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| <p><center>'''Monthly Performance of Airline in Asia Pacific''' </center></p> | | <p><center>'''Monthly Performance of Airline in Asia Pacific''' </center></p> |
− | [[File:1718T1G9 BackgroundSurvey1.png|300px|center]] | + | [[File:1718T1G9 BackgroundSurvey1.png|400px|center]] |
| <p><center>'''Source''': https://www.flightstats.com/company/monthly-performance-reports/airlines/</center></p> | | <p><center>'''Source''': https://www.flightstats.com/company/monthly-performance-reports/airlines/</center></p> |
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Line 119: |
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| | <p><center> '''Trends in the Causes of Flight Delay in US''' </center></p> | | | <p><center> '''Trends in the Causes of Flight Delay in US''' </center></p> |
− | [[File:1718T1G9 BackgroundSurvey2.png|300px|center]] | + | [[File:1718T1G9 BackgroundSurvey2.png|400px|center]] |
| <p><center> '''Source''': https://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/2012_04_13.pdf </center> </p> | | <p><center> '''Source''': https://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/2012_04_13.pdf </center> </p> |
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| | <p><center> '''Global Digital Attack Network''' </center></p> | | | <p><center> '''Global Digital Attack Network''' </center></p> |
− | [[File:1718T1G9 BackgroundSurvey3.png|300px|center]] | + | [[File:1718T1G9 BackgroundSurvey3.png|400px|center]] |
| <p><center> '''Source''': http://www.digitalattackmap.com/#anim=1&color=0&country=US&list=0&time=17475&view=map </center></p> | | <p><center> '''Source''': http://www.digitalattackmap.com/#anim=1&color=0&country=US&list=0&time=17475&view=map </center></p> |
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| * The graph vividly displays the path with its origin and destination | | * The graph vividly displays the path with its origin and destination |
| * When mouse hovers on the path, the label shows up with its detailed information | | * When mouse hovers on the path, the label shows up with its detailed information |
− | |}
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− | <br/><div style="background: #347473; padding: 15px; font-weight: bold; line-height: 0.3em; text-indent: 15px;letter-spacing:-0.08em;font-size:20px"><font color=#fbfcfd face="Century Gothic">PROPOSED STORYBOARD</font></div>
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− | {| class="wikitable" style="background-color:#FFFFFF;" width="100%"
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− | ! style="font-weight: bold;background: #56C0BE;color:#fbfcfd;width: 50%;" | Proposed Layout
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− | ! style="font-weight: bold;background: #56C0BE;color:#fbfcfd;" | How Analyst Can Conduct Analysis
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− | |-
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− | | [[File:HSH Home.png|600px|center]]
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− | # Introduce analysts to the topic of HDB Resale Price and the objectives of the visualization project
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− | # Upon clicking "Find your Dream Home", analysts will then begin their process of exploration
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− | | [[File:HSH Insight1.png|600px|center]]
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− | # When a user enters our app, we will show them a brief history of HDB followed by the problem that most young people are facing with regards to understanding the HDB situation.
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− | # The 2 screens of different insights we are trying to show will be displayed as 2 clickable buttons so that it is easy for a user to know what exactly he wants to look for at a glance.
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− | # The dual axis bar-line chart will indicate the number of units sold vs the HDB price index to show the performing over the years.
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− | # The scatter plot groups attacks based on 3 main categories - Planning Area, HDB resale price, Life left on lease. Firstly, by grouping based on planning area, it shows the the different HDB of how many years they left on lease. The size of the circle indicate the resale price of the HDB
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− | | [[File:HSH Insight2.png|600px|center]]
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− | # In the next phase of the exploration, we
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− | # The radar chart shows 5 different governance indicators that defines how well a HDB is price with several other indicators. The closer the area is to the center of the chart, the less well the indicators is. Upon mouse-over of each area, one can also retrieve the exact values of each governance indicator.
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− | # Using the map of Singapore as a filter condition, analysts can selected their choice of HDB to see if its near any bus stop, MRT, schools.
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− | # By looking at both charts, the analyst will then be able to compare and establish possible linkages between the different indicators for HDB over the years. As such, these 2 charts are placed side by side to assist the analyst in their data exploration.
