15 tilecode

Working with Tilecode 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

latlon2tilecode¶

In [2]:

from vgrid.conversion.latlon2dggs import latlon2tilecode

lat = 10.775276
lon = 106.706797
res = 14
tilecode_id = latlon2tilecode(lat, lon, res)
tilecode_id

from vgrid.conversion.latlon2dggs import latlon2tilecode lat = 10.775276 lon = 106.706797 res = 14 tilecode_id = latlon2tilecode(lat, lon, res) tilecode_id

Out[2]:

'z14x13048y7698'

Tilecode to Shapely Polygon¶

In [3]:

from vgrid.conversion.dggs2geo.tilecode2geo  import tilecode2geo

tilecode_geo = tilecode2geo(tilecode_id)
tilecode_geo

from vgrid.conversion.dggs2geo.tilecode2geo import tilecode2geo tilecode_geo = tilecode2geo(tilecode_id) tilecode_geo

Out[3]:

Tilecode to GeoJSON¶

In [4]:

from vgrid.conversion.dggs2geo.tilecode2geo import tilecode2geojson

tilecode_geojson = tilecode2geojson(tilecode_id)
tilecode_geojson

from vgrid.conversion.dggs2geo.tilecode2geo import tilecode2geojson tilecode_geojson = tilecode2geojson(tilecode_id) tilecode_geojson

Out[4]:

{'type': 'FeatureCollection',
 'features': [{'type': 'Feature',
   'geometry': {'type': 'Polygon',
    'coordinates': (((106.69921875, 10.768555807732435),
      (106.72119140625, 10.768555807732435),
      (106.72119140625, 10.790140750321735),
      (106.69921875, 10.790140750321735),
      (106.69921875, 10.768555807732435)),)},
   'properties': {'tilecode_id': 'z14x13048y7698',
    'resolution': 14,
    'center_lat': 10.7793483,
    'center_lon': 106.7102051,
    'cell_width': 2403.192,
    'cell_height': 2387.578,
    'cell_area': 5737603.485,
    'cell_perimeter': 9581.368}}]}

Vector to Tilecode¶

In [5]:

from vgrid.conversion.vector2dggs.vector2tilecode import vector2tilecode

file_path = (
    "https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/polygon.geojson"
)
vector_to_tilecode = vector2tilecode(file_path, resolution=18, compact=False, 
                                    predicate = "intersects",output_format="gpd")
# Visualize the output
vector_to_tilecode.plot(edgecolor="white")

from vgrid.conversion.vector2dggs.vector2tilecode import vector2tilecode file_path = ( "https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/polygon.geojson" ) vector_to_tilecode = vector2tilecode(file_path, resolution=18, compact=False, predicate = "intersects",output_format="gpd") # Visualize the output vector_to_tilecode.plot(edgecolor="white")

Processing features: 100%|██████████| 4/4 [00:00<00:00, 194.15it/s]

Out[5]:

<Axes: >

Tilecode Compact¶

In [6]:

from vgrid.conversion.dggscompact.tilecodecompact import tilecodecompact

tilecode_compacted = tilecodecompact(vector_to_tilecode, tilecode_id="tilecode", output_format="gpd")
tilecode_compacted.plot(edgecolor="white")

from vgrid.conversion.dggscompact.tilecodecompact import tilecodecompact tilecode_compacted = tilecodecompact(vector_to_tilecode, tilecode_id="tilecode", output_format="gpd") tilecode_compacted.plot(edgecolor="white")

Out[6]:

<Axes: >

Tilecode Expand¶

In [7]:

from vgrid.conversion.dggscompact.tilecodecompact import tilecodeexpand

tilecode_expanded = tilecodeexpand(vector_to_tilecode, resolution=19, output_format="gpd")
tilecode_expanded.plot(edgecolor="white")

from vgrid.conversion.dggscompact.tilecodecompact import tilecodeexpand tilecode_expanded = tilecodeexpand(vector_to_tilecode, resolution=19, output_format="gpd") tilecode_expanded.plot(edgecolor="white")

Out[7]:

<Axes: >

Tilecode Binning¶

In [16]:

from vgrid.binning.tilecodebin import tilecodebin
import geopandas as gpd
file_path = (
    "https://raw.githubusercontent.com/opengeoshub/vopendata/main/csv/dist1_pois.csv"
)
stats = "count"
tilecode_bin =  tilecodebin(file_path, resolution=18, stats=stats, 
                # numeric_field="confidence",
                # category="category",
                output_format="gpd")
tilecode_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.tilecodebin import tilecodebin import geopandas as gpd file_path = ( "https://raw.githubusercontent.com/opengeoshub/vopendata/main/csv/dist1_pois.csv" ) stats = "count" tilecode_bin = tilecodebin(file_path, resolution=18, stats=stats, # numeric_field="confidence", # category="category", output_format="gpd") tilecode_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 Tilecode DGGS: 768 cells [00:00, 8895.22 cells/s]

Out[16]:

<Axes: >

Raster to Tilecode¶

In [9]:

# %pip install folium

# %pip install folium

In [10]:

from vgrid.conversion.raster2dggs.raster2tilecode    import raster2tilecode
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_tilecode =  raster2tilecode(raster_file,output_format="gpd")

# Visualize the output
import folium

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

tilecode_layer = folium.GeoJson(
    raster_to_tilecode,
    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=["tilecode", "band_1", "band_2", "band_3"],
        aliases=["Tilecode 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(tilecode_layer.get_bounds())

