Identification of Spatial Patterns in Water Distribution Pipe Failure Data Using Spatial Autocorrelation Analysis
Publication: Journal of Water Resources Planning and Management
Volume 145, Issue 12
Abstract
Identifying spatial patterns of water distribution pipe breaks can improve the understanding of the significant factors that promote the deterioration of water infrastructure systems. Such understanding can be translated into better failure prediction models or leveraged by water municipalities to improve their maintenance operations and asset management. Herein, spatial autocorrelation analysis, based on global and local Moran’s I, is proposed for the identification of failure clusters in water distribution networks. The proposed approach extends traditional spatial autocorrelation analysis by accounting for network structure in the formulation of the spatial weights and is adaptable to different levels of spatial resolution. For the studied system, the results revealed that pipe failures exhibit a significant degree of spatial clustering. The locations of statistically significant hot- and coldspots of pipe failures were identified. Characteristics of the underlying pipe network, including pipe density, material, and age, were found to be the main drivers for spatial clustering in the failure data. Beyond water distribution networks, the proposed computational approach can be applied to detect and locate patterns of spatial events in other networked infrastructure systems and to identify local network characteristics that give rise to such patterns.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions (e.g., anonymized data). Pipe failure data and network GIS records were provided by the water utility under a confidentiality agreement between the water utility and the second author. The data are available by request from the authors and may only be provided after obtaining the utility’s approval and undergoing potential anonymization. The spatial autocorrelation analysis (SAA) was conducted using the Python Spatial Analysis Library (PySAL) (Rey and Anselin 2010). Other Python libraries used for data analysis and visualization include NumPy, pandas, geopandas, and matplotlib (Hunter 2007; McKinney 2010; Van Der Walt et al. 2011). The Python code is available from the authors upon request.
Acknowledgments
This work was supported by the University of Texas at Austin Startup Grant and under Cooperative Agreement No. 83595001 awarded by the US Environmental Protection Agency to The University of Texas at Austin. It has not been formally reviewed by EPA. The views expressed in this document are solely those of the authors and do not necessarily reflect those of the agency. EPA does not endorse any products or commercial services mentioned in this publication.
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Published In
Journal of Water Resources Planning and Management
Volume 145 • Issue 12 • December 2019
Copyright
©2019 American Society of Civil Engineers.
History
Received: Oct 26, 2018
Accepted: Apr 18, 2019
Published online: Sep 28, 2019
Published in print: Dec 1, 2019
Discussion open until: Feb 28, 2020
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