New Orleans, Louisiana
June 26, 2016
June 26, 2016
August 28, 2016
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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