Project Groups

ISSS608 Visual Analytics and Applications

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Weekly Session

DataViz Makeover

Assignment

Visual Analytics Project

Resources

Project Groups

Please provide project description the project title and an abstract of your project. The abstract should not be more than 350 words. You are also required to include project blog link and the names of team member.

Project Team	Project Title/Description	Project Web Blog	Project Member
Group 1	Understanding Airbnb listings in Australia The abundance of Airbnb data provides great opportunity to conduct a variety of data analyses to understand the residential short-lease rental market. The dataset that has be scrapped on the Airbnb web and made publicly available by Inside Airbnb provides geospatial, textual, and quantitative data on each of the listings listed on the web. This project provides an analytics platform for interested parties (especially non-data specialists) to conduct exploratory spatial data, text, cluster, and regression analysis on the Australia Airbnb dataset using simple and user-friendly interactive dashboards that does not require programming knowledge.	Project Blog Link	Jason TEY Shou Heng Louelle TEO Fengmin WONG Kian Hoong (Andy)
Group 2	Project Title and Abstract	Project Blog Link	Choong Shi Lian Selene Jiang Weiling Angeline Wong Wei Sheng Dylan
Group 3	Project Title and Abstract Understanding Key Stories Covered In the Media and How Readers Engaged With News As we become more and more inundated with news from various digital sources today, understanding what the key stories are across the digital spectrum is becoming more and more challenging. As such, we are interested in understanding how to best present a visual snapshot of the key stories that are covered in local media and identifying how readers engaged with the news.	Project Blog Link	Gerald Chee Nam Jihun Atticus Foo
Group 4	Visualisation and Analysis of Patient Psychosocial Acuity (VAPPA) The Community Care Team (CCT) of the Singapore General Hospital aims to facilitate person-centered care in the community and to enable patients to remain in the community as long as possible. This is achieved through collaboration with community partners to meet patient psychosocial needs with health and social issues. The CCT collects data on the patient socio-demographics, location and psychosocial acuity to understand the psychosocial needs of patients in the community, and to devise targeted intervention strategies to address them. Our project aims to develop an interactive application to enhance the visualisation and analysis of the data collected by the CCT.	Project Blog Link	Lin YongYan Siow Mun Chai Tan Wei Li, Amanda
Group 5	Predicting whether an individual would go for the H1N1 vaccine Vaccination is a crucial public health measure to flatten the curve in a pandemic. By looking at a dataset that contains the personal demographics and attitudes of respondents in the USA towards H1N1 vaccination, we hope to predict whether an individual would go for the vaccine.	Project Blog Link	Hai Dan Lim Pek Loong Desmond Tay Kai Lin
Group 6	Our Shiny PET: A Predictive, Exploratory and Text Application for Airbnb Data The increasing availability of data has resulted in the increased demand for data driven decisions. Although there is an extensive range of commercial statistical tools, they are often subscription-based and demand good technical knowledge to mine and draw insights from. Therefore, it may not appeal to the average user. As such, our project aims to develop a user-friendly application that will enable users to make data-driven decisions without the need to understand programming languages or have extensive statistical knowledge. We will use Airbnb data as our baseline for this project - data generated is rich in information, which consists of structured, unstructured (textual), and location data. With this application, users will be able to perform text analysis on review and listing data to generate more quantitative insights. The exploratory module allows users to identify interesting patterns based on selected variables. Findings from the exploratory module will be further augmented in the confirmatory module where selection of statistical methods will be guided based on user’s chosen variables. Finally, the predictive module enables users to prepare and build a variety of prediction models without needing to have in-depth understanding of the predictive models and its algorithms.	Project Blog Link	Ang Su Yiin Chua Si Min Joey Kevin Gunawan Albindo
Group 7	Project Title and Abstract	Project Blog Link	Elaine Lee Teo Chye Teck (Lance)
Group 8	Project Title and Abstract	Project Blog Link	Team member 1 Team member 2 Team member 3
