An Introduction to the Storefront Index


Cortright and Mahmoudi acquired their data through a third party, but Oxnard’s data portal was all I needed, although some additional preprocessing steps were required. I had to convert the Business Data dataset’s 11,000 business licenses into a list of storefronts. Doing this required de-duplication and manual curation of some categories, but the end result was a list of Oxnard’s 1,200 storefronts.

Figure 1: filtering out Oxnard’s neighborless and distant storefronts


After performing the steps described in the methodology section, the original count of 1,200 storefronts was reduced down to 963. This final number represents Oxnard’s own storefront index score. Surprisingly, the score of 963 is competitive with the quantities attributed to larger cities (circa 2014) in Cortright and Mahmoudi’s research, including the cities of Austin and Pittsburgh. This number may be deceiving, however, due to the use of different datasets.

Figure 2: Strip malls influence the index.


While the Storefront Index is supposed to measure economic strength, it is also supposed to shed light on vibrant communities. In this sense, fast food restaurants and big box retailers don’t contribute as much to a neighborhood’s character as do independent bookstores, pinball arcades, and bars. The Storefront Index would need to acknowledge this difference to better reflect neighborhood quality.



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Zachary Kitt

Zachary Kitt

Writer of code. Interested in data-driven policy. Graduate of @JacksonYale and @UCSBGlobal.