resolve new conflicts

notebooks/Implementations/MENA_Benchmarking/NTL_zonal_stats.ipynb

@@ -0,0 +1,108 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Benchmarking cities in MENA\n",
+    "\n",
+    "In support of an upcoming Urban flagship report, the MENA team is looking for a series of zonal statistics:\n",
+    "\n",
+    "- Nighttime Lights, Population, and built-area:  \n",
+    "  - Entire FUA  \n",
+    "  - Its associated urban center / “core”  \n",
+    "  - Associated “periphery”  \n",
+    "\n",
+    "The unit of analysis is the Functional Urban Areas (FUAs) from the [UCDB Database](https://human-settlement.emergency.copernicus.eu/ghs_stat_ucdb2015mt_r2019a.php). For each FUA, we need to grab the associated urban periphary (lower threshold urban areas)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "import os\n",
+    "\n",
+    "import geopandas as gpd\n",
+    "\n",
+    "\n",
+    "sys.path.append(\"C:/WBG/Work/Code/GOSTrocks/src\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_folder = \"C:/WBG/Work/data\"\n",
+    "ucdb_file = os.path.join(\n",
+    "    data_folder,\n",
+    "    \"URBAN\",\n",
+    "    \"GHS_STAT_UCDB2015MT_GLOBE_R2019A\",\n",
+    "    \"GHS_STAT_UCDB2015MT_GLOBE_R2019A_V1_2.gpkg\",\n",
+    ")\n",
+    "fua_file = os.path.join(\n",
+    "    data_folder, \"URBAN\", \"GHS_FUA_UCDB2015_GLOBE_R2019A_54009_1K_V1_0.gpkg\"\n",
+    ")\n",
+    "\n",
+    "out_folder = \"C:/WBG/Work/MENA_Urban/\"\n",
+    "urban_res_folder = os.path.join(out_folder, \"urban_data\")\n",
+    "\n",
+    "for out_folder in [urban_res_folder]:\n",
+    "    if not os.path.exists(out_folder):\n",
+    "        os.makedirs(out_folder)\n",
+    "\n",
+    "urban_periphary_file = os.path.join(urban_res_folder, \"urban_periphary.gpkg\")\n",
+    "urban_core_file = os.path.join(urban_res_folder, \"urban_core.gpkg\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inD = gpd.read_file(ucdb_file)\n",
+    "inF = gpd.read_file(fua_file)\n",
+    "inD = inD.to_crs(inF.crs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inF.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fua_peripheries = inF.copy()\n",
+    "for idx, row in inF.iterrows():\n",
+    "    # grab the related UCDBs\n",
+    "    ucdb_ids = row[\"UC_IDs\"].split(\";\")\n",
+    "    ucdb_ids = [int(x) for x in ucdb_ids]\n",
+    "    sel_cores = inD.loc[inD[\"ID_HDC_G0\"].isin(ucdb_ids)]\n",
+    "    periphery_geom = row[\"geometry\"].difference(sel_cores.unary_union)\n",
+    "    fua_peripheries.loc[idx, \"geometry\"] = periphery_geom\n",
+    "\n",
+    "fua_peripheries.to_file(os.path.join(out_folder, \"FUA_peripheries.gpkg\"), driver=\"GPKG\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

