The State Changers met to discuss an issue relating to the Google Maps API. The problem was about extracting county data from user zip codes for a nonprofit. This is critical for them because the nonprofit needs to know which county its users are in to obtain certain grants. A difficulty arose when the Google Maps API did not always provide county data, causing some inconsistencies in the data results with some zip codes not being associated with a county.
The API returns a heavily detailed response requiring some data manipulation to get the precise information needed. Strategies for handling this issue involved filtering through the results to target types that include 'administrative area level two' which apparently represents the county data. It was suggested that the data manipulation could be efficiently handled using solutions like Pipe Dream or Xano, with a bit of JavaScript. Dart could also be used but might introduce unwanted complexity. An issue was acknowledged with county categorization since it's not uniformly structured across the United States, hence leading to possible inconsistencies with the Google API results. An alternative suggested solution was to purchase a database of consistent county data related to zip codes. This could be used to bypass the unreliable Google Maps API and would provide more consistent naming conventions for counties. Specific reference was made to a database of 42,000 records that could be purchased for a minimal cost compared to the time and resources spent trying to rectify the Google Maps API inconsistencies. The above meeting would be of interest to those working with data-intensive APIs, as well as organizations working with local grants or demographics who need consistent geo-locational information. Furthermore, developers familiar with JavaScript, Pipe Dream, Xano, or Dart might find the conversation insightful for implementing data filtering across APIs.
(Source: Office Hour 8/24/2023 )
