31:T9f9,md`# GBC Impact Calculations
### Introduction This document outlines Integral’s methodology for calculating the energy performance of each building in Fannie Mae’s residential portfolio, which will help the organization determine eligibility for their Green Building Loan program. In addition, the performance calculations will help Fannie Mae estimate the environmental impact of their entire portfolio and identify opportunities to reduce energy use, and associated carbon emissions at scale. Integral’s methodology builds on the work of previous consultants who developed the “Green Building Certification Impact Workbook.” Their work relied on a variety of data sources and academic studies, including those published by ASHRAE, the [Pacific Northwest National Laboratory (PNNL)](https://www.pnnl.gov/), and the Lawrence Berkeley National Laboratory (LBNL). In order to estimate energy performance of a multifamily project, they established an average energy use intensity for all multifamily buildings across the country using survey data from Fannie Mae, and then adjusted that average according to the energy code standard used by the project (e.g. ASHRAE 90.1-2004, IECC 2015). The adjustments were based on published studies comparing the various code standards. While this approach can provide reasonable estimates for energy use at the national level, it lacks the nuance needed to estimate energy use for different types of residential properties in different climate regions. Integral’s approach leverages the database of commercial and residential prototype energy models published by PNNL to bring more regional specificity to the energy performance calculations. The database contains results from energy models of low-rise, mid-rise, and high-rise multifamily residential buildings in each climate zone, for ASHRAE 90.1-2004 to ASHRAE 90.1-2016 and IECC 2006 to IECC 2015. By using actual model data from these major code standards, we can more accurately estimate the energy performance of different types of residential properties around the county. In addition, the model data allows us to estimate carbon emissions for each project, by combining the expected electricity consumption (a data point not available from national averages) with the EPA’s database of regional grid emissions factors. The result of this approach is a system for calculating energy use and carbon emissions that acknowledges programmatic differences within the multifamily portfolio and regional differences in energy consumption.`32:T810,zones = { const path = d3.geoPath(); const parent = document.createElement("div"); const svg = d3.create("svg") .attr("viewBox", "0 0 960 600") .style("width", "100%") .style("height", "auto"); //const tooltip = d3.select(document.createElement("div")).call(createTooltip); svg.append("g") .selectAll("path") .data(topojson.feature(us, us.objects.counties).features) .join("path") .attr("fill", d => zone(data.get(d.id))) .attr("d", path) .on("mouseover", function(d) { d3.select(this).attr("fill", chroma(zone(data.get(d.id))).darken().hex()); // tooltip // .style("opacity", 1) // .html(zoneTooltip(d)); }) .on("mouseleave", function(d) { d3.select(this).attr("fill", zone(data.get(d.id))); // tooltip.style("opacity", 0) }) .append("title").html(d => zoneTooltip(d)); svg.append("path") .datum(topojson.mesh(us, us.objects.states, (a, b) => a !