2c:null 2d:["$","title",null,{"children":"Demo Data Catalogue | Emu Analytics | Observable"}] 32:[] 30:{"id":"05408df97e0d881e","slug":"demo-data-catalogue","title":"Demo Data Catalogue","thumbnail":"9efd81d6bcc7dadcff535f25b8aea1b12ff639989bae4e317f7867939d326ad7","access_level":"public","create_time":"$D2021-02-03T17:41:58.252Z","update_time":"$D2023-12-05T15:10:56.754Z","view_time":null,"first_public_time":"$D2021-02-03T18:07:41.502Z","has_unpublished_edits":null,"fork":true,"stars":0,"starred":false,"owner":{"type":"team","id":"374dcc63ddd06df5","login":"emuanalytics","name":"Emu Analytics","avatar_url":"https://avatars-next.observableusercontent.com/be971faab0532b04c1173a4e3af506aaf7228fe01f5d68db47f0459eff95d96d","tier":"pro","bio":""},"version":322,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"bdf516c467dde4e8","login":"robinsummerhill","name":"Robin Summerhill","avatar_url":"https://avatars-next.observableusercontent.com/a9ff6855f02eecd3b45d5c8360553ae7dde7a80f4b79d56742af72792b79ad58","tier":null,"bio":"Head of Technology at Emu Analytics. Geospatial visualisation wizardry."}],"members":[{"type":"team","id":"374dcc63ddd06df5","login":"emuanalytics","name":"Emu Analytics","avatar_url":"https://avatars-next.observableusercontent.com/be971faab0532b04c1173a4e3af506aaf7228fe01f5d68db47f0459eff95d96d","tier":"pro","role":"editor"},{"type":"individual","id":"bdf516c467dde4e8","login":"robinsummerhill","name":"Robin Summerhill","avatar_url":"https://avatars-next.observableusercontent.com/a9ff6855f02eecd3b45d5c8360553ae7dde7a80f4b79d56742af72792b79ad58","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":4,"collection":{"id":"a648749434bc8b5a","slug":"flo-w-examples","title":"Flo.w Examples","owner":{"id":"374dcc63ddd06df5","login":"emuanalytics","name":"Emu Analytics"}},"type":"notebook","body":{"titling":"auto","title":"Demo Data Catalogue","cells":[{"id":0,"value":"md`# Demo Data Catalogue`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":137,"value":"toc('h2')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":96,"value":"md`## Demo API Key`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":101,"value":"md`The API key shown below provides read-only access to the demo data described in this catalogue:`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":9,"value":"apiKey = 'b6b77cc4-904a-46d1-aa0f-3bf3848ce4c7'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":104,"value":"md`## Available Datasets`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":39,"value":"datasetTable(context)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":107,"value":"md`----`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":43,"value":"md`## \\`airquality\\` - point`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":53,"value":"md`The \\`airquality\\` dataset contains NO₂ (nitrogen dioxide) readings from various air quality sensors around London. NO₂ concentration is in units of ppm (parts per million).\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":50,"value":"datasetAttributes('airquality', context)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":62,"value":"html`\n\n\n\n \n \n \n\n \n \n circle-color: rgba(255, 255, 255, 0.3);\n circle-stroke-color: yellow;\n circle-stroke-width: interpolate(linear(), zoom(), 10, 0, 11, 2);\n circle-radius: @no2 ? @no2 / 5 : 5;\n \n\n \n \n text-field: @name;\n text-size: interpolate(linear(), zoom(), 12, 10, 18, 20);\n text-color: white;\n text-variable-anchor: ['top'];\n text-radial-offset: 1.8;\n \n\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":69,"value":"md`\n----\n## \\`london_boroughs\\` - polygon`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":72,"value":"md`The \\`london_boroughs\\` dataset contains polygons for the 33 London boroughs.\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":76,"value":"datasetAttributes('london_boroughs', context)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":87,"value":"html`\n\n\n\n \n \n \n\n \n \n line-width: 2;\n line-color: white;\n \n\n \n \n text-field: @name;\n text-color: white;\n text-size: 10;\n \n\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":112,"value":"md`\n----\n## \\`borough-data\\` - non-geospatial`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":129,"value":"md`The \\`borough-data\\` dataset contains various employment and income indicators for the 33 London boroughs. Can be combined with the the \\`london_boroughs\\` dataset using [data join](https://developer.emu-analytics.net/docs/flow-rdf/components/map/map-data-join.html#examples).`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":114,"value":"datasetAttributes('borough_data', context)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":134,"value":"md`### Sample Data`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":120,"value":"table(sampleBoroughData, {\n style: 'compact'\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":118,"value":"sampleBoroughData = context.flowClient.datasets.query('borough_data', {\n limit: 10\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":250,"value":"md`\n----\n## \\`pop_density\\` - polygon`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":257,"value":"md`The \\`pop_density\\` dataset contains LSOA ([lower super output areas](https://data.london.gov.uk/dataset/super-output-area-population-lsoa-msoa-london)) polygons for England and Wales. Each LSOA has attributes for area, population and population density (pop/km²).