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A set of utilities for manipulating (Geo)JSON and (Geo)TIFF data.

Project description

Package Description
Hydrodata Access NWIS, HCDN 2009, NLCD, and SSEBop databases
PyGeoOGC Query data from any ArcGIS RESTful-, WMS-, and WFS-based services
PyGeoUtils Convert responses from PyGeoOGC’s supported web services to datasets
PyNHD Access NLDI and WaterData web services for navigating the NHDPlus database
Py3DEP Access topographic data through the 3D Elevation Program (3DEP) web service
PyDaymet Access the Daymet database for daily climate data

PyGeoUtils: Manipulate (Geo)JSON and (Geo)TIFF data

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🚨 This package is under heavy development and breaking changes are likely to happen. 🚨


PyGeoUtils is a part of Hydrodata software stack and provides utilities for manipulating (Geo)JSON and (Geo)TIFF data. These utilities are:

  • json2geodf: For converting (Geo)JSON objects to GroPandas dataframe.
  • arcgis2geojson: For converting ESRIGeoJSON objects to standard GeoJSON format.
  • gtiff2xarray: For converting (Geo)TIFF objects to xarray datasets.
  • xarray_geomask: For masking a xarray.Dataset or xarray.DataArray using a polygon.

All these function handle all necessary CRS transformations. Moreover, requests for additional functionalities can be submitted via issue tracker.


You can install PyGeoUtils using pip after installing libgdal on your system (for example, in Ubuntu run sudo apt install libgdal-dev):

$ pip install pygeoutils

Alternatively, PyGeoUtils can be installed from the conda-forge repository using Conda:

$ conda install -c conda-forge pygeoutils

Quick start

To demonstrate capabilities of PyGeoUtils lets use PyGeoOGC to access National Wetlands Inventory from WMS, and FEMA National Flood Hazard via WFS, then convert the output to GeoDataFrame and xarray.Dataset using PyGeoUtils.

import pygeoutils as geoutils
from pygeoogc import WFS, WMS
from shapely.geometry import Polygon

geometry =  Polygon(
        [-118.72, 34.118],
        [-118.31, 34.118],
        [-118.31, 34.518],
        [-118.72, 34.518],
        [-118.72, 34.118],

url_wms = ""
wms = WMS(
r_dict = wms.getmap_bybox(
wetlands = geoutils.gtiff2xarray(r_dict, geometry, "epsg:4326")

url_wfs = ""
wfs = WFS(
r = wfs.getfeature_bybox(geometry.bounds, box_crs="epsg:4326")
flood = geoutils.json2geodf(r.json(), "epsg:4269", "epsg:4326")


Contributions are very welcomed. Please read CONTRIBUTING.rst file for instructions.

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