diff --git a/CHANGELOG.md b/CHANGELOG.md
index e400e6e..1c7f2d0 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -13,7 +13,8 @@ versions of pandas (>= 2.2), GeoPandas (>= 1.0), scikit-learn and statsmodels.
 
 ### Added
 - `crime_mapping.get_city_shape("Natal, RN, Brazil")` — thin wrapper around
-  `osmnx.geocode_to_gdf` returning the raw city shape.
+  `osmnx.geocode_to_gdf` returning the raw city shape; `osmnx` is now a core
+  dependency (the `osm` extra is kept, empty, for compatibility).
 - `examples/natal.ipynb`: an executed end-to-end walkthrough on Natal with
   synthetic data, also rendered in the documentation.
 - `PredictionPipeline.evaluate` accepts a list of scorings and returns a
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 06ae5fb..5afe947 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -8,7 +8,7 @@ Thanks for your interest in Predspot! Issues and pull requests are welcome.
 git clone https://github.com/adaj/predspot.git
 cd predspot
 python -m venv .venv && source .venv/bin/activate
-pip install -e ".[dev,osm,contour]"
+pip install -e ".[dev,contour,examples]"
 ```
 
 ## Checks
diff --git a/README.md b/README.md
index a341d08..9fefa72 100644
--- a/README.md
+++ b/README.md
@@ -36,8 +36,14 @@ Install from PyPI (Python 3.10 or newer):
 
 ```bash
 pip install predspot              # core
-pip install "predspot[osm]"       # + study areas from OpenStreetMap (osmnx)
 pip install "predspot[contour]"   # + GeoJSON contour export (geojsoncontour)
+pip install "predspot[examples]"  # + jupyter, to run the example notebooks
+```
+
+Not on PyPI yet? Install straight from GitHub:
+
+```bash
+pip install "git+https://github.com/adaj/predspot.git"
 ```
 
 Basic usage example:
@@ -81,7 +87,7 @@ mapping = QuadratCount(tfreq='W', grid=create_gridhexagonal(study_area_gdf, reso
 ### Study area from OpenStreetMap and synthetic data
 
 You do not need real data to try Predspot. Fetch a city boundary from
-OpenStreetMap (`pip install "predspot[osm]"`) and generate synthetic events
+OpenStreetMap and generate synthetic events
 with spatial hotspots and realistic temporal patterns (trend, annual cycle,
 day-of-week and hour-of-day profiles):
 
@@ -276,7 +282,7 @@ From source, for development:
 ```bash
 git clone https://github.com/adaj/predspot.git
 cd predspot
-pip install -e ".[dev,osm,contour]"
+pip install -e ".[dev,contour,examples]"
 ruff check src tests   # lint
 pytest                 # ~10 s
 ```
diff --git a/docs/getting-started/installation.md b/docs/getting-started/installation.md
index ed42c27..96f10da 100644
--- a/docs/getting-started/installation.md
+++ b/docs/getting-started/installation.md
@@ -10,16 +10,17 @@ Optional extras add features that need heavier dependencies:
 
 | Extra | Installs | Enables |
 |-------|----------|---------|
-| `osm` | [osmnx](https://osmnx.readthedocs.io) | [`load_study_area`][predspot.crime_mapping.load_study_area] — study areas from OpenStreetMap |
 | `contour` | [geojsoncontour](https://github.com/bartromgens/geojsoncontour) | [`contour_geojson`][predspot.utilities.contour_geojson] — GeoJSON contour export |
+| `examples` | jupyter, nbconvert, ipykernel | Running the example notebooks |
 | `dev` | pytest, ruff, build, twine | Running the test suite and building the package |
 
 ```bash
-pip install "predspot[osm,contour]"
+pip install "predspot[contour,examples]"
 ```
 
 The core dependencies — pandas, GeoPandas, Shapely, NumPy, SciPy, scikit-learn,
-statsmodels and Matplotlib — are installed automatically.
+statsmodels, Matplotlib and [osmnx](https://osmnx.readthedocs.io) (study areas
+from OpenStreetMap) — are installed automatically.
 
 !!! tip "conda users"
     GeoPandas and its GEOS/PROJ stack install fine from PyPI wheels nowadays,
@@ -31,7 +32,7 @@ statsmodels and Matplotlib — are installed automatically.
 ```bash
 git clone https://github.com/adaj/predspot.git
 cd predspot
-pip install -e ".[dev,osm,contour]"
+pip install -e ".[dev,contour,examples]"
 pytest
 ```
 
diff --git a/docs/getting-started/quickstart.md b/docs/getting-started/quickstart.md
index b9750eb..d2c4bfe 100644
--- a/docs/getting-started/quickstart.md
+++ b/docs/getting-started/quickstart.md
@@ -8,7 +8,7 @@ synthetic data so you can run it as is. Every step is explained in depth in the
 
 Predspot needs the boundary of the region you are studying as a GeoDataFrame
 with a CRS. The easiest way is to fetch it from OpenStreetMap
-(`pip install "predspot[osm]"`):
+(osmnx is installed with Predspot):
 
 ```python
 from predspot import load_study_area
diff --git a/docs/guide/data.md b/docs/guide/data.md
index 137fad6..ba8e039 100644
--- a/docs/guide/data.md
+++ b/docs/guide/data.md
@@ -38,7 +38,7 @@ removed, but grid cells are only created where they intersect it.
     place with [osmnx](https://osmnx.readthedocs.io) (Nominatim) and returns
     its administrative boundary. Be specific — add the state and country —
     so that the first match is the boundary you want; `which_result` lets you
-    pick another match. Requires `pip install "predspot[osm]"`.
+    pick another match. osmnx is installed with Predspot.
 
