Spatial Relation tests#

[1]:

import starepandas
import geopandas
import matplotlib.pyplot as plt

Cities#

[2]:

cities = ['Buenos Aires', 'Brasilia', 'Santiago',
          'Bogota', 'Caracas', 'Sao Paulo', 'Bridgetown']

latitudes = [-34.58, -15.78, -33.45, 4.60, 10.48, -23.55, 13.1]
longitudes = [-58.66, -47.91, -70.66, -74.08, -66.86, -46.63, -59.62]

data =  {'City': cities,
         'Latitude': latitudes,
         'Longitude': longitudes}

cities = starepandas.STAREDataFrame(data)
stare = starepandas.sids_from_xy(cities.Longitude, cities.Latitude, level=27)
geom = geopandas.points_from_xy(cities.Longitude, cities.Latitude, crs='EPSG:4326')
cities.set_sids(stare, inplace=True)
cities.set_geometry(geom, inplace=True)
cities.add_trixels(inplace=True)

Countries#

[3]:

countries = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
countries = countries.sort_values(by='name')
samerica = countries[countries.continent=='South America']

sids = starepandas.sids_from_gdf(samerica, level=7, force_ccw=True)
samerica = starepandas.STAREDataFrame(samerica, sids=sids)
trixels = samerica.make_trixels()
samerica.set_trixels(trixels, inplace=True)

[4]:

fig, ax = plt.subplots(figsize=(3,7), dpi=100)
ax.grid(True)

samerica.plot(ax=ax, trixels=True, color='y', lw=1, zorder=0)
samerica.plot(ax=ax, trixels=False, facecolor='none', edgecolor='blue', lw=1, zorder=1)
cities.plot(ax=ax, trixels=False, color='r', zorder=2)

[4]:

<AxesSubplot:>

../../_images/examples_notebooks_spatial_relations_6_1.png

[5]:

# The cities trixels are too small to be visible,
# but they do exist:
cities[0:1].plot(trixels=True)

[5]:

<AxesSubplot:>

../../_images/examples_notebooks_spatial_relations_7_1.png

Intersects Points#

[6]:

brasilia = cities[cities.City=='Brasilia']

[7]:

# We do intersect of single feauture and collection;
# Intersects of two collections only is defined row-wise

# Conventional intersect
intersects_conv = samerica.intersects(brasilia.iloc[0].geometry)

# Stare intersect
intersects_stare = samerica.stare_intersects(brasilia.iloc[0].sids)

[8]:

sids = samerica[samerica['name']=='Brazil'].make_sids(level=5)
sids

[8]:

29    [2749447572509687812, 2796735368597078020, 286...
Name: sids, dtype: object

[9]:

cities

[9]:

	City	Latitude	Longitude	sids	geometry	trixels
0	Buenos Aires	-34.58	-58.66	2663379193440875387	POINT (-58.66000 -34.58000)	POLYGON ((-58.66000 -34.58000, -58.66000 -34.5...
1	Brasilia	-15.78	-47.91	2867415364672350651	POINT (-47.91000 -15.78000)	POLYGON ((-47.91000 -15.78000, -47.91000 -15.7...
2	Santiago	-33.45	-70.66	2723774768829278555	POINT (-70.66000 -33.45000)	POLYGON ((-70.66000 -33.45000, -70.66000 -33.4...
3	Bogota	4.60	-74.08	2667981979956219515	POINT (-74.08000 4.60000)	POLYGON ((-74.08000 4.60000, -74.08000 4.60000...
4	Caracas	10.48	-66.86	2494081632617553403	POINT (-66.86000 10.48000)	POLYGON ((-66.86000 10.48000, -66.86000 10.480...
5	Sao Paulo	-23.55	-46.63	2803225788975740475	POINT (-46.63000 -23.55000)	POLYGON ((-46.63000 -23.55000, -46.63000 -23.5...
6	Bridgetown	13.10	-59.62	2518254660685127707	POINT (-59.62000 13.10000)	POLYGON ((-59.62000 13.10000, -59.62000 13.100...

[10]:

sum(~cities.stare_intersects(sids.iloc[0]))

[10]:

Intersects Polygons#

[11]:

# Sao Paulo Sate
fname = 'zip://../tests/data/sp_state.zip'
sao_paulo = geopandas.read_file(fname) # Nice flex
sao_paulo = sao_paulo[sao_paulo.mslink==221]
sids = starepandas.sids_from_gdf(sao_paulo, level=10, force_ccw=True)
sao_paulo = starepandas.STAREDataFrame(sao_paulo, sids=sids)

[12]:

sum(~samerica.stare_intersects(sao_paulo['sids'].iloc[0]))

[12]:

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