Difference between revisions of "Group1 Introduction"

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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="#FFFFFF">About</font>]]
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[[Project Timeline| <font color="#FFFFFF">Data Preparation</font>]]
  
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== About this project ==
 
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The project here aims to solve the questions posed in the Visual Analytics Science and Technology challenge [[http://www.vacommunity.org/VAST+Challenge+2017]]. The team chose the project, as it gives a chance to apply varied concepts of visualizing through geography, image sensing and sensor detection. An amalgamation of the three different mini challenges [[http://www.vacommunity.org/VAST+Challenge+2017#Overview]] allows the group to build an unified application to explore the unknown, to unlock the potential reasons on why the Red Pipit bird is disappearing. Whilst this might not be a real world data set, the application of these kind of data is very much pragmatic.
 
  
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==Motivation==
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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>
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==Feedback==
  
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In regions with minimal human access such as the wildlife preserve shown in the challenge, drones are fast gaining traction. This kind of application is aimed to provide an insight on how to carry out path analysis, how to trace anomalies based on wind data and how to use satellite imagery, to get a clearer picture and draw out an inference.
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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].

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.

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