12 geohash

Working withGgeohash in Vgrid DGGS¶

Full Vgrid DGGS documentation is available at vgrid document.

To work with Vgrid DGGS directly in GeoPandas and Pandas, please use vgridpandas. Full Vgridpandas DGGS documentation is available at vgridpandas document.

To work with Vgrid DGGS in QGIS, install the Vgrid Plugin.

To visualize DGGS in Maplibre GL JS, try the vgrid-maplibre library.

For an interactive demo, visit the Vgrid Homepage.

Install vgrid¶

Uncomment the following line to install vgrid.

In [1]:

# %pip install vgrid --upgrade

# %pip install vgrid --upgrade

latlon2geohash¶

In [2]:

from vgrid.conversion.latlon2dggs import latlon2geohash

lat = 10.775276
lon = 106.706797
res = 6
geohash_id = latlon2geohash(lat, lon, res)
geohash_id

from vgrid.conversion.latlon2dggs import latlon2geohash lat = 10.775276 lon = 106.706797 res = 6 geohash_id = latlon2geohash(lat, lon, res) geohash_id

Out[2]:

'w3gvk1'

Geohash to Polygon¶

In [3]:

from vgrid.conversion.dggs2geo.geohash2geo import geohash2geo

geohash_geo = geohash2geo(geohash_id)
geohash_geo

from vgrid.conversion.dggs2geo.geohash2geo import geohash2geo geohash_geo = geohash2geo(geohash_id) geohash_geo

Out[3]:

Geohash to GeoJSON¶

In [ ]:

from vgrid.conversion.dggs2geo.geohash2geo import geohash2geojson

geohash_geojson = geohash2geojson(geohash_id)
# geohash_geojson

from vgrid.conversion.dggs2geo.geohash2geo import geohash2geojson geohash_geojson = geohash2geojson(geohash_id) # geohash_geojson

Vector to Geohash¶

In [1]:

from vgrid.conversion.vector2dggs.vector2geohash import vector2geohash

file_path = "https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/polygon2.geojson"
vector_to_geohash = vector2geohash(
    file_path,
    resolution=7,
    compact=True,
    topology=True,
    predicate="intersects",
    output_format="gpd",
)
# Visualize the output
vector_to_geohash.plot(edgecolor="white")

from vgrid.conversion.vector2dggs.vector2geohash import vector2geohash file_path = "https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/polygon2.geojson" vector_to_geohash = vector2geohash( file_path, resolution=7, compact=True, topology=True, predicate="intersects", output_format="gpd", ) # Visualize the output vector_to_geohash.plot(edgecolor="white")

Processing features: 100%|██████████| 1/1 [00:00<00:00,  1.77it/s]

Out[1]:

<Axes: >

Geohash Compact¶

In [2]:

from vgrid.conversion.dggscompact.geohashcompact import geohashcompact

geohash_compacted = geohashcompact(
    vector_to_geohash, geohash_id="geohash", output_format="gpd"
)
geohash_compacted.plot(edgecolor="white")

from vgrid.conversion.dggscompact.geohashcompact import geohashcompact geohash_compacted = geohashcompact( vector_to_geohash, geohash_id="geohash", output_format="gpd" ) geohash_compacted.plot(edgecolor="white")

Out[2]:

<Axes: >

Geohash Expand¶

In [3]:

from vgrid.conversion.dggscompact.geohashcompact import geohashexpand

geohash_expanded = geohashexpand(vector_to_geohash, resolution=7, output_format="gpd")
geohash_expanded.plot(edgecolor="white")

from vgrid.conversion.dggscompact.geohashcompact import geohashexpand geohash_expanded = geohashexpand(vector_to_geohash, resolution=7, output_format="gpd") geohash_expanded.plot(edgecolor="white")

Out[3]:

<Axes: >

Geohash Binning¶

In [8]:

from vgrid.binning.geohashbin import geohashbin

file_path = (
    "https://raw.githubusercontent.com/opengeoshub/vopendata/main/csv/dist1_pois.csv"
)
stats = "count"
geohash_bin = geohashbin(
    file_path,
    resolution=7,
    stats=stats,
    # numeric_field="confidence",
    # category="category",
    output_format="gpd",
)
geohash_bin.plot(
    column=stats,  # numeric column to base the colors on
    cmap="Spectral_r",  # color scheme (matplotlib colormap)
    legend=True,
    linewidth=0.2,  # boundary width (optional)
)

from vgrid.binning.geohashbin import geohashbin file_path = ( "https://raw.githubusercontent.com/opengeoshub/vopendata/main/csv/dist1_pois.csv" ) stats = "count" geohash_bin = geohashbin( file_path, resolution=7, stats=stats, # numeric_field="confidence", # category="category", output_format="gpd", ) geohash_bin.plot( column=stats, # numeric column to base the colors on cmap="Spectral_r", # color scheme (matplotlib colormap) legend=True, linewidth=0.2, # boundary width (optional) )

Generating Geohash DGGS: 100%|██████████| 744/744 [00:00<00:00, 12166.08 cells/s]

