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

fabi-cast / nature-warm-world