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 Shapely 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 [4]:

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

Out[4]:

{'type': 'FeatureCollection', 'features': []}

Vector to Geohash¶

In [5]:

from vgrid.conversion.vector2dggs.vector2geohash import vector2geohash

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

Processing features: 100%|██████████| 4/4 [00:00<00:00, 102.97it/s]

Out[5]:

<Axes: >

Geohash Compact¶

In [6]:

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[6]:

<Axes: >

Geohash Expand¶

In [7]:

from vgrid.conversion.dggscompact.geohashcompact import geohashexpand

geohash_expanded = geohashexpand(
    vector_to_geohash, resolution=8, output_format="gpd"        
)
geohash_expanded.plot(edgecolor="white")

from vgrid.conversion.dggscompact.geohashcompact import geohashexpand geohash_expanded = geohashexpand( vector_to_geohash, resolution=8, output_format="gpd" ) geohash_expanded.plot(edgecolor="white")

Out[7]:

<Axes: >

Geohash Binning¶

In [3]:

from vgrid.binning.geohashbin import geohashbin
import geopandas as gpd
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 import geopandas as gpd 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, 8853.06 cells/s]

Out[3]:

<Axes: >

Raster to Geohash¶

In [9]:

# %pip install folium

# %pip install folium

In [10]:

from vgrid.conversion.raster2dggs.raster2geohash    import raster2geohash
from vgrid.utils.io import download_file          

raster_url = (
    "https://raw.githubusercontent.com/opengeoshub/vopendata/main/raster/rgb.tif"
)
raster_file = download_file(raster_url)

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 from vgrid.utils.io import download_file raster_url = ( "https://raw.githubusercontent.com/opengeoshub/vopendata/main/raster/rgb.tif" ) raster_file = download_file(raster_url) 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

WARNING [rasterio._env:368  open()] CPLE_AppDefined in PROJ: proj_create_from_database: Cannot find proj.db
WARNING [rasterio._env:368  open()] CPLE_AppDefined in PROJ: proj_create_from_database: Cannot find proj.db

rgb.tif already exists. Skip downloading. Set overwrite=True to overwrite.
Nearest geohash resolution determined: 10

Resampling: 100%|██████████| 4380/4380 [00:00<00:00, 122710.10 cells/s]
Converting to GeoDataFrame: 100%|██████████| 4307/4307 [00:00<00:00, 53947.20 cells/s]

Out[10]:

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

Geohash Generator¶

In [11]:

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, 6075.67 cells/s]

Out[11]:

<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, 12238.34 cells/s]

Out[1]:

	geohash	resolution	center_lat	center_lon	cell_width	cell_height	cell_area	cell_perimeter	geometry	crossed	norm_area	ipq	zsc
0	445	3	-78.046875	-85.078125	30638.101	157001.499	5.106049e+09	379048.869	POLYGON ((-85.78125 -78.75, -84.375 -78.75, -8...	False	0.328026	0.446585	0.668267
1	uqb	3	83.671875	11.953125	19225.557	157050.292	2.718492e+09	348720.779	POLYGON ((11.25 82.96875, 12.65625 82.96875, 1...	False	0.174643	0.280920	0.530017
2	nxd	3	-47.109375	116.015625	105320.146	156336.991	1.668573e+10	526125.489	POLYGON ((115.3125 -47.8125, 116.71875 -47.812...	False	1.071937	0.757489	0.870324
3	vut	3	71.015625	86.484375	52893.772	156901.708	8.013363e+09	415949.629	POLYGON ((85.78125 70.3125, 87.1875 70.3125, 8...	False	0.514800	0.582027	0.762901
4	c7m	3	63.984375	-116.015625	70570.828	156764.626	1.079198e+10	451213.306	POLYGON ((-116.71875 63.28125, -115.3125 63.28...	False	0.693306	0.666112	0.816148

Distribution of Geohash Area Distortions¶

In [2]:

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)

Distribution of Geohash IPQ Compactness¶

In [3]:

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

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

Geohash Statistics¶

In [1]:

from vgrid.stats import geohashstats            
geohashstats()

from vgrid.stats import geohashstats geohashstats()

Out[1]:

	resolution	number_of_cells	avg_edge_len_m	avg_cell_area_m2
0	1	32	3.992437e+06	1.593955e+13
1	2	1024	7.057698e+05	4.981110e+11
2	3	32768	1.247636e+05	1.556597e+10
3	4	1048576	2.205531e+04	4.864365e+08
4	5	33554432	3.898864e+03	1.520114e+07
5	6	1073741824	6.892283e+02	4.750356e+05
6	7	34359738368	1.218395e+02	1.484486e+04
7	8	1099511627776	2.153838e+01	4.639020e+02
8	9	35184372088832	3.807484e+00	1.449694e+01
9	10	1125899906842624	6.730745e-01	4.530293e-01
10	11	36028797018963968	1.189839e-01	1.415717e-02
11	12	1152921504606846976	2.103358e-02	4.424114e-04