cuSpatial Python User’s Guide#
cuSpatial is a GPU-accelerated Python library for spatial data analysis including distance and
trajectory computations, spatial data indexing and spatial join operations. cuSpatial’s
Python API provides an accessible interface to high-performance spatial algorithms accelerated by CUDA-enabled GPUs.
Contents#
This guide provides a working example for all of the python API components of cuSpatial.
The following list links to each subsection.
Installing cuSpatial#
Read the RAPIDS Quickstart Guide to learn more about installing all RAPIDS libraries, including cuSpatial.
If you are working on a system with a CUDA-enabled GPU and have CUDA installed, uncomment the
following cell and install cuSpatial:
[ ]:
# !conda create -n rapids-24.10 -c rapidsai -c conda-forge -c nvidia \
# cuspatial=24.10 python=3.9 cudatoolkit=11.5
For other options to create a RAPIDS environment, such as docker or build from source, see
If you wish to contribute to cuSpatial, you should create a source build using the excellent rapids-compose
GPU accelerated memory layout#
cuSpatial uses
GeoArrow buffers, a GPU-friendly data format for geometric data that is wellsuited for massively parallel programming. See [I/O]((#Input-/-Output) on the fastest methods to get your
data into cuSpatial. GeoArrow extends PyArrow bindings and introduces several new types suited
GeoArrow stores heterogeneous types of Features. DataFrames of geometry objects and their
metadata can be loaded and transformed in a method similar to those in GeoPandas.GeoSeries.
[1]:
# Imports used throughout this notebook.
import cuspatial
import cudf
import cupy
import geopandas
import os
import pandas as pd
import numpy as np
from shapely.geometry import *
from shapely import wkt
[2]:
# For deterministic result
np.random.seed(0)
cupy.random.seed(0)
Input / Output#
The primary method of loading features into cuSpatial is using cuspatial.from_geopandas.
One can also create feature geometries directly using any Python buffer that supports
__array_interface__ for coordinates and their feature offsets.cuspatial.from_geopandas#
The easiest way to get data into cuSpatial is via cuspatial.from_geopandas.
[3]:
host_dataframe = geopandas.read_file('https://naturalearth.s3.amazonaws.com/110m_cultural/ne_110m_admin_0_countries.zip')
host_dataframe = host_dataframe.set_crs(4326)
gpu_dataframe = cuspatial.from_geopandas(host_dataframe)
print(gpu_dataframe.head())
featurecla scalerank LABELRANK SOVEREIGNT SOV_A3 \
0 Admin-0 country 1 6 Fiji FJI
1 Admin-0 country 1 3 United Republic of Tanzania TZA
2 Admin-0 country 1 7 Western Sahara SAH
3 Admin-0 country 1 2 Canada CAN
4 Admin-0 country 1 2 United States of America US1
ADM0_DIF LEVEL TYPE TLC ADMIN ... \
0 0 2 Sovereign country 1 Fiji ...
1 0 2 Sovereign country 1 United Republic of Tanzania ...
2 0 2 Indeterminate 1 Western Sahara ...
3 0 2 Sovereign country 1 Canada ...
4 1 2 Country 1 United States of America ...
FCLASS_TR FCLASS_ID FCLASS_PL FCLASS_GR FCLASS_IT \
0 None None None None None
1 None None None None None
2 Unrecognized Unrecognized Unrecognized None None
3 None None None None None
4 None None None None None
FCLASS_NL FCLASS_SE FCLASS_BD FCLASS_UA \
0 None None None None
1 None None None None
2 Unrecognized None None None
3 None None None None
4 None None None None
geometry
0 MULTIPOLYGON (((180 -16.06713, 180 -16.55522, ...
1 POLYGON ((33.90371 -0.95, 34.07262 -1.05982, 3...
2 POLYGON ((-8.66559 27.65643, -8.66512 27.58948...
3 MULTIPOLYGON (((-122.84 49, -122.97421 49.0025...
4 MULTIPOLYGON (((-122.84 49, -120 49, -117.0312...
[5 rows x 169 columns]
(GPU)
Geopandas and cuDF integration#
Both types of series are stored on the GPU, and
GeoSeries is represented internally using GeoArrow data layout.One of the most important features of cuSpatial is that it is highly integrated with
cuDF.You can use any
cuDF operation on cuSpatial non-feature columns, and most operations will workwith a
geometry column. Operations that reduce or collate the number of rows in your DataFrame,for example
groupby, are not supported at this time.[4]:
gpu_dataframe = cuspatial.from_geopandas(host_dataframe)
continents_dataframe = gpu_dataframe.sort_values("NAME")
print(continents_dataframe.head())
