Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2cec534e-3da5-4d59-9dac-cbe4fd7e549d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install OWSlib\n",
    "#!pip install OWSLib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d71792a4-c1a0-44c3-9cf1-42eea9cd0ff8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from owslib.ogcapi.records import Records\n",
    "w = Records('https://emotional.byteroad.net/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1853abbd-4c50-4dbc-9460-18bbceaa68b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "records = w.records()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d7ff7c0b-e5cd-41f8-8184-d7430070d990",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ec_catalog']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "d2bbe2a6-e567-4e07-afb8-e0ecd2caafda",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_catalogue = w.collection('ec_catalog')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1f8edb89-beb1-4ca7-8046-ec1bf55092e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_catalogue_queryables = w.collection_queryables('ec_catalog')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2dee7552-a564-4b8b-969b-21dd25ba14f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'type': 'object',\n",
       " 'title': 'eMOTIONAL Cities Metadata catalog',\n",
       " 'properties': {'geometry': {'$ref': 'https://geojson.org/schema/Geometry.json',\n",
       "   'x-ogc-role': 'primary-geometry'},\n",
       "  'created': {'title': 'created', 'type': 'string'},\n",
       "  'dataCollectionTool': {'title': 'dataCollectionTool', 'type': 'string'},\n",
       "  'description': {'title': 'description', 'type': 'string'},\n",
       "  'formats': {'title': 'formats', 'type': 'string'},\n",
       "  'keywords': {'title': 'keywords', 'type': 'string'},\n",
       "  'license': {'title': 'license', 'type': 'string'},\n",
       "  'title': {'title': 'title', 'type': 'string'},\n",
       "  'type': {'title': 'type', 'type': 'string'},\n",
       "  'updated': {'title': 'updated', 'type': 'string'}},\n",
       " '$schema': 'http://json-schema.org/draft/2019-09/schema',\n",
       " '$id': 'https://emotional.byteroad.net/collections/ec_catalog/queryables'}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_catalogue_queryables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "3b1e8538-9e25-401e-ae3f-f5e9c41aae1f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'type': 'FeatureCollection',\n",
       " 'features': [{'id': 'hex350_grid_cardio_1920',\n",
       "   'type': 'Feature',\n",
       "   'conformsTo': ['http://www.opengis.net/spec/ogcapi-records-1/1.0/req/record-core'],\n",
       "   'time': {'interval': ['2019-01-01T00:00:00Z', '2020-12-31T23:59:59Z'],\n",
       "    'resolution': 'P1D'},\n",
       "   'geometry': {'type': 'Polygon',\n",
       "    'coordinates': [[[-0.260765354541958, 51.409628133733456],\n",
       "      [0.126720196946648, 51.409628133733456],\n",
       "      [0.126720196946648, 51.58003067802845],\n",
       "      [-0.260765354541958, 51.58003067802845],\n",
       "      [-0.260765354541958, 51.409628133733456]]]},\n",
       "   'properties': {'created': '2024-08-06T13:47:25Z',\n",
       "    'updated': '2024-08-06T13:47:25Z',\n",
       "    'type': 'dataset',\n",
       "    'title': 'Prevalence rates of cardiovascular diseases in London',\n",
       "    'description': 'The mapping results of urban health outcomes (Prevalence rates of cardiovascular diseases) in 350m hexagonal grids of Inner London',\n",
       "    'keywords': ['urban health',\n",
       "     'health outcomes',\n",
       "     'physical health',\n",
       "     'urban health mapping',\n",
       "     'inner london',\n",
       "     ''],\n",
       "    'language': {'code': 'en', 'name': 'English', 'dir': 'ltr'},\n",
       "    'themes': [{'concepts': [{'id': 'human health'}],\n",
       "      'scheme': 'https://www.eionet.europa.eu/gemet/en/themes/'}],\n",
       "    'formats': ['HTML', 'GeoJSON'],\n",
       "    'contacts': [{'name': ' University of Cambridge'}],\n",
       "    'license': 'CC BY-NC 4.0'},\n",
       "   'links': [{'href': 'https://emotional.byteroad.net?f=json',\n",
       "     'rel': 'root',\n",
       "     'type': 'application/json',\n",
       "     'title': 'The landing page of this server as JSON'},\n",
       "    {'href': 'https://emotional.byteroad.net/collections/ec_catalog/hex350_grid_cardio_1920?f=json',\n",
       "     'rel': 'self',\n",
       "     'type': 'application/json',\n",
       "     'title': 'This document as JSON'},\n",
       "    {'href': 'https://emotional.byteroad.net/collections/hex350_grid_cardio_1920?f=json',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/geo+json',\n",
       "     'title': 'OGC API Features for hex350_grid_cardio_1920'},\n",
       "    {'href': 'https://emotional.byteroad.net/collections/hex350_grid_cardio_1920/tiles?f=json',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/geo+json',\n",
       "     'title': 'OGC API Tiles for hex350_grid_cardio_1920'},\n",
       "    {'href': 'https://emotional-cities.s3.eu-central-1.amazonaws.com/geojson/hex350_grid_cardio_1920.geojson',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/geo+json',\n",
       "     'title': 'GeoJson download link for hex350_grid_cardio_1920'},\n",
       "    {'href': 'https://emotional-cities.s3.eu-central-1.amazonaws.com/geoparquet/hex350_grid_cardio_1920.parquet',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/vnd.apache.parquet',\n",
       "     'title': 'GeoParquet download link for hex350_grid_cardio_1920'},\n",
       "    {'href': 'https://emotional-cities.s3.eu-central-1.amazonaws.com/geopackage/hex350_grid_cardio_1920.gpkg',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/x-sqlite3',\n",
       "     'title': 'GeoPackage download link for hex350_grid_cardio_1920'},\n",
       "    {'href': 'https://emotional.byteroad.net/geoserver/ows?service=WMS&version=1.3.0&request=GetMap&crs={crs}&bbox={bbox}&layers=hex350_grid_cardio_1920&width={width}&height={height}&format=image/png',\n",
       "     'rel': 'item',\n",
       "     'type': 'image/png',\n",
       "     'title': 'OGC Web Map Service (WMS)',\n",
       "     'templated': True,\n",
       "     'variables': {'crs': {'description': 'http://www.opengis.net/def/crs/OGC/1.3/CRS84',\n",
       "       'type': 'string',\n",
       "       'enum': ['EPSG:4326']},\n",
       "      'bbox': {'description': '...',\n",
       "       'type': 'array',\n",
       "       'items': {'type': 'number', 'format': 'double'},\n",
       "       'minItems': 4,\n",
       "       'maxItems': 4},\n",
       "      'width': {'description': '...',\n",
       "       'type': 'number',\n",
       "       'format': 'integer',\n",
       "       'minimum': 600,\n",
       "       'maximum': 5000},\n",
       "      'height': {'description': '...',\n",
       "       'type': 'number',\n",
       "       'format': 'integer',\n",
       "       'minimum': 600,\n",
       "       'maximum': 5000}}}]},\n",
       "  {'id': 'hex350_grid_obesity_1920',\n",
       "   'type': 'Feature',\n",
       "   'conformsTo': ['http://www.opengis.net/spec/ogcapi-records-1/1.0/req/record-core'],\n",
       "   'time': {'interval': ['2019-01-01T00:00:00Z', '2020-12-31T23:59:59Z'],\n",
       "    'resolution': 'P1D'},\n",
       "   'geometry': {'type': 'Polygon',\n",
       "    'coordinates': [[[-0.260765354541958, 51.409628133733456],\n",
       "      [0.126720196946648, 51.409628133733456],\n",
       "      [0.126720196946648, 51.58003067802845],\n",
       "      [-0.260765354541958, 51.58003067802845],\n",
       "      [-0.260765354541958, 51.409628133733456]]]},\n",
       "   'properties': {'created': '2024-08-06T13:47:25Z',\n",
       "    'updated': '2024-08-06T13:47:25Z',\n",
       "    'type': 'dataset',\n",
       "    'title': 'Prevalence rates of obesity in London',\n",
       "    'description': 'The mapping results of urban health outcomes (Prevalence rates of obesity) in 350m hexagonal grids of Inner London',\n",
       "    'keywords': ['urban health',\n",
       "     'health outcomes',\n",
       "     'physical health',\n",
       "     'urban health mapping',\n",
       "     'inner london',\n",
       "     ''],\n",
       "    'language': {'code': 'en', 'name': 'English', 'dir': 'ltr'},\n",
       "    'themes': [{'concepts': [{'id': 'human health'}],\n",
       "      'scheme': 'https://www.eionet.europa.eu/gemet/en/themes/'}],\n",
       "    'formats': ['HTML', 'GeoJSON'],\n",
       "    'contacts': [{'name': ' University of Cambridge'}],\n",
       "    'license': 'CC BY-NC 4.0'},\n",
       "   'links': [{'href': 'https://emotional.byteroad.net?f=json',\n",
       "     'rel': 'root',\n",
       "     'type': 'application/json',\n",
       "     'title': 'The landing page of this server as JSON'},\n",
       "    {'href': 'https://emotional.byteroad.net/collections/ec_catalog/hex350_grid_obesity_1920?f=json',\n",
       "     'rel': 'self',\n",
       "     'type': 'application/json',\n",
       "     'title': 'This document as JSON'},\n",
       "    {'href': 'https://emotional.byteroad.net/collections/hex350_grid_obesity_1920?f=json',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/geo+json',\n",
       "     'title': 'OGC API Features for hex350_grid_obesity_1920'},\n",
       "    {'href': 'https://emotional.byteroad.net/collections/hex350_grid_obesity_1920/tiles?f=json',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/geo+json',\n",
       "     'title': 'OGC API Tiles for hex350_grid_obesity_1920'},\n",
       "    {'href': 'https://emotional-cities.s3.eu-central-1.amazonaws.com/geojson/hex350_grid_obesity_1920.geojson',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/geo+json',\n",
       "     'title': 'GeoJson download link for hex350_grid_obesity_1920'},\n",
       "    {'href': 'https://emotional-cities.s3.eu-central-1.amazonaws.com/geoparquet/hex350_grid_obesity_1920.parquet',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/vnd.apache.parquet',\n",
       "     'title': 'GeoParquet download link for hex350_grid_obesity_1920'},\n",
       "    {'href': 'https://emotional-cities.s3.eu-central-1.amazonaws.com/geopackage/hex350_grid_obesity_1920.gpkg',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/x-sqlite3',\n",
       "     'title': 'GeoPackage download link for hex350_grid_obesity_1920'},\n",
       "    {'href': 'https://emotional.byteroad.net/geoserver/ows?service=WMS&version=1.3.0&request=GetMap&crs={crs}&bbox={bbox}&layers=hex350_grid_obesity_1920&width={width}&height={height}&format=image/png',\n",
       "     'rel': 'item',\n",
       "     'type': 'image/png',\n",
       "     'title': 'OGC Web Map Service (WMS)',\n",
       "     'templated': True,\n",
       "     'variables': {'crs': {'description': 'http://www.opengis.net/def/crs/OGC/1.3/CRS84',\n",
       "       'type': 'string',\n",
       "       'enum': ['EPSG:4326']},\n",
       "      'bbox': {'description': '...',\n",
       "       'type': 'array',\n",
       "       'items': {'type': 'number', 'format': 'double'},\n",
       "       'minItems': 4,\n",
       "       'maxItems': 4},\n",
       "      'width': {'description': '...',\n",
       "       'type': 'number',\n",
       "       'format': 'integer',\n",
       "       'minimum': 600,\n",
       "       'maximum': 5000},\n",
       "      'height': {'description': '...',\n",
       "       'type': 'number',\n",
       "       'format': 'integer',\n",
       "       'minimum': 600,\n",
       "       'maximum': 5000}}}]},\n",
       "  {'id': 'hex350_grid_depression_1920',\n",
       "   'type': 'Feature',\n",
       "   'conformsTo': ['http://www.opengis.net/spec/ogcapi-records-1/1.0/req/record-core'],\n",
       "   'time': {'interval': ['2019-01-01T00:00:00Z', '2020-12-31T23:59:59Z'],\n",
       "    'resolution': 'P1D'},\n",
       "   'geometry': {'type': 'Polygon',\n",
       "    'coordinates': [[[-0.260765354541958, 51.409628133733456],\n",
       "      [0.126720196946648, 51.409628133733456],\n",
       "      [0.126720196946648, 51.58003067802845],\n",
       "      [-0.260765354541958, 51.58003067802845],\n",
       "      [-0.260765354541958, 51.409628133733456]]]},\n",
       "   'properties': {'created': '2024-08-06T13:47:25Z',\n",
       "    'updated': '2024-08-06T13:47:25Z',\n",
