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Sustainability Intelligence: Emergence and Use of Big Data for Sustainable Urban Planning

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2016 ASEE Annual Conference & Exposition


New Orleans, Louisiana

Publication Date

June 26, 2016

Start Date

June 26, 2016

End Date

August 28, 2016





Conference Session

Environmental Engineering Division Poster Session

Tagged Division

Environmental Engineering

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Paper Authors


Ben D. Radhakrishnan National University

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Prof. Ben D Radhakrishnan is currently a full time Faculty in the School of Engineering, Technology and Media (SETM), National University, San Diego, California, USA. He is the Lead Faculty for MS Sustainability Management Program. He develops and teaches Engineering and Sustainability Management graduate level courses. Ben has taught Sustainability workshops in Los Angeles (Army) and San Diego (SDGE). His special interests and research include promoting Leadership in Sustainability Practices, energy management of Data Centers and to establish Sustainable strategies for enterprises. He is an Affiliate Researcher at Lawrence Berkeley National Laboratory, Berkeley, CA, focusing on the energy efficiency of IT Equipment in a Data Centers.
As a means of promoting student-centric learning, Prof. Radhakrishnan has successfully introduced games in to his sustainability classes where students demonstrate the 3s of sustainability, namely, Environment, Economics and Equity, through games. Students learn about conservation (energy, water, waste, equity, etc.) through games and quantifying the results. He has published papers on this subject and presented them in conferences.
Before his teaching career, he had a very successful corporate management career working in R&D at Lucent Technologies and as the Director of Global Technology Management at Qualcomm. He had initiated and managed software development for both the companies in India.
Prof. Radhakrishnan holds Masters Degrees (M.Tech, M.S., M.B.A) and Sustainable Business Practices certification from University of California San Diego.

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Jodi Reeves National University

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Dr. Jodi Reeves is an Associate Professor and Department Chair of Applied Engineering at National University in San Diego, CA. She teaches courses in design engineering, engineering management, and data analytics. Prior to academia, she worked for almost ten years as a quality control manager, engineering project manager, and senior scientist responsible for failure analysis of thin film materials. She invented new quality control tools and supervised interns from local universities and community colleges as part of a $5.0 million technical workforce development initiative funded by New York State. She has published diverse articles on topics ranging from engineering education to high temperature superconductors and has spoken at many national and international conferences. Her doctorate in materials science and engineering are from the University of Wisconsin, Madison, and she holds five patents.

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Jeremiah Jack Ninteman National University

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Mr. Ninteman is a graduate of National University's Master of Science in Data Analytics program in San Diego, Ca. Prior to attending National University, Mr Ninteman earned a B.A. from San Diego Christian College and worked as business analyst in the commercial real estate industry where he acquired an interest in big data and analytics. Mr. Ninteman has since supported the development of business intelligence applications in facility and construction management for the instialltions department of the U.S. Bureau of Naval Medicine and Surgery and currently works as an analytics consultant with Booz Allen Hamilton, Inc., supporting operations research and data science efforts at the U.S. Space and Naval Warfare Systems Command. He hold a B.A., a M.S. and is a Tableau certified associate.

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Charles Hahm

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Large numbers of mobile and other electronic devices today generate a constant stream of data in large volumes. With the abundance of inexpensive processing power in the electronic devices and in the cloud, processing these big data streams to provide business intelligence with predictive models is already becoming an established practice in enterprises. Business intelligence predictive models is a tool that helps enterprises to study all options available to help change or innovate their products and services to optimize the use of resources. In today’s world urban area planning is one of the most challenging issues towards maintaining a balance among the three elements of sustainability, namely environment, economics and equity (or social justice). With an estimate of about 50% of the world population living in urban areas today along with the advent and use of modern communication devices (huge adoptions of smart phones, tablets, traffic sensors, wireless utility readers, etc.), a tremendous amount of big data is being generated which can be used for a more balanced urban planning. This research paper will focus on the emergence of modern urban big data and their use in building predictive models giving quantified options towards a more sustainable urban planning. Big Data can be used for many different aspects of Urban Planning, such as transportation, housing, energy use, pollution abatement, health, etc. With the unstoppable climate change happening around the world, urban planning will not only be a local or regional issue, is an international issue.

The dynamics of a modern urban area operations generate large amounts of data. This research paper will examine currently generated urban big data (e.g. traffic data) and those that can be potentially generated, and their use with predictive modelling focused on sustainable urban planning.

Big Data processing is drawing huge attention in engineering schools. Sustainable urban planning using big data was addressed in the school of engineering bringing Sustainability Management and Data Analytics programs together (both graduate programs) – as a capstone project for one of the teams in the MS Data Analytics program. The exposure and implementation of this project demonstrates the use of modern platform tools for big data processing and solving a sustainability real world problem giving the students an edge in the job market.

The paper will specifically demonstrate an implementation and use of southern California’s traffic big data (collected by the California’s department of Transportation) that evaluates alternative modes of transportation and the associated reduction in Carbon dioxide emissions. The research formulates predictive modelling using Tableau software platform to generate a comprehensive interactive dashboard focused on the highways in the San Diego, CA area. Reductions in carbon dioxide will relate to the improving the three elements of sustainability noted earlier, with environment being the highest priority. This provides options to planners to help design the best alternative transportation systems.

The paper will explore other areas of urban operations for modelling and their advantages for a more sustainable urban planning.

Radhakrishnan, B. D., & Reeves, J., & Ninteman, J. J., & Hahm, C. (2016, June), Sustainability Intelligence: Emergence and Use of Big Data for Sustainable Urban Planning Paper presented at 2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana. 10.18260/p.25985

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