With geospatial insights, planning departments can improve equity in transportation networks by analyzing their accessibility and proximity to these key locations. Understanding the socioeconomic landscape by adding demographic data to a transportation mapping analysis helps planners to identify gaps in access, as well as barriers to access like unaffordable transit options. While existing infrastructure may have been developed without these issues in mind, the wide array of tools and data now available to planning departments enables communities to increase accessibility and equity in transportation.
Where to get transportation mapping data
To effectively leverage geospatial technology for these transportation use cases, planning departments require high-precision data about the various features involved in their network, as well as about the surrounding environment and population. Sourcing this data can sometimes be challenging, but there are multiple ways to go about getting the information needed for transportation mapping.
First-party data collection
Like any organization, transportation departments usually possess their own first-party data they’ve collected internally. This can include network usage metrics, such as how many people ride a certain subway route each day or how often buses stop at a particular location. First-party data is incredibly valuable for understanding unique characteristics of specific transportation networks, and there are often no other sources of such hyper-local information. However, there are also limitations to first-party data. Many of the relevant datasets planners must layer into their network analysis are not actively maintained by transportation departments, leading them to rely on stale data or else create it themselves. This is often the case with land cover data, an integral part of understanding site suitability and other components of transportation planning.
First-party data can also be derived from previous network plans or maps, but depending on when those plans were created, the data may not be readily usable. For example, a planning department may have the original plans for their community’s sidewalk network on paper, but no digital representation of that network to leverage in geospatial analytics. Not to mention, the paper map is most likely outdated. To get the sidewalk data into a usable format for GIS, and update it to reflect the network’s current state, the department will need to digitize it.
One option for transportation planning organizations to acquire the data they need but do not already have in a usable format is to manually digitize paper maps or imagery. Most GIS programs include a tool for manually tracing and georeferencing real-world features on a raster image, resulting in interactive vector map features that can be used for deeper analysis. While this is an effective way to extract pieces of data from otherwise static sources of information, it is an extremely tedious and time-consuming process at-scale. Manual digitization requires the full attention of a trained GIS professional, making it an expensive task. For reference, San Bernardino County Transportation Authority (SBCTA) worked with civil engineers at Fehr & Peers to manually digitize their over 17,000 miles of sidewalks from geospatial imagery; after six months of resource-intensive work, only 750 miles were mapped.
Even if planners have a full geospatial dataset of their transportation networks, keeping them up-to-date can require manual digitization. As an example, it took the GIS team in Collier County, Florida four years to manually digitize all of the driveways and access roads throughout their county, not accounting for the inevitable change that occurred within that time. While manual digitization can be a helpful tool for spot checking and updating minor gaps in data, it is not a sustainable or efficient way for transportation planning departments to acquire the data they need about the communities they serve.
Thankfully, AI is alleviating many of the challenges transportation planning departments experience when sourcing geospatial data. What used to take weeks, months, and even years of tedious work by a trained GIS professional now takes just a fraction of that time, freeing them up for what really matters - analysis and planning.
Ecopia’s AI-based mapping systems ingest high-resolution imagery from our global partner network and extract the real-world features needed for strategic decision-making at-scale. Unlike other automation tools, Ecopia’s AI-powered technology maintains the same level of quality and accuracy you would expect of a trained GIS professional manually digitizing a map. Using the examples we previously provided, Ecopia was able to digitize 17 layers of detailed transportation features across San Bernardino County in just three months, and all of Collier County’s driveways and access roads within four weeks. Ecopia’s change detection capabilities ensure that any alterations to these features are captured in future updates of the data, keeping GIS teams informed with the most up-to-date understanding of their communities.