Data Verification Methodology to Facilitate Employment Database Updates for Transportation Planning. Issue 5 (May 2022)

• Record Type: Journal Article
• Authors: Kramer, Eric; Noh, Hyunsoo; Li, Xiao
• Is Part Of: Transportation research record. Volume 2676:Issue 5(2022)
• Page Start: 560
• Page End: 570
• Publication Date: 2022-05
• Subjects: data and data science -- urban transportation data and information systems -- data analysis -- data cleaning -- data validation; Transportation -- Periodicals; Roads; Transport -- Périodiques; Routes -- Périodiques; Routes -- Conception et construction -- Périodiques; Roads; Transportation; 388.05
• DOI: 10.1177/03611981221090243
• Languages: English
• ISSNs: 0361-1981

Abstract

Researchers on travel behavior and regional economic trends increasingly rely on multiple data sources to locate employers and site-specific employment. In a previous study, we proposed a method to assess and integrate multiple sources of employment data using three components: the Google Places application programming interface (API), a business existence verification model, and manual reviews of sampled data. This paper updates our previous methodology with a dual conditional classification of incoming and previously verified employment data made possible by checks using Google Places API and two rounds of string comparisons for both business names and establishment locations. The resulting match classes distinguish well-matched or confirmed business listings from those that require additional review to evaluate potential business closure or relocation. This screening process, augmented with fuzzy logic string matching techniques, reduces the effort needed to update employer information and assists with automated data standardization and deduplication, integrating incoming employment information with a database of verified employers.