25:["$","title",null,{"children":"Rockwood Identity Atlas | Chloe Bui | Observable"}] 49:T486,Plot.plot({marginRight: 210, label: null, x: { axis: "top", label: "Difference in Employment by Industry", labelAnchor: "center", tickFormat: "+", percent: true }, color: { scheme: "PiYG", type: "ordinal" }, marks: [ Plot.barX(mc_industry_compare.filter((d )=> (d.Year==`${Year_industry}`)), { x: "Difference", fy: "Industry", fill: "Comparison_Region", y: "Comparison_Region", //fill: (d) => d.Difference > 0, sort: { y: "x" } }), Plot.gridX({ stroke: "white", strokeOpacity: 0.5 }), d3 .groups(mc_industry_compare.filter((d )=> (d.Year==`${Year_industry}`)), (d) => d.Difference > 0) .map(([growth, states]) => [ Plot.axisY({ x: 0, ticks: states.map((d) => d.Industry), tickSize: 0, anchor: growth ? "left" : "right" }), Plot.textX(states, { x: "Difference", y: "Industry", text: ((f) => (d) => f(d.Difference))(d3.format("+.1%")), textAnchor: growth ? "start" : "end", dx: growth ? 4 : -4, }) ]), Plot.ruleX([0]) ] })4a:T44e,Plot.plot({marginLeft: 210, label: null, x: { axis: "top", label: "Difference in Employment by Industry (%)", labelAnchor: "center", tickFormat: "+", tickFormat: (d) => d + "%" //percent: true }, y: { axis: null}, color: { legend: true, domain: ["East County", "Rockwood"], range: ["#4269d0", "#ff725c"] }, marks: [ Plot.barX(mc_industry_compare.filter((d )=> (d.Year==`${Year_industry}`)), { x: "Difference", fy: "Industry", fill: d => d.Comparison_Region === "East County" ? "#4269d0" : "#ff725c", y: "Comparison_Region", //tip : "Y", sort: { fy: "x" } }), Plot.text(mc_industry_compare.filter(d => d.Year == `${Year_industry}`), { x: "Difference", fy: "Industry", text: d => `${d.Difference}%`, fill: "black", //dx: d => d.Difference < 0 ? -10 : 10,// Adjust text placement based on value lineAnchor: "middle", textAnchor: "middle", //d => d.Difference < 0 ? "end" : "start", y: "Comparison_Region" }), Plot.ruleX([0]) ] })4b:T440,a1a=Plot.plot({ marginLeft : 60, marks: [ Plot.barY(mc_mhi_1223_race.filter((d )=> (d.Year==`${Year_mhi_race}`)), { x: "Race", y: "Average Median Income", fx: "Region", fill: "Race", tip : "X", }), Plot.ruleY([0]), ], color: { legend : true, domain: ["White alone", "Black alone", "Asian alone", "American Indian and Alaska Native alone","Hispanic or Latino","Some other race alone","Two or more races", "All races" ], scheme: "Spectral" }, x: { axis: null, domain: ["White alone", "Black alone", "Asian alone", "American Indian and Alaska Native alone", "Hispanic or Latino", "Some other race alone", "Two or more races", "All races"]}, fx: {domain: ["Rockwood","East County","West County"], label : null}, y: { grid: true, //domain: [0, 60000], //ticks: [0, 10000, 20000, 30000, 40000, 50000, 60000] }, title: "Average Median Household Income by Race in "+ `${Year_mhi_race}`, caption: "Source: "+ `${Year_mhi_race}` +" US Census Bureau ACS 5 Year Estimates" });4c:T45c,a4=Plot.plot({ marks: [ Plot.ruleY([0]), Plot.lineY(race_4region, {x: "Year", y: "Total Population", stroke: "Region", strokeWidth: 3, tip : "x"}), Plot.dot(race_4region, { x: "Year", y: "Total Population", fill: "Region", r: 4 // Radius of the dots }), Plot.text(race_4region.filter(d => d.Year === d3.max(race_4region, d => d.Year)), { x: "Year", y: "Total Population", text: d => d.Region, dx: 35, // Offset text to the right of the line end dy: -5, fill: "Region", fontSize: 12, fontWeight: "bold", }) ], title: " Total Population in 4 regions", caption: "Source: 2012-2023 US Census Bureau ACS 5 Year