whataflight - data visualisation on flight and tourism data around the world
TRANSCRIPT
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VISUALISING
FLIGHTS
VISUALISING
FLIGHTSNICOLA GRECO
MEKHI DHESI VIRGINIA ALONSO
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VISUALISING
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Content !
!
data !
process !
team
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VISUALISING
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WHAT ARE WE DOING?
We want to visualise flight data and link this to !
• tourism and related expenditure, • growth of airports • tweets sent from airports.
Per continent. Through time.
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Context !
what are we interested in !
how did we get to the idea
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VISUALISING
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WHAT DATA ARE WE USING?
LOCATION
TIME
AIRPORT DATA
TOURISM
IMMIGRATION
SOCIAL MEDIA
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VISUALISING
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STEP 1: GATHERING DATA
PROPRIETARY DATAOPEN DATA
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VISUALISING
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STEP 1: GATHERING DATA
PROPRIETARY DATAOPEN DATA
Community based
Public but proprietary
Confidential data
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VISUALISING
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CONTACTING THE INDUSTRY
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VISUALISING
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CONTACTING THE INDUSTRY
: (
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VISUALISING
FLIGHTSVISUALISING
FLIGHTS
CONTACTING THE INDUSTRY
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VISUALISING
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CONTACTING THE INDUSTRY: )
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VISUALISING
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CONTACTING THE INDUSTRY
AIRPORT TRANSFERS DATA
to judge the quality of public system, airport centrality and
safety of cities
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VISUALISING
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DISCOVERING DATA PROVIDERS
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VISUALISING
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http://openflights.org/data.html
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VISUALISING
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http://openflights.org/data.html
6977 5903
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VISUALISING
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http://data.un.org/DocumentData.aspx?id=353#19
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VISUALISING
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http://data.worldbank.org/indicator/ST.INT.ARVL/countries/1W?display=default
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#twitteranalysisIf we could gain assess to the Twitter API: !Analysis of tweets !#airport !Plot a tweet density map !If not globally !#heathrow – analysis destinations
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VISUALISING
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STEP 2: STANDARDISING THE DATA
.CSV FILES
.JSON FILES
.XLS FILES
• the different fields don’t match • airplane data in vector format • others have geo-cordinates • we don’t know where airports have been open so we will
scrape data from DBpedia. !
We will use Python parsing and data structures to standardise the data
!
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STEP 3: CORRELATE THE DATA
• Airports opening v. Tourism (expenditure & people)Tourism expenditure v. number of tourists Airports opening v. Growth of country !
• Airline routes — per continent (in and out) !
• Graph and statistical analysis on routes:Aim I: define the top connected areas per continentAim II: identify longest and shortest journeys
!
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STEP 3: VISUALISE THE DATA
Choropleth map • growth of airports • increase in tourism per country Map of flight routes • representing airports as nodes/deduce
linking airports and most visited cities
Articulation points graph • most connected cities !
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STEP 3: VISUALISE THE DATA
Statistical visualisation • for a variety of our data !!Density map for tweets per airport • deduce the most social airport/
destinations of twitter users
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VISUALISING
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VISUALISING
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Chloropleth Map
Number of Tourists each
year (over time duration)
Tourist Expenditure in
country
Net Immigration Number of airports/flights from
airport
GDP
Population
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Python: Statistical purposes Pandas Numpy
Python: Visualisation Basemap
Matplotlib
Python: Graphs Networkx
Python: Text analysis NLTK
HTML and SVG for real-time in case we wanna be adventurous
STEP 5: IDENTIFYING SUITABLE TECHNOLOGIES
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Mekhi: Basemap and Numpy expert
Virginia: NLTK and MatPlotLib master
Nicola: Code juggler and data wrangler
Hacker
Statistician
Artist
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MISSION
Not just a visualisation of data, but a story with !
equilibrium of colours, proportions !
finding interesting correlations !!
#sexybarcharts
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VISUALISING
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NEXT IS WHAT HAPPENS NEXT
Data-Driven Journalism
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ENJOY YOUR
FLIGHT:)