diff --git a/python/cuspatial/benchmarks/api/bench_api.py b/python/cuspatial/benchmarks/api/bench_api.py
index 261746f1c..0ad32b4ed 100644
--- a/python/cuspatial/benchmarks/api/bench_api.py
+++ b/python/cuspatial/benchmarks/api/bench_api.py
@@ -121,14 +121,18 @@ def bench_points_in_spatial_window(benchmark, gpu_dataframe):
     geometry = gpu_dataframe["geometry"]
     mean_x, std_x = (geometry.polygons.x.mean(), geometry.polygons.x.std())
     mean_y, std_y = (geometry.polygons.y.mean(), geometry.polygons.y.std())
+    points = cuspatial.GeoSeries.from_points_xy(
+        cudf.DataFrame(
+            {"x": geometry.polygons.x, "y": geometry.polygons.y}
+        ).interleave_columns()
+    )
     benchmark(
         cuspatial.points_in_spatial_window,
+        points,
         mean_x - std_x,
         mean_x + std_x,
         mean_y - std_y,
         mean_y + std_y,
-        geometry.polygons.x,
-        geometry.polygons.y,
     )
 
 
@@ -136,13 +140,16 @@ def bench_quadtree_on_points(benchmark, gpu_dataframe):
     polygons = gpu_dataframe["geometry"].polygons
     x_points = (cupy.random.random(10000000) - 0.5) * 360
     y_points = (cupy.random.random(10000000) - 0.5) * 180
+    points = cuspatial.GeoSeries.from_points_xy(
+        cudf.DataFrame({"x": x_points, "y": y_points}).interleave_columns()
+    )
+
     scale = 5
     max_depth = 7
     min_size = 125
     benchmark(
         cuspatial.quadtree_on_points,
-        x_points,
-        y_points,
+        points,
         polygons.x.min(),
         polygons.x.max(),
         polygons.y.min(),
@@ -160,9 +167,11 @@ def bench_quadtree_point_in_polygon(benchmark, polygons):
     scale = 5
     max_depth = 7
     min_size = 125
+    points = cuspatial.GeoSeries.from_points_xy(
+        cudf.DataFrame({"x": x_points, "y": y_points}).interleave_columns()
+    )
     point_indices, quadtree = cuspatial.quadtree_on_points(
-        x_points,
-        y_points,
+        points,
         polygons.x.min(),
         polygons.x.max(),
         polygons.y.min(),
@@ -215,9 +224,11 @@ def bench_quadtree_point_to_nearest_linestring(benchmark):
     polygons = gpu_countries["geometry"].polygons
     points_x = gpu_cities["geometry"].points.x
     points_y = gpu_cities["geometry"].points.y
+    points = cuspatial.GeoSeries.from_points_xy(
+        cudf.DataFrame({"x": points_x, "y": points_y}).interleave_columns()
+    )
     point_indices, quadtree = cuspatial.quadtree_on_points(
-        points_x,
-        points_y,
+        points,
         polygons.x.min(),
         polygons.x.max(),
         polygons.y.min(),
diff --git a/python/cuspatial/cuspatial/core/spatial/filtering.py b/python/cuspatial/cuspatial/core/spatial/filtering.py
index d05e6dd49..be7689e1d 100644
--- a/python/cuspatial/cuspatial/core/spatial/filtering.py
+++ b/python/cuspatial/cuspatial/core/spatial/filtering.py
@@ -1,13 +1,14 @@
-# Copyright (c) 2022, NVIDIA CORPORATION.
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
 
 from cudf import DataFrame
 from cudf.core.column import as_column
 
 from cuspatial._lib import spatial_window
-from cuspatial.utils.column_utils import normalize_point_columns
+from cuspatial.core.geoseries import GeoSeries
+from cuspatial.utils.column_utils import contains_only_points
 
 
-def points_in_spatial_window(min_x, max_x, min_y, max_y, xs, ys):
+def points_in_spatial_window(points: GeoSeries, min_x, max_x, min_y, max_y):
     """Return only the subset of coordinates that fall within a
     rectangular window.
 
