diff --git a/CHANGELOG.md b/CHANGELOG.md
index d2ee400..0078d84 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -12,6 +12,7 @@ Added:
  - Module `preprocess` to derive inputs from external datasets, including terrain masks from Copernicus DEM data (`compute_terrain_mask`, `compute_terrain_mask_for_station`, `compute_terrain_mask_for_platform`, `get_copernicus_dem_tile_urls`, and `sample_dem_elevation`, with an `effective_earth_radius_factor` for refraction, also an argument of `compute_terrain_elevation_angle`), and gridded weights from GeoTIFF rasters (`read_raster_weights`).
  - Generation method `generate_points_random` for random points, uniform by area or weighted by a raster (for example, of population counts or densities), optionally within a mask, with a seed for reproducibility.
  - Analysis methods `collect_region_observations` and `collect_multi_region_observations` to collect the periods (start and end) when an instrument observes any part of a region (shapely `Polygon` or `MultiPolygon`), using the footprints of pointed and conical instruments and the field of regard of other instruments. Each observation records its swath (the part of the region swept by the footprint) as its geometry and identifies the region by the hash of its geometry (`target_hash`). Methods `aggregate_observations` and `reduce_observations` combine the swaths of a region into the part of it observed.
+ - Analysis methods `find_coincident_observations`, the periods when two or more satellites observe the same point at once (or within a maximum time difference), and `find_coincident_ground_track`, the overlapping footprints of pairs of satellites at the same time (or within a maximum time difference), with example notebook `FindCoincidentObservations` (Metop-SG-B ICI and NASA FALCON).
  - Analysis methods `compute_access_periods`, the periods when a satellite is in view of a point above a minimum elevation angle, and `compute_region_access_periods`, a conservative superset of the periods when an instrument's field of regard may observe any part of a region (for example, to cull analysis times).
  - Support for shapely `Point` objects (longitude, latitude, and optional elevation) wherever a TAT-C `Point` is accepted (`collect_observations`, `collect_multi_observations`, `compute_dop`, and `GeneralPerturbationsOrbit.get_observation_events`), in anticipation of replacing TAT-C points.
  - Object schema `ConicalInstrument` (included in `AllInstruments`) for conically scanning instruments, observed when a point crosses the scanned cone, and utility method `compute_cone_and_azimuth`.
diff --git a/docs/api_reference/analysis/coincidence.rst b/docs/api_reference/analysis/coincidence.rst
new file mode 100644
index 0000000..0556461
--- /dev/null
+++ b/docs/api_reference/analysis/coincidence.rst
@@ -0,0 +1,10 @@
+Coincident Observations
+=======================
+
+Find Coincident Observations
+----------------------------
+.. autofunction:: tatc.analysis.find_coincident_observations
+
+Find Coincident Ground Track
+----------------------------
+.. autofunction:: tatc.analysis.find_coincident_ground_track
diff --git a/docs/api_reference/analysis/index.rst b/docs/api_reference/analysis/index.rst
index c70079e..6657fe0 100644
--- a/docs/api_reference/analysis/index.rst
+++ b/docs/api_reference/analysis/index.rst
@@ -9,6 +9,7 @@ Analysis functions simulate mission operation to compute key performance metrics
 
   coverage
   track
+  coincidence
   latency
   dop
   ro_coverage
diff --git a/docs/examples/FindCoincidentObservations.ipynb b/docs/examples/FindCoincidentObservations.ipynb
new file mode 100644
index 0000000..a13e205
--- /dev/null
+++ b/docs/examples/FindCoincidentObservations.ipynb
@@ -0,0 +1,1595 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "65d857e9",
+   "metadata": {},
+   "source": [
+    "# Find Coincident Observations\n",
+    "\n",
+    "This example demonstrates how to identify *coincident* observations, when two or more observing platforms observe the same place at (nearly) the same time, using two TAT-C analysis functions:\n",
+    "\n",
+    "* `find_coincident_observations` finds the periods when observations of points (as collected by `collect_observations`) by different satellites overlap in time.\n",
+    "* `find_coincident_ground_track` finds the overlapping footprints of different satellites (as collected by `collect_ground_track`).\n",
+    "\n",
+    "The scenario pairs a hypothetical three-satellite constellation of EUMETSAT Metop Second Generation B-series (Metop-SG-B) satellites, carrying the Ice Cloud Imager (ICI), with the first elements of NASA's proposed Fleet for the Atmosphere Linking Commercial Observations with NASA (FALCON) constellation. Coincident observations support cross-calibration and synergistic retrievals: for example, the PolSIR science team expects ICI to \"serve as a reference for PolSIR\", which covers the same sub-millimeter spectral range ([Bennartz and Wu, 2025](https://lps25.esa.int/lps25-presentations/presentations/1496/_1496.pdf))."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "faff3230",
+   "metadata": {},
+   "source": [
+    "## Scenario\n",
+    "\n",
+    "All parameters come from publicly available sources, listed below. Where a source gives a range or omits a value, the assumption made here is marked *assumed*.\n",
+    "\n",
+    "| Element | Parameter | Value | Source |\n",
+    "| --- | --- | --- | --- |\n",
+    "| Metop-SG-B | Orbit | sun-synchronous, 832 km, 09:30 local time of descending node | [ESA, MetOp-SG facts and figures](https://www.esa.int/Applications/Observing_the_Earth/Meteorological_missions/MetOp_Second_Generation/MetOp-SG_facts_and_figures) |\n",
+    "| | Satellites | B1, B2, and B3 are planned to operate in sequence (2026-2048); here, *assumed* to fly together, evenly phased in one orbital plane | [WMO OSCAR](https://space.oscar.wmo.int/instruments/view/ici) |\n",
+    "| MWI and ICI | Scan | conical, forward view over +/-65 deg of azimuth (about 1700 km swath), yaw-steered platform | [MWI-ICI Science Plan (EUMETSAT, 2017)](https://user.eumetsat.int/s3/eup-strapi-media/pdf_science_epssg_mwi_ici_plan_6c6e1a4b97.pdf), Sec. 4 |\n",
+    "| | Cone (offset) angle | MWI 44.82 deg, ICI 44.6 deg (53 deg incidence) | MWI-ICI Science Plan, Sec. 4.1-4.2 |\n",
+    "| FALCON | Elements | begins with the INCUS and PolSIR missions; later elements (FALCON Lidar, FALCON Radar, STRIVE, and a commercial radiometer) are omitted, as FALCON's orbital architecture is still under investigation | [NASA RFI NNH26ZDA007L (2026)](https://go.nasa.gov/atmrorfi), Sec. 1 |\n",
+    "| PolSIR | Orbit | two satellites, 51.2 deg inclination, 550 km, 90 deg apart in right ascension of the ascending node | [Bennartz and Wu (2025)](https://lps25.esa.int/lps25-presentations/presentations/1496/_1496.pdf) |\n",
+    "| | Radiometer | cross-track scanning, 325 and 683 GHz; scan range *assumed* +/-45 deg | [eoPortal](https://www.eoportal.org/satellite-missions/polsir) |\n",
+    "| INCUS | Orbit | three satellites 30 s and 90 s apart; 38 deg inclination; altitude 450-550 km (*assumed* 500 km) | [CSU INCUS mission](https://incus.colostate.edu/mission/mission-instruments), [van den Heever et al. (2025)](https://vandenheever.atmos.colostate.edu/vdhpage/presentation_files/Dolan_AGU_Fall_M_2025.pdf) |\n",
+    "| | Instruments | Ka-band radar on each satellite (10 km swath); cross-track radiometer on the middle satellite (<500 km swath, *assumed* 500 km) | CSU INCUS mission, van den Heever et al. (2025) |\n",
+    "\n",
+    "The analysis starts on 1 July 2029, after FALCON's planned launches. The relative phasing of the orbits (FALCON's right ascensions of the ascending node and the Metop-SG-B satellites' positions in their plane) is *assumed*; it changes when and where coincidences occur, but less so how often over a week, as the non-sun-synchronous FALCON orbits precess relative to Metop-SG-B by several degrees per day."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "206d50a6",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-23T22:57:00.762898Z",
+     "iopub.status.busy": "2026-10-23T22:57:00.762898Z",
+     "iopub.status.idle": "2026-10-23T22:57:03.046044Z",
+     "shell.execute_reply": "2026-10-23T22:57:03.044012Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from datetime import datetime, time, timedelta, timezone\n",
+    "\n",
+    "import cartopy.crs as ccrs\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "from tatc.schemas import (\n",
+    "    CircularOrbit,\n",
+    "    ConicalInstrument,\n",
+    "    PointedInstrument,\n",
+    "    Satellite,\n",
+    "    SunSynchronousOrbit,\n",
+    "    WalkerConstellation,\n",
+    ")\n",
+    "from tatc.utils import (\n",
+    "    ViewGeometry,\n",
+    "    compute_along_track_field_of_view,\n",
+    "    swath_width_to_field_of_regard,\n",
+    ")\n",
+    "\n",
+    "START = datetime(2029, 7, 1, tzinfo=timezone.utc)\n",
+    "END = START + timedelta(days=7)\n",
+    "TIME_STEP = 60  # seconds between ground track samples\n",
+    "METOP_ALTITUDE = 832e3\n",
+    "POLSIR_ALTITUDE = 550e3\n",
+    "INCUS_ALTITUDE = 500e3\n",
+    "\n",
+    "# colors (in fixed order) for the FALCON instruments\n",
+    "COLORS = {\"PolSIR\": \"#2a78d6\", \"INCUS radiometer\": \"#eb6834\", \"INCUS radar\": \"#1baf7a\"}\n",
+    "GRAY = \"#8a8985\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "244640fb",
+   "metadata": {},
+   "source": [
+    "## Choosing MWI or ICI\n",
+    "\n",