== b)) .attr("fill", "none") .attr("stroke", "white") .attr("stroke-linejoin", "round") .attr("d", path); // svg.on("mousemove", function(d) { // let [x,y] = d3.mouse(this); // y +=20; // if(x>width/2) x-= 100; // tooltip // .style("left", x + "px") // .style("top", y + "px") // }) // svg.selectAll("circle") // .data(places).join("circle") // .attr("cx", function (d) { return projection([d.lon, d.lat])[0]; }) // .attr("cy", function (d) { return projection([d.lon, d.lat])[1]; }) // .attr("r", "4px") // .attr("fill", "#333333") // .attr("stroke", "#ffffff") // .attr("stroke-width", 2) // .append("title").html(d => cityTooltip(d)); // .on("mouseover", function(d) { // tooltip // .style("opacity", 1) // .html(cityTooltip(d)); // }) // .on("mouseleave", function(d) { // tooltip.style("opacity", 0) // }); parent.appendChild(svg.node()); // parent.appendChild(tooltip.node()); return parent }33:T421,md`

### Calculating Carbon Emissions While there are many factors that may influence carbon emissions from building energy consumption, from time-of-day use to the way regional transmission organizations (RTO’s) buy and sell energy, the EPA’s [Emissions and Generation Resource Integrated Database (eGRID)](https://www.epa.gov/energy/emissions-generation-resource-integrated-database-egrid) is a reasonably accurate source of emissions data for annual emissions calculations. The eGRID database contains information about grid emissions factors for every zip code, along with information about the types of fuels used for generation (e.g. coal, gas, nuclear, etc.) and generation capacities. The figure below shows the distribution of zip codes in the U.S., colored by eGRID region. The map was developed by combining GeoJson data for each zip code tabulation area in the U.S. with the EPA’s eGRID database. Note that the GeoJson data only covers zip code tabulation areas from 2010, while the eGrid database includes the 2016 codes.`34:Td9f,// grids = { // const path = d3.geoPath(); // const parent = document.createElement("div"); // const svg = d3.create("svg") // .attr("viewBox", "0 0 960 600") // .style("width", "100%") // .style("height", "auto"); // // const tooltip = d3.select(document.createElement("div")).call(createTooltip); // svg.append("g") // .selectAll(".gridregions") // // .data(topojson.feature(us, us.objects.counties).features) // .data(merged) // .join("path") // .attr("class", "gridregions") // .attr("fill", d => grid(d.Emissions)) // .attr("stroke", "white") // .attr("stroke-width", 0.5) // .attr("stroke-linejoin", "round") // .attr("d", path) // .on("mouseover", function(d) { // d3.selectAll(".gridregions") // .attr("fill", d => chroma(grid(d.Emissions)).desaturate(5).hex()) // .style("opacity", 0.5); // d3.select(this) // .attr("fill", d => grid(d.Emissions)) // .style("opacity", 1); // // tooltip // // .style("opacity", 1) // // .html(gridTooltip(d)); // }) // .on("mouseleave", function(d) { // d3.selectAll(".gridregions") // .attr("fill", d => grid(d.Emissions)) // .style("opacity", 1); // // tooltip.style("opacity", 0); // }) // .append("title").html(d => gridTooltip(d)); // svg.on("mousemove", function(d) { // let [x,y] = d3.mouse(this); // y +=20; // if(x>width/2) x-= 100; // // tooltip // // .style("left", x + "px") // // .style("top", y + "px") // }) // parent.appendChild(svg.node()); // // parent.appendChild(tooltip.node()); // return parent // } grids = { const path = d3.geoPath(); const parent = document.createElement("div"); const svg = d3.create("svg") .attr("viewBox", "0 0 960 600") .style("width", "100%") .style("height", "auto"); // const tooltip = d3.select(document.createElement("div")).call(createTooltip); svg.append("g") .selectAll(".gridregions") .data(topojson.feature(zips, zips.objects.zip).features) .join("path") .attr("class", "gridregions") .attr("fill", d => grid(zip_data.get(d.properties.GEOID10))) .attr("stroke", "none") .attr("stroke-width", 0.5) .attr("stroke-linejoin", "round") .attr("d", path); // .on("mouseover", function(d) { // d3.selectAll(".gridregions") // .attr("fill", d => chroma(grid(d.Emissions)).desaturate(5).hex()) // .style("opacity", 0.5); // d3.select(this) // .attr("fill", d => grid(d.Emissions)) // .style("opacity", 1); // // tooltip // // .style("opacity", 