Transforming addresses into geographic coordinates (geocoding) is not just a technical process, but a vital one for precise location data applications. We require exact spot data, making the conversion of addresses into map points a critical step for apps like the HealthPorta API. The local geocoding system is synonymous with enhancing the speed of geo-search functionalities. This is particularly crucial for services that rely on high location precision. In essence, we need to obtain the coordinates of the address or someplace locally to enable quick and efficient searches around that location.
Internal geocoding is important for a platform like HealthPorta API, as we focus on delivering precise healthcare location services in less time. The integration of a geocoding system directly into the framework, HealthPorta API offers more efficient, location-based search capabilities. The functionality is indispensable for users who need to find healthcare services within specific geographical boundaries quickly and accurately, for instance, by searching for the closest pharmacy or doctor.
We use PostgreSQL, so the best solution to avoid creating a zoo of different technologies is to combine the search by provider types and their health insurance coverage with the geo-search. PostgreSQL has PostGIS as the particular extension that works with GEO data. It provides special data types and indexes for Geometry and Geography operations. Additionally to the data types, we need the actual data about all streets, cities, and states. As an official source, the TIGER database provided by the U.S. Census Bureau is an essential data source for local geocoding. It doesn’t look hard initially, as most of the work is done by others, and you need to send the pack of commands to get it locally. However, some difficulties still need to be addressed, as you need to import every state separately, and running this operation manually is quite dull. To keep the data current, you will need automation in your operations.