featurecla scalerank LABELRANK SOVEREIGNT SOV_A3 ADM0_DIF \
103 Admin-0 country 1 3 Afghanistan AFG 0
125 Admin-0 country 1 6 Albania ALB 0
82 Admin-0 country 1 3 Algeria DZA 0
74 Admin-0 country 1 3 Angola AGO 0
159 Admin-0 country 1 4 Antarctica ATA 0
LEVEL TYPE TLC ADMIN ... FCLASS_TR FCLASS_ID \
103 2 Sovereign country 1 Afghanistan ... None None
125 2 Sovereign country 1 Albania ... None None
82 2 Sovereign country 1 Algeria ... None None
74 2 Sovereign country 1 Angola ... None None
159 2 Indeterminate 1 Antarctica ... None None
FCLASS_PL FCLASS_GR FCLASS_IT FCLASS_NL FCLASS_SE FCLASS_BD FCLASS_UA \
103 None None None None None None None
125 None None None None None None None
82 None None None None None None None
74 None None None None None None None
159 None None None None None None None
geometry
103 POLYGON ((66.51861 37.36278, 67.07578 37.35614...
125 POLYGON ((21.02004 40.84273, 20.99999 40.58, 2...
82 POLYGON ((-8.6844 27.39574, -8.66512 27.58948,...
74 MULTIPOLYGON (((12.99552 -4.7811, 12.63161 -4....
159 MULTIPOLYGON (((-48.66062 -78.04702, -48.1514 ...
[5 rows x 169 columns]
(GPU)
You can also convert between GPU-backed
cuspatial.GeoDataFrame and CPU-backedgeopandas.GeoDataFrame with from_geopandas and to_geopandas, enabling you totake advantage of any native GeoPandas operation. Note, however, that GeoPandas runs on
the CPU and therefore will not have as high performance as cuSpatial operations. The following
example displays the
Polygon associated with the first item in the dataframe sortedalphabetically by name.
[5]:
gpu_dataframe = cuspatial.from_geopandas(host_dataframe)
sorted_dataframe = gpu_dataframe.sort_values("NAME")
sorted_dataframe = sorted_dataframe.to_geopandas()
sorted_dataframe['geometry'].iloc[0]
[5]:
Trajectories#
A trajectory is a
LineString coupled with a time sample for each point in the LineString.Use
cuspatial.trajectory.derive_trajectories to group trajectory datasets and sort by time.
cuspatial.derive_trajectories#
[6]:
# 1m random trajectory samples
ids = cupy.random.randint(1, 400, 1000000)
timestamps = cupy.random.random(1000000)*1000000
xy= cupy.random.random(2000000)
trajs = cuspatial.GeoSeries.from_points_xy(xy)
sorted_trajectories, trajectory_offsets = \
cuspatial.core.trajectory.derive_trajectories(ids, trajs, timestamps)
# sorted_trajectories is a DataFrame containing all trajectory samples
# sorted first by `object_id` and then by `timestamp`.
print(sorted_trajectories.head())
# trajectory_offsets is a Series containing the start position of each
# trajectory in sorted_trajectories.
print(trajectory_offsets)
object_id x y timestamp
0 1 0.680146 0.874341 1970-01-01 00:00:00.125
1 1 0.843522 0.044402 1970-01-01 00:00:00.834
2 1 0.837039 0.351025 1970-01-01 00:00:01.335
3 1 0.946184 0.479038 1970-01-01 00:00:01.791
4 1 0.117322 0.182117 1970-01-01 00:00:02.474
0 0
1 2455
2 4899
3 7422
4 9924
...
394 987408
395 989891
396 992428
397 994975
398 997448
Length: 399, dtype: int32
derive_trajectories sorts the trajectories by object_id, then timestamp, and returns atuple containing the sorted trajectory data frame in the first index position and the offsets
buffer defining the start and stop of each trajectory in the second index position.
cuspatial.trajectory_distances_and_speeds#
Use
trajectory_distance_and_speed to calculate the overall distance travelled in meters andthe speed of a set of trajectories with the same format as the result returned by
derive_trajectories.[7]:
trajs = cuspatial.GeoSeries.from_points_xy(
sorted_trajectories[["x", "y"]].interleave_columns()
)
d_and_s = cuspatial.core.trajectory.trajectory_distances_and_speeds(
len(cudf.Series(ids).unique()),
sorted_trajectories['object_id'],
trajs,
sorted_trajectories['timestamp']
)
print(d_and_s.head())
distance speed
trajectory_id
0 1.278996e+06 1280.320089
1 1.267179e+06 1268.370390
2 1.294437e+06 1295.905261
3 1.323413e+06 1323.956714
4 1.309590e+06 1311.561012
Finally, compute the bounding boxes of trajectories that follow the format of the above two
examples:
Bounding#
Compute the bounding boxes of n polygons or linestrings:
cuspatial.trajectory_bounding_boxes#
trajectory_bounding_boxes works out of the box with the values returned by derive_trajectories.Its arguments are the number of incoming objects, the offsets of those objects, and x and y point buffers.
[7]:
bounding_boxes = cuspatial.core.trajectory.trajectory_bounding_boxes(
len(cudf.Series(ids, dtype="int32").unique()),
sorted_trajectories['object_id'],
trajs
)
print(bounding_boxes.head())
x_min y_min x_max y_max
0 0.000361 0.000170 0.999582 0.999485
1 0.000184 0.000647 0.999939 0.999884
2 0.000461 0.001395 0.999938 0.999297
3 0.000093 0.000073 0.999819 0.999544
4 0.000105 0.000112 0.999952 0.999013
cuspatial.polygon_bounding_boxes#
polygon_bounding_boxes supports more complex geometry objects such as Polygons with multiplerings. The combination of
part_offset and ring_offset allows the function to use only theexterior ring for computing the bounding box.
