Evaluating two freely available geocoding tools for geographical inconsistencies and geocoding errors

Geographic location plays a vital role in a variety of socioeconomic and environmental decisions, such as in selecting sites for new businesses or providing location-based services [1, 2]; detecting, valuing, and defining protected marine areas [3]; locating prospective areas for grid-connected offshore wind power development [3]; identifying disease-prone areas [4]; responding to crime and natural hazards; and locating customer-friendly shopping malls [5]. Geocoding, the process of assigning coordinates (latitude and longitude) to a physical location, has helped various industries improve performance through spatial analysis [6, 7]. However, the accuracy and reliability of geocoded results have always been a matter of concern among the geospatial analytics community [4, 7–10]. Senaratne et al. (2017) provide a detailed review of the various methods applied in assessment of the quality of locational analytics. The authors report that accuracy measurement is the most frequent and reliable technique currently in practice [11]. They define accuracy as the degree of closeness between measured and actual values, noting that it may vary with use of various geocoding tools [11]. Geocoding is highly prone to error for various reasons, including lack of coverage (local vs. global); lack of complete, correct, consistent, and updated reference databases; and the making of inappropriate assumptions [7, 9, 10, 12, 13]. All these may affect match rate and positional accuracy [7]. Additionally, incorrect geocoding may bias the results of spatial analysis, resulting in misclassification of actual physical locations that may adversely affect research outcomes or location-based business decisions [14, 15]. Accordingly, understanding and addressing these geocoding challenges is vital [16]. Yet geocoding processes and error handling have been largely ignored in some studies [14, 15].

Several studies have offered a comparative analysis of various free or subscription-based geocoding services. For example, Karimi et al. [13] have evaluated the matching rate of geocoded addresses using web-based geocoding services, including Virtual Earth, Google Maps, Geocoder.us, MapQuest, and Yahoo Maps. In contrast, Swift and his team members [18] assessed seven commercial geocoding services and one open-source geocoding service—Centrus, Geolytics, ERSI Address Locator, Geocoder.us, Google Earth, Google Maps API, and the Yahoo API and USC Geocoding Platforms, respectively—to match accurately geocoded addresses. The authors selected 50 addresses for this purpose and found that only 42% of samples matched their reference data, 54% of addresses matched parcel centroids, and only 4% addresses matched USPS ZIP code centroids [18]. All the geocoding tools tested produced varying results, indicating that analysts should indeed take care when geocoding physical locations, especially when doing so for purposes of location-based analysis, and should take that same care when selecting geocoding tools in the first place.

This study compares the use of two commonly used free geocoding tools for research and business purposes: Google Sheets, which is a Google offering, and ggmap, which is an R package. No comparison of these tools has yet appeared in mainstream journals.

ggmap is one of the most widely used geospatial R packages in a variety of domains. For example, it was used to geocode helminth (nematodes popularly known as roundworms) host–parasite interactions that helped establish the London Natural History Museum’s Host–Parasite Database [19]; was used in a big-data environment to geocode customer movements from homes to shopping centers [20]; and was used to site locations for implementation of a U.S. federal program offering families and children healthful foods during the summer months, administered by the U.S. Department of Agriculture [21]. Google Sheets, or Google Spreadsheets, by contrast, has gained little attention among geospatial analytics communities, even though it has been applied to an array of domains, such as in the geocoding of socioeconomic historical data for visualization of urban geographies [22] and in the public health domain [23, 24]. Google Sheets provides advantages not seen in ggmap, because it does not require coding and is a web-based application. By contrast, ggmap runs through the RStudio software and requires a sequence of queries; even so, it is widely used and accepted by researchers and professionals the world over.

Although the geocoding tools Google Sheets and ggmap use a common map reference, they produce varying results. In addition, specific formatting, free of problematic characters such as the ampersand, is required for correct geocoding by ggmap. Google Sheets, by contrast, features a user-friendly environment that does more to aid production of reliable geocoding results. Regardless, users of geocoding tools should not wholly rely on whichever tool they use but rather should always verify their results by the methods outlined in this study or by any other established approach. The visualizing of geocoded results on a map using QGIS, ArcGIS, Google Earth, OSM, or R can help in identifying and resolving potential challenges to accuracy. Certainly other factors not covered in this study could also produce erroneous geocoded results, so analysts should carefully evaluate their results and report them in detail, taking particular care when geocoding physical locations in bulk. This study seeks merely to compare the geocoding respective potentials of two freely available geocoding tools for research purposes, not to promote or undermine either of them. Reporting positional accuracy challenges and methods of resolving them can help users of geospatial analytics conduct efficient and accurate spatial analysis.