Estimates", y: { grid: true, label: "Population", domain: [0, 4500000], tickFormat: "s", tickFontSize: 50 } , x: { domain: [2011.5, 2024.5], ticks: ["2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021", "2022", "2023"], tickFormat: (d) => d } })4d:T577,a6=Plot.plot({ marks: [ Plot.ruleY([0]), Plot.lineY(race_4region.filter((d )=> (d.Race =="People of color")), {x: "Year", y: "Percentage", stroke: "Region", strokeWidth: 3, tip : "x"}), Plot.dot(race_4region.filter((d )=> (d.Race =="People of color")), { x: "Year", y: "Percentage", fill: "Region", r: 4, // Radius of the dots title: d => `${d.Year} - ${d.Region}: ${d.Percentage}%` // Tooltip for dots similar to line }), Plot.text(race_4region.filter(d => d.Race =="People of color" && d.Year === d3.max(race_4region, d => d.Year)), { x: "Year", y: "Percentage", text: d => d.Region, dx: 35, // Offset text to the right of the line end dy: -5, fill: "Region", fontSize: 12, fontWeight: "bold", }) ], title: " People of color in 4 regions", caption: "Source: 2012-2023 US Census Bureau ACS 5 Year Estimates", y: { grid: true, label: "Percent people of color", domain: [20, 60], tickFormat: (d) => d + "%", ticks: [20,30,40,50,60], nice: false, tickFontSize: 50 } , x: { domain: [2011.5, 2024.5], ticks: ["2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021", "2022", "2023"], tickFormat: (d) => d } })4e:T4cd,a7=Plot.plot({ marks: [ Plot.ruleY([0]), Plot.lineY(race_4region.filter((d )=> (d.Race =="Black alone")), {x: "Year", y: "Percentage", stroke: "Region", strokeWidth: 3, tip : "x"}), Plot.dot(race_4region.filter((d )=> (d.Race =="Black alone")), { x: "Year", y: "Percentage", fill: "Region", r: 4 }), Plot.text(race_4region.filter(d => d.Race =="Black alone" && d.Year === d3.max(race_4region, d => d.Year)), { x: "Year", y: "Percentage", text: d => d.Region, dx: 39, dy: 0, fill: "Region", fontSize: 12, fontWeight: "bold", }) ], title: " Black Identifying in 4 regions", caption: "Source: 2012-2023 US Census Bureau ACS 5 Year Estimates", y: { grid: true, label: "Percent black people", domain: [0, 12], tickFormat: (d) => d + "%", ticks: [0,3,6,9,12], nice: false, tickFontSize: 50 } , x: { domain: [2011.5, 2024.5], ticks: ["2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021", "2022", "2023"], tickFormat: (d) => d } })4f:T4a4,a14=Plot.plot({ marks: [ Plot.ruleY([15]), Plot.lineY(age_1765_4region1.filter((d )=> (d.Age_Group =="0_17")), {x: "Year", y: "Percentage", stroke: "Region", strokeWidth: 3, tip : "x"}), Plot.dot(age_1765_4region1.filter((d )=> (d.Age_Group =="0_17")), { x: "Year", y: "Percentage", fill: "Region", r: 4 }), Plot.text(age_1765_4region1.filter(d => d.Age_Group =="0_17" && d.Year === d3.max(age_1765_4region1, d => d.Year)), { x: "Year", y: "Percentage", text: d => d.Region, dx: 39, dy: 0, fill: "Region", fontSize: 12, fontWeight: "bold", }) ], title: " Percent Youth Ages 0-17 in 4 regions", caption: "Source: 2012-2023 US Census Bureau ACS 5 Year Estimates", y: { grid: true, label: "Percent", domain: [15, 30], tickFormat: (d) => d + "%", ticks: [15,20,25,30], nice: false, tickFontSize: 50 } , x: { domain: [2011.5, 2024.5], ticks: ["2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021", "2022", "2023"], tickFormat: (d) => d } })50:T4b4,a15=Plot.plot({ marks: [ Plot.ruleY([8]), Plot.lineY(age_1765_4region1.filter((d )=> (d.Age_Group =="65_plus")), {x: "Year", y: "Percentage", stroke: "Region", strokeWidth: 3, tip : "x"}), Plot.dot(age_1765_4region1.filter((d )=> (d.Age_Group =="65_plus")), { x: "Year", y: "Percentage", fill: "Region", r: 4 }), Plot.text(age_1765_4region1.filter(d => d.Age_Group =="65_plus" && d.Year === d3.max(age_1765_4region1, d => d.Year)), { x: "Year", y: "Percentage", text: d => d.Region, dx: 39, dy: 0, fill: "Region", fontSize: 12, fontWeight: "bold", }) ], title: " Percent Seniors Ages 65 and Over in 4 regions", caption: "Source: 2012-2023 US Census Bureau ACS 5 Year Estimates", y: { grid: true, label: "Percent", domain: [8, 20], tickFormat: (d) => d + "%", ticks: [8,12,16,20], nice: false, tickFontSize: 50 } , x: { domain: [2011.5, 2024.5], ticks: ["2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021", "2022", "2023"], tickFormat: (d) => d } })51:T48b,a23=Plot.plot({ marginLeft : 60, marks: [ Plot.ruleY([30000]), Plot.lineY(mhi_4region, {x: "Year", y: "Average Median Household Income", stroke: "Region", strokeWidth: 3, tip : "x"}), Plot.dot(mhi_4region, { x: "Year", y: "Average Median Household Income", fill: "Region", r: 4 // Radius of the dots }), Plot.text(mhi_4region.filter(d => d.Year === d3.max(mhi_4region, d => d.Year)), { x: "Year", y: "Average Median Household Income", text: d => d.Region, dx: 39, // Offset text to the right of the line end dy: 0, fill: "Region", fontSize: 12, fontWeight: "bold", }) ], title: " Average Median Household Income ", caption: "Source: 2012-2023 US Census Bureau ACS 5 Year Estimates", y: { grid: true, domain: [30000, 110000], //ticks: [30000,60000,90000,120000], } , x: { domain: [2011.5, 2024.5], ticks: ["2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021", "2022", "2023"], tickFormat: (d) => d } })52:T57c,a28=Plot.plot({ marks: [ Plot.ruleY([0]), Plot.lineY(income_4region.filter((d )=> (d.Gender==`${Gender2}` && d.Income_Group==`${IncomeRange}`)) , {x: "Year", y: "Percentage", stroke: "Region", strokeWidth: 3, tip : "x"}), Plot.dot(income_4region.filter((d )=> (d.Gender==`${Gender2}` && d.Income_Group==`${IncomeRange}`)), { x: "Year", y: "Percentage", fill: "Region", r: 4 // Radius of the dots }), Plot.text(income_4region_new.filter(d => d.Year === d3.max(income_4region_new, d => d.Year)), { x: "Year", y: "Percentage", text: d => d.Region, dx: 39, // Offset text to the right of the line end dy: 0, fill: "Region", fontSize: 12, fontWeight: "bold", }) ], title: Gender2=== "both" ? "Percentage of population with Annual Income range of "+`${IncomeRange}` : "Percentage of "+ `${Gender2}` +" population with Annual Income range of "+ `${IncomeRange}`, caption: "Source: 2012-2023 US Census Bureau ACS 5 Year Estimates", y: { grid: true, tickFormat: (d) => d + "%", tickFontSize: 50 } , x: { domain: [2011.5, 2024.5], ticks: ["2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021", "2022", "2023"], tickFormat: (d) => d } })53:T4dc,a31=Plot.plot({ marks: [ Plot.ruleY([25]), Plot.lineY(rent_4region, {x: "Year", y: "Average Percent Income on rent", stroke: "Region", strokeWidth: 3, tip : "x"}), Plot.dot(rent_4region, { x: "Year", y: "Average Percent Income on rent", fill: "Region", r: 4 // Radius of the dots }), Plot.text(rent_4region.filter(d => d.Year === d3.max(rent_4region, d => d.Year)), { x: "Year", y: "Average Percent Income on rent", text: d => d.Region, dx: 39, // Offset text to the right of the line end //dy: d => d.Region === "Oregon" ? 