+    "Metop-SG-B carries two conical imagers with the same scan sector, +/-65 deg of azimuth ahead of the satellite. They differ in their cone angle, the angle of the scanned cone from nadir. Each is modeled as a `ConicalInstrument` whose along-track field of view is the distance the satellite advances in one ground track time step (see `compute_along_track_field_of_view`), so that consecutive footprints tile its swath."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "75d0a1d8",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-23T22:57:03.048225Z",
+     "iopub.status.busy": "2026-10-23T22:57:03.048225Z",
+     "iopub.status.idle": "2026-10-23T22:57:03.095652Z",
+     "shell.execute_reply": "2026-10-23T22:57:03.095652Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cone angle (deg)</th>\n",
+       "      <th>swath width (km)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>MWI</th>\n",
+       "      <td>44.8</td>\n",
+       "      <td>1615.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ICI</th>\n",
+       "      <td>44.6</td>\n",
+       "      <td>1600.7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     cone angle (deg)  swath width (km)\n",
+       "MWI              44.8            1615.2\n",
+       "ICI              44.6            1600.7"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def conical_imager(name, cone_angle):\n",
+    "    return ConicalInstrument(\n",
+    "        name=name,\n",
+    "        cone_angle=cone_angle,\n",
+    "        scan_center_azimuth=0,\n",
+    "        scan_half_width=65,\n",
+    "        along_track_field_of_view=compute_along_track_field_of_view(\n",
+    "            METOP_ALTITUDE, TIME_STEP, 98.7\n",
+    "        ),\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "mwi = conical_imager(\"MWI\", 44.82)\n",
+    "ici = conical_imager(\"ICI\", 44.6)\n",
+    "pd.DataFrame(\n",
+    "    {\n",
+    "        \"cone angle (deg)\": [i.cone_angle for i in (mwi, ici)],\n",
+    "        \"swath width (km)\": [i.get_swath_width(METOP_ALTITUDE) / 1e3 for i in (mwi, ici)],\n",
+    "    },\n",
+    "    index=[\"MWI\", \"ICI\"],\n",
+    ").round(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "297f57ca",
+   "metadata": {},
+   "source": [
+    "ICI's smaller cone angle gives it the narrower swath, so its coverage is the more constrained of the two (it also samples along track more coarsely, about 15 km per scan rather than MWI's 10 km, according to [WMO OSCAR](https://space.oscar.wmo.int/instruments/view/ici)). The rest of this example uses ICI, whose sub-millimeter channels also overlap PolSIR's.\n",
+    "\n",
+    "The three Metop-SG-B satellites share one sun-synchronous orbital plane, evenly phased by a Walker constellation with one plane."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "66e9a070",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-23T22:57:03.095652Z",
+     "iopub.status.busy": "2026-10-23T22:57:03.095652Z",
+     "iopub.status.idle": "2026-10-23T22:57:03.162578Z",
+     "shell.execute_reply": "2026-10-23T22:57:03.161793Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Metop-SG-B 1', 'Metop-SG-B 2', 'Metop-SG-B 3']"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metop = WalkerConstellation(\n",
+    "    name=\"Metop-SG-B\",\n",
+    "    orbit=SunSynchronousOrbit(\n",
+    "        mean_altitude=METOP_ALTITUDE,\n",
+    "        equator_crossing_time=time(9, 30),\n",
+    "        equator_crossing_ascending=False,\n",
+    "        epoch=START,\n",
+    "    ),\n",
+    "    number_satellites=3,\n",
+    "    number_planes=1,\n",
+    "    instruments=[ici],\n",
+    ").generate_members()\n",
+    "[satellite.name for satellite in metop]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d1d045d",
+   "metadata": {},
+   "source": [
+    "## FALCON Constellation\n",
+    "\n",
+    "The PolSIR and INCUS radiometers scan across track; INCUS's radars view a narrow strip at nadir. Each is modeled as a nadir-pointed `PointedInstrument` with a scan view geometry, a cross-track field of view sized to its swath, and an along-track field of view for the ground track time step. The INCUS satellites share an orbit, with the second and third satellites trailing the first by 30 s and 120 s."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "1ff13df8",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-23T22:57:03.162578Z",
+     "iopub.status.busy": "2026-10-23T22:57:03.162578Z",
+     "iopub.status.idle": "2026-10-23T22:57:03.344192Z",
+     "shell.execute_reply": "2026-10-23T22:57:03.344192Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>satellite</th>\n",
+       "      <th>mission</th>\n",
+       "      <th>constellation</th>\n",
+       "      <th>instruments</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Metop-SG-B 1</td>\n",
+       "      <td>Metop-SG-B</td>\n",
+       "      <td>Metop-SG-B</td>\n",
+       "      <td>ICI (1601 km)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Metop-SG-B 2</td>\n",
+       "      <td>Metop-SG-B</td>\n",
+       "      <td>Metop-SG-B</td>\n",
+       "      <td>ICI (1601 km)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Metop-SG-B 3</td>\n",
+       "      <td>Metop-SG-B</td>\n",
+       "      <td>Metop-SG-B</td>\n",
+       "      <td>ICI (1601 km)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>PolSIR-1</td>\n",
+       "      <td>PolSIR</td>\n",
+       "      <td>FALCON</td>\n",
+       "      <td>PolSIR (1154 km)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>PolSIR-2</td>\n",
+       "      <td>PolSIR</td>\n",
+       "      <td>FALCON</td>\n",
+       "      <td>PolSIR (1154 km)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>INCUS-A</td>\n",
+       "      <td>INCUS</td>\n",
+       "      <td>FALCON</td>\n",
+       "      <td>INCUS radar (10 km)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>INCUS-B</td>\n",
+       "      <td>INCUS</td>\n",
+       "      <td>FALCON</td>\n",
+       "      <td>INCUS radar (10 km), INCUS radiometer (500 km)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>INCUS-C</td>\n",
+       "      <td>INCUS</td>\n",
+       "      <td>FALCON</td>\n",
+       "      <td>INCUS radar (10 km)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      satellite     mission constellation  \\\n",
+       "0  Metop-SG-B 1  Metop-SG-B    Metop-SG-B   \n",
+       "1  Metop-SG-B 2  Metop-SG-B    Metop-SG-B   \n",
+       "2  Metop-SG-B 3  Metop-SG-B    Metop-SG-B   \n",
+       "3      PolSIR-1      PolSIR        FALCON   \n",
+       "4      PolSIR-2      PolSIR        FALCON   \n",
+       "5       INCUS-A       INCUS        FALCON   \n",
+       "6       INCUS-B       INCUS        FALCON   \n",
+       "7       INCUS-C       INCUS        FALCON   \n",
+       "\n",
+       "                                      instruments  \n",
+       "0                                   ICI (1601 km)  \n",
+       "1                                   ICI (1601 km)  \n",
+       "2                                   ICI (1601 km)  \n",
+       "3                                PolSIR (1154 km)  \n",
+       "4                                PolSIR (1154 km)  \n",
+       "5                             INCUS radar (10 km)  \n",
+       "6  INCUS radar (10 km), INCUS radiometer (500 km)  \n",
+       "7                             INCUS radar (10 km)  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def cross_track_instrument(name, altitude, inclination, cross_track_field_of_view):\n",
+    "    return PointedInstrument(\n",
+    "        name=name,\n",
+    "        field_of_regard=cross_track_field_of_view,\n",
+    "        cross_track_field_of_view=cross_track_field_of_view,\n",
+    "        along_track_field_of_view=compute_along_track_field_of_view(\n",
+    "            altitude, TIME_STEP, inclination\n",
+    "        ),\n",
+    "        is_rectangular=True,\n",
+    "        view_geometry=ViewGeometry.SCAN,\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "polsir_radiometer = cross_track_instrument(\"PolSIR\", POLSIR_ALTITUDE, 51.2, 90)\n",
+    "polsir = [\n",
+    "    Satellite(\n",
+    "        name=f\"PolSIR-{i + 1}\",\n",
+    "        orbit=CircularOrbit(\n",
+    "            mean_altitude=POLSIR_ALTITUDE,\n",
+    "            inclination=51.2,\n",
+    "            right_ascension_ascending_node=raan,\n",
+    "            epoch=START,\n",
+    "        ),\n",
+    "        instruments=[polsir_radiometer],\n",
+    "    )\n",
+    "    for i, raan in enumerate([0, 90])\n",
+    "]\n",
+    "\n",
+    "incus_radar = cross_track_instrument(\n",
+    "    \"INCUS radar\", INCUS_ALTITUDE, 38, swath_width_to_field_of_regard(INCUS_ALTITUDE, 10e3)\n",
+    ")\n",
+    "incus_radiometer = cross_track_instrument(\n",
+    "    \"INCUS radiometer\", INCUS_ALTITUDE, 38, swath_width_to_field_of_regard(INCUS_ALTITUDE, 500e3)\n",
+    ")\n",
+    "incus_orbit = CircularOrbit(\n",
+    "    mean_altitude=INCUS_ALTITUDE, inclination=38, right_ascension_ascending_node=45, epoch=START\n",
+    ")\n",
+    "incus_rate = 360 / incus_orbit.get_orbit_period().total_seconds()  # deg/s\n",
+    "incus = [\n",
+    "    Satellite(\n",
+    "        name=f\"INCUS-{name}\",\n",
+    "        orbit=incus_orbit.model_copy(update={\"true_anomaly\": (-incus_rate * delay) % 360}),\n",
+    "        instruments=[incus_radar, incus_radiometer] if name == \"B\" else [incus_radar],\n",
+    "    )\n",
+    "    for name, delay in [(\"A\", 0), (\"B\", 30), (\"C\", 120)]\n",
+    "]\n",
+    "\n",
+    "satellites = metop + polsir + incus\n",
+    "mission = {s.name: \"Metop-SG-B\" if s in metop else s.name.split(\"-\")[0] for s in satellites}\n",
+    "constellation = {s.name: \"Metop-SG-B\" if s in metop else \"FALCON\" for s in satellites}\n",
+    "pd.DataFrame(\n",
+    "    [\n",
+    "        {\n",
+    "            \"satellite\": s.name,\n",
+    "            \"mission\": mission[s.name],\n",
+    "            \"constellation\": constellation[s.name],\n",
