The Use of Population Forecasting and Cluster Analysis in Water Utility Master Planning

Features

  • Author: Lillian Pierson
  • Date: 30 Apr 2013
  • Copyright: Image appears courtesy of iStock Photo

When Dr. Prasad Chittaluru was a young engineer carrying out water system modeling and design work, he looked around his industry and saw a lot of safe, middle-of-the-road solutions. Since errors in water system design are costly and sometimes fatal, design engineers are very reluctant to use novel and innovative solutions on the problems that they face. “These tried-and-true solutions have worked in the past and will continue to work if applied correctly, therefore why change?,” thinks today’s typical design engineer; And thus, business continues as usual.

thumbnail image: The Use of Population Forecasting and Cluster Analysis in Water Utility Master Planning

Dr. Chittaluru, however, is far from typical. When he looked around his industry he saw opportunities for improvement and optimization everywhere. He understood the traditional methods and approaches to engineering, yet he saw how much improvement could be made if only engineers were harnessing the power of their data. It was Dr. Chittaluru’s passion for innovative data-driven solutions that led him to create a business through which he could help engineers and key decision-makers use their data in ways that optimize their infrastructure development and maintenance expenditures.

Introduction

Dr. Prasad Chittaluru and his company Epic Engineering and Consulting Group of Orlando, Florida, have spent the last 15 years developing novel data science methods to solve environmental engineering problems that affect the water utility sector. Water utility master planning is just one application of these powerful methods. In water utility master planning, Dr. Chittaluru uses population growth projections from statistical analyses commissioned by the state, and then models this data spatially in order to predict the future of development and water demand within a utility’s service area. Municipalities are able to use this type of model in planning ahead for future infrastructure development.

          

Population Allocation Analysis

Local population growth and density is determined through a “top-down” allocation analysis. Dr. Chittaluru uses a 20-year population projection dataset from The University of Florida’s Bureau of Economic and Business Research (BEBR). BEBR uses five different statistical techniques to produce population growth forecasts at the regional level.

For specific details about how the Bureau applies these techniques in population forecasting, please see the BEBR website. For purposes of utility master planning analyses, the population forecasting data is analyzed against local data for future land use, planned developments, and traffic statistics. These datasets are mapped and spatial clustering is analyzed in ArcView GIS to develop a spatial model for 20-year population projections across the utility service area. Lastly, this model is projected to a theoretical planning area for analysis against data generated in the water demand allocation analysis.

Water Demand Allocation Analysis

In contrast to population allocation analyses, local water demand allocation is determined through a “bottom-up” approach. Water demand is calculated from historical and current data that is housed in Utilities Customer Information Systems, water treatment plant SCADA record systems, and databases for water treatment plant operational data. Once the average demand for the region has been determined, a model for the spatial allocation of this demand must be generated. This is done by examining the current individual water usage per customer per physical address. Water demand allocation data is mapped spatially across individual addresses in the service area. This data model is then projected to the theoretical planning area for comparison against data from the population allocation analysis.

The End Result

The spatial data models that are generated in the population and demand allocation analyses are combined in the theoretical planning area. This area is simply an area for spatial overlay and extrapolation from these models. The predicted population counts and densities from the population allocation analysis are multiplied by the demand from existing water customers as determined in the demand allocation analysis. The product of this operation is a model that forecasts regional development, population growth, developmental spatial clustering, and the water demand for the service area over the next 20 years. This model is then taken to its most granular level by projecting it onto current parcel data for the service area. The 20-year projection for population and water demand per parcel is taken from this final model.

"...with these insights municipalities are able to begin preparing to fund and commission water utility infrastructure expansions that will be needed 10, or even 20, years down the line."

The final analysis for utility master planning involves the spatial analysis of projected demand allocations against projected population densities across the utility service area. From this model, Dr. Chittaluru predicts where development will occur and the prime locales for future water infrastructure expansion. Using these methods he is able to tell municipalities where they will need to expand their systems and even to what capacity.

This forward-looking perspective allows decision-makers to make sound infrastructure investment decisions, rather than having to rely upon a best-guess or hunch as to where their systems should be expanded next. It gives municipalities a long-term, reliable vision for their growth and removes the guess work that can often occur when making long-term decisions based on short-term or otherwise limited information. These insights translate to tremendous tax dollars saved. Not only that, with these insights municipalities are able to begin preparing to fund and commission water utility infrastructure expansions that will be needed 10, or even 20, years down the line.

Prasad Chittaluru, Ph.D., PE, PMP, GISP, BCEE

Dr. Chittaluru is an experienced technical professional in the areas of program and project management, enterprise systems design, implementation and maintenance, master planning and hydraulic modeling. He has extensive experience in business process optimization, compiling systems development requirements, concept development and implementation of software solutions to serve the requirements identified. His domain experience includes working with DOTs, MPOs, government agencies, utilities, public works and businesses. He has an excellent understanding of business needs of utilities, transportation agencies, public works and planning agencies and managed the development of large enterprise information management systems for government agencies across the United States. He has served as project manager and lead technical professional for many master planning, transportation planning and systems development projects.

 

 

Lillian Pierson is a Data Analytics Engineer and GIS Specialist with Orange County Government. She is also an environmental engineer and digital humanitarian. She volunteers with the Stand By Volunteer Task Force, Ushahidi, and Crisis Mappers, and is also a tech journalist.

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