       "    'type': 'dataset',\n",
       "    'title': 'Prevalence rates of depression in London',\n",
       "    'description': 'The mapping results of urban health outcomes (Prevalence rates of depression) in 350m hexagonal grids of Inner London',\n",
       "    'keywords': ['urban health',\n",
       "     'health outcomes',\n",
       "     'mental health',\n",
       "     'wellbeing',\n",
       "     'urban health mapping',\n",
       "     'inner london',\n",
       "     ''],\n",
       "    'language': {'code': 'en', 'name': 'English', 'dir': 'ltr'},\n",
       "    'themes': [{'concepts': [{'id': 'human health'}],\n",
       "      'scheme': 'https://www.eionet.europa.eu/gemet/en/themes/'}],\n",
       "    'formats': ['HTML', 'GeoJSON'],\n",
       "    'contacts': [{'name': ' University of Cambridge'}],\n",
       "    'license': 'CC BY-NC 4.0'},\n",
       "   'links': [{'href': 'https://emotional.byteroad.net?f=json',\n",
       "     'rel': 'root',\n",
       "     'type': 'application/json',\n",
       "     'title': 'The landing page of this server as JSON'},\n",
       "    {'href': 'https://emotional.byteroad.net/collections/ec_catalog/hex350_grid_depression_1920?f=json',\n",
       "     'rel': 'self',\n",
       "     'type': 'application/json',\n",
       "     'title': 'This document as JSON'},\n",
       "    {'href': 'https://emotional.byteroad.net/collections/hex350_grid_depression_1920?f=json',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/geo+json',\n",
       "     'title': 'OGC API Features for hex350_grid_depression_1920'},\n",
       "    {'href': 'https://emotional.byteroad.net/collections/hex350_grid_depression_1920/tiles?f=json',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/geo+json',\n",
       "     'title': 'OGC API Tiles for hex350_grid_depression_1920'},\n",
       "    {'href': 'https://emotional-cities.s3.eu-central-1.amazonaws.com/geojson/hex350_grid_depression_1920.geojson',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/geo+json',\n",
       "     'title': 'GeoJson download link for hex350_grid_depression_1920'},\n",
       "    {'href': 'https://emotional-cities.s3.eu-central-1.amazonaws.com/geoparquet/hex350_grid_depression_1920.parquet',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/vnd.apache.parquet',\n",
       "     'title': 'GeoParquet download link for hex350_grid_depression_1920'},\n",
       "    {'href': 'https://emotional-cities.s3.eu-central-1.amazonaws.com/geopackage/hex350_grid_depression_1920.gpkg',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/x-sqlite3',\n",
       "     'title': 'GeoPackage download link for hex350_grid_depression_1920'},\n",
       "    {'href': 'https://emotional.byteroad.net/geoserver/ows?service=WMS&version=1.3.0&request=GetMap&crs={crs}&bbox={bbox}&layers=hex350_grid_depression_1920&width={width}&height={height}&format=image/png',\n",
       "     'rel': 'item',\n",
       "     'type': 'image/png',\n",
       "     'title': 'OGC Web Map Service (WMS)',\n",
       "     'templated': True,\n",
       "     'variables': {'crs': {'description': 'http://www.opengis.net/def/crs/OGC/1.3/CRS84',\n",
       "       'type': 'string',\n",
       "       'enum': ['EPSG:4326']},\n",
       "      'bbox': {'description': '...',\n",
       "       'type': 'array',\n",
       "       'items': {'type': 'number', 'format': 'double'},\n",
       "       'minItems': 4,\n",
       "       'maxItems': 4},\n",
       "      'width': {'description': '...',\n",
       "       'type': 'number',\n",
       "       'format': 'integer',\n",
       "       'minimum': 600,\n",
       "       'maximum': 5000},\n",
       "      'height': {'description': '...',\n",
       "       'type': 'number',\n",
       "       'format': 'integer',\n",
       "       'minimum': 600,\n",
       "       'maximum': 5000}}}]},\n",
       "  {'id': 'hex350_grid_mental_1920',\n",
       "   'type': 'Feature',\n",
       "   'conformsTo': ['http://www.opengis.net/spec/ogcapi-records-1/1.0/req/record-core'],\n",
       "   'time': {'interval': ['2019-01-01T00:00:00Z', '2020-12-31T23:59:59Z'],\n",
       "    'resolution': 'P1D'},\n",
       "   'geometry': {'type': 'Polygon',\n",
       "    'coordinates': [[[-0.260765354541958, 51.409628133733456],\n",
       "      [0.126720196946648, 51.409628133733456],\n",
       "      [0.126720196946648, 51.58003067802845],\n",
       "      [-0.260765354541958, 51.58003067802845],\n",
       "      [-0.260765354541958, 51.409628133733456]]]},\n",
       "   'properties': {'created': '2024-08-06T13:47:25Z',\n",
       "    'updated': '2024-08-06T13:47:25Z',\n",
       "    'type': 'dataset',\n",
       "    'title': 'Prevalence rates of mental health issues in London',\n",
       "    'description': 'The mapping results of urban health outcomes (Prevalence rates of mental health issues) in 350m hexagonal grids of Inner London',\n",
       "    'keywords': ['urban health',\n",
       "     'health outcomes',\n",
       "     'mental health',\n",
       "     'wellbeing',\n",
       "     'urban health mapping',\n",
       "     'inner london',\n",
       "     ''],\n",
       "    'language': {'code': 'en', 'name': 'English', 'dir': 'ltr'},\n",
       "    'themes': [{'concepts': [{'id': 'human health'}],\n",
       "      'scheme': 'https://www.eionet.europa.eu/gemet/en/themes/'}],\n",
       "    'formats': ['HTML', 'GeoJSON'],\n",
       "    'contacts': [{'name': ' University of Cambridge'}],\n",
       "    'license': 'CC BY-NC 4.0'},\n",
       "   'links': [{'href': 'https://emotional.byteroad.net?f=json',\n",
       "     'rel': 'root',\n",
       "     'type': 'application/json',\n",
       "     'title': 'The landing page of this server as JSON'},\n",
       "    {'href': 'https://emotional.byteroad.net/collections/ec_catalog/hex350_grid_mental_1920?f=json',\n",
       "     'rel': 'self',\n",
       "     'type': 'application/json',\n",
       "     'title': 'This document as JSON'},\n",
       "    {'href': 'https://emotional.byteroad.net/collections/hex350_grid_mental_1920?f=json',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/geo+json',\n",
       "     'title': 'OGC API Features for hex350_grid_mental_1920'},\n",
       "    {'href': 'https://emotional.byteroad.net/collections/hex350_grid_mental_1920/tiles?f=json',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/geo+json',\n",
       "     'title': 'OGC API Tiles for hex350_grid_mental_1920'},\n",
       "    {'href': 'https://emotional-cities.s3.eu-central-1.amazonaws.com/geojson/hex350_grid_mental_1920.geojson',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/geo+json',\n",
       "     'title': 'GeoJson download link for hex350_grid_mental_1920'},\n",
       "    {'href': 'https://emotional-cities.s3.eu-central-1.amazonaws.com/geoparquet/hex350_grid_mental_1920.parquet',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/vnd.apache.parquet',\n",
       "     'title': 'GeoParquet download link for hex350_grid_mental_1920'},\n",
       "    {'href': 'https://emotional-cities.s3.eu-central-1.amazonaws.com/geopackage/hex350_grid_mental_1920.gpkg',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/x-sqlite3',\n",
       "     'title': 'GeoPackage download link for hex350_grid_mental_1920'},\n",
       "    {'href': 'https://emotional.byteroad.net/geoserver/ows?service=WMS&version=1.3.0&request=GetMap&crs={crs}&bbox={bbox}&layers=hex350_grid_mental_1920&width={width}&height={height}&format=image/png',\n",
       "     'rel': 'item',\n",
       "     'type': 'image/png',\n",
       "     'title': 'OGC Web Map Service (WMS)',\n",
       "     'templated': True,\n",
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       "    'description': 'Area Deprivation Index state score in 2020. The Area Deprivation Index (ADI) ranks neighborhoods on the basis of socioeconomic disadvantage in the areas of income, education, employment, and housing quality. Areas with greater disadvantage are ranked higher. National scores are normalized to the whole country, and state scores are normalized to a particular state. Higher Area Deprivation Index scores have been shown to correlate with worse health outcomes in measures such as life expectancy. This index was created by researchers at the University of Wisconsin-Madison based on a methodology originally developed by the Health Resources and Services Administration. Areas on this map are ranked against other areas within the state. State scores represent deciles. In other words, they are divided into 10 groups of the same size, where 1 is the lowest rate of disadvantage and 10 is the highest.',\n",
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       "    'title': 'Percentage of people with Disability',\n",
       "    'description': 'Estimated percent of the civilian noninstitutionalized population with one or more types of disabilities, between 2017-2021. Percentage calculations are suppressed in cases where the denominator of the calculation was less than 10 of the unit that is being described (e.g., households, people, householders, etc.).',\n",
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       "    'description': ' National walkability index in 2021. The National Walkability Index identifies areas with mixtures of land use and transportation infrastructure that may encourage walking as a mode of transportation. This index is comprised of four ranked measures: intersection density, distance to the nearest transit stop, employment diversity, and employment and housing diversity. More walkable areas rate higher on intersection density, have lower distances to the nearest transit stop, and have higher employment and employment plus housing diversity.The Environmental Protection Agency’s (EPA) Smart Location Database was created to address the demand for tools that compare location efficiency. The Smart Location Database (SLD) summarizes several demographic, employment, and built environment variables for every Census block group.',\n",
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       "    'title': 'Percent of People Travel by Bicycle to Work',\n",
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       "    'description': 'Estimated average travel time to work in 2017-2021. The average travel time to work is the number of minutes required to reach place of employment for workers age 16 years or older. Averages are suppressed in cases where the sample of the average was less than 10 of the unit that is being described (e.g., households, people, householders, etc.).',\n",
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       "     'title': 'OGC API Features for lansing_city_census_tracts_traveltimetowork_17_21'},\n",
       "    {'href': 'https://emotional.byteroad.net/collections/lansing_city_census_tracts_traveltimetowork_17_21/tiles?f=json',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/geo+json',\n",
       "     'title': 'OGC API Tiles for lansing_city_census_tracts_traveltimetowork_17_21'},\n",
       "    {'href': 'https://emotional-cities.s3.eu-central-1.amazonaws.com/geojson/lansing_city_census_tracts_traveltimetowork_17_21.geojson',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/geo+json',\n",
       "     'title': 'GeoJson download link for lansing_city_census_tracts_traveltimetowork_17_21'},\n",
       "    {'href': 'https://emotional-cities.s3.eu-central-1.amazonaws.com/geoparquet/lansing_city_census_tracts_traveltimetowork_17_21.parquet',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/vnd.apache.parquet',\n",
       "     'title': 'GeoParquet download link for lansing_city_census_tracts_traveltimetowork_17_21'},\n",
       "    {'href': 'https://emotional-cities.s3.eu-central-1.amazonaws.com/geopackage/lansing_city_census_tracts_traveltimetowork_17_21.gpkg',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/x-sqlite3',\n",
       "     'title': 'GeoPackage download link for lansing_city_census_tracts_traveltimetowork_17_21'},\n",
       "    {'href': 'https://emotional.byteroad.net/geoserver/ows?service=WMS&version=1.3.0&request=GetMap&crs={crs}&bbox={bbox}&layers=lansing_city_census_tracts_traveltimetowork_17_21&width={width}&height={height}&format=image/png',\n",
       "     'rel': 'item',\n",
       "     'type': 'image/png',\n",
       "     'title': 'OGC Web Map Service (WMS)',\n",
       "     'templated': True,\n",
