BACKGROUND: The process of geocoding produces output coordinates of varying degrees of quality. Previous studies have revealed that simply excluding records with low-quality geocodes from analysis can introduce significant bias, but depending on the number and severity of the inaccuracies, their inclusion may also lead to bias. Little quantitative research has been presented on the cost and/or effectiveness of correcting geocodes through manual interactive processes, so the most cost effective methods for improving geocoded data are unclear. The present work investigates the time and effort required to correct geocodes contained in five health-related datasets that represent examples of data commonly used in Health GIS. RESULTS: Geocode correction was attempted on five health-related datasets containing a total of 22,317 records. The complete processing of these data took 11.4 weeks (427 hours), averaging 69 seconds of processing time per record. Overall, the geocodes associated with 12,280 (55%) of records were successfully improved, taking 95 seconds of processing time per corrected record on average across all five datasets. Geocode correction improved the overall match rate (the number of successful matches out of the total attempted) from 79.3 to 95%. The spatial shift between the location of original successfully matched geocodes and their corrected improved counterparts averaged 9.9 km per corrected record. After geocode correction the number of city and USPS ZIP code accuracy geocodes were reduced from 10,959 and 1,031 to 6,284 and 200, respectively, while the number of building centroid accuracy geocodes increased from 0 to 2,261. CONCLUSION: The results indicate that manual geocode correction using a web-based interactive approach is a feasible and cost effective method for improving the quality of geocoded data. The level of effort required varies depending on the type of data geocoded. These results can be used to choose between data improvement options (e.g., manual intervention, pseudocoding/geo-imputation, field GPS readings).
BACKGROUND: The process of geocoding produces output coordinates of varying degrees of quality. Previous studies have revealed that simply excluding records with low-quality geocodes from analysis can introduce significant bias, but depending on the number and severity of the inaccuracies, their inclusion may also lead to bias. Little quantitative research has been presented on the cost and/or effectiveness of correcting geocodes through manual interactive processes, so the most cost effective methods for improving geocoded data are unclear. The present work investigates the time and effort required to correct geocodes contained in five health-related datasets that represent examples of data commonly used in Health GIS. RESULTS: Geocode correction was attempted on five health-related datasets containing a total of 22,317 records. The complete processing of these data took 11.4 weeks (427 hours), averaging 69 seconds of processing time per record. Overall, the geocodes associated with 12,280 (55%) of records were successfully improved, taking 95 seconds of processing time per corrected record on average across all five datasets. Geocode correction improved the overall match rate (the number of successful matches out of the total attempted) from 79.3 to 95%. The spatial shift between the location of original successfully matched geocodes and their corrected improved counterparts averaged 9.9 km per corrected record. After geocode correction the number of city and USPS ZIP code accuracy geocodes were reduced from 10,959 and 1,031 to 6,284 and 200, respectively, while the number of building centroid accuracy geocodes increased from 0 to 2,261. CONCLUSION: The results indicate that manual geocode correction using a web-based interactive approach is a feasible and cost effective method for improving the quality of geocoded data. The level of effort required varies depending on the type of data geocoded. These results can be used to choose between data improvement options (e.g., manual intervention, pseudocoding/geo-imputation, field GPS readings).
Authors: Gerard Rushton; Marc P Armstrong; Josephine Gittler; Barry R Greene; Claire E Pavlik; Michele M West; Dale L Zimmerman Journal: Am J Prev Med Date: 2006-02 Impact factor: 5.043
Authors: Ronit Peled; Haim Reuveni; Joseph S Pliskin; Itzhak Benenson; Erez Hatna; Asher Tal Journal: Int J Health Geogr Date: 2006-01-17 Impact factor: 3.918
Authors: Matthew J Strickland; Csaba Siffel; Bennett R Gardner; Alissa K Berzen; Adolfo Correa Journal: Environ Health Date: 2007-04-04 Impact factor: 5.984
Authors: Julia Green Brody; Ann Aschengrau; Wendy McKelvey; Ruthann A Rudel; Christopher H Swartz; Theresa Kennedy Journal: Environ Health Perspect Date: 2004-06 Impact factor: 9.031
Authors: Qi Yan; Zeyan Liew; Karan Uppal; Xin Cui; Chenxiao Ling; Julia E Heck; Ondine S von Ehrenstein; Jun Wu; Douglas I Walker; Dean P Jones; Beate Ritz Journal: Environ Int Date: 2019-06-20 Impact factor: 9.621
Authors: Julia E Heck; Andrew S Park; Jiaheng Qiu; Myles Cockburn; Beate Ritz Journal: J Expo Sci Environ Epidemiol Date: 2013-11-27 Impact factor: 5.563
Authors: Beate R Ritz; Angelika D Manthripragada; Sadie Costello; Sarah J Lincoln; Matthew J Farrer; Myles Cockburn; Jeff Bronstein Journal: Environ Health Perspect Date: 2009-02-22 Impact factor: 9.031
Authors: James D Hibbert; Angela D Liese; Andrew Lawson; Dwayne E Porter; Robin C Puett; Debra Standiford; Lenna Liu; Dana Dabelea Journal: Int J Health Geogr Date: 2009-10-08 Impact factor: 3.918