29:null 2a:["$","title",null,{"children":"Deep Dive on Location Scoring | Targomo | Observable"}] 2f:T4ff,## Glossary Before jumping in, let's take a look at some important concepts: ### Criteria Criteria are the **base measurements** which Targomo uses to evaluate a location. They can be based on **points of interest** (nearest, count), **statistics** (basic summary stats, combined equations), or **advanced mathematical analyses**. ### Criteria Group Criteria Groups are **collections** of criteria which represent a larger concept. ### Scores Scores are the **measurements of** individual **criteria** for a specific location. Scores are raw values, which represent the measurement method for the specific criteria.
If the criteria were _count of reachable cafés_, the score might be **15**. If the criteria were _${tex`m^2`} reachable area_, the score might be **5506.97**.
However, to be meaningful, scores should be _normalized_. We'll see how to do this. ### Ratings The true value of the Location Scoring API is the ratings. Ratings are pre-defined collections of criteria, the scores of which are **always normalized to a surrounding area**, by the service. To avoid comparing a location in (massive) Berlin to one in (smaller) Stuttgart, ratings are always relative to their surrounding boundary. --- In this notebook we'll explore each of these concepts.30:T417,{ let container = html`

`; // Give the container dimensions. yield container; // set the targomo client const client = tgmCoreClient("westcentraleurope"); // Create the "map" object with the mapboxgl.Map constructor, referencing // the container div let map = new mapboxgl.Map({ container, center: centroid, zoom: 10.5, style: client.basemaps.getGLStyleURL("Light"), pitch: 45, attributionControl: false }) .addControl(new mapboxgl.NavigationControl()) .addControl( new mapboxgl.AttributionControl({ compact: true, customAttribution: attribution }) ); let markers = []; for (let i = 0; i < locations.length; ++i) { const popup = new mapboxgl.Popup({ offset: 25, closeButton: false }).setText("Location " + (i + 1)); let marker = new mapboxgl.Marker({ draggable: false, color: getColor(i, locations.length) }); marker.setLngLat([locations[i].lng, locations[i].lat]); marker.setPopup(popup); marker.addTo(map); markers.push(marker); } const osmQuery = { key: "group", value: "supermarket" }; map.on("load", async () => { await addStatisticsLayer(); // Showing each cell's population map.on("click", "statistics", function (e) { var description = "Population: ".concat( Math.trunc(e.features[0].properties[stats.id]) ); new mapboxgl.Popup().setLngLat(e.lngLat).setHTML(description).addTo(map); }); }); const stats = { group: 100, id: 3 }; async function addStatisticsLayer() { const populationMeta = await tgmCoreClient( "westcentraleurope" ).statistics.metadataKey(stats.group, { id: stats.id }); // get the vectortiles url const tileRoute = await tgmCoreClient( "westcentraleurope" ).statistics.tileRoute(stats.group, [{ id: stats.id }]); const colorBreakpointValues = calculateColorBreakpoints(populationMeta); const colorDef = buildColorDefinition(colorBreakpointValues); const breakpointsValues = calculateHeightBreakpoints(populationMeta); const heightDef = calculateCellHeights(breakpointsValues); addTilesToMap(tileRoute, stats.group, colorDef, heightDef); hideUnrecheableCells(); } function calculateColorBreakpoints(metadata) { const colors = chroma.scale("YlOrRd").correctLightness().colors(7); return metadata.breakpoints.kmeans.c7.map((b, i) => [b, colors[i]]); } function buildColorDefinition(colorBreakpoints) { return [ "interpolate", ["linear"], ["get", `${stats.id}`], ...colorBreakpoints.flat() ]; } function calculateHeightBreakpoints(metadata) { return metadata.breakpoints.equal_interval.c9.map((b, i) => [b, i * 125]); } function calculateCellHeights(heightBreakpoints) { return [ "interpolate", ["linear"], ["get", `${stats.id}`], ...heightBreakpoints.flat() ]; } function addTilesToMap(tileRoute, statsGroup, colorDef, heightDef) { map.addLayer( { id: "statistics", type: "fill-extrusion", source: { type: "vector", tiles: [tileRoute], minzoom: 10 }, "source-layer": `${statsGroup}`, // source-layer is the statistic group id layout: {}, paint: { "fill-extrusion-color": colorDef, "fill-extrusion-opacity": 0.75, "fill-extrusion-height": heightDef } }, "place_other" ); } async function hideUnrecheableCells() { // Getting reachable cells const statistics = await tgmCoreClient( "westcentraleurope" ).statistics.travelTimes(locations, { maxEdgeWeight: maxEdgeWeight * 60, travelType: Object.keys(travel)[0], statisticsGroup: stats.group, statistics: [{ id: stats.id }] }); // Showing only reachable cells map.setFilter("statistics", [ "match", ["get", "id"], Object.keys(statistics).map((k) => +k), true, false ]); } }33:T51a,{ let container = html`