1) // // .html(gridTooltip(d)); // }) // .on("mouseleave", function(d) { // d3.selectAll(".gridregions") // .attr("fill", d => grid(d.Emissions)) // .style("opacity", 1); // // tooltip.style("opacity", 0); // }) // .append("title").html(d => gridTooltip(d)); // svg.on("mousemove", function(d) { // let [x,y] = d3.mouse(this); // y +=20; // if(x>width/2) x-= 100; // // tooltip // // .style("left", x + "px") // // .style("top", y + "px") // }) parent.appendChild(svg.node()); // parent.appendChild(tooltip.node()); return parent }35:T50b,codes = { const path = d3.geoPath(); const parent = document.createElement("div"); const svg = d3.create("svg") .attr("viewBox", "0 0 960 600") .style("width", "100%") .style("height", "auto"); // const tooltip = d3.select(document.createElement("div")).call(createTooltip); svg.append("g") .selectAll("path") .data(topojson.feature(us, us.objects.states).features) .join("path") .attr("fill", d => code(stateCodes.get(d.id))) .attr("stroke", "white") .attr("stroke-linejoin", "round") .attr("d", path) .on("mouseover", function(d) { d3.select(this).attr("fill", chroma(code(stateCodes.get(d.id))).darken().hex()); // tooltip // .style("opacity", 1) // .html(codeTooltip(d)); }) .on("mouseleave", function(d) { d3.select(this).attr("fill", code(stateCodes.get(d.id))); // tooltip.style("opacity", 0); }) .append("title").html(d => codeTooltip(d)); svg.on("mousemove", function(d) { let [x,y] = d3.mouse(this); y +=20; if(x>width/2) x-= 100; // tooltip // .style("left", x + "px") // .style("top", y + "px"); }) parent.appendChild(svg.node()); // parent.appendChild(tooltip.node()); return parent }36:T4fc,baseline_legend = { const svg = d3.select(DOM.svg(960, 50)) .style("font", "11px sans-serif"); const defs = svg.append("defs"); const linearGradient = defs.append("linearGradient") .attr("id", "linear-gradient") .attr("x1", "0%") .attr("y1", "0%") .attr("x2", "100%") .attr("y2", "0%"); linearGradient.selectAll("stop") .data([ {offset: 0, color: d3.interpolateYlOrBr(0)}, {offset: 0.25, color: d3.interpolateYlOrBr(.25)}, {offset: 0.5, color: d3.interpolateYlOrBr(.5)}, {offset: 0.75, color: d3.interpolateYlOrBr(.75)}, {offset: 1, color: d3.interpolateYlOrBr(1)} ]) .enter().append("stop") .attr("offset", function(d) { return d.offset; }) .attr("stop-color", function(d) { return d.color; }); svg.append("rect") .attr("width", 300) .attr("height",10) .attr("y", 20) .attr("x", 40) .style("fill", "url(#linear-gradient)"); svg.selectAll("text") .data(d3.extent(Array.from(data.values(), d => d[4]))) .join("text") .attr("x", (d, i) => i * 350) .attr("y", 25) .attr("dy", "0.35em") .html((d, i) => `${_.round(d, 2)} ${i != 0 ? "lbs CO₂e / ft²-yr" : ""}`); return svg.node(); }37:Tb63,combined = { const path = d3.geoPath(); const parent = document.createElement("div"); const svg = d3.create("svg") .attr("viewBox", "0 0 960 600") .style("width", "100%") .style("height", "auto"); const tooltip = d3.select("body").append("div") .attr("class", "tooltip") .style("border-radius", "3px") .style("pointer-events", "none") .style("display", "none") .style("position", "absolute") .style("z-index", "1000") .style("padding", "12px") .style("font-weight", "regular") .style("font-family", "Open Sans") .style("font-size", "12px") .style("background", "white") .style("box-shadow", "0 0 10px rgba(0,0,0,.25)") .style("color", "#333333") .style("line-height", "1.6") .style("pointer-events", "none"); const lookup = { "ASHRAE2004": "ASHRAE 90.1-2004", "ASHRAE2007": "ASHRAE 90.1-2007", "ASHRAE2010": "ASHRAE 90.1-2010", "ASHRAE2016": "ASHRAE 90.1-2016", "IECC2006": "IECC 2006", "IECC2009": "IECC 2009", "IECC2012": "IECC 2012", "IECC2015": "IECC 2015", "TITLE242016": "Title 24-2016" }; const tipContent = d => ` ${d.properties.name}, ${states.get(d.id.slice(0, 2)).name}
State Energy Code (Closest Match): ${lookup[data.get(d.id)[3]]}
Climate Zone: ${data.get(d.id)[0]}
eGRID Region: ${data.get(d.id)[1]}
Estimated Emissions: ${_.round(data.get(d.id)[4], 1)} lbs CO₂e / ft²-yr` svg.append("g") .selectAll("path") .data(topojson.feature(us, us.objects.counties).features) .join("path") .attr("fill", d => emissions(data.get(d.id))) .attr("d", path) .on("mouseover", function(d) { tooltip.style("display", null); d3.select(this).attr("fill", chroma(emissions(data.get(d.id))).darken().hex()); tooltip.html(tipContent(d)) .style("left", (d3.event.pageX) + "px") .style("top", (d3.event.pageY - 28) + "px"); }) .on("mousemove", function(d) { tooltip .style("left", (d3.event.pageX) + "px") .style("top", (d3.event.pageY - 28) + "px"); }) .on("mouseout", function(d) { d3.select(this).attr("fill", emissions(data.get(d.id))); tooltip.style("display", "none"); }) svg.append("path") .datum(topojson.mesh(us, us.objects.states, (a, b) => a !== b)) .attr("fill", "none") .attr("stroke", "white") .attr("stroke-linejoin", "round") .attr("d", path); // svg.on("mousemove", function(d) { // let [x,y] = d3.mouse(this); // y +=20; // if(x>width/2) x-= 100; // // tooltip // // .style("left", x + "px") // // .style("top", y + "px"); // }) parent.appendChild(svg.node()); // parent.appendChild(tooltip.node()); return parent }38:[] 2f:{"title":"GBC Impact Calculations","cells":[{"id":0,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":295,"value":"md`\n
\n### PNNL Prototype Models\nResearchers at PNNL maintain a database of models for every major energy code standard in the U.S., from ASHRAE 90.1 2004-2016 to IECC 2006-2015. Results are available, as energyPlus output files, for low-rise, mid-rise, and high-rise buildings in all climate zones.
`","pinned":false,"hidden":false,"mode":"ojs"},{"id":754,"value":"md`\n### ASHRAE 90.1 and IECC Baselines\nThe map below shows the major ASHRAE climate zones (1-8), along with the reference city location for each PNNL energy model. The reference cities were chosen by PNNL based on the degree to which they represent an average for each climate zone, in terms of heating and cooling degree days. In certain instances, the dataset from PNNL didn’t contain results for all ASHRAE 90.1 and IECC code standards. For example, the residential dataset, which include low-rise multifamily and single-family homes, only contains models for IECC standards. In those instances, we interpolated the results for ASHRAE 90.1 models based on the relative differences between IECC and ASHRAE, in each climate zone, for more complete datasets. By filling in this missing data, we developed a full set of results for low-rise, mid-rise, and high-rise buildings, for each ASHRAE and IECC code standard, in each climate zone.
`","pinned":false,"hidden":false,"mode":"ojs"},{"id":288,"value":"zone_legend = {\n \n const color = d3.scaleOrdinal(d3.schemeRdPu[8]).domain(d3.range(1, 9).reverse());\n \n const svg = d3.select(DOM.svg(960, color.domain().length * 24 + 60))\n .style(\"font\", \"11px sans-serif\");\n\n const g = svg.append(\"g\")\n .selectAll(\"g\")\n .data(color.domain().slice().reverse())\n .enter().append(\"g\")\n .attr(\"transform\", (d, i) => `translate(${0}, ${i*24 + 20})`);\n\n g.append(\"rect\")\n .attr(\"width\", 20)\n .attr(\"height\",20)\n .attr(\"fill\", color);\n \n\n g.append(\"text\")\n .attr(\"x\", 30)\n .attr(\"y\", 9.5)\n .attr(\"dy\", \"0.35em\")\n .text((d, i) => `${i === 0 ? \"Climate Zone\": \"\"} ${d}`);\n \n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":25,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":611,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":291,"value":"grid_legend = {\n \n const color = d3.scaleLinear()\n .domain(d3.extent(Array.from(data.values(), d => d[2])))\n .range([d3.interpolateRdPu(0), d3.interpolateRdPu(1)]);\n \n const svg = d3.select(DOM.svg(960, color.domain().length * 24 + 60))\n .style(\"font\", \"11px sans-serif\");\n\n const g = svg.append(\"g\")\n .selectAll(\"g\")\n .data(color.domain().slice().reverse())\n .enter().append(\"g\")\n .attr(\"transform\", (d, i) => `translate(${0}, ${i*24 + 20})`);\n\n g.append(\"rect\")\n .attr(\"width\", 20)\n .attr(\"height\",20)\n .attr(\"fill\", color);\n\n g.append(\"text\")\n .attr(\"x\", 30)\n .attr(\"y\", 9.5)\n .attr(\"dy\", \"0.35em\")\n .html((d, i) => `${i === 0 ? \"Highest Factor (MROE)\": \"Lowest Factor (NYUP)\"}`);\n \n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":213,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":783,"value":"md`\n

\n### State Energy Codes\nThe U.S. Department of Energy (DOE) regularly updates the [status of energy codes](https://www.energycodes.gov/status-state-energy-code-adoption) in each state for both commercial and residential properties. Based on this data, as well as the results from the PNNL prototype models and the eGRID emissions factors, Integral produced an estimate for baseline carbon emissions for projects in each county. While there are inherent limitations to using the state energy code as a baseline (e.g. more stringent local codes in areas with higher population densities), this approach is reasonably accurate for calculating carbon emissions with a limited set of building information (location, size, type). The map below shows each state energy code (or closest match to the PNNL prototype model dataset), colored according to expected performance.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":608,"value":"code_legend = {\n \n const color = d3.scaleOrdinal(d3.schemeYlGnBu[8]).domain([\n \"ASHRAE 2004 or no standard\",\n \"IECC 2006\",\n \"ASHRAE 2007\",\n \"ASHRAE 2010\",\n \"IECC 2012\",\n \"IECC 2015 or equivalent\",\n \"ASHRAE 2016 or equivalent\",\n \"Title 24-2016\"\n ]);\n \n const svg = d3.select(DOM.svg(960, color.domain().length * 24 + 60))\n .style(\"font\", \"11px sans-serif\");\n\n const g = svg.append(\"g\")\n .selectAll(\"g\")\n .data(color.domain().slice().reverse())\n .enter().append(\"g\")\n .attr(\"transform\", (d, i) => `translate(${0}, ${i*24 + 20})`);\n\n g.append(\"rect\")\n .attr(\"width\", 20)\n .attr(\"height\",20)\n .attr(\"fill\", color);\n \n\n g.append(\"text\")\n .attr(\"x\", 30)\n .attr(\"y\", 9.5)\n .attr(\"dy\", \"0.35em\")\n .text((d, i) => d);\n \n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":617,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":789,"value":"md`\n

\n### Baseline Emissions\nPutting it all together, the maps below demonstrates the variation in predicted emissions for each county. Only data for mid-rise multifamily units is shown. Data for high-rise and low-rise properties is included in the full dataset, and will be included as a dropdown in future iterations of this graphic.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":380,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":377,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":444,"value":"md`\n

\n### Conclusion and Additional Information\nMoving forward, Integral will use this calculation methodology in conjunction with an audit of various green building certification systems (e.g. LEED, Green Globes, PHIUS+) to estimate the potential for energy and carbon savings for each project. Integral will also continue to advise Fannie Mae on an appropriate baseline standard for their Green Building Loan program.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":11,"value":"d3 = require('d3@5.9.2');","pinned":false,"hidden":false,"mode":"ojs"},{"id":13,"value":"topojson = require('topojson-client@3.0.0/dist/topojson-client.js')","pinned":false,"hidden":false,"mode":"ojs"},{"id":15,"value":"us = d3.json(\"https://cdn.jsdelivr.net/npm/us-atlas@2/us/10m.json\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":840,"value":"zips = d3.json(\"https://elementa.nyc/projects/archive/energycodes/data/zipcodes.json\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":17,"value":"states = new Map(us.objects.states.geometries.map(d => [d.id, d.properties]))","pinned":false,"hidden":false,"mode":"ojs"},{"id":19,"value":"data = Object.assign(new Map(await d3.csv(\"https://elementa.nyc/projects/archive/energycodes/data/mid_baselines.csv\", ({FIPS, Zone, Grid, Emissions, Code, Baseline}) => [FIPS, [Zone, Grid, +Emissions, Code, +Baseline]])), {title: [\"Climate Zone\", \"eGRID\", \"Grid Emissions Factor\", \"State Energy Code\", \"Baseline Emissions\"]})","pinned":false,"hidden":false,"mode":"ojs"},{"id":853,"value":"zip_data = Object.assign(new Map(await d3.csv(\"https://elementa.nyc/projects/archive/energycodes/data/grid_emissions_factors_zip.csv\", ({Zip, Grid, Emissions}) => [d3.format(\"05\")(Zip), [Grid, +Emissions]])), {title: [\"eGRID Region\", \"Grid Emissions Factor\"]})","pinned":false,"hidden":false,"mode":"ojs"},{"id":27,"value":"zone = {\n return value => {\n if (!value) return \"#ccc\";\n\n let [climate_zone, grid, emissions_factor, state_code, baseline_emissions, ashrae2013_emissions] = value;\n const color = d3.scaleOrdinal(d3.schemeRdPu[8]).domain(d3.range(1, 9).reverse());\n \n const climate = +climate_zone.slice(0, 1);\n const moisture = climate_zone.slice(1, 2);\n \n let primary = color(climate);\n \n // switch(moisture) {\n // case \"B\":\n // primary = chroma.mix(primary, '#bd1e54', 0.3).hex();\n // break;\n // case \"C\":\n // primary = chroma.mix(primary, '#bd1e54', 0.6).hex();\n // break;\n // default:\n // break;\n // }\n \n return primary;\n \n };\n}\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":210,"value":"grid = {\n return value => {\n\n if (!value) return \"#ccc\";\n \n const normalized = d3.scaleLinear([0, 1]).domain(d3.extent(merged, d => d.Emissions));\n\n return d3.interpolateRdPu(normalized(value[1]));\n \n };\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":637,"value":"code = {\n return value => {\n if (!value) return \"#ccc\";\n\n const color = d3.scaleOrdinal(d3.schemeYlGnBu[8]).domain([\n \"ASHRAE2004\",\n \"IECC2006\",\n \"ASHRAE2007\",\n \"ASHRAE2010\",\n \"IECC2012\",\n \"IECC2015\",\n \"ASHRAE2016\",\n \"TITLE242016\"\n ]);\n \n return color(value);\n \n };\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":382,"value":"emissions = {\n return value => {\n if (!value) return \"#ccc\";\n\n const color = d3.scaleSequential(d3.interpolateYlOrBr)\n .domain(d3.extent(Array.from(data.values(), d => d[4])));\n // .range([\"#f3f3f3\", \"#0c0c0c\"])\n // .interpolate(d3.interpolateYlOrBr);\n \n return color(value[4]);\n \n };\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":115,"value":"chroma = require('chroma-js')","pinned":false,"hidden":false,"mode":"ojs"},{"id":178,"value":"zoneTooltip = function(d) {\n // return `${d.properties.name}, ${states.get(d.id.slice(0, 2)).name}\n //
\n // Climate Zone ${data.get(d.id)[0]}\n //
`\n \n return `${d.properties.name}, ${states.get(d.id.slice(0, 2)).name}\nClimate Zone ${data.get(d.id)[0]}`\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":632,"value":"codeTooltip = function(d) {\n const lookup = {\n \"ASHRAE2004\": \"ASHRAE 90.1-2004\",\n \"ASHRAE2007\": \"ASHRAE 90.1-2007\",\n \"ASHRAE2010\": \"ASHRAE 90.1-2010\",\n \"ASHRAE2016\": \"ASHRAE 90.1-2016\",\n \"IECC2006\": \"IECC 2006\",\n \"IECC2009\": \"IECC 2009\",\n \"IECC2012\": \"IECC 2012\",\n \"IECC2015\": \"IECC 2015\",\n \"TITLE242016\": \"Title 24-2016\"\n };\n\n// return `${d.properties.name}\n//
\n// ${lookup[stateCodes.get(d.id)]}\n//
