VISUALISING

FLIGHTS

VISUALISING

FLIGHTSNICOLA GRECO

MEKHI DHESI VIRGINIA ALONSO

VISUALISING

FLIGHTSVISUALISING

FLIGHTS

Content !

!

data !

process !

team

VISUALISING

FLIGHTSVISUALISING

FLIGHTS

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.

VISUALISING

FLIGHTSVISUALISING

FLIGHTS

Context !

what are we interested in !

how did we get to the idea

VISUALISING

FLIGHTSVISUALISING

FLIGHTS

WHAT DATA ARE WE USING?

LOCATION

TIME

AIRPORT DATA

TOURISM

IMMIGRATION

SOCIAL MEDIA

VISUALISING

FLIGHTSVISUALISING

FLIGHTS

STEP 1: GATHERING DATA

PROPRIETARY DATAOPEN DATA

VISUALISING

FLIGHTSVISUALISING

FLIGHTS

STEP 1: GATHERING DATA

PROPRIETARY DATAOPEN DATA

Community based

Public but proprietary

Confidential data

VISUALISING

FLIGHTSVISUALISING

FLIGHTS

CONTACTING THE INDUSTRY

VISUALISING

FLIGHTSVISUALISING

FLIGHTS

CONTACTING THE INDUSTRY

: (

VISUALISING

FLIGHTSVISUALISING

FLIGHTS

CONTACTING THE INDUSTRY

VISUALISING

FLIGHTSVISUALISING

FLIGHTS

CONTACTING THE INDUSTRY: )

VISUALISING

FLIGHTSVISUALISING

FLIGHTS

CONTACTING THE INDUSTRY

AIRPORT TRANSFERS DATA

to judge the quality of public system, airport centrality and

safety of cities

VISUALISING

FLIGHTSVISUALISING

FLIGHTS

DISCOVERING DATA PROVIDERS

VISUALISING

FLIGHTS

http://openflights.org/data.html

VISUALISING

FLIGHTS

http://openflights.org/data.html

6977 5903

VISUALISING

FLIGHTS

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

VISUALISING

FLIGHTS

http://data.worldbank.org/indicator/ST.INT.ARVL/countries/1W?display=default

VISUALISING

FLIGHTS

#twitteranalysisIf we could gain assess to the Twitter API: !Analysis of tweets !#airport !Plot a tweet density map !If not globally !#heathrow – analysis destinations

VISUALISING

FLIGHTSVISUALISING

FLIGHTS

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

!

VISUALISING

FLIGHTSVISUALISING

FLIGHTS

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

!

VISUALISING

FLIGHTS0

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 !

VISUALISING

FLIGHTS0

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

VISUALISING

FLIGHTS

VISUALISING

FLIGHTS

Chloropleth Map

Number of Tourists each

year (over time duration)

Tourist Expenditure in

country

Net Immigration Number of airports/flights from

airport

GDP

Population

VISUALISING

FLIGHTS

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

VISUALISING

FLIGHTS

Mekhi: Basemap and Numpy expert

Virginia: NLTK and MatPlotLib master

Nicola: Code juggler and data wrangler

Hacker

Statistician

Artist

VISUALISING

FLIGHTS

MISSION

Not just a visualisation of data, but a story with !

equilibrium of colours, proportions !

finding interesting correlations !!

#sexybarcharts

VISUALISING

FLIGHTS

NEXT IS WHAT HAPPENS NEXT

Data-Driven Journalism

VISUALISING

FLIGHTS

ENJOY YOUR

FLIGHT:)