Young+Nightclub Score

${name}: ${ combinedResult[name].scores.weightedAverage.toFixed(2) 100 }/100*

` ); let marker = new mapboxgl.Marker({ draggable: false, color: getColorScale( combinedResult[name].scores.weightedAverage, locations.length ) }); marker.setLngLat([locations[i].lng, locations[i].lat]); marker.setPopup(popup); marker.addTo(map); } }34:[{"id":"e6193b9c3a8b106a1318b4265160ae64b4d02ac82f2b257501bc018fd6f586f149332e744377abc141582974c1094f11482193ccce394f42ba4553aad9564bda","href":"https://static.observableusercontent.com/files/e6193b9c3a8b106a1318b4265160ae64b4d02ac82f2b257501bc018fd6f586f149332e744377abc141582974c1094f11482193ccce394f42ba4553aad9564bda","name":"Screen Shot 2022-04-21 at 15.25.51.png","type":"image/png","lastModified":1650547576999.066,"size":614990}] 2d:{"id":"1a672ba798011d3d","slug":"deep-dive-on-location-scoring","title":"Deep Dive on Location Scoring","thumbnail":"b9f2b94bb730e27833e543e9659d15d38910c4000d1a927a2af5a68e3e043776","access_level":"public","create_time":"$D2022-01-07T11:09:02.195Z","update_time":"$D2025-05-07T08:30:35.924Z","view_time":null,"first_public_time":"$D2022-04-22T13:48:30.434Z","has_unpublished_edits":null,"fork":false,"stars":3,"starred":false,"owner":{"type":"team","id":"b873ed05f5a7d959","login":"targomo","name":"Targomo","avatar_url":"https://avatars-next.observableusercontent.com/39645f1242d4403a667e1c0e54a0d6755d36418f30088e89a9c65e6f84f9a3bf","tier":"starter_2024"},"version":1368,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"a217b68901b4272f","login":"targomo","name":"Targomo","avatar_url":"https://avatars-next.observableusercontent.com/39645f1242d4403a667e1c0e54a0d6755d36418f30088e89a9c65e6f84f9a3bf","tier":null},{"type":"individual","id":"9e1c3fb87df742fb","login":"alexperathoner","name":"Alex Perathoner","avatar_url":"https://avatars-next.observableusercontent.com/08735dedad3bbf25efd8747dab81b538a8679f29bc7fc4dcf938a4ddb520f068","tier":null},{"type":"individual","id":"59ca7144f8ccf1d1","login":"abenrob","name":"Adam Roberts","avatar_url":"https://avatars-next.observableusercontent.com/35b4f71c800d4e6c9e2cfa93d55a4f89515f41428620d3050060df33504db2e1","tier":null}],"members":[{"type":"individual","id":"a217b68901b4272f","login":"targomo","name":"Targomo","avatar_url":"https://avatars-next.observableusercontent.com/39645f1242d4403a667e1c0e54a0d6755d36418f30088e89a9c65e6f84f9a3bf","tier":null,"role":"editor"}],"comments":[{"id":"4bf93c3c2ee88124","reply_to_id":null,"content":"Let's start with many more. At least 10, if not many more. Consider just making a grid (https://turfjs.org/docs/#pointGrid) and mapping the features to the id,lng,lat format. This will make it easy to default to lots of points.","create_time":"$D2022-01-17T13:47:42.123Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"a217b68901b4272f","login":"targomo","name":"Targomo","avatar_url":"https://avatars-next.observableusercontent.com/39645f1242d4403a667e1c0e54a0d6755d36418f30088e89a9c65e6f84f9a3bf","tier":null}},{"id":"f00acd7f1da3639f","reply_to_id":"4bf93c3c2ee88124","content":"Id can just be `Location ${index+1}`...","create_time":"$D2022-01-17T13:52:13.253Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"a217b68901b4272f","login":"targomo","name":"Targomo","avatar_url":"https://avatars-next.observableusercontent.com/39645f1242d4403a667e1c0e54a0d6755d36418f30088e89a9c65e6f84f9a3bf","tier":null}}],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":1,"collection":{"id":"37ae7df3d4c3aafb","slug":"targomo-deep-dives","title":"Targomo Deep Dives","owner":{"id":"b873ed05f5a7d959","login":"targomo","name":"Targomo"}},"type":"notebook","body":{"title":"Deep Dive on Location Scoring","cells":[{"id":47,"value":"tgmIntro(\n \"Deep Dive on Location Scoring\"\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1332,"value":"image = FileAttachment(\"Screen Shot 2022-04-21 at 15.25.51.