A Hybrid Geocoding Methodology for Spatio-Temporal Data

As tools for collecting data continue to evolve and improve, the information available for research is expanding rapidly. Increasingly, this information is of a spatio-temporal nature, which enables tracking of phenomena through both space and time. Despite the increasing availability of spatio-temporal data, however, the methods for processing and analyzing these data are lacking. Existing geocoding techniques are no exception. Geocoding enables the geographic location of people and events to be known and tracked. However, geocoded information is highly generalized and subject to various interpolation errors. In addition, geocoding for spatio-temporal data is especially challenging because of the inherent dynamism of associated data. This article presents a methodology for geocoding spatio-temporal data in ArcGIS that utilizes several additional supporting procedures to enhance spatial accuracy, including the use of supplementary land use information, aerial photographs and local knowledge. This hybrid methodology allows for the tracking of phenomenon through space and over time. It is also able to account for reporting inconsistencies, which is a common feature of spatio-temporal data. The utility of this methodology is demonstrated using an application to spatio-temporal address records for a highly mobile group of convicted felons in Hamilton County, Ohio.