Sungsoon Hwang, Kashica Webber-Ritchey, Elizabeth Moxley
{"title":"Comparison of GPS imputation methods in environmental health research.","authors":"Sungsoon Hwang, Kashica Webber-Ritchey, Elizabeth Moxley","doi":"10.4081/gh.2022.1081","DOIUrl":null,"url":null,"abstract":"<p><p>Assessment of personal exposure in the external environment commonly relies on global positioning system (GPS) measurements. However, it has been challenging to determine exposures accurately due to missing data in GPS trajectories. In environmental health research using GPS, missing data are often discarded or are typically imputed based on the last known location or linear interpolation. Imputation is said to mitigate bias in exposure measures, but methods used are hardly evaluated against ground truth. Widely used imputation methods assume that a person is either stationary or constantly moving during the missing interval. Relaxing this assumption, we propose a method for imputing locations as a function of a person's likely movement state (stop, move) during the missing interval. We then evaluate the proposed method in terms of the accuracy of imputed location, movement state, and daily mobility measures such as the number of trips and time spent on places visited. Experiments based on real data collected by participants (n=59) show that the proposed approach outperforms existing methods. Imputation to the last known location can lead to large deviation from the actual location when gap distance is large. Linear interpolation is shown to result in large errors in mobility measures. Researchers should be aware that the different treatment of missing data can affect the spatiotemporal accuracy of GPS-based exposure assessments.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":null,"pages":null},"PeriodicalIF":1.0000,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geospatial Health","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.4081/gh.2022.1081","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0
Abstract
Assessment of personal exposure in the external environment commonly relies on global positioning system (GPS) measurements. However, it has been challenging to determine exposures accurately due to missing data in GPS trajectories. In environmental health research using GPS, missing data are often discarded or are typically imputed based on the last known location or linear interpolation. Imputation is said to mitigate bias in exposure measures, but methods used are hardly evaluated against ground truth. Widely used imputation methods assume that a person is either stationary or constantly moving during the missing interval. Relaxing this assumption, we propose a method for imputing locations as a function of a person's likely movement state (stop, move) during the missing interval. We then evaluate the proposed method in terms of the accuracy of imputed location, movement state, and daily mobility measures such as the number of trips and time spent on places visited. Experiments based on real data collected by participants (n=59) show that the proposed approach outperforms existing methods. Imputation to the last known location can lead to large deviation from the actual location when gap distance is large. Linear interpolation is shown to result in large errors in mobility measures. Researchers should be aware that the different treatment of missing data can affect the spatiotemporal accuracy of GPS-based exposure assessments.
期刊介绍:
The focus of the journal is on all aspects of the application of geographical information systems, remote sensing, global positioning systems, spatial statistics and other geospatial tools in human and veterinary health. The journal publishes two issues per year.