diff --git a/examples/python/air_traffic_data/README.md b/examples/python/air_traffic_data/README.md
index deff871db74c..a85f4150b5ac 100644
--- a/examples/python/air_traffic_data/README.md
+++ b/examples/python/air_traffic_data/README.md
@@ -1,24 +1,46 @@
 <!--[metadata]
 title = "Air Traffic Data"
 tags = ["2d", "3d", "map", "crs"]
-description = "Display aircraft flight trajectories"
-build_args = ["--jpeg-quality=50"]
+description = "Display aircraft traffic data"
 -->
 
 
 Display air traffic data kindly provided by [INVOLI](https://www.involi.com).
 
+<picture>
+  <img src="https://static.rerun.io/air_traffic_data/4a68b46a404c4f9e3c082f57a8a8ed4bf5b9b236/full.png" alt="">
+  <source media="(max-width: 480px)" srcset="https://static.rerun.io/air_traffic_data/4a68b46a404c4f9e3c082f57a8a8ed4bf5b9b236/480w.png">
+  <source media="(max-width: 768px)" srcset="https://static.rerun.io/air_traffic_data/4a68b46a404c4f9e3c082f57a8a8ed4bf5b9b236/768w.png">
+  <source media="(max-width: 1024px)" srcset="https://static.rerun.io/air_traffic_data/4a68b46a404c4f9e3c082f57a8a8ed4bf5b9b236/1024w.png">
+  <source media="(max-width: 1200px)" srcset="https://static.rerun.io/air_traffic_data/4a68b46a404c4f9e3c082f57a8a8ed4bf5b9b236/1200w.png">
+</picture>
 
-```bash
-# install dependencies
-pip install -r examples/python/air_traffic_data/requirements.txt
+This example demonstrates multiple aspects of the Rerun viewer:
+
+- Use of the [map view](https://rerun.io/docs/reference/types/views/map_view).
+- Use of [pyproj](https://pyproj4.github.io/pyproj/stable/) to transform geospatial data from one coordinate system to another.
+- Use [GeoPandas](https://geopandas.org/en/stable/) to load geospatial data into a 3D view.
+- Use [Polars]https://pola.rs) to batch data to be sent via [`rr.send_columns()`](https://rerun.io/docs/howto/logging/send-columns) (use `--batch`).
 
-# run with demo dataset
-python examples/python/air_traffic_data/main.py --dataset 10min
 
-# run with custom dataset
-python examples/python/air_traffic_data/main.py --dir path/to/my/dataset/directory
+## Run the code
 
-# more options
-python examples/python/air_traffic_data/main.py --help
+To run this example, make sure you have Python version at least 3.9, the Rerun repository checked out and the latest SDK installed:
+```bash
+pip install --upgrade rerun-sdk  # install the latest Rerun SDK
+git clone git@github.com:rerun-io/rerun.git  # Clone the repository
+cd rerun
+git checkout latest  # Check out the commit matching the latest SDK release
+```
+Install the necessary libraries specified in the requirements file:
+```bash
+pip install -e examples/python/air_traffic_data
+```
+To experiment with the provided example, simply execute the main Python script:
+```bash
+python -m air_traffic_data
+```
+If you wish to customize it, explore additional features, or save it use the CLI with the `--help` option for guidance:
+```bash
+python -m air_traffic_data --help
 ```
diff --git a/examples/python/air_traffic_data/air_traffic_data.py b/examples/python/air_traffic_data/air_traffic_data.py
index d79019be8bab..4394959b0fad 100644
--- a/examples/python/air_traffic_data/air_traffic_data.py
+++ b/examples/python/air_traffic_data/air_traffic_data.py
@@ -5,6 +5,7 @@
 import itertools
 import json
 import re
+import typing
 import zipfile
 from argparse import ArgumentParser
 from pathlib import Path
@@ -41,11 +42,11 @@ def download_with_progress(url: str, what: str) -> io.BytesIO:
     resp = requests.get(url, stream=True)
     total_size = int(resp.headers.get("content-length", 0))
     with tqdm(
-        desc=f"Downloading {what}…",
-        total=total_size,
-        unit="iB",
-        unit_scale=True,
-        unit_divisor=1024,
+            desc=f"Downloading {what}…",
+            total=total_size,
+            unit="iB",
+            unit_scale=True,
+            unit_divisor=1024,
     ) as progress:
         download_file = io.BytesIO()
         for data in resp.iter_content(chunk_size):
@@ -72,7 +73,7 @@ def shapely_geom_to_numpy(geom: shapely.Geometry) -> list[npt.NDArray[np.float64
 
 
 def log_region_boundaries_for_country(
-    country_code: str, level: int, color: tuple[float, float, float], crs: CRS
+        country_code: str, level: int, color: tuple[float, float, float], crs: CRS
 ) -> None:
     """Log some boundaries for the given country and level."""
