10 qtm

Working with QTM 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 [ ]:

# %pip install vgrid --upgrade

# %pip install vgrid --upgrade

latlon2qtm¶

In [1]:

from vgrid.conversion.latlon2dggs import latlon2qtm

lat = 10.775276
lon = 106.706797
res = 14
qtm_id = latlon2qtm(lat, lon, res)
qtm_id

from vgrid.conversion.latlon2dggs import latlon2qtm lat = 10.775276 lon = 106.706797 res = 14 qtm_id = latlon2qtm(lat, lon, res) qtm_id

Out[1]:

'42012323131211'

QTM to Shapely Polygon¶

In [2]:

from vgrid.conversion.dggs2geo.qtm2geo  import qtm2geo

qtm_geo = qtm2geo(qtm_id)
qtm_geo

from vgrid.conversion.dggs2geo.qtm2geo import qtm2geo qtm_geo = qtm2geo(qtm_id) qtm_geo

Out[2]:

QTM to GeoJSON¶

In [3]:

from vgrid.conversion.dggs2geo.qtm2geo import qtm2geojson

qtm_geojson = qtm2geojson(qtm_id)
qtm_geojson

from vgrid.conversion.dggs2geo.qtm2geo import qtm2geojson qtm_geojson = qtm2geojson(qtm_id) qtm_geojson

Out[3]:

{'type': 'FeatureCollection',
 'features': [{'type': 'Feature',
   'geometry': {'type': 'Polygon',
    'coordinates': (((106.70909228465132, 10.7666015625),
      (106.71146419641514, 10.777587890625),
      (106.6985891412885, 10.777587890625),
      (106.70909228465132, 10.7666015625)),)},
   'properties': {'qtm': '42012323131211',
    'resolution': 14,
    'center_lat': 10.7739258,
    'center_lon': 106.7063819,
    'avg_edge_len': 1440.993,
    'cell_area': 855603.691,
    'cell_perimeter': 4322.978}}]}

Vector to QTM¶

In [4]:

from vgrid.conversion.vector2dggs.vector2qtm import vector2qtm

file_path = (
    "https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/polygon.geojson"
)
vector_to_qtm = vector2qtm(file_path, resolution=17, compact=False, 
                            predicate = "intersects", output_format="gpd")
# Visualize the output
vector_to_qtm.plot(edgecolor="white")

from vgrid.conversion.vector2dggs.vector2qtm import vector2qtm file_path = ( "https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/polygon.geojson" ) vector_to_qtm = vector2qtm(file_path, resolution=17, compact=False, predicate = "intersects", output_format="gpd") # Visualize the output vector_to_qtm.plot(edgecolor="white")

Processing features: 100%|██████████| 4/4 [00:00<00:00, 75.15it/s]

Out[4]:

<Axes: >

QTM Compact¶

In [5]:

from vgrid.conversion.dggscompact import qtmcompact

qtm_compacted = qtmcompact(vector_to_qtm, qtm_id="qtm", output_format="gpd")
qtm_compacted.plot(edgecolor="white")

from vgrid.conversion.dggscompact import qtmcompact qtm_compacted = qtmcompact(vector_to_qtm, qtm_id="qtm", output_format="gpd") qtm_compacted.plot(edgecolor="white")

Out[5]:

<Axes: >

QTM Expand¶

In [6]:

from vgrid.conversion.dggscompact import qtmexpand

qtm_expanded = qtmexpand(vector_to_qtm, resolution=18, qtm_id="qtm", output_format="gpd")
qtm_expanded.plot(edgecolor="white")

from vgrid.conversion.dggscompact import qtmexpand qtm_expanded = qtmexpand(vector_to_qtm, resolution=18, qtm_id="qtm", output_format="gpd") qtm_expanded.plot(edgecolor="white")

Out[6]:

<Axes: >

QTM Binning¶

In [10]:

from vgrid.binning.qtmbin import qtmbin
import geopandas as gpd
file_path = (
    "https://raw.githubusercontent.com/opengeoshub/vopendata/main/csv/dist1_pois.csv"
)
stats = "count"
qtm_bin = qtmbin(file_path, resolution=16, stats=stats, 
                # numeric_field="confidence",
                # category="category",
                output_format="gpd")
qtm_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.qtmbin import qtmbin import geopandas as gpd file_path = ( "https://raw.githubusercontent.com/opengeoshub/vopendata/main/csv/dist1_pois.csv" ) stats = "count" qtm_bin = qtmbin(file_path, resolution=16, stats=stats, # numeric_field="confidence", # category="category", output_format="gpd") qtm_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 QTM DGGS: 100%|██████████| 16/16 [00:00<00:00, 153.29it/s]

Out[10]:

<Axes: >

Raster to QTM¶

In [ ]:

# %pip install folium

# %pip install folium

In [11]:

from vgrid.conversion.raster2dggs.raster2qtm import raster2qtm
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_qtm =  raster2qtm(raster_file,output_format="gpd")

# Visualize the output
import folium

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

qtm_layer = folium.GeoJson(
    raster_to_qtm,
    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=["qtm", "band_1", "band_2", "band_3"],
        aliases=["QTM 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(qtm_layer.get_bounds())

