Targomo’s benchmark service enables the visualization and analysis of multi-factor reachability metrics, allowing one to paint custom, precise view of the character of an area.
Reachable vs. stationary
The benchmark service consists of a base statistics layer, which is used to pre-generate full-coverage reachabilty values for individual statistics as well as points of interest (POI). The specific analysis is based on the reachability of a statistic value or POI as measured from an individual statistics cell. Wheras general statistics and point-in-polygon style coverages show the value which is present within the boundaries of the cell in question, Targomo’s service calculates individual reachability catchements for every single cell and every transport mode, and stores these values for reference and re-use.
What this means is that instead of asking “what is the population by 250m cell for Oslo?”
We can now ask “what is the reachable population by car in 10 minutes for 250m cells for Oslo?”
Or “what is the reachable population by foot in 20 minutes for 250m cells for Oslo?”
Additionally, we can visualize POI data in place of the population data; restaurants reachable within 10 minutes on foot in this case
Although reachable population by itself is interesting, the truly powerful feature of the benchmarks service is that different metrics can be combined to create complex characterizations of an area based on the reachability of the surroundings.
If we are trying to characterize a lifestyle or type of person, we will want to consider many different factors. For example, if we wanted to visualize “access to eating out” we might want to know where areas are which are close to cafes by foot, and close to restaurants by car.
But wait - cars have a much further reach, so we are really seeing the effect of car/restaurant reachability stronger than cafe. If we weight restaurants reachable by car by 0.25, we see the effect of the cafes still show through.
There are several interesting use-cases for the benchmark service:
Generate and visualize lifestyle scores
A specific target population most likely has multi-factor groupings of data that characterize an idea that is interesting to them. By showing these groups where there are areas of a high incidence of their “lifestyle”, they can feel more connected to, and empowered about the true nature of the location in question. Examples of these lifestyle scores:
- young parent: reachable proximity to childcare, kindergartens and pediatricians could help a young parent feel more confident about an area where they are hoping to live
- tourism: reachable proximity to museums, beaches, restaurants could help a vacation apartment searcher click through more quickly
- nigthlife: access to pubs, nightclubs, fast food and a population between 20 and 30 could motivate a young apartment hunter that the area is where it’s at, or help an older retiree know they might find it too noisy.