 
@@ -202,6 +203,14 @@ def natural_keys(path: Path) -> list[int | str]:
     return sorted(directory.rglob("*.json"), key=natural_keys)
 
 
+class Logger(typing.Protocol):
+    def process_measurement(self, measurement: Measurement) -> None:
+        pass
+
+    def flush(self) -> None:
+        pass
+
+
 # ================================================================================================
 # Simple logger
 
@@ -232,9 +241,9 @@ def process_measurement(self, measurement: Measurement) -> None:
         entity_path = f"aircraft/{measurement.icao_id}"
 
         if (
-            measurement.latitude is not None
-            and measurement.longitude is not None
-            and measurement.barometric_altitude is not None
+                measurement.latitude is not None
+                and measurement.longitude is not None
+                and measurement.barometric_altitude is not None
         ):
             rr.log(
                 entity_path,
@@ -280,7 +289,7 @@ def process_measurement(self, measurement: Measurement) -> None:
         if len(self._measurements) >= 8192:
             self.flush()
 
-    def flush(self):
+    def flush(self) -> None:
         # !!! the raw data is not sorted by timestamp, so we sort it here
         df = polars.DataFrame(self._measurements).sort("timestamp")
         self._measurements = []
@@ -368,14 +377,14 @@ def log_everything(paths: list[Path], raw: bool, batch: bool, batch_size: int) -
     proj = Transformer.from_crs("EPSG:4326", utm_crs, always_xy=True)
 
     rr.set_time_seconds("unix_time", 0)
-    for country_code, (level, color) in itertools.product(["DE", "FR", "CH"], [(0, (1, 0.5, 0.5))]):
+    for country_code, (level, color) in itertools.product(["DE", "CH"], [(0, (1, 0.5, 0.5))]):
         log_region_boundaries_for_country(country_code, level, color, utm_crs)
 
     # Exaggerate altitudes
     rr.log("aircraft", rr.Transform3D(scale=[1, 1, 10]), static=True)
 
     if batch:
-        logger = MeasurementBatchLogger(proj, batch_size)
+        logger: Logger = MeasurementBatchLogger(proj, batch_size)
     else:
         logger = MeasurementLogger(proj, raw)
 
@@ -431,7 +440,12 @@ def main() -> None:
             with zipfile.ZipFile(zip_data) as zip_ref:
                 zip_ref.extractall(dataset_directory)
 
-    blueprint = rrb.Horizontal(rrb.Spatial3DView(origin="/"), rrb.TimeSeriesView(origin="/aircraft"))
+    # TODO(ab): this blueprint would be massively improved by setting the 3D view's orbit point to FRA's coordinates.
