{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "981c91b0-058a-47ad-8167-276fa713f062",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Installed geopandas 1.1.4.\n",
      "Installed matplotlib 3.11.1.\n",
      "requests already installed (version 2.34.2).\n"
     ]
    }
   ],
   "source": [
    "# Run this cell to work in colab\n",
    "import sys, os\n",
    "from pathlib import Path\n",
    "\n",
    "# Colab-specific setup\n",
    "if 'google.colab' in sys.modules:\n",
    "    if not os.path.exists(\"ioer-conference-2026-haclathon\"):\n",
    "        !git clone -q https://github.com/ioer-dresden/ioer-conference-2026-haclathon.git\n",
    "    %cd -q ioer-conference-2026-haclathon/notebooks\n",
    "\n",
    "# Install required packages\n",
    "pyexec = sys.executable\n",
    "!../py/modules/pkginstall.sh \"{pyexec}\" geopandas matplotlib requests"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4376e4f-fcb6-4a12-8c73-fc795d110143",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "# 🌿 Urban Green Cooling Benifits with ioerDATA: From API to Insight\n",
    "\n",
    "* **Authors**: Marzan Tasnim Oyshi (IOER) & Maria Nieswand (IOER)\n",
    "* **Topics**: Urban Green Infrastructure, Climate Regulation, Ecosystem Services, Open Data Re-use, Reproducible Research\n",
    "*  **Badges**: ![ioerDATA](https://img.shields.io/badge/Data-ioerDATA-green?style=flat-square) ![Dataverse API](https://img.shields.io/badge/Access-Dataverse_API-blueviolet?style=flat-square) ![FAIR Data](https://img.shields.io/badge/Principle-FAIR_Data-brightgreen?style=flat-square) ![Colab](https://img.shields.io/badge/Colab-Tested-yellow?style=flat-square&logo=googlecolab&logoColor=white) ![Jupyter](https://img.shields.io/badge/Jupyter4NFDI-Ready-orange?style=flat-square&logo=jupyter)\n",
    "```{admonition} Summary\n",
    ":class: hint\n",
    "How much can urban green infrastructure contribute to climate regulation in German cities and how many people benefit from it?\n",
    "\n",
    "In this chapter, we reuse the openly published ioerDATA replication package **Climate Regulation in Cities** to explore a national ecosystem-service indicator for urban climate regulation.\n",
    "\n",
    "We will:\n",
    "\n",
    "- access an openly published replication package,\n",
    "- explore spatial indicators for German cities,\n",
    "- investigate cooling capacity provided by urban green infrastructure,\n",
    "- compare cooling capacity with population benefit,\n",
    "- create reproducible maps and visualisations,\n",
    "- and explore how the data could support urban planning.\n",
    "\n",
    "The aim is to demonstrate how published research data can be **reused, explored, and extended**.\n",
    "```\n",
    "\n",
    "---\n",
    "\n",
    "## 1. Why does urban green matter?\n",
    "\n",
    "Cities are particularly vulnerable to heat.\n",
    "\n",
    "Buildings, sealed surfaces, roads, and other artificial surfaces can store heat and contribute to the **urban heat island effect**. Green infrastructure can counteract some of these effects through shading, evapotranspiration, and other local climate-regulation processes.\n",
    "\n",
    "Urban green infrastructure includes elements such as:\n",
    "\n",
    "- trees,\n",
    "- parks,\n",
    "- urban forests,\n",
    "- gardens,\n",
    "- grass and vegetated surfaces,\n",
    "- and other green spaces.\n",
    "\n",
    "But simply asking **\"How green is a city?\"** is not enough.\n",
    "\n",
    "For climate adaptation, we are also interested in:\n",
    "\n",
    "> **Where does urban green provide cooling capacity, and how many people may benefit from it?**\n",
    "\n",
    "This notebook explores that question using an openly available research dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "290b6ec0-f2a2-498b-adc1-9c3b02f531bc",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "---\n",
    "## 2. From publication to reusable research data\n",
    "\n",
    "The analysis is based on the ioerDATA replication package:\n",
    "\n",
    "> **Replication package for: Climate Regulation in Cities**\n",
    "\n",
    "The dataset provides a national indicator of local climate regulation by urban green infrastructure for **165 German cities with more than 50,000 inhabitants**.\n",
    "\n",
    "It contains information on:\n",
    "\n",
    "- urban green infrastructure,\n",
    "- cooling capacity,\n",
    "- population,\n",
    "- and the proportion of inhabitants benefiting from climate-regulating ecosystem services.\n",
    "\n",
    "The replication package accompanies the publication:\n",
    "\n",
    "*Assessment and Monitoring of Local Climate Regulation in Cities by Green Infrastructure — A National Ecosystem Service Indicator for Germany.*\n",
