“You cannot manage what you cannot measure,” as the saying goes. One of the key frustrations that led to the development of this website—as both a platform and a tool—was the lack of readily accessible and easily understandable transport network maps. Whether in physical form or published online by government agencies, such maps are often difficult to find or use. This gap hampers the work of a wide range of stakeholders: planners and infrastructure managers who need accurate data to make informed decisions; academic researchers who could contribute technical insights, even on a voluntary basis; and the general public, who deserve transparency and clarity in how transport networks are shaped and maintained.
In particular, geotagged transport infrastructure need to be proliferated and used - particularly in cases where no official infrasturcture inventories are kept and made publicly availabe (for several reasons). Getting access requires some level of proficiency when it comes to dealing with platforms which may be able to provide such.
Geotagged transport infrastructure data is crucial for informed planning, investment, and policymaking in the transport sector. By linking infrastructure assets—such as roads, railways, ports, and transit stations—to precise geographic coordinates, stakeholders can visualize spatial patterns, identify connectivity gaps, and assess exposure to climate and disaster risks. This data enables more accurate modeling of accessibility, travel demand, and emissions, especially in rapidly urbanizing or underserved regions. It also supports efficient monitoring of infrastructure development, maintenance needs, and equity in service provision. Ultimately, geotagged data is a foundational layer for building smarter, more resilient, and inclusive transport systems.
As a first step in contributing to improving transport planning in cities (Philippines as a priority), you may find a collection of historical transport network datasets in this website. I had extracted 2015 and 2024 datasets from OSM through the ohsome historical database facility. The files for each city comprises of static images, geojson files which you can directly use in GIS programs such as QGIS, as well as summaries in excel (lengths) for various types of infrastructure (primarily different types of roads).
Of course there are limitations, as these are basedon OpenStreet Map (OSM) data. At its best, OSM offers a freely accessible, continuously updated, and impressively detailed global map database—a democratic counterweight to proprietary geospatial platforms. Its granularity, particularly in urban areas and developing regions with active contributor communities, allows planners, researchers, and NGOs to trace roads, footpaths, bus stops, and bike lanes that might not appear in official records. For cash-strapped institutions, it offers a viable alternative to expensive commercial datasets.
But OSM’s open model is both its strength and its Achilles’ heel. Coverage is uneven, reflecting where contributors are active rather than where needs are greatest. Rural areas, lower-income countries, and less digitally connected communities are often underrepresented. Data quality varies: while some areas boast high precision and tagging consistency, others suffer from outdated entries, inconsistent schemas, or gaps in infrastructure types. Importantly, because OSM is built for general-purpose mapping rather than specialized infrastructure inventories, it may lack the technical rigor or classifications demanded by engineers or national transport agencies.
It is a powerful starting point—flexible, scalable, and transparent—but not a panacea. It serves best when used critically, supplemented with local knowledge or official records, and understood for what it is: a civic commons, not a certified dataset.