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3,"value":"md`## Global scoring\nThe Location Scoring API is a resource to analyse the quality of a given list of locations according to **different metrics**.\n\nIt enables you to **combine** the **features** of many of Targomo's APIs into one single request.\n\nYou can define **different criteria** (POI reachability, statistics reachability, gravitational model requests) and can request those criteria for a list of locations. The Scoring API will calculate and combine all results into a single response.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":707,"value":"$2f","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":15,"value":"md`
\n## Criteria\nLet's start by creating a criterion.
A criterion defines **what** the **rating tells** us. \n\nFor this first example we'll use a **simple** criterion, but keep in mind that they can get **as complex as you want**!\n\nIn particular, we are interested in the *number of reachable night clubs*, with a given mean of transport, in a given time:`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1239,"value":"viewof travel_time = Inputs.select(\n [\n { mode: { walk: {} }, time: 25 },\n { mode: { transit: {} }, time: 20 },\n { mode: { bike: {} }, time: 15 }\n ],\n {\n label: \"Travel mode/time:\",\n format: (x) => `${Object.keys(x.mode)} - ${x.time} minutes`\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":8,"value":"simpleCriteria = {\n return {\n nightClubs: {\n type: \"poiCoverageCount\",\n osmTypes: [\n {\n key: \"group\",\n value: \"nightclub\"\n }\n ],\n maxEdgeWeight: maxEdgeWeight * 60,\n edgeWeight: \"time\",\n travelMode: travel,\n coreServiceUrl: \"https://api.targomo.com/westcentraleurope\"\n }\n };\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":299,"value":"md`
\n## Locations\nLet's proceed by defining a bunch of locations:`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":22,"value":"locations = {\n var extent = [52.46, 13.27, 52.57, 13.53];\n var cellSide = 5;\n\n var grid = turf.pointGrid(extent, cellSide);\n return grid.features.map((e, index) => {\n return {\n id: `Location ${index + 1}`,\n lat: e.geometry.coordinates[0],\n lng: e.geometry.coordinates[1]\n };\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1114,"value":"$30","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":306,"value":"md`
\n## Scoring\nWe can now pass everything to the *Location Scoring Service* to retrieve the scoring of these locations. As we defined the criterion to match the number of reachable night clubs, that's what the scoring represents in this case :`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":24,"value":"simpleResponse = {\n let response = await fetch(\n `https://api.targomo.com/quality/v1/scores?showDetails=true&apiKey=${targomoKey()}`,\n {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({ locations, criteria: { ...simpleCriteria } }),\n mode: \"cors\"\n }\n ).then((response) => {\n if (!response.ok) {\n console.log(response);\n throw new Error(response.status);\n }\n return response.json();\n });\n return response[\"data\"];\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":666,"value":"Inputs.table(extractScores(simpleResponse, [\"nightClubs\"]), {\n sort: \"nightClubs\",\n reverse: true\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":89,"value":"Let's take a look a map showing the nearby night clubs, coloured in red if reachable, retrieved using the [POI API](https://observablehq.com/@targomo/points-of-interest-for-reference-map) (you can click on the markers to see their name):\n