    "\n",
    "This gives us an opportunity to move beyond simply reading a scientific publication.\n",
    "\n",
    "Instead, we can directly inspect and reuse the underlying research data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cba7381b-f4f7-4390-a2f3-296623c7ef65",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "---\n",
    "## Reproducibility first\n",
    "\n",
    "A scientific figure is much more useful when we can understand:\n",
    "\n",
    "1. **where the data came from,**\n",
    "2. **how it was processed,**\n",
    "3. **which indicators were created,**\n",
    "4. **and how the final visualisation was produced.**\n",
    "\n",
    "This notebook therefore keeps the complete workflow visible and executable.\n",
    "\n",
    "The same data can then be reused for questions that were not necessarily part of the original publication."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b99b1249-8bd2-4650-b64a-e2b43208d1e3",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "---\n",
    "## 3. Access the replication package\n",
    "\n",
    "The dataset is published through **ioerDATA**, which is based on Dataverse.\n",
    "\n",
    "Instead of manually downloading the GeoPackage, we can retrieve it programmatically.\n",
    "\n",
    "This is useful because the source of the data becomes part of the analysis itself."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa869237-0fea-4fd6-a453-fc4fa400de69",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "### Setup\n",
    "\n",
    "Import the libraries needed for this chapter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4be0be14-0718-4087-88dd-56fb849d80c9",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Installed libraries ✓\n"
     ]
    }
   ],
   "source": [
    "#import cell\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patheffects as pe\n",
    "import requests\n",
    "\n",
    "from pathlib import Path\n",
    "from getpass import getpass\n",
    "\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import matplotlib.patheffects as pe\n",
    "\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "print(\"Installed libraries ✓\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac84b0ce-9247-4a28-a41c-fbe64ff06294",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "### Check the dataset contents\n",
    "\n",
    "Before downloading the data, we first query the ioerDATA API to see which files are included in the replication package.\n",
    "\n",
    "The request may take a few moments. A loading indicator will appear while the metadata is being retrieved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8a3d795b-aeb0-46e4-af48-07e0eba06d95",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⏳ Retrieving dataset information from ioerDATA...\n",
      "✓ Done! Found 14 files:\n",
      "\n",
      "  • Assessment_and_Monitoring_of_Local_Climate_Regulation.pdf\n",
      "  • Climate_regulation_in_cities_as_an_ecosystem_service_German.pdf\n",
      "  • Cooling_Capacity_2018_buffered.gdb.zip\n",
      "  • Documentation.md\n",
      "  • Figure_1_Urban_green_Infrastructure_Syrbe_KLu-04.png\n",
      "  • Map_1_Climate_regulation_Air_Photo.jpg\n",
      "  • Map_2_Climate_regulation_Tree_Cover.jpg\n",
      "  • Map_3_Climate_regulation_Population.jpg\n",
      "  • Map_4_Climate_regulation_Cooling_Capacity.jpg\n",
      "  • Map_5_Cities_cooling_capacity.jpg\n",
      "  • Map_6_Cities_inhabitants_cooling_capacity.jpg\n",
      "  • README.md\n",
      "  • Stadtklima_Skript.py\n",
      "  • climate_regulation_in_cities.gpkg\n"
     ]
    }
   ],
   "source": [
    "# DOI of the ioerDATA replication package\n",
    "dataset_doi = \"doi:10.71830/AFW3N3\"\n",
    "\n",
    "# Build the Dataverse API URL for the dataset\n",
    "api_url = (\n",
    "    \"https://data.fdz.ioer.de/api/datasets/:persistentId/\"\n",
    "    f\"?persistentId={dataset_doi}\"\n",
    ")\n",
    "\n",
    "# Show a loading message while requesting the metadata\n",
    "print(\"⏳ Retrieving dataset information from ioerDATA...\")\n",
    "\n",
    "# Request metadata from the ioerDATA Dataverse API\n",
    "response = requests.get(api_url, timeout=60)\n",
    "response.raise_for_status()\n",
    "\n",
    "# Convert the API response to JSON\n",
    "metadata = response.json()\n",
    "\n",
    "# Extract the files from the latest dataset version\n",
    "files = metadata[\"data\"][\"latestVersion\"][\"files\"]\n",
    "\n",
    "# Confirm that the request has finished\n",
    "print(f\"✓ Done! Found {len(files)} files:\\n\")\n",
    "\n",
    "# Display the available filenames\n",
    "for item in files:\n",
