Both companies and individuals have many factors to consider when choosing a location for a home or new business. Considerations could include cost, footfall and proximity to other places of interest.
The TravelTime platform allows users to visualise a travel time area on a map. Travel times are calculated by different modes of transport to multiple locations. This tool can help users make long-term decisions about location, such as:
- Where to buy a new home
- Where to place a new office
- Where to hire new staff
- Where to look for a new job
The TravelTime was used as part of a data visualization project at KU Leuven University in Belgium. The team at KU focused on young families looking for their first home. They used the TravelTime API to help these families decide where to live.
Families were able to visualise their travel times to unique places of interest. This visualization allowed them to find the ideal location in which to settle down. For example, each parent could enter the postcode of their office. They could also enter their maximum travel time and preferred a mode of transport. Parents could then view areas that fell within the travel time to work for both of them.
Families could also view this data in relation to local schools in the area. Parents were able to search by travel time to different types of educational institution. For example, an area might have a nursery close to a train station. In this case, a parent could see that they could walk their child to the nursery before catching the train to work.
The KU team were able to show families the ideal region to live in by creating a map that was personal to the user. These types of visualization can lead to an increased conversion rate. Users are only shown results that are relevant to their unique set of circumstances. This means that they are more likely to get in touch.
Businesses often have to handle a large volume of location data. This makes it difficult to see which locations are best suited to the circumstances. Isochrone data visualization helps simplify this process because visuals are easier to understand.
For example, when moving home, future homeowners have to assess a lot of factors. They may consider the number of bedrooms, local schools and their daily commute. Allowing users to picture where they can reach simplifies this process.
For educational institutions, it can be difficult to have an overview of available spaces for different types of education. It is often the case that certain schools are oversubscribed. Catchment areas also involve complex management.
The TravelTime allows users to view catchment areas. They could then layer their map to see the spread of school children. This could include the students’ ages and parents' travel times. When viewed together, this information can help educational institutions to make strategic decisions.
The TravelTime can give users an understanding of the coverage of schools. This can help institutions to make decisions, such as:
- where to open new schools
- where to improve failing schools
- where to redraw the boundaries to relieve pressure
The team at KU Leuven used the TravelTime in two ways. Firstly, they used the time maps with a single origin and a specified arrival time. This was for individual travel time searches requested by the families. Secondly, they used the same function to calculate a set of travel time data ahead of the project. This was to make it easy for the families to search by education type and travel time.
For the individual searches, the user could enter any location, mode of transport and maximum travel time. The team at KU always set the arrival time to be a weekday at 8:30 am to align with school and work start times.
The student tool would then request TravelTime data for this information. These results were returned on a map presented to the user. The team at KU then calculated overlap to show ideal regions.
The KU team also calculated data sets for educational institution information in advance. They started with a dataset containing information on the Flemish education system. This dataset contained all schools, their locations, contact information and details on the available curriculums at these schools. It also contained data on all types of education - primary and secondary school, higher education and special education.
Using the TravelTime API, the students calculated the locations that could be reached from these school within 5, 10, 15, 20 and 30 minutes. They made this calculation for all modes of transport. The user could then search by these criteria. The search data would be loaded from the pre-generated datasets. This could be overlapped with other data (places of interest or other education data) in order to determine the ideal region for the families to live in.
Wouter Beert, a member of the KU Leuven team that used the TravelTime API says:
“The TravelTime had everything we needed and it was easily accessible. The API was the key to our project as it allowed our users to visualise different layers of data and put complex information into context. Our project shows just how useful the TravelTime API can be in the visualization of data”.
The team at KU Leuven used the TravelTime API to create isochrone maps that provide powerful visualizations of information. This will allow educational institutions to make strategic decisions about school locations and catchment areas. It will also help individuals make informed decisions about where to work or live. Get more information on making isochrones.