[8]:
single_polygons = cuspatial.from_geopandas(
host_dataframe['geometry'][host_dataframe['geometry'].type == "Polygon"]
)
bounding_box_polygons = cuspatial.core.spatial.bounding.polygon_bounding_boxes(
single_polygons
)
print(bounding_box_polygons.head())
minx miny maxx maxy
0 29.339998 -11.720938 40.316590 -0.950000
1 -17.063423 20.999752 -8.665124 27.656426
2 46.466446 40.662325 87.359970 55.385250
3 55.928917 37.144994 73.055417 45.586804
4 12.182337 -13.257227 31.174149 5.256088
cuspatial.linestring_bounding_boxes#
Equivalently, we can treat trajectories as Linestrings and compute the same bounding boxes from
the above trajectory calculation more generally:
[9]:
lines = cuspatial.GeoSeries.from_linestrings_xy(
trajs.points.xy, trajectory_offsets, cupy.arange(len(trajectory_offsets))
)
trajectory_bounding_boxes = cuspatial.core.spatial.bounding.linestring_bounding_boxes(
lines, 0.0001
)
print(trajectory_bounding_boxes.head())
minx miny maxx maxy
0 0.000261 0.000070 0.999682 0.999585
1 0.000084 0.000547 1.000039 0.999984
2 0.000361 0.001295 1.000038 0.999397
3 -0.000007 -0.000027 0.999919 0.999644
4 0.000005 0.000012 1.000052 0.999113
Projection#
cuSpatial provides a simple sinusoidal longitude / latitude to Cartesian coordinate transform.
This function requires an origin point to determine the scaling parameters for the lonlat inputs.
cuspatial.sinusoidal_projection#
The following cell converts the lonlat coordinates of the country of Afghanistan to Cartesian
coordinates in km, centered around the center of the country, suitable for graphing and display.
[10]:
gpu_dataframe = cuspatial.from_geopandas(host_dataframe)
afghanistan = gpu_dataframe['geometry'][gpu_dataframe['NAME'] == 'Afghanistan']
points = cuspatial.GeoSeries.from_points_xy(afghanistan.polygons.xy)
projected = cuspatial.sinusoidal_projection(
afghanistan.polygons.x.mean(),
afghanistan.polygons.y.mean(),
points
)
print(projected.head())
0 POINT (112.174 -281.59)
1 POINT (62.152 -280.852)
2 POINT (-5.573 -257.391)
3 POINT (-33.071 -243.849)
4 POINT (-98.002 -279.54)
dtype: geometry
Distance#
cuSpatial provides a growing suite of distance computation functions. Parallel distance functions
come in two main forms: pairwise, which computes a distance for each corresponding pair of input
geometries; and all-pairs, which computes a distance for the each element of the Cartesian product
of input geometries (for each input geometry in A, compute the distance from A to each input geometry in B).”
Two pairwise distance functions are included in cuSpatial:
haversine and pairwise_linestring.The
hausdorff clustering distances algorithm is also available, computing the hausdorffdistance across the cartesian product of its single input.
cuspatial.directed_hausdorff_distance#
The directed Hausdorff distance from one space to another is the greatest of all the distances
between any point in the first space to the closet point in the second. This is especially useful
as a similarity metric between trajectories.
[11]:
coordinates = sorted_trajectories[['x', 'y']].interleave_columns()
spaces = cuspatial.GeoSeries.from_multipoints_xy(
coordinates, trajectory_offsets
)
hausdorff_distances = cuspatial.core.spatial.distance.directed_hausdorff_distance(
spaces
)
print(hausdorff_distances.head())
0 1 2 3 4 5 6 \
0 0.000000 0.034755 0.031989 0.031959 0.031873 0.038674 0.029961
1 0.030328 0.000000 0.038672 0.032086 0.031049 0.032170 0.032275
2 0.027640 0.030539 0.000000 0.036737 0.033055 0.043447 0.028812
3 0.031497 0.033380 0.035224 0.000000 0.032581 0.035484 0.030339
4 0.031079 0.032256 0.035731 0.039084 0.000000 0.036416 0.031369
7 8 9 ... 388 389 390 391 \
0 0.029117 0.040962 0.033259 ... 0.031614 0.036447 0.035548 0.028233
1 0.030215 0.034443 0.032998 ... 0.030594 0.035665 0.031473 0.031916
2 0.031807 0.039269 0.033250 ... 0.031998 0.033636 0.034646 0.032615
3 0.034792 0.045755 0.031810 ... 0.033623 0.031359 0.034923 0.032287
4 0.030388 0.033751 0.034029 ... 0.030705 0.040339 0.034328 0.029027
392 393 394 395 396 397
0 0.034176 0.030057 0.033863 0.031111 0.034590 0.033850
1 0.037483 0.033489 0.041403 0.029784 0.035374 0.038179
2 0.036681 0.030642 0.038432 0.032481 0.034810 0.036695
3 0.032808 0.029771 0.040891 0.030802 0.032279 0.038443
4 0.035645 0.027703 0.037529 0.029356 0.031260 0.035501
[5 rows x 398 columns]
cuspatial.haversine_distance#
Haversine distance is the great circle distance between longitude and latitude pairs. cuSpatial
uses the
lon/lat ordering to better reflect the cartesian coordinates of great circlecoordinates:
x/y.
[12]:
gpu_dataframe = cuspatial.from_geopandas(host_dataframe)
polygons_first = gpu_dataframe['geometry'][0:10]
polygons_second = gpu_dataframe['geometry'][10:30]
points_first = polygons_first.polygons.xy[0:1000]
points_second = polygons_second.polygons.xy[0:1000]
first = cuspatial.GeoSeries.from_points_xy(points_first)
second = cuspatial.GeoSeries.from_points_xy(points_second)
# The number of coordinates in two sets of polygons vary, so
# we'll just compare the first set of 1000 values here.
distances_in_meters = cuspatial.haversine_distance(
first, second
)
cudf.Series(distances_in_meters).head()
[12]:
0 9959.695143
1 9803.166859
2 9876.857085
3 9925.097106
4 9927.268486
Name: None, dtype: float64
This above method reads the GeoPandas data from CPU memory into GPU memory and then cuSpatial processes it. If the data is already in a cuDF GPU dataframe, you can quickly calculate Haversine distances using the method below. This maximizes speed by keeping all the processing on the GPU and is very useful when working on large datasets.
[13]:
# Generate data to be used to create a cuDF dataframe.
# The data to be processed by Haversine MUST be a Float.
a = {"latitude":[17.1167, 17.1333, 25.333, 25.255, 24.433, 24.262, 35.317, 34.21, 34.566, 31.5, 36.7167, 30.5667, 28.05, 22.8, 35.7297, 36.97, 36.78, 36.8, 36.8, 36.72],
"longitude": [-61.7833, -61.7833, 55.517, 55.364, 54.651, 55.609, 69.017, 62.228, 69.212, 65.85, 3.25, 2.8667, 9.6331, 5.4331, 0.65, 7.79, 3.07, 3.03, 3.04, 4.05]}
df = cudf.DataFrame(data=a)
# Create cuSpatial GeoSeries from cuDF Dataframe
cuGeoSeries = cuspatial.GeoSeries.from_points_xy(df[['longitude', 'latitude']].interleave_columns())
# Create Comparator cuSpatial GeoSeries from a comparator point
df['atlanta_lat'] = 33.7490
df['atlanta_lng'] = -84.3880
atlGeoSeries = cuspatial.GeoSeries.from_points_xy(df[['atlanta_lat', 'atlanta_lng']].interleave_columns())
# Calculate Haversine Distance of cuDF dataframe to comparator point
df['atlanta_dist'] = cuspatial.haversine_distance(cuGeoSeries, atlGeoSeries)
print(df.head())
latitude longitude atlanta_lat atlanta_lng atlanta_dist
0 17.1167 -61.7833 33.749 -84.388 11961.556540
1 17.1333 -61.7833 33.749 -84.388 11963.392729
2 25.3330 55.5170 33.749 -84.388 12243.126130
3 25.2550 55.3640 33.749 -84.388 12233.867463
4 24.4330 54.6510 33.749 -84.388 12139.822218
Pairwise distance#
pairwise_linestring_distance computes the distance between a GeoSeries of Linestrings oflength
n and a corresponding GeoSeries of Linestrings of n length. It returns theminimum distance from any point in the first linestring of the pair to the nearest segment
or point within the second Linestring of the pair.
The input accepts a pair of geoseries as input sequences of linestring arrays.
The below example uses the polygons from
naturalearth_lowres and treats them as linestrings.The first example computes the distances between all polygons and themselves, while the second
example computes the distance between the first 50 polygons and the second 50 polygons.
cuspatial.pairwise_linestring_distance#
[14]:
gpu_boundaries = cuspatial.from_geopandas(host_dataframe.geometry.boundary)
zeros = cuspatial.pairwise_linestring_distance(
gpu_boundaries[0:50],
gpu_boundaries[0:50]
)
print(zeros.head())
lines1 = gpu_boundaries[0:50]
lines2 = gpu_boundaries[50:100]
distances = cuspatial.pairwise_linestring_distance(
lines1, lines2
)
print(distances.head())
0 0.0
1 0.0
2 0.0
3 0.0
4 0.0
dtype: float64
0 152.200610
1 44.076445
2 2.417269
3 44.197151
4 75.821029
dtype: float64
pairwise_point_linestring_distance computes the distance between pairs of points andlinestrings. It can be used with polygons treated as linestrings as well. In the following
example the minimum distance from a country’s center to it’s border is computed.
cuspatial.pairwise_point_linestring_distance#
Using WGS 84 Pseudo-Mercator, distances are in meters.
[15]:
# Convert input dataframe to Pseudo-Mercator projection.
host_dataframe3857 = host_dataframe.to_crs(3857)
polygons = host_dataframe3857[host_dataframe3857['geometry'].type == "Polygon"]
gpu_polygons = cuspatial.from_geopandas(polygons)
# Extract mean_x and mean_y from each country
mean_x = [gpu_polygons['geometry'].iloc[[ix]].polygons.x.mean() for ix in range(len(gpu_polygons))]
mean_y = [gpu_polygons['geometry'].iloc[[ix]].polygons.y.mean() for ix in range(len(gpu_polygons))]
# Convert mean_x/mean_y values into Points for use in API.
points = cuspatial.GeoSeries([Point(point) for point in zip(mean_x, mean_y)])
# Convert Polygons into Linestrings for use in API.
linestring_df = cuspatial.from_geopandas(geopandas.geoseries.GeoSeries(
[MultiLineString(mapping(polygons['geometry'].iloc[ix])["coordinates"]) for ix in range(len(polygons))]
))
gpu_polygons['border_distance'] = cuspatial.pairwise_point_linestring_distance(
points, linestring_df
)
print(gpu_polygons.head())
featurecla scalerank LABELRANK SOVEREIGNT \
1 Admin-0 country 1 3 United Republic of Tanzania
2 Admin-0 country 1 7 Western Sahara
5 Admin-0 country 1 3 Kazakhstan
6 Admin-0 country 1 3 Uzbekistan
11 Admin-0 country 1 2 Democratic Republic of the Congo
SOV_A3 ADM0_DIF LEVEL TYPE TLC \
1 TZA 0 2 Sovereign country 1
2 SAH 0 2 Indeterminate 1
5 KA1 1 1 Sovereignty 1
6 UZB 0 2 Sovereign country 1
11 COD 0 2 Sovereign country 1
ADMIN ... FCLASS_ID FCLASS_PL \
1 United Republic of Tanzania ... None None
2 Western Sahara ... Unrecognized Unrecognized
5 Kazakhstan ... None None
6 Uzbekistan ... None None
11 Democratic Republic of the Congo ... None None
FCLASS_GR FCLASS_IT FCLASS_NL FCLASS_SE FCLASS_BD FCLASS_UA \
1 None None None None None None
2 None None Unrecognized None None None
5 None None None None None None
6 None None None None None None
11 None None None None None None
geometry border_distance
1 POLYGON ((3774143.866 -105758.362, 3792946.708... 8047.288391
2 POLYGON ((-964649.018 3205725.605, -964597.245... 593137.492497
5 POLYGON ((9724867.413 6311418.173, 9640131.701... 37091.213890
6 POLYGON ((6230350.563 5057973.384, 6225978.591... 278633.467299
11 POLYGON ((3266113.592 -501451.658, 3286149.877... 35812.988244
[5 rows x 170 columns]
(GPU)
cuspatial.pairwise_point_polygon_distance#
Using WGS 84 Pseudo-Mercator, distances are in meters.
[16]:
countries = host_dataframe
cities = geopandas.read_file('https://naturalearth.s3.amazonaws.com/110m_cultural/ne_110m_populated_places_simple.zip')
cities = cities.to_crs(3857)
gpu_cities = cuspatial.from_geopandas(cities)
gpu_countries = cuspatial.from_geopandas(countries)
dist = cuspatial.pairwise_point_polygon_distance(
gpu_cities.geometry[:len(gpu_countries)], gpu_countries.geometry
)
gpu_countries["distance_from"] = cities.name
gpu_countries["distance"] = dist
print(gpu_countries.head())
featurecla scalerank LABELRANK SOVEREIGNT SOV_A3 \
0 Admin-0 country 1 6 Fiji FJI
1 Admin-0 country 1 3 United Republic of Tanzania TZA
2 Admin-0 country 1 7 Western Sahara SAH
3 Admin-0 country 1 2 Canada CAN
4 Admin-0 country 1 2 United States of America US1
ADM0_DIF LEVEL TYPE TLC ADMIN ... \
0 0 2 Sovereign country 1 Fiji ...
1 0 2 Sovereign country 1 United Republic of Tanzania ...
2 0 2 Indeterminate 1 Western Sahara ...
3 0 2 Sovereign country 1 Canada ...
4 1 2 Country 1 United States of America ...
FCLASS_PL FCLASS_GR FCLASS_IT FCLASS_NL FCLASS_SE FCLASS_BD \
0 None None None None None None
1 None None None None None None
2 Unrecognized None None Unrecognized None None
3 None None None None None None
4 None None None None None None
FCLASS_UA geometry distance_from \
0 None MULTIPOLYGON (((180 -16.06713, 180 -16.55522, ... Vatican City
1 None POLYGON ((33.90371 -0.95, 34.07262 -1.05982, 3... San Marino
2 None POLYGON ((-8.66559 27.65643, -8.66512 27.58948... Vaduz
3 None MULTIPOLYGON (((-122.84 49, -122.97421 49.0025... Lobamba
4 None MULTIPOLYGON (((-122.84 49, -120 49, -117.0312... Luxembourg
distance
0 5.329915e+06
1 5.628613e+06
2 6.057264e+06
3 4.626961e+06
4 6.415631e+06
[5 rows x 171 columns]
(GPU)
cuspatial.pairwise_linestring_polygon_distance#
Using WGS 84 Pseudo-Mercator, distances are in meters.
[17]:
# all driveways within 2km range of central park, nyc
if not os.path.exists("./streets_3857.csv"):
import osmnx as ox
graph = ox.graph_from_point((40.769361, -73.977655), dist=2000, network_type="drive")
nodes, streets = ox.graph_to_gdfs(graph)
streets = streets.to_crs(3857)
streets = streets.reset_index(drop=True)
streets.index.name = "index"
streets[["name", "geometry"]].to_csv("streets_3857.csv")
# The data is under notebooks/streets_3857.csv
streets = pd.read_csv("./streets_3857.csv", index_col="index")
streets.geometry = streets.geometry.apply(wkt.loads)
streets = geopandas.GeoDataFrame(streets)
streets.head()
[17]:
| name | geometry | |
|---|---|---|
| index | ||
| 0 | Columbus Avenue | LINESTRING (-8234860.077 4980333.535, -8234863... |
| 1 | West 80th Street | LINESTRING (-8235173.854 4980508.442, -8235160... |
| 2 | Amsterdam Avenue | LINESTRING (-8235173.854 4980508.442, -8235168... |
| 3 | West 80th Street | LINESTRING (-8235369.475 4980617.398, -8235347... |
| 4 | Broadway | LINESTRING (-8235369.475 4980617.398, -8235373... |
[19]:
# The polygon of the Empire State Building
if not os.path.exists("./esb_3857.csv"):
import osmnx as ox
esb = ox.features.features_from_place('Empire State Building, New York', tags={"building": True})
esb = esb.to_crs(3857)
esb = esb.geometry.reset_index(drop=True)
esb.index.name = "index"
esb.to_csv("esb_3857.csv")
# The data is under notebooks/esb_3857.csv
esb = pd.read_csv("./esb_3857.csv", index_col="index")
esb.geometry = esb.geometry.apply(wkt.loads)
esb = geopandas.GeoDataFrame(esb)
esb = pd.concat([esb.iloc[0:1]] * len(streets))
esb.head()
/raid/jlamb/miniforge/envs/cuspatial-dev/lib/python3.11/site-packages/osmnx/features.py:294: DeprecationWarning: The 'unary_union' attribute is deprecated, use the 'union_all()' method instead.
polygon = gdf_place["geometry"].unary_union
[19]:
| geometry | |
|---|---|
| index | |
| 0 | POLYGON ((-8236139.639 4975314.625, -8235990.3... |
| 0 | POLYGON ((-8236139.639 4975314.625, -8235990.3... |
| 0 | POLYGON ((-8236139.639 4975314.625, -8235990.3... |
| 0 | POLYGON ((-8236139.639 4975314.625, -8235990.3... |
| 0 | POLYGON ((-8236139.639 4975314.625, -8235990.3... |
[20]:
# Straight line distance between the driveways to the Empire State Building
gpu_streets = cuspatial.from_geopandas(streets.geometry)
gpu_esb = cuspatial.from_geopandas(esb.geometry)
dist = cuspatial.pairwise_linestring_polygon_distance(gpu_streets, gpu_esb).rename("dist")
pd.concat([streets["name"].reset_index(drop=True), dist.to_pandas()], axis=1)
[20]:
| name | dist | |
|---|---|---|
| 0 | Columbus Avenue | 4993.583717 |
| 1 | West 80th Street | 5103.472213 |
| 2 | Amsterdam Avenue | 5208.373183 |
| 3 | West 80th Street | 5275.851781 |
| 4 | Broadway | 5178.999774 |
| ... | ... | ... |
| 1862 | West 82nd Street | 5411.762092 |
| 1863 | Broadway | 5476.345847 |
| 1864 | West 84th Street | 5613.403002 |
| 1865 | West 75th Street | 4750.092380 |
| 1866 | Broadway | 4638.894939 |
1867 rows × 2 columns
cuspatial.pairwise_polygon_distance#
Using WGS 84 Pseudo-Mercator, distances are in meters.
[21]:
african_countries = gpu_countries[gpu_countries.CONTINENT == "Africa"].sort_values("POP_EST", ascending=False)
asian_countries = gpu_countries[gpu_countries.CONTINENT == "Asia"].sort_values("POP_EST", ascending=False)
[22]:
# Straight line distance between the top 10 most populated countries in Asia and Africa
population_top10_africa = african_countries[:10].reset_index(drop=True)
population_top10_asia = asian_countries[:10].reset_index(drop=True)
dist = cuspatial.pairwise_polygon_distance(
population_top10_africa.geometry, population_top10_asia.geometry)
cudf.concat([
population_top10_africa["NAME"].rename("Africa"),
population_top10_asia["NAME"].rename("Asia"),
dist.rename("dist")], axis=1
)
[22]:
| Africa | Asia | dist | |
|---|---|---|---|
| 0 | Nigeria | China | 64.966620 |
| 1 | Ethiopia | India | 25.598868 |
| 2 | Egypt | Indonesia | 60.717434 |
| 3 | Dem. Rep. Congo | Pakistan | 37.489668 |
| 4 | South Africa | Bangladesh | 72.860545 |
| 5 | Tanzania | Japan | 97.872886 |
| 6 | Kenya | Philippines | 75.450451 |
| 7 | Uganda | Vietnam | 69.827567 |
| 8 | Algeria | Turkey | 17.927419 |
| 9 | Sudan | Iran | 13.990335 |
Filtering#
The filtering module contains points_in_spatial_window, which returns from a set of points only those points that fall within a spatial window defined by four bounding coordinates: min_x, max_x, min_y, and max_y. The following example finds only the points of polygons that fall within 1 standard deviation of the mean of all of the polygons.
