Neighborhood Data Types
We organize informal neighborhood data making it practical to use and simple to obtain.
Our neighborhood data includes the following: boundaries, names, relationships, and corresponding ZIP/postal codes. Additional data, such as demographics and business listings are also available.
Boundaries
Neighborhoods are informally defined. They are not defined by municipal governments, but defined for historical or cultural reasons. For example, downtown usually refers to a city’s commercial center, while New York’s TriBeCa is short for the “Triangle Below Canal Street.” There is no hard and fast rule that dictates how neighborhoods are defined or named.
Due to the informal nature of neighborhoods, boundaries can sometimes become fuzzy. While it may seem like a good idea to draw hard boundaries between neighborhoods, exclusive boundaries don’t accurately describe locations situated on or close to the boundary.
Fuzzy algorithms account for informal and organic boundaries when neighborhoods overlap
Urban Mapping’s neighborhood data defines boundaries in a way that accounts for the informal nature of urban geography. Through recognizing that a location can technically be in two or more neighborhoods, we are able to eliminate binary boundaries and replace them with conditional boundaries. These conditional boundaries incorporate the fuzzy space that exists between neighborhoods and where neighborhood overlap occurs. This results in neighborhood boundaries that are not only more realistic and accurate, but also more in tune with the way people view informal space.
Names and aliases
A neighborhood may have multiple names. For the most part, these multiple neighborhood names can be grouped into two categories: synonyms, names that are equivalent; and exonyms.
Exonyms are names that are not used by locals. For example, Bastille Quarter is the English exonym for Quartier de la Bastille in Paris, France.
Urban Mapping’s neighborhood data accounts for both synonyms and exonyms.
Relationships
Some neighborhoods overlap, contain, or border other neighborhoods. Urban Mapping’s API categorizes neighborhood data into a hierarchical structure. This hierarchy will therefore determine the dominant neighborhood definition.
Dominance example: Although X resides in both NoHo and SoHo, SoHo is the dominant neighborhood because it is more significant
The dominance factor is established by Urban Mapping’s network of researchers, who use their local expert knowledge to gather data and analyze historical/cultural trends. This information is then normalized and the dominance factor is determined.
ZIP and postal codes
Although neighborhood boundaries are different from ZIP and postal code boundaries, our Neighborhood Database includes ZIP/postal code information. This information is useful for direct mail marketing campaigns and for tying online marketing efforts to offline activites.