    "    print(f\"  • {item['dataFile']['filename']}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10f08bb1-0304-4493-be61-fe57026252bc",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "---\n",
    "## 3. Download the replication package\n",
    "\n",
    "The replication package is published on **ioerDATA** and can be accessed through the Dataverse API.\n",
    "\n",
    "While many files are publicly available, some are **restricted** and require authentication. By creating a free **ioerDATA account**, you can generate a **personal API token** that allows this notebook to securely access all files your account is authorized to use.\n",
    "\n",
    "**Already have an ioerDATA account?** Simply log in.\n",
    "**New to ioerDATA?** Sign up for an account and follow the steps below to create your personal API token.\n",
    "\n",
    "![ioerDATA login](../resources/dataverse.png \"ioerDATA login\")\n",
    "![ioerDATA API](../resources/dataverse_api.png \"ioerDATA API\")\n",
    "\n",
    "> ⚠️ **Keep your API token private.** Never save it in the notebook or commit it to GitHub."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e43c6126-ae7b-49bd-b8e2-58e57f6f31c6",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "🔑 Paste your ioerDATA API token:  ········\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "⏳ Authenticating and retrieving dataset information...\n",
      "✓ Ready! 14 files found.\n",
      "📁 Files will be stored in: /home/jovyan/work/hackathon-ioer-conference-2026/notebooks/data/raw\n"
     ]
    }
   ],
   "source": [
    "# ioerDATA Dataverse address and dataset DOI\n",
    "base_url = \"https://data.fdz.ioer.de\"\n",
    "persistent_id = \"doi:10.71830/AFW3N3\"\n",
    "\n",
    "# Ask for the personal API token securely.\n",
    "# The entered token will not be displayed in the notebook.\n",
    "api_token = getpass(\"🔑 Paste your ioerDATA API token: \")\n",
    "headers = {\"X-Dataverse-key\": api_token}\n",
    "\n",
    "print(\"\\n⏳ Authenticating and retrieving dataset information...\")\n",
    "\n",
    "# Request metadata for the latest version of the dataset\n",
    "url = f\"{base_url}/api/datasets/:persistentId/\"\n",
    "\n",
    "response = requests.get(\n",
    "    url,\n",
    "    params={\"persistentId\": persistent_id},\n",
    "    headers=headers,\n",
    "    timeout=60\n",
    ")\n",
    "\n",
    "# Stop with a clear error if the request was unsuccessful\n",
    "response.raise_for_status()\n",
    "\n",
    "# Extract the list of files from the API response\n",
    "metadata = response.json()\n",
    "files = metadata[\"data\"][\"latestVersion\"][\"files\"]\n",
    "\n",
    "# Create a folder for the downloaded files\n",
    "data_dir = Path(\"data/raw\")\n",
    "data_dir.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "# Confirm that everything is ready for the download\n",
    "print(f\"✓ Ready! {len(files)} files found.\")\n",
    "print(f\"📁 Files will be stored in: {data_dir.resolve()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26df772c-ef3a-4c19-9d3d-46964106d557",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "### Download the dataset files\n",
    "\n",
    "The files are now downloaded to a local `data/raw` folder.\n",
    "\n",
    "A progress bar shows the download status for each file. Restricted files are downloaded only if your ioerDATA account has permission.\n",
    "\n",
    "If you are running this notebook in **Google Colab**, you can also package the downloaded files into a ZIP archive and download them to your computer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d517aa56-e821-4cfc-ae89-88b30b9a3994",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📁 Download folder:\n",
      "/home/jovyan/work/hackathon-ioer-conference-2026/notebooks/data/raw\n",
      "\n",
      "📦 14 files found in the replication package:\n",
      "\n",
      " 1. Assessment_and_Monitoring_of_Local_Climate_Regulation.pdf (public)\n",
      " 2. Climate_regulation_in_cities_as_an_ecosystem_service_German.pdf (public)\n",
      " 3. Cooling_Capacity_2018_buffered.gdb.zip (public)\n",
      " 4. Documentation.md (public)\n",
      " 5. Figure_1_Urban_green_Infrastructure_Syrbe_KLu-04.png (public)\n",
      " 6. Map_1_Climate_regulation_Air_Photo.jpg (public)\n",
      " 7. Map_2_Climate_regulation_Tree_Cover.jpg (public)\n",
      " 8. Map_3_Climate_regulation_Population.jpg (public)\n",
      " 9. Map_4_Climate_regulation_Cooling_Capacity.jpg (public)\n",
      "10. Map_5_Cities_cooling_capacity.jpg (public)\n",
      "11. Map_6_Cities_inhabitants_cooling_capacity.jpg (public)\n",
      "12. README.md (public)\n",
      "13. Stadtklima_Skript.py (public)\n",
      "14. climate_regulation_in_cities.gpkg (restricted)\n",
