multiurbanpy

Computing multi-scale urban features (building areas and volumes, tree canopy, terrain/topographic indices...) in Python.

multiurbanpy is an analogy to the multilandr package [1] to compute multi-scale landscape metrics in R. However, instead of lansdcape metrics¹, multiurbanpy computes mutli-scale metrics for urban landscapes such as building areas and volumes, tree canopy cover or topographic features.

Example application to compute the proportion of tree canopy around (with multiple buffer radii) weather stations in Zurich, Switzerland:

(C) OpenStreetMap contributors, tiles style by Humanitarian OpenStreetMap Team hosted by OpenStreetMap France

Overview

Start by instantiating a MultiScaleFeatureComputer for a given region of interest. Then, given a list of site locations, you can compute urban features at multiple scales, i.e., based on the landscape surrounding each site for multiple buffer radii:

import swisstopopy

import multiurbanpy as mup

# parameters
region = "EPFL"
crs = "epsg:2056"
buffer_dists = [10, 25, 50, 100]
grid_res = 200

# instantiate the multi-scale feature computer
msfc = mup.MultiScaleFeatureComputer(region=region, crs=crs)

# generate a regular grid of points/sites within the region
site_gser = msfc.generate_regular_grid_gser(grid_res, geometry_type="point")

# get a tree canopy raster from swisstopo data
dst_filepath = "tree-canopy.tif"
swisstopopy.get_tree_canopy_raster(region, dst_filepath)
tree_val = 1  # pixel value representing a tree in the canopy raster

# generate a DEM raster from swisstopo data
dem_filepath = "dem.tif"
swisstopopy.get_dem_raster(region, dem_filepath)

# compute multi-scale features

# building areas from OpenStreetMap buildings (via osmnx)
features_df = pd.concat(
    [
        msfc.compute_building_features(site_gser, buffer_dists),
        msfc.compute_tree_features(
            tree_canopy_filepath, site_gser, buffer_dists, tree_val
        ),
        msfc.compute_topo_features_df(
            dem_filepath,
            site_gser,
            buffer_dists,
        ),
    ],
    axis="columns",
)
features_df.head()

grid_cell_id	buffer_dist	building_area	tree_canopy	slope	northness	tpi
0	10	313.654849	0.000000	0.020963	0.180932	-0.006561
	25	3920.685613	0.014260	0.052408	0.023872	0.036682
	50	31365.484905	0.047746	0.070575	-0.006432	-0.075104
	100	250923.879244	0.043386	0.073637	0.006363	-0.716217
1	10	627.309698	0.000000	0.095521	0.228504	0.080963

See the overview notebook and the API documentation for more details on the features of multiurbanpy.

Installation

Like many other geospatial Python packages, multiurbanpy requires many base C libraries that cannot be installed with pip. Accordingly, the best way to install multiurbanpy is to use conda/mamba, i.e., in a given conda environment, run:

# or mamba install -c conda-forge geopandas
conda install -c conda-forge geopandas

Within the same conda environment, you can then install multiurbanpy using pip:

pip install https://github.com/martibosch/multiurbanpy/archive/main.zip

Acknowledgements

• This package was created with the martibosch/cookiecutter-geopy-package project template.

Footnotes

1. You can use the MultiScaleAnalysis class of pylandstats [2] to compute multi-scale landscape metrics in Python.

References

1. Huais, P. Y. (2024). Multilandr: An r package for multi-scale landscape analysis. Landscape Ecology, 39(8), 140.
2. Bosch, M. (2019). PyLandStats: An open-source Pythonic library to compute landscape metrics. PloS one, 14(12), e0225734.