fix spellings

notebooks/Implementations/JUP_SURGP_GLO_B_D__LEI_Evaluation.ipynb

@@ -67,7 +67,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Vizualize raster data - GHSL"
+    "# Visualize raster data - GHSL"
    ]
   },
   {

notebooks/Implementations/KAZ_SCADR_UrbanizationReview/Urban_metrics_Combine_All.ipynb

@@ -5,7 +5,7 @@
    "metadata": {},
    "source": [
     "# Combine All Urban Metrics\n",
-    "Run the 4 seperate notebooks seperately, and then this notebook last to combine all of the results into one shapefile\n",
+    "Run the 4 separate notebooks separately, and then this notebook last to combine all of the results into one shapefile\n",
     "\n",
     "Check to see that all of the results have the same number of rows"
    ]

notebooks/Implementations/KAZ_SCADR_UrbanizationReview/Urban_metrics_Fullness.ipynb

@@ -138,7 +138,7 @@
     "\n",
     "    input_shapes_gpd = gpd.read_file(shpName)\n",
     "\n",
-    "    # psuedocode\n",
+    "    # pseudocode\n",
     "    # For each Shape:\n",
     "    # Select all built-up pixels that are mostly within shape\n",
     "    # Area of shape = sum of all pixels * area of each pixel\n",
@@ -197,7 +197,7 @@
     "        # print(\"print metrics_scalar\")\n",
     "        # print(metrics_scalar)\n",
     "\n",
-    "        # and concatinate it with the row's shape\n",
+    "        # and concatenate it with the row's shape\n",
     "        new_temp_gdf = pd.concat([temp_gdf.reset_index(drop=True), metrics_df], axis=1)\n",
     "\n",
     "        # print(\"print new_temp_gdf\")\n",

notebooks/Implementations/KAZ_SCADR_UrbanizationReview/Urban_metrics_Shape.ipynb

@@ -379,7 +379,7 @@
     "        metrics_scalar[k] = [metrics[k]]\n",
     "    metrics_df = pd.DataFrame(metrics_scalar)\n",
     "\n",
-    "    # and concatinate it with the row's shape\n",
+    "    # and concatenate it with the row's shape\n",
     "    new_temp_gdf_proj = pd.concat(\n",
     "        [temp_gdf_proj.reset_index(drop=True), metrics_df], axis=1\n",
     "    )\n",

notebooks/Implementations/KAZ_SCADR_UrbanizationReview/Urban_metrics_Sprawl.ipynb

@@ -134,7 +134,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Psuedocode\n",
+    "# Pseudocode\n",
     "\n",
     "# pop_values = []\n",
     "# For each Shape/FUA:\n",
@@ -193,7 +193,7 @@
     "        # geometry\n",
     "        d[\"geometry\"] = d.apply(lambda row: Point(row[\"x\"], row[\"y\"]), axis=1)\n",
     "\n",
-    "        # exlude pixels with value less than 77\n",
+    "        # exclude pixels with value less than 77\n",
     "        print(len(d))\n",
     "\n",
     "        # print(d)\n",
@@ -238,7 +238,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Psuedocode\n",
+    "# Pseudocode\n",
     "\n",
     "# for each Shape/FUA:\n",
     "# pixel_count_below_median = 0\n",
@@ -288,7 +288,7 @@
     "\n",
     "        d = gpd.GeoDataFrame({\"col\": col, \"row\": row, \"val\": val})\n",
     "\n",
-    "        # exlude pixels with value less than 77\n",
+    "        # exclude pixels with value less than 77\n",
     "        d = d[d.val > 77]\n",
     "        d_count = len(d)\n",
     "        # print(f\"d_count is {d_count}\")\n",
@@ -322,7 +322,7 @@
     "        # print(\"print metrics_scalar\")\n",
     "        # print(metrics_scalar)\n",
     "\n",
-    "        # and concatinate it with the row's shape\n",
+    "        # and concatenate it with the row's shape\n",
     "        new_temp_gdf = pd.concat([temp_gdf.reset_index(drop=True), metrics_df], axis=1)\n",
     "\n",
     "        # print(\"print new_temp_gdf\")\n",

notebooks/Implementations/KAZ_SCADR_UrbanizationReview/Urban_metrics_Structure.ipynb