      "\n",
      "⬇ Starting downloads...\n",
      "\n",
      "\n",
      "[1/14] Assessment_and_Monitoring_of_Local_Climate_Regulation.pdf (public)\n"
     ]
    },
    {
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       "Downloading: 0.00B [00:00, ?B/s]"
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✓ Saved to: /home/jovyan/work/hackathon-ioer-conference-2026/notebooks/data/raw/Assessment_and_Monitoring_of_Local_Climate_Regulation.pdf\n",
      "\n",
      "[2/14] Climate_regulation_in_cities_as_an_ecosystem_service_German.pdf (public)\n"
     ]
    },
    {
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✓ Saved to: /home/jovyan/work/hackathon-ioer-conference-2026/notebooks/data/raw/Climate_regulation_in_cities_as_an_ecosystem_service_German.pdf\n",
      "\n",
      "[3/14] Cooling_Capacity_2018_buffered.gdb.zip (public)\n"
     ]
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       "Downloading: 0.00B [00:00, ?B/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✓ Saved to: /home/jovyan/work/hackathon-ioer-conference-2026/notebooks/data/raw/Cooling_Capacity_2018_buffered.gdb.zip\n",
      "\n",
      "[4/14] Documentation.md (public)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "04f465ebfafa4457a0dc368e5eacbaae",
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       "Downloading: 0.00B [00:00, ?B/s]"
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✓ Saved to: /home/jovyan/work/hackathon-ioer-conference-2026/notebooks/data/raw/Documentation.md\n",
      "\n",
      "[5/14] Figure_1_Urban_green_Infrastructure_Syrbe_KLu-04.png (public)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d4ab39401a894e939caa221ffc54de3d",
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       "version_minor": 0
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       "Downloading: 0.00B [00:00, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✓ Saved to: /home/jovyan/work/hackathon-ioer-conference-2026/notebooks/data/raw/Figure_1_Urban_green_Infrastructure_Syrbe_KLu-04.png\n",
      "\n",
      "[6/14] Map_1_Climate_regulation_Air_Photo.jpg (public)\n"
     ]
    },
    {
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       "model_id": "1b11bf461c224e3694c65fc4821596d8",
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       "Downloading: 0.00B [00:00, ?B/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✓ Saved to: /home/jovyan/work/hackathon-ioer-conference-2026/notebooks/data/raw/Map_1_Climate_regulation_Air_Photo.jpg\n",
      "\n",
      "[7/14] Map_2_Climate_regulation_Tree_Cover.jpg (public)\n"
     ]
    },
    {
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       "Downloading: 0.00B [00:00, ?B/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✓ Saved to: /home/jovyan/work/hackathon-ioer-conference-2026/notebooks/data/raw/Map_2_Climate_regulation_Tree_Cover.jpg\n",
      "\n",
      "[8/14] Map_3_Climate_regulation_Population.jpg (public)\n"
     ]
    },
    {
     "data": {
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       "model_id": "6e2d316d08684342b930cc964da3520f",
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       "version_minor": 0
      },
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       "Downloading: 0.00B [00:00, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✓ Saved to: /home/jovyan/work/hackathon-ioer-conference-2026/notebooks/data/raw/Map_3_Climate_regulation_Population.jpg\n",
      "\n",
      "[9/14] Map_4_Climate_regulation_Cooling_Capacity.jpg (public)\n"
     ]
    },
    {
     "data": {
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       "model_id": "00746dca69eb4a5981a986351e9a887f",
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       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✓ Saved to: /home/jovyan/work/hackathon-ioer-conference-2026/notebooks/data/raw/Map_4_Climate_regulation_Cooling_Capacity.jpg\n",
      "\n",
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     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8efd4a070c454cc886a9825adcc3f0ff",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading: 0.00B [00:00, ?B/s]"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✓ Saved to: /home/jovyan/work/hackathon-ioer-conference-2026/notebooks/data/raw/Map_5_Cities_cooling_capacity.jpg\n",
      "\n",
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     ]
    },
    {
     "data": {
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       "model_id": "39278f3189514c65abc03c63993a16d7",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✓ Saved to: /home/jovyan/work/hackathon-ioer-conference-2026/notebooks/data/raw/Map_6_Cities_inhabitants_cooling_capacity.jpg\n",