+    blueprint = rrb.Vertical(
+        rrb.Horizontal(rrb.Spatial3DView(origin="/"), rrb.MapView(origin="/")),
+        rrb.TimeSeriesView(origin="/aircraft"),
+        row_shares=[3, 1],
+    )
     rr.script_setup(args, "rerun_example_air_traffic_data", default_blueprint=blueprint)
 
     paths = get_paths_for_directory(dataset_directory)
diff --git a/pixi.lock b/pixi.lock
index fff4677f244d..7ea327375897 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -2136,6 +2136,7 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.0-hebfffa5_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2024.8.30-pyhd8ed1ab_0.conda
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/pixman-0.43.2-h59595ed_0.conda
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+      - conda: https://conda.anaconda.org/conda-forge/linux-64/pyproj-3.7.0-py311h0f98d5a_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.11.10-hc5c86c4_3_cpython.conda
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+      - pypi: examples/python/air_traffic_data
       - pypi: examples/python/arkit_scenes
       - pypi: examples/python/blueprint
       - pypi: examples/python/blueprint_stocks
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+  - pytest-cov ; extra == 'dev'
+  - pytest-xdist ; extra == 'dev'
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+  - black ; extra == 'dev'
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   - numpy
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   timestamp: 1713667175451
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+  - __glibc >=2.17,<3.0.a0
+  - certifi
+  - libgcc >=13
+  - proj >=9.5.0,<9.6.0a0
+  - python >=3.11,<3.12.0a0
+  - python_abi 3.11.* *_cp311
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/pyproj?source=hash-mapping
+  size: 562000
+  timestamp: 1727795513365
+- kind: conda
+  name: pyproj
+  version: 3.7.0
+  build: py311h50e4d0a_0
+  subdir: osx-64
+  url: https://conda.anaconda.org/conda-forge/osx-64/pyproj-3.7.0-py311h50e4d0a_0.conda
+  sha256: a269c953b5c7d789e330db13cb693d6aec6f176cc249cd36607850e826f3deae
+  md5: 53d4a946f02d1b811035310cf575990c
+  depends:
+  - __osx >=10.13
+  - certifi
+  - proj >=9.5.0,<9.6.0a0
+  - python >=3.11,<3.12.0a0
+  - python_abi 3.11.* *_cp311
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/pyproj?source=hash-mapping
+  size: 498197
+  timestamp: 1727795466468
+- kind: conda
+  name: pyproj
+  version: 3.7.0
+  build: py311h90dcb63_0
+  subdir: win-64
+  url: https://conda.anaconda.org/conda-forge/win-64/pyproj-3.7.0-py311h90dcb63_0.conda
+  sha256: 36644db864de2ae57573d61bff0c46f3e5cb1efc86bd43756fdec9365ee8b5b3
+  md5: 423256766e9b9ba6c01c39b0ea992f1e
+  depends:
+  - certifi
+  - proj >=9.5.0,<9.6.0a0
+  - python >=3.11,<3.12.0a0
+  - python_abi 3.11.* *_cp311
+  - ucrt >=10.0.20348.0
+  - vc >=14.2,<15
+  - vc14_runtime >=14.29.30139
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/pyproj?source=hash-mapping
+  size: 760981
+  timestamp: 1727796239898
+- kind: conda
+  name: pyproj
+  version: 3.7.0
+  build: py311hb4b81e0_0
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/pyproj-3.7.0-py311hb4b81e0_0.conda