**Reachable Night Clubs** => by ${travelMode} within ${maxEdgeWeight} minutes.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":404,"value":"$31","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":295,"value":"md`The values returned by the *Location Scoring Service* match the number of reachable night clubs, per location.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":320,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":339,"value":"As already mentioned, criterion can use **any** of the already existing **services**.
Let's use the **statistics** service to get the number of 20-29 year old citizens around the locations (refer to the [documentation](https://docs.targomo.com/#tag/Statistics-Context-API) to find which *statisticGroupId* and *statisticIds* you want):","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":348,"value":"statisticsCriteria = {\n return {\n youngPopulation: {\n type: \"statisticsSum\",\n statisticGroupId: 100,\n statisticsIds: [3],\n referenceStatisticsIds: [],\n maxEdgeWeight: maxEdgeWeight * 60,\n edgeWeight: \"time\",\n travelMode: travel,\n coreServiceUrl: \"https://api.targomo.com/westcentraleurope\"\n }\n };\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":458,"value":"Let's look at the Location Scoring response:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":408,"value":"statisticsResponse = {\n let response = await fetch(\n `https://api.targomo.com/quality/v1/scores?showDetails=true&apiKey=${targomoKey()}`,\n {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({ locations, criteria: { ...statisticsCriteria } }),\n mode: \"cors\"\n }\n ).then((response) => {\n if (!response.ok) throw new Error(response.status);\n return response.json();\n });\n return response[\"data\"]\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":641,"value":"Inputs.table(extractScores(statisticsResponse, [\"youngPopulation\"]), {\n sort: \"youngPopulation\",\n reverse: true,\n format: {\n youngPopulation: (x) => Math.trunc(x)\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":462,"value":"We can again use [another service](https://docs.targomo.com/#operation/getStatisticsTilesV1GroupZXYmvt) to double check these values (try clicking on the map to see the number of citizens in each cell or on the markers to see their names):\n

**Reachable Population** => by ${travelMode} within ${maxEdgeWeight} minutes.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":508,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1015,"value":"---\n## Increasing complexity\nThe number of 20-29 years old people and the number of night clubs, as absolute values, can be useful in some contexts. But what if we want to know how many clubs there are, **in relation** to the number of people?\n\nIn this (and many other!) cases we can use the *mathAggregation* criterion type, which allows us to carry out **complex operations** using **other criterion as parameters**:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1022,"value":"complexCriteria = {\n return {\n nightClubVSyoungPopulation: {\n type: \"mathAggregation\",\n mathExpression: \"{nightClubs}/max(1,{youngPopulation})\", // Be careful: we don't want to divide by 0\n criterionParameters: {\n youngPopulation: {\n type: \"statisticsDistance\",\n statisticCollectionId: 198,\n statisticsIds: [49],\n referenceStatisticsIds: [],\n maxEdgeWeight: maxEdgeWeight * 60,\n edgeWeight: \"time\",\n travelMode: travel,\n coreServiceUrl: \"https://api.targomo.com/westcentraleurope\"\n },\n nightClubs: {\n type: \"poiCoverageCount\",\n osmTypes: [\n {\n key: \"group\",\n value: \"nightclub\"\n }\n ],\n maxEdgeWeight: maxEdgeWeight * 60,\n edgeWeight: \"time\",\n travelMode: travel,\n coreServiceUrl: \"https://api.targomo.com/westcentraleurope\"\n }\n }\n }\n };\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1038,"value":"complexResponse = {\n let response = await fetch(\n `https://api.targomo.com/quality/v1/scores?showDetails=true&apiKey=${targomoKey()}`,\n {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({ locations, criteria: { ...complexCriteria } }),\n mode: \"cors\"\n }\n ).then((response) => {\n if (!response.ok) throw new Error(response.status);\n return response.json();\n });\n return response[\"data\"];\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1046,"value":"Inputs.table(extractScores(complexResponse, [\"nightClubVSyoungPopulation\"]), {\n sort: \"nightClubVSyoungPopulation\",\n reverse: true,\n format: {\n nightClubVSyoungPopulation: (x) => x.toFixed(6)\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":514,"value":"---\n\n## Normalization\nHowever, scores alone are not inherently valuable. You need to combine and **compare** them with each other.
We tried doing that by calculating the ratio of the two criterion. But, as you can see, even in our simple example there are some problems: locations 1 and 10, for instance, have both a score of 0. But they aren't the same.