      "\n",
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     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d9848b0a40c24cac87ca16e9ab7b62e2",
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       "version_minor": 0
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "✓ Saved to: /home/jovyan/work/hackathon-ioer-conference-2026/notebooks/data/raw/README.md\n",
      "\n",
      "[13/14] Stadtklima_Skript.py (public)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a95375840df4431fa9751b6d12ee4d93",
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      ]
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✓ Saved to: /home/jovyan/work/hackathon-ioer-conference-2026/notebooks/data/raw/Stadtklima_Skript.py\n",
      "\n",
      "[14/14] climate_regulation_in_cities.gpkg (restricted)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c6e2628410a14d5eafc1d587ebf3c170",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading: 0.00B [00:00, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✓ Saved to: /home/jovyan/work/hackathon-ioer-conference-2026/notebooks/data/raw/climate_regulation_in_cities.gpkg\n",
      "\n",
      "✅ Download process complete.\n",
      "📁 Available files are stored in:\n",
      "/home/jovyan/work/hackathon-ioer-conference-2026/notebooks/data/raw\n"
     ]
    }
   ],
   "source": [
    "# Show where files will be stored\n",
    "print(f\"📁 Download folder:\\n{data_dir.resolve()}\\n\")\n",
    "\n",
    "# Show the complete list before downloading anything\n",
    "print(f\"📦 {len(files)} files found in the replication package:\\n\")\n",
    "\n",
    "for i, item in enumerate(files, start=1):\n",
    "    file = item[\"dataFile\"]\n",
    "    filename = file[\"filename\"]\n",
    "    access = \"restricted\" if item.get(\"restricted\") else \"public\"\n",
    "\n",
    "    print(f\"{i:>2}. {filename} ({access})\")\n",
    "\n",
    "print(\"\\n⬇ Starting downloads...\\n\")\n",
    "\n",
    "# Download files one by one\n",
    "for i, item in enumerate(files, start=1):\n",
    "    file = item[\"dataFile\"]\n",
    "    filename = file[\"filename\"]\n",
    "    output = data_dir / filename\n",
    "    access = \"restricted\" if item.get(\"restricted\") else \"public\"\n",
    "\n",
    "    print(f\"\\n[{i}/{len(files)}] {filename} ({access})\")\n",
    "\n",
    "    # Request the file as a stream so it can be downloaded in chunks\n",
    "    response = requests.get(\n",
    "        f\"{base_url}/api/access/datafile/{file['id']}\",\n",
    "        headers=headers,\n",
    "        stream=True,\n",
    "        timeout=120\n",
    "    )\n",
    "\n",
    "    # Skip restricted files if the account does not have permission\n",
    "    if response.status_code in (401, 403):\n",
    "        print(\"⏭ Skipped — no permission\")\n",
    "        continue\n",
    "\n",
    "    # Stop if another download error occurs\n",
    "    response.raise_for_status()\n",
    "\n",
    "    # Get the expected file size, if provided by the server\n",
    "    total_size = int(response.headers.get(\"content-length\", 0))\n",
    "\n",
    "    # Save the file while showing download progress\n",
    "    with open(output, \"wb\") as f:\n",
    "        with tqdm(\n",
    "            total=total_size,\n",
    "            unit=\"B\",\n",
    "            unit_scale=True,\n",
    "            unit_divisor=1024,\n",
    "            desc=\"Downloading\",\n",
    "            leave=True\n",
    "        ) as progress:\n",
    "\n",
    "            for chunk in response.iter_content(chunk_size=1024 * 1024):\n",
    "                if chunk:\n",
    "                    f.write(chunk)\n",
    "                    progress.update(len(chunk))\n",
    "\n",
    "    print(f\"✓ Saved to: {output.resolve()}\")\n",
    "\n",
    "# Final summary\n",
    "print(\"\\n✅ Download process complete.\")\n",
    "print(f\"📁 Available files are stored in:\\n{data_dir.resolve()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0c439ada-3f24-4127-978e-38ace81bc1b3",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloaded: Assessment_and_Monitoring_of_Local_Climate_Regulation.pdf (public)\n",
      "Downloaded: Climate_regulation_in_cities_as_an_ecosystem_service_German.pdf (public)\n",
      "Downloaded: Cooling_Capacity_2018_buffered.gdb.zip (public)\n",
      "Downloaded: Documentation.md (public)\n",
      "Downloaded: Figure_1_Urban_green_Infrastructure_Syrbe_KLu-04.png (public)\n",
      "Downloaded: Map_1_Climate_regulation_Air_Photo.jpg (public)\n",
      "Downloaded: Map_2_Climate_regulation_Tree_Cover.jpg (public)\n",
      "Downloaded: Map_3_Climate_regulation_Population.jpg (public)\n",
