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Visualisation cartographique de données de recensement d’oiseaux

Database used

The Cornell Lab has authorized us on reasoned request to use to a part of the database EBird. This represents all censuses of North America between 2003 and 2016. Each year corresponds to a csv. Each line of the base corresponds to a census. The Fields of the Base are:

  • SAMPLING EVENT ID: nominal, unique census identifier
  • LOC ID: nominal, unique identifier of the geographical location
  • LATITUDE: quantitative continuous
  • LONGITUDE: quantitative continuous
  • YEAR: categorial ordinal
  • MONTH: categorial ordinal
  • DAY: categorial ordinal
  • TIME: TimeStamp
  • COUNTRY: catégorial nominal
  • STATE PROVINCE: catégorial nominal
  • COUNTY: catégorial nominal
  • COUNT TYPE: catégorial nominal
  • EFFORT HRS: quantitativ continuous, duration of observation in hours
  • EFFORT DISTANCE KM: quantitativ continueous, distance traveled during the observation
  • EFFORT AREA HA: continuous quantitative, area covered during the observation
  • OBSERVER ID: nominal categorical, unique identifier of the observer
  • NUMBER OBSERVERS: quantative (integer), number of observers
  • GROUP ID: catégorial nominal
  • {Species name}: quantitative (integer), more than 3000 columns (1 per species) with the number of individuals observed

Proposed visualization

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Get started

Download the repository

Dependencies

Note that this project has been performed using python3

Dependencies : Plotly, Dask (pip3 install youNameIt)

You will also need the Geos library. For Mac users, brew install geos

The last dependency is basemap. You can install it using this command :

sudo -H pip3 install https://github.com/matplotlib/basemap/archive/v1.1.0.tar.gz

In command line: python Birds_viz.py

More insight

See the report: Report_Birds_viz.pdf

DataViz

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