@@ -19,32 +20,40 @@ def points_in_spatial_window(min_x, max_x, min_y, max_y, xs, ys):
 
     Parameters
     ----------
-    min_x
+    points: GeoSeries
+        A geoseries of points
+    min_x: float
         lower x-coordinate of the query window
-    max_x
+    max_x: float
         upper x-coordinate of the query window
-    min_y
+    min_y: float
         lower y-coordinate of the query window
-    max_y
+    max_y: float
         upper y-coordinate of the query window
-    xs
-        column of x-coordinates that may fall within the window
-    ys
-        column of y-coordinates that may fall within the window
 
     Returns
     -------
-    result : cudf.DataFrame
-        subset of `(x, y)` pairs above that fall within the window
+    result : GeoSeries
+        subset of `points` above that fall within the window
 
     Notes
     -----
     * Swaps ``min_x`` and ``max_x`` if ``min_x > max_x``
     * Swaps ``min_y`` and ``max_y`` if ``min_y > max_y``
     """
-    xs, ys = normalize_point_columns(as_column(xs), as_column(ys))
-    return DataFrame._from_data(
+
+    if len(points) == 0:
+        return GeoSeries([])
+
+    if not contains_only_points(points):
+        raise ValueError("GeoSeries must contain only points.")
+
+    xs = as_column(points.points.x)
+    ys = as_column(points.points.y)
+
+    res_xy = DataFrame._from_data(
         *spatial_window.points_in_spatial_window(
             min_x, max_x, min_y, max_y, xs, ys
         )
-    )
+    ).interleave_columns()
+    return GeoSeries.from_points_xy(res_xy)
diff --git a/python/cuspatial/cuspatial/core/spatial/indexing.py b/python/cuspatial/cuspatial/core/spatial/indexing.py
index 435355819..52d63e274 100644
--- a/python/cuspatial/cuspatial/core/spatial/indexing.py
+++ b/python/cuspatial/cuspatial/core/spatial/indexing.py
@@ -1,18 +1,19 @@
-# Copyright (c) 2022, NVIDIA CORPORATION.
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
 
 import warnings
 
 from cudf import DataFrame, Series
 from cudf.core.column import as_column
 
+from cuspatial import GeoSeries
 from cuspatial._lib.quadtree import (
     quadtree_on_points as cpp_quadtree_on_points,
 )
-from cuspatial.utils.column_utils import normalize_point_columns
+from cuspatial.utils.column_utils import contains_only_points
 
 
 def quadtree_on_points(
-    xs, ys, x_min, x_max, y_min, y_max, scale, max_depth, max_size
+    points: GeoSeries, x_min, x_max, y_min, y_max, scale, max_depth, max_size
 ):
     """
     Construct a quadtree from a set of points for a given area-of-interest
@@ -20,10 +21,8 @@ def quadtree_on_points(
 
     Parameters
     ----------
-    xs
-        Column of x-coordinates for each point.
-    ys
-        Column of y-coordinates for each point.
+    points
+        Series of points.
     x_min
         The lower-left x-coordinate of the area of interest bounding box.
     x_max
@@ -157,7 +156,12 @@ def quadtree_on_points(
         Length: 120, dtype: int32
     """
 
-    xs, ys = normalize_point_columns(as_column(xs), as_column(ys))
+    if not len(points) == 0 and not contains_only_points(points):
+        raise ValueError("GeoSeries must contain only points.")
+
+    xs = as_column(points.points.x)
+    ys = as_column(points.points.y)
+
     x_min, x_max, y_min, y_max = (
         min(x_min, x_max),
         max(x_min, x_max),
diff --git a/python/cuspatial/cuspatial/tests/spatial/filtering/test_points_in_spatial_window.py b/python/cuspatial/cuspatial/tests/spatial/filtering/test_points_in_spatial_window.py
index 677f37eca..214a44b82 100644
--- a/python/cuspatial/cuspatial/tests/spatial/filtering/test_points_in_spatial_window.py
+++ b/python/cuspatial/cuspatial/tests/spatial/filtering/test_points_in_spatial_window.py
@@ -1,26 +1,25 @@
-# Copyright (c) 2019, NVIDIA CORPORATION.
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
 