+    "            \"instruments\": \", \".join(\n",
+    "                f\"{i.name} ({i.get_swath_width(s.orbit.mean_altitude) / 1e3:.0f} km)\"\n",
+    "                for i in s.instruments\n",
+    "            ),\n",
+    "        }\n",
+    "        for s in satellites\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a68732c",
+   "metadata": {},
+   "source": [
+    "## Coincident Observations of Points\n",
+    "\n",
+    "`collect_observations` collects the observations of points of interest, here a Fibonacci lattice of points about 750 km apart between 60 deg south and 60 deg north (the latitudes FALCON observes), by every instrument (`instrument_index=None`) over one week."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b925222e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-23T22:57:03.346599Z",
+     "iopub.status.busy": "2026-10-23T22:57:03.346599Z",
+     "iopub.status.idle": "2026-10-23T22:59:59.763430Z",
+     "shell.execute_reply": "2026-10-23T22:59:59.762708Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>observations</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>satellite</th>\n",
+       "      <th>instrument</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>INCUS-A</th>\n",
+       "      <th>INCUS radar</th>\n",
+       "      <td>93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">INCUS-B</th>\n",
+       "      <th>INCUS radar</th>\n",
+       "      <td>101</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>INCUS radiometer</th>\n",
+       "      <td>4557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>INCUS-C</th>\n",
+       "      <th>INCUS radar</th>\n",
+       "      <td>95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metop-SG-B 1</th>\n",
+       "      <th>ICI</th>\n",
+       "      <td>9937</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metop-SG-B 2</th>\n",
+       "      <th>ICI</th>\n",
+       "      <td>9941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metop-SG-B 3</th>\n",
+       "      <th>ICI</th>\n",
+       "      <td>9948</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PolSIR-1</th>\n",
+       "      <th>PolSIR</th>\n",
+       "      <td>10581</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PolSIR-2</th>\n",
+       "      <th>PolSIR</th>\n",
+       "      <td>10568</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               observations\n",
+       "satellite    instrument                    \n",
+       "INCUS-A      INCUS radar                 93\n",
+       "INCUS-B      INCUS radar                101\n",
+       "             INCUS radiometer          4557\n",
+       "INCUS-C      INCUS radar                 95\n",
+       "Metop-SG-B 1 ICI                       9937\n",
+       "Metop-SG-B 2 ICI                       9941\n",
+       "Metop-SG-B 3 ICI                       9948\n",
+       "PolSIR-1     PolSIR                   10581\n",
+       "PolSIR-2     PolSIR                   10568"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from tatc.analysis import collect_observations\n",
+    "from tatc.generation import generate_points_fibonacci_lattice\n",
+    "\n",
+    "points = generate_points_fibonacci_lattice(750e3)\n",
+    "points = points[points.geometry.y.abs() <= 60].reset_index(drop=True)\n",
+    "observations = collect_observations(\n",
+    "    list(points.geometry), satellites, START, END, instrument_index=None\n",
+    ")\n",
+    "observations.groupby([\"satellite\", \"instrument\"]).size().rename(\"observations\").to_frame()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "510a1559",
+   "metadata": {},
+   "source": [
+    "`find_coincident_observations` treats each satellite as a distinct platform, so observations by two Metop-SG-B satellites would coincide with each other. To find only coincidences *between* the constellations, the `satellite` column is relabeled with the constellation before the call, making each constellation one platform.\n",
+    "\n",
+    "Observations coincide if the gap between their periods is at most `max_time_difference`, here 15 minutes. Each coincident period is the time that observations by both constellations, each widened by half of `max_time_difference`, overlap; `satellite` and `instrument` list the contributors in order of their start."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f97b7fcd",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-23T22:59:59.764543Z",
+     "iopub.status.busy": "2026-10-23T22:59:59.764543Z",
+     "iopub.status.idle": "2026-10-23T23:00:08.233042Z",
+     "shell.execute_reply": "2026-10-23T23:00:08.232048Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>target_hash</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>satellite</th>\n",
+       "      <th>instrument</th>\n",
+       "      <th>start</th>\n",
+       "      <th>epoch</th>\n",
+       "      <th>end</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3ced23b233272b4b</td>\n",
+       "      <td>POINT Z (144.31672 15.55517 0)</td>\n",
+       "      <td>FALCON, Metop-SG-B</td>\n",
+       "      <td>INCUS radiometer, ICI</td>\n",
+       "      <td>2029-07-01 00:00:11.254653012+00:00</td>\n",
+       "      <td>2029-07-01 00:07:44.582561277+00:00</td>\n",
+       "      <td>2029-07-01 00:15:17.910469543+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>c4f2cff22543b4af</td>\n",
+       "      <td>POINT Z (149.05274 19.08616 0)</td>\n",
+       "      <td>Metop-SG-B, FALCON</td>\n",
+       "      <td>ICI, INCUS radiometer</td>\n",
+       "      <td>2029-07-01 00:00:39.953887246+00:00</td>\n",
+       "      <td>2029-07-01 00:08:46.280635710+00:00</td>\n",
+       "      <td>2029-07-01 00:16:52.607384174+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>62c3f641ea5270a7</td>\n",
+       "      <td>POINT Z (139.58069 12.08383 0)</td>\n",
+       "      <td>FALCON, FALCON, Metop-SG-B</td>\n",
+       "      <td>INCUS radiometer, INCUS radar, ICI</td>\n",
+       "      <td>2029-07-01 00:01:22.792409721+00:00</td>\n",
+       "      <td>2029-07-01 00:08:01.967124430+00:00</td>\n",
+       "      <td>2029-07-01 00:14:41.141839139+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>b3750db3a0eb9b9b</td>\n",
+       "      <td>POINT Z (134.84467 8.65701 0)</td>\n",
+       "      <td>FALCON, Metop-SG-B</td>\n",
+       "      <td>INCUS radiometer, ICI</td>\n",
+       "      <td>2029-07-01 00:02:47.759772846+00:00</td>\n",
+       "      <td>2029-07-01 00:07:26.834445955+00:00</td>\n",
+       "      <td>2029-07-01 00:12:05.909119064+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>c6abca90563d73b7</td>\n",
+       "      <td>POINT Z (142.08067 50.03491 0)</td>\n",
+       "      <td>Metop-SG-B, FALCON</td>\n",
+       "      <td>ICI, PolSIR</td>\n",
+       "      <td>2029-07-01 00:11:01.180069730+00:00</td>\n",
+       "      <td>2029-07-01 00:12:56.219171430+00:00</td>\n",
+       "      <td>2029-07-01 00:14:51.258273131+00:00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        target_hash                        geometry  \\\n",
+       "0  3ced23b233272b4b  POINT Z (144.31672 15.55517 0)   \n",
+       "1  c4f2cff22543b4af  POINT Z (149.05274 19.08616 0)   \n",
+       "2  62c3f641ea5270a7  POINT Z (139.58069 12.08383 0)   \n",
+       "3  b3750db3a0eb9b9b   POINT Z (134.84467 8.65701 0)   \n",
+       "4  c6abca90563d73b7  POINT Z (142.08067 50.03491 0)   \n",
+       "\n",
+       "                    satellite                          instrument  \\\n",
+       "0          FALCON, Metop-SG-B               INCUS radiometer, ICI   \n",
+       "1          Metop-SG-B, FALCON               ICI, INCUS radiometer   \n",
+       "2  FALCON, FALCON, Metop-SG-B  INCUS radiometer, INCUS radar, ICI   \n",
+       "3          FALCON, Metop-SG-B               INCUS radiometer, ICI   \n",
+       "4          Metop-SG-B, FALCON                         ICI, PolSIR   \n",
+       "\n",
+       "                                start                               epoch  \\\n",
+       "0 2029-07-01 00:00:11.254653012+00:00 2029-07-01 00:07:44.582561277+00:00   \n",
+       "1 2029-07-01 00:00:39.953887246+00:00 2029-07-01 00:08:46.280635710+00:00   \n",
+       "2 2029-07-01 00:01:22.792409721+00:00 2029-07-01 00:08:01.967124430+00:00   \n",
+       "3 2029-07-01 00:02:47.759772846+00:00 2029-07-01 00:07:26.834445955+00:00   \n",
+       "4 2029-07-01 00:11:01.180069730+00:00 2029-07-01 00:12:56.219171430+00:00   \n",
+       "\n",
+       "                                  end  \n",
+       "0 2029-07-01 00:15:17.910469543+00:00  \n",
+       "1 2029-07-01 00:16:52.607384174+00:00  \n",
+       "2 2029-07-01 00:14:41.141839139+00:00  \n",
+       "3 2029-07-01 00:12:05.909119064+00:00  \n",
+       "4 2029-07-01 00:14:51.258273131+00:00  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from tatc.analysis import find_coincident_observations\n",
+    "\n",
+    "by_constellation = observations.assign(satellite=observations.satellite.map(constellation))\n",
+    "coincident = find_coincident_observations(\n",
+    "    by_constellation, max_time_difference=timedelta(minutes=15)\n",
+    ")\n",
+    "display(coincident.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1783dc20",
+   "metadata": {},
+   "source": [
+    "The number of coincidences depends strongly on the maximum time difference. Observations of a point by satellites in different orbits rarely overlap exactly; most coincidences are near-simultaneous passes separated by a few minutes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a522c880",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-23T23:00:08.236192Z",
+     "iopub.status.busy": "2026-10-23T23:00:08.236192Z",
+     "iopub.status.idle": "2026-10-23T23:00:49.795989Z",