       "     'variables': {'crs': {'description': 'http://www.opengis.net/def/crs/OGC/1.3/CRS84',\n",
       "       'type': 'string',\n",
       "       'enum': ['EPSG:4326']},\n",
       "      'bbox': {'description': '...',\n",
       "       'type': 'array',\n",
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       "       'type': 'number',\n",
       "       'format': 'integer',\n",
       "       'minimum': 600,\n",
       "       'maximum': 5000}}}]},\n",
       "  {'id': 'lansing_blockgroup_pedestrianorientedroadnetworkdensity_2021',\n",
       "   'type': 'Feature',\n",
       "   'conformsTo': ['http://www.opengis.net/spec/ogcapi-records-1/1.0/req/record-core'],\n",
       "   'time': {'interval': ['2021-01-01T00:00:00Z', '2021-12-31T23:59:59Z'],\n",
       "    'resolution': 'P1D'},\n",
       "   'geometry': {'type': 'Polygon',\n",
       "    'coordinates': [[[-84.61528312089224, 42.655510004126654],\n",
       "      [-84.49916050045216, 42.655510004126654],\n",
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       "      [-84.61528312089224, 42.769793884562326],\n",
       "      [-84.61528312089224, 42.655510004126654]]]},\n",
       "   'properties': {'created': '2024-08-06T13:47:25Z',\n",
       "    'updated': '2024-08-06T13:47:25Z',\n",
       "    'type': 'dataset',\n",
       "    'title': 'Pedestrian-Oriented Road Network Density ',\n",
       "    'description': 'Pedestrian-oriented road network density in 2021. This was calculated by dividing the number of roadway links from intersection to intersection by the land area of the block group. Pedestrian-oriented roadways exclude auto-oriented roadways, and include roadways with low speeds and pathways or trails that prohibit automobiles.The Environmental Protection Agency’s (EPA) Smart Location Database was created to address the demand for tools that compare location efficiency. The Smart Location Database (SLD) summarizes several demographic, employment, and built environment variables for every Census block group.',\n",
       "    'keywords': ['pedestrian road network', 'walkability', 'lansing'],\n",
       "    'language': {'code': 'en', 'name': 'English', 'dir': 'ltr'},\n",
       "    'themes': [{'concepts': [{'id': 'infrastructure'}],\n",
       "      'scheme': 'https://www.eionet.europa.eu/gemet/en/themes/'}],\n",
       "    'formats': ['HTML', 'GeoJSON'],\n",
       "    'contacts': [{'name': 'Michigan State University'}],\n",
       "    'license': 'CC BY-NC 4.0'},\n",
       "   'links': [{'href': 'https://emotional.byteroad.net?f=json',\n",
       "     'rel': 'root',\n",
       "     'type': 'application/json',\n",
       "     'title': 'The landing page of this server as JSON'},\n",
       "    {'href': 'https://emotional.byteroad.net/collections/ec_catalog/lansing_blockgroup_pedestrianorientedroadnetworkdensity_2021?f=json',\n",
       "     'rel': 'self',\n",
       "     'type': 'application/json',\n",
       "     'title': 'This document as JSON'},\n",
       "    {'href': 'https://emotional.byteroad.net/collections/lansing_blockgroup_pedestrianorientedroadnetworkdensity_2021?f=json',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/geo+json',\n",
       "     'title': 'OGC API Features for lansing_blockgroup_pedestrianorientedroadnetworkdensity_2021'},\n",
       "    {'href': 'https://emotional.byteroad.net/collections/lansing_blockgroup_pedestrianorientedroadnetworkdensity_2021/tiles?f=json',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/geo+json',\n",
       "     'title': 'OGC API Tiles for lansing_blockgroup_pedestrianorientedroadnetworkdensity_2021'},\n",
       "    {'href': 'https://emotional-cities.s3.eu-central-1.amazonaws.com/geojson/lansing_blockgroup_pedestrianorientedroadnetworkdensity_2021.geojson',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/geo+json',\n",
       "     'title': 'GeoJson download link for lansing_blockgroup_pedestrianorientedroadnetworkdensity_2021'},\n",
       "    {'href': 'https://emotional-cities.s3.eu-central-1.amazonaws.com/geoparquet/lansing_blockgroup_pedestrianorientedroadnetworkdensity_2021.parquet',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/vnd.apache.parquet',\n",
       "     'title': 'GeoParquet download link for lansing_blockgroup_pedestrianorientedroadnetworkdensity_2021'},\n",
       "    {'href': 'https://emotional-cities.s3.eu-central-1.amazonaws.com/geopackage/lansing_blockgroup_pedestrianorientedroadnetworkdensity_2021.gpkg',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/x-sqlite3',\n",
       "     'title': 'GeoPackage download link for lansing_blockgroup_pedestrianorientedroadnetworkdensity_2021'},\n",
       "    {'href': 'https://emotional.byteroad.net/geoserver/ows?service=WMS&version=1.3.0&request=GetMap&crs={crs}&bbox={bbox}&layers=lansing_blockgroup_pedestrianorientedroadnetworkdensity_2021&width={width}&height={height}&format=image/png',\n",
       "     'rel': 'item',\n",
       "     'type': 'image/png',\n",
       "     'title': 'OGC Web Map Service (WMS)',\n",
       "     'templated': True,\n",
       "     'variables': {'crs': {'description': 'http://www.opengis.net/def/crs/OGC/1.3/CRS84',\n",
       "       'type': 'string',\n",
       "       'enum': ['EPSG:4326']},\n",
       "      'bbox': {'description': '...',\n",
       "       'type': 'array',\n",
       "       'items': {'type': 'number', 'format': 'double'},\n",
       "       'minItems': 4,\n",
       "       'maxItems': 4},\n",
       "      'width': {'description': '...',\n",
       "       'type': 'number',\n",
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       "       'maximum': 5000},\n",
       "      'height': {'description': '...',\n",
       "       'type': 'number',\n",
       "       'format': 'integer',\n",
       "       'minimum': 600,\n",
       "       'maximum': 5000}}}]},\n",
       "  {'id': 'lansing_blockgroup_multimodalroadnetworkdensity_2021',\n",
       "   'type': 'Feature',\n",
       "   'conformsTo': ['http://www.opengis.net/spec/ogcapi-records-1/1.0/req/record-core'],\n",
       "   'time': {'interval': ['2021-01-01T00:00:00Z', '2021-12-31T23:59:59Z'],\n",
       "    'resolution': 'P1D'},\n",
       "   'geometry': {'type': 'Polygon',\n",
       "    'coordinates': [[[-84.61528312089224, 42.655510004126654],\n",
       "      [-84.49916050045216, 42.655510004126654],\n",
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       "      [-84.61528312089224, 42.769793884562326],\n",
       "      [-84.61528312089224, 42.655510004126654]]]},\n",
       "   'properties': {'created': '2024-08-06T13:47:25Z',\n",
       "    'updated': '2024-08-06T13:47:25Z',\n",
       "    'type': 'dataset',\n",
       "    'title': 'Multi-modal Road Network Density ',\n",
       "    'description': 'Multi-modal road network density in 2021. This was calculated by dividing the number of roadway links from intersection to intersection by the land area of the block group. Multi-modal roadways exclude auto-oriented roadways, and include roadways with moderate to low speeds. The Environmental Protection Agency’s (EPA) Smart Location Database was created to address the demand for tools that compare location efficiency. The Smart Location Database (SLD) summarizes several demographic, employment, and built environment variables for every Census block group.',\n",
       "    'keywords': ['road network density', 'lansing'],\n",
       "    'language': {'code': 'en', 'name': 'English', 'dir': 'ltr'},\n",
       "    'themes': [{'concepts': [{'id': 'infrastructure'}],\n",
       "      'scheme': 'https://www.eionet.europa.eu/gemet/en/themes/'}],\n",
       "    'formats': ['HTML', 'GeoJSON'],\n",
       "    'contacts': [{'name': 'Michigan State University'}],\n",
       "    'license': 'CC BY-NC 4.0'},\n",
       "   'links': [{'href': 'https://emotional.byteroad.net?f=json',\n",
       "     'rel': 'root',\n",
       "     'type': 'application/json',\n",
       "     'title': 'The landing page of this server as JSON'},\n",
       "    {'href': 'https://emotional.byteroad.net/collections/ec_catalog/lansing_blockgroup_multimodalroadnetworkdensity_2021?f=json',\n",
       "     'rel': 'self',\n",
       "     'type': 'application/json',\n",
       "     'title': 'This document as JSON'},\n",
       "    {'href': 'https://emotional.byteroad.net/collections/lansing_blockgroup_multimodalroadnetworkdensity_2021?f=json',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/geo+json',\n",
       "     'title': 'OGC API Features for lansing_blockgroup_multimodalroadnetworkdensity_2021'},\n",
       "    {'href': 'https://emotional.byteroad.net/collections/lansing_blockgroup_multimodalroadnetworkdensity_2021/tiles?f=json',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/geo+json',\n",
       "     'title': 'OGC API Tiles for lansing_blockgroup_multimodalroadnetworkdensity_2021'},\n",
       "    {'href': 'https://emotional-cities.s3.eu-central-1.amazonaws.com/geojson/lansing_blockgroup_multimodalroadnetworkdensity_2021.geojson',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/geo+json',\n",
       "     'title': 'GeoJson download link for lansing_blockgroup_multimodalroadnetworkdensity_2021'},\n",
       "    {'href': 'https://emotional-cities.s3.eu-central-1.amazonaws.com/geoparquet/lansing_blockgroup_multimodalroadnetworkdensity_2021.parquet',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/vnd.apache.parquet',\n",
       "     'title': 'GeoParquet download link for lansing_blockgroup_multimodalroadnetworkdensity_2021'},\n",
       "    {'href': 'https://emotional-cities.s3.eu-central-1.amazonaws.com/geopackage/lansing_blockgroup_multimodalroadnetworkdensity_2021.gpkg',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/x-sqlite3',\n",
       "     'title': 'GeoPackage download link for lansing_blockgroup_multimodalroadnetworkdensity_2021'},\n",
       "    {'href': 'https://emotional.byteroad.net/geoserver/ows?service=WMS&version=1.3.0&request=GetMap&crs={crs}&bbox={bbox}&layers=lansing_blockgroup_multimodalroadnetworkdensity_2021&width={width}&height={height}&format=image/png',\n",
       "     'rel': 'item',\n",
       "     'type': 'image/png',\n",
       "     'title': 'OGC Web Map Service (WMS)',\n",
       "     'templated': True,\n",
       "     'variables': {'crs': {'description': 'http://www.opengis.net/def/crs/OGC/1.3/CRS84',\n",
       "       'type': 'string',\n",
       "       'enum': ['EPSG:4326']},\n",
       "      'bbox': {'description': '...',\n",
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       "       'type': 'number',\n",
       "       'format': 'integer',\n",
       "       'minimum': 600,\n",
       "       'maximum': 5000}}}]},\n",
       "  {'id': 'lansing_censustracts_environmentalhealthindex_2015',\n",
       "   'type': 'Feature',\n",
       "   'conformsTo': ['http://www.opengis.net/spec/ogcapi-records-1/1.0/req/record-core'],\n",
       "   'time': {'interval': ['2015-01-01T00:00:00Z', '2015-12-31T23:59:59Z'],\n",
       "    'resolution': 'P1D'},\n",
       "   'geometry': {'type': 'Polygon',\n",
       "    'coordinates': [[[-84.61528312089224, 42.655510004126654],\n",
       "      [-84.49916050045216, 42.655510004126654],\n",
       "      [-84.49916050045216, 42.769793884562326],\n",
       "      [-84.61528312089224, 42.769793884562326],\n",
       "      [-84.61528312089224, 42.655510004126654]]]},\n",
       "   'properties': {'created': '2024-08-06T13:47:25Z',\n",
       "    'updated': '2024-08-06T13:47:25Z',\n",
       "    'type': 'dataset',\n",
       "    'title': 'Environmental Health Index',\n",
       "    'description': \"Environmental health index, as of 2015. This index summarizes potential exposure to harmful toxins at a neighborhood level. Values are inverted and percentile ranked nationally. The values range from 0 to 100, with the higher index values suggesting less exposure to toxins harmful to human health and, thus, better environmental quality in an area. HUD created this index using 2005 National Air Toxics Assessment data. For more information, please see HUD's Affirmatively Furthering Fair Housing website.\",\n",
       "    'keywords': ['environmental health index', 'lansing'],\n",
       "    'language': {'code': 'en', 'name': 'English', 'dir': 'ltr'},\n",
       "    'themes': [{'concepts': [{'id': 'infrastructure'}],\n",
       "      'scheme': 'https://www.eionet.europa.eu/gemet/en/themes/'}],\n",
