whataflight - data visualisation on flight and tourism data around the world

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VISUALISING

FLIGHTS

VISUALISING

FLIGHTSNICOLA GRECO

MEKHI DHESI VIRGINIA ALONSO

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Content !

!

data !

process !

team

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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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WHAT DATA ARE WE USING?

LOCATION

TIME

AIRPORT DATA

TOURISM

IMMIGRATION

SOCIAL MEDIA

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STEP 1: GATHERING DATA

PROPRIETARY DATAOPEN DATA

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STEP 1: GATHERING DATA

PROPRIETARY DATAOPEN DATA

Community based

Public but proprietary

Confidential data

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CONTACTING THE INDUSTRY

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CONTACTING THE INDUSTRY

: (

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CONTACTING THE INDUSTRY

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CONTACTING THE INDUSTRY: )

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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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DISCOVERING DATA PROVIDERS

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http://openflights.org/data.html

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http://openflights.org/data.html

6977 5903

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http://data.un.org/DocumentData.aspx?id=353#19

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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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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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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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NEXT IS WHAT HAPPENS NEXT

Data-Driven Journalism

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ENJOY YOUR

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