Abstract
Following the Flint drinking water crisis, a service line (SL) replacement program was implemented to replace lead SLs and galvanized SLs connecting residences to Flint's water system, leading to the excavation and inspection over a 5-year period (2016-2020) of a total of 26,750 lines, representing close to 50% of all tax parcels in the City of Flint. These recent data were used to validate an earlier geospatial model created by residual indicator kriging (IK) to predict the probability that a home has a lead, galvanized, or copper private-side SL based on neighboring house inspections (i.e., 3254 homes visited in 2017) and secondary information (i.e., built year and city records on SL composition). Receiver operating characteristic curves indicated an average frequency of detection (i.e., area under the curve [AUC]) of 0.9 for copper and galvanized material and 0.6 for lead service lines. Predicting the composition of SL at unmonitored residences by IK, however, can result in negative probabilities of occurrence and probabilities that do not sum to 1. These limitations were overcome by adopting simplicial IK, whereby data undergo a logratio transform before the geospatial analysis. This first application of a compositional approach to SL data improved the detection of lead SLs (AUC = 0.8 vs. 0.6) while providing coherent predictions. Incorporating secondary information, in particular using standardized cokriging and a new rescaled cross-semivariogram estimator introduced to correct for geographical clustering of house inspections, increased the accuracy of the prediction.