       "    'formats': ['HTML', 'GeoJSON'],\n",
       "    'contacts': [{'name': 'Michigan State University'}],\n",
       "    'license': 'CC BY-NC 4.0'},\n",
       "   'links': [{'href': 'https://emotional.byteroad.net?f=json',\n",
       "     'rel': 'root',\n",
       "     'type': 'application/json',\n",
       "     'title': 'The landing page of this server as JSON'},\n",
       "    {'href': 'https://emotional.byteroad.net/collections/ec_catalog/lansing_censustracts_environmentalhealthindex_2015?f=json',\n",
       "     'rel': 'self',\n",
       "     'type': 'application/json',\n",
       "     'title': 'This document as JSON'},\n",
       "    {'href': 'https://emotional.byteroad.net/collections/lansing_censustracts_environmentalhealthindex_2015?f=json',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/geo+json',\n",
       "     'title': 'OGC API Features for lansing_censustracts_environmentalhealthindex_2015'},\n",
       "    {'href': 'https://emotional.byteroad.net/collections/lansing_censustracts_environmentalhealthindex_2015/tiles?f=json',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/geo+json',\n",
       "     'title': 'OGC API Tiles for lansing_censustracts_environmentalhealthindex_2015'},\n",
       "    {'href': 'https://emotional-cities.s3.eu-central-1.amazonaws.com/geojson/lansing_censustracts_environmentalhealthindex_2015.geojson',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/geo+json',\n",
       "     'title': 'GeoJson download link for lansing_censustracts_environmentalhealthindex_2015'},\n",
       "    {'href': 'https://emotional-cities.s3.eu-central-1.amazonaws.com/geoparquet/lansing_censustracts_environmentalhealthindex_2015.parquet',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/vnd.apache.parquet',\n",
       "     'title': 'GeoParquet download link for lansing_censustracts_environmentalhealthindex_2015'},\n",
       "    {'href': 'https://emotional-cities.s3.eu-central-1.amazonaws.com/geopackage/lansing_censustracts_environmentalhealthindex_2015.gpkg',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/x-sqlite3',\n",
       "     'title': 'GeoPackage download link for lansing_censustracts_environmentalhealthindex_2015'},\n",
       "    {'href': 'https://emotional.byteroad.net/geoserver/ows?service=WMS&version=1.3.0&request=GetMap&crs={crs}&bbox={bbox}&layers=lansing_censustracts_environmentalhealthindex_2015&width={width}&height={height}&format=image/png',\n",
       "     'rel': 'item',\n",
       "     'type': 'image/png',\n",
       "     'title': 'OGC Web Map Service (WMS)',\n",
       "     'templated': True,\n",
       "     'variables': {'crs': {'description': 'http://www.opengis.net/def/crs/OGC/1.3/CRS84',\n",
       "       'type': 'string',\n",
       "       'enum': ['EPSG:4326']},\n",
       "      'bbox': {'description': '...',\n",
       "       'type': 'array',\n",
       "       'items': {'type': 'number', 'format': 'double'},\n",
       "       'minItems': 4,\n",
       "       'maxItems': 4},\n",
       "      'width': {'description': '...',\n",
       "       'type': 'number',\n",
       "       'format': 'integer',\n",
       "       'minimum': 600,\n",
       "       'maximum': 5000},\n",
       "      'height': {'description': '...',\n",
       "       'type': 'number',\n",
       "       'format': 'integer',\n",
       "       'minimum': 600,\n",
       "       'maximum': 5000}}}]},\n",
       "  {'id': 'urban_image_stimulus_set_variables',\n",
       "   'type': 'Feature',\n",
       "   'conformsTo': ['http://www.opengis.net/spec/ogcapi-records-1/1.0/req/record-core'],\n",
       "   'time': {'interval': ['2023-01-01T00:00:00Z', '2023-12-31T23:59:59Z'],\n",
       "    'resolution': 'P1D'},\n",
       "   'geometry': {'type': 'Polygon',\n",
       "    'coordinates': [[[-84.61528312089224, 42.655510004126654],\n",
       "      [-84.49916050045216, 42.655510004126654],\n",
       "      [-84.49916050045216, 42.769793884562326],\n",
       "      [-84.61528312089224, 42.769793884562326],\n",
       "      [-84.61528312089224, 42.655510004126654]]]},\n",
       "   'properties': {'created': '2024-08-06T13:47:25Z',\n",
       "    'updated': '2024-08-06T13:47:25Z',\n",
       "    'type': 'dataset',\n",
       "    'title': 'Urban Image Stimulus Set (WP5)',\n",
       "    'description': 'Social media data, such as photos and status posts, can be tagged with location information (geotagging). This geotagged information could be used for urban spatial analysis to explore neighborhood characteristics or mobility patterns. Here, we share an urban image stimulus set from the city of Lisbon. The stimulus set consists of 160 geotagged urban space photographs extracted from the Flickr social media platform. We categorized these images into two putative groups by photographer status (residents and tourists) with 80 images belonging to each group. In addition, we divided the city into 100 x 100 meter cells to calculate the cell image density (number of images in each cell) and the cell green index (Normalized Difference Vegetation Index of each cell) and assigned these values to each geotagged image. Finally, we also computed the popularity of each image (normalized views on the social network). Overall, this stimulus set can facilitate future research exploring human-urban environment interaction patterns, especially if complemented with survey/neuroimaging measures or machine-learning analyses.',\n",
       "    'keywords': [''],\n",
       "    'language': {'code': 'en', 'name': 'English', 'dir': 'ltr'},\n",
       "    'themes': [],\n",
       "    'formats': ['HTML', 'GeoJSON'],\n",
       "    'contacts': [{'name': 'MSU'}],\n",
       "    'license': 'Creative Commons (CC BY 4.0)'},\n",
       "   'links': [{'href': 'https://emotional.byteroad.net?f=json',\n",
       "     'rel': 'root',\n",
       "     'type': 'application/json',\n",
       "     'title': 'The landing page of this server as JSON'},\n",
       "    {'href': 'https://emotional.byteroad.net/collections/ec_catalog/urban_image_stimulus_set_variables?f=json',\n",
       "     'rel': 'self',\n",
       "     'type': 'application/json',\n",
       "     'title': 'This document as JSON'}]},\n",
       "  {'id': 'lisbon_life_births_rate',\n",
       "   'type': 'Feature',\n",
       "   'conformsTo': ['http://www.opengis.net/spec/ogcapi-records-1/1.0/req/record-core'],\n",
       "   'time': {'interval': ['2021-01-01T00:00:00Z', '2021-12-31T23:59:59Z'],\n",
       "    'resolution': 'P1D'},\n",
       "   'geometry': {'type': 'Polygon',\n",
       "    'coordinates': [[[-9.229835564339478, 38.69139933303907],\n",
       "      [-9.08738707964508, 38.69139933303907],\n",
       "      [-9.08738707964508, 38.79675969109248],\n",
       "      [-9.229835564339478, 38.79675969109248],\n",
       "      [-9.229835564339478, 38.69139933303907]]]},\n",
       "   'properties': {'created': '2024-08-06T13:47:25Z',\n",
       "    'updated': '2024-08-06T13:47:25Z',\n",
       "    'type': 'dataset',\n",
       "    'title': 'Life births rate',\n",
       "    'description': 'Life births rate in 2021, per 1000 inhabitants.',\n",
       "    'keywords': ['life births', 'urban health', 'health', 'igot', 'lisbon'],\n",
       "    'language': {'code': 'en', 'name': 'English', 'dir': 'ltr'},\n",
       "    'themes': [{'concepts': [{'id': 'urban health data'}],\n",
       "      'scheme': 'https://www.eionet.europa.eu/gemet/en/themes/'}],\n",
       "    'formats': ['HTML', 'GeoJSON'],\n",
       "    'contacts': [{'name': 'Paulo Morgado'}],\n",
       "    'license': 'Creative Commons (CC BY 4.0)'},\n",
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       "    'updated': '2024-08-06T13:47:25Z',\n",
       "    'type': 'dataset',\n",
       "    'title': 'Altimetry',\n",
       "    'description': 'Altimetry level in meters.',\n",
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       "    'language': {'code': 'en', 'name': 'English', 'dir': 'ltr'},\n",
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       "  {'id': 'lisbon_beds_tourist_accomodations',\n",
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       "   'properties': {'created': '2024-08-06T13:47:25Z',\n",
       "    'updated': '2024-08-06T13:47:25Z',\n",
       "    'type': 'dataset',\n",
       "    'title': 'Beds / customers in tourist accommodations',\n",
       "    'description': 'Beds / customers in tourist accommodations in 2021, per 1000 inhabitants.',\n",
       "    'keywords': ['urban density',\n",
       "     'urban design',\n",
       "     'urban planning',\n",
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       "     'variables': {'crs': {'description': 'http://www.opengis.net/def/crs/OGC/1.3/CRS84',\n",
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       "  {'id': 'lisbon_fast_food_outlets_density',\n",
       "   'type': 'Feature',\n",
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       "    'type': 'dataset',\n",
       "    'title': 'Density of fast-food outlets',\n",
       "    'description': \"Kernal density of fast-food outlets (from 100 Montaditos, Burger King, Burger Ranch, Domino's Pizza, McDonald's, Pizza Hut, Telepizza, KFC, Taco Bell, Subway, Pans & Company, and Papa John's chains) in march 2022.\",\n",
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       "  {'id': 'lisbon_ndvi',\n",
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       "    'updated': '2024-04-26T15:31:03Z',\n",
       "    'type': 'dataset',\n",
       "    'title': 'Normalized Difference Vegetation Index (NDVI)',\n",
       "    'description': 'Mean, in 2021, of Normalized Difference Vegetation Index (NDVI).',\n",
       "    'keywords': ['green', 'urban', 'green areas', 'igot', 'lisbon'],\n",
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       "     'templated': True,\n",
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       "  {'id': 'lisbon_noise',\n",
       "   'type': 'Feature',\n",
       "   'conformsTo': ['http://www.opengis.net/spec/ogcapi-records-1/1.0/req/record-core'],\n",
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       "    'updated': '2024-04-26T15:31:03Z',\n",
       "    'type': 'dataset',\n",
       "    'title': 'Noise level',\n",
       "    'description': 'Mean noise level during the day, afternoon and night (Lden) in 2021, in weighted decibel.',\n",
       "    'keywords': ['pollution',\n",
       "     'noise',\n",
       "     'noise pollution',\n",
       "     'urban planning',\n",
       "     'igot',\n",
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       "      [12.3813907315, 55.6281278107]]]},\n",
       "   'properties': {'created': '2024-04-26T15:31:03Z',\n",
       "    'updated': '2024-04-26T15:31:03Z',\n",
       "    'type': 'dataset',\n",
       "    'title': 'Land Use and POIs for Copenhagen',\n",
       "    'description': 'OpenStreetMap (OSM) POI and land-use data extracted and aggregated for each of the zones in the Copenhagen area',\n",
       "    'keywords': ['land use', 'poi', 'urban planning', 'dtu', 'copenhagen'],\n",
       "    'language': {'code': 'en', 'name': 'English', 'dir': 'ltr'},\n",
       "    'themes': [{'concepts': [{'id': 'physical environment data'}],\n",
       "      'scheme': 'https://www.eionet.europa.eu/gemet/en/themes/'}],\n",
       "    'formats': ['HTML', 'GeoJSON'],\n",
       "    'contacts': [{'name': 'Carlos Lima Azevedo'}],\n",
       "    'license': 'Creative Commons (CC BY 4.0)'},\n",
       "   'links': [{'href': 'https://emotional.byteroad.net?f=json',\n",
       "     'rel': 'root',\n",
       "     'type': 'application/json',\n",
       "     'title': 'The landing page of this server as JSON'},\n",
       "    {'href': 'https://emotional.byteroad.net/collections/ec_catalog/landuse_poi_wzones?f=json',\n",
       "     'rel': 'self',\n",
       "     'type': 'application/json',\n",
       "     'title': 'This document as JSON'},\n",
       "    {'href': 'https://emotional.byteroad.net/collections/landuse_poi_wzones?f=json',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/geo+json',\n",
       "     'title': 'OGC API Features for landuse_poi_wzones'},\n",
       "    {'href': 'https://emotional.byteroad.net/collections/landuse_poi_wzones/tiles?f=json',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/geo+json',\n",
       "     'title': 'OGC API Tiles for landuse_poi_wzones'},\n",