10 : 0, // Conditional dy based on Region dy: 0, fill: "Region", fontSize: 12, fontWeight: "bold", }) ], title: " Average Percentage of Household Income on rent", caption: "Source: 2012-2023 US Census Bureau ACS 5 Year Estimates", y: { grid: true, domain: [25, 45], tickFormat: (d) => d + "%", tickFontSize: 50 } , x: { domain: [2011.5, 2024.5], ticks: ["2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021", "2022", "2023"], tickFormat: (d) => d } })54:T4eb,a35=Plot.plot({ marks: [ Plot.ruleY([0]), Plot.lineY(rentpt_4region.filter((d )=> (d.Income_percentage==`${IncomePct}` )) , {x: "Year", y: "Percentage", stroke: "Region", strokeWidth: 3, tip : "x"}), Plot.dot(rentpt_4region.filter((d )=> (d.Income_percentage==`${IncomePct}`)), { x: "Year", y: "Percentage", fill: "Region", r: 4 // Radius of the dots }), Plot.text(rentpt_4region_new.filter(d => d.Year === d3.max(income_4region_new, d => d.Year)), { x: "Year", y: "Percentage", text: d => d.Region, dx: 39, // Offset text to the right of the line end dy: 0, fill: "Region", fontSize: 12, fontWeight: "bold", }) ], title: " Percentage of housing units with "+ `${IncomePct}`+ " Household Income for Gross Rent", caption: "Source: 2012-2023 US Census Bureau ACS 5 Year Estimates", y: { grid: true, tickFormat: (d) => d + "%", tickFontSize: 50 } , x: { domain: [2011.5, 2024.5], ticks: ["2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021", "2022", "2023"], tickFormat: (d) => d } })55:T406,a45=Plot.plot({ marginLeft : 60, marks: [ Plot.barY(tenpop_4region.filter((d )=> (d.Year==`${Year_tenpop}`)), { x: "Tenure type", y: "Population estimate", fx: "Region", fill: "Tenure type", }), Plot.ruleY([0]), Plot.text(tenpop_4region.filter((d )=> (d.Year==`${Year_tenpop}`)), { text: d => `${Number(d['Population estimate']).toLocaleString()}`, x: "Tenure type", y: "Population estimate", fx: "Region", dy: -7, fill: "black" })], color: { legend : true, scheme: "Warm"}, x: { axis: null}, fx: { domain: [//"Oregon", "West County", "East County", "Rockwood"], label : null}, y: { grid: true, domain: [0, 300000], ticks: [0, 50000, 100000, 150000, 200000, 250000, 300000], label: "Population Estimate" }, title: "Owners and Renters by Region in "+ `${Year_tenpop}`, caption: "Source: "+ `${Year_tenpop}`+" US Census Bureau ACS 5 Year Estimates" });56:T4b3,a59=Plot.plot({ marks: [ Plot.ruleY([0]), Plot.lineY(rpov_4region.filter((d )=> (d.Range==`${Range}` )) , {x: "Year", y: "Percentage", stroke: "Region", strokeWidth: 3, tip : "x"}), Plot.dot(rpov_4region.filter((d )=> (d.Range==`${Range}`)), { x: "Year", y: "Percentage", fill: "Region", r: 4 // Radius of the dots }), Plot.text(rpov_4region_new.filter(d => d.Year === d3.max(rpov_4region_new, d => d.Year)), { x: "Year", y: "Percentage", text: d => d.Region, dx: 39, // Offset text to the right of the line end dy: 0, fill: "Region", fontSize: 12, fontWeight: "bold", }) ], title: " Percentage of Group with ratio of Income to Poverty Level of "+ `${Range}`, caption: "Source: 2012-2023 US Census Bureau ACS 5 Year Estimates", y: { grid: true, tickFormat: (d) => d + "%", tickFontSize: 50 } , x: { domain: [2011.5, 2024.5], ticks: ["2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021", "2022", "2023"], tickFormat: (d) => d } })57:T4fd,a63=Plot.plot({ marks: [ Plot.ruleY([0]), Plot.lineY(pov_4region.filter((d )=> (d.Gender==`${Gender_pov}` && d[`Poverty status`]==`${pov_status}`)) , {x: "Year", y: "Percentage", stroke: "Region", strokeWidth: 3, tip : "x"}), Plot.dot(pov_4region.filter((d )=> (d.Gender==`${Gender_pov}` && d[`Poverty status`]==`${pov_status}`)), { x: "Year", y: "Percentage", fill: "Region", r: 4 // Radius of the dots }), Plot.text(pov_4region_new.filter(d => d.Year === d3.max(pov_4region_new, d => d.Year)), { x: "Year", y: "Percentage", text: d => d.Region, dx: 39, // Offset text to the right of the line end dy: 0, fill: "Region", fontSize: 12, fontWeight: "bold", }) ], title: " Percentage of "+ `${Gender_pov}`+ " gender "+ `${pov_status}`, caption: "Source: 2012-2023 US Census Bureau ACS 5 Year Estimates", y: { grid: true, tickFormat: (d) => d + "%", tickFontSize: 50 } , x: { domain: [2011.5, 2024.5], ticks: ["2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021", "2022", "2023"], tickFormat: (d) => d } })58:T664,a72=Plot.plot({ marks: [ Plot.ruleY([0]), Plot.lineY(incov_4region.filter((d )=> (d.Gender==`${Gender_incov}` && d[`Insurance coverage`]==`${Incov}` && d[`Age Range`]==`${age_range}`)) , {x: "Year", y: "Percentage", stroke: "Region", strokeWidth: 3, tip : "x"}), Plot.dot(incov_4region.filter((d )=> (d.Gender==`${Gender_incov}` && d[`Insurance coverage`]==`${Incov}` && d[`Age Range`]==`${age_range}`)), { x: "Year", y: "Percentage", fill: "Region", r: 4 // Radius of the dots }), Plot.text(incov_4region_new.filter(d => d.Year === d3.max(incov_4region_new, d => d.Year)), { x: "Year", y: "Percentage", text: d => d.Region, dx: 39, // Offset text to the right of the line end dy: 0, fill: "Region", fontSize: 12, fontWeight: "bold", }) ], title: " Percentage of "+ `${Gender_incov}`+ " gender of "+ `${age_range}`+ " with health insurance status is "+`${Incov}`, title: Incov === "yes" ? " Percentage of "+ `${Gender_incov}`+ " gender of "+ `${age_range}`+ " with health insurance" : " Percentage of "+ `${Gender_incov}`+ " gender of "+ `${age_range}`+ " without health insurance", caption: "Source: 2012-2023 US Census Bureau ACS 5 Year Estimates", y: { domain: [0,100], grid: true, tickFormat: (d) => d + "%" } , x: { domain: [2011.5, 2024.5], ticks: ["2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021", "2022", "2023"], tickFormat: (d) => d } })59:T5d6,a74=Plot.plot({ marginLeft : 60, title: Incov2 === "yes" ? "Percentage of population with health insurance" : "Percentage of population without health insurance", caption: "Source: "+ `${Year_incov}` +" US Census Bureau ACS 5 Year Estimates", x: { padding: 0.5, // Adjust spacing between bars, smaller values mean thinner bars, domain: ["Oregon", "West County", "East County", "Rockwood"], label: null}, y: { grid: true, tickFormat: (d) => d + "%", label: "Percentage"}, marks: [ Plot.barY(incov_4region.filter((d )=> (d.Gender=="both" && d[`Insurance coverage`]==`${Incov2}` && d[`Age Range`] =="all ages" && d[`Year`]==`${Year_incov}`)), {x: "Region", y: "Percentage", fill: d => d.Region === "Oregon" ? "#efb118" : d.Region === "West County" ? "#3ca951" : d.Region === "East County" ? "#4269d0" : "#ff725c" }), Plot.ruleY([0]), Plot.text(incov_4region.filter((d )=> (d.Gender=="both" && d[`Insurance coverage`]==`${Incov2}` && d[`Age Range`] =="all ages" && d[`Year`]==`${Year_incov}`)), { text: d => `${d['Percentage']}%`, x: "Region", y: "Percentage", dy: 13, fontSize: 13, fontWeight: "bold", fill: "white" })] })5a:T626,viewof checkbox = Inputs.checkbox( topic1 === "Health Outcomes" ? ["Arthritis", "Current Asthma", "COPD", "Cancer (non-skin) or Melanoma", "Coronary Heart Disease", "Depression", "Diabetes", "Obesity", "Stroke"] : topic1 === "Health Risk Behaviors" ? ["Binge Drinking", "Current Cigarette Smoking", "Physical Inactivity", "Short Sleep Duration"] : topic1 === "Disability" ? ["Any Disability", "Cognitive Disability", "Hearing Disability", "Independent Living Disability", "Mobility Disability", "Self-care Disability", "Vision Disability"] : topic1 === "Health Status" ? ["Frequent Mental Distress", "Frequent Physical Distress", "General Health"] : ["Annual Checkup", "Colorectal Cancer Screening", "Dental Visit", "Health Insurance", "Mammography"], {label: "SELECT SUB-CATEGORY", value: topic1 === "Health Outcomes" ? ["Arthritis", "Current Asthma", "COPD", "Cancer (non-skin) or Melanoma", "Coronary Heart Disease", "Depression", "Diabetes", "Obesity", "Stroke"] : topic1 === "Health Risk Behaviors" ? ["Binge Drinking", "Current Cigarette Smoking", "Physical Inactivity", "Short Sleep Duration"] : topic1 === "Disability" ? ["Any Disability", "Cognitive Disability", "Hearing Disability", "Independent Living Disability", "Mobility Disability", "Self-care Disability", "Vision Disability"] : topic1 === "Health Status" ? ["Frequent Mental Distress", "Frequent Physical Distress", "General Health"] : ["Annual Checkup", "Colorectal Cancer Screening", "Dental Visit", "Health Insurance", "Mammography"]})5b:T533,a78= Plot.plot({ width: 600, // Chart width height: 400, // Chart height marginLeft: 240, // Space for y-axis labels marginTop: 30, // Space for the x-axis at the top title: "Prevalence of " + topic1 + " in Multnomah County in 2022", caption: "Source: Centers for Disease Control and Prevention", label: null, color: { scheme: "reds", domain: [0, 100], legend: true, label: "Percentage Range" }, x: { domain: ["Rockwood", "East County", "West County"], axis: "top"// Move the x-axis to the top }, marks: [ Plot.cell(filteredData, { x: "Region", y: "Sub_category", fill: "Percentage", rx: 5 // Rounded cell corners for aesthetics }), Plot.text(filteredData, { x: "Region", y: "Sub_category", text: d => `${d.Percentage}%`, fontSize: 14, // Increase text size fontWeight: "bold", // Make text bold textAnchor: "middle", fill: (d) => (d.Percentage > 50 ? "white" : "black") }) ], style: { "text-anchor": "middle", "font-size": "15px", // Set font size for tick labels "font-weight": "bold", // Set font weight for axis labels "color": "black", // Optional: Set text color "tick-line-width": 0, // Remove tick lines (horizontal and vertical) "stroke-width": 0 } 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Bui","avatar_url":"https://avatars-next.observableusercontent.com/a1dc092075d75f22b8331655ed10563c4738bdff4834cca0d856f476e0281bdd","tier":"starter_2024","bio":"","followed_by_you":false,"follows_you":false},"version":2338,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"a0996923973394b5","login":"cdco","name":"Chloe Bui","avatar_url":"https://avatars-next.observableusercontent.com/5f2909d79277de6d1b62b68b7fa0450afbfead99f832337373b3f8cf2570bfac","tier":null,"bio":"","followed_by_you":false,"follows_you":false}],"members":[{"type":"team","id":"38687cf2b05a9fb2","login":"cdco","name":"Chloe Bui","avatar_url":"https://avatars-next.observableusercontent.com/a1dc092075d75f22b8331655ed10563c4738bdff4834cca0d856f476e0281bdd","tier":"starter_2024","role":"editor"},{"type":"individual","id":"a0996923973394b5","login":"cdco","name":"Chloe Bui","avatar_url":"https://avatars-next.observableusercontent.com/5f2909d79277de6d1b62b68b7fa0450afbfead99f832337373b3f8cf2570bfac","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Rockwood Identity Atlas","cells":[{"id":928,"value":"# Rockwood Identity Atlas","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":924,"value":"a1=html`These tables and charts below show the demographic data of Rockwood compare to other regions, including: Oregon, West County (refers to all the census tracts west of 82nd Avenue in Multnomah County) and East County (refers to all the census tracts east of 82nd Avenue in Multnomah County except the census tracts that contain Rockwood.) `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2139,"value":"# Industry","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2142,"value":"mc_industry_1223 = await FileAttachment(\"mc_industry_1223.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"mc_industry_1223","database":"$undefined"},{"id":2147,"value":"viewof Region_industry = Inputs.select([\"Oregon\", \"West County\", \"East County\", \"Rockwood\"],\n {label: \"SELECT REGION\", value: \"Rockwood\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2150,"value":"viewof Year_industry = Inputs.select([2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023],\n {label: \"SELECT YEAR\", value : 2023})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2153,"value":"viewof Gender_industry = Inputs.select([\"male\", \"female\", \"both\"],\n {label: \"SELECT GENDER\", value: \"both\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2156,"value":"Plot.plot({\n marginLeft: 300,\n x: { axis: null, domain: [0,15], },\n y: { label: null },\n marks: [\n Plot.barX(mc_industry_1223.filter((d )=> (d.Gender==`${Gender_industry}` && d.Region==`${Region_industry}` && d.Year==`${Year_industry}`)), \n { x: \"percentage\",\n y: \"Industry\",\n fill: \"#EFB118\",\n sort: { y: \"x\", reverse: true }\n }),\n\n Plot.text(mc_industry_1223.filter((d )=> (d.Gender==`${Gender_industry}` && d.Region==`${Region_industry}` && d.Year==`${Year_industry}`)), \n { text: d => `${d['percentage']}%`,\n x: \"percentage\",\n y: \"Industry\",\n fontWeight: \"bold\",\n //textAnchor: \"end\",\n dx: 14,\n fill: \"black\"\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2181,"value":"mc_industry_compare = await FileAttachment(\"mc_industry_compare.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"mc_industry_compare","database":"$undefined"},{"id":2184,"value":"$49","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2204,"value":"$4a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2245,"value":"mc_rent_23 = await FileAttachment(\"mc_rent_23.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"mc_rent_23","database":"$undefined"},{"id":2295,"value":"rockwood_pob_asian = await FileAttachment(\"rw_pob_asian.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"rockwood_pob_asian","database":"$undefined"},{"id":2298,"value":"Plot.waffleY(rockwood_pob_asian, Plot.groupZ({y: \"count\"}, \n {fill: \"country\", sort: \"country\", unit: 10})).plot(\n { y: { axis: null }, \n height: 400, \n title: \"Asian Foreign-born population in Rockwood (2023)\",\n color: {legend: true}})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2319,"value":"rw_pob_central_americas = await FileAttachment(\"rw_pob_central_americas.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"rw_pob_central_americas","database":"$undefined"},{"id":2321,"value":"Plot.waffleY(rw_pob_central_americas, Plot.groupZ({y: \"count\"}, \n {fill: \"country\", sort: \"country\", unit: 10})).plot(\n { y: { axis: null }, \n height: 300, \n title: \"Central America Foreign-born population in Rockwood (2023)\",\n color: {legend: true}})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2328,"value":"rw_pob_eu = await FileAttachment(\"rw_pob_eu.