+import geopandas as gpd
 import pytest
-
-import cudf
+from geopandas.testing import assert_geoseries_equal
+from shapely.geometry import Point
 
 import cuspatial
 
 
 def test_zeros():
     result = cuspatial.points_in_spatial_window(  # noqa: F841
-        0, 0, 0, 0, cudf.Series([0.0]), cudf.Series([0.0])
+        cuspatial.GeoSeries([Point(0, 0)]), 0, 0, 0, 0
     )
     assert result.empty
 
 
 def test_centered():
-    result = cuspatial.points_in_spatial_window(
-        -1, 1, -1, 1, cudf.Series([0.0]), cudf.Series([0.0])
-    )
-    cudf.testing.assert_frame_equal(
-        result, cudf.DataFrame({"x": [0.0], "y": [0.0]})
-    )
+    s = cuspatial.GeoSeries([Point(0, 0)])
+    result = cuspatial.points_in_spatial_window(s, -1, 1, -1, 1)
+
+    assert_geoseries_equal(result.to_geopandas(), gpd.GeoSeries([Point(0, 0)]))
 
 
 @pytest.mark.parametrize(
@@ -29,38 +28,48 @@ def test_centered():
 def test_corners(coords):
     x, y = coords
     result = cuspatial.points_in_spatial_window(
-        -1.1, 1.1, -1.1, 1.1, cudf.Series([x]), cudf.Series([y])
-    )
-    cudf.testing.assert_frame_equal(
-        result, cudf.DataFrame({"x": [x], "y": [y]})
+        cuspatial.GeoSeries([Point(x, y)]), -1.1, 1.1, -1.1, 1.1
     )
+    assert_geoseries_equal(result.to_geopandas(), gpd.GeoSeries([Point(x, y)]))
 
 
 def test_pair():
     result = cuspatial.points_in_spatial_window(
-        -1.1, 1.1, -1.1, 1.1, cudf.Series([0.0, 1.0]), cudf.Series([1.0, 0.0])
+        cuspatial.GeoSeries([Point(0, 1), Point(1, 0)]), -1.1, 1.1, -1.1, 1.1
     )
-    cudf.testing.assert_frame_equal(
-        result, cudf.DataFrame({"x": [0.0, 1.0], "y": [1.0, 0.0]})
+    assert_geoseries_equal(
+        result.to_geopandas(), gpd.GeoSeries([Point(0, 1), Point(1, 0)])
     )
 
 
 def test_oob():
     result = cuspatial.points_in_spatial_window(
-        -1, 1, -1, 1, cudf.Series([-2.0, 2.0]), cudf.Series([2.0, -2.0])
+        cuspatial.GeoSeries([Point(-2.0, 2.0), Point(2.0, -2.0)]),
+        -1,
+        1,
+        -1,
+        1,
     )
-    cudf.testing.assert_frame_equal(result, cudf.DataFrame({"x": [], "y": []}))
+    assert_geoseries_equal(result.to_geopandas(), gpd.GeoSeries([]))
 
 
 def test_half():
     result = cuspatial.points_in_spatial_window(
+        cuspatial.GeoSeries(
+            [
+                Point(-1.0, 1.0),
+                Point(1.0, -1.0),
+                Point(3.0, 3.0),
+                Point(-3.0, -3.0),
+            ]
+        ),
         -2,
         2,
         -2,
         2,
-        cudf.Series([-1.0, 1.0, 3.0, -3.0]),
-        cudf.Series([1.0, -1.0, 3.0, -3.0]),
     )
-    cudf.testing.assert_frame_equal(
-        result, cudf.DataFrame({"x": [-1.0, 1.0], "y": [1.0, -1.0]})
+
+    assert_geoseries_equal(
+        result.to_geopandas(),
+        gpd.GeoSeries([Point(-1.0, 1.0), Point(1.0, -1.0)]),
     )
diff --git a/python/cuspatial/cuspatial/tests/spatial/indexing/test_indexing.py b/python/cuspatial/cuspatial/tests/spatial/indexing/test_indexing.py
index 4163f547b..9ede78630 100644
--- a/python/cuspatial/cuspatial/tests/spatial/indexing/test_indexing.py
+++ b/python/cuspatial/cuspatial/tests/spatial/indexing/test_indexing.py
@@ -1,6 +1,7 @@
-# Copyright (c) 2020, NVIDIA CORPORATION.
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
 import numpy as np
 import pytest
+from shapely.geometry import Point
 
 import cudf
 
@@ -13,8 +14,7 @@
 def test_empty():
     # empty should not throw
     order, quadtree = cuspatial.quadtree_on_points(
-        cudf.Series([]),  # x
-        cudf.Series([]),  # y
+        cuspatial.GeoSeries([]),
         *bbox_1,  # bbox
         1,  # scale
         1,  # max_depth
@@ -34,11 +34,9 @@ def test_empty():
     )
 
 
-@pytest.mark.parametrize("dtype", ["float32", "float64"])
-def test_one_point(dtype):
+def test_one_point():
     order, quadtree = cuspatial.quadtree_on_points(
-        cudf.Series([0.5]).astype(dtype),  # x
-        cudf.Series([0.5]).astype(dtype),  # y
+        cuspatial.GeoSeries([Point(0.5, 0.5)]),
         *bbox_1,  # bbox
         1,  # scale
         1,  # max_depth
@@ -58,11 +56,9 @@ def test_one_point(dtype):
     )
 
 
-@pytest.mark.parametrize("dtype", ["float32", "float64"])
-def test_two_points(dtype):
+def test_two_points():
     order, quadtree = cuspatial.quadtree_on_points(
-        cudf.Series([0.5, 1.5]).astype(dtype),  # x
-        cudf.Series([0.5, 1.5]).astype(dtype),  # y
+        cuspatial.GeoSeries([Point(0.5, 0.5), Point(1.5, 1.5)]),
         *bbox_2,  # bbox
         1,  # scale
         1,  # max_depth
@@ -84,161 +80,162 @@ def test_two_points(dtype):
 
