Because findERnow gives exact directions to the 24/7 emergency facilities in NEDI-USA, we aimed to ensure that coordinates assigned to every facility in NEDI-USA were no more than 1/8th of a mile (approximately one block) from the ER’s actual location.
To achieve this ambitious objective, we embarked on a research-quality “data cleaning.” First, we compared two national hospital databases, resolving thousands of identified discrepancies between the addresses listed in each database through web searches and phone calls to facilities. Second, because only one of those hospital databases contained geographic coordinates in addition to addresses, we sent our edited list of ER facility addresses to a geocoding company, which generated geographic coordinates for each. We then compared this generated list with the coordinates in the national hospital database. All facilities whose coordinates differed by more than 1/8th of a mile between the two sources were marked for further investigation. Third, we used Google Maps to resolve these discrepancies and generate correct geocodes because findERnow utilizes Google Maps. Facility addresses were entered into Google Maps and coordinates were generated using manual geocoding tools available on the Google Maps website.
For the purposes of this project, a geocode was considered to be accurate if the coordinates matched either: 1) the placement of the corresponding hospital symbol or facility outline on Google maps, 2) the image of the facility on Google Maps, or 3) the directions to the facility as reported on the its website. Finally, all coordinates obtained using Google Maps were entered back into the program to ensure they went to the correct facility address. This process yielded the database underlying findERnow. We are extremely confident that it is more accurate than any other database available today.
Through this process, EMNet staff discovered many factors which complicate the accurate generation of geocodes. Below, we list some of the most common: