Score${labels[j] && ` (${labels[j]})`} Income${labels[i] && ` (${labels[i]})`}

2e:null 2f:["$","title",null,{"children":"NYC Building Energy Ratings (FY2020) | Vivek Rao | Observable"}] 34:T46a,

..
A – score is equal to or greater than 85;

..
B – score is equal to or greater than 70 but less than 85;

..
C – score is equal to or greater than 55 but less than 70;

..
D – score is less than 55;

..
F – for buildings that didn’t submit required benchmarking information;

..
N – for buildings exempted from benchmarking or not covered by the Energy Star program.

35:T494,legend = () => { const k = 24; const arrow = DOM.uid(); return svg` ${d3.cross(d3.range(n), d3.range(n)).map( ([i, j]) => svg` Score${labels[j] && ` (${labels[j]})`} Income${labels[i] && ` (${labels[i]})`} ` )} Higher Score More Income `; }36:[{"id":"dcd4a66e2a6abbe6915c3977509226bd455af0b15e1a22f4947a31294e9762d622806fe766f50bc5091a19407763052ceff00d1c0f265b62ca810209835fbdbe","href":"https://static.observableusercontent.com/files/dcd4a66e2a6abbe6915c3977509226bd455af0b15e1a22f4947a31294e9762d622806fe766f50bc5091a19407763052ceff00d1c0f265b62ca810209835fbdbe","name":"bldg_scores.geojson","type":"application/geo+json","lastModified":1640511898417.872,"size":49968453},{"id":"dfc4478c1b2e1150a701da1866da600632c01e21c8211bbd8b9fe170ea7c4833eeb87c8c92a0b8d72dd0e8bab4c0cb9f5a68ed5b46cb0cfddcf39497a92683a3","href":"https://static.observableusercontent.com/files/dfc4478c1b2e1150a701da1866da600632c01e21c8211bbd8b9fe170ea7c4833eeb87c8c92a0b8d72dd0e8bab4c0cb9f5a68ed5b46cb0cfddcf39497a92683a3","name":"modzcta_merge@3.geojson","type":"application/geo+json","lastModified":1640510149252.957,"size":6610770},{"id":"27533e763496120f9288cc337db5400d439a3de13add743d6bf95f45283ca02167f6782152d1fea24054f4d6743d29294e9f2f27d93579825ae076acdfb755f6","href":"https://static.observableusercontent.com/files/27533e763496120f9288cc337db5400d439a3de13add743d6bf95f45283ca02167f6782152d1fea24054f4d6743d29294e9f2f27d93579825ae076acdfb755f6","name":"observable.csv","type":"text/csv","lastModified":1640522003958.805,"size":2643}] 32:{"id":"d2cdab00861ac6cf","slug":"nyc-buildings-sustainability-fy2020","title":"NYC Building Energy Ratings (FY2020)","thumbnail":"9e769e9cbc10ecb3964fbbc722a98ef0418b50a41c3b6385c4e3b4cc59f3ff0f","access_level":"public","create_time":"$D2021-12-21T14:27:29.523Z","update_time":"$D2022-03-13T22:36:37.852Z","view_time":null,"first_public_time":"$D2021-12-31T10:12:02.669Z","has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"0b85d005387c1032","login":"vivrao9","name":"Vivek Rao","avatar_url":"https://avatars-next.observableusercontent.com/bef3b5fc8c76d305b837832196006da09e14e5ff3812149e979a7f004178f2bf","tier":"starter_2024"},"version":1063,"publish_version":1063,"role":null,"authored":false,"authors":[{"type":"individual","id":"16d1a97215fda001","login":"vivrao9","name":"Vivek Rao","avatar_url":"https://avatars-next.observableusercontent.com/bef3b5fc8c76d305b837832196006da09e14e5ff3812149e979a7f004178f2bf","tier":null}],"members":[{"type":"individual","id":"16d1a97215fda001","login":"vivrao9","name":"Vivek Rao","avatar_url":"https://avatars-next.observableusercontent.com/bef3b5fc8c76d305b837832196006da09e14e5ff3812149e979a7f004178f2bf","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"NYC Building Energy Ratings (FY2020)","cells":[{"id":0,"value":"# NYC Building Energy Ratings (FY2020)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":5,"value":"`The City of New York's Building Department makes publicly available every buildings ENERGY Star score on May 1 every year. This data can be downloaded from the link` `here`.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":12,"value":"`After using Tabula, Python and merging with data from the Census, the data file below is a geoJSON of paths for NYC building footprints, where each building had corresponding information pertaining to ENERGY score, grade, year constructed, zip code etc. Take a look at the data for yourself.