fabi-cast/nature-warm-world

This is a repository containing all the material relevant to my Capstone Project at Data Analytics Bootcamp (Ironhack)

Nature in a Warming World

Capstone project in Data Analytics Bootcamp, Dic. 2019

Content

• Project Description
• Goal
• Data Sources
• Workflow
• Tech Stack
• Organization
• Links

Project Description

A global analysis about the status of Wildlife and their relationship with the main variables affected by climate change.

Data Sources

1. International Union for Conservation of Nature IUCN RED List API -v3: Threatened species around the World data. In order to access the data it is necessary to ask for a token.

2. World Bank: Climate change knowdledge portal: Climate change related variables, precipitation and average temperatures monthly and yearly for each country.

3. National Centers for Environmental Information: Temperature Anomalies dataset.

4. EONET API v.2.1: Natural events ocurrences around the World.

5. NASA Climate: Sea level rising measurements.

Organization

This repository contains:

• data-cleaning.ipnyb: this notebook shows the extraction and cleaning process of the datasets used in this project. Also, the curated data is exported as .csv file.
• nature-warming-world.ipnyb: this notebook contains the exploratory data analysis and data visualization tasks.
• data: This folder contains the curated data using through the analysis. By policies of the IUCN API, it is not possible to distribute the original data set.
• charts: Contains static images of the data viz created. Most of them are originaly interactive, produced using Bokeh, Holoviews, HvPlot and Plotly. Unfortunately, they cannot be rendered in GitHub.

Workflow

1. Asking the right questions:

• Is climate change an uniform phenomenon around the planet?
• What are the species more threatened by global warming in each biorealm?
• How do CO2 emissions, temperature and precipitation vary over time in each biorealm?
• Are these variables correlated with the loss of species in each biorealm?

2. Data wrangling: We start by gathering data from the respective APIs and from the other sources. The cleaning process is a little bit extensive, so we have dedicated a separated notebook showing this process. As final step of this stage, we export curated data to the output folder.

3. Exploratory Data Analysis: First, we wanted to investigate whether is possible to cluster using K-means regions of the planet using temperature and precipitation data. Although this attempt did not give us a meaningful clustering for the problem we are adressing, we decided to classify regions by Biorealms to conduct our analysis.

4. Data Visualization.

5. Presentation.

Tech Stack

• Pandas
• Numpy
• Bokeh
• HoloViews
• Hvplot
• Matplotlib
• Seaborn
• Statsmodels
• Tableau

Environment Dependencies:

• _ipyw_jlab_nb_ext_conf=0.1.0=py37_0
• alabaster=0.7.12=py37_0
• anaconda=2019.10=py37_0
• anaconda-client=1.7.2=py37_0
• anaconda-navigator=1.9.7=py37_0
• anaconda-project=0.8.3=py_0
• appnope=0.1.0=py37_0
• appscript=1.1.0=py37h1de35cc_0
• asn1crypto=1.0.1=py37_0
• astroid=2.3.1=py37_0
• astropy=3.2.2=py37h1de35cc_0
• atomicwrites=1.3.0=py37_1
• attrs=19.2.0=py_0
• babel=2.7.0=py_0
• backcall=0.1.0=py37_0
• backports=1.0=py_2
• backports.functools_lru_cache=1.5=py_2
• backports.os=0.1.1=py37_0
• backports.shutil_get_terminal_size=1.0.0=py37_2
• backports.tempfile=1.0=py_1
• backports.weakref=1.0.post1=py_1
• beautifulsoup4=4.8.0=py37_0
• bitarray=1.0.1=py37h1de35cc_0
• bkcharts=0.2=py37_0
• blas=1.0=mkl
• bleach=3.1.0=py37_0
• blosc=1.16.3=hd9629dc_0
• bokeh=1.3.4=py37_0
• boto=2.49.0=py37_0
• bottleneck=1.2.1=py37h1d22016_1
• bzip2=1.0.8=h1de35cc_0
• ca-certificates=2019.8.28=0
• cairo=1.14.12=hc4e6be7_4
• cartopy=0.17.0=py37haea56ea_1
• certifi=2019.9.11=py37_0
• cffi=1.12.3=py37hb5b8e2f_0
• cftime=1.0.4.2=py37h1d22016_0
• chardet=3.0.4=py37_1003
• click=7.0=py37_0
• click-plugins=1.1.1=py_0
• cligj=0.5.0=py37_0
• cloudpickle=1.2.2=py_0
• clyent=1.2.2=py37_1
• colorama=0.4.1=py37_0
• colorcet=2.0.2=py_0
• colorlover=0.3.0=py_0
• conda=4.7.12=py37_0
• conda-build=3.18.9=py37_3
• conda-env=2.6.0=1
• conda-package-handling=1.6.0=py37h1de35cc_0
• conda-verify=3.4.2=py_1
• contextlib2=0.6.0=py_0
• cryptography=2.7=py37ha12b0ac_0
• cufflinks-py=0.13.0=py_0
• curl=7.65.3=ha441bb4_0
• cycler=0.10.0=py37_0
• cython=0.29.13=py37h0a44026_0
• cytoolz=0.10.0=py37h1de35cc_0
• dask=2.5.2=py_0
• dask-core=2.5.2=py_0
• datashader=0.8.0=py_0
• datashape=0.5.4=py37_1
• dbus=1.13.6=h90a0687_0
• decorator=4.4.0=py37_1
• defusedxml=0.6.0=py_0
• distributed=2.5.2=py_0
• docutils=0.15.2=py37_0
• entrypoints=0.3=py37_0
• et_xmlfile=1.0.1=py37_0
• expat=2.2.6=h0a44026_0
• fastcache=1.1.0=py37h1de35cc_0
• filelock=3.0.12=py_0
• fiona=1.8.4=py37h9a122fd_0
• flask=1.1.1=py_0
• fontconfig=2.13.0=h5d5b041_1
• freetype=2.9.1=hb4e5f40_0
• freexl=1.0.5=h1de35cc_0
• fsspec=0.5.2=py_0
• future=0.17.1=py37_0
• fuzzywuzzy=0.17.0=py_0
• gdal=2.3.3=py37hbe65578_0
• geopandas=0.6.1=py_0
• geos=3.7.1=h0a44026_0
• geoviews=1.6.5=py_0
• geoviews-core=1.6.5=py_0
• get_terminal_size=1.0.0=h7520d66_0
• gettext=0.19.8.1=h15daf44_3
• gevent=1.4.0=py37h1de35cc_0
• giflib=5.1.4=h1de35cc_1
• glib=2.56.2=hd9629dc_0
• glob2=0.7=py_0
• gmp=6.1.2=hb37e062_1
• gmpy2=2.0.8=py37h6ef4df4_2
• greenlet=0.4.15=py37h1de35cc_0
• h5py=2.9.0=py37h3134771_0
• hdf4=4.2.13=h39711bb_2
• hdf5=1.10.4=hfa1e0ec_0
• heapdict=1.0.1=py_0
• holoviews=1.12.7=py_0
• html5lib=1.0.1=py37_0
• hvplot=0.5.2=py_0
• icu=58.2=h4b95b61_1
• idna=2.8=py37_0
• imageio=2.6.0=py37_0
• imagesize=1.1.0=py37_0
• importlib_metadata=0.23=py37_0
• intel-openmp=2019.4=233
• ipykernel=5.1.2=py37h39e3cac_0
• ipython=7.8.0=py37h39e3cac_0
• ipython_genutils=0.2.0=py37_0
• ipywidgets=7.5.1=py_0
• isort=4.3.21=py37_0
• itsdangerous=1.1.0=py37_0
• jbig=2.1=h4d881f8_0
• jdcal=1.4.1=py_0
• jedi=0.15.1=py37_0
• jinja2=2.10.3=py_0
• joblib=0.13.2=py37_0
• jpeg=9b=he5867d9_2
• json-c=0.13.1=h3efe00b_0
• json5=0.8.5=py_0
• jsonschema=3.0.2=py37_0
• matplotlib=3.1.1=py37h54f8f79_0
• matplotlib-base=3.1.1=py37h3a684a6_1
• mccabe=0.6.1=py37_1
• mistune=0.8.4=py37h1de35cc_0
• mkl=2019.4=233
• mkl-service=2.3.0=py37hfbe908c_0
• mkl_fft=1.0.14=py37h5e564d8_0
• mkl_random=1.1.0=py37ha771720_0
• mock=3.0.5=py37_0
• more-itertools=7.2.0=py37_0
• mpc=1.1.0=h6ef4df4_1
• mpfr=4.0.1=h3018a27_3
• mpmath=1.1.0=py37_0
• msgpack-python=0.6.1=py37h04f5b5a_1
• multipledispatch=0.6.0=py37_0
• munch=2.5.0=py_0
• navigator-updater=0.2.1=py37_0
• nbconvert=5.6.0=py37_1
• nbformat=4.4.0=py37_0
• ncurses=6.1=h0a44026_1
• netcdf4=1.4.2=py37h13743db_0
• networkx=2.3=py_0
• nltk=3.4.5=py37_0
• nodejs=10.13.0=h0a44026_0
• nose=1.3.7=py37_2
• notebook=6.0.1=py37_0
• numba=0.45.1=py37h6440ff4_0
• numexpr=2.7.0=py37h7413580_0
• numpy=1.17.2=py37h99e6662_0
• numpy-base=1.17.2=py37h6575580_0
• numpydoc=0.9.1=py_0
• olefile=0.46=py37_0
• openjpeg=2.3.0=hb95cd4c_1
• openpyxl=3.0.0=py_0
• openssl=1.1.1d=h1de35cc_2
• owslib=0.19.0=py_0
• packaging=19.2=py_0
• pandas=0.25.1=py37h0a44026_0
• pandoc=2.2.3.2=0
• pandocfilters=1.4.2=py37_1
• panel=0.6.4=py_0
• param=1.9.2=py_0
• parso=0.5.1=py_0
• partd=1.0.0=py_0
• path.py=12.0.1=py_0
• pathlib2=2.3.5=py37_0
• patsy=0.5.1=py37_0
• pcre=8.43=h0a44026_0
• pep8=1.7.1=py37_0
• pexpect=4.7.0=py37_0
• pickleshare=0.7.5=py37_0
• pillow=6.2.0=py37hb68e598_0
• pip=19.2.3=py37_0
• pixman=0.38.0=h1de35cc_0
• pkginfo=1.5.0.1=py37_0
• plotly=4.2.1=py_0
• pluggy=0.13.0=py37_0
• ply=3.11=py37_0
• poppler=0.65.0=ha097c24_1
• poppler-data=0.4.9=0
• proj4=5.2.0=h0a44026_1
• prometheus_client=0.7.1=py_0
• prompt_toolkit=2.0.10=py_0
• psutil=5.6.3=py37h1de35cc_0
• ptyprocess=0.6.0=py37_0
• py=1.8.0=py37_0
• py-lief=0.9.0=py37h1413db1_2
• pycodestyle=2.5.0=py37_0
• pycosat=0.6.3=py37h1de35cc_0
• pycparser=2.19=py37_0
• pycrypto=2.6.1=py37h1de35cc_9
• pyct=0.4.6=py_0
• pyct-core=0.4.6=py_0
• pycurl=7.43.0.3=py37ha12b0ac_0
• pyepsg=0.4.0=py_0
• pyflakes=2.1.1=py37_0
• pygments=2.4.2=py_0
• pykdtree=1.3.1=py37h3b54f70_1002
• pylint=2.4.2=py37_0
• pyodbc=4.0.27=py37h0a44026_0
• pyopenssl=19.0.0=py37_0
• pyparsing=2.4.2=py_0
• pyproj=1.9.6=py37h9c430a6_0
• pyqt=5.9.2=py37h655552a_2
• pyrsistent=0.15.4=py37h1de35cc_0
• pyshp=2.1.0=py_0