       "    {'href': 'https://emotional-cities.s3.eu-central-1.amazonaws.com/geojson/landuse_poi_wzones.geojson',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/geo+json',\n",
       "     'title': 'GeoJson download link for landuse_poi_wzones'},\n",
       "    {'href': 'https://emotional-cities.s3.eu-central-1.amazonaws.com/geoparquet/landuse_poi_wzones.parquet',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/vnd.apache.parquet',\n",
       "     'title': 'GeoParquet download link for landuse_poi_wzones'},\n",
       "    {'href': 'https://emotional-cities.s3.eu-central-1.amazonaws.com/geopackage/landuse_poi_wzones.gpkg',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/x-sqlite3',\n",
       "     'title': 'GeoPackage download link for landuse_poi_wzones'},\n",
       "    {'href': 'https://emotional.byteroad.net/geoserver/ows?service=WMS&version=1.3.0&request=GetMap&crs={crs}&bbox={bbox}&layers=landuse_poi_wzones&width={width}&height={height}&format=image/png',\n",
       "     'rel': 'item',\n",
       "     'type': 'image/png',\n",
       "     'title': 'OGC Web Map Service (WMS)',\n",
       "     'templated': True,\n",
       "     'variables': {'crs': {'description': 'http://www.opengis.net/def/crs/OGC/1.3/CRS84',\n",
       "       'type': 'string',\n",
       "       'enum': ['EPSG:4326']},\n",
       "      'bbox': {'description': '...',\n",
       "       'type': 'array',\n",
       "       'items': {'type': 'number', 'format': 'double'},\n",
       "       'minItems': 4,\n",
       "       'maxItems': 4},\n",
       "      'width': {'description': '...',\n",
       "       'type': 'number',\n",
       "       'format': 'integer',\n",
       "       'minimum': 600,\n",
       "       'maximum': 5000},\n",
       "      'height': {'description': '...',\n",
       "       'type': 'number',\n",
       "       'format': 'integer',\n",
       "       'minimum': 600,\n",
       "       'maximum': 5000}}}]},\n",
       "  {'id': 'noise_wzones',\n",
       "   'type': 'Feature',\n",
       "   'conformsTo': ['http://www.opengis.net/spec/ogcapi-records-1/1.0/req/record-core'],\n",
       "   'time': {'interval': ['2017-01-01T00:00:00Z', '2017-12-31T23:59:59Z'],\n",
       "    'resolution': 'P1D'},\n",
       "   'geometry': {'type': 'Polygon',\n",
       "    'coordinates': [[[12.3813907315, 55.6281278107],\n",
       "      [12.7560245038, 55.6281278107],\n",
       "      [12.7560245038, 55.7859716623],\n",
       "      [12.3813907315, 55.7859716623],\n",
       "      [12.3813907315, 55.6281278107]]]},\n",
       "   'properties': {'created': '2024-04-26T15:31:03Z',\n",
       "    'updated': '2024-04-26T15:31:03Z',\n",
       "    'type': 'dataset',\n",
       "    'title': 'Noise data for the zones considered in the greater Copenhagen',\n",
       "    'description': 'Mean of the total noise levels for each noise point within that particular zone. Source: https://mst.dk/service/miljoegis/hent-data/. Level Noise6 over 75 dB 5 70-75 dB4 65-70dB3 60-65 dB2 55-60 dB1 50-60 dB',\n",
       "    'keywords': ['noise', 'pollution', 'urban planning', 'dtu', 'copenhagen'],\n",
       "    'language': {'code': 'en', 'name': 'English', 'dir': 'ltr'},\n",
       "    'themes': [{'concepts': [{'id': 'physical environment data'}],\n",
       "      'scheme': 'https://www.eionet.europa.eu/gemet/en/themes/'}],\n",
       "    'formats': ['HTML', 'GeoJSON'],\n",
       "    'contacts': [{'name': 'Carlos Lima Azevedo'}],\n",
       "    'license': 'Creative Commons (CC BY 4.0)'},\n",
       "   'links': [{'href': 'https://emotional.byteroad.net?f=json',\n",
       "     'rel': 'root',\n",
       "     'type': 'application/json',\n",
       "     'title': 'The landing page of this server as JSON'},\n",
       "    {'href': 'https://emotional.byteroad.net/collections/ec_catalog/noise_wzones?f=json',\n",
       "     'rel': 'self',\n",
       "     'type': 'application/json',\n",
       "     'title': 'This document as JSON'},\n",
       "    {'href': 'https://emotional.byteroad.net/collections/noise_wzones?f=json',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/geo+json',\n",
       "     'title': 'OGC API Features for noise_wzones'},\n",
       "    {'href': 'https://emotional.byteroad.net/collections/noise_wzones/tiles?f=json',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/geo+json',\n",
       "     'title': 'OGC API Tiles for noise_wzones'},\n",
       "    {'href': 'https://emotional-cities.s3.eu-central-1.amazonaws.com/geojson/noise_wzones.geojson',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/geo+json',\n",
       "     'title': 'GeoJson download link for noise_wzones'},\n",
       "    {'href': 'https://emotional-cities.s3.eu-central-1.amazonaws.com/geoparquet/noise_wzones.parquet',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/vnd.apache.parquet',\n",
       "     'title': 'GeoParquet download link for noise_wzones'},\n",
       "    {'href': 'https://emotional-cities.s3.eu-central-1.amazonaws.com/geopackage/noise_wzones.gpkg',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/x-sqlite3',\n",
       "     'title': 'GeoPackage download link for noise_wzones'},\n",
       "    {'href': 'https://emotional.byteroad.net/geoserver/ows?service=WMS&version=1.3.0&request=GetMap&crs={crs}&bbox={bbox}&layers=noise_wzones&width={width}&height={height}&format=image/png',\n",
       "     'rel': 'item',\n",
       "     'type': 'image/png',\n",
       "     'title': 'OGC Web Map Service (WMS)',\n",
       "     'templated': True,\n",
       "     'variables': {'crs': {'description': 'http://www.opengis.net/def/crs/OGC/1.3/CRS84',\n",
       "       'type': 'string',\n",
       "       'enum': ['EPSG:4326']},\n",
       "      'bbox': {'description': '...',\n",
       "       'type': 'array',\n",
       "       'items': {'type': 'number', 'format': 'double'},\n",
       "       'minItems': 4,\n",
       "       'maxItems': 4},\n",
       "      'width': {'description': '...',\n",
       "       'type': 'number',\n",
       "       'format': 'integer',\n",
       "       'minimum': 600,\n",
       "       'maximum': 5000},\n",
       "      'height': {'description': '...',\n",
       "       'type': 'number',\n",
       "       'format': 'integer',\n",
       "       'minimum': 600,\n",
       "       'maximum': 5000}}}]},\n",
       "  {'id': 'london_whitechapel_sub-oe309002',\n",
       "   'conformsTo': ['http://www.opengis.net/spec/ogcapi-records-1/1.0/req/record-core'],\n",
       "   'type': 'Feature',\n",
       "   'time': {'interval': ['2023-06-19T15:21:10Z', '2023-06-19T15:45:52Z'],\n",
       "    'resolution': 'PT1S'},\n",
       "   'geometry': {'type': 'Polygon',\n",
       "    'coordinates': [[[-0.068561911, 51.512126272],\n",
       "      [-0.068561911, 51.515988267],\n",
       "      [-0.061986911, 51.515988267],\n",
       "      [-0.061986911, 51.512126272],\n",
       "      [-0.068561911, 51.512126272]]]},\n",
       "   'properties': {'created': '2023-08-29T09:53:19Z',\n",
       "    'updated': '2023-08-29T14:24:29Z',\n",
       "    'type': 'dataset',\n",
       "    'title': 'London Outdoor Walk: WhiteChapel Subject OE309002',\n",
       "    'description': 'Outdoor walk platform benchmarks',\n",
       "    'keywords': ['London', 'Outdoor', 'Walk', 'Microclimate', 'Biosignals'],\n",
       "    'language': {'code': 'en', 'name': 'English', 'dir': 'ltr'},\n",
       "    'contacts': [{'name': 'NeuroGEARS Ltd',\n",
       "      'organization': 'NeuroGEARS Ltd',\n",
       "      'email': [{'value': 'contact@neurogears.org'}]}],\n",
       "    'themes': [],\n",
       "    'formats': ['CSV', 'GeoJSON'],\n",
       "    'license': 'CC BY-NC 4.0',\n",
       "    'dataCollectionTool': 'Bonsai'},\n",
       "   'links': [{'rel': 'item',\n",
       "     'type': 'image/png',\n",
       "     'title': 'OGC Web Map Service (WMS)',\n",
       "     'href': 'https://emotional.byteroad.net/geoserver/ows?service=WMS&version=1.3.0&request=GetMap&crs={crs}&bbox={bbox}&layers=london_whitechapel_sub-oe309002&width={width}&height={height}&format=image/png',\n",
       "     'templated': True,\n",
       "     'variables': {'crs': {'description': '...',\n",
       "       'type': 'string',\n",
       "       'enum': ['EPSG:4326', 'EPSG:3857']},\n",
       "      'bbox': {'description': '...',\n",
       "       'type': 'array',\n",
       "       'items': {'type': 'number', 'format': 'double'},\n",
       "       'minItems': 4,\n",
       "       'maxItems': 4},\n",
       "      'width': {'description': '...',\n",
       "       'type': 'number',\n",
       "       'format': 'integer',\n",
       "       'minimum': 600,\n",
       "       'maximum': 5000},\n",
       "      'height': {'description': '...',\n",
       "       'type': 'number',\n",
       "       'format': 'integer',\n",
       "       'minimum': 600,\n",
       "       'maximum': 5000}}},\n",
       "    {'rel': 'item',\n",
       "     'type': 'application/geo+json',\n",
       "     'title': 'OGC API Features',\n",
       "     'href': 'https://emotional.byteroad.net/collections/london_whitechapel_sub-oe309002'},\n",
       "    {'rel': 'item',\n",
       "     'type': 'application/geo+json',\n",
       "     'title': 'OGC API Tiles',\n",
       "     'href': 'https://emotional.byteroad.net/collections/london_whitechapel_sub-oe309002/tiles'}],\n",
       "   'm': 'API'},\n",
       "  {'geometry': {'coordinates': [[[-9.229835564339478, 38.69139933303907],\n",
       "      [-9.08738707964508, 38.69139933303907],\n",
       "      [-9.08738707964508, 38.79675969109248],\n",
       "      [-9.229835564339478, 38.79675969109248],\n",
       "      [-9.229835564339478, 38.69139933303907]]],\n",
       "    'type': 'Polygon'},\n",
       "   'recordUpdated': '2023-09-22T15:51:31Z',\n",
       "   'type': 'Feature',\n",
       "   '@timestamp': '2023-09-22T15:51:31Z',\n",
       "   'recordCreated': '2023-09-22T15:51:31Z',\n",
       "   'properties': {'language': {'code': 'en', 'name': 'English', 'dir': 'ltr'},\n",
       "    'title': 'fMRI Raw Data (WP5: Brain as predictor)',\n",
       "    'description': 'Raw fMRI Data for 77 subjects collected at MSU while they viewed and rated urban spaces of Lisbon for preference in the MRI scanner',\n",
       "    'license': 'Creative Commons (CC BY 4.0)',\n",
       "    'themes': [],\n",
       "    'contacts': [{'name': 'MSU'}],\n",
       "    'formats': ['HTML', 'GeoJSON'],\n",
       "    'created': '2023-09-22T15:51:31Z',\n",
       "    'type': 'dataset',\n",
       "    'updated': '2023-09-22T15:51:31Z'},\n",
       "   '@version': '1',\n",
       "   'time': {'resolution': 'P1D',\n",
       "    'interval': ['2023-01-01T00:00:00Z', '2023-12-31T23:59:59Z']},\n",
       "   'id': 'fmri_raw_data_wp5_brain_as_predictor',\n",
       "   'conformsTo': ['http://www.opengis.net/spec/ogcapi-records-1/1.0/req/record-core'],\n",
       "   'links': [{'type': 'application/json',\n",
       "     'rel': 'root',\n",
       "     'title': 'The landing page of this server as JSON',\n",
       "     'href': 'https://emotional.byteroad.net?f=json'},\n",
       "    {'type': 'text/html',\n",
       "     'rel': 'root',\n",
       "     'title': 'The landing page of this server as HTML',\n",
       "     'href': 'https://emotional.byteroad.net?f=html'},\n",
       "    {'rel': 'self',\n",
       "     'type': 'application/geo+json',\n",
       "     'title': 'This document as GeoJSON',\n",
       "     'href': 'https://emotional.byteroad.net/collections/ec_catalog/items/urban_image_stimulus_set_variables?f=json'},\n",
       "    {'rel': 'alternate',\n",
       "     'type': 'application/ld+json',\n",
       "     'title': 'This document as RDF (JSON-LD)',\n",
       "     'href': 'https://emotional.byteroad.net/collections/ec_catalog/items/urban_image_stimulus_set_variables?f=jsonld'},\n",
       "    {'rel': 'alternate',\n",
       "     'type': 'text/html',\n",
       "     'title': 'This document as HTML',\n",
       "     'href': 'https://emotional.byteroad.net/collections/ec_catalog/items/urban_image_stimulus_set_variables?f=html'},\n",
       "    {'rel': 'resources',\n",
       "     'type': 's3-bucket',\n",
       "     'title': 'eMOTIONAL Cities S3 Bucket',\n",
       "     'href': 'https://emotional-cities.s3.eu-central-1.amazonaws.com/data/msu/fMRI+Raw+Data+(WP5%3A+Brain+as+predictor)/'},\n",
       "    {'rel': 'collection',\n",