 === "From a file"
 
diff --git a/examples/build_natal_notebook.py b/examples/build_natal_notebook.py
index b6e8863..578bb7f 100644
--- a/examples/build_natal_notebook.py
+++ b/examples/build_natal_notebook.py
@@ -37,7 +37,7 @@ def code(text):
 6. forecast the next months and check how well the true hotspots are recovered.
 
 Every intermediate object is displayed so you can see exactly what flows
-between the steps. Requirements: `pip install "predspot[osm]" matplotlib`.
+between the steps. Requirements: `pip install predspot jupyter` (or `pip install "predspot[examples]"`).
 """)
 
 code("""
diff --git a/pyproject.toml b/pyproject.toml
index 89b35b0..abded01 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -40,10 +40,17 @@ dependencies = [
   "scikit-learn>=1.3",
   "statsmodels>=0.14",
   "matplotlib>=3.7",
+  "osmnx>=2.0",
 ]
 
 [project.optional-dependencies]
 contour = ["geojsoncontour>=0.4"]
+osm = []  # kept for compatibility: osmnx is now a core dependency
+examples = [
+  "jupyter>=1.0",
+  "nbconvert>=7",
+  "ipykernel>=6",
+]
 docs = [
   "mkdocs>=1.6,<2",
   "mkdocs-material>=9.5,<10",
diff --git a/src/predspot/crime_mapping.py b/src/predspot/crime_mapping.py
index f2ccfa9..36a3608 100644
--- a/src/predspot/crime_mapping.py
+++ b/src/predspot/crime_mapping.py
@@ -94,8 +94,7 @@ def load_study_area(place, crs=WGS84, which_result=None):
     """
     Fetch the boundary polygon of a place from OpenStreetMap.
 
-    Uses `osmnx <https://osmnx.readthedocs.io>`_ (optional dependency:
-    ``pip install predspot[osm]``) to geocode ``place`` with Nominatim and
+    Uses `osmnx <https://osmnx.readthedocs.io>`_ to geocode ``place`` with Nominatim and
     return its administrative boundary, ready to be used as the
     ``study_area`` of [`Dataset`][predspot.Dataset] or as the ``bbox`` of the
     ``create_grid*`` functions.
@@ -122,7 +121,7 @@ def load_study_area(place, crs=WGS84, which_result=None):
         import osmnx as ox
     except ImportError as exc:
         raise ImportError(
-            "load_study_area requires the optional dependency `osmnx`: pip install predspot[osm]"
+            "load_study_area requires `osmnx` (installed with predspot): pip install osmnx"
         ) from exc
     logger.debug("Geocoding study area %r with osmnx", place)
     gdf = ox.geocode_to_gdf(place, which_result=which_result)
@@ -145,7 +144,7 @@ def get_city_shape(place_query):
     A thin wrapper around ``osmnx.geocode_to_gdf`` that returns the raw
     Nominatim result. Prefer [`load_study_area`][predspot.crime_mapping.load_study_area]
     when you want the result validated (polygon geometry, tidy columns).
-    Requires the optional ``osmnx`` dependency (``pip install predspot[osm]``).
+    Uses ``osmnx``, installed with Predspot.
 
     Args:
         place_query (str): Name of the place, in a format Nominatim accepts,
@@ -163,7 +162,7 @@ def get_city_shape(place_query):
         import osmnx as ox
     except ImportError as exc:
         raise ImportError(
-            "get_city_shape requires the optional dependency `osmnx`: pip install predspot[osm]"
+            "get_city_shape requires `osmnx` (installed with predspot): pip install osmnx"
         ) from exc
     return ox.geocode_to_gdf(place_query)
 
diff --git a/tests/test_load_study_area.py b/tests/test_load_study_area.py
index 92b7640..c15f2bc 100644
--- a/tests/test_load_study_area.py
+++ b/tests/test_load_study_area.py
@@ -56,7 +56,7 @@ def test_load_study_area_rejects_points(monkeypatch):
 
 def test_load_study_area_without_osmnx(monkeypatch):
     monkeypatch.setitem(sys.modules, "osmnx", None)
-    with pytest.raises(ImportError, match="predspot\\[osm\\]"):
+    with pytest.raises(ImportError, match="pip install osmnx"):
         load_study_area("Natal, Brazil")
 
 
@@ -82,5 +82,5 @@ def test_get_city_shape_returns_raw_geocode(monkeypatch):
 
 def test_get_city_shape_without_osmnx(monkeypatch):
     monkeypatch.setitem(sys.modules, "osmnx", None)
-    with pytest.raises(ImportError, match="predspot\\[osm\\]"):
+    with pytest.raises(ImportError, match="pip install osmnx"):
         get_city_shape("Natal, RN, Brazil")