Out[8]:

<Axes: >

Raster to Geohash¶

Download and open raster¶

In [1]:

from vgrid.utils.io import download_file
import rasterio
from rasterio.plot import show

raster_url = (
    "https://raw.githubusercontent.com/opengeoshub/vopendata/main/raster/rgb.tif"
)
raster_file = download_file(raster_url)
src = rasterio.open(raster_file, "r")
print(src.meta)
show(src)

from vgrid.utils.io import download_file import rasterio from rasterio.plot import show raster_url = ( "https://raw.githubusercontent.com/opengeoshub/vopendata/main/raster/rgb.tif" ) raster_file = download_file(raster_url) src = rasterio.open(raster_file, "r") print(src.meta) show(src)

rgb.tif already exists. Skip downloading. Set overwrite=True to overwrite.
{'driver': 'GTiff', 'dtype': 'uint8', 'nodata': None, 'width': 240, 'height': 147, 'count': 3, 'crs': CRS.from_wkt('GEOGCS["WGS 84",DATUM["World Geodetic System 1984",SPHEROID["WGS 84",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST]]'), 'transform': Affine(2.6640125000199077e-06, 0.0, 106.708118755,
       0.0, -2.6640136054383103e-06, 10.812568272)}

Out[1]:

<Axes: >

Convert raster to Geohash¶

In [10]:

# %pip install folium

# %pip install folium

In [2]:

from vgrid.conversion.raster2dggs.raster2geohash import raster2geohash

raster_to_geohash = raster2geohash(raster_file, output_format="gpd")

# Visualize the output
import folium

m = folium.Map(tiles="CartoDB positron", max_zoom=28)

geohash_layer = folium.GeoJson(
    raster_to_geohash,
    style_function=lambda x: {
        "fillColor": f"rgb({x['properties']['band_1']}, {x['properties']['band_2']}, {x['properties']['band_3']})",
        "fillOpacity": 1,
        "color": "black",
        "weight": 1,
    },
    popup=folium.GeoJsonPopup(
        fields=["geohash", "band_1", "band_2", "band_3"],
        aliases=["Geohash ID", "Band 1", "Band 2", "Band 3"],
        style="""
            background-color: white;
            border: 2px solid black;
            border-radius: 3px;
            box-shadow: 3px;
        """,
    ),
).add_to(m)

m.fit_bounds(geohash_layer.get_bounds())

# Display the map
m

from vgrid.conversion.raster2dggs.raster2geohash import raster2geohash raster_to_geohash = raster2geohash(raster_file, output_format="gpd") # Visualize the output import folium m = folium.Map(tiles="CartoDB positron", max_zoom=28) geohash_layer = folium.GeoJson( raster_to_geohash, style_function=lambda x: { "fillColor": f"rgb({x['properties']['band_1']}, {x['properties']['band_2']}, {x['properties']['band_3']})", "fillOpacity": 1, "color": "black", "weight": 1, }, popup=folium.GeoJsonPopup( fields=["geohash", "band_1", "band_2", "band_3"], aliases=["Geohash ID", "Band 1", "Band 2", "Band 3"], style=""" background-color: white; border: 2px solid black; border-radius: 3px; box-shadow: 3px; """, ), ).add_to(m) m.fit_bounds(geohash_layer.get_bounds()) # Display the map m

Cell size: 0.08638527081938627 m2
Nearest geohash resolution determined: 10

Converting raster to Geohash: 100%|██████████| 4380/4380 [00:00<00:00, 22380.30 cells/s]

Out[2]:

Make this Notebook Trusted to load map: File -> Trust Notebook

Geohash Generator¶

In [12]:

from vgrid.generator.geohashgrid import geohashgrid

# geohash_grid = geohashgrid(resolution=1,output_format="gpd")
geohash_grid = geohashgrid(
    resolution=7,
    bbox=[106.699007, 10.762811, 106.717674, 10.778649],
    output_format="gpd",
)
geohash_grid.plot(edgecolor="white")

from vgrid.generator.geohashgrid import geohashgrid # geohash_grid = geohashgrid(resolution=1,output_format="gpd") geohash_grid = geohashgrid( resolution=7, bbox=[106.699007, 10.762811, 106.717674, 10.778649], output_format="gpd", ) geohash_grid.plot(edgecolor="white")

Generating Geohash DGGS: 100%|██████████| 180/180 [00:00<00:00, 6404.50 cells/s]

Out[12]:

<Axes: >

Geohash Inspect¶

In [1]:

from vgrid.stats.geohashstats import geohashinspect

resolution = 3
geohash_inspect = geohashinspect(resolution)
geohash_inspect.head()

from vgrid.stats.geohashstats import geohashinspect resolution = 3 geohash_inspect = geohashinspect(resolution) geohash_inspect.head()

Generating Geohash DGGS: 100%|██████████| 32768/32768 [00:02<00:00, 14171.03 cells/s]