       "     'type': 'application/json',\n",
       "     'title': 'eMOTIONAL Cities Metadata catalog',\n",
       "     'href': 'https://emotional.byteroad.net/collections/ec_catalog'}],\n",
       "   'm': 'API'},\n",
       "  {'id': 'lisbon_vibrancy_index',\n",
       "   'type': 'Feature',\n",
       "   'conformsTo': ['http://www.opengis.net/spec/ogcapi-records-1/1.0/req/record-core'],\n",
       "   'time': {'interval': ['2023-01-01T00:00:00Z', '2023-12-31T23:59:59Z'],\n",
       "    'resolution': 'P1D'},\n",
       "   'geometry': {'type': 'Polygon',\n",
       "    'coordinates': [[[-9.229835564339478, 38.69139933303907],\n",
       "      [-9.08738707964508, 38.69139933303907],\n",
       "      [-9.08738707964508, 38.79675969109248],\n",
       "      [-9.229835564339478, 38.79675969109248],\n",
       "      [-9.229835564339478, 38.69139933303907]]]},\n",
       "   'properties': {'created': '2024-08-06T13:58:32Z',\n",
       "    'updated': '2024-08-06T13:58:32Z',\n",
       "    'type': 'dataset',\n",
       "    'title': 'Vibrancy index',\n",
       "    'description': \"Vibrancy index, calculated by the Shannon's diversity index based on the Points of Interest obtained from OpenStreetMap (methodology of Botta & Gutiérrez-Roig, 2021).\",\n",
       "    'keywords': ['urban vitality', 'urban planning', 'igot', 'lisbon'],\n",
       "    'language': {'code': 'en', 'name': 'English', 'dir': 'ltr'},\n",
       "    'themes': [{'concepts': [{'id': 'physical environment data'}],\n",
       "      'scheme': 'https://www.eionet.europa.eu/gemet/en/themes/'}],\n",
       "    'formats': ['HTML', 'GeoJSON'],\n",
       "    'contacts': [{'name': 'Paulo Morgado'}],\n",
       "    'license': 'Creative Commons (CC BY 4.0)'},\n",
       "   'links': [{'href': 'https://emotional.byteroad.net?f=json',\n",
       "     'rel': 'root',\n",
       "     'type': 'application/json',\n",
       "     'title': 'The landing page of this server as JSON'},\n",
       "    {'href': 'https://emotional.byteroad.net/collections/ec_catalog/lisbon_vibrancy_index?f=json',\n",
       "     'rel': 'self',\n",
       "     'type': 'application/json',\n",
       "     'title': 'This document as JSON'},\n",
       "    {'href': 'https://emotional.byteroad.net/collections/lisbon_vibrancy_index?f=json',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/geo+json',\n",
       "     'title': 'OGC API Features for lisbon_vibrancy_index'},\n",
       "    {'href': 'https://emotional.byteroad.net/collections/lisbon_vibrancy_index/tiles?f=json',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/geo+json',\n",
       "     'title': 'OGC API Tiles for lisbon_vibrancy_index'},\n",
       "    {'href': 'https://emotional-cities.s3.eu-central-1.amazonaws.com/geojson/lisbon_vibrancy_index.geojson',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/geo+json',\n",
       "     'title': 'GeoJson download link for lisbon_vibrancy_index'},\n",
       "    {'href': 'https://emotional-cities.s3.eu-central-1.amazonaws.com/geoparquet/lisbon_vibrancy_index.parquet',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/vnd.apache.parquet',\n",
       "     'title': 'GeoParquet download link for lisbon_vibrancy_index'},\n",
       "    {'href': 'https://emotional-cities.s3.eu-central-1.amazonaws.com/geopackage/lisbon_vibrancy_index.gpkg',\n",
       "     'rel': 'item',\n",
       "     'type': 'application/x-sqlite3',\n",
       "     'title': 'GeoPackage download link for lisbon_vibrancy_index'},\n",
       "    {'href': 'https://emotional.byteroad.net/geoserver/ows?service=WMS&version=1.3.0&request=GetMap&crs={crs}&bbox={bbox}&layers=lisbon_vibrancy_index&width={width}&height={height}&format=image/png',\n",
       "     'rel': 'item',\n",
       "     'type': 'image/png',\n",
       "     'title': 'OGC Web Map Service (WMS)',\n",
       "     'templated': True,\n",
       "     'variables': {'crs': {'description': 'http://www.opengis.net/def/crs/OGC/1.3/CRS84',\n",
       "       'type': 'string',\n",
       "       'enum': ['EPSG:4326']},\n",
       "      'bbox': {'description': '...',\n",
       "       'type': 'array',\n",
       "       'items': {'type': 'number', 'format': 'double'},\n",
       "       'minItems': 4,\n",
       "       'maxItems': 4},\n",
       "      'width': {'description': '...',\n",
       "       'type': 'number',\n",
       "       'format': 'integer',\n",
       "       'minimum': 600,\n",
       "       'maximum': 5000},\n",
       "      'height': {'description': '...',\n",
       "       'type': 'number',\n",
       "       'format': 'integer',\n",
       "       'minimum': 600,\n",
       "       'maximum': 5000}}}]}],\n",
       " 'numberMatched': 114,\n",
       " 'numberReturned': 114,\n",
       " 'links': [{'type': 'application/geo+json',\n",
       "   'rel': 'self',\n",
       "   'title': 'This document as GeoJSON',\n",
       "   'href': 'https://emotional.byteroad.net/collections/ec_catalog/items?f=json&limit=200'},\n",
       "  {'rel': 'alternate',\n",
       "   'type': 'application/ld+json',\n",
       "   'title': 'This document as RDF (JSON-LD)',\n",
       "   'href': 'https://emotional.byteroad.net/collections/ec_catalog/items?f=jsonld&limit=200'},\n",
       "  {'type': 'text/html',\n",
       "   'rel': 'alternate',\n",
       "   'title': 'This document as HTML',\n",
       "   'href': 'https://emotional.byteroad.net/collections/ec_catalog/items?f=html&limit=200'},\n",
       "  {'type': 'application/json',\n",
       "   'title': 'eMOTIONAL Cities Metadata catalog',\n",
       "   'rel': 'collection',\n",
       "   'href': 'https://emotional.byteroad.net/collections/ec_catalog/items'}],\n",
       " 'timeStamp': '2024-12-20T16:08:08.057506Z'}"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_catalogue_query = w.collection_items('ec_catalog', limit=200)\n",
    "my_catalogue_query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "8fa9cd40-d165-4efa-ad6e-1586c3a03623",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "114"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_catalogue_query['numberMatched']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "4306ca9e-f902-4dce-ae38-b43c2a6aea0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CC BY-NC 4.0\n",
      "CC BY-NC 4.0\n",
      "CC BY-NC 4.0\n",
      "CC BY-NC 4.0\n",
      "CC BY-NC 4.0\n",
      "CC BY-NC 4.0\n",
      "CC BY-NC 4.0\n",
      "CC BY-NC 4.0\n",
      "CC BY-NC 4.0\n",
      "CC BY-NC 4.0\n",
      "CC BY-NC 4.0\n",
      "CC BY-NC 4.0\n",
      "CC BY-NC 4.0\n",
      "CC BY-NC 4.0\n",
      "CC BY-NC 4.0\n",
      "CC BY-NC 4.0\n",
      "CC BY-NC 4.0\n",
      "CC BY-NC 4.0\n",
      "CC BY-NC 4.0\n",
      "CC BY-NC 4.0\n",
      "Attribution-NonCommercial-NoDerivatives 4.0 International\n",
      "Attribution-NonCommercial-NoDerivatives 4.0 International\n",
      "Attribution-NonCommercial-NoDerivatives 4.0 International\n",
      "Attribution-NonCommercial-NoDerivatives 4.0 International\n",
      "Attribution-NonCommercial-NoDerivatives 4.0 International\n",
      "Attribution-NonCommercial-NoDerivatives 4.0 International\n",
      "Attribution-NonCommercial-NoDerivatives 4.0 International\n",
      "Attribution-NonCommercial-NoDerivatives 4.0 International\n",
      "Attribution-NonCommercial-NoDerivatives 4.0 International\n",
      "Attribution-NonCommercial-NoDerivatives 4.0 International\n",
      "Attribution-NonCommercial-NoDerivatives 4.0 International\n",
      "Attribution-NonCommercial-NoDerivatives 4.0 International\n",
      "Attribution-NonCommercial-NoDerivatives 4.0 International\n",
      "CC BY-NC 4.0\n",
      "CC BY-NC 4.0\n",
      "CC BY-NC 4.0\n",
      "CC BY-NC 4.0\n",
      "CC BY-NC 4.0\n",
      "CC BY-NC 4.0\n",
      "CC BY-NC 4.0\n"
     ]
    }
   ],
   "source": [
    "for i in range(total):\n",
    "    print(my_catalogue_query['features'][i]['properties']['license'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "80343c67-e7d2-49fb-b1d2-0066382a4a37",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['created', 'updated', 'type', 'title', 'description', 'keywords', 'language', 'themes', 'formats', 'contacts', 'license'])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_catalogue_query['features'][0]['properties'].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "35aa3ae6-70f4-4a7f-8b65-79b1371a7b68",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['urban health',\n",
       " 'health outcomes',\n",
       " 'physical health',\n",
       " 'urban health mapping',\n",
       " 'inner london',\n",
       " '']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_catalogue_query['features'][0]['properties']['keywords']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "12f55c96-6f48-4474-a39e-3441b4d00d86",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in /home/joana/git/ec-data-analysis/lib/python3.12/site-packages (2.2.3)\n",
      "Requirement already satisfied: numpy>=1.26.0 in /home/joana/git/ec-data-analysis/lib/python3.12/site-packages (from pandas) (2.2.0)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /home/joana/git/ec-data-analysis/lib/python3.12/site-packages (from pandas) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /home/joana/git/ec-data-analysis/lib/python3.12/site-packages (from pandas) (2024.2)\n",
      "Requirement already satisfied: tzdata>=2022.7 in /home/joana/git/ec-data-analysis/lib/python3.12/site-packages (from pandas) (2024.2)\n",
      "Requirement already satisfied: six>=1.5 in /home/joana/git/ec-data-analysis/lib/python3.12/site-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n"
     ]
    }
   ],
   "source": [
    "#! pip install pandas\n",
    "import pandas as pd\n",
    "df = pd.DataFrame(columns=['keyword'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67d6ab7e-d10b-4af1-baf8-69ceb34faa41",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "983d20f9-0362-4df4-8e56-d44c8fe73b8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "urban health\n",
      "health outcomes\n",
      "physical health\n",
      "urban health mapping\n",
      "inner london\n",
      "\n",
      "urban health\n",
      "health outcomes\n",
      "physical health\n",
      "urban health mapping\n",
      "inner london\n",
      "\n",
      "urban health\n",
      "health outcomes\n",
      "mental health\n",
      "wellbeing\n",
      "urban health mapping\n",
      "inner london\n",
      "\n",
      "urban health\n",
      "health outcomes\n",
      "mental health\n",
      "wellbeing\n",
      "urban health mapping\n",
      "inner london\n",
      "\n",
      "urban health\n",
      "health outcomes\n",
      "mental health\n",
      "wellbeing\n",
      "urban health mapping\n",
      "inner london\n",
      "\n",
      "urban health\n",
      "health determinants\n",
      "physical environment\n",
      "urban health mapping\n",
      "inner london\n",
      "\n",
      "urban health\n",
      "health determinants\n",
      "physical environment\n",
      "urban health mapping\n",
      "inner london\n",
      "\n",
      "urban health\n",
      "health determinants\n",
      "physical environment\n",
      "urban health mapping\n",
      "inner london\n",
      "\n",
      "urban health\n",
      "health determinants\n",
      "physical environment\n",
      "urban health mapping\n",
      "inner london\n",
      "\n",
      "urban health\n",
      "health determinants\n",
      "physical environment\n",
      "urban health mapping\n",
      "inner london\n",
      "\n",
      "urban health\n",
      "health determinants\n",
      "physical environment\n",
      "urban health mapping\n",
      "inner london\n",
      "\n",
      "urban health\n",
      "health determinants\n",
      "physical environment\n",
      "urban health mapping\n",
      "inner london\n",
      "\n",
      "urban health\n",
      "health determinants\n",
      "physical environment\n",
      "urban health mapping\n",
      "inner london\n",
      "\n",
      "urban health\n",
      "health determinants\n",
      "physical environment\n",
      "urban health mapping\n",
      "inner london\n",
      "\n",
      "urban health\n",
      "health determinants\n",
      "physical environment\n",
      "urban health mapping\n",
      "inner london\n",
      "\n",
      "urban health\n",
      "health determinants\n",
      "physical environment\n",
      "urban health mapping\n",
