Reverse geocoding

Reverse geocoding is the process of back (reverse) coding of a point location (latitude, longitude) to a readable address or place name. This permits the identification of nearby street addresses, places, and/or areal subdivisions such as neighbourhoods, county, state, or country. Combined with geocoding and routing services, reverse geocoding is a critical component of mobile location-based services and Enhanced 911 to convert a coordinate obtained by GPS to a readable street address which is easier to understand by the end user.

Reverse geocoding can be carried out systematically by services which process a coordinate similarly to the geocoding process. For example, when a GPS coordinate is entered the street address is interpolated from a range assigned to the road segment in a reference dataset that the point is nearest to. If the user provides a coordinate near the midpoint of a segment that starts with address 1 and ends with 100, the returned street address will be somewhere near 50. This approach to reverse geocoding does not return actual addresses, only estimates of what should be there based on the predetermined range. Alternatively, coordinates for reverse geocoding can also be selected on an interactive map, or extracted from static maps by georeferencing them in a GIS with predefined spatial layers to determine the coordinates of a displayed point. Many of the same limitations of geocoding are similar with reverse geocoding.

Public reverse geocoding services are becoming increasingly available through APIs and other web services as well as mobile phone applications.^[1] These services require manual input of a coordinate, capture from a localization tool (mostly GPS, but also cell tower signals or WiFi traces^[2]), or selection of a point on an interactive map; to look up a street address or neighboring places. Examples of these services include the GeoNames reverse geocoding web service which has tools to identify nearest street address, place names, Wikipedia articles, country, county subdivisions, neighborhoods, and other location data from a coordinate. Google has also published a reverse geocoding API which can be adapted for online reverse geocoding tools, which uses the same street reference layer as Google maps.^[3]

Privacy concerns

Geocoding and reverse geocoding have raised potential privacy concerns, especially regarding the ability to reverse engineer street addresses from published static maps. By digitizing published maps it is possible to georeference them by overlaying with other spatial layers and then extract point locations which can be used to identify individuals or reverse geocoded to obtain a street address of the individual. This has potential implications to determine locations for patients or study participants from maps published in medical literature as well as potentially sensitive information published in other journalistic sources.

In one study a map of Hurricane Katrina mortality locations published in a Baton Rouge, Louisiana, paper was examined. Using GPS locations obtained from houses where fatalities occurred, the authors were able to determine the relative error between the true house locations and the location determined by georeferencing the published map. The authors found that approximately 45% of the points extracted from the georeferenced map were within 10 meters of a household's GPS obtained point.^[4] Another study found similar results in examining hypothetical low and high-resolution patient address maps similar to what might be found published in medical journals. They found approximately 26% of points obtained from a low-resolution map and 79% from a high-resolution map were matched precisely with the true location.^[5]

The findings from these studies raise concerns regarding the potential use of georeferencing and reverse geocoding of published maps to elucidate sensitive or private information on mapped individuals. Guidelines for the display and publication of potentially sensitive information are inconsistently applied and no uniform procedure has been identified. The use of blurring algorithms which shift the location of mapped points have been proposed as a solution. In addition, where direct reference to the geography of the area mapped is not required, it may be possible to use abstract space on which to display spatial patterns.

Footnotes

1. https://software.intel.com/en-us/context-sensing-sdk
2. Danalet, Antonin; Farooq, Bilal; Bierlaire, Michel (2014). "A Bayesian approach to detect pedestrian destination-sequences from WiFi signatures". Transportation Research Part C: Emerging Technologies 44: 146–170. doi:10.1016/j.trc.2014.03.015.
3. Google Codesource reverse Geocoding API
4. Curtis, A. J., Mills, J. W., & Leitner, M. (2006) Spatial confidentiality and GIS: re-engineering mortality locations from published maps about Hurricane Katrina J Health Geogr, 5, 44.
5. Brownstein, J. S., Cassa, C. A., Kohane, I. S., & Mandl, K. D. (2006) An unsupervised classification method for inferring original case locations from low-resolution disease maps Int J Health Geogr, 5, 56