`","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":19,"value":"footprint = FileAttachment(\"bldg_scores.geojson\").json()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":21,"value":"`New York City has over one million structures, but not every one is subject to its ENERGY Star energy review. Buildings that are subject to the local law are scored on a scale between 0 and 100, and corresponding grades 'A', 'B', 'C', 'D', and 'F' are awarded based on the score. The conversion can be found below:`","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":25,"value":"$34","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":226,"value":"`As of FY2020, most buildings in NYC score a D. Take a look the data for yourself:`","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":432,"value":"### Most buildings in NYC score a D in their energy rating","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":279,"value":"value_counts = {\n const svg = d3.select(DOM.svg(width, 150));\n\n // draw bars\n let u = svg\n .append(\"g\")\n .selectAll(\"rect\")\n .data(grade_count)\n .join(\"rect\")\n .attr(\"x\", margin.left * 3.5)\n .attr(\"y\", (d, i) => i * 25 + margin.top)\n .attr(\"width\", (d) => d[1] * 0.01 + \"%\")\n .attr(\"height\", 20)\n .attr(\"fill\", (d) => fill_ftprnt(d[0]));\n\n // add labels\n let labels = svg\n .append(\"g\")\n .selectAll(\"text\")\n .data(grade_count)\n .join(\"text\")\n .attr(\"x\", margin.left * 1.5)\n .attr(\"y\", (d, i) => i * 25 + margin.top + 17)\n .text((d) => d[0])\n .style(\"font-family\", \"sans-serif\")\n .style(\"font-size\", \"1rem\");\n\n // add title\n // let title = svg\n // .append(\"text\")\n // .text(\"Distribution of ENERGY Star grades across NYC buildings\")\n // .attr(\"x\", margin.left)\n // .attr(\"y\", margin.top)\n // .style(\"font-family\", \"sans-serif\")\n // .style(\"font-size\", \"1.25rem\");\n\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":230,"value":"`The average energy score in the data is ${d3.mean(footprint.features, (d) => +d.properties.score).toFixed(2)}. `","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":32,"value":"`Let's plot this data on a map! Every building shape is filled with its corresponding grade color.`","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":46,"value":"map1 = {\n const svg = d3.select(DOM.svg(width, height));\n\n let projection = d3\n .geoMercator()\n .scale(1000)\n .fitSize([width, height], footprint);\n\n let geoGenerator = d3.geoPath().projection(projection);\n\n // Join the FeatureCollection's features array to path elements\n let u = svg\n .append(\"g\")\n .selectAll(\"path\")\n .data(footprint.features)\n .join(\"path\")\n .attr(\"d\", geoGenerator)\n .attr(\"fill\", (d) => {\n return fill_ftprnt(d.properties.grade);\n });\n\n // add in zip code boundaries\n let z = svg\n .append(\"g\")\n .selectAll(\"path\")\n .data(modzcta.features)\n .join(\"path\")\n .attr(\"d\", geoGenerator)\n .attr(\"stroke\", \"#eeeeee\")\n .attr(\"fill\", \"none\");\n\n // zoom\n // https://stackoverflow.com/questions/46773545/how-to-implement-mouse-wheel-zoom-in-d3-like-google-map-with-overlay-and-ctrl\n\n return svg.node();\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":994,"value":"// in this map, we're going to try to find the average energy rating by ZIP code\n// WIP\n\navg_score_by_zip = {\n const svg = d3.select(DOM.svg(width, height));\n\n let projection = d3\n .geoMercator()\n .scale(500)\n .fitSize([width, height], footprint);\n\n let geoGenerator = d3.geoPath().projection(projection);\n\n // Join the FeatureCollection's features array to path elements\n // let u = svg\n // .append(\"g\")\n // .selectAll(\"path\")\n // .data(footprint.features)\n // .join(\"path\")\n // .attr(\"d\", geoGenerator)\n // .attr(\"fill\", (d) => {\n // return fill_ftprnt(d.properties.grade);\n // });\n\n // add in zip code boundaries\n let z = svg\n .append(\"g\")\n .selectAll(\"path\")\n .data(modzcta.features)\n .join(\"path\")\n .attr(\"d\", geoGenerator)\n .attr(\"fill\", (d) => fill_ftprnt(d3.map(mapd, (d) => d[1][0])));\n\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1027,"value":"d3.map(mapd, (d) => d[1][0])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1038,"value":"d3.map(modzcta.features, (d) => fill_ftprnt(d3.map(mapd, (d) => d[1][0])))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":506,"value":"## Let's group scores by ZIP code and median income","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":885,"value":"`Let's use the median income and median energy rating from each ZIP code and plot a bivariate choropleth to see if there is a relationship between income and energy ratings. For examples, do ZIP codes with a lower income tend to have lower energy scores too?