starepandas 0.6.6

STARE pandas extensions

STAREPandas

STAREpandas adds SpatioTemporal Adaptive Resolution Encoding (STARE) support to pandas DataFrames.

Introduction

STAREPandas is the STARE pendant to GeoPandas. It makes working with geospatial data in python easier. It provides file and database I/O functionality and allows to easily perform STARE based spatial operations that would otherwise require a (STARE-extended) spatial database or a geographic information system.

In STAREDataFrames, geometries are represented as sets of STARE triangles or ”trixels”; analogously to GeoPandas geodataframes which represent geometries as WKT. In STARE dataframes, points are represented as STARE trixels at the HTM tree’s leaf level. Polygons are represented as sets of STARE trixels that cover the polygon.

STAREPandas also extends the geopandas file I/O functionality to load some (raster) formats of remote sensing granules and tiles (MOD09, MOD09GA, VNP03) through pyhdf and netcdf4.

Installation

pyhdf

STAREPandas depends on pyhdf to read hdf4-eos granules, requiring libhdf4-dev, to build.

Tested on python 3.7.6

On Ubuntu 20.04:

apt install libhdf4-dev 

On Centos7:

yum install hdf-devel.x86_64

Alternatively, pyhdf can also be found on conda

conda install -c conda-forge pyhdf

pystare

STAREPandas is built on top of pystare.

pip3 install pystare

STAREPandas

It is recommendable to install pip packages in a Virtual Environment

mkvirtualevironment starepandas

Make sure pip is up-to-date.

Then install STAREPandas from github.

pip3 install starepandas

Note

Some of the examples require Rtree-linux to be installed to run geopandas spatial joins. As of 2020-08-20, I could not make this work on Centos7 with rtree>0.9 (9.4) as it requires GLIBCXX_3.4.21. I therefor downgrade rtree to rtree-0.8.3 on Centos7

pip3 install "rtree>=0.8,<0.9

This is likely related to rtree issue 120

Tests

cd starepandas/
pytests

Some of the examples further require bokeh and pandas_bokeh

Documentation

starepandas uses sphinx

The dependencies are in docs/source/requirements.txt

pip3 install -r docs/source/requirements.txt

Build the docs with e.g.

cd docs/
make html 

Features and usage

The examples/ folder contains notebooks that highlight the usage.

STAREPandas helps integrating STARE in the geospatial data workflow. Building on top of fiona and geopandas, STAREPandas allows to read almost any vector-based spatial data format and convert lat/lon and well-known-text (WKT) representation to STARE indices and covers.

path = geopandas.datasets.get_path('naturalearth_lowres')
world = geopandas.read_file(path)
africa = world[world.continent == 'Africa']
stare = starepandas.sids_from_gdf(africa, level=7, force_ccw=True)
africa = starepandas.STAREDataFrame(africa, stare=stare)

STAREPandas extends the geopandas rich plotting abilities and provides a simple method to generate visualizations of trixels:

trixels = africa.make_trixels()
africa.set_trixels(trixels, inplace=True)
africa.plot(ax=ax, trixels=True, boundary=True, column='name', linewidth=0.2)

STAREPandas extends the file I/O capability with the ability to read common remote-sensing granule data from HDF and netCDF files. STARE indices for the granules can either be generated on demand or read from a companion / sidedcar file.

path= 'data/MYD05_L2.A2020060.1635.061.2020061153519.hdf'
modis = starepandas.read_mod09(path, add_stare=True, adapt_resolution=True)

STAREPandas allows to carry out STARE-based spatial relation tests and spatial joins.

cities = ['Buenos Aires', 'Brasilia', 'Santiago',
          'Bogota', 'Caracas', 'Sao Paulo', 'Bridgetown']
latitudes = [-34.58, -15.78, -33.45, 4.60, 10.48, -23.55, 13.1]
longitudes = [-58.66, -47.91, -70.66, -74.08, -66.86, -46.63, -59.62]
data = {'City': cities,
        'Latitude': latitudes, 'Longitude': longitudes}
cities = starepandas.STAREDataFrame(data)
stare = starepandas.sids_from_xy(cities.Longitude, cities.Latitude, level=27)
cities.set_sids(stare, inplace=True)

countries = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
countries = countries.sort_values(by='name')
samerica = countries[countries.continent == 'South America']
stare = starepandas.sids_from_gdf(samerica, level=10, force_ccw=True)
samerica = starepandas.STAREDataFrame(samerica, stare=stare)

starepandas.stare_join(samerica, cities, how='left').head()

STAREPandas further allows for STARE-bases intersections:

fname = 'zip://data/amapoly_ivb.zip'
amazon = geopandas.read_file(fname)  # Nice flex
amazon = amazon.to_crs('EPSG:4326')

stare = starepandas.sids_from_gdf(amazon, level=10, force_ccw=True)
amazon = starepandas.STAREDataFrame(amazon, stare=stare)

stare_amazon = samerica.stare_intersection(amazon.make_sids.iloc[0])

Acknowledgments

2018-2021 STARE development supported by NASA/ACCESS-17 grant 80NSSC18M0118.