Learn about Local Logic Location scores

Local Logic

Local Logic

Location data shouldn’t play hard to get.

6 min read

Jul 22, 2021

Local Logic was founded on two beliefs:

  1. Location has a huge impact on the way that cities are developed and how their citizens live their lives: Where you live shapes which transportation modes you’ll use, what kinds of people you’ll befriend, what ideas you’ll be exposed to. Where you set up your store determines who’ll shop there, what kind of clients you’ll attract, how much money you’ll make. Where you build a new transit station impacts how many people will use it, how the land around it will develop, how nearby transportation patterns will change.
  2. Communicating the value of location is hard. People have long known that some locations are much more valuable than others, but quantifying that value has been challenging. Valuing more tangible things is straightforward: it is obvious that, all other things being equal, a 3-bedroom house is more valuable than a 2-bedroom one. But location is different; it is not necessarily obvious why a property on one street might be more valuable than a similar property two streets to the south.

    There are a few reasons for this:
  • Location is multi-faceted. Things like distance to amenities, transportation options, demographics, street appearance, noise levels, and so on all factor into the value of a location.
  • Location means trade-offs. There is no perfect location. It’s convenient to live close to a highway if you travel frequently, but then you have to deal with the noise. It’s great to have frequent transit within walking distance, but that requires a high population density, which means less space for everyone. As you get closer to one amenity, you get farther from other ones.
  • The experience of a location can be hard to explain. If you walk down two commercial streets in two different cities, you will likely prefer one over the other. However, you may not be able to articulate why you prefer it. Numerous factors play into these kinds of preferences - everything from the architecture to the consistency of the building setbacks to the building heights to street widths to tree sizes to traffic volumes. It’s hard to pin down why a location evokes a certain feeling, positive or otherwise.
  • Different people value different aspects of location. A family with young children might be well served by being close to schools. A college student might want to live close to bars. A dress shop might benefit from being near other clothing stores. A factory might need a railway or an airport nearby to ship goods. Everyone wants different things in a location.

For all these reasons, it’s hard to put a clear value on a location.

This is why we built our Location Scores: to quantify what seems unquantifiable.

Our Location Scores API allows quick context to be displayed for many factors

Location scores

Our location scores are a collection of numerical ratings that evaluate different aspects of a location - things like transit quality, access to restaurants, and quietness.

We have 17 scores in total, across three broad categories, learn more about them here.

We did not develop a single, unified score to rate a location, as not everyone values the same things in a location.

This PDF describes what the values of 0-10 mean for their respective scores.

Scale of analysis


Some of our competitors have built their own scores that assess location characteristics. Often, these scores are calculated at the neighborhood level, as shown below:

Screenshot of neighbourhoods

This is fine for a high-level analysis, but there can be substantial variation within a single neighborhood.

Grid cells

Other competitors, like WalkScore, calculate their scores by placing a grid over a city and calculating one value per grid cell. This approach offers increased granularity over a neighborhood-based approach, as well as mathematical regularity.

Screenshot of gridcells

However, this approach is divorced from the reality of a city: grid cells don’t correspond to any meaningful location that you interact with. Local Logic’s philosophy is that to properly quantify a city, you need to work with its own unique geography, rather than imposing an abstract logic on it.

Street segments

Instead of these two approaches, Local Logic calculates its scores at the street-segment level. A street segment is a section of a street between two adjacent intersections; for example, Lexington Ave in New York City between 42nd Street and 43rd Street. In the following image, the red lines are street segments, starting and ending with white-dot intersections:

Screenshot of segments

Using street segments as the base unit of analysis allows for a high level of granularity: we can easily distinguish the strengths & weaknesses of two streets that are very close, such as a busy commercial street on one side of a block and a calm residential street on the opposite side. Additionally, street segments are tangible: we experience the public realm of cities primarily at the level of the street, making the street segment a meaningful, relatable reference point. Comparing the map below, which demonstrates Local Logic’s street segment approach, with the maps above using the neighborhood and grid cell approaches, we can see that our street segment approach provides a more nuanced and granular perspective on what it’s like to be in any given place in a city.

Screenshot of segments

Use in our products

Location scores are very versatile, and are at the core of most of Local Logic’s products.

Local Content

In Local Content, users can drop a pin on a map and see how the address in question ranks on each of our 17 scores. When users click on any one of the scores, relevant points of interest are displayed on the map, allowing the user to understand where the score comes from and to explore the specific POIs in question.

Screenshot of Local Content

Learn more about Local Content

Local Maps

In Local Maps, users can select one or more of our scores to visualize the spatial distribution of scores throughout a city. For example, by selecting the transit-friendly and high school scores at the same time, a user can explore how regions of a city, all the way down to the street segment, score on the combined index chosen by the user.

Screenshot of Local Maps

Learn more about Local Maps

Local Profiles

Our Local Profiles product generates text descriptions of individual neighborhoods. Behind the scenes, the product converts numerical location scores into text descriptions and enriches them with details of relevant points of interest, amenities, and services.

Screenshot of Local Profiles

Learn more about Local Profiles

Lifestyle Match

Location scores are combined to allow users to create their own personalized, unified location score for each property in a real-estate search:

Screenshot of Lifestyle Match

Learn more about Lifestyle Match

Local Analytics

Location scores are combined with other factors such as demographics and market data to help real-estate developers find an ideal place to build. We have also added market data, and more data that impacts business critical decisions.

Screenshot of Local Analytics

Learn more about Local Analytics