Difference between revisions of "ISSS608 2016 17T1 Group1 Proposal"

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=Background=
  
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Singapore is a highly urbanised city state with a good network of public transport.  It is important to understand how the population flows within the city state through the public transport system.  The public transport planners will need to understand the characteristics of the population commuting on the public transport system, especially the public bus system.  The public bus system has a greater reach to majority of the population and is more dynamic in nature as compared to the train system.  By understanding the characteristics of the commuters, it will allow the planners to twist the public bus system in order to make it more efficiency and support the initiative of a car-lite society.
  
 +
 
 +
=Challenges=
  
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=Motivation=
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An Ezlink passenger travel record over 7 days has 50 millions + rows of data and 50+ variables.  The raw data was too large for visualisation tools such as Tableau and D3.JS to read and process.  Even loading the data into JMP proved to be a challenge and on average it took at least 30 mins to read in the csv data and convert into JMP format.  Hence the ability to visualise the raw data was very limited.
  
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There is no shortage of visualisation software on the market, both commercial and open-source. However, very few of them emphasize and utilise the science of visualisation to guide the users towards deriving deep insights from the data.
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=Project Objectives=
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<br>
 
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We believe that statistical learning can also benefit from better visualisation coupled with interactivity. Business users should be able to perform data modelling on their own, and understand the implications of the decision parameters and assess their models through visualisation as easily as data exploration. With the aim of realising such a capability, we built a web-based tool that is accessible to everyone.
 
  
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=Review and Critic on other Visualisation Tools=
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The objective of the project was to prepare and manipulate the raw data into manageable data size for analysis on visualisation tools such as Tableau and D3.JS.
  
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Mention what tableau and SAS can do...
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=Motivation=
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=Selection of Tools=
 
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R scripting language was chosen due to the wide range of statistical learning libraries available. Of equal importance is the availability of Shiny, an R package and web application framework, which can build interactive web applications quickly in the same environment.  Shiny also has a comprehensive list of widgets to implement interactive features such as selection button and input slider. It also allows any User-Interface interactions like click, hover, brush for users to perform deeper exploration of the data.
 
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=Design Framework=
 
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A detail description of the design principles used and data visualisation elements built
 
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=Demonstration=
 
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Sample test cases
 
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=Discussion=
 
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What has the audience learned from your work? What new insights or practices has your system enabled? A full blown user study is not expected, but informal observations of use that help evaluate your system are encouraged.
 
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=Future Work=
 
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A description of how your system could be extended or refined.
 
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=Installation guide=
 
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No installation is required, you can access the application in the following link: https://oohshinyviztool.shinyapps.io/server<br>
 
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However, the next section shows the steps required to host shiny application on Shinyapps.io.
 
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==Deploy the application online==
 
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You can choose to host the shiny application on your own server. Or you can choose to host it on shinyapps.io by RStudio. The following steps describe the steps to host the application on
 
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shinyapps.io.<br>
 
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'''Step 1''': Go to http://www.shinyapps.io/ to sign up for a free account which allows you to host up to 5 applications and utilise 25 active hours.<br><br>
 
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'''Step 2''': You will get your personal token and secret number, as well as determine the name of your application link.<br><br>
 
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In your Rstudio, <br>
 
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'''Step 3''': Key in the following in the console: <br>
 
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install.packages('devtools')<br>
 
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devtools::install_github('rstudio/shinyapps')<br>
 
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devtools::install_github('rstudio/rsconnect')<br>
 
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library(shinyapps)<br>
 
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library(rsconnect)<br><br>
 
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'''Step 4''': Go to Tools -> Global Options -> Publishing -> Connect <br>
 
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paste the following in the empty box:
 
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shinyapps::setAccountInfo(name="''Your account name''", token="''Personal Token issued''", secret="''Secret number issued''")
 
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<br>
 
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'''Step 5''': Key in the following in the console: <br>
 
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file_directory = 'C:/Users/app'<br>
 
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setwd(file_directory)<br>
 
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[Note: use the file directory which you store the R codes for the application.]<br><br>
 
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'''Step 6''': Key in the following in the console: <br>
 
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deployApp(appName="server")<br><br>
 
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'''Step 7'''': The application should open on your browser.<br>
 
  
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With this project we hope to help the public transport planners to study the commuter's movement on the public bus system.
  
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=User Guide=
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Step-by-step guide on how to use the data visualisation functions designed.
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=Visualisation Modelling=  
  
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=References=
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We are exploring using either D3.JS or R Shiny to create visualisation model such as the Chord Diagram or sparklines to analysis the flow pattern.
 +
 +
 
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=Data Sources=

Latest revision as of 22:26, 28 November 2016

PROPOSAL   POSTER   REPORT


Background

Singapore is a highly urbanised city state with a good network of public transport. It is important to understand how the population flows within the city state through the public transport system. The public transport planners will need to understand the characteristics of the population commuting on the public transport system, especially the public bus system. The public bus system has a greater reach to majority of the population and is more dynamic in nature as compared to the train system. By understanding the characteristics of the commuters, it will allow the planners to twist the public bus system in order to make it more efficiency and support the initiative of a car-lite society.


Challenges

An Ezlink passenger travel record over 7 days has 50 millions + rows of data and 50+ variables. The raw data was too large for visualisation tools such as Tableau and D3.JS to read and process. Even loading the data into JMP proved to be a challenge and on average it took at least 30 mins to read in the csv data and convert into JMP format. Hence the ability to visualise the raw data was very limited.

Project Objectives

The objective of the project was to prepare and manipulate the raw data into manageable data size for analysis on visualisation tools such as Tableau and D3.JS.


Motivation

With this project we hope to help the public transport planners to study the commuter's movement on the public bus system.


Visualisation Modelling

We are exploring using either D3.JS or R Shiny to create visualisation model such as the Chord Diagram or sparklines to analysis the flow pattern.


Data Sources

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