      "Downloaded: Map_4_Climate_regulation_Cooling_Capacity.jpg (public)\n",
      "Downloaded: Map_5_Cities_cooling_capacity.jpg (public)\n",
      "Downloaded: Map_6_Cities_inhabitants_cooling_capacity.jpg (public)\n",
      "Downloaded: README.md (public)\n",
      "Downloaded: Stadtklima_Skript.py (public)\n",
      "Downloaded: climate_regulation_in_cities.gpkg (restricted)\n"
     ]
    }
   ],
   "source": [
    "for item in files:\n",
    "    file = item[\"dataFile\"]\n",
    "    filename = file[\"filename\"]\n",
    "    output = data_dir / filename\n",
    "\n",
    "    response = requests.get(\n",
    "        f\"{base_url}/api/access/datafile/{file['id']}\",\n",
    "        headers=headers,\n",
    "        stream=True\n",
    "    )\n",
    "\n",
    "    if response.status_code in (401, 403):\n",
    "        print(f\"Skipped: {filename} — no permission\")\n",
    "        continue\n",
    "\n",
    "    response.raise_for_status()\n",
    "\n",
    "    with open(output, \"wb\") as f:\n",
    "        for chunk in response.iter_content(1024 * 1024):\n",
    "            if chunk:\n",
    "                f.write(chunk)\n",
    "\n",
    "    access = \"restricted\" if item.get(\"restricted\") else \"public\"\n",
    "    print(f\"Downloaded: {filename} ({access})\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3e18cec-3170-4b60-85ca-b2fe6004fef8",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "---\n",
    "## FAIR Data in Practice\n",
    "\n",
    "This replication package illustrates how the **FAIR principles** can support reproducible research:\n",
    "\n",
    "- **Findable** — the dataset has a persistent DOI and searchable metadata.\n",
    "- **Accessible** — data and metadata can be accessed through ioerDATA and its Dataverse API. Restricted files remain available through controlled access.\n",
    "- **Interoperable** — spatial data is provided in standard formats such as GeoPackage.\n",
    "- **Reusable** — documentation, metadata and provenance allow the data to be understood and used beyond the original study.\n",
    "\n",
    "> **FAIR does not necessarily mean open.**\n",
    "> Restricted data can still be FAIR when access conditions are clearly described and authorised users can access the data through a transparent process."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cdb0950-8042-4347-a3ca-667502d776ac",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "---\n",
    "## 4. Load the spatial data\n",
    "\n",
    "The main spatial dataset is stored as a GeoPackage. We load it with GeoPandas and inspect the available indicators before mapping them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "54aa9c8d-ff3f-4872-9a78-0657c70fce18",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features: 165\n",
      "CRS: EPSG:3035\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGS</th>\n",
       "      <th>GEN</th>\n",
       "      <th>NUTS</th>\n",
       "      <th>Shape_Leng</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>Pop_Benefit_Percent</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>09563000</td>\n",
       "      <td>Fürth</td>\n",
       "      <td>DE253</td>\n",
       "      <td>49209.551437</td>\n",
       "      <td>6.334375e+07</td>\n",
       "      <td>49.550707</td>\n",
       "      <td>MULTIPOLYGON (((4392577.758 2936451.016, 43926...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>09162000</td>\n",
       "      <td>München</td>\n",
       "      <td>DE212</td>\n",
       "      <td>118084.194383</td>\n",
       "      <td>3.108342e+08</td>\n",
       "      <td>70.270877</td>\n",
       "      <td>MULTIPOLYGON (((4432133.707 2774479.63, 443212...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>09461000</td>\n",
       "      <td>Bamberg</td>\n",
       "      <td>DE241</td>\n",
       "      <td>44492.667416</td>\n",
       "      <td>5.464215e+07</td>\n",
       "      <td>77.048748</td>\n",
       "      <td>MULTIPOLYGON (((4388203.406 2978639.586, 43882...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>09262000</td>\n",
       "      <td>Passau</td>\n",
       "      <td>DE222</td>\n",
       "      <td>61360.769487</td>\n",
       "      <td>6.956591e+07</td>\n",
       "      <td>78.069768</td>\n",
       "      <td>MULTIPOLYGON (((4570163.06 2838978.815, 457018...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>09564000</td>\n",
       "      <td>Nürnberg</td>\n",
       "      <td>DE254</td>\n",
       "      <td>153997.952475</td>\n",
       "      <td>1.866370e+08</td>\n",
       "      <td>46.746620</td>\n",
       "      <td>MULTIPOLYGON (((4401927.814 2917518.273, 44019...</td>\n",
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       "        AGS       GEN   NUTS     Shape_Leng    Shape_Area  \\\n",