The **same value** for **different zone** can have very **different meaning**.\n
That's why **normalization** plays a huge role in the Location Scoring Service.\n\n
Let's do a basic normalization:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":828,"value":"normalizedNightClubs = {\n let newObj = copyObject(simpleResponse);\n let [min, max] = getMinMaxFrom(simpleResponse);\n\n Object.values(newObj).map((e) => {\n e.scores[\"nightClubsNormalized\"] =\n (e.scores[\"nightClubs\"] - min) / (max - min); // Simple normalization formula: = (value - min) / (max - min)\n });\n return newObj;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":899,"value":"Inputs.table(\n extractScores(normalizedNightClubs, [\"nightClubs\", \"nightClubsNormalized\"]),\n {\n sort: \"nightClubsNormalized\",\n reverse: true\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":926,"value":"Let's do the same thing for *youngPopulation* as well:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":931,"value":"normalizedYoungPopulation = {\n let newObj = copyObject(statisticsResponse);\n let [min, max] = getMinMaxFrom(statisticsResponse);\n\n Object.values(newObj).map((e) => {\n e.scores[\"youngPopulationNormalized\"] =\n (e.scores[\"youngPopulation\"] - min) / (max - min);\n });\n return newObj;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":935,"value":"Inputs.table(\n extractScores(normalizedYoungPopulation, [\n \"youngPopulation\",\n \"youngPopulationNormalized\"\n ]),\n {\n sort: \"youngPopulationNormalized\",\n reverse: true\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":943,"value":"We can now **combine** both normalized values:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":947,"value":"viewof nightClubsWeightRaw = Inputs.range([0, 1], {\n step: 0.05,\n label: \"Night clubs weight:\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":950,"value":"viewof youngPopulationWeightRaw = Inputs.range([0, 1], {\n step: 0.05,\n label: \"Young population weight:\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":953,"value":"combinedResult = {\n let result = {};\n let [youngPopulationWeight, nightClubsWeight] = getWeights();\n\n Object.keys(normalizedNightClubs).map((key) => {\n let nightClubValue = normalizedNightClubs[key].scores.nightClubsNormalized;\n let youngPopulationValue =\n normalizedYoungPopulation[key].scores.youngPopulationNormalized;\n\n result[key] = {\n scores: {\n weightedAverage:\n (nightClubValue * nightClubsWeight +\n youngPopulationValue * youngPopulationWeight) /\n (nightClubsWeight + youngPopulationWeight)\n }\n };\n });\n return result;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":962,"value":"Inputs.table(extractScores(combinedResult, [\"weightedAverage\"]), {\n sort: \"weightedAverage\",\n reverse: true\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1206,"value":"Let's visualize this result:\n
Locations with higher scores are more red, lower scores are more yellow.\n

**Night Clubs** (${nightClubsWeightRaw}), **Young Population** (${youngPopulationWeightRaw}) => by ${travelMode} within ${maxEdgeWeight} minutes.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1179,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1122,"value":"---\n\n## Ratings\n\nAs we have seen, **scores need** to be **normalized**, ideally with as many locations as possible. \n\nTargomo can generate and provide _**RATINGS**_ - with ratings, entire **reference areas** (for example the province or state) are **pre-calculated**, allowing for instant, **already normalized** scores for any location, and for any number of locations.\n\n\nRatings feature:\n- **Faster** calculation times than normalized scores, as they work on **pre-calculated data**\n- **Full coverage**, as new locations' ratings are calculated **instantly**, by comparing raw values to relevant normalization in the reference dataset.\n\nRatings are **custom solutions**, [contact us](https://www.targomo.com/about-us/contact-us/) for a demo or to discuss the process.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1030,"value":"