+     "shell.execute_reply": "2026-10-23T23:00:49.795280Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>coincidences</th>\n",
+       "      <th>points with a coincidence (%)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max time difference (min)</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>965</td>\n",
+       "      <td>61.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1816</td>\n",
+       "      <td>72.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>3576</td>\n",
+       "      <td>83.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>4502</td>\n",
+       "      <td>92.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>6873</td>\n",
+       "      <td>96.1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                           coincidences  points with a coincidence (%)\n",
+       "max time difference (min)                                             \n",
+       "0                                   965                           61.7\n",
+       "5                                  1816                           72.7\n",
+       "15                                 3576                           83.8\n",
+       "30                                 4502                           92.7\n",
+       "60                                 6873                           96.1"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rows = []\n",
+    "for minutes in [0, 5, 15, 30, 60]:\n",
+    "    result = find_coincident_observations(\n",
+    "        by_constellation, max_time_difference=timedelta(minutes=minutes)\n",
+    "    )\n",
+    "    rows.append(\n",
+    "        {\n",
+    "            \"max time difference (min)\": minutes,\n",
+    "            \"coincidences\": len(result),\n",
+    "            \"points with a coincidence (%)\": 100 * result.target_hash.nunique() / len(points),\n",
+    "        }\n",
+    "    )\n",
+    "pd.DataFrame(rows).set_index(\"max time difference (min)\").round(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19a6dc54",
+   "metadata": {},
+   "source": [
+    "Each coincidence lists the instruments that contributed to it. Most pair ICI with PolSIR, whose two satellites observe more often than the single INCUS radiometer; the INCUS radars, with their 10 km swath, rarely pass over a point of the lattice."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "bf3e9d18",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-23T23:00:49.797336Z",
+     "iopub.status.busy": "2026-10-23T23:00:49.797336Z",
+     "iopub.status.idle": "2026-10-23T23:00:49.812705Z",
+     "shell.execute_reply": "2026-10-23T23:00:49.812705Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>coincidences</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FALCON instruments</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>PolSIR</th>\n",
+       "      <td>3040</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>INCUS radiometer</th>\n",
+       "      <td>517</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>INCUS radar, INCUS radiometer</th>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>INCUS radiometer, PolSIR</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               coincidences\n",
+       "FALCON instruments                         \n",
+       "PolSIR                                 3040\n",
+       "INCUS radiometer                        517\n",
+       "INCUS radar, INCUS radiometer            17\n",
+       "INCUS radiometer, PolSIR                  2"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def falcon_instruments(instruments):\n",
+    "    return \", \".join(sorted(set(instruments.split(\", \")) - {\"ICI\"}))\n",
+    "\n",
+    "\n",
+    "coincident.instrument.map(falcon_instruments).value_counts().rename_axis(\n",
+    "    \"FALCON instruments\"\n",
+    ").rename(\"coincidences\").to_frame()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45f943a2",
+   "metadata": {},
+   "source": [
+    "Coincident observations have the columns of collected observations, so `aggregate_observations` and `reduce_observations` compute their statistics: here, the number of coincidences (`samples`) at each point over the week and the mean time between them (`revisit`). Coincidences are most frequent near 50 to 60 deg north and south, where PolSIR's ground tracks turn and are densest, and points without any coincidence in the week lie mostly at lower latitudes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "0d6ca7d0",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-23T23:00:49.812705Z",
+     "iopub.status.busy": "2026-10-23T23:00:49.812705Z",
+     "iopub.status.idle": "2026-10-23T23:00:53.331505Z",
+     "shell.execute_reply": "2026-10-23T23:00:53.331095Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>samples</th>\n",
+       "      <th>revisit</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>839.000000</td>\n",
+       "      <td>723</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>4.262217</td>\n",
+       "      <td>1 days 04:45:39.341271966</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>2.423117</td>\n",
+       "      <td>0 days 22:00:08.682351193</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0 days 00:09:02.321197530</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0 days 17:44:03.930860342</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>0 days 23:34:31.711996125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>1 days 07:11:52.029683234</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>6 days 13:00:15.277743627</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          samples                    revisit\n",
+       "count  839.000000                        723\n",
+       "mean     4.262217  1 days 04:45:39.341271966\n",
+       "std      2.423117  0 days 22:00:08.682351193\n",
+       "min      1.000000  0 days 00:09:02.321197530\n",
+       "25%      2.000000  0 days 17:44:03.930860342\n",
+       "50%      4.000000  0 days 23:34:31.711996125\n",
+       "75%      6.000000  1 days 07:11:52.029683234\n",
+       "max     11.000000  6 days 13:00:15.277743627"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x450 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from tatc.analysis import aggregate_observations, reduce_observations\n",
+    "\n",
+    "reduced = reduce_observations(aggregate_observations(coincident))\n",
+    "display(reduced[[\"samples\", \"revisit\"]].describe())\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(9, 4.5), subplot_kw={\"projection\": ccrs.PlateCarree()})\n",
+    "points[~points.point_id.isin(reduced.target_hash)].plot(\n",
+    "    ax=ax, color=GRAY, markersize=12, marker=\"x\", linewidth=1, label=\"no coincidence\"\n",
+    ")\n",
+    "reduced.plot(\n",
+    "    ax=ax,\n",
+    "    column=\"samples\",\n",
+    "    cmap=\"Blues\",\n",
+    "    vmin=0,\n",
+    "    markersize=14,\n",
+    "    legend=True,\n",
+    "    legend_kwds={\"label\": \"Coincidences per week\", \"shrink\": 0.6},\n",
+    ")\n",
+    "ax.coastlines(color=GRAY, linewidth=0.5)\n",
+    "ax.set_extent([-180, 180, -65, 65], crs=ccrs.PlateCarree())\n",
+    "ax.legend(\n",
+    "    handles=[plt.Line2D([], [], marker=\"x\", color=GRAY, linestyle=\"none\")],\n",
+    "    labels=[\"no coincidence\"],\n",
+    "    loc=\"lower left\",\n",
+    "    fontsize=8,\n",
+    "    frameon=False,\n",
+    ")\n",
+    "ax.set_title(\"ICI and FALCON coincident observations (max. time difference 15 min)\", fontsize=10)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "491b63f6",
+   "metadata": {},
+   "source": [
+    "Coincidences among more than two platforms use `min_platforms`. Relabeling the `satellite` column with each mission (Metop-SG-B, PolSIR, or INCUS) finds the points observed by all three missions within the maximum time difference, which are rare. (A mission appears once per instrument that contributes.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "a530f1b6",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-23T23:00:53.333505Z",
+     "iopub.status.busy": "2026-10-23T23:00:53.332506Z",
+     "iopub.status.idle": "2026-10-23T23:01:00.470730Z",
+     "shell.execute_reply": "2026-10-23T23:01:00.470730Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>target_hash</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>satellite</th>\n",
+       "      <th>instrument</th>\n",
+       "      <th>start</th>\n",
+       "      <th>epoch</th>\n",
+       "      <th>end</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>678670b6a603cfbd</td>\n",
+       "      <td>POINT Z (125.79967 -38.27067 0)</td>\n",
+       "      <td>Metop-SG-B, INCUS, PolSIR</td>\n",
+       "      <td>ICI, INCUS radiometer, PolSIR</td>\n",
+       "      <td>2029-07-07 14:20:29.222356576+00:00</td>\n",
+       "      <td>2029-07-07 14:21:54.033398351+00:00</td>\n",
+       "      <td>2029-07-07 14:23:18.844440127+00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>dba788a000f429d1</td>\n",
+       "      <td>POINT Z (172.73285 38.39704 0)</td>\n",
+       "      <td>Metop-SG-B, INCUS, PolSIR</td>\n",
+       "      <td>ICI, INCUS radiometer, PolSIR</td>\n",
+       "      <td>2029-07-07 23:05:31.588398224+00:00</td>\n",
+       "      <td>2029-07-07 23:06:35.859609001+00:00</td>\n",
+       "      <td>2029-07-07 23:07:40.130819778+00:00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        target_hash                         geometry  \\\n",
+       "0  678670b6a603cfbd  POINT Z (125.79967 -38.27067 0)   \n",
+       "1  dba788a000f429d1   POINT Z (172.73285 38.39704 0)   \n",
+       "\n",
+       "                   satellite                     instrument  \\\n",