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| |} | | |} |
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Problem & Motivation
Flight delays has been a very common problem for travelers, the delay can be attributable to various problems, such as, aircraft issues, weather issues at origin airport or/and destination airport. The delay has no doubts will disappoint air travelers and affect their flight experience greatly. Thus, in this project, our team aims to investigate the performance of different airlines and flight delays in detail.
In addition, airport network is a very critical and complex transportation infrastructure for a nation, it is increasingly important for public policy considerations. The disruptions of the airport network, caused by terrorist attack, disease transmission or other reasons, can lead to huge economic loss. Thus, the study on the airport network can assist us better understand the relationship between different airports, for example, identify most critical airport, and take proactive measures to prevent occurrence of disruptions.
Objectives
In this project, we will adopt visualization techniques to:
- Demographics of student alcohol consumptionAnalyse airport network connectivity
- Analyse flight delays for different airlines
- Evaluate on-time performance for airlines and aircrafts
With the visualization, airline companies will become aware of its on-time performance among all airlines and meanwhile have a better idea on areas where greater attention should be placed on routine operation, such as service or aircraft maintenance.
Our visualization will also provide a detailed insight on airport network, it will speed up the decision making process when faced with infectious diseases and terrorist attacks.
Selected Dataset
We have obtained the dataset from Kaggle, which can be download from https://www.kaggle.com/usdot/flight-delays/data
Dataset/Source
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Data Attributes
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Rationale Of Usage
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airline.csv
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- IATA_Code, String, Airline identifier
- Airline, String, Airport Name
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This data is used to identify and provide detailed information about the different airlines.
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airport.csv
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- IATA_Code, String, Location identifier
- Airport, String, Airport Name
- City, String, City of Airport
- State, String, State of Airport
- Country, String, State of Airport
- Latitude, Numeric, Latitude of the Airport
- Longitude, Numeric, Longitude of the Airport
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This data is used to identify and provide detailed information about different airport. It complements the main dataset by providing detailed location information about latitude and longitude, city, state and country of the airport.
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flights.csv
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- Year, Numeric, Year of the flight
- Month, Numeric, Month of the flight
- Day, Numeric, Day of the flight
- Day_of_Week, Numeric, Day of week of the flight
- Airline, String, Airline identifier
- Tail_Number, String, Aircraft identifier
- Origin_Airport, String, Departing airport
- Destination_Airport, String, Destination airport
- Departure_Delary, Numeric, Total delay on Departure, negative value indicates the flight departs before scheduled time
- Arrival_Delay, Numeric, Total delay on arrival, it is derived from the difference of arrival_time and scheduled_arrival, negative value
- indicates the flight arrived before scheduled time.
- Diverted,Numeric (binary data), Aircraft landed on airport that out of schedule
- Cancelled, Numeric (binary data), 1 means cancelled
- Cancellation_Reason, String, Reason for Cancellation of flight: A - Airline/Carrier; B - Weather; C - National Air System; D -
Security
- Air_System_Delay, String, Delay caused by air system
- Security_Delay, String, Delay caused by security
- Airline_Delay, String, Delay caused by airline
- Late_Aircraft_Delay, String, Delay caused by aircraft
- Weather_Delay, String, Delay caused by weather
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This data is used as the major source of information in our project. We mainly use this data to analyse flight delays and reasons of delay. In addition, the data will be used investigate airport network and analyse airport network relationship by different centrality measures, such as betweenness centrality, degree centrality.
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Background Survey of Related Work
Technical Complexity
Below are the list of technical challenges that team may be faced with when developing the visualization application.
Technical Challenges
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How To Resolve
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Unfamiliar with D3.js libraries and building D3 application |
- Attend D3.js workshop
- Individual learning on how to build D3 application
- Peer Learning
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Lack of knowledge on how to integrate tableau work with D3 application |
- Research on how to integrate tableau work with D3 application
- Conduct early integration so that team have enough time to tackle some potential errors.
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Insufficient metadata for the source data |
- Research in the official website of US Department of Transportation
- Arrange team discussion to facilitate the understanding for the source data
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Tools/Technology
Below are the tools/technologies we will use when developing the visualization
- Excel
- Tableau
- D3.js
- Gephi
Project Milestones
References