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Utilities for manipulating geospatial, (Geo)JSON, and (Geo)TIFF data.

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Utilities for manipulating geospatial, (Geo)JSON, and (Geo)TIFF data

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PyGeoUtils: Utilities for (Geo)JSON and (Geo)TIFF Conversion

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PyGeoUtils is a part of HyRiver software stack that is designed to aid in hydroclimate analysis through web services. This package provides utilities for manipulating (Geo)JSON and (Geo)TIFF responses from web services. These utilities are:

  • Coordinates: Generate validated and normalized coordinates in WGS84.

  • GeoBSpline: Create B-spline from a geopandas.GeoDataFrame of points.

  • smooth_linestring: Smooth a shapely.geometry.LineString using B-spline.

  • bspline_curvature: Compute tangent angles, curvature, and radius of curvature of a B-Spline at any points along the curve.

  • arcgis2geojson: Convert ESRIGeoJSON format to GeoJSON.

  • break_lines: Break lines at specified points in a given direction.

  • gtiff2xarray: Convert (Geo)Tiff byte responses to xarray.Dataset.

  • json2geodf: Create geopandas.GeoDataFrame from (Geo)JSON responses

  • snap2nearest: Find the nearest points on a line to a set of points.

  • xarray2geodf: Vectorize a xarray.DataArray to a geopandas.GeoDataFrame.

  • geodf2xarray: Rasterize a geopandas.GeoDataFrame to a xarray.DataArray.

  • xarray_geomask: Mask a xarray.Dataset based on a geometry.

  • query_indices: A wrapper around geopandas.sindex.query_bulk. However, instead of returning an array of positional indices, it returns a dictionary of indices where keys are the indices of the input geometry and values are a list of indices of the tree geometries that intersect with the input geometry.

  • nested_polygons: Determining nested (multi)polygons in a geopandas.GeoDataFrame.

  • multi2poly: For converting a MultiPolygon to a Polygon in a geopandas.GeoDataFrame.

  • geometry_reproject: For reprojecting a geometry (bounding box, list of coordinates, or any shapely.geometry) to a new CRS.

You can find some example notebooks here.

You can also try using PyGeoUtils without installing it on your system by clicking on the binder badge. A Jupyter Lab instance with the HyRiver stack pre-installed will be launched in your web browser, and you can start coding!

Moreover, requests for additional functionalities can be submitted via issue tracker.


If you use any of HyRiver packages in your research, we appreciate citations:

    author = {Chegini, Taher and Li, Hong-Yi and Leung, L. Ruby},
    doi = {10.21105/joss.03175},
    journal = {Journal of Open Source Software},
    month = {10},
    number = {66},
    pages = {1--3},
    title = {{HyRiver: Hydroclimate Data Retriever}},
    volume = {6},
    year = {2021}


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

We start by smoothing a shapely.geometry.LineString using B-spline:

import pygeoutils as pgu
from shapely import LineString

line = LineString(
        (-97.06138, 32.837),
        (-97.06133, 32.836),
        (-97.06124, 32.834),
        (-97.06127, 32.832),
line = pgu.geometry_reproject(line, 4326, 5070)
sp = pgu.smooth_linestring(line, 5070, 5)
line_sp = pgu.geometry_reproject(sp.line, 5070, 4326)

Next, we use PyGeoOGC to access National Wetlands Inventory from WMS, and FEMA National Flood Hazard via WFS, then convert the output to xarray.Dataset and GeoDataFrame, respectively.

from pygeoogc import WFS, WMS, ServiceURL
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],
crs = 4326

wms = WMS(
r_dict = wms.getmap_bybox(
canopy = pgu.gtiff2xarray(r_dict, geometry, crs)

mask = canopy > 60
canopy_gdf = pgu.xarray2geodf(canopy, "float32", mask)

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


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

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