cuspatial.points_in_spatial_window#
[23]:
gpu_dataframe = cuspatial.from_geopandas(host_dataframe)
geometry = gpu_dataframe['geometry']
points = cuspatial.GeoSeries.from_points_xy(geometry.polygons.xy)
mean_x, std_x = (geometry.polygons.x.mean(), geometry.polygons.x.std())
mean_y, std_y = (geometry.polygons.y.mean(), geometry.polygons.y.std())
avg_points = cuspatial.points_in_spatial_window(
points,
mean_x - std_x,
mean_x + std_x,
mean_y - std_y,
mean_y + std_y
)
print(avg_points.head())
0 POINT (33.90371 -0.95)
1 POINT (34.07262 -1.05982)
2 POINT (37.69869 -3.09699)
3 POINT (37.7669 -3.67712)
4 POINT (39.20222 -4.67677)
dtype: geometry
With some careful grouping, one can reconstruct the original complete polygons that fall within the range.
Set Operations#
Linestring Intersections#
cuSpatial provides a linestring-linestring intersection algorithm to compute the overlapping geometries between two linestrings. The API also returns the ids for each returned geometry to help user to trace back the source geometry.
[24]:
from cuspatial.core.binops.intersection import pairwise_linestring_intersection
usa_boundary = cuspatial.from_geopandas(host_dataframe[host_dataframe.NAME == "United States of America"].geometry.boundary)
canada_boundary = cuspatial.from_geopandas(host_dataframe[host_dataframe.NAME == "Canada"].geometry.boundary)
list_offsets, geometries, look_back_ids = pairwise_linestring_intersection(usa_boundary, canada_boundary)
[25]:
# The first integer series shows that the result contains 1 row (since we only have 1 pair of linestrings as input).
# This row contains 144 geometires.
list_offsets
[25]:
<cudf.core.column.numerical.NumericalColumn object at 0x7f525c27a570>
[
0,
142
]
dtype: int32
[31]:
# The second element is a geoseries that contains the intersecting geometries, with 144 rows, including points and linestrings.
geometries
[31]:
0 POINT (-130.53611 54.80275)
1 POINT (-130.53611 54.80278)
2 POINT (-130.53611 54.80275)
3 POINT (-129.98 55.285)
4 POINT (-130.53611 54.80278)
...
137 LINESTRING (-120 49, -117.03121 49)
138 LINESTRING (-122.84 49, -120 49)
139 LINESTRING (-117.03121 49, -107.05 49)
140 LINESTRING (-83.89077 46.11693, -83.61613 46.1...
141 LINESTRING (-82.69009 41.67511, -82.43928 41.6...
Length: 142, dtype: geometry
[26]:
# The third element is a dataframe that contains IDs to the input segments and linestrings, 4 for each result row.
# Each represents ids to lhs, rhs linestring and segment ids.
look_back_ids
[26]:
| lhs_linestring_id | lhs_segment_id | rhs_linestring_id | rhs_segment_id | |
|---|---|---|---|---|
| 0 | [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, ... | [18, 16, 18, 15, 17, 137, 14, 16, 13, 15, 14, ... | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... | [9, 10, 10, 11, 11, 28, 12, 12, 13, 13, 14, 15... |
Spatial Joins#
cuSpatial provides a number of functions to facilitate high-performance spatial joins, including unindexed and quadtree-indexed point-in-polygon and quadtree-indexed point to nearest linestring.
The API for spatial joins does not yet match GeoPandas, but with knowledge of cuSpatial data formats you can call cuspatial.point_in_polygon for large numbers of points on 32 polygons or less, or call cuspatial.quadtree_point_in_polygon for large numbers of points and polygons.
Unindexed Point-in-polygon Join#
cuspatial.point_in_polygon#
[27]:
single_polygons = host_dataframe[host_dataframe['geometry'].type == "Polygon"]
gpu_dataframe = cuspatial.from_geopandas(single_polygons)
x_points = (cupy.random.random(10000000) - 0.5) * 360
y_points = (cupy.random.random(10000000) - 0.5) * 180
xy = cudf.DataFrame({"x": x_points, "y": y_points}).interleave_columns()
points = cuspatial.GeoSeries.from_points_xy(xy)
short_dataframe = gpu_dataframe.iloc[0:31]
geometry = short_dataframe['geometry']
points_in_polygon = cuspatial.point_in_polygon(
points, geometry
)
sum_of_points_in_polygons_0_to_31 = points_in_polygon.sum()
sum_of_points_in_polygons_0_to_31.head()
[27]:
1 11896
2 1268
5 50835
6 7792
11 29318
dtype: int64
cuSpatial includes another join algorithm,
quadtree_point_in_polygon that uses an indexingquadtree for faster calculations.
quadtree_point_in_polygon also supports a number ofpolygons limited only by memory constraints.
