A Probabilistic Geocoding System based on a National Address File

Abstract

Abstract. It is estimated that between 80 % and 90 % of governmental and business data collections contain address information. Geocoding – the process of assigning geographic coordinates to addresses – is becoming increasingly important in many application areas that involve the analysis and mining of such data. In many cases, address records are captured and/or stored in a free-form or inconsistent manner. This fact complicates the task of robustly matching such addresses to spatiallyannotated reference data. In this paper we describe a geocoding system that is based on a comprehensive high-quality geocoded national address database. It uses a learning address parser based on hidden Markov models to separate free-form addresses into components, and a rule-based matching engine to determine the best set of candidate matches to a reference file. The geocoding software modules are implemented (as part of the Febrl open source data linkage system) in the object-oriented language Python, which allows rapid prototype development and testing.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 35 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.