Address matching bias: ignorance is not bliss

Abstract

PurposeThe purpose of this paper is to show that despite the critical importance of using accurate data when identifying geographic patterns and studying hotspots, few have explored the data quality issues introduced by Geographic Information Systems (GIS) software applications. While software manufacturers provide some information about the address matching process, critical details are left out or are buried in technical, and sometimes proprietary, jargon. The purpose of this paper is to address these issues.Design/methodology/approachThe paper demonstrates, with three datasets of 100 cases each, how the assumptions built into popular GIS software produce systematically missing data during the data importing process commonly referred to as address matching.FindingsInclusion of directional indicators and zip codes are more important than previously thought. The results highlight the critical need to provide complete descriptions of research methodology. All geographic analyses must be accompanied with: information about the hit rate (percent of cases plotted), details about the software and process used to import tabular crime data, information about the software parameters set for the importation process (geocoding preferences), reference information about the street file used; and, an examination of the missing cases to identify some of the sampling error. When forecasting crime issues or identifying hot spots, analysts must be cognizant of the differential impact this bias will have on the generalizability of the results.Originality/valueThe paper explores previously neglected issues in data quality introduced by GIS software applications.