@@ -158,7 +158,7 @@
     "        # print(\"print metrics_scalar\")\n",
     "        # print(metrics_scalar)\n",
     "\n",
-    "        # and concatinate it with the row's shape\n",
+    "        # and concatenate it with the row's shape\n",
     "        new_temp_gdf = pd.concat([temp_gdf.reset_index(drop=True), metrics_df], axis=1)\n",
     "\n",
     "        # print(\"print new_temp_gdf\")\n",
@@ -185,7 +185,7 @@
     "        #         metrics_scalar['intersection_density_km'] = 0\n",
     "        #         metrics_scalar['street_density_km'] = 0\n",
     "        #         metrics_df = pd.DataFrame(metrics_scalar)\n",
-    "        # and concatinate it with the row's shape\n",
+    "        # and concatenate it with the row's shape\n",
     "        #         new_temp_gdf = pd.concat([temp_gdf.reset_index(drop=True), metrics_df], axis=1)\n",
     "        #         output_new_temp_gdf = output_new_temp_gdf.append(new_temp_gdf, ignore_index=True)\n",
     "        continue"

notebooks/Implementations/Slum_Mapping/slumML/STEP1.ipynb

@@ -106,7 +106,7 @@
     "# Prepare the original shape file\n",
     "original = gpd.read_file(f)  # Read ESEI shapefile\n",
     "if original.crs != WGS:\n",
-    "    original = original.to_crs(WGS)  # Convert the spatial referenct to WGS if it is not\n",
+    "    original = original.to_crs(WGS)  # Convert the spatial referenced to WGS if it is not\n",
     "\n",
     "original[\"PID\"] = original.index + 1\n",
     "\n",
@@ -115,7 +115,7 @@
     "fil = original.copy()\n",
     "\n",
     "fil = fil.to_crs(UTM)  # Convert the spatial reference to UTM\n",
-    "# Adding attributes to the shapefile: area, geomerty, and PID (unique IDs)\n",
+    "# Adding attributes to the shapefile: area, geometry, and PID (unique IDs)\n",
     "fil[\"area\"] = fil.area\n",
     "fil[\"centroid\"] = fil[\"geometry\"].centroid\n",
     "\n",
@@ -262,8 +262,8 @@
     "            print(\"%s rows completed at %s\" % (counter, time.ctime()))\n",
     "\n",
     "        \"\"\"\n",
-    "        # this functionality saves progress in case the process cannot be finished in one sitting. \n",
-    "        # ideally, finish the processing in one sitting. \n",
+    "        # this functionality saves progress in case the process cannot be finished in one sitting.\n",
+    "        # ideally, finish the processing in one sitting.\n",
     "        old = 0\n",
     "        if counter % save_thresh == 0:\n",
     "            saver = pd.DataFrame(bundle)\n",

notebooks/Implementations/Slum_Mapping/slumML/STEP2.ipynb

@@ -98,7 +98,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pth = \"/content/drive/MyDrive/Colab Notebooks/slumML/data/Yaounde/\"  # Directory to save model, ouputs\n",
+    "pth = \"/content/drive/MyDrive/Colab Notebooks/slumML/data/Yaounde/\"  # Directory to save model, outputs\n",
     "building_file = \"/content/drive/MyDrive/Colab Notebooks/slumML/data/Yaounde/Yaounde_DA_morphology.shp\"  # Specify the processed building footprint data\n",
     "sample_file = \"/content/drive/MyDrive/Colab Notebooks/slumML/data/Yaounde/Yaounde_sample_data.shp\"  # Specify the sample data"
    ]

notebooks/Implementations/URB_DECAT_B_ExploringGHSSMODcode.ipynb

@@ -6,7 +6,7 @@
    "metadata": {},
    "source": [
     "# Exploring DEGURBA\n",
-    "The Degree of Urbanization methodology developed by the European Commission provides a consistent definition of urban through application of population density and total population theresholds to gridded population data."
+    "The Degree of Urbanization methodology developed by the European Commission provides a consistent definition of urban through application of population density and total population thresholds to gridded population data."
    ]
   },
   {
@@ -253,7 +253,7 @@
     "            xx = shape(cShape)\n",
     "            xx = Polygon(xx.exterior)\n",
     "            cShape = xx.__geo_interface__\n",
-    "        # If the shape is urban, claculate total pop\n",
+    "        # If the shape is urban, calculate total pop\n",
     "        mask = rasterize(\n",
     "            [(cShape, 0)],\n",
     "            out_shape=data[0, :, :].shape,\n",