Group 9	Enabling optimization of bike-sharing operations – Bluebikes The advent of shared bikes has provided people with a new way of commuting, and has picked up rapidly due to its convenience and low cost. However, there are still some problems at the current stage, such as an over-accumulation of bikes at certain areas leading to inconveniences to the public. On the flip side, there could be insufficient supply of bikes at selected stations during peak periods leading to potential users choosing an alternate form of transport. There is also the issue of overused bikes lacking maintenance/servicing at the right time intervals. There is currently no platform that provides an integrated analytics capability to perform exploratory analysis of the trip data and gather insights to improve the operations. This is the gap that our team is intrigued to close. We would like to design an interactive application that will help the executives of Blue Bikes to analyze and visualize users’ trip data. This application would serve as the go-to analytics platform for gathering insights on the bike sharing operations and facilitate decision making on improvement ideas. The objective of this project is to create an app using R-Shiny that will enable Bluebikes to focus on the operational optimization of their bike fleet supply at each of the stations via: • Exploratory and Confirmatory interface to analyze bike trip duration and intensity of bike station activity. • Analyze the deficit or excess of bikes that are moving in and out of the numerous bike stations. • Optimize the utilization rate of their entire bike fleet. • Track and determine the right time to perform servicing and maintenance on the bike fleet.	Project Blog Link	Liu Jie Wang Ziqi Vikram Shashank Dandekar
Group 10	Understanding Prime Mover (PM) Waiting Time in Yard The study objective is to seek insight from Prime Mover (PM) Operations from port operations to identify common characteristics exhibited by PM with high and low waiting times, through understanding of PM events and operational data. This, in turn, enables us to pinpoint and identify correlated attributes and embark on further study to improve the overall productivity of PM operations and resource utilisation through active targeting of activities contributing to the PM waiting time.	/ Project Blog Link	Li Zhenglong Lim Kai Chin
Group 11	The Prime Crime Area Spatio-Temporal Analysis With the limited police resources and possible adverse impact when crime occurs, analytics on crime has been done as far back as in the 1800s (Hunt, 2019). Crime occurrence was found to have spatial patterns, and thus predictive analytics should be possible. However, mixed results were obtained in the research to determine whether predictive policing results to lower crime rates (Meijer & Wessels, 2019). Thus, it is more beneficial to use analytics to determine areas with a higher risk of crime and to discover the underlying factors to the increased risk. Traditionally, crime analysis is done manually or through a spreadsheet program (RAND Corporation, 2013). Using demographic, socio-economic and crime rate data of the Greater London Region, retrieved from the London Datastore, this project would give the users an easier way to do the crime analysis using a web application. In this project, 3 key analysis will be performed: Exploratory Data Analysis: Finding spatial hotspots and how crime rates have changed over the years. Clustering Analysis: Finding similar local authority districts (LADs) based on the crime rates and other influencing factors. Regression Modelling: Forecasting crime rate in each LAD.	Project Blog Link	GABRIELLA PAULINE DJOJOSAPUTRO TAN HUIYONG TOH MEI SIEW
Group 12	Investing 101: A visual and predictive guide for the rookie investor Existing financial data websites such as Yahoo Finance do a good job in providing historical price data and technical indicators, but the beginner investor lacks the knowledge to properly utilise and benefit from these. In addition, we have also identified several gaps in such websites. For one, these websites do not provide tools to allow the user to compare stocks meaningfully or zoom in to the statistical properties of financial returns. For example, a user is unable to conduct correlation analysis or visualize the distribution of returns. Secondly, these websites also do not provide any form of forecasting to aid in investors’ decisions. This project aims to improve on the current offering of financial data websites by including the following key modules: Exploratory Data Analysis: Key visualizations and analysis of key financial asset returns metrics Time-Series Forecasting: Predicting financial asset prices using an ARIMA model Time-Series Clustering: Clustering financial assets based on historical returns	Project Blog Link	Andre Lee Boey Yi Heen Ng Weekien
Group 13	The Impact of Lifestyle and Family Background on Grades of High School Students In the past many years, there has been an emphasis on education around the world because of the impact it a person, be it in terms of employment opportunities and quality of life. It is hence important to know what are factors that affect one’s academic performance. While there are many factors that can impact a person’s academic performance, family background and one’s lifestyle are two of the larger factors. Since there are many sub-factors in family background and lifestyle choices, the motivation of this study is to look deeper at these sub-factors to see which are the factors that have a greater correlation in the impact on a student’s grades. More specifically, this study aims to study the correlation between each factor and a student’s grades, as well as aiming to build a model that can accurately determine the academic performance of a student. From the findings, targeted help may be administered to students in these specific areas attributing to poor grades in school, therein helping them have a higher chance of a better future.	Project Blog Link	Lim Jun Jie Timothy Tang Haozheng Wu Yufeng
Group 14	Project Title and Abstract	Project Blog Link	Team member 1 Team member 2 Team member 3
Group 15	Project Title and Abstract	Project Blog Link	Team member 1 Team member 2 Team member 3
Group 16	Project Title and Abstract	Project Blog Link	Cheryl Pay Wei Lin Chong Jia Jun Louis Lau Wei Han Amos