Leveraging Disparate Parcel-Level Data to Improve Classification and Analysis of Urban Nonresidential Water Demand
Publication: Journal of Water Resources Planning and Management
Volume 146, Issue 1
Abstract
This study details a novel procedure for analyzing water demands in the nonresidential sector (i.e., commercial, industrial, and institutional users). Nonresidential customers are classified into “subsectors” based on economic, land-use, and property appraisal data sets and analyzed using a linear mixed-effects regression modeling framework, which controls for random fluctuations around mean monthly parcel-level water demand, for a 4-year study period in Austin, Texas. Classification of nonresidential customers can improve the explanatory power of statistical models over models without any classification ( and 0.431, respectively). Additional improvement is seen by explicitly using the economic, land-use, and property data on which subsectors are based (), at the cost of computational expense and added model complexity. Results indicate that the subsector classification provides the best explanation of variation in monthly water usage at the parcel level, followed by conditioned floor area and number of employees. These results can improve traditional water demand forecasting techniques for the nonresidential sector and reveal subsector-specific trends that might otherwise be obscured without classification of customers.
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Data Availability Statement
The following data, models, or code used during the study were provided by a third party:
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Business locations and summary data set containing NAICS code, number of employees, and address: InfoGroup, Inc., via ESRI Business Analyst Extension for ArcGIS (https://www.esri.com/en-us/arcgis/products/arcgis-business-analyst/overview);
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Anonymized monthly billing data set for study area including monthly water demand (in gallons), customer type (via rate code), and account identifiers: City of Austin, Austin Water;
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Anonymized land-use GIS polygon layer for study area: City of Austin, Austin Water;
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Anonymized property appraisal data set including improvement characteristics (age, gross square footage, number of floors): Travis County Appraisal District.
Direct requests for these materials may be submitted to the provider as indicated in the acknowledgments.
The following data, models, or code generated or used during the study are available from the corresponding author by request:
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All R code used for data curation and statistical analysis available from the corresponding author upon request.
Acknowledgments
The authors would like to thank Joseph Smith, P.E. and Katherine Jashinski, P.E. from the Austin Water utility, for providing access to data sets used in this study as well as initial data curation, and for providing guidance with interpreting billing data and model specification. The authors would also like to thank Dr. Michael J. Mahometa and Erika Hale from the University of Texas at Austin Department of Statistics and Data Sciences for their assistance in model specification and troubleshooting. Finally, the authors would like to thank the Texas Section of the WateReuse organization (Grant No. UTA17-001138) and the National Science Foundation (Award No. 1560451) for funding this research.
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Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 146 • Issue 1 • January 2020
Copyright
©2019 American Society of Civil Engineers.
History
Received: Jul 12, 2018
Accepted: Apr 17, 2019
Published online: Oct 29, 2019
Published in print: Jan 1, 2020
Discussion open until: Mar 29, 2020
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