      "inner london\n",
      "\n",
      "urban health\n",
      "health determinants\n",
      "socioeconomic environment\n",
      "urban health mapping\n",
      "inner london\n",
      "\n",
      "urban health\n",
      "health determinants\n",
      "socioeconomic environment\n",
      "urban health mapping\n",
      "inner london\n",
      "\n",
      "urban health\n",
      "health determinants\n",
      "heath behviour\n",
      "urban health mapping\n",
      "inner london\n",
      "\n",
      "urban health\n",
      "health determinants\n",
      "heath behviour\n",
      "urban health mapping\n",
      "inner london\n",
      "\n",
      "test dataset\n",
      "test dataset\n",
      "test dataset\n",
      "test dataset\n",
      "test dataset\n",
      "test dataset\n",
      "test dataset\n",
      "test dataset\n",
      "test dataset\n",
      "test dataset\n",
      "test dataset\n",
      "test dataset\n",
      "test dataset\n",
      "boundary\n",
      "block group\n",
      "lansing\n",
      "boundary\n",
      "lansing\n",
      "boundary\n",
      "census tract\n",
      "lansing\n",
      "gender ratio\n",
      "lansing\n",
      "older adults\n",
      "seniors\n",
      "lansing\n",
      "income\n",
      "per capita income\n",
      "lansing\n",
      "area deprivation index\n",
      "usa\n",
      "disability\n",
      "lansing\n",
      "disability\n",
      "lansing\n",
      "depression\n",
      "lansing\n",
      "copd\n",
      "lansing\n",
      "coronary hearth disease\n",
      "lansing\n",
      "asthma\n",
      "lansing\n",
      "high blood pressure\n",
      "lansing\n",
      "stroke\n",
      "lansing\n",
      "diabetes\n",
      "lansing\n",
      "obesity\n",
      "lansing\n",
      "physical health\n",
      "lansing\n",
      "pysical activity\n",
      "lansing\n",
      "life expectancy\n",
      "lansing\n",
      "walkability index\n",
      "lansing\n",
      "crime\n",
      "lansing\n",
      "access to food\n",
      "lansing\n",
      "low income\n",
      "low access to food\n",
      "lansing\n",
      "bike to work\n",
      "lansing\n",
      "travel time to work\n",
      "lansing\n",
      "pedestrian road network\n",
      "walkability\n",
      "lansing\n",
      "road network density\n",
      "lansing\n",
      "environmental health index\n",
      "lansing\n",
      "\n",
      "life births\n",
      "urban health\n",
      "health\n",
      "igot\n",
      "lisbon\n",
      "mortality\n",
      "urban health\n",
      "health\n",
      "igot\n",
      "lisbon\n",
      "diabetes\n",
      "urban health\n",
      "health\n",
      "igot\n",
      "lisbon\n",
      "alcohol\n",
      "urban health\n",
      "health\n",
      "life style\n",
      "igot\n",
      "lisbon\n",
      "tobacco\n",
      "urban health\n",
      "health\n",
      "life style\n",
      "igot\n",
      "lisbon\n",
      "obesity\n",
      "urban health\n",
      "health\n",
      "igot\n",
      "lisbon\n",
      "hypertension\n",
      "urban health\n",
      "health\n",
      "igot\n",
      "lisbon\n",
      "dementia\n",
      "urban health\n",
      "mental health\n",
      "lisbon\n",
      "igot\n",
      "sentiment\n",
      "twitter\n",
      "social media\n",
      "igot\n",
      "lisbon\n",
      "urban design\n",
      "urban planning\n",
      "urban determinants\n",
      "igot\n",
      "lisbon\n",
      "urban design\n",
      "urban planning\n",
      "urban determinants\n",
      "igot\n",
      "lisbon\n",
      "urban design\n",
      "urban planning\n",
      "urban determinants\n",
      "igot\n",
      "lisbon\n",
      "urban design\n",
      "urban planning\n",
      "urban determinants\n",
      "igot\n",
      "lisbon\n",
      "urban design\n",
      "urban planning\n",
      "urban determinants\n",
      "igot\n",
      "lisbon\n",
      "social determinants\n",
      "urban design\n",
      "igot\n",
      "lisbon\n",
      "social determinants\n",
      "urban design\n",
      "igot\n",
      "lisbon\n",
      "built environment\n",
      "urban design\n",
      "urban fabric\n",
      "igot\n",
      "lisbon\n",
      "built environment\n",
      "urban design\n",
      "urban fabric\n",
      "igot\n",
      "lisbon\n",
      "built environment\n",
      "urban design\n",
      "urban fabric\n",
      "igot\n",
      "lisbon\n",
      "built environment\n",
      "urban design\n",
      "urban fabric\n",
      "igot\n",
      "lisbon\n",
      "urban design\n",
      "urban health\n",
      "walkability\n",
      "igot\n",
      "lisbon\n",
      "urban morphology\n",
      "igot\n",
      "lisbon\n",
      "urban density\n",
      "urban design\n",
      "urban planning\n",
      "igot\n",
      "lisbon\n",
      "urban planning\n",
      "urban health\n",
      "igot\n",
      "lisbon\n",
      "green\n",
      "urban\n",
      "green areas\n",
      "igot\n",
      "lisbon\n",
      "pollution\n",
      "noise\n",
      "noise pollution\n",
      "urban planning\n",
      "igot\n",
      "lisbon\n",
      "air pollution\n",
      "pollution\n",
      "air quality\n",
      "urban planning\n",
      "igot\n",
      "lisbon\n",
      "weather\n",
      "urban climate\n",
      "urban comfort\n",
      "climate change\n",
      "igot\n",
      "lisbon\n",
      "weather\n",
      "urban climate\n",
      "urban comfort\n",
      "climate change\n",
      "igot\n",
      "lisbon\n",
      "weather\n",
      "urban climate\n",
      "urban comfort\n",
      "climate change\n",
      "igot\n",
      "lisbon\n",
      "air pollution\n",
      "pollution\n",
      "air quality\n",
      "urban planning\n",
      "igot\n",
      "lisbon\n",
      "green\n",
      "urban\n",
      "green areas\n",
      "igot\n",
      "lisbon\n",
      "drug prescription\n",
      "urban health\n",
      "mental health\n",
      "lisbon\n",
      "igot\n",
      "drug prescription\n",
      "urban health\n",
      "mental health\n",
      "lisbon\n",
      "igot\n",
      "drug prescription\n",
      "urban health\n",
      "mental health\n",
      "lisbon\n",
      "igot\n",
      "anxiety\n",
      "urban health\n",
      "mental health\n",
      "lisbon\n",
      "igot\n",
      "depression\n",
      "urban health\n",
      "mental health\n",
      "lisbon\n",
      "igot\n",
      "air pollution\n",
      "pollution\n",
      "air quality\n",
      "urban planning\n",
      "dtu\n",
      "copenhagen\n",
      "urban health\n",
      "health\n",
      "dtu\n",
      "copenhagen\n",
      "urban health\n",
      "health\n",
      "dtu\n",
      "copenhagen\n",
      "urban health\n",
      "health\n",
      "dtu\n",
      "copenhagen\n",
      "urban health\n",
      "health\n",
      "dtu\n",
      "copenhagen\n",
      "urban health\n",
      "health\n",
      "dtu\n",
      "copenhagen\n",
      "urban health\n",
      "health\n",
      "dtu\n",
      "copenhagen\n",
      "urban health\n",
      "health\n",
      "dtu\n",
      "copenhagen\n",
      "zones\n",
      "traffic zones\n",
      "dtu\n",
      "copenhagen\n",
      "land use\n",
      "poi\n",
      "urban planning\n",
      "dtu\n",
      "copenhagen\n",
      "noise\n",
      "pollution\n",
      "urban planning\n",
      "dtu\n",
      "copenhagen\n",
      "London\n",
      "Outdoor\n",
      "Walk\n",
      "Microclimate\n",
      "Biosignals\n",
      "urban vitality\n",
      "urban planning\n",
      "igot\n",
      "lisbon\n"
     ]
    }
   ],
   "source": [
    "for key in my_catalogue_query['features']:\n",
    "    if 'keywords' in key['properties']:\n",
    "        #print(key['properties']['keywords'])\n",
    "        #print(type(key['properties']['keywords']))\n",
    "        for val in key['properties']['keywords']:\n",
    "            new_row = pd.DataFrame({'keyword': [val]})\n",
    "            df = pd.concat([df, new_row], ignore_index=True)\n",
    "            print(val)\n",
    "    #else:\n",
    "        #print (key['properties'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "c8c7462d-a338-458b-adb7-89b4da4f5fb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>keyword</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>urban health</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>health outcomes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>physical health</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>urban health mapping</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>inner london</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                keyword\n",
       "0          urban health\n",
       "1       health outcomes\n",
       "2       physical health\n",
       "3  urban health mapping\n",
       "4          inner london"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "57360863-aedc-4b84-adc5-6acf01990061",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "my_catalogue_query2 = w.collection_items('ec_catalog', q='London', limit=100)\n",
    "total = my_catalogue_query2['numberMatched']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "22380826-6202-4598-aab0-fa64a52182e8",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting WordCloud\n",
      "  Downloading wordcloud-1.9.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.4 kB)\n",
      "Requirement already satisfied: numpy>=1.6.1 in /home/joana/git/ec-data-analysis/lib/python3.12/site-packages (from WordCloud) (2.2.0)\n",
      "Collecting pillow (from WordCloud)\n",
      "  Downloading pillow-11.0.0-cp312-cp312-manylinux_2_28_x86_64.whl.metadata (9.1 kB)\n",
      "Collecting matplotlib (from WordCloud)\n",
      "  Downloading matplotlib-3.10.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)\n",
      "Collecting contourpy>=1.0.1 (from matplotlib->WordCloud)\n",
      "  Downloading contourpy-1.3.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.4 kB)\n",
      "Collecting cycler>=0.10 (from matplotlib->WordCloud)\n",
      "  Downloading cycler-0.12.1-py3-none-any.whl.metadata (3.8 kB)\n",
      "Collecting fonttools>=4.22.0 (from matplotlib->WordCloud)\n",
      "  Downloading fonttools-4.55.3-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (165 kB)\n",
      "\u001b[2K     \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m165.1/165.1 kB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\n",
      "Collecting kiwisolver>=1.3.1 (from matplotlib->WordCloud)\n",
      "  Downloading kiwisolver-1.4.7-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.3 kB)\n",
      "Requirement already satisfied: packaging>=20.0 in /home/joana/git/ec-data-analysis/lib/python3.12/site-packages (from matplotlib->WordCloud) (24.2)\n",
      "Collecting pyparsing>=2.3.1 (from matplotlib->WordCloud)\n",
      "  Using cached pyparsing-3.2.0-py3-none-any.whl.metadata (5.0 kB)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /home/joana/git/ec-data-analysis/lib/python3.12/site-packages (from matplotlib->WordCloud) (2.9.0.post0)\n",
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      "\u001b[?25hUsing cached pyparsing-3.2.0-py3-none-any.whl (106 kB)\n",
      "Installing collected packages: pyparsing, pillow, kiwisolver, fonttools, cycler, contourpy, matplotlib, WordCloud\n",
      "Successfully installed WordCloud-1.9.4 contourpy-1.3.1 cycler-0.12.1 fonttools-4.55.3 kiwisolver-1.4.7 matplotlib-3.10.0 pillow-11.0.0 pyparsing-3.2.0\n"
     ]
    }
   ],
   "source": [
    "! pip install WordCloud\n",
    "from wordcloud import WordCloud\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "f44138da-d580-44fa-a675-e870ede1a3cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "text = \" \".join(df['keyword'].dropna())  # .dropna() to remove any NaN values\n",
    "\n",
    "# Generate the word cloud\n",
    "wordcloud = WordCloud(width=800, height=400, background_color='white').generate(text)\n",
    "\n",
    "# Display the word cloud using matplotlib\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.imshow(wordcloud, interpolation='bilinear')\n",
    "plt.axis(\"off\")  # Hide axes\n",
    "plt.savefig('wordcloud_image.png', format='png', bbox_inches='tight', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "a6398baa-4769-456a-825e-651e162dc57f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "5aca9cb5-3b2d-49ba-a69c-729b62d5de4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_catalogue_query2 = w.collection_items('ec_catalog', q='London', limit=100)\n",