\n\n## Congratulations!\nYou just learned to effectively **compare** _multiple locations_ using the **_Location Scoring API_**!","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1083,"value":"
\n\n---\n\n## Pricing\nThe *Location Scoring API* offers *scores* and *ratings*, which have different usages. As such, they are priced differently:\n- **Scores** use a combination of a multitude of other services, their pricing is based on the end assets they request.\n - For example, a criterion using points of interest will be priced following the *Points of Interest* pricing. You can check them out [here](https://www.targomo.com/developers/pricing/), or you can use the [simulator](https://www.targomo.com/developers/pricing/calculator/). A score for a location which references 4 different points of interest will log 4 operations. Likewise for statistics-based scores.\n- **Ratings**, on the other hand, are more complex and offer more value. Therefore you need to be onboarded and you should [contact us](https://www.targomo.com/about-us/contact-us/) for a demo or to discuss the process.\n\n---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":27,"value":"md`

\n## General utilities used for the examples\n\nMost of what you'll find here is similar to how you'd do it outside of [observable](https://observablehq.com), but there are several differences... Refer to our [developer resources](https://targomo.com/developers/documentation/javascript/) for a more direct path!`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":106,"value":"mapboxgl = {\n const gl = await require(\"mapbox-gl@1.13.1\");\n const href = await require.resolve(\"mapbox-gl@1.13.1/dist/mapbox-gl.css\");\n document.head.appendChild(html``);\n return gl;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":63,"value":"travel = travel_time.mode","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1261,"value":"travelMode = Object.keys(travel)[0]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":80,"value":"maxEdgeWeight = travel_time.time","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1280,"value":"pointsExtent = [52.46, 13.27, 52.57, 13.53]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1284,"value":"centroid = turf\n .centroid(turf.bboxPolygon(pointsExtent))\n .geometry.coordinates.reverse()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":600,"value":"function extractScores(data, values) {\n let result = [];\n for (const key of Object.keys(data)) {\n result.push({ \"Location name\": key });\n values.map((value) => {\n result[result.length - 1][value] = data[key].scores[value];\n });\n }\n return result;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":774,"value":"color_scale1 = chroma.scale(\"YlGnBu\").correctLightness()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1197,"value":"color_scale2 = chroma.scale(\"YlOrRd\").correctLightness()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":769,"value":"function getColor(i, t) {\n return color_scale1(i / t).hex();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1185,"value":"function getColorScale(i) {\n return color_scale2(i).hex();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":714,"value":"turf = require(\"@turf/turf\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":833,"value":"function getMinMaxFrom(response) {\n let min = Number.POSITIVE_INFINITY;\n let max = Number.NEGATIVE_INFINITY;\n let arr = Object.values(response);\n\n for (var i = arr.length - 1; i >= 0; i--) {\n let key = Object.keys(arr[i].scores)[0];\n let tmp = arr[i].scores[key];\n if (tmp < min) min = tmp;\n if (tmp > max) max = tmp;\n }\n return [min, max];\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":893,"value":"function copyObject(obj) {\n return JSON.parse(JSON.stringify(obj));\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":778,"value":"import { chroma } from \"@gka/chroma-js\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":749,"value":"html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":986,"value":"function getWeights() {\n let youngPopulationWeight = youngPopulationWeightRaw;\n let nightClubsWeight = nightClubsWeightRaw;\n // Preventing weights to both be 0\n if (youngPopulationWeightRaw == 0 && nightClubsWeightRaw == 0) {\n youngPopulationWeight = 1;\n nightClubsWeight = 1;\n }\n return [youngPopulationWeight, nightClubsWeight];\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":29,"value":"md`__Get re-usable targomo elements__`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":31,"value":"import {\n tgmCoreClient,\n tgmNotice,\n attribution,\n targomoKey,\n tgmLoader,\n tgmIntro\n} from \"@targomo/re-usable-targomo-resources\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":33,"value":"md`Make sure you get your _own_ API key!`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W34","stdlib":"1","titling":"auto"}} 2e:"$2d:body"

Young+Nightclub Score

${name}: ${ combinedResult[name].scores.weightedAverage.toFixed(2) * 100 }/100

${name}: ${ combinedResult[name].scores.weightedAverage.toFixed(2) 100 }/100*