# Display the map
m

from vgrid.conversion.raster2dggs.raster2qtm import raster2qtm 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_qtm = raster2qtm(raster_file,output_format="gpd") # Visualize the output import folium m = folium.Map(tiles="CartoDB positron", max_zoom=28) qtm_layer = folium.GeoJson( raster_to_qtm, 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=["qtm", "band_1", "band_2", "band_3"], aliases=["QTM 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(qtm_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 qtm resolution determined: 24

Resampling: 100%|██████████| 3812/3812 [00:00<00:00, 6954.41 cells/s]
Converting raster to QTM: 100%|██████████| 3711/3711 [00:00<00:00, 7855.96 cells/s]

Out[11]:

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

QTM Generator¶

In [12]:

from vgrid.generator.qtmgrid import qtmgrid
qtm_grid = qtmgrid(resolution=3,output_format="gpd")
# qtm_grid = qtmgrid(resolution=18,bbox=[106.699007, 10.762811, 106.717674, 10.778649],output_format="gpd")
qtm_grid.plot(edgecolor='white')

from vgrid.generator.qtmgrid import qtmgrid qtm_grid = qtmgrid(resolution=3,output_format="gpd") # qtm_grid = qtmgrid(resolution=18,bbox=[106.699007, 10.762811, 106.717674, 10.778649],output_format="gpd") qtm_grid.plot(edgecolor='white')

Generating QTM DGGS: 100%|██████████| 3/3 [00:00<00:00, 205.75it/s]

Out[12]:

<Axes: >

QTM Inspect¶

In [13]:

from vgrid.stats.qtmstats import qtminspect
resolution = 6
qtm_inspect = qtminspect(resolution)
qtm_inspect.head()

from vgrid.stats.qtmstats import qtminspect resolution = 6 qtm_inspect = qtminspect(resolution) qtm_inspect.head()

Generating QTM DGGS: 100%|██████████| 6/6 [00:00<00:00,  6.22it/s]

Out[13]:

	qtm	resolution	center_lat	center_lon	avg_edge_len	cell_area	cell_perimeter	geometry	crossed	norm_area	ipq	zsc
0	1	6	45.0	-135.0	1.000756e+07	6.375820e+13	30022685.63	POLYGON ((-180 0, -90 0, -90 90, -180 90, -180...	False	512.5	0.888888	0.882057
1	2	6	45.0	-45.0	1.000756e+07	6.375820e+13	30022685.63	POLYGON ((-90 0, 0 0, 0 90, -90 90, -90 0))	False	512.5	0.888888	0.882057
2	3	6	45.0	45.0	1.000756e+07	6.375820e+13	30022685.63	POLYGON ((0 0, 90 0, 90 90, 0 90, 0 0))	False	512.5	0.888888	0.882057
3	4	6	45.0	135.0	1.000756e+07	6.375820e+13	30022685.63	POLYGON ((90 0, 180 0, 180 90, 90 90, 90 0))	False	512.5	0.888888	0.882057
4	5	6	-45.0	-135.0	1.000756e+07	6.375820e+13	30022685.63	POLYGON ((-180 -90, -90 -90, -90 0, -180 0, -1...	False	512.5	0.888888	0.882057

Distribution of QTM Area Distortions¶

In [14]:

from vgrid.stats.qtmstats import qtm_norm_area
qtm_norm_area(qtm_inspect)

from vgrid.stats.qtmstats import qtm_norm_area qtm_norm_area(qtm_inspect)

Distribution of QTM IPQ Compactness¶

In [15]:

from vgrid.stats.qtmstats import qtm_compactness
qtm_compactness(qtm_inspect)

from vgrid.stats.qtmstats import qtm_compactness qtm_compactness(qtm_inspect)

QTM Statistics¶

In [16]:

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

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

Out[16]:

	resolution	number_of_cells	avg_edge_len_km	avg_cell_area_km2
0	1	8	12134.383498	6.375820e+07
1	2	32	6067.191749	1.593955e+07
2	3	128	3033.595875	3.984888e+06
3	4	512	1516.797937	9.962219e+05
4	5	2048	758.398969	2.490555e+05
5	6	8192	379.199484	6.226387e+04
6	7	32768	189.599742	1.556597e+04
7	8	131072	94.799871	3.891492e+03
8	9	524288	47.399936	9.728730e+02
9	10	2097152	23.699968	2.432182e+02
10	11	8388608	11.849984	6.080456e+01
11	12	33554432	5.924992	1.520114e+01
12	13	134217728	2.962496	3.800285e+00
13	14	536870912	1.481248	9.500713e-01
14	15	2147483648	0.740624	2.375178e-01
15	16	8589934592	0.370312	5.937945e-02
16	17	34359738368	0.185156	1.484486e-02
17	18	137438953472	0.092578	3.711216e-03
18	19	549755813888	0.046289	9.278040e-04
19	20	2199023255552	0.023144	2.319510e-04
20	21	8796093022208	0.011572	5.798775e-05
21	22	35184372088832	0.005786	1.449694e-05
22	23	140737488355328	0.002893	3.624234e-06
23	24	562949953421312	0.001447	9.060586e-07