    "total = my_catalogue_query2['numberMatched']\n",
    "total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "9a290637-b8c9-4e3b-9b4b-4104cfa5f644",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "London Outdoor Walk: WhiteChapel Subject OE309002\n",
      "Map of the ratio of male in London\n",
      "Land use diversity in London\n",
      "Population density map in London\n",
      "Map of the ratio of elder people in London\n",
      "London outdoor walk test data: Humidity CO2\n",
      "London outdoor walk test data: Temperature CO2\n",
      "London outdoor walk test data: GPS Data\n",
      "London outdoor walk test data: Humidity\n",
      "Prevalence rates of obesity in London\n",
      "Prevalence rates of depression in London\n",
      "Prevalence rates of dementia in London\n",
      "Annual mean NO2 concentration in London\n",
      "Major summer heat spots in London\n",
      "Cycling routes density map in London\n",
      "Map of ratio of active people in Inner London\n",
      "Map of number of recorded crimes in Inner London\n",
      "London outdoor walk test data: Air Quality Humidity\n",
      "London outdoor walk test data: Air Quality Temperature\n",
      "London outdoor walk test data: CO2 Concentration\n",
      "London outdoor walk test data: GPS Internal Clock\n",
      "London outdoor walk test data: Sound Pressure levels\n",
      "Prevalence rates of cardiovascular diseases in London\n",
      "Proportion of population with access to public open space in London\n",
      "Normalized difference vegetation index (NDVI) in London\n",
      "London outdoor walk test data: Air Quality Air Pressure\n",
      "London outdoor walk test data: Ambient Light\n",
      "London outdoor walk test data: Synchronization pulses\n",
      "Prevalence rates of mental health issues in London\n",
      "London outdoor walk test data: Air Quality Air Quality Index\n",
      "Annual average noise levels of rail noise in London\n",
      "Risk of flooding from rivers and seas in London\n",
      "Annual mean PM2.5 based on output areas average concentrations in London\n",
      "Annual mean PM10 based on output areas average concentrations in London\n"
     ]
    }
   ],
   "source": [
    "for i in range(total):\n",
    "    print(my_catalogue_query2['features'][i]['properties']['title'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "af2028ac-bacb-4852-965e-584ccb5e6fb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_catalogue_query2 = w.collection_items('ec_catalog', q='Copenhagen', limit=100)\n",
    "total = my_catalogue_query2['numberMatched']\n",
    "total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "cd29504c-402b-4db0-8e8e-d2b278b8c515",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Land Use and POIs for Copenhagen\n",
      "Spatial Zones for the greater Copenhagen Area\t\n",
      "Air pollution data for the zones considered in the greater Copenhagen\n",
      "Demographic and health 2012\n",
      "Demographic and health 2013\n",
      "Demographic and health 2014\n",
      "Demographic and health 2015\n",
      "Demographic and health 2016\n",
      "Demographic and health 2017\n",
      "Demographic and health 2018\n",
      "Noise data for the zones considered in the greater Copenhagen\n"
     ]
    }
   ],
   "source": [
    "for i in range(total):\n",
    "    print(my_catalogue_query2['features'][i]['properties']['title'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "697cef4e-fe7e-4a62-ae91-a5e478a9974b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_catalogue_query2 = w.collection_items('ec_catalog', q='Lisbon', limit=100)\n",
    "total = my_catalogue_query2['numberMatched']\n",
    "total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "3954f3a3-34e4-40a3-928c-fc8267c9a2a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fMRI Raw Data (WP5: Brain as predictor)\n",
      "Altimetry\n",
      "Urban Image Stimulus Set (WP5)\n",
      "Mortality rate\n",
      "Prevalence of patients with Diabetes Mellitus\n",
      "Prevalence of patients with obesity\n",
      "Prevalence of patients with hypertension\n",
      "Positive tweets\n",
      "Youth people ratio\n",
      "Elderly people ratio\n",
      "Density of fast-food outlets\n",
      "Normalized Difference Vegetation Index (NDVI)\n",
      "Distance to green spaces\n",
      "Vibrancy index\n",
      "Life births rate\n",
      "Prevalence of patients diagnosed with dementia\n",
      "Walkability index\n",
      "Patients diagnosed with anxiety disorder\n",
      "Patients diagnosed with depressive disorder\n",
      "Prevalence of patients with chronic alcohol abuse\n",
      "Prevalence of patients with tobacco abuse\n",
      "Purchasing power\n",
      "Unemployed people ratio\n",
      "People with low literacy level ratio\n",
      "Population density\n",
      "Gender ratio\n",
      "Average age of buildings\n",
      "Buildings with repair needs ratio\n",
      "Average building height\n",
      "Building area ratio\n",
      "Beds / customers in tourist accommodations\n",
      "Noise level\n",
      "Drug prescription of anxiolytics (N05B)\n",
      "Drug prescription of antidepressants (N06A)\n",
      "Drug prescription of antidementia (N06D)\n",
      "Particulate Matter (PM2.5)\n",
      "Mean temperature\n",
      "Vulnerability to excessive heat index\n",
      "Vulnerability to flash floods index\n",
      "Nitrogen Dioxide (NO2)\n"
     ]
    }
   ],
   "source": [
    "for i in range(total):\n",
    "    print(my_catalogue_query2['features'][i]['properties']['title'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "7508113b-e829-4c78-8367-0f5c8fd71f93",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_catalogue_query2 = w.collection_items('ec_catalog', q='Michigan', limit=100)\n",
    "total = my_catalogue_query2['numberMatched']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "4fdc1f02-c935-44f9-be1b-fee811e5aa59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "d3d66cdc-b22b-43ca-8e16-708265bc4ee7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Block Group\n",
      "City Boundary\n",
      "Census Tracts\n",
      "Gender Ratio\n",
      "Percentage, Ratio and Number of People Aged 65+\n",
      "Per Capita Income\n",
      "Area Deprivation Index-State\n",
      "Percentage of people with Disability\n",
      "Number of People with Disability (aged 5 and above)\n",
      "Crude percent of adults with depression\n",
      "CrudeP - Chroni Obstructive Pulmonary Disease - Age Above 18 years\n",
      "CrudeP - Coronary Heart Disease - Age Above 18 years\n",
      "CrudeP - Current Ashthma - Age Above 18 years\n",
      "CrudeP - High Blood Pressure - Age Above 18 years\n",
      "CrudeP - Stroke -Age Above 18 Years\n",
      "CrudeP - Diabeties - Age Above 18 years\n",
      " CrudeP - Obesity - Age Above 18 years\n",
      "CrudeP - Physical Health Not Good for People Aged Above 18 Years\n",
      "CrudeP - No Leisure Time Physical Activity - Age Above 18 years\n",
      "Life Expectancy at birth\n",
      "National Walkability Index\n",
      "Lansing crime data\n",
      "Low Access to Food\n",
      "Low Income Low Access to Food\n",
      "Percent of People Travel by Bicycle to Work\n",
      "Average Travel Time to Work\n",
      "Pedestrian-Oriented Road Network Density \n",
      "Multi-modal Road Network Density \n",
      "Environmental Health Index\n"
     ]
    }
   ],
   "source": [
    "for i in range(total):\n",
    "    print(my_catalogue_query2['features'][i]['properties']['title'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "23eec59c-ca8e-4b0c-90af-8c79e048af14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_catalogue_query2 = w.collection_items('ec_catalog', q='Lisbon', limit=100)\n",
    "total = my_catalogue_query2['numberMatched']\n",
    "total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "93f0db9a-5533-4c49-a80f-8bf99287735e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fMRI Raw Data (WP5: Brain as predictor)\n",
      "Altimetry\n",
      "Urban Image Stimulus Set (WP5)\n",
      "Mortality rate\n",
      "Prevalence of patients with Diabetes Mellitus\n",
      "Prevalence of patients with obesity\n",
      "Prevalence of patients with hypertension\n",
      "Positive tweets\n",
      "Youth people ratio\n",
      "Elderly people ratio\n",
      "Density of fast-food outlets\n",
      "Normalized Difference Vegetation Index (NDVI)\n",
      "Distance to green spaces\n",
      "Vibrancy index\n",
      "Life births rate\n",
      "Prevalence of patients diagnosed with dementia\n",
      "Walkability index\n",
      "Patients diagnosed with anxiety disorder\n",
      "Patients diagnosed with depressive disorder\n",
      "Prevalence of patients with chronic alcohol abuse\n",
      "Prevalence of patients with tobacco abuse\n",
      "Purchasing power\n",
      "Unemployed people ratio\n",
      "People with low literacy level ratio\n",
      "Population density\n",
      "Gender ratio\n",
      "Average age of buildings\n",
      "Buildings with repair needs ratio\n",
      "Average building height\n",
      "Building area ratio\n",
      "Beds / customers in tourist accommodations\n",
      "Noise level\n",
      "Drug prescription of anxiolytics (N05B)\n",
      "Drug prescription of antidepressants (N06A)\n",
      "Drug prescription of antidementia (N06D)\n",
      "Particulate Matter (PM2.5)\n",
      "Mean temperature\n",
      "Vulnerability to excessive heat index\n",
      "Vulnerability to flash floods index\n",
      "Nitrogen Dioxide (NO2)\n"
     ]
    }
   ],
   "source": [
    "for i in range(total):\n",
    "    print(my_catalogue_query2['features'][i]['properties']['title'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "e4342abf-148d-4aff-a0e3-34fdc0730fb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          geo  collections\n",
      "0      London           34\n",
      "1    Michigan           29\n",
      "2      Lisbon           40\n",
      "3  Copenhagen           11\n"
     ]
    }
   ],
   "source": [
    "data = {\n",
    "    'geo': ['London', 'Michigan', 'Lisbon', 'Copenhagen'],\n",
    "    'collections': [34, 29, 40, 11]\n",
    "}\n",
    "\n",
    "# Create the DataFrame\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# Display the DataFrame\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "e00e7714-d14a-4541-902e-d4dbcab57d8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "plt.pie(df['collections'], labels=df['geo'], autopct='%1.1f%%', startangle=140)\n",
    "\n",
    "# Add a title\n",
    "plt.title('Collections by Region')\n",
    "\n",
    "plt.savefig('collections_by_region.png', format='png', bbox_inches='tight', dpi=300)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "76bd8679-b1b8-4bdc-9a6e-a27c220e1329",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install pyproj\n",
    "from owslib.wfs import WebFeatureService\n",
    "wfs20 = WebFeatureService(url='https://emotional.byteroad.net/geoserver/ows?service=WFS', version='2.0.0')\n",
    "wfs20.identification.title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "9d60509e-d2ba-4d3b-a81f-e3d2b6ab6b92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "103"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list(wfs20.contents))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "77f064fd-62d2-456f-9d50-d140cfc298bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    status  collections\n",
      "0   public          103\n",
      "1  private           11\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = {\n",
    "    'status': ['public', 'private'],\n",
    "    'collections': [103, 11]\n",
    "}\n",
    "\n",
    "# Create the DataFrame\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# Display the DataFrame\n",
    "print(df)\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.bar(df['status'], df['collections'], color='skyblue', edgecolor='black')\n",
    "\n",
    "# Add title and labels\n",
    "plt.title('EC Datasets')\n",
    "plt.xlabel('Category')\n",
    "plt.ylabel('Values')\n",
    "plt.savefig('dataset_status.png', format='png', bbox_inches='tight', dpi=300)\n",
    "# Show the plot\n",
    "plt.tight_layout()  # Adjust layout for better fit\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b4bdb65-3dcb-4899-b263-b72c7d8ca36e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}