Whataflight - Data Visualisation on Flight and Tourism Data around the World
•
0 likes•881 views
Recommended
Recommended
More Related Content
Similar to Whataflight - Data Visualisation on Flight and Tourism Data around the World
Similar to Whataflight - Data Visualisation on Flight and Tourism Data around the World (20)
Recently uploaded
Recently uploaded (20)
Whataflight - Data Visualisation on Flight and Tourism Data around the World
- 3. VISUALISING FLIGHTS VISUALISING 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.
- 4. VISUALISING FLIGHTS VISUALISING FLIGHTS Context ! what are we interested in ! how did we get to the idea
- 5. VISUALISING FLIGHTS VISUALISING FLIGHTS WHAT DATA ARE WE USING? LOCATION TIME AIRPORT DATA TOURISM IMMIGRATION SOCIAL MEDIA
- 6. VISUALISING FLIGHTS VISUALISING FLIGHTS STEP 1: GATHERING DATA PROPRIETARY DATAOPEN DATA
- 7. VISUALISING FLIGHTS VISUALISING FLIGHTS STEP 1: GATHERING DATA PROPRIETARY DATAOPEN DATA Community based Public but proprietary Confidential data
- 12. VISUALISING FLIGHTS VISUALISING FLIGHTS CONTACTING THE INDUSTRY AIRPORT TRANSFERS DATA to judge the quality of public system, airport centrality and safety of cities
- 17. VISUALISING FLIGHTS http://data.worldbank.org/indicator/ST.INT.ARVL/countries/1W?display=default
- 18. VISUALISING FLIGHTS #twitteranalysis If we could gain assess to the Twitter API: ! Analysis of tweets ! #airport ! Plot a tweet density map ! If not globally ! #heathrow – analysis destinations
- 19. VISUALISING FLIGHTS VISUALISING 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
- 20. VISUALISING FLIGHTS VISUALISING 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 continent Aim II: identify longest and shortest journeys !
- 21. 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 !
- 22. 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
- 24. 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
- 25. 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
- 26. VISUALISING FLIGHTS Mekhi: Basemap and Numpy expert Virginia: NLTK and MatPlotLib master Nicola: Code juggler and data wrangler Hacker Statistician Artist
- 27. VISUALISING FLIGHTS MISSION Not just a visualisation of data, but a story with ! equilibrium of colours, proportions ! finding interesting correlations ! ! #sexybarcharts
- 28. VISUALISING FLIGHTS NEXT IS WHAT HAPPENS NEXT Data-Driven Journalism