Difference between revisions of "Kabak: Report"

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Revision as of 15:51, 22 November 2016


OVERVIEW

DATA PREPARATION

ANALYSIS


As a small red dot with no natural resources, the Singapore government is always coming up with new initiatives to encourage Singaporeans to reduce their energy consumption and wastage. Recently, the Singapore Power (SP) introduced a utilities bill redesign in a bid to help fellow Singaporeans track and reduce their energy and water consumption. However, through our team's primary observation the current energy consumption data and statistic provided on the Energy Market Authority is not provided in a user friendly manner. While the data is wholesome, it lacks interactivity and appeal for layman user to conduct basic data exploration. Furthermore, the data provided also does not link to other factors such as the population demographics, which could potentially provide some interesting insights on the usage patterns of Singaporean households.

Hence, to align with the government's goal to reduce energy consumption and wastage across the island and improve public awareness and education on energy consumption in Singapore, our team set out to explore ways to improve how energy consumption data is being visualized and displayed. In order to better visualize the energy consumption across the various households in Singapore, our team set out to explore the energy consumption data available on the Energy Market Authority. As the data provided is in a rather raw form, our team performed a series of data processing and cleaning to prepare the data in an ideal fashion required in the later phase of our project. The key visualization question or team wished to explore using our visual application is:

“How does factors identified affect the energy consumption pattern across Singapore? Is there any significant patterns or changes of concern?”

Having set our data visualization objectives, our team then made use of d3.js to develop an interactive visual application that is friendly towards end users that may not be equipped with the know-how of data analysis.