`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":255,"value":"datasetAttributes('pop_density', context)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":240,"value":"html`\n\n\n\n \n \n \n\n \n \n fill-color: from-palette('RdYlGn', @pop_den, 0, 1000, 3000, 5000, 50000, true);\n fill-outline-color: white;\n \n\n \n \n text-field: @lsoa11nm;\n text-color: white;\n text-size: 10;\n \n\n\n`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":297,"value":"md`\n----\n## \\`greenspace\\` - polygon`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":295,"value":"md`The \\`greenspace\\` dataset contains LSOA ([lower super output areas](https://data.london.gov.uk/dataset/super-output-area-population-lsoa-msoa-london)) polygons for England and Wales. A greenspace index is calculated for each LSOA by dividing the area classified as greenspace by the total area of the LSOA.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":292,"value":"datasetAttributes(\"greenspace\", context)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":284,"value":"html`\n\n\n\n \n \n \n\n \n \n fill-color: from-palette('RdYlGn', @greenspace_index, 0, 0.006981, 0.027576, 0.066794, 0.70094);\n fill-outline-color: white;\n \n\n \n \n text-field: @name;\n text-color: white;\n text-size: 10;\n \n\n\n`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":171,"value":"md`\n----\n## \\`rail_lines\\` - line`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":176,"value":"md`The \\`rail_lines\\` dataset contains line segments for England, Wales and Scotland railway lines.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":167,"value":"datasetAttributes('rail_lines', context)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":169,"value":"html`\n\n\n\n \n \n \n\n \n \n line-color: red;\n \n\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":180,"value":"md`\n----\n## \\`dft_roadworks_live\\` - point`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":211,"value":"md`The \\`dft_roadworks_live\\` dataset contains live roadwork updates in the UK. The dataset is based on an 'upsert' so only contains the event update for every roadwork. However to avoid seeing stale updates on the map its best to use another query to return only the very latest updates in a time window or to filter on active roadworks only. \\`dft_roadworks_filtered\\`, used in the map below, is an example of this in action.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":215,"value":"datasetAttributes('dft_roadworks_live', context)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":218,"value":"html`\n\n\n\n \n \n \n \n \n\n \n \n circle-radius: 4;\n circle-color: pink;\n circle-opacity: 0.6;\n \n\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":182,"value":"md`\n----\n## \\`opensky_events_live\\` - point`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":195,"value":"md`The \\`opensky_events_live\\` dataset contains live aircraft positions globally. The dataset is based on an 'upsert' so only contains the latest positions for every aircraft. However to avoid seeing stale positions on the map its best to use another query to return only the very latest positions in a time window. \\`opensky_events_filtered\\`, used in the map below, is an example of this in action.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":198,"value":"datasetAttributes('opensky_events_live', context)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":187,"value":"html`\n\n\n\n \n \n \n \n \n\n \n \n circle-radius: 4;\n circle-color: yellow;\n circle-opacity: 0.6;\n \n\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":183,"value":"md`\n----\n## \\`train_positions_live\\` - point`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":228,"value":"md`The \\`train_positions_live\\` dataset contains live train positions in the UK. The dataset is based on an 'upsert', but its primary key is based on headcode and STANOX (location). This means that the dataset will hold all the latest data for all routes in the UK, not just the individual trains. As a result its possible to right historical queries against. In the below example the dataset \\`train_positions_filtered\\` simply returns the latest trains in the last given time interval.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":231,"value":"datasetAttributes('train_positions_live', context)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":234,"value":"html`\n\n\n\n \n \n \n \n \n\n \n \n circle-radius: 4;\n circle-color: rgb(102, 255, 255);\n circle-opacity: 0.6;\n \n\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":4,"value":"md`## Appendix `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":11,"value":"import {\n initializeFlow,\n datasetTable,\n tileSourceTable,\n datasetSource,\n datasetAttributes\n} from '@emuanalytics/flow-helper-library'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":14,"value":"context = {\n const context = await initializeFlow(apiKey);\n return context;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":20,"value":"import {\n button,\n slider,\n number,\n text,\n select,\n date,\n time\n} from \"@robinsummerhill/inputs\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":23,"value":"import { table } from '@tmcw/tables'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":123,"value":"// Make tables horizontally scrollable\nhtml`\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":139,"value":"import { toc } from \"@mbostock/toc\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":207,"value":"html`\n\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W32","stdlib":"1"}} 31:"$30:body"