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 [1]:

# %pip install vgrid --upgrade

# %pip install vgrid --upgrade

latlon2qtm¶

In [2]:

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[2]:

'42012323131211'

QTM to Polygon¶

In [3]:

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[3]:

QTM to GeoJSON¶

In [ ]:

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

Vector to QTM¶

In [21]:

from vgrid.conversion.vector2dggs.vector2qtm import vector2qtm

file_path = "https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/polygon2.geojson"
vector_to_qtm = vector2qtm(
    file_path,
    resolution=17,
    compact=True,
    topology=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/polygon2.geojson" vector_to_qtm = vector2qtm( file_path, resolution=17, compact=True, topology=False, predicate="intersects", output_format="gpd", ) # Visualize the output vector_to_qtm.plot(edgecolor="white")

Processing features: 100%|██████████| 1/1 [00:00<00:00,  2.61it/s]

Out[21]:

<Axes: >

QTM Compact¶

In [6]:

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[6]:

<Axes: >

QTM Expand¶

In [7]:

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[7]:

<Axes: >

QTM Binning¶

In [8]:

from vgrid.binning.qtmbin import qtmbin

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 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, 65.29it/s]

Out[8]:

<Axes: >

Raster to QTM¶

Download and open raster¶

In [9]:

# %pip install folium

# %pip install folium

In [10]:

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)

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.
{'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[10]:

<Axes: >

Convert raster to QTM¶

In [11]:

from vgrid.conversion.raster2dggs.raster2qtm import raster2qtm

raster_to_qtm = raster2qtm(raster_file, resolution=23, 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 raster_to_qtm = raster2qtm(raster_file, resolution=23, 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

Converting raster to QTM: 100%|██████████| 998/998 [00:01<00:00, 982.06 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")
qtm_grid.to_file("qtm.geojson")

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") qtm_grid.to_file("qtm.geojson")

Generating QTM DGGS: 100%|██████████| 3/3 [00:00<00:00, 27.52it/s]

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 [00:05<00:00,  1.02it/s]

Out[13]:

	qtm	resolution	center_lat	center_lon	avg_edge_len	cell_area	cell_perimeter	geometry	crossed	norm_area	ipq	zsc	cvh
0	1	6	45.0	-135.0	1.000756e+07	6.375820e+13	3.002269e+07	POLYGON ((-180 0, -90 0, -90 90, -180 90, -180...	False	512.5	0.888888	0.882057	1.0
1	2	6	45.0	-45.0	1.000756e+07	6.375820e+13	3.002269e+07	POLYGON ((-90 0, 0 0, 0 90, -90 90, -90 0))	False	512.5	0.888888	0.882057	1.0
2	3	6	45.0	45.0	1.000756e+07	6.375820e+13	3.002269e+07	POLYGON ((0 0, 90 0, 90 90, 0 90, 0 0))	False	512.5	0.888888	0.882057	1.0
3	4	6	45.0	135.0	1.000756e+07	6.375820e+13	3.002269e+07	POLYGON ((90 0, 180 0, 180 90, 90 90, 90 0))	False	512.5	0.888888	0.882057	1.0
4	5	6	-45.0	-135.0	1.000756e+07	6.375820e+13	3.002269e+07	POLYGON ((-180 -90, -90 -90, -90 0, -180 0, -1...	False	512.5	0.888888	0.882057	1.0

QTM Normalized Area Histogram¶

In [14]:

from vgrid.stats.qtmstats import qtm_norm_area_hist

qtm_norm_area_hist(qtm_inspect)

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

Distribution of QTM Area Distortions¶

In [15]:

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)

QTM 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 [16]:

from vgrid.stats.qtmstats import qtm_compactness_ipq_hist

qtm_compactness_ipq_hist(qtm_inspect)

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

Distribution of QTM IPQ Compactness¶

In [17]:

from vgrid.stats.qtmstats import qtm_compactness_ipq

qtm_compactness_ipq(qtm_inspect)

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

QTM 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 [18]:

from vgrid.stats.qtmstats import qtm_compactness_cvh_hist

qtm_compactness_cvh_hist(qtm_inspect)

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

Distribution of QTM Convex hull Compactness¶

In [19]:

from vgrid.stats.qtmstats import qtm_compactness_cvh

qtm_compactness_cvh(qtm_inspect)

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

QTM Statistics¶

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

In [20]:

from vgrid.stats import qtmstats

qtmstats("km")

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

Out[20]:

	resolution	number_of_cells	avg_edge_len_km	avg_cell_area_km2	cls_km
0	1	8	12134.383462	6.375820e+07	9209.090165
1	2	32	6067.191731	1.593955e+07	4528.782056
2	3	128	3033.595866	3.984888e+06	2255.434481
3	4	512	1516.797933	9.962219e+05	1126.612534
4	5	2048	758.398966	2.490555e+05	563.168635
5	6	8192	379.199483	6.226387e+04	281.567127
6	7	32768	189.599742	1.556597e+04	140.781415
7	8	131072	94.799871	3.891492e+03	70.390439
8	9	524288	47.399935	9.728730e+02	35.195186
9	10	2097152	23.699968	2.432182e+02	17.597589
10	11	8388608	11.849984	6.080456e+01	8.798794
11	12	33554432	5.924992	1.520114e+01	4.399397
12	13	134217728	2.962496	3.800285e+00	2.199698
13	14	536870912	1.481248	9.500713e-01	1.099849
14	15	2147483648	0.740624	2.375178e-01	0.549925
15	16	8589934592	0.370312	5.937945e-02	0.274962
16	17	34359738368	0.185156	1.484486e-02	0.137481
17	18	137438953472	0.092578	3.711216e-03	0.068741
18	19	549755813888	0.046289	9.278040e-04	0.034370
19	20	2199023255552	0.023144	2.319510e-04	0.017185
20	21	8796093022208	0.011572	5.798775e-05	0.008593
21	22	35184372088832	0.005786	1.449694e-05	0.004296
22	23	140737488355328	0.002893	3.624234e-06	0.002148
23	24	562949953421312	0.001447	9.060586e-07	0.001074