+       "0  Metop-SG-B, INCUS, PolSIR  ICI, INCUS radiometer, PolSIR   \n",
+       "1  Metop-SG-B, INCUS, PolSIR  ICI, INCUS radiometer, PolSIR   \n",
+       "\n",
+       "                                start                               epoch  \\\n",
+       "0 2029-07-07 14:20:29.222356576+00:00 2029-07-07 14:21:54.033398351+00:00   \n",
+       "1 2029-07-07 23:05:31.588398224+00:00 2029-07-07 23:06:35.859609001+00:00   \n",
+       "\n",
+       "                                  end  \n",
+       "0 2029-07-07 14:23:18.844440127+00:00  \n",
+       "1 2029-07-07 23:07:40.130819778+00:00  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "by_mission = observations.assign(satellite=observations.satellite.map(mission))\n",
+    "find_coincident_observations(\n",
+    "    by_mission, min_platforms=3, max_time_difference=timedelta(minutes=15)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1531581",
+   "metadata": {},
+   "source": [
+    "## Coincident Ground Tracks\n",
+    "\n",
+    "Points sample coincidences at discrete locations. Ground tracks instead give the *areas* observed in common. `collect_ground_track` collects every instrument's footprint at each time step (here, every minute for the week); with the along-track fields of view chosen above, consecutive footprints tile each swath.\n",
+    "\n",
+    "`find_coincident_ground_track` pairs the footprints of different satellites whose times differ by at most `max_time_difference` and which overlap, recording the columns of both footprints (suffixed `_1` and `_2`, with `time_1` no later than `time_2`) and their overlap as the geometry. As before, the `satellite` column is relabeled with the constellation (keeping each satellite's name in a `member` column), so that only pairs between the constellations are found."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "f43964c4",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-23T23:01:00.473884Z",
+     "iopub.status.busy": "2026-10-23T23:01:00.473884Z",
+     "iopub.status.idle": "2026-10-23T23:01:31.439633Z",
+     "shell.execute_reply": "2026-10-23T23:01:31.439633Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time_1</th>\n",
+       "      <th>satellite_1</th>\n",
+       "      <th>instrument_1</th>\n",
+       "      <th>valid_obs_1</th>\n",
+       "      <th>member_1</th>\n",
+       "      <th>time_2</th>\n",
+       "      <th>satellite_2</th>\n",
+       "      <th>instrument_2</th>\n",
+       "      <th>valid_obs_2</th>\n",
+       "      <th>member_2</th>\n",
+       "      <th>time_difference</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2029-07-01 00:02:00+00:00</td>\n",
+       "      <td>FALCON</td>\n",
+       "      <td>INCUS radiometer</td>\n",
+       "      <td>True</td>\n",
+       "      <td>INCUS-B</td>\n",
+       "      <td>2029-07-01 00:15:00+00:00</td>\n",
+       "      <td>Metop-SG-B</td>\n",
+       "      <td>ICI</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Metop-SG-B 2</td>\n",
+       "      <td>0 days 00:13:00</td>\n",
+       "      <td>POLYGON Z ((132.11799 3.50472 0, 132.60076 3.0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2029-07-01 00:02:00+00:00</td>\n",
+       "      <td>FALCON</td>\n",
+       "      <td>INCUS radar</td>\n",
+       "      <td>True</td>\n",
+       "      <td>INCUS-A</td>\n",
+       "      <td>2029-07-01 00:15:00+00:00</td>\n",
+       "      <td>Metop-SG-B</td>\n",
+       "      <td>ICI</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Metop-SG-B 2</td>\n",
+       "      <td>0 days 00:13:00</td>\n",
+       "      <td>POLYGON Z ((132.47031 5.86551 0, 132.47747 5.8...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2029-07-01 00:02:00+00:00</td>\n",
+       "      <td>FALCON</td>\n",
+       "      <td>INCUS radiometer</td>\n",
+       "      <td>True</td>\n",
+       "      <td>INCUS-B</td>\n",
+       "      <td>2029-07-01 00:16:00+00:00</td>\n",
+       "      <td>Metop-SG-B</td>\n",
+       "      <td>ICI</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Metop-SG-B 2</td>\n",
+       "      <td>0 days 00:14:00</td>\n",
+       "      <td>POLYGON Z ((131.7861 2.42426 0, 131.42029 2.11...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2029-07-01 00:03:00+00:00</td>\n",
+       "      <td>FALCON</td>\n",
+       "      <td>INCUS radar</td>\n",
+       "      <td>True</td>\n",
+       "      <td>INCUS-A</td>\n",
+       "      <td>2029-07-01 00:13:00+00:00</td>\n",
+       "      <td>Metop-SG-B</td>\n",
+       "      <td>ICI</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Metop-SG-B 2</td>\n",
+       "      <td>0 days 00:10:00</td>\n",
+       "      <td>POLYGON Z ((134.54656 7.60036 0, 134.89614 7.8...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2029-07-01 00:03:00+00:00</td>\n",
+       "      <td>FALCON</td>\n",
+       "      <td>INCUS radar</td>\n",
+       "      <td>True</td>\n",
+       "      <td>INCUS-B</td>\n",
+       "      <td>2029-07-01 00:14:00+00:00</td>\n",
+       "      <td>Metop-SG-B</td>\n",
+       "      <td>ICI</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Metop-SG-B 2</td>\n",
+       "      <td>0 days 00:11:00</td>\n",
+       "      <td>POLYGON Z ((132.68877 6.15494 0, 133.02192 6.4...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     time_1 satellite_1      instrument_1  valid_obs_1  \\\n",
+       "0 2029-07-01 00:02:00+00:00      FALCON  INCUS radiometer         True   \n",
+       "1 2029-07-01 00:02:00+00:00      FALCON       INCUS radar         True   \n",
+       "2 2029-07-01 00:02:00+00:00      FALCON  INCUS radiometer         True   \n",
+       "3 2029-07-01 00:03:00+00:00      FALCON       INCUS radar         True   \n",
+       "4 2029-07-01 00:03:00+00:00      FALCON       INCUS radar         True   \n",
+       "\n",
+       "  member_1                    time_2 satellite_2 instrument_2  valid_obs_2  \\\n",
+       "0  INCUS-B 2029-07-01 00:15:00+00:00  Metop-SG-B          ICI         True   \n",
+       "1  INCUS-A 2029-07-01 00:15:00+00:00  Metop-SG-B          ICI         True   \n",
+       "2  INCUS-B 2029-07-01 00:16:00+00:00  Metop-SG-B          ICI         True   \n",
+       "3  INCUS-A 2029-07-01 00:13:00+00:00  Metop-SG-B          ICI         True   \n",
+       "4  INCUS-B 2029-07-01 00:14:00+00:00  Metop-SG-B          ICI         True   \n",
+       "\n",
+       "       member_2 time_difference  \\\n",
+       "0  Metop-SG-B 2 0 days 00:13:00   \n",
+       "1  Metop-SG-B 2 0 days 00:13:00   \n",
+       "2  Metop-SG-B 2 0 days 00:14:00   \n",
+       "3  Metop-SG-B 2 0 days 00:10:00   \n",
+       "4  Metop-SG-B 2 0 days 00:11:00   \n",
+       "\n",
+       "                                            geometry  \n",
+       "0  POLYGON Z ((132.11799 3.50472 0, 132.60076 3.0...  \n",
+       "1  POLYGON Z ((132.47031 5.86551 0, 132.47747 5.8...  \n",
+       "2  POLYGON Z ((131.7861 2.42426 0, 131.42029 2.11...  \n",
+       "3  POLYGON Z ((134.54656 7.60036 0, 134.89614 7.8...  \n",
+       "4  POLYGON Z ((132.68877 6.15494 0, 133.02192 6.4...  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from tatc.analysis import collect_ground_track, find_coincident_ground_track\n",
+    "\n",
+    "times = pd.date_range(START, END, freq=f\"{TIME_STEP}s\", inclusive=\"left\")\n",
+    "ground_track = collect_ground_track(satellites, list(times), instrument_index=None)\n",
+    "pairs = find_coincident_ground_track(\n",
+    "    ground_track.assign(\n",
+    "        member=ground_track.satellite, satellite=ground_track.satellite.map(constellation)\n",
+    "    ),\n",
+    "    max_time_difference=timedelta(minutes=15),\n",
+    ")\n",
+    "display(pairs.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89656082",
+   "metadata": {},
+   "source": [
+    "Each pair's FALCON instrument is the one not on Metop-SG-B. Dissolving the overlaps of each instrument gives the area observed in common with ICI over the week (computed in an equal-area projection), and the time differences (multiples of the one-minute time step) show how close in time the coincidences are."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "a47f5d25",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-23T23:01:31.440814Z",
+     "iopub.status.busy": "2026-10-23T23:01:31.440814Z",
+     "iopub.status.idle": "2026-10-23T23:01:33.467001Z",
+     "shell.execute_reply": "2026-10-23T23:01:33.467001Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pairs</th>\n",
+       "      <th>ICI first (%)</th>\n",
+       "      <th>median time difference (min)</th>\n",
+       "      <th>coincident area (million km2)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>falcon_instrument</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>PolSIR</th>\n",
+       "      <td>13202</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>327.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>INCUS radiometer</th>\n",
+       "      <td>3339</td>\n",
+       "      <td>51.1</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>95.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>INCUS radar</th>\n",
+       "      <td>5500</td>\n",
+       "      <td>50.8</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>8.8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   pairs  ICI first (%)  median time difference (min)  \\\n",
+       "falcon_instrument                                                       \n",
+       "PolSIR             13202           52.0                           8.0   \n",
+       "INCUS radiometer    3339           51.1                           8.0   \n",
+       "INCUS radar         5500           50.8                           8.0   \n",
+       "\n",
+       "                   coincident area (million km2)  \n",
