minor changes

Author: mpho-sadiki

Datasets/Africapolis_urban_agglomerations.ipynb

@@ -1275,7 +1275,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,

Real_world_examples/Urban_green_space.ipynb

@@ -487,7 +487,7 @@
    "id": "75bf8c27-104d-438e-bbd1-f273eac41eb6",
    "metadata": {},
    "source": [
-    "#### Convert results to dataFrame\n",
+    "#### Convert results to DataFrame\n",
     "The results collected from the previous step are converted into a Pandas DataFrame.\n",
     "This allows for easier processing and analysis of land cover statistics per urban agglomeration."
    ]
@@ -559,7 +559,7 @@
    "id": "384147a9-eb8f-444d-b98e-ddc55ce18bcb",
    "metadata": {},
    "source": [
-    "#### Reindex dataFrames to include all land cover classes\n",
+    "#### Reindex DataFrames to include all land cover classes\n",
     "The histograms might not include all possible land cover classes, especially if certain classes are absent in some areas.\n",
     "Here, we ensure that all classes are present by reindexing the DataFrames and filling missing values with zero.\n",
     "Columns are renamed for clarity."
@@ -586,7 +586,7 @@
    "id": "a2e1e0f8-9ca7-4004-859b-bc13e4f49c14",
    "metadata": {},
    "source": [
-    "#### Combine histogram data with original dataFrame\n",
+    "#### Combine histogram data with original DataFrame\n",
     "Now that the histograms are properly formatted, we merge them with the original DataFrame.\n",
     "Only `agglosID` and `geometry` from the original DataFrame are retained along with the processed histogram data."
    ]
@@ -636,7 +636,7 @@
    "id": "0e3416a5-60c6-4563-87c3-a3be677bf41e",
    "metadata": {},
    "source": [
-    "#### Organize and format the final dataFrame\n",
+    "#### Organize and format the final DataFrame\n",
     "To ensure clarity, we explicitly order the fraction columns for both years.\n",
     "The final DataFrame includes `agglosID`, `geometry`, and all fraction values, rounded to two decimal places."
    ]
@@ -1166,7 +1166,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,

Real_world_examples/Urban_tree_extent.ipynb

@@ -35,7 +35,7 @@
     "In this notebook, we implement an algorithm for mapping urban tree extent using the [Digital Earth Africa Sentinel-2 Annual GeoMAD](https://docs.digitalearthafrica.org/en/latest/data_specs/GeoMAD_specs.html), incorporating time series analysis to assess changes in tree cover over multiple years. \n",
     "\n",
     "### Description\n",
-    "This notebook demostrates how to load and visualise urban tree extent.\n",
+    "This notebook demonstrates how to load and visualise urban tree extent.\n",
     "The notebook covers:\n",
     "\n",
     "1. **Define the area of interest (AOI)**: The default method computes urban tree extent for the area of interest (AOI) that you define. This AOI is used to select urban agglomerations from the [Africapolis dataset](https://docs.digitalearthafrica.org/en/latest/data_specs/Africapolis_urban_specs.html). However, if you do not wish to use an agglomeration and prefer to proceed only with the AOI you defined, you can comment out cells 4-7. This will allow you to work exclusively with your specified area without selecting any agglomerations.\n",
@@ -1227,7 +1227,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,