 @pytest.mark.parametrize("dtype", ["float32", "float64"])
 def test_small_number_of_points(dtype):
+    x = cudf.Series(
+        [
+            1.9804558865545805,
+            0.1895259128530169,
+            1.2591725716781235,
+            0.8178039499335275,
+            0.48171647380517046,
+            1.3890664414691907,
+            0.2536015260915061,
+            3.1907684812039956,
+            3.028362149164369,
+            3.918090468102582,
+            3.710910700915217,
+            3.0706987088385853,
+            3.572744183805594,
+            3.7080407833612004,
+            3.70669993057843,
+            3.3588457228653024,
+            2.0697434332621234,
+            2.5322042870739683,
+            2.175448214220591,
+            2.113652420701984,
+            2.520755151373394,
+            2.9909779614491687,
+            2.4613232527836137,
+            4.975578758530645,
+            4.07037627210835,
+            4.300706849071861,
+            4.5584381091040616,
+            4.822583857757069,
+            4.849847745942472,
+            4.75489831780737,
+            4.529792124514895,
+            4.732546857961497,
+            3.7622247877537456,
+            3.2648444465931474,
+            3.01954722322135,
+            3.7164018490892348,
+            3.7002781846945347,
+            2.493975723955388,
+            2.1807636574967466,
+            2.566986568683904,
+            2.2006520196663066,
+            2.5104987015171574,
+            2.8222482218882474,
+            2.241538022180476,
+            2.3007438625108882,
+            6.0821276168848994,
+            6.291790729917634,
+            6.109985464455084,
+            6.101327777646798,
+            6.325158445513714,
+            6.6793884701899,
+            6.4274219368674315,
+            6.444584786789386,
+            7.897735998643542,
+            7.079453687660189,
+            7.430677191305505,
+            7.5085184104988,
+            7.886010001346151,
+            7.250745898479374,
+            7.769497359206111,
+            1.8703303641352362,
+            1.7015273093278767,
+            2.7456295127617385,
+            2.2065031771469,
+            3.86008672302403,
+            1.9143371250907073,
+            3.7176098065039747,
+            0.059011873032214,
+            3.1162712022943757,
+            2.4264509160270813,
+            3.154282922203257,
+        ]
+    ).astype(
+        dtype
+    )  # x
+    y = cudf.Series(
+        [
+            1.3472225743317712,
+            0.5431061133894604,
+            0.1448705855995005,
+            0.8138440641113271,
+            1.9022922214961997,
+            1.5177694304735412,
+            1.8762161698642947,
+            0.2621847215928189,
+            0.027638405909631958,
+            0.3338651960183463,
+            0.9937713340192049,
+            0.9376313558467103,
+            0.33184908855075124,
+            0.09804238103130436,
+            0.7485845679979923,
+            0.2346381514128677,
+            1.1809465376402173,
+            1.419555755682142,
+            1.2372448404986038,
+            1.2774712415624014,
+            1.902015274420646,
+            1.2420487904041893,
+            1.0484414482621331,
+            0.9606291981013242,
+            1.9486902798139454,
+            0.021365525588281198,
+            1.8996548860019926,
+            0.3234041700489503,
+            1.9531893897409585,
+            0.7800065259479418,
+            1.942673409259531,
+            0.5659923375279095,
+            2.8709552313924487,
+            2.693039435509084,
+            2.57810040095543,
+            2.4612194182614333,
+            2.3345952955903906,
+            3.3999020934055837,
+            3.2296461832828114,
+            3.6607732238530897,
+            3.7672478678985257,
+            3.0668114607133137,
+            3.8159308233351266,
+            3.8812819070357545,
+            3.6045900851589048,
+            2.5470532680258002,
+            2.983311357415729,
+            2.2235950639628523,
+            2.5239201807166616,
+            2.8765450351723674,
+            2.5605928243991434,
+            2.9754616970668213,
+            2.174562817047202,
+            3.380784914178574,
+            3.063690547962938,
+            3.380489849365283,
+            3.623862886287816,
+            3.538128217886674,
+            3.4154469467473447,
+            3.253257011908445,
+            4.209727933188015,
+            7.478882372510933,
+            7.474216636277054,
+            6.896038613284851,
+            7.513564222799629,
+            6.885401350515916,
+            6.194330707468438,
+            5.823535317960799,
+            6.789029097334483,
+            5.188939408363776,
+            5.788316610960881,
+        ]
+    ).astype(dtype)
+    xy = cudf.DataFrame({"x": x, "y": y}).interleave_columns()
+    points = cuspatial.GeoSeries.from_points_xy(xy)
     order, quadtree = cuspatial.quadtree_on_points(
-        cudf.Series(
-            [
-                1.9804558865545805,
-                0.1895259128530169,
-                1.2591725716781235,
-                0.8178039499335275,
-                0.48171647380517046,
-                1.3890664414691907,
-                0.2536015260915061,
-                3.1907684812039956,
-                3.028362149164369,
-                3.918090468102582,