+       "falcon_instrument                                 \n",
+       "PolSIR                                     327.4  \n",
+       "INCUS radiometer                            95.0  \n",
+       "INCUS radar                                  8.8  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metop_first = pairs.satellite_1 == \"Metop-SG-B\"\n",
+    "pairs[\"falcon_instrument\"] = pairs.instrument_2.where(metop_first, pairs.instrument_1)\n",
+    "pairs[\"metop_first\"] = metop_first\n",
+    "areas = pairs.dissolve(by=\"falcon_instrument\")\n",
+    "pd.DataFrame(\n",
+    "    {\n",
+    "        \"pairs\": pairs.groupby(\"falcon_instrument\").size(),\n",
+    "        \"ICI first (%)\": 100 * pairs.groupby(\"falcon_instrument\").metop_first.mean(),\n",
+    "        \"median time difference (min)\": pairs.groupby(\"falcon_instrument\").time_difference.median().dt.total_seconds() / 60,\n",
+    "        \"coincident area (million km2)\": areas.to_crs(\"EPSG:6933\").area / 1e12,\n",
+    "    }\n",
+    ").reindex(COLORS).round(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "09a3467c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-23T23:01:33.468005Z",
+     "iopub.status.busy": "2026-10-23T23:01:33.468005Z",
+     "iopub.status.idle": "2026-10-23T23:01:33.742314Z",
+     "shell.execute_reply": "2026-10-23T23:01:33.742314Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, (ax, hist) = plt.subplots(\n",
+    "    2,\n",
+    "    1,\n",
+    "    figsize=(9, 7),\n",
+    "    height_ratios=[2, 1],\n",
+    "    subplot_kw={},\n",
+    ")\n",
+    "ax.remove()\n",
+    "ax = fig.add_subplot(2, 1, 1, projection=ccrs.PlateCarree())\n",
+    "for instrument, color in COLORS.items():\n",
+    "    if instrument in areas.index:\n",
+    "        areas.loc[[instrument]].plot(\n",
+    "            ax=ax, color=color, alpha=0.6 if instrument != \"INCUS radar\" else 1, edgecolor=\"none\"\n",
+    "        )\n",
+    "ax.coastlines(color=GRAY, linewidth=0.5)\n",
+    "ax.set_extent([-180, 180, -65, 65], crs=ccrs.PlateCarree())\n",
+    "ax.legend(\n",
+    "    handles=[plt.Rectangle((0, 0), 1, 1, color=c) for c in COLORS.values()],\n",
+    "    labels=list(COLORS),\n",
+    "    loc=\"lower left\",\n",
+    "    fontsize=8,\n",
+    "    frameon=False,\n",
+    ")\n",
+    "ax.set_title(\"Areas observed by ICI and a FALCON instrument within 15 min (one week)\", fontsize=10)\n",
+    "\n",
+    "bins = np.arange(-0.5, 16)\n",
+    "hist.hist(\n",
+    "    [pairs[pairs.falcon_instrument == i].time_difference.dt.total_seconds() / 60 for i in COLORS],\n",
+    "    bins=bins,\n",
+    "    color=list(COLORS.values()),\n",
+    "    label=list(COLORS),\n",
+    "    rwidth=0.85,\n",
+    ")\n",
+    "hist.set(xlabel=\"Time difference (min)\", ylabel=\"Coincident footprint pairs\")\n",
+    "hist.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "hist.set_ylim(top=1.25 * hist.get_ylim()[1])\n",
+    "hist.legend(frameon=False, fontsize=8, ncol=3, loc=\"upper center\")\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f92eb4c7",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "* ICI's narrower swath (about 1600 km at 832 km altitude) makes it the more constrained of Metop-SG-B's two conical imagers.\n",
+    "* With a 15 minute window, ICI on three Metop-SG-B satellites and the FALCON instruments observe most of the points between 60 deg south and 60 deg north in common at least once a week, mostly with PolSIR.\n",
+    "* Coincidences with all three missions at once are rare.\n",
+    "* Coincident footprints are spread evenly over time differences up to the maximum, so the coincident area grows with the window.\n",
+    "* The INCUS radars' narrow swath makes their coincidences with ICI strips along their ground tracks, which points spaced hundreds of kilometers apart rarely sample but ground tracks capture.\n",
+    "\n",
+    "These results depend on assumptions about the orbits' relative phasing and the instruments' scan ranges (see Scenario), which can be updated as the missions' designs are published."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "tatc",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/index.rst b/docs/examples/index.rst
index 23c830b..14aa3de 100644
--- a/docs/examples/index.rst
+++ b/docs/examples/index.rst
@@ -13,6 +13,7 @@ The following provide examples of TAT-C use in a Jupyter notebook environment.
   CollectRadarTrack.ipynb
   CollectPlatformTrack.ipynb
   ComputeCoverage.ipynb
+  FindCoincidentObservations.ipynb
   ComputeLatency.ipynb
   ComputeDOP.ipynb
   CollectRO.ipynb
diff --git a/src/tatc/analysis/__init__.py b/src/tatc/analysis/__init__.py
index d1026ec..be350e5 100644
--- a/src/tatc/analysis/__init__.py
+++ b/src/tatc/analysis/__init__.py
@@ -2,6 +2,10 @@
 Defines analysis functions.
 """
 
+from .coincidence import (
+    find_coincident_ground_track,
+    find_coincident_observations,
+)
 from .coverage_metrics import (
     aggregate_observations,
     grid_observations,
@@ -79,6 +83,8 @@
     "compute_platform_track",
     "compute_radar_track",
     "compute_region_access_periods",
+    "find_coincident_ground_track",
+    "find_coincident_observations",
     "grid_latencies",
     "grid_observations",
     "reduce_latencies",
diff --git a/src/tatc/analysis/coincidence.py b/src/tatc/analysis/coincidence.py
new file mode 100644
index 0000000..57c4c0d
--- /dev/null
+++ b/src/tatc/analysis/coincidence.py
@@ -0,0 +1,256 @@
+"""
+Methods to find coincident observations by two or more observing
+platforms: the periods when platforms observe the same point and the
+areas that their ground tracks observe in common.
+
+@author: Paul T. Grogan <paul.grogan@asu.edu>
+"""
+
+from __future__ import annotations
+
+from datetime import timedelta
+from itertools import combinations
+
+import geopandas as gpd
+import numpy as np
+import pandas as pd
+import shapely
+
+from .coverage_metrics import _get_target_keys
+
+
+def _check_max_time_difference(max_time_difference: timedelta) -> pd.Timedelta:
+    """
+    Checks the maximum time difference of coincident observations.
+
+    Args:
+        max_time_difference (datetime.timedelta): The maximum time difference.
+
+    Returns:
+        pandas.Timedelta: the maximum time difference
+    """
+    value = pd.Timedelta(max_time_difference)
+    if value < pd.Timedelta(0):
+        raise ValueError(
+            f"max_time_difference must be non-negative, not {max_time_difference}"
+        )
+    return value
+
+
+def _get_empty_coincidence_frame() -> gpd.GeoDataFrame:
+    """
+    Gets an empty data frame for coincident observations.
+
+    Returns:
+        geopandas.GeoDataFrame: Empty data frame.
+    """
+    columns = {
+        "target_hash": pd.Series([], dtype="str"),
+        "geometry": pd.Series([], dtype="object"),
+        "satellite": pd.Series([], dtype="str"),
+        "instrument": pd.Series([], dtype="str"),
+        "start": pd.Series([], dtype="datetime64[ns, utc]"),
+        "epoch": pd.Series([], dtype="datetime64[ns, utc]"),
+        "end": pd.Series([], dtype="datetime64[ns, utc]"),
+    }
+    return gpd.GeoDataFrame(columns, crs="EPSG:4326")
+
+
+def _find_coincident_periods(
+    periods: pd.DataFrame, min_platforms: int
+) -> list[tuple[pd.Timestamp, pd.Timestamp]]:
+    """
+    Finds the maximal periods when at least a minimum number of platforms
+    observe at once, sweeping over the start and end of each platform's
+    (closed) observation periods.
+
+    Args:
+        periods (pandas.DataFrame): The observation periods, with columns
+                `platform`, `start`, and `end`.
+        min_platforms (int): The minimum number of platforms.
+
+    Returns:
+        list[tuple[pandas.Timestamp, pandas.Timestamp]]: the start and end
+            of each coincident period, in time order
+    """
+    starts, ends = [], []
+    for _, platform in periods.groupby("platform"):
+        # merge the platform's overlapping periods so that it counts once
+        platform = platform.sort_values("start")
+        group = (platform["start"] > platform["end"].shift().cummax()).cumsum()
+        merged = platform.groupby(group).agg(start=("start", "min"), end=("end", "max"))
+        starts.append(merged["start"])
+        ends.append(merged["end"])
+    times = pd.concat(starts + ends, ignore_index=True)
+    events = pd.DataFrame(
+        {"time": times, "change": np.repeat([1, -1], len(times) // 2)}
+    )
+    # sweep the events in time order, with starts before ends at the same
+    # time so that periods which touch count as coincident at that instant
+    events = events.sort_values(["time", "change"], ascending=[True, False])
+    active = events["change"].cumsum().to_numpy() >= min_platforms
+    was_active = np.concatenate(([False], active[:-1]))
+    return list(
+        zip(events["time"][active & ~was_active], events["time"][~active & was_active])
+    )
+
+
+def find_coincident_observations(
+    observations: gpd.GeoDataFrame,
+    min_platforms: int = 2,
+    max_time_difference: timedelta = timedelta(0),
+) -> gpd.GeoDataFrame:
+    """
+    Find coincident observations of points: the periods when at least
+    `min_platforms` satellites observe the same target (the same
+    `target_hash`, see `_get_target_keys`) at once, as collected by
+    `tatc.analysis.point_sampling.collect_observations`. Observations by
+    different instruments on the same satellite are not coincident with
+    each other.
+
+    Each observation period is widened by half of `max_time_difference`
+    before and after, so that two observations coincide if the gap between
+    their periods is at most `max_time_difference` (for instantaneous
+    observations, if their epochs differ by at most `max_time_difference`).