Out[1]:

	geohash	resolution	center_lat	center_lon	cell_width	cell_height	cell_area	cell_perimeter	geometry	crossed	norm_area	ipq	zsc	cvh
0	1rz	3	-45.703125	-113.203125	108131.362462	156298.383490	1.711594e+10	531605.099750	POLYGON ((-113.90625 -46.40625, -112.5 -46.406...	False	1.099574	0.761084	0.872386	1.0
1	h3j	3	-83.671875	18.984375	15394.637692	157050.292252	2.718492e+09	348720.779077	POLYGON ((18.28125 -84.375, 19.6875 -84.375, 1...	False	0.174643	0.280920	0.530017	1.0
2	n1m	3	-82.265625	97.734375	19225.556881	157040.827204	3.319000e+09	356351.878996	POLYGON ((97.03125 -82.96875, 98.4375 -82.9687...	False	0.213222	0.328442	0.573097	1.0
3	ck2	3	69.609375	-123.046875	56502.699442	156877.037096	8.580812e+09	423150.545233	POLYGON ((-123.75 68.90625, -122.34375 68.9062...	False	0.551255	0.602211	0.776016	1.0
4	pcm	3	-82.265625	176.484375	19225.556881	157040.827204	3.319000e+09	356351.878996	POLYGON ((175.78125 -82.96875, 177.1875 -82.96...	False	0.213222	0.328442	0.573097	1.0

Geohash Normalized Area Histogram¶

In [2]:

from vgrid.stats.geohashstats import geohash_norm_area_hist

geohash_norm_area_hist(geohash_inspect)

from vgrid.stats.geohashstats import geohash_norm_area_hist geohash_norm_area_hist(geohash_inspect)

Distribution of Geohash Area Distortions¶

In [3]:

from vgrid.stats.geohashstats import geohash_norm_area

geohash_norm_area(geohash_inspect)

from vgrid.stats.geohashstats import geohash_norm_area geohash_norm_area(geohash_inspect)

Geohash IPQ Compactness Histogram¶

Isoperimetric Inequality (IPQ) Compactness (suggested by Osserman, 1978):

$$C_{IPQ} = \frac{4 \pi A}{p^2}$$ The range of the IPQ compactness metric is [0,1].

A circle represents the maximum compactness with a value of 1.

As shapes become more irregular or elongated, their compactness decreases toward 0.

In [4]:

from vgrid.stats.geohashstats import geohash_compactness_ipq_hist

geohash_compactness_ipq_hist(geohash_inspect)

from vgrid.stats.geohashstats import geohash_compactness_ipq_hist geohash_compactness_ipq_hist(geohash_inspect)

Distribution of Geohash IPQ Compactness¶

In [5]:

from vgrid.stats.geohashstats import geohash_compactness_ipq

geohash_compactness_ipq(geohash_inspect)

from vgrid.stats.geohashstats import geohash_compactness_ipq geohash_compactness_ipq(geohash_inspect)

Geohash Convex hull Compactness Histogram:¶

$$C_{CVH} = \frac{A}{A_{CVH}}$$

The range of the convex hull compactness metric is [0,1].

As shapes become more concave, their convex hull compactness decreases toward 0.

In [6]:

from vgrid.stats.geohashstats import geohash_compactness_cvh_hist

geohash_compactness_cvh_hist(geohash_inspect)

from vgrid.stats.geohashstats import geohash_compactness_cvh_hist geohash_compactness_cvh_hist(geohash_inspect)

Distribution of Geohash Convex hull Compactness¶

In [7]:

from vgrid.stats.geohashstats import geohash_compactness_cvh

geohash_compactness_cvh(geohash_inspect)

from vgrid.stats.geohashstats import geohash_compactness_cvh geohash_compactness_cvh(geohash_inspect)

Geohash Statistics¶

Characteristic Length Scale (CLS - suggested by Ralph Kahn): the diameter of a spherical cap of the same cell's area

In [9]:

from vgrid.stats import geohashstats

geohashstats()

from vgrid.stats import geohashstats geohashstats()

Out[9]:

	resolution	number_of_cells	avg_edge_len_m	avg_cell_area_m2	cls_m
0	1	32	3.992437e+06	1.593955e+13	4.528782e+06
1	2	1024	7.057698e+05	4.981110e+11	7.965056e+05
2	3	32768	1.247636e+05	1.556597e+10	1.407814e+05
3	4	1048576	2.205531e+04	4.864365e+08	2.488675e+04
4	5	33554432	3.898864e+03	1.520114e+07	4.399397e+03
5	6	1073741824	6.892283e+02	4.750356e+05	7.777108e+02
6	7	34359738368	1.218395e+02	1.484486e+04	1.374812e+02
7	8	1099511627776	2.153838e+01	4.639020e+02	2.430346e+01
8	9	35184372088832	3.807484e+00	1.449694e+01	4.296286e+00
9	10	1125899906842624	6.730745e-01	4.530293e-01	7.594832e-01
10	11	36028797018963968	1.189839e-01	1.415716e-02	1.342589e-01
11	12	1152921504606846976	2.103358e-02	4.424114e-04	2.373385e-02