       "0  09563000     Fürth  DE253   49209.551437  6.334375e+07   \n",
       "1  09162000   München  DE212  118084.194383  3.108342e+08   \n",
       "2  09461000   Bamberg  DE241   44492.667416  5.464215e+07   \n",
       "3  09262000    Passau  DE222   61360.769487  6.956591e+07   \n",
       "4  09564000  Nürnberg  DE254  153997.952475  1.866370e+08   \n",
       "\n",
       "   Pop_Benefit_Percent                                           geometry  \n",
       "0            49.550707  MULTIPOLYGON (((4392577.758 2936451.016, 43926...  \n",
       "1            70.270877  MULTIPOLYGON (((4432133.707 2774479.63, 443212...  \n",
       "2            77.048748  MULTIPOLYGON (((4388203.406 2978639.586, 43882...  \n",
       "3            78.069768  MULTIPOLYGON (((4570163.06 2838978.815, 457018...  \n",
       "4            46.746620  MULTIPOLYGON (((4401927.814 2917518.273, 44019...  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the path to the downloaded GeoPackage\n",
    "gpkg_path = data_dir / \"climate_regulation_in_cities.gpkg\"\n",
    "\n",
    "# Load the spatial dataset as a GeoDataFrame\n",
    "gdf = gpd.read_file(gpkg_path)\n",
    "\n",
    "# Display basic information about the dataset\n",
    "print(f\"Features: {len(gdf)}\")\n",
    "print(f\"CRS: {gdf.crs}\")\n",
    "\n",
    "# Preview the first five rows\n",
    "gdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbba7cf7-152a-42e7-94a5-87a444f7abdf",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "This is the checkpoint where you identify the exact columns for:\n",
    "\n",
    ">city name, cooling capacity, population benefit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ada1ae19-86dc-45f0-ba13-80a3ab59d4ce",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AGS',\n",
       " 'GEN',\n",
       " 'NUTS',\n",
       " 'Shape_Leng',\n",
       " 'Shape_Area',\n",
       " 'Pop_Benefit_Percent',\n",
       " 'geometry']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List all attribute columns available in the spatial dataset\n",
    "gdf.columns.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60e75c69-7908-4437-a32f-cd906d401320",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "---\n",
    "## 5. Where is climate-regulation capacity high?\n",
    "\n",
    "Urban green infrastructure provides different levels of cooling capacity across German cities.\n",
    "\n",
    "Mapping the indicator helps reveal where climate-regulation potential is comparatively high or low."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1c927e47-dd18-4f88-8496-5cd4b9d50001",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Map Context Prepared!\n"
     ]
    }
   ],
   "source": [
    "# Prepare the data and geographic context for the map\n",
    "\n",
    "# Select the indicator to visualize and the column containing city names\n",
    "value_col = \"Pop_Benefit_Percent\"\n",
    "name_col = \"GEN\"\n",
    "\n",
    "# Reproject the city data to WGS84 for mapping\n",
    "gdf_wgs = gdf.to_crs(\"EPSG:4326\")\n",
    "\n",
    "# Load country boundaries from Natural Earth\n",
    "world = gpd.read_file(\n",
    "    \"https://naturalearth.s3.amazonaws.com/110m_cultural/ne_110m_admin_0_countries.zip\"\n",
    ")\n",
    "\n",
    "# Select Germany to provide geographic context\n",
    "germany = world[world[\"NAME\"] == \"Germany\"]\n",
    "\n",
    "# Select major cities to label without overcrowding the map\n",
    "major_cities = {\n",
    "    \"Berlin\", \"Hamburg\", \"München\", \"Dresden\",\n",
    "    \"Köln\", \"Leipzig\", \"Frankfurt am Main\", \"Bremen\"\n",
    "}\n",
    "\n",
    "# Keep only the selected cities for map labels\n",
    "labels = gdf_wgs[gdf_wgs[name_col].isin(major_cities)]\n",
    "print (\"Map Context Prepared!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1c7de5f7-f653-4fc1-992c-2d1bce42166b",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the map and set the figure size\n",
    "fig, ax = plt.subplots(figsize=(9, 9))\n",
    "\n",
    "# Map the percentage of population benefiting from urban climate regulation\n",
    "gdf_wgs.plot(\n",
    "    column=value_col,\n",
    "    cmap=\"viridis\",\n",
    "    legend=True,\n",
    "    edgecolor=\"white\",\n",
    "    linewidth=0.3,\n",
    "    ax=ax\n",
    ")\n",
    "\n",
    "# Add the German national boundary for geographic context\n",
    "germany.boundary.plot(\n",
    "    ax=ax,\n",
    "    color=\"black\",\n",
    "    linewidth=0.8\n",
    ")\n",
    "\n",
    "# Add labels for selected major cities\n",
    "for _, row in labels.iterrows():\n",
    "\n",
    "    # Find a suitable point inside each city geometry for the label\n",
