Project Groups

ISSS608 Visual Analytics and Applications

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Visual Analytics Project

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Project Groups

Please change Your Team name to your project topic and change student name to your own name

Project Team	Project Title/Description	Project Artifacts	Project Member
Group 1: Team S-MALL	Understanding traffic patterns using network graph visualisations The project aims to illustrate the power of visual analytics in highlight patterns exhibited by vehicles when traversing through various traffic corridors. By linking the information captured in RFID tags when vehicles move through checkpoints, an interactive application is designed. This will help to unravel insights such as frequently travelled corridors, preferred routes amongst vehicles, traffic density, etc. The application will be primarily developed using R, and specifically the versatile ggraph package, which helps to develop powerful network visualisations. ggraph has been chosen as it is a recent release (Feb 2017), that exhibits the power of R and the ggplot architecture in incorporating network visualisations. Though ggplot tools have existed to visualise network patterns previously, the use of this package helps in making neater visualisations that help the user understand better. The motivation for this project stems from the traffic accumulation key problem found in most cities. Though the dataset pertains to a set of vehicles travelling through a wildlife preserve, the ideology can be applied to planning of roads and associated establishments. Urban planning needs to cater to robust planning of vehicle corridors to minimise disruptions in flow, and improve productivity. The interactive application helps the user understand linkages between various points in a predefined vicinity. The timestamp information of vehicle passages present in the data helps to understand various parameters such as traffic density, preferred corridors for vehicles and their speeds. The application devised here is also aimed to support the traffic authorities to identify what other alternative corridors might exist for reaching from Point A to point B. In addition, network measures such as the betweenness, the connectivity and closeness of various nodes, are also provided.	Proposal Report Application Poster	AAA BBB CCC
Group 4: Show me the money	Ever wonder how far bitcoin value could go? Read on Bitcoin has recently garnered mixed reviews from two extreme ends. From China banning bitcoin to Chicago Mercantile Exchange supporting the futures trading of bitcoin. And there are plenty more remarks from big investment banks as well as regulators. The value of bitcoin has rose more than 700% since the start of January 2017. If for any reason why people are excited or agitated about bitcoin, it has to do with its volatile value. Looking up on the internet, there is hardly any reports that delve any further other than time series data of bitcoin price. It is very tempting to speculate that the price will continue to go up. If it does, by how much? If it doesn’t, how deep is the pit hole? How is the performance compared to other instruments? Any wise investor and information hungry scavengers alike would like to understand more of the recent price trend. In this project, we will perform limited technical analysis, time series analysis and return analysis through the usage of R. There are various packages which proof useful when it comes to quick data crunching and visually beautiful presentation. The data what we will be using is per minute close price since 2009. Ie, many data points. There are volume information as well to accompany our analysis. Apart from price, we are also obtaining price information of index and other commonly traded commodities as a return comparison with bitcoin. Lastly, we hope this report will provide a better compass to one who has the money and guts to take a bet on bitcoin-roller-coaster -ride or to by-standers (like ourselves) who observe from a far.	Proposal	DENG Yuetong YAU Hon Tak

Team	Members
Group 1	GE BIN			LIM LIANG DANNY			RYAN CHIA
Group 2	Rachel Tong			Nurul Asyikeen Binte Azhar			Matilda Tan Ying Xuan
Group 3	CHEN ZHENGJIAN			XIAO ZHENYU			ZHENG MIANYI
Group 4	Yau Hon Tak			Deng Yuetong
Group 5	Wang Rui			Wu Yuqing			Xing Siyuan
Group 6	Wang Yizhou			Zhou Chen			Zhang Lidan
Group 7	Zhang Peng			Wang Shang
Group 8	Fam Guo Teng			Wang Yuchen			Xu Yanru
Group 10	Ma Xiaoliu			Deng Chunling			AISHWARYA MOHAN
Group 11	FOO CELONG RAYMOND			GOH JUN JIE ANTHONY			KARAN JYOTI KHANNA
Group 12	HE ZIWEN			GONGQIANG			KYONG HWAN KIM