+    Each coincident period is maximal: it spans the time that at least
+    `min_platforms` widened periods overlap, even as the satellites
+    observing change; `satellite`/`instrument` record every contributor to
+    that period, comma-separated and paired, in order of their start time.
+    `epoch` is the midpoint of the coincident period.
+
+    The result has the columns of `aggregate_observations` inputs, so
+    `aggregate_observations` and `reduce_observations` compute the access
+    and revisit of coincident observations.
+
+    Args:
+        observations (geopandas.GeoDataFrame): The collected observations of
+                points.
+        min_platforms (int): The minimum number of satellites (at least 2)
+                observing at once.
+        max_time_difference (datetime.timedelta): The maximum time
+                difference between coincident observations.
+
+    Returns:
+        geopandas.GeoDataFrame: The data frame of coincident observations,
+            sorted by start time.
+    """
+    if min_platforms < 2:
+        raise ValueError(f"min_platforms must be at least 2, not {min_platforms}")
+    half_difference = _check_max_time_difference(max_time_difference) / 2
+    if observations.empty:
+        return _get_empty_coincidence_frame()
+    if not (observations.geom_type == "Point").all():
+        raise ValueError(
+            "observations must be of points (see find_coincident_ground_track "
+            "for the footprints of ground tracks)"
+        )
+    records = []
+    for _, gdf in observations.groupby(_get_target_keys(observations)):
+        periods = pd.DataFrame(
+            {
+                "platform": gdf["satellite"],
+                "start": gdf["start"] - half_difference,
+                "end": gdf["end"] + half_difference,
+            }
+        )
+        for start, end in _find_coincident_periods(periods, min_platforms):
+            # each observation overlapping the period contributes to it
+            contributors = gdf[
+                (periods["start"] <= end) & (periods["end"] >= start)
+            ].sort_values("start", kind="stable")
+            pairs = list(
+                dict.fromkeys(
+                    zip(contributors["satellite"], contributors["instrument"])
+                )
+            )
+            records.append(
+                {
+                    "target_hash": gdf["target_hash"].iloc[0],
+                    "geometry": gdf.geometry.iloc[0],
+                    "satellite": ", ".join(satellite for satellite, _ in pairs),
+                    "instrument": ", ".join(instrument for _, instrument in pairs),
+                    "start": start,
+                    "epoch": start + (end - start) / 2,
+                    "end": end,
+                }
+            )
+    if len(records) == 0:
+        return _get_empty_coincidence_frame()
+    return (
+        gpd.GeoDataFrame(records, crs=observations.crs)
+        .sort_values("start", kind="stable")
+        .reset_index(drop=True)
+    )
+
+
+def find_coincident_ground_track(
+    ground_track: gpd.GeoDataFrame,
+    max_time_difference: timedelta = timedelta(0),
+) -> gpd.GeoDataFrame:
+    """
+    Find coincident observations of ground tracks: the pairs of footprints
+    of different satellites, as collected by
+    `tatc.analysis.ground_track.collect_ground_track`, whose times differ
+    by at most `max_time_difference` and which overlap (share a positive
+    area, in longitude/latitude coordinates). Footprints of different
+    instruments on the same satellite are not coincident with each other.
+
+    Each pair records the columns of both footprints, suffixed `_1` and
+    `_2` (for example, `time_1`, `satellite_1`, and `valid_obs_1`) and
+    ordered so that `time_1` is no later than `time_2`, the time difference
+    `time_difference` (`time_2` less `time_1`), and the overlap of the
+    footprints as its geometry. Dissolving the result (for example, after
+    selecting the pairs of valid observations) gives the area observed in
+    common.
+
+    Footprints are first matched by overlap, then by time, so the cost
+    grows with the number of times each area is observed.
+
+    Args:
+        ground_track (geopandas.GeoDataFrame): The collected ground track.
+        max_time_difference (datetime.timedelta): The maximum time
+                difference between coincident footprints.
+
+    Returns:
+        geopandas.GeoDataFrame: The data frame of coincident footprints,
+            sorted by `time_1` and `time_2`.
+    """
+    max_time_difference = _check_max_time_difference(max_time_difference)
+    track = ground_track.sort_values("time", kind="stable").reset_index(drop=True)
+    geometries = track.geometry.to_numpy()
+    satellites = track["satellite"].to_numpy()
+    times = pd.DatetimeIndex(track["time"])
+    # find the overlapping footprints of each pair of satellites
+    first, second = [np.array([], dtype=int)], [np.array([], dtype=int)]
+    for satellite_1, satellite_2 in combinations(pd.unique(satellites), 2):
+        index_1 = np.flatnonzero(satellites == satellite_1)
+        index_2 = np.flatnonzero(satellites == satellite_2)
+        tree = shapely.STRtree(geometries[index_2])
+        query_1, query_2 = tree.query(geometries[index_1], predicate="intersects")
+        first.append(index_1[query_1])
+        second.append(index_2[query_2])
+    first, second = np.concatenate(first), np.concatenate(second)
+    # order each pair by time (rows are sorted by time)
+    first, second = np.minimum(first, second), np.maximum(first, second)
+    time_difference = times[second] - times[first]
+    selected = np.asarray(time_difference <= max_time_difference)
+    first, second = first[selected], second[selected]
+    overlap = shapely.intersection(geometries[first], geometries[second])
+    # exclude footprints that only touch
+    selected = shapely.area(overlap) > 0
+    first, second = first[selected], second[selected]
+    columns = [column for column in track.columns if column != track.geometry.name]
+    pairs = gpd.GeoDataFrame(
+        {
+            **{
+                f"{column}_{suffix}": track[column].iloc[index].reset_index(drop=True)
+                for suffix, index in ((1, first), (2, second))
+                for column in columns
+            },
+            "time_difference": (times[second] - times[first]).to_series(
+                index=pd.RangeIndex(len(first))
+            ),
+        },
+        geometry=gpd.GeoSeries(overlap[selected], crs=track.crs),
+    )
+    return pairs.sort_values(["time_1", "time_2"], kind="stable").reset_index(drop=True)
diff --git a/tests/analysis/test_coincidence.py b/tests/analysis/test_coincidence.py
new file mode 100644
index 0000000..a2c82a1
--- /dev/null
+++ b/tests/analysis/test_coincidence.py
@@ -0,0 +1,353 @@
+"""
+Unit tests for the coincident observation analysis functions.
+
+@author Paul T. Grogan <paul.grogan@asu.edu>
+"""
+
+from datetime import datetime, timedelta, timezone
+
+import geopandas as gpd
+import pandas as pd
+from shapely.geometry import Point as ShapelyPoint, box
+
+from tatc.analysis import (
+    aggregate_observations,
+    collect_ground_track,
+    collect_observations,
+    find_coincident_ground_track,
+    find_coincident_observations,
+)
+from tatc.schemas import Instrument, Satellite
+
+from .common import IssConstellationTestCase
+
+T0 = datetime(2022, 6, 1, tzinfo=timezone.utc)
+
+
+def _minutes(value):
+    return T0 + timedelta(minutes=value)
+
+
+def _make_observation(satellite, start, end, instrument="Test", target_hash="0"):
+    """
+    Build a synthetic observation record matching the schema
+    `collect_observations` produces, with start and end in minutes after T0.
+    """
+    return {
+        "target_hash": target_hash,
+        "geometry": ShapelyPoint(0, 0),
+        "satellite": satellite,
+        "instrument": instrument,
+        "start": pd.Timestamp(_minutes(start)),
+        "epoch": pd.Timestamp(_minutes((start + end) / 2)),
+        "end": pd.Timestamp(_minutes(end)),
+    }
+
+
+def _make_footprint(satellite, time, geometry, instrument="Test"):
+    """
+    Build a synthetic footprint record matching the schema
+    `collect_ground_track` produces, with time in minutes after T0.
+    """
+    return {
+        "time": pd.Timestamp(_minutes(time)),
+        "satellite": satellite,
+        "instrument": instrument,
+        "valid_obs": True,
+        "geometry": geometry,
+    }
+
+
+class TestFindCoincidentObservations(IssConstellationTestCase):
+    """
+    Unit tests for `find_coincident_observations`.
+    """
+
+    def test_overlap(self):
+        """
+        Test that the coincident period of two satellites is the overlap of
+        their observation periods.
+        """
+        observations = gpd.GeoDataFrame(
+            [_make_observation("A", 0, 10), _make_observation("B", 6, 15)],
+            crs="EPSG:4326",
+        )
+        results = find_coincident_observations(observations)
+        self.assertEqual(len(results.index), 1)
+        self.assertEqual(results.iloc[0].satellite, "A, B")
+        self.assertEqual(results.iloc[0].instrument, "Test, Test")
+        self.assertEqual(results.iloc[0].start, _minutes(6))
+        self.assertEqual(results.iloc[0].end, _minutes(10))
+        self.assertEqual(results.iloc[0].epoch, _minutes(8))
+
+    def test_no_overlap(self):
+        """
+        Test that separate observation periods are not coincident, giving an
+        empty data frame with the result columns.
+        """
+        observations = gpd.GeoDataFrame(
+            [_make_observation("A", 0, 10), _make_observation("B", 12, 15)],
+            crs="EPSG:4326",
+        )
+        results = find_coincident_observations(observations)
+        self.assertTrue(results.empty)
+        self.assertListEqual(
+            list(results.columns),
+            [
+                "target_hash",
+                "geometry",
+                "satellite",
+                "instrument",
+                "start",
+                "epoch",
+                "end",
+            ],
+        )
+
+    def test_max_time_difference(self):
+        """
+        Test that observations coincide if the gap between their periods is
+        at most the maximum time difference, widening each period by half.