+  sha256: c2c06cca4ce9fd72f1bf8d69703a8c5c2252efd497619d09f47d95d18d3cccf9
+  md5: e3c2e5ded5b6e4b3c2600d033929645c
+  depends:
+  - __osx >=11.0
+  - certifi
+  - proj >=9.5.0,<9.6.0a0
+  - python >=3.11,<3.12.0a0
+  - python >=3.11,<3.12.0a0 *_cpython
+  - python_abi 3.11.* *_cp311
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/pyproj?source=hash-mapping
+  size: 501267
+  timestamp: 1727795522741
+- kind: conda
+  name: pyproj
+  version: 3.7.0
+  build: py311hf06f8e6_0
+  subdir: linux-aarch64
+  url: https://conda.anaconda.org/conda-forge/linux-aarch64/pyproj-3.7.0-py311hf06f8e6_0.conda
+  sha256: 1138944dff2e206cf18ba172e3f2723ce7bafa48f1aee78e01ec9d78e7626e80
+  md5: cce0e19078131aec5b6f7ece87df8120
+  depends:
+  - certifi
+  - libgcc >=13
+  - proj >=9.5.0,<9.6.0a0
+  - python >=3.11,<3.12.0a0
+  - python_abi 3.11.* *_cp311
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/pyproj?source=hash-mapping
+  size: 552175
+  timestamp: 1727797057849
 - kind: pypi
   name: pyquaternion
   version: 0.9.9
@@ -34770,7 +35405,7 @@ packages:
   sha256: 9006b1b7ca8bd9c90ba0bf0d7a00641b7dd13a6de76a2828f79ec5b853a4ef98
   requires_dist:
   - numpy
-  - requests<3,>=2.31
+  - requests>=2.31,<3
   - rerun-sdk
   - trimesh==3.15.2
   editable: true
@@ -35114,7 +35749,7 @@ packages:
   requires_dist:
   - numpy
   - opencv-python>4.6
-  - requests<3,>=2.31
+  - requests>=2.31,<3
   - rerun-sdk
   - tqdm
   editable: true
@@ -36454,11 +37089,11 @@ packages:
   path: examples/python/segment_anything_model
   sha256: 85bc241bedf212c63a39d0251a9dcc0fb3a435087a024d4eafd7f49342a75926
   requires_dist:
+  - segment-anything @ git+https://github.com/facebookresearch/segment-anything.git
   - numpy
   - opencv-python
-  - requests<3,>=2.31
+  - requests>=2.31,<3
   - rerun-sdk
-  - segment-anything @ git+https://github.com/facebookresearch/segment-anything.git
   - torch==2.2.2
   - torchvision
   - tqdm
@@ -36666,7 +37301,7 @@ packages:
   requires_dist:
   - mesh-to-sdf @ git+https://github.com/marian42/mesh_to_sdf.git
   - numpy
-  - requests<3,>=2.31
+  - requests>=2.31,<3
   - rerun-sdk
   - scikit-learn>=1.1.3
   - trimesh==3.15.2
@@ -36884,6 +37519,96 @@ packages:
   - certifi
   - jsonschema>=3.0
   requires_python: '>=3.6'
+- kind: conda
+  name: sqlite
+  version: 3.46.1
+  build: h2466b09_0
+  subdir: win-64
+  url: https://conda.anaconda.org/conda-forge/win-64/sqlite-3.46.1-h2466b09_0.conda
+  sha256: fdee2e0c16ece695fde231d80242121b5ff610a4f66164f931e2a7622815c3ae
+  md5: 19c50225f5fbbb15d80063a68e52c8bb
+  depends:
+  - libsqlite 3.46.1 h2466b09_0
+  - ucrt >=10.0.20348.0
+  - vc >=14.2,<15
+  - vc14_runtime >=14.29.30139
+  license: Unlicense
+  purls: []
+  size: 886067
+  timestamp: 1725354209514
+- kind: conda
+  name: sqlite
+  version: 3.46.1
+  build: h3b4c4e4_0
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/sqlite-3.46.1-h3b4c4e4_0.conda
+  sha256: 91c8becaddc4593adba50eb27c4e47deafd879cfc3a569cc6db767b5ee6d8146
+  md5: 78996531776f6a277cac5a14cf590b6a
+  depends:
+  - __osx >=11.0
+  - libsqlite 3.46.1 hc14010f_0
+  - libzlib >=1.3.1,<2.0a0
+  - ncurses >=6.5,<7.0a0
+  - readline >=8.2,<9.0a0
+  license: Unlicense