# Display the map
m

from vgrid.conversion.raster2dggs.raster2tilecode import raster2tilecode 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_tilecode = raster2tilecode(raster_file,output_format="gpd") # Visualize the output import folium m = folium.Map(tiles="CartoDB positron", max_zoom=28) tilecode_layer = folium.GeoJson( raster_to_tilecode, 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=["tilecode", "band_1", "band_2", "band_3"], aliases=["Tilecode 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(tilecode_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 tilecode resolution determined: 26

Resampling: 100%|██████████| 8925/8925 [00:00<00:00, 163480.10 cells/s]
Converting raster to Tilecode: 100%|██████████| 8806/8806 [00:00<00:00, 38007.93 cells/s]

Out[10]:

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

Tilecode Generator¶

In [11]:

from vgrid.generator.tilecodegrid import tilecodegrid
# tilecode_grid = tilecodegrid(resolution=2,output_format="gpd")
tilecode_grid = tilecodegrid(resolution=18,bbox=[106.699007, 10.762811, 106.717674, 10.778649],output_format="gpd")
tilecode_grid.plot(edgecolor="white")

from vgrid.generator.tilecodegrid import tilecodegrid # tilecode_grid = tilecodegrid(resolution=2,output_format="gpd") tilecode_grid = tilecodegrid(resolution=18,bbox=[106.699007, 10.762811, 106.717674, 10.778649],output_format="gpd") tilecode_grid.plot(edgecolor="white")

Generating Tilecode DGGS: 195 cells [00:00, 4683.80 cells/s]

Out[11]:

<Axes: >

Tilecode Inspect¶

In [12]:

from vgrid.stats.tilecodestats import tilecodeinspect 
resolution = 7
tilecode_inspect = tilecodeinspect(resolution)    
tilecode_inspect.head()

from vgrid.stats.tilecodestats import tilecodeinspect resolution = 7 tilecode_inspect = tilecodeinspect(resolution) tilecode_inspect.head()

Generating Tilecode DGGS: 16384 cells [00:01, 8448.16 cells/s]

Out[12]:

	tilecode	resolution	center_lat	center_lon	cell_width	cell_height	cell_area	cell_perimeter	geometry	crossed	norm_area	ipq	zsc
0	z7x0y0	7	84.926801	-178.59375	28454.129	27771.077	7.711209e+08	111092.678	POLYGON ((-180 84.80247, -177.1875 84.80247, -...	False	0.024863	0.785166	0.886095
1	z7x0y1	7	84.671917	-178.59375	29879.298	29162.169	8.503136e+08	116657.765	POLYGON ((-180 84.54136, -177.1875 84.54136, -...	False	0.027416	0.785166	0.886095
2	z7x0y2	7	84.404267	-178.59375	31375.156	30622.280	9.375993e+08	122499.013	POLYGON ((-180 84.26717, -177.1875 84.26717, -...	False	0.030231	0.785167	0.886096
3	z7x0y3	7	84.123216	-178.59375	32945.097	32154.730	1.033796e+09	128629.712	POLYGON ((-180 83.97926, -177.1875 83.97926, -...	False	0.033332	0.785167	0.886096
4	z7x0y4	7	83.828101	-178.59375	34592.666	33762.981	1.139804e+09	135063.726	POLYGON ((-180 83.67694, -177.1875 83.67694, -...	False	0.036750	0.785168	0.886096

Distribution of Tilecode Area Distortions¶

In [13]:

from vgrid.stats.tilecodestats import tilecode_norm_area
tilecode_norm_area(tilecode_inspect)

from vgrid.stats.tilecodestats import tilecode_norm_area tilecode_norm_area(tilecode_inspect)

Distribution of Tilecode IPQ Compactness¶

In [14]:

from vgrid.stats.tilecodestats import tilecode_compactness
tilecode_compactness(tilecode_inspect)

from vgrid.stats.tilecodestats import tilecode_compactness tilecode_compactness(tilecode_inspect)

Tilecode Statistics¶

In [15]:

from vgrid.stats import tilecodestats            
tilecodestats('km')

from vgrid.stats import tilecodestats tilecodestats('km')

Out[15]:

	resolution	number_of_cells	avg_edge_len_km	avg_cell_area_km2
0	0	1	22584.632492	5.100656e+08
1	1	4	11292.316246	1.275164e+08
2	2	16	5646.158123	3.187910e+07
3	3	64	2823.079061	7.969775e+06
4	4	256	1411.539531	1.992444e+06
5	5	1024	705.769765	4.981110e+05
6	6	4096	352.884883	1.245277e+05
7	7	16384	176.442441	3.113194e+04
8	8	65536	88.221221	7.782984e+03
9	9	262144	44.110610	1.945746e+03
10	10	1048576	22.055305	4.864365e+02
11	11	4194304	11.027653	1.216091e+02
12	12	16777216	5.513826	3.040228e+01
13	13	67108864	2.756913	7.600570e+00
14	14	268435456	1.378457	1.900143e+00
15	15	1073741824	0.689228	4.750356e-01
16	16	4294967296	0.344614	1.187589e-01
17	17	17179869184	0.172307	2.968973e-02
18	18	68719476736	0.086154	7.422432e-03
19	19	274877906944	0.043077	1.855608e-03
20	20	1099511627776	0.021538	4.639020e-04
21	21	4398046511104	0.010769	1.159755e-04
22	22	17592186044416	0.005385	2.899387e-05
23	23	70368744177664	0.002692	7.248468e-06
24	24	281474976710656	0.001346	1.812117e-06
25	25	1125899906842624	0.000673	4.530293e-07
26	26	4503599627370496	0.000337	1.132573e-07
27	27	18014398509481984	0.000168	2.831433e-08
28	28	72057594037927936	0.000084	7.078583e-09
29	29	288230376151711744	0.000042	1.769646e-09