    "    p = row.geometry.representative_point()\n",
    "\n",
    "    # Add the city name\n",
    "    txt = ax.text(\n",
    "        p.x,\n",
    "        p.y,\n",
    "        row[name_col],\n",
    "        fontsize=7,\n",
    "        ha=\"center\"\n",
    "    )\n",
    "\n",
    "    # Add a white outline to make labels easier to read\n",
    "    txt.set_path_effects([\n",
    "        pe.withStroke(linewidth=2, foreground=\"white\")\n",
    "    ])\n",
    "\n",
    "# Add a descriptive title and remove map axes\n",
    "ax.set_title(\"Population Benefiting from Urban Climate Regulation\")\n",
    "ax.set_axis_off()\n",
    "\n",
    "# Display the finished map\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cab8fca-1a80-4045-9803-e8b8e034fce2",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "### What does the map show?\n",
    "\n",
    "The indicator represents the **share of inhabitants benefiting from the cooling effect of urban green infrastructure**.\n",
    "\n",
    "The map reveals that this benefit varies between German cities. This shifts the focus from simply asking *where green infrastructure exists* to asking:\n",
    "\n",
    "> **How effectively does urban green infrastructure provide climate-regulation benefits to people?**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d18276db-c583-4050-88f8-3d27fd51cb77",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "---\n",
    "## 6. From replication to exploration\n",
    "\n",
    "Reproducing the indicator map is only the starting point.\n",
    "\n",
    "Because the replication package provides reusable spatial data, we can explore additional questions:\n",
    "\n",
    "- Which cities show particularly high or low population benefit?\n",
    "- How do cities compare with each other?\n",
    "- What might these differences mean for urban green planning?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bbe29322-2372-4f96-9402-3be9fc7a57e8",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select the 10 cities with the highest population benefit\n",
    "# and sort them for a clear horizontal bar chart\n",
    "top = gdf.nlargest(10, value_col).sort_values(value_col)\n",
    "\n",
    "# Create the figure\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "\n",
    "# Compare the population benefit across the selected cities\n",
    "ax.barh(\n",
    "    top[name_col],\n",
    "    top[value_col]\n",
    ")\n",
    "\n",
    "# Add a descriptive axis label and title\n",
    "ax.set_xlabel(\"Population benefiting (%)\")\n",
    "ax.set_title(\"Cities with High Population Benefit from UGI\")\n",
    "\n",
    "# Adjust spacing so labels are not cut off\n",
    "plt.tight_layout()\n",
    "\n",
    "# Display the finished chart\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdac97a6-37f5-4ae1-bbb2-d0d830cfa9a7",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "---\n",
    "## Try it yourself\n",
    "\n",
    "Open data makes it possible to move beyond reproduction.\n",
    "\n",
    "Try changing the analysis:\n",
    "\n",
    "- Find the cities with the **lowest** population benefit.\n",
    "- Select a city you know and compare it with others.\n",
    "- Explore another file from the replication package.\n",
    "\n",
    "> **Replication reproduces evidence. Reuse creates opportunities for new questions.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac3aa09a-5f5d-40ba-918f-719c876c4f74",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "---\n",
    "## Conclusion\n",
    "\n",
    "This example moves from:\n",
    "\n",
    "**open research data → spatial indicator → city comparison → planning question**\n",
    "\n",
    "Urban green infrastructure is not only about the amount of green space. Its relevance also depends on the **climate-regulation service it provides and the population that benefits from it**.\n",
    "\n",
    "The ioerDATA replication package makes this evidence accessible for reproduction, exploration, and further research."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90008b08-f0b5-4a6e-9299-3fb550f56c29",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "---\n",
    "## Acknowledgements\n",
    "\n",
    "This contribution builds on the broader [**ioerDATA training materials**](https://github.com/ioer-dresden/jupyter-book-ioerdata) developed at IOER.\n",
    "\n",
    ">The author gratefully acknowledges **Cruickshank, Claudia** for feedback and refinement & **Dunkel, Alexander** for technical support and earlier training resources.\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba2633e1-c24b-4244-939e-54d13fdaed7e",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
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