Difference between revisions of "Group1 Introduction"

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<font size = 5; color="#FFFFFF">Unlocking insights from the VAST Challenge 2017</font>
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<font size = 5; color="#FFFFFF">Discovering traffic patterns by using network graph visualisations</font>
 
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[[Introduction| <font color="#1C1C1C">Introduction</font>]]
 
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[[About| <font color="#FFFFFF">About</font>]]
 
[[About| <font color="#FFFFFF">About</font>]]
  
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[[Project Proposal| <font color="#FFFFFF">Project Proposal</font>]]
 
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[[Project Timeline| <font color="#FFFFFF">Data Preparation</font>]]
  
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== Welcome to Group 1 project page ==
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==Welcome to Group 1 project page==
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<p><span style="font-size: 11pt; font-family: Arial; color: #000000; background-color: transparent; font-weight: 400; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">The page covers the work carried out to develop an interactive visualisation and application to solve real world business problems. The report outlines the motivation behind the application developed, covering how a particular data set can be tweaked to visualise traffic networks, and derive patterns out of it. The framework for the application is R, with special focus on the </span><strong><em><span style="font-size: 11pt; font-family: Arial; color: #000000; background-color: transparent; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">ggraph </span></em></strong><span style="font-size: 11pt; font-family: Arial; color: #000000; background-color: transparent; font-weight: 400; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">and the </span><strong><em><span style="font-size: 11pt; font-family: Arial; color: #000000; background-color: transparent; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">ggplotly </span></em></strong><span style="font-size: 11pt; font-family: Arial; color: #000000; background-color: transparent; font-weight: 400; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">packages. The report also covers the previous work conducted in the field of traffic network visualisation and other tools that exist to perform similar visualisations. A full step user guide is also provided for easy replication. The report concludes with a coverage of the assumptions and limitations of the application. For readers wishing to get more context quickly on the application, you may refer to the [[Poster|Poster tab]]. </span></p>
  
The wiki contains a guide on the project carried out by Group 1, which pertains to the investigation of corridors of vehicle traffic. It will cover the following.
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==Feedback==
  
*What was the motivation behind the project?
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After reading the page, kindly spend 2 minutes of your valuable time to leave your feedback [https://goo.gl/forms/10ABscIrqDPjrHcr2 here].
*What data sets were used? How was project planning carried out?
 
*Project deliverables timeline
 
*Application of the analysis carried out here
 
*Poster
 
*Related links and references
 

Latest revision as of 15:17, 7 August 2017

Discovering traffic patterns by using network graph visualisations

Introduction

About

Project Proposal

Data Preparation

App & Deliverables

Poster

 

Welcome to Group 1 project page

The page covers the work carried out to develop an interactive visualisation and application to solve real world business problems. The report outlines the motivation behind the application developed, covering how a particular data set can be tweaked to visualise traffic networks, and derive patterns out of it. The framework for the application is R, with special focus on the ggraph and the ggplotly packages. The report also covers the previous work conducted in the field of traffic network visualisation and other tools that exist to perform similar visualisations. A full step user guide is also provided for easy replication. The report concludes with a coverage of the assumptions and limitations of the application. For readers wishing to get more context quickly on the application, you may refer to the Poster tab.

Feedback

After reading the page, kindly spend 2 minutes of your valuable time to leave your feedback here.