`\n \n \n return `${d.properties.name}\n${lookup[stateCodes.get(d.id)]}`\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":251,"value":"emTooltip = function(d) {\n const lookup = {\n \"ASHRAE2004\": \"ASHRAE 90.1-2004\",\n \"ASHRAE2007\": \"ASHRAE 90.1-2007\",\n \"ASHRAE2010\": \"ASHRAE 90.1-2010\",\n \"ASHRAE2016\": \"ASHRAE 90.1-2016\",\n \"IECC2006\": \"IECC 2006\",\n \"IECC2009\": \"IECC 2009\",\n \"IECC2012\": \"IECC 2012\",\n \"IECC2015\": \"IECC 2015\",\n \"TITLE242016\": \"Title 24-2016\"\n };\n \n // return `${d.properties.name}, ${states.get(d.id.slice(0, 2)).name}\n //
\n // State Energy Code (Closest Match): ${lookup[data.get(d.id)[3]]}\n //
\n // ${_.round(data.get(d.id)[4], 1)} lbs CO₂e / ft²-yr\n //
`\n \n return `${d.properties.name}, ${states.get(d.id.slice(0, 2)).name}\nState Energy Code (Closest Match): ${lookup[data.get(d.id)[3]]}\n${_.round(data.get(d.id)[4], 1)} lbs CO₂e / ft²-yr`\n \n \n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":439,"value":"gridTooltip = function(d) {\n // return `${d.Region}\n //
\n // ${_.round(d.Emissions, 2)} lbs CO₂e / MWh\n //
`\n \n return `${d.Region}\n${_.round(d.Emissions, 2)} lbs CO₂e / MWh`\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":358,"value":"cityTooltip = function(d) {\n return `${d.city}, ${d.state}\n
`\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":184,"value":"createTooltip = function(el) {\n el\n .style(\"position\", \"absolute\")\n .attr(\"id\", \"zone_tooltip\")\n .style(\"pointer-events\", \"none\")\n .style(\"top\", 0)\n .style(\"opacity\", 0)\n .style(\"background\", \"white\")\n .style(\"border-radius\", \"2px\")\n .style(\"box-shadow\", \"0 0 10px rgba(0,0,0,.25)\")\n .style(\"padding\", \"12px\")\n .style(\"font\", \"11px sans-serif\")\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":261,"value":"_ = require('lodash')","pinned":false,"hidden":false,"mode":"ojs"},{"id":325,"value":"places = {\n// let places = (await d3.csv(\"https://gist.githubusercontent.com/jameswperakis/f75204f6f8dd222da39e4a7274dff584/raw/299e49044f36736ff123ea20ced4038d7cd06916/place_references\", d3.autoType));\n \n// return places;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":330,"value":"projection = d3.geoAlbersUsa()\n .scale(1280)\n .translate([480, 300])","pinned":false,"hidden":false,"mode":"ojs"},{"id":549,"value":"//not needed - apprently topomerge works with holes....\nfunction browsermerge(arr) {\n \n let sets = arr.map((d, i) => d.concat([i])).slice();\n let stop = 0;\n \n while(sets.flat().length > Array.from(new Set(sets.flat())).length) {\n \n for (let i of _.range(sets.length)) {\n let pull = [];\n let reduced = sets.reduce((acc, cur, idx, src) => { \n if (_.intersection(acc, cur).length > 0) {\n if (i != idx) pull.push(idx);\n return acc.concat(cur);\n }\n else {\n return acc;\n }\n }, sets[i]);\n \n if (_.difference(reduced, sets[i]).length > 0) {\n sets[i] = Array.from(new Set(reduced));\n pull.map(d => { sets[d] = []});\n break;\n }\n }\n \n \n stop += 1;\n if (stop > 30000 ) {\n console.log(`Loop Error: ${stop}`);\n break;\n }\n \n }\n \n return sets.filter(d => d.length > 0);\n\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":552,"value":"merged = {\n const subregions = Array.from(new Set([...data].map(d => d[1][1])));\n \n \n const mergedSubregions = subregions.map(region => {\n let emissions = [...data].filter(d => { return d[1][1] === region})[0][1][2];\n let merged = topojson.merge(us, us.objects.counties.geometries.filter(d => { return data.get(d.id)[1] === region}));\n merged.Region = region;\n merged.Emissions = emissions;\n return merged;\n });\n \n return mergedSubregions.flat();\n}\n \n","pinned":false,"hidden":false,"mode":"ojs"},{"id":625,"value":"stateCodes = {\n const map = new Map();\n \n [...data].map(d => {\n map.set(d[0].slice(0, 2), d[1][3]);\n });\n \n return map;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":629,"value":"topojson.feature(us, us.objects.states).features","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W38","stdlib":"1","titling":"auto"}