notebooks/Implementations/URB_SEAU1_NovelUrbanization/NGA_specific_results.ipynb

@@ -98,7 +98,7 @@
     "                dou_urban_1k_files.append(os.path.join(root, f))\n",
     "            if f.endswith(\"_cc.tif\") or f.endswith(\"_co.tif\") or f.endswith(\"_ur.tif\"):\n",
     "                db_urban_1k_files.append(os.path.join(root, f))\n",
-    "            \n",
+    "\n",
     "pop_files = list(set([\"_\".join(os.path.basename(x).split(\"_\")[:2]) + \".tif\" for x in dou_urban_files]))\n",
     "pop_files = [os.path.join(urban_folder, x) for x in pop_files]"
    ]
@@ -168,10 +168,9 @@
     "    cur_name = os.path.basename(urban_file).replace(\".tif\", \"\")\n",
     "    cur_res_2018 = [x[0] for x in list(curR.sample(hh_2018_pairs))]\n",
     "    out_hh_2018[cur_name] = cur_res_2018\n",
-    "    \n",
+    "\n",
     "    cur_res_2022 = [x[0] for x in list(curR.sample(hh_2022_pairs))]\n",
-    "    out_hh_2022[cur_name] = cur_res_2022\n",
-    "    "
+    "    out_hh_2022[cur_name] = cur_res_2022\n"
    ]
   },
   {
@@ -268,17 +267,17 @@
    "source": [
     "final_res = adm1_bounds.copy()\n",
     "for pop_layer in pop_files:\n",
-    "    # zonal stats on DOU filess\n",
-    "    pop_name = os.path.basename(pop_layer)[:-4]    \n",
+    "    # zonal stats on DOU files\n",
+    "    pop_name = os.path.basename(pop_layer)[:-4]\n",
     "    dou_urban_file    = os.path.join(urban_folder, f'{pop_name}_urban.tif')\n",
     "    dou_hd_urban_file = os.path.join(urban_folder, f'{pop_name}_urban_hd.tif')\n",
-    "    \n",
+    "\n",
     "    help_xx = helper.summarize_population(pop_layer, adm1_bounds, dou_urban_file, dou_hd_urban_file)\n",
     "    zonal_res = help_xx.calculate_zonal()\n",
     "    zonal_res = zonal_res.loc[:,[x for x in zonal_res.columns if \"SUM\" in x]]\n",
     "    for col in zonal_res.columns:\n",
     "        final_res[col] = zonal_res[col]\n",
-    "    \n",
+    "\n",
     "    # zonal stats on DB files\n",
     "    db_cc_file = os.path.join(urban_folder, f'{pop_name}d10b3000_cc.tif')\n",
     "    db_co_file = os.path.join(urban_folder, f'{pop_name}d10b3000_co.tif')\n",
@@ -297,7 +296,7 @@
     "            final_res[col] = zonal_res[col]\n",
     "    else:\n",
     "        tPrint(f\"Cannot process {pop_name} for DB\")\n",
-    "        \n",
+    "\n",
     "    tPrint(pop_name)"
    ]
   },

notebooks/Implementations/URB_SEAU1_NovelUrbanization/novelUrbanization.py

@@ -143,7 +143,7 @@ def generate_combo_layer(self, pop_type="gpo", res="", debug=False):
                 print(p)
 
         if len(sel_rasters) > 0:
-            # Open all the ratser files and covert to pixel-level summary numbers
+            # Open all the ratser files and convert to pixel-level summary numbers
             idx = 0
             for cur_raster in sel_rasters:
                 curR = rasterio.open(cur_raster)

notebooks/Tutorials/LEI_Example.ipynb

@@ -8,7 +8,7 @@
     "\n",
     "More details on the wiki - https://github.com/worldbank/GOST_Urban/wiki/Landscape-Expansion-Index\n",
     "\n",
-    "The Landscape Expansion Index measures the nature of urbanization, quantifying the new urban landscape as one of the following three categories. The process works by isolating the areas of new urban footprint in your study area, buffering those by a set amount (300 m) and intersecting the buffer donut with the original urban area. LEI is calculated as the ratio of the area of the buffer to the area of the old built area within the buffer (the threshold for each class is customizeable). \n",
+    "The Landscape Expansion Index measures the nature of urbanization, quantifying the new urban landscape as one of the following three categories. The process works by isolating the areas of new urban footprint in your study area, buffering those by a set amount (300 m) and intersecting the buffer donut with the original urban area. LEI is calculated as the ratio of the area of the buffer to the area of the old built area within the buffer (the threshold for each class is customizable). \n",
     "\n",
     "| Expansion Type | Description | \n",
     "| --- | --- |\n",
@@ -255,8 +255,14 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "worldbank",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "name": "python",
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,