• pysocks=1.7.1=py37_0
• pytables=3.5.2=py37h5bccee9_1
• pytest=5.2.1=py37_0
• pytest-arraydiff=0.3=py37h39e3cac_0
• pytest-astropy=0.5.0=py37_0
• pytest-doctestplus=0.4.0=py_0
• pytest-openfiles=0.4.0=py_0
• pytest-remotedata=0.3.2=py37_0
• python=3.7.4=h359304d_1
• python-dateutil=2.8.0=py37_0
• python-levenshtein=0.12.0=py37h0b31af3_1001
• python-libarchive-c=2.8=py37_13
• python.app=2=py37_9
• pytz=2019.3=py_0
• pyviz_comms=0.7.2=py_0
• pywavelets=1.0.3=py37h1d22016_1
• pyyaml=5.1.2=py37h1de35cc_0
• pyzmq=18.1.0=py37h0a44026_0
• qt=5.9.7=h468cd18_1
• qtawesome=0.6.0=py_0
• qtconsole=4.5.5=py_0
• qtpy=1.9.0=py_0
• readline=7.0=h1de35cc_5
• requests=2.22.0=py37_0
• retrying=1.3.3=py37_2
• ripgrep=0.10.0=hc07d326_0
• rope=0.14.0=py_0
• rtree=0.8.3=py37_0
• ruamel_yaml=0.15.46=py37h1de35cc_0
• scikit-image=0.15.0=py37h0a44026_0
• scikit-learn=0.21.3=py37h27c97d8_0
• scipy=1.3.1=py37h1410ff5_0
• seaborn=0.9.0=py37_0
• send2trash=1.5.0=py37_0
• setuptools=41.4.0=py37_0
• shapely=1.6.4=py37he8793f5_0
• simplegeneric=0.8.1=py37_2
• singledispatch=3.4.0.3=py37_0
• sip=4.19.8=py37h0a44026_0
• six=1.12.0=py37_0
• snappy=1.1.7=he62c110_3
• snowballstemmer=2.0.0=py_0
• sortedcollections=1.1.2=py37_0
• sortedcontainers=2.1.0=py37_0
• soupsieve=1.9.3=py37_0
• sphinx=2.2.0=py_0
• sphinxcontrib=1.0=py37_1
• sphinxcontrib-applehelp=1.0.1=py_0
• sphinxcontrib-devhelp=1.0.1=py_0
• sphinxcontrib-htmlhelp=1.0.2=py_0
• sphinxcontrib-jsmath=1.0.1=py_0
• sphinxcontrib-qthelp=1.0.2=py_0
• sphinxcontrib-serializinghtml=1.1.3=py_0
• sphinxcontrib-websupport=1.1.2=py_0
• spyder=3.3.6=py37_0
• spyder-kernels=0.5.2=py37_0
• sqlalchemy=1.3.9=py37h1de35cc_0
• sqlite=3.30.0=ha441bb4_0
• statsmodels=0.10.1=py37h1d22016_0
• sympy=1.4=py37_0
• tbb=2019.8=h04f5b5a_0
• tblib=1.4.0=py_0
• terminado=0.8.2=py37_0
• testpath=0.4.2=py37_0
• tk=8.6.8=ha441bb4_0
• toolz=0.10.0=py_0
• tornado=6.0.3=py37h1de35cc_0
• tqdm=4.36.1=py_0
• traitlets=4.3.3=py37_0
• unicodecsv=0.14.1=py37_0
• unixodbc=2.3.7=h1de35cc_0
• urllib3=1.24.2=py37_0
• wcwidth=0.1.7=py37_0
• webencodings=0.5.1=py37_1
• werkzeug=0.16.0=py_0
• wheel=0.33.6=py37_0
• widgetsnbextension=3.5.1=py37_0
• wrapt=1.11.2=py37h1de35cc_0
• wurlitzer=1.0.3=py37_0
• xarray=0.14.0=py_0
• xerces-c=3.2.2=h44e365a_0
• xlrd=1.2.0=py37_0
• xlsxwriter=1.2.1=py_0
• xlwings=0.15.10=py37_0
• xlwt=1.3.0=py37_0
• xz=5.2.4=h1de35cc_4
• yaml=0.1.7=hc338f04_2
• zeromq=4.3.1=h0a44026_3
• zict=1.0.0=py_0
• zipp=0.6.0=py_0
• zlib=1.2.11=h1de35cc_3
• zstd=1.3.7=h5bba6e5_0
• pip:
  • geocoder==1.38.1
  • geohash-hilbert==1.3.1
  • geopip==1.1
  • lightning-python==1.2.1
  • ratelim==0.1.6
  • squarify==0.4.3

Links

IUCN

Global Carbon Atlas

Goddard Institute for Space Studies