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Visibility from GeoPandas data

Generate a visibility analysis from GeoPandas data (or a file opened with GeoPandas).

import geopandas as gpd
from cityseer.metrics import visibility

Open a GeoPandas dataset.

bldgs_gdf = gpd.read_file("data/madrid_buildings/madrid_bldgs.gpkg")
bldgs_gdf.head()

	mean_height	area	perimeter	compactness	orientation	volume	floor_area_ratio	form_factor	corners	shape_index	fractal_dimension	geometry
0	NaN	187.418714	58.669276	0.491102	40.235999	NaN	NaN	NaN	4	0.700787	1.026350	POLYGON ((448688.642 4492911, 448678.351 44928...
1	7.0	39.082821	26.992208	0.472874	10.252128	273.579749	78.165643	5.410857	4	0.687658	1.041691	POLYGON ((440862.665 4482604.017, 440862.64 44...
2	7.0	39.373412	27.050303	0.475086	10.252128	275.613883	78.746824	5.400665	4	0.689265	1.040760	POLYGON ((440862.681 4482608.269, 440862.665 4...
3	7.5	37.933979	26.739878	0.464266	10.252129	284.504846	75.867959	5.513124	4	0.681371	1.045072	POLYGON ((440862.705 4482612.365, 440862.681 4...
4	7.0	39.013701	26.972641	0.472468	10.183618	273.095907	78.027402	5.412350	4	0.687363	1.041798	POLYGON ((440880.29 4482607.963, 440880.274 44...

bldgs_gdf.crs

<Projected CRS: EPSG:25830>
Name: ETRS89 / UTM zone 30N
Axis Info [cartesian]:
- E[east]: Easting (metre)
- N[north]: Northing (metre)
Area of Use:
- name: Europe between 6°W and 0°W: Faroe Islands offshore; Ireland - offshore; Jan Mayen - offshore; Norway including Svalbard - offshore; Spain - mainland - onshore and offshore.
- bounds: (-6.0, 35.26, 0.01, 80.49)
Coordinate Operation:
- name: UTM zone 30N
- method: Transverse Mercator
Datum: European Terrestrial Reference System 1989 ensemble
- Ellipsoid: GRS 1980
- Prime Meridian: Greenwich

Use the visibility module to create a visibility analysis from GeoPandas data. The visibility will be generated based on the GeoPandas data provided to the visibility_from_gpd method.

Pay particular attention to:

The bounds parameter, which defines the geographical area in the same CRS as your dataset. The order is Left, Bottom, Right, Top.
The out_path parameter, which specifies where to save the output files. The filepath must exist. The extension of the output file will be .tif and the file will be saved in GeoTIFF format, which can be opened in QGIS.
The from_crs_code parameter, which specifies the coordinate reference system (CRS) to use for the input data. This should match the CRS of your GeoPandas dataset. If you are unsure, you can check the CRS of your GeoDataFrame using .crs.
The to_crs_code parameter, which specifies the coordinate reference system (CRS) to use for the output. The default is the local UTM, but you can change it to any valid CRS code.
The view_distance and resolution parameters, which control the analysis granularity.

There is a performance trade-off for the bounds, view_distance, and resolution. A larger view_distance or higher resolution will result in a more detailed graph but may take longer to compute and require more memory.

visibility.visibility_from_gpd(
    bldgs_gdf,
    bounds=(439658, 4473632, 440914, 4474972),
    out_path="recipes/visibility/images/madrid_vis",
    from_crs_code=25830,  # set geopandas CRS
    to_crs_code=25830,  # set output CRS
    view_distance=150,  # can use a larger view distance - but slower
    resolution=2,  # can use a higher resolution - but slower
)

INFO:cityseer.metrics.visibility:Preparing buildings raster.
INFO:cityseer.metrics.visibility:Running visibility.

The output will generate three files:

A density measure - the number of visible pixels.
A farness measure - the summed distance to visible pixels - favours farther views (up to view_distance).
A harmonic closeness measure - the summed inverse of the distance to visible pixels - favours close adjacency to open spaces.

Visualised as farness with values clipped to de-accentuate rooftops while recovering detail in relation to the ground.

Conclusion

This notebook demonstrated how to generate a visibility analysis from a GeoPandas GeoDataFrame of building footprints using visibility_from_gpd. By specifying bounds, CRS, view distance, and resolution, the method produces density, farness, and harmonic closeness rasters that capture the spatial pattern of visual openness and enclosure across the Madrid study area.

Next steps: To incorporate terrain elevation data, see Visibility from Raster.