One of my favorite German words is bummeln (pronunciation). I like this word not just because of how it rolls off the tongue, but because it describes one of my favorite activities: strolling. Aimlessly walking. Soaking it all in. Bumming around the town. (Francophiles may prefer 'flâneur'. Fine.) When we stroll — aimless, but attentive — we get a real feel for a place: Does it have parks? Do certain businesses predominate? Is it safely walkable? Is there something nearby producing a bad smell, or persistent noise?
This kind of understanding isn’t just for nerds– it can be a real business differentiator. From real estate pricing to site selection, spatial determinants of health and geo-targeting, being able to incorporate data about place leads to much better analyses, models and outcomes. But it’s really hard to do.
Let’s just imagine you acquired some data, like fire stations/protections for sections of Manhattan and Brooklyn. In the screenshot below, the data are aggregated to the zipcode level, as is quite common. Zip code aggregation means that any address in a zip code gets the same value as every other address in the zipcode, which here is just the total count of fire stations in the zip code. If you’re a data person, you can imagine a table where every row is a zip code. A few things of note here: the patchwork-like look– it’s hard to see trends, and the ‘hard’ edges, which imply a discontinuity.
The way the team at iggy thinks about place data is much different, and not coincidentally, closely related to the strolling we described above. Check out the screenshot below, where we’ve identified the strollable area around every address (more precisely, the area one can walk to within 10 minutes in every direction), and then counted up the fire stations in that area. Every address has a (potentially) unique value, because the walkable area may differ from one place to the next. If you’re a data person, imagine a table where every address is a row. The result is more color ramping, visible trends, and clear variation!
Let’s get practical: if I wanted to (naively) insure a house against fire, I’d probably give the same rate to everyone in the same zipcode if I were using the first dataset. But if I were using the second, I’d probably vary rates within zipcodes. The zipcode level, as well as any other made-up administrative boundary such as a census block or even an ‘as the crow flies’ radius-based area (which is quite typical in geospatial analytics), obscures really vital variation that isochrones reveal. This variation is really hard to achieve on your own, it’s powerful, and it’s unique to iggy.
Beyond defining the area that matters when considering place data, we provide a wide range of data that represent place. From the number of restaurants, grocery stores, gyms, or hospitals nearby, to whether there is a body of water accessible, and tons else (See our current data dictionary). We also provide helpful normalizations that help contextualize the data even further, such as area- and population-based normalizations. You can use this data directly in a model, visualize it on a map alongside other data to make a decision, or even associate it as metadata to place-based data in your warehouse for further analysis or product development. You don’t have to source, unify, clean, normalize, store, or transform the data. We take care of all of that for you so you can get back to what you do best.
Lindsay Pettingill, CEO