-                3.710910700915217,
-                3.0706987088385853,
-                3.572744183805594,
-                3.7080407833612004,
-                3.70669993057843,
-                3.3588457228653024,
-                2.0697434332621234,
-                2.5322042870739683,
-                2.175448214220591,
-                2.113652420701984,
-                2.520755151373394,
-                2.9909779614491687,
-                2.4613232527836137,
-                4.975578758530645,
-                4.07037627210835,
-                4.300706849071861,
-                4.5584381091040616,
-                4.822583857757069,
-                4.849847745942472,
-                4.75489831780737,
-                4.529792124514895,
-                4.732546857961497,
-                3.7622247877537456,
-                3.2648444465931474,
-                3.01954722322135,
-                3.7164018490892348,
-                3.7002781846945347,
-                2.493975723955388,
-                2.1807636574967466,
-                2.566986568683904,
-                2.2006520196663066,
-                2.5104987015171574,
-                2.8222482218882474,
-                2.241538022180476,
-                2.3007438625108882,
-                6.0821276168848994,
-                6.291790729917634,
-                6.109985464455084,
-                6.101327777646798,
-                6.325158445513714,
-                6.6793884701899,
-                6.4274219368674315,
-                6.444584786789386,
-                7.897735998643542,
-                7.079453687660189,
-                7.430677191305505,
-                7.5085184104988,
-                7.886010001346151,
-                7.250745898479374,
-                7.769497359206111,
-                1.8703303641352362,
-                1.7015273093278767,
-                2.7456295127617385,
-                2.2065031771469,
-                3.86008672302403,
-                1.9143371250907073,
-                3.7176098065039747,
-                0.059011873032214,
-                3.1162712022943757,
-                2.4264509160270813,
-                3.154282922203257,
-            ]
-        ).astype(
-            dtype
-        ),  # x
-        cudf.Series(
-            [
-                1.3472225743317712,
-                0.5431061133894604,
-                0.1448705855995005,
-                0.8138440641113271,
-                1.9022922214961997,
-                1.5177694304735412,
-                1.8762161698642947,
-                0.2621847215928189,
-                0.027638405909631958,
-                0.3338651960183463,
-                0.9937713340192049,
-                0.9376313558467103,
-                0.33184908855075124,
-                0.09804238103130436,
-                0.7485845679979923,
-                0.2346381514128677,
-                1.1809465376402173,
-                1.419555755682142,
-                1.2372448404986038,
-                1.2774712415624014,
-                1.902015274420646,
-                1.2420487904041893,
-                1.0484414482621331,
-                0.9606291981013242,
-                1.9486902798139454,
-                0.021365525588281198,
-                1.8996548860019926,
-                0.3234041700489503,
-                1.9531893897409585,
-                0.7800065259479418,
-                1.942673409259531,
-                0.5659923375279095,
-                2.8709552313924487,
-                2.693039435509084,
-                2.57810040095543,
-                2.4612194182614333,
-                2.3345952955903906,
-                3.3999020934055837,
-                3.2296461832828114,
-                3.6607732238530897,
-                3.7672478678985257,
-                3.0668114607133137,
-                3.8159308233351266,
-                3.8812819070357545,
-                3.6045900851589048,
-                2.5470532680258002,
-                2.983311357415729,
-                2.2235950639628523,
-                2.5239201807166616,
-                2.8765450351723674,
-                2.5605928243991434,
-                2.9754616970668213,
-                2.174562817047202,
-                3.380784914178574,
-                3.063690547962938,
-                3.380489849365283,
-                3.623862886287816,
-                3.538128217886674,
-                3.4154469467473447,
-                3.253257011908445,
-                4.209727933188015,
-                7.478882372510933,
-                7.474216636277054,
-                6.896038613284851,
-                7.513564222799629,
-                6.885401350515916,
-                6.194330707468438,
-                5.823535317960799,
-                6.789029097334483,
-                5.188939408363776,
-                5.788316610960881,
-            ]
-        ).astype(
-            dtype
-        ),  # y
+        points,
         0,  # x_min
         8,  # x_max
         0,  # y_min
diff --git a/python/cuspatial/cuspatial/tests/spatial/join/test_spatial_join.py b/python/cuspatial/cuspatial/tests/spatial/join/test_spatial_join.py
index 13e5fa0ed..190fca450 100644
--- a/python/cuspatial/cuspatial/tests/spatial/join/test_spatial_join.py
+++ b/python/cuspatial/cuspatial/tests/spatial/join/test_spatial_join.py
@@ -210,9 +210,16 @@
 