+  purls: []
+  size: 822440
+  timestamp: 1725353761204
+- kind: conda
+  name: sqlite
+  version: 3.46.1
+  build: h578a6b9_0
+  subdir: linux-aarch64
+  url: https://conda.anaconda.org/conda-forge/linux-aarch64/sqlite-3.46.1-h578a6b9_0.conda
+  sha256: ae45a7f2aefff9570f9330c0c167085228dadc7754c634a949358b7ef6f4a723
+  md5: 6eed5c8ca8a58cd77a6f515997a4173a
+  depends:
+  - libgcc >=13
+  - libsqlite 3.46.1 hc4a20ef_0
+  - libzlib >=1.3.1,<2.0a0
+  - ncurses >=6.5,<7.0a0
+  - readline >=8.2,<9.0a0
+  license: Unlicense
+  purls: []
+  size: 1068759
+  timestamp: 1725354119401
+- kind: conda
+  name: sqlite
+  version: 3.46.1
+  build: h9eae976_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.46.1-h9eae976_0.conda
+  sha256: 8c6245f988a2e1f4eef8456726b9cc46f2462448e61daa4bad2f9e4ca601598a
+  md5: b2b3e737da0ae347e16ef1970a5d3f14
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  - libsqlite 3.46.1 hadc24fc_0
+  - libzlib >=1.3.1,<2.0a0
+  - ncurses >=6.5,<7.0a0
+  - readline >=8.2,<9.0a0
+  license: Unlicense
+  purls: []
+  size: 859188
+  timestamp: 1725353670478
+- kind: conda
+  name: sqlite
+  version: 3.46.1
+  build: he26b093_0
+  subdir: osx-64
+  url: https://conda.anaconda.org/conda-forge/osx-64/sqlite-3.46.1-he26b093_0.conda
+  sha256: 668dcc8c38aabf42de440f682de4afe11f390b1dc5b49e09b34501bbf19571c8
+  md5: 56a8cc349cf8e2310ee0e52f90247dab
+  depends:
+  - __osx >=10.13
+  - libsqlite 3.46.1 h4b8f8c9_0
+  - libzlib >=1.3.1,<2.0a0
+  - ncurses >=6.5,<7.0a0
+  - readline >=8.2,<9.0a0
+  license: Unlicense
+  purls: []
+  size: 912164
+  timestamp: 1725353686354
 - kind: pypi
   name: stack-data
   version: 0.6.3
@@ -36917,9 +37642,9 @@ packages:
   path: examples/python/structure_from_motion
   sha256: b20b79aa7bb2b4225b37d3cb28872a70dc7e9ab2ca9ab138b90d60fc8d7b4c15
   requires_dist:
-  - numpy
   - opencv-python>4.6
-  - requests<3,>=2.31
+  - numpy
+  - requests>=2.31,<3
   - rerun-sdk
   - tqdm
   editable: true
diff --git a/rerun_py/rerun_sdk/rerun/_send_columns.py b/rerun_py/rerun_sdk/rerun/_send_columns.py
index cd4e4e2df01a..993d19060e35 100644
--- a/rerun_py/rerun_sdk/rerun/_send_columns.py
+++ b/rerun_py/rerun_sdk/rerun/_send_columns.py
@@ -5,7 +5,7 @@
 import pyarrow as pa
 import rerun_bindings as bindings
 
-from ._baseclasses import Archetype, ComponentBatchMixin, ComponentColumn
+from ._baseclasses import Archetype, ComponentBatchMixin, ComponentColumn, ComponentBatchLike
 from ._log import IndicatorComponentBatch
 from .any_value import AnyBatchValue
 from .error_utils import catch_and_log_exceptions
@@ -119,11 +119,11 @@ def as_arrow_array(self) -> pa.Array:
 
 @catch_and_log_exceptions()
 def send_columns(
-    entity_path: str,
-    times: Iterable[TimeColumnLike],
-    components: Iterable[Union[ComponentBatchMixin, ComponentColumn, AnyBatchValue]],
-    recording: RecordingStream | None = None,
-    strict: bool | None = None,
+        entity_path: str,
+        times: Iterable[TimeColumnLike],
+        components: Iterable[Union[ComponentBatchLike]],
+        recording: RecordingStream | None = None,
+        strict: bool | None = None,
 ) -> None:
     r"""
     Send columnar data to Rerun.