Extracting large-scale POI data for a defined land area through APIs

Locations that act as prominent attractors for humans can be defined as the Point of Interests (POIs). Restaurants, schools, and transit terminals are some excellent examples of it. Historically, these have been added to the maps utilizing symbols and labels by cartographers, and the increasing digitization assisted the institution of digital maps and the POIs. Contemporarily, POI data are provided by several Location Based Services (LBSs), comprising comparative advantages and disadvantages. However, all these sources provide the data for the users through the different requests attached to their Application Programming Interfaces (APIs). In this context, APIs limit the data extraction to balance the distribution of its resources and its pricing strategies which halt the users from achieving their highest potential in different applications accompanied by POI data. To this end, this study proposes a methodology to collect the POI data for a defined land area in large and store it locally for further analytical utilizations. This approach performs the iteration of API calls that can be used for a defined land area efficiently and cost-effectively. It was tested for an area of 49km
²
in a country in the South Asian region and could extract 50,207 POIs with an average API calling time of 3 seconds. In future, application users of POI data are beneficial through this approach in conducting their tasks without bounding to the typical limitations. The implementation of the proposed methodology has been made publicly available at http://github.com/dineth33/Extracting-1arge-sca1e-POI-data