 @pytest.mark.parametrize("dtype", [np.float32, np.float64])
 def test_empty(dtype):
+    points = cuspatial.GeoSeries.from_points_xy(
+        cudf.DataFrame(
+            {
+                "x": cudf.Series([], dtype=dtype),  # x
+                "y": cudf.Series([], dtype=dtype),  # y
+            }
+        ).interleave_columns()
+    )
     order, quadtree = cuspatial.quadtree_on_points(
-        cudf.Series([], dtype=dtype),  # x
-        cudf.Series([], dtype=dtype),  # y
+        points,
         *bbox_1,  # bbox
         1,  # scale
         1,  # max_depth
@@ -254,11 +261,18 @@ def test_polygon_join_small(dtype):
     min_size = 12
     points_x = small_points_x.astype(dtype)
     points_y = small_points_y.astype(dtype)
+    points = cuspatial.GeoSeries.from_points_xy(
+        cudf.DataFrame(
+            {
+                "x": points_x,  # x
+                "y": points_y,  # y
+            }
+        ).interleave_columns()
+    )
     poly_points_x = small_poly_xs.astype(dtype)
     poly_points_y = small_poly_ys.astype(dtype)
     point_indices, quadtree = cuspatial.quadtree_on_points(
-        points_x,
-        points_y,
+        points,
         x_min,
         x_max,
         y_min,
@@ -310,11 +324,18 @@ def test_linestring_join_small(dtype):
     expansion_radius = 2.0
     points_x = small_points_x.astype(dtype)
     points_y = small_points_y.astype(dtype)
+    points = cuspatial.GeoSeries.from_points_xy(
+        cudf.DataFrame(
+            {
+                "x": points_x,  # x
+                "y": points_y,  # y
+            }
+        ).interleave_columns()
+    )
     linestring_points_x = small_poly_xs.astype(dtype)
     linestring_points_y = small_poly_ys.astype(dtype)
     point_indices, quadtree = cuspatial.quadtree_on_points(
-        points_x,
-        points_y,
+        points,
         x_min,
         x_max,
         y_min,
@@ -411,11 +432,18 @@ def test_quadtree_point_in_polygon_small(dtype):
     min_size = 12
     points_x = small_points_x.astype(dtype)
     points_y = small_points_y.astype(dtype)
+    points = cuspatial.GeoSeries.from_points_xy(
+        cudf.DataFrame(
+            {
+                "x": points_x,  # x
+                "y": points_y,  # y
+            }
+        ).interleave_columns()
+    )
     poly_points_x = small_poly_xs.astype(dtype)
     poly_points_y = small_poly_ys.astype(dtype)
     point_indices, quadtree = cuspatial.quadtree_on_points(
-        points_x,
-        points_y,
+        points,
         x_min,
         x_max,
         y_min,
@@ -501,11 +529,18 @@ def run_test_quadtree_point_to_nearest_linestring_small(
     expansion_radius = 2.0
     points_x = small_points_x.astype(dtype)
     points_y = small_points_y.astype(dtype)
+    points = cuspatial.GeoSeries.from_points_xy(
+        cudf.DataFrame(
+            {
+                "x": points_x,  # x
+                "y": points_y,  # y
+            }
+        ).interleave_columns()
+    )
     linestring_points_x = small_poly_xs.astype(dtype)
     linestring_points_y = small_poly_ys.astype(dtype)
     point_indices, quadtree = cuspatial.quadtree_on_points(
-        points_x,
-        points_y,
+        points,
         x_min,
         x_max,
         y_min,