`","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":909,"value":"`Note: So as to not extrapolate, we will only include ZIP codes where we have at least 50 energy ratings. Zip codes with fewer than 50 are excluded and are represented by crosshatches.`","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":702,"value":"score_by_income = {\n const svg = d3.select(DOM.svg(width, height));\n\n let projection = d3\n .geoMercator()\n .scale(500)\n .fitSize([width, height], modzcta);\n\n const path = d3.geoPath().projection(projection);\n\n // append legend\n svg\n .append(legend)\n .attr(\n \"transform\",\n \"translate(\" +\n (margin.left + 150) +\n \",\" +\n (height - margin.bottom - 75) +\n \")\"\n );\n\n // add zip code boundaries\n svg\n .append(\"g\")\n .selectAll(\"path\")\n .data(modzcta.features)\n .join(\"path\")\n .attr(\"fill\", (d) => {\n let var_ = color(mapd.get(d.properties.modzcta));\n return var_ == \"#ffffff\" ? \"url(#crosshatch)\" : var_;\n }) //(d) => color(mapd.get(d.id)))\n .attr(\"d\", path)\n .attr(\"stroke\", \"#4d4d4d\");\n\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":916,"value":"`We see there is a somewhat clear, but not very strong visual relationship between income and energy ratings.`","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1054,"value":"// in this code cell, we will find out the number of ZIP codes that fall in each cell of the above choropleth's legend\n// more values along the diagonal may indicate a relationship between income and energy ratings\n// WIP","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":597,"value":"## Data Transformation","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":687,"value":"data = FileAttachment(\"observable.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":801,"value":"mapd = d3.rollup(\n data,\n (v) => [d3.mean(v, (d) => +d.score), d3.mean(v, (d) => +d.median_income)],\n // score: d3.mean(v, (d) => +d.score),\n // median_income: d3.mean(v, (d) => +d.median_income)\n (d) => +d.modzcta\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":40,"value":"## Appendix","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":898,"value":"### Bivariate stuff","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":722,"value":"x_score = d3.scaleQuantile(\n Array.from(data.values(), (d) => +d[\"score\"]),\n d3.range(n)\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":730,"value":"y_score = d3.scaleQuantile(\n Array.from(data.values(), (d) => +d[\"median_income\"]),\n d3.range(n)\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":720,"value":"color = {\n return (value) => {\n if (!value) return \"#ffffff\";\n let [a, b] = value;\n return colors[y_score(b) + x_score(a) * n];\n };\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":697,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":667,"value":"colors = [\n \"#e8e8e8\",\n \"#ace4e4\",\n \"#5ac8c8\",\n \"#dfb0d6\",\n \"#a5add3\",\n \"#5698b9\",\n \"#be64ac\",\n \"#8c62aa\",\n \"#3b4994\"\n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":659,"value":"labels = [\"low\", \"\", \"high\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":661,"value":"n = 3","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":902,"value":"### Other","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":569,"value":"// include zip code geographical information\nmodzcta = FileAttachment(\"modzcta_merge@3.geojson\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":242,"value":"grade_count = d3.sort(\n d3.rollup(\n footprint.features,\n (v) => v.length,\n (d) => d.properties.grade\n )\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":98,"value":"fill_ftprnt = d3\n .scaleOrdinal()\n .domain([\"A\", \"B\", \"C\", \"D\", \"F\"])\n .range([\"#00af50\", \"#d0e43f\", \"#ffc000\", \"#fe0000\", \"#2e2e2e\"])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":49,"value":"height = 600","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":293,"value":"margin = ({ left: 20, right: 20, top: 20, bottom: 20 })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":36,"value":"d3 = require(\"d3@6\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":594,"value":"l = require(\"lodash\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":920,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"}],"files":"$W36","stdlib":"1","titling":"auto"}} 33:"$32:body"