap (right)
Clear presentation of topography in complex pedestrian environments is essential. The map is intended to aid decisions about the best way to navigate obstructions or to find access points. In the examples above, busy road junctions, barriers to walking and multiple levels make these areas difficult (dangerous even) to traverse on foot.
Unfortunately, OSM is not sufficiently detailed and consistent for such areas, so we usually use the licenced data, which is geometrically more detailed and consistent for producing detailed base geometry. Even this data requires graphic tidying and simplification to be fully fit for purpose.
Where a smaller scale, more generalised result is acceptable (e.g. the NTA Ireland example below), OSM is satisfactory due to the reduced level of detail. In this case OSM provides a much lower cost mapping option which can be rolled out over larger areas for regional transport information. NTA Ireland aims to extend this Dublin basemap for use across the whole country.
A wayfinding basemap for NTA Ireland presents a smaller scale area map around 67 Dublin LUAS tram stops for this generalised mapping (right) OSM is an adequate source (left)
Once the topographic basemap is complete, content layers can be generated. These are the named features that add interest and legibility to the map. There are so many possible features that an editorial selection process is required involving many stakeholders such as local authority departments, local interest groups and end users.
Content layers, such as places of interest (POIs) and landmarks, are typically sourced from diverse datasets. Many of these are collated and maintained by local authority custodians and are therefore highly accurate, updated and comprehensive. Examples are data from the schools board, tourism and fire department.
As a source of POI features OSM can provide lots of this input, but OSM requires improved classification, often needs updating and suffers from inconsistent naming conventions. Correct placenaming and hierarchy are essential components of city wayfinding mapping.
Having said that, OSM is a very helpful supplimentary source of data. Cycle infrastructure data, for example, is typically hard to find as the current trend to prioritise cycling in cities is fairly recent. OSM data can help here, being typically generated by enthusiasts, and therefore well surveyed.
Layers of symbology showing transport information are central to a city wayfinding system, integrating walking and cycling with public transport journeys. Public transport departments are therefore a key stakeholder, if not the owner, of the wayfinding system. As such, this data is sourced directly from the relative departments and rigorously maintained. Compared to this level of updatedness and accuracy, OSM has little value to add.
An interesting application of OSM source data could be the development of mobile digital wayfinding basemaps. The small screen area favours small scale, generalised mapping, mitigating some of the detail issues applying to large, printed maps. In addition, the map presentation is created and labelled ‘on-the-fly’ so precision is not required for crafting the position of names and icons. The digital basemap will be royalty-free which can also be an advantage.
Mobile apps could use smaller scale mapping where royalty-free OSM could be an advantage
We regard the interest in OSM from commercial organisations as good news for city wayfinding. As OSM becomes more popular, and wealthy corporations decide to work on improving it, the quality and consistency will improve. It will rapidly become a more reliable source of data, requiring less checking and correcting. This will make OSM more interesting for cities wishing to create and distribute map products based on their unique content and identity.