+        """
+        observations = gpd.GeoDataFrame(
+            [_make_observation("A", 0, 10), _make_observation("B", 12, 15)],
+            crs="EPSG:4326",
+        )
+        self.assertTrue(
+            find_coincident_observations(
+                observations, max_time_difference=timedelta(minutes=1)
+            ).empty
+        )
+        results = find_coincident_observations(
+            observations, max_time_difference=timedelta(minutes=4)
+        )
+        self.assertEqual(len(results.index), 1)
+        self.assertEqual(results.iloc[0].start, _minutes(10))
+        self.assertEqual(results.iloc[0].end, _minutes(12))
+
+    def test_same_satellite(self):
+        """
+        Test that observations by different instruments on the same
+        satellite are not coincident with each other.
+        """
+        observations = gpd.GeoDataFrame(
+            [
+                _make_observation("A", 0, 10, instrument="X"),
+                _make_observation("A", 5, 15, instrument="Y"),
+            ],
+            crs="EPSG:4326",
+        )
+        self.assertTrue(find_coincident_observations(observations).empty)
+
+    def test_maximal_period(self):
+        """
+        Test that a coincident period spans the time that at least two
+        satellites observe, even as the satellites observing change, and
+        records each satellite and instrument once.
+        """
+        observations = gpd.GeoDataFrame(
+            [
+                _make_observation("A", 0, 10),
+                _make_observation("B", 2, 4, instrument="X"),
+                _make_observation("B", 2, 4, instrument="X"),
+                _make_observation("C", 3, 12),
+                _make_observation("D", 20, 25),
+            ],
+            crs="EPSG:4326",
+        )
+        results = find_coincident_observations(observations)
+        self.assertEqual(len(results.index), 1)
+        self.assertEqual(results.iloc[0].satellite, "A, B, C")
+        self.assertEqual(results.iloc[0].instrument, "Test, X, Test")
+        self.assertEqual(results.iloc[0].start, _minutes(2))
+        self.assertEqual(results.iloc[0].end, _minutes(10))
+
+    def test_min_platforms(self):
+        """
+        Test that a minimum of three satellites requires all three to
+        observe at once.
+        """
+        observations = gpd.GeoDataFrame(
+            [
+                _make_observation("A", 0, 10),
+                _make_observation("B", 2, 8),
+                _make_observation("C", 6, 12),
+            ],
+            crs="EPSG:4326",
+        )
+        results = find_coincident_observations(observations, min_platforms=3)
+        self.assertEqual(len(results.index), 1)
+        self.assertEqual(results.iloc[0].satellite, "A, B, C")
+        self.assertEqual(results.iloc[0].start, _minutes(6))
+        self.assertEqual(results.iloc[0].end, _minutes(8))
+
+    def test_isolates_targets(self):
+        """
+        Test that observations of different targets are not coincident.
+        """
+        observations = gpd.GeoDataFrame(
+            [
+                _make_observation("A", 0, 10, target_hash="0"),
+                _make_observation("B", 5, 15, target_hash="1"),
+                _make_observation("C", 5, 15, target_hash="1"),
+            ],
+            crs="EPSG:4326",
+        )
+        results = find_coincident_observations(observations)
+        self.assertEqual(len(results.index), 1)
+        self.assertEqual(results.iloc[0].target_hash, "1")
+        self.assertEqual(results.iloc[0].satellite, "B, C")
+
+    def test_invalid_arguments(self):
+        """
+        Test that invalid arguments and region observations raise errors.
+        """
+        observations = gpd.GeoDataFrame(
+            [_make_observation("A", 0, 10), _make_observation("B", 6, 15)],
+            crs="EPSG:4326",
+        )
+        with self.assertRaises(ValueError):
+            find_coincident_observations(observations, min_platforms=1)
+        with self.assertRaises(ValueError):
+            find_coincident_observations(
+                observations, max_time_difference=timedelta(minutes=-1)
+            )
+        observations.geometry = [box(0, 0, 1, 1), box(0, 0, 1, 1)]
+        with self.assertRaises(ValueError):
+            find_coincident_observations(observations)
+
+    def test_collect_observations(self):
+        """
+        Test that the coincident observations of a constellation lie within
+        observations by at least two of its satellites, and aggregate.
+        """
+        satellites = self.constellation.generate_members()
+        observations = collect_observations(
+            ShapelyPoint(0, 45), satellites, T0, T0 + timedelta(days=1)
+        )
+        # successive passes of the constellation are about 40 minutes apart
+        max_time_difference = timedelta(minutes=45)
+        results = find_coincident_observations(
+            observations, max_time_difference=max_time_difference
+        )
+        self.assertFalse(results.empty)
+        half = max_time_difference / 2
+        for result in results.itertuples():
+            contributors = observations[
+                (observations.start - half <= result.end)
+                & (observations.end + half >= result.start)
+            ]
+            self.assertGreaterEqual(contributors.satellite.nunique(), 2)
+            self.assertSetEqual(
+                set(result.satellite.split(", ")), set(contributors.satellite)
+            )
+        self.assertEqual(len(aggregate_observations(results).index), len(results.index))
+
+
+class TestFindCoincidentGroundTrack(IssConstellationTestCase):
+    """
+    Unit tests for `find_coincident_ground_track`.
+    """
+
+    def test_overlap(self):
+        """
+        Test that the overlapping footprints of two satellites at the same
+        time are coincident, with their overlap as the geometry.
+        """
+        ground_track = gpd.GeoDataFrame(
+            [
+                _make_footprint("A", 0, box(0, 0, 2, 2)),
+                _make_footprint("B", 0, box(1, 1, 3, 3)),
+                _make_footprint("B", 1, box(1, 1, 3, 3)),
+            ],
+            crs="EPSG:4326",
+        )
+        results = find_coincident_ground_track(ground_track)
+        self.assertEqual(len(results.index), 1)
+        self.assertEqual(results.iloc[0].satellite_1, "A")
+        self.assertEqual(results.iloc[0].satellite_2, "B")
+        self.assertEqual(results.iloc[0].time_1, _minutes(0))
+        self.assertEqual(results.iloc[0].time_2, _minutes(0))
+        self.assertEqual(results.iloc[0].time_difference, timedelta(0))
+        self.assertTrue(results.iloc[0].valid_obs_1 and results.iloc[0].valid_obs_2)
+        self.assertTrue(results.iloc[0].geometry.equals(box(1, 1, 2, 2)))
+        self.assertEqual(results.crs, ground_track.crs)
+
+    def test_max_time_difference(self):
+        """
+        Test that footprints coincide if their times differ by at most the
+        maximum time difference, ordered by time.
+        """
+        ground_track = gpd.GeoDataFrame(
+            [
+                _make_footprint("B", 0, box(1, 1, 3, 3)),
+                _make_footprint("A", 5, box(0, 0, 2, 2)),
+            ],
+            crs="EPSG:4326",
+        )
+        self.assertTrue(find_coincident_ground_track(ground_track).empty)
+        results = find_coincident_ground_track(
+            ground_track, max_time_difference=timedelta(minutes=5)
+        )
+        self.assertEqual(len(results.index), 1)
+        self.assertEqual(results.iloc[0].satellite_1, "B")
+        self.assertEqual(results.iloc[0].satellite_2, "A")
+        self.assertEqual(results.iloc[0].time_difference, timedelta(minutes=5))
+
+    def test_same_satellite_and_touching(self):
+        """
+        Test that footprints of the same satellite, or that only touch,
+        are not coincident.
+        """
+        ground_track = gpd.GeoDataFrame(
+            [
+                _make_footprint("A", 0, box(0, 0, 2, 2), instrument="X"),
+                _make_footprint("A", 0, box(1, 1, 3, 3), instrument="Y"),
+                _make_footprint("B", 0, box(3, 3, 4, 4)),
+            ],
+            crs="EPSG:4326",
+        )
+        results = find_coincident_ground_track(ground_track)
+        self.assertTrue(results.empty)
+        self.assertIn("time_difference", results.columns)
+        self.assertIn("satellite_2", results.columns)
+
+    def test_empty(self):
+        """
+        Test that an empty ground track has no coincident footprints.
+        """
+        ground_track = collect_ground_track([], [])
+        results = find_coincident_ground_track(ground_track)
+        self.assertTrue(results.empty)
+        with self.assertRaises(ValueError):
+            find_coincident_ground_track(
+                ground_track, max_time_difference=timedelta(minutes=-1)
+            )
+
+    def test_collect_ground_track(self):
+        """
+        Test that the coincident footprints of a constellation lie within
+        the footprints of both satellites.
+        """
+        instrument = Instrument(name="Test", field_of_regard=60.0)
+        satellites = [
+            Satellite(name=member.name, orbit=member.orbit, instruments=[instrument])
+            for member in self.constellation.generate_members()
+        ]
+        times = [T0 + timedelta(minutes=i) for i in range(0, 24 * 60, 2)]
+        ground_track = collect_ground_track(satellites, times)
+        results = find_coincident_ground_track(
+            ground_track, max_time_difference=timedelta(minutes=10)
+        )
+        self.assertFalse(results.empty)
+        self.assertTrue((results.satellite_1 != results.satellite_2).all())
+        self.assertTrue((results.time_difference <= timedelta(minutes=10)).all())
+        self.assertTrue((results.time_1 <= results.time_2).all())
+        footprints = ground_track.set_index(["time", "satellite"]).geometry
+        for result in results.itertuples():
+            for time, satellite in (
+                (result.time_1, result.satellite_1),
+                (result.time_2, result.satellite_2),
+            ):
+                footprint = footprints.loc[(time, satellite)]
+                self.assertTrue(footprint.buffer(1e-9).contains(result.geometry))