Quadtree Indexing#
The indexing module is used to create a spatial quadtree. Use
cuspatial.quadtree_on_points(
points,
x_min,
x_max,
y_min,
y_max,
scale,
max_depth,
max_size
)
to create the quadtree object that is used by the
quadtree_point_in_polygonfunction in the
join module.The function uses a set of points and a user-defined bounding box to build an
indexing quad tree. Be sure to adjust the parameters appropriately, with larger
parameter values for larger datasets.
scale: A scaling function that increases the size of the point space from anorigin defined by
{x_min, y_min}. This can increase the likelihood of generatingwell-separated quads.
max_depth: In order for a quadtree to index points effectively, it must have a depth that is log-scaled with the size of the number of points. Each level of thequad tree contains 4 quads. The number of available quads \(q\) for indexing is then
equal to \(q = 4^{d}\) where \(d\) is the
max_depth parameter. With an input sizeof
10m points and max_depth = 7, \(\frac{10^6}{4^7}\) points will be mostefficiently packed into the leaves of the quad tree.
max_size: The maximum number of points allowed in an internal node before it is split into four leaf notes. As the quadtree is generated, a leaf node containing usable index points will be created as points are added. If the number of points in this leaf exceeds max_size, the leaf will be subdivided, with four new leaves added and the original node removed from the set of leaves. This number is probably optimized in most datasets by making it a significant fraction of the optimal leaf
size computation from above. Consider \(10,000,000 / 4^7 / 4 = 153\).
cuspatial.quadtree_on_points#
[28]:
x_points = (cupy.random.random(10000000) - 0.5) * 360
y_points = (cupy.random.random(10000000) - 0.5) * 180
xy = cudf.DataFrame({"x": x_points, "y": y_points}).interleave_columns()
points = cuspatial.GeoSeries.from_points_xy(xy)
scale = 5
max_depth = 7
max_size = 125
point_indices, quadtree = cuspatial.quadtree_on_points(points,
x_points.min(),
x_points.max(),
y_points.min(),
y_points.max(),
scale,
max_depth,
max_size)
print(point_indices.head())
print(quadtree.head())
0 1507
1 1726
2 4242
3 7371
4 11341
dtype: uint32
key level is_internal_node length offset
0 0 0 True 4 2
1 1 0 True 2 6
2 0 1 True 4 8
3 1 1 True 4 12
4 2 1 True 2 16
Indexed Spatial Joins#
The quadtree spatial index (point_indices and quadtree) is used by quadtree_point_in_polygon and quadtree_point_to_nearest_linestring to accelerate larger spatial joins. quadtree_point_in_polygon depends on a number of intermediate products calculated here using the following functions.
cuspatial.join_quadtree_and_bounding_boxes#
cuspatial.quadtree_point_in_polygon#
[29]:
polygons = gpu_dataframe['geometry']
poly_bboxes = cuspatial.polygon_bounding_boxes(
polygons
)
intersections = cuspatial.join_quadtree_and_bounding_boxes(
quadtree,
poly_bboxes,
polygons.polygons.x.min(),
polygons.polygons.x.max(),
polygons.polygons.y.min(),
polygons.polygons.y.max(),
scale,
max_depth
)
polygons_and_points = cuspatial.quadtree_point_in_polygon(
intersections,
quadtree,
point_indices,
points,
polygons
)
print(polygons_and_points.head())
Empty DataFrame
Columns: [polygon_index, point_index]
Index: []
You can see above that polygon 270 maps to the first 5 points. In order to bring this back to
a specific row of the original dataframe, the individual polygons must be mapped back to their
original MultiPolygon row. This is left an an exercise.
cuspatial.quadtree_point_to_nearest_linestring#
cuspatial.quadtree_point_to_nearest_linestring can be used to find the Polygon or Linestringnearest to a set of points from another set of mixed geometries.
[30]:
gpu_countries = cuspatial.from_geopandas(countries[countries['geometry'].type == "Polygon"])
gpu_cities = cuspatial.from_geopandas(cities[cities['geometry'].type == 'Point'])
[31]:
polygons = gpu_countries['geometry'].polygons
boundaries = cuspatial.GeoSeries.from_linestrings_xy(
cudf.DataFrame({"x": polygons.x, "y": polygons.y}).interleave_columns(),
polygons.ring_offset,
cupy.arange(len(polygons.ring_offset))
)
point_indices, quadtree = cuspatial.quadtree_on_points(gpu_cities['geometry'],
polygons.x.min(),
polygons.x.max(),
polygons.y.min(),
polygons.y.max(),
scale,
max_depth,
max_size)
poly_bboxes = cuspatial.linestring_bounding_boxes(
boundaries,
2.0
)
intersections = cuspatial.join_quadtree_and_bounding_boxes(
quadtree,
poly_bboxes,
polygons.x.min(),
polygons.x.max(),
polygons.y.min(),
polygons.y.max(),
scale,
max_depth
)
result = cuspatial.quadtree_point_to_nearest_linestring(
intersections,
quadtree,
point_indices,
gpu_cities['geometry'],
boundaries
)
print(result.head())
Empty DataFrame
Columns: [point_index, linestring_index, distance]
Index: []
Images used with permission from Wikipedia Creative Commons
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