diff --git a/book/geospatial/leafmap.ipynb b/book/geospatial/leafmap.ipynb
index 64e43de..ff00b0f 100644
--- a/book/geospatial/leafmap.ipynb
+++ b/book/geospatial/leafmap.ipynb
@@ -513,7 +513,7 @@
    "source": [
     "m = leafmap.Map()\n",
     "url = \"https://github.com/opengeos/datasets/releases/download/world/world_cities.csv\"\n",
-    "m.add_marker_cluster(url, x=\"latitude\", y=\"longitude\", layer_name=\"World cities\")\n",
+    "m.add_marker_cluster(url, x=\"longitude\", y=\"latitude\", layer_name=\"World cities\")\n",
     "m"
    ]
   },
@@ -1524,7 +1524,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,
diff --git a/book/geospatial/leafmap.md b/book/geospatial/leafmap.md
index 95fb219..9b75366 100644
--- a/book/geospatial/leafmap.md
+++ b/book/geospatial/leafmap.md
@@ -264,7 +264,7 @@ For a large number of points, you can group them into clusters. This method redu
 ```{code-cell} ipython3
 m = leafmap.Map()
 url = "https://github.com/opengeos/datasets/releases/download/world/world_cities.csv"
-m.add_marker_cluster(url, x="latitude", y="longitude", layer_name="World cities")
+m.add_marker_cluster(url, x="longitude", y="latitude", layer_name="World cities")
 m
 ```