notebooks/Implementations/URB_LKA_CCDR_Support/LKA_Urban_Comp.ipynb

@@ -0,0 +1,131 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9fd5e3dd",
+   "metadata": {},
+   "source": [
+    "# Urbanization comparison\n",
+    "\n",
+    "Compare the multiple urban extents in the project\n",
+    "\n",
+    "1. Official administrative boundaries provided by the project team.  \n",
+    "  a. Need to get metadata on these boundaries from Swati  \n",
+    "2. Official administrative boundaries with buffers  \n",
+    "  a. Why these specific buffer distances?  \n",
+    "3. DoU boundaries  \n",
+    "  a. Which population source  \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c6da02f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import rasterio\n",
+    "\n",
+    "import pandas as pd\n",
+    "import geopandas as gpd\n",
+    "\n",
+    "import GOSTurban.UrbanRaster as urban"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df1bf2ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "base_folder = \"C:/WBG/Work/LKA_URB_CCDR/Data\"\n",
+    "urban_folder = os.path.join(base_folder, \"Urban_extents\")\n",
+    "population_folder = os.path.join(base_folder, \"Population\")\n",
+    "\n",
+    "worldpop_file = os.path.join(population_folder, \"lka_ppp_2020_UNadj.tif\")\n",
+    "combo_extents = os.path.join(urban_folder, \"combo_extents.shp\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b135cd57",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Combine all the urban extents into a single file\n",
+    "if not os.path.exists(combo_extents):\n",
+    "    urban_extents = [\n",
+    "        x\n",
+    "        for x in os.listdir(os.path.join(urban_folder, \"Final_AoI\"))\n",
+    "        if x.endswith(\".shp\")\n",
+    "    ]\n",
+    "    all_extents = []\n",
+    "    for urban_extent in urban_extents:\n",
+    "        urban_extent_gdf = gpd.read_file(\n",
+    "            os.path.join(urban_folder, \"Final_AoI\", urban_extent)\n",
+    "        )\n",
+    "        type = urban_extent.split(\"_\")[1].replace(\".shp\", \"\").lower()\n",
+    "        if type == \"colombo\":\n",
+    "            type = \"city\"\n",
+    "            if \"buffer\" in urban_extent:\n",
+    "                type = \"20km\"\n",
+    "        urban_extent_gdf[\"Type\"] = type\n",
+    "        all_extents.append(urban_extent_gdf)\n",
+    "\n",
+    "    all_extents_gdf = gpd.GeoDataFrame(pd.concat(all_extents, ignore_index=True))\n",
+    "    all_extents_gdf.to_file(combo_extents)\n",
+    "else:\n",
+    "    all_extents_gdf = gpd.read_file(combo_extents)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "97add9be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# extract urban extents from population grids\n",
+    "for pop_layer, name in [[worldpop_file, \"WorldPop\"]]:\n",
+    "    urb_out_file = os.path.join(urban_folder, \"DOU_urb_extent_%s.shp\" % name)\n",
+    "    hd_out_file = os.path.join(urban_folder, \"DOU_hd_extent_%s.shp\" % name)\n",
+    "    if not os.path.exists(hd_out_file):\n",
+    "        curPop = rasterio.open(pop_layer)\n",
+    "        urban_calculator = urban.urbanGriddedPop(curPop)\n",
+    "        urban_extents = urban_calculator.calculateUrban(\n",
+    "            densVal=3, totalPopThresh=5000, smooth=False, queen=False, verbose=True\n",
+    "        )\n",
+    "        urban_extents[\"Type\"] = name\n",
+    "        urban_extents.to_file(urb_out_file)\n",
+    "\n",
+    "        hd_urban_extents = urban_calculator.calculateUrban(\n",
+    "            densVal=15,\n",
+    "            totalPopThresh=50000,\n",
+    "            smooth=True,\n",
+    "            queen=True,  # high density extents use queen's case contiguity, and\n",
+    "            verbose=True,\n",
+    "        )  # High density extents have hole smoothing applied.\n",
+    "        hd_urban_extents[\"Type\"] = name\n",
+    "        hd_urban_extents.to_file(hd_out_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "50115a0d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/Implementations/URB_LKA_CCDR_Support/README.md

@@ -0,0 +1,12 @@
+# Urbanization for CCDR
+
+The Sri Lanka CCDR team is looking for support in urban deep dives on 9 provincial capitals. The following analysis will be completed:
+
+| Analysis | Phase | Notebook | Plan |
+| --- | --- | --- | --- |
+| Urbanization analysis | 1 |  | Compare official and buffered boundaries to quantified boundaries (DoU); summarize population within all boundaries |
+| Urban expansion |  1 |  | Summariuze GHSL within urban extents |
+| Urban Poverty |  3 |  | Analyze high-resolution satellite imagery to identify informality in residential settlements |
+| Urban economics | 2 |  | Summarize monthly nighttime lights |
+| Status of infrastructure | 2 |  | TBD |
+| GHG emissions | 1 |  | Summarize Brian B's GHG emissions data |