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"rw_pob_eu","database":"$undefined"},{"id":2330,"value":"Plot.waffleY(rw_pob_eu, Plot.groupZ({y: \"count\"}, \n {fill: \"country\", sort: \"country\", unit: 10})).plot(\n { y: { axis: null }, \n height: 300, \n title: \"European Foreign-born population in Rockwood (2023)\",\n color: {legend: true}})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2334,"value":"rw_pob_af = await FileAttachment(\"rw_pob_af.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"rw_pob_af","database":"$undefined"},{"id":2336,"value":"Plot.waffleY(rw_pob_af, Plot.groupZ({y: \"count\"}, \n {fill: \"country\", sort: \"country\", unit: 10})).plot(\n { y: { axis: null }, \n height: 300, \n title: \"African Foreign-born population in Rockwood (2023)\",\n color: {legend: true}})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":29,"value":"a2= md`# 1. Race`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":136,"value":"a3= md`**Data source**: US Census Bureau ACS 5-year estimate \nTable B03002 (Hispanic or Latino Origin by Race)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1009,"value":"race_4region = await FileAttachment(\"race_4region@1.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"race_4region","database":"$undefined"},{"id":2136,"value":"SELECT \"Region\", \"Year\", \"Total_Pop\" AS \"Total Population\", LN(\"Total_Pop\") AS \"Log total population\",\n \"Race\", \"Estimate\" AS \"Population Estimate\", \"Percentage\" FROM \"race_4region\"","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"race_4region@1.csv"},{"id":2033,"value":"viewof tab1 = Inputs.table(race_4region.map(d => {\n return {Region: d.Region, Year: d.Year, \n 'Total Population': d['Total Population'], Race: d.Race,\n 'Population Estimate': d['Population Estimate'], Percentage: d.Percentage\n }}))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2270,"value":"mc_mhi_1223_race = await FileAttachment(\"mc_mhi_1223_race@1.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"mc_mhi_1223_race","database":"$undefined"},{"id":2261,"value":"viewof Year_mhi_race = Inputs.select([2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023],\n {label: \"SELECT YEAR\", value : 2023})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2263,"value":"$4b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":938,"value":"$4c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":952,"value":"a5=Plot.plot({\n marks: [\n Plot.barY(race_4region.filter((d )=> (d.Race !=\"People of color\")), \n {fx: \"Region\",\n x: \"Year\",\n y: \"Percentage\",\n fill: \"Race\",\n z: {value: d => d.variableOrder_race}, // Use the factor for sorting\n order: \"variableOrder_race\", // Ensure the stacking order follows the factor\n tip : \"X\",\n }),\n Plot.ruleY([0])\n ],\n color: {\n legend : true,\n domain: categoryOrder2,\n scheme: \"spectral\"\n },\n x: { axis: null },\n y: { grid: true,\n domain:[0,100],\n tickFormat: (d) => d + \"%\",\n ticks: [0,20,40,60,80, 100]},\n fx: { label: null,\n domain: [\"Oregon\", \"West County\", \"East County\", \"Rockwood\"]},\n title: \"Race distribution by percent in 4 regions\",\n caption: \"Source: 2012-2023 US Census Bureau ACS 5 Year Estimates\"\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":978,"value":"$4d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":984,"value":"$4e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":141,"value":"a8=html`To analyze the significant changes in the percentage of the Black community in Rockwood, we will examine the shifts in each census tract of Rockwood from 2012 to 2023.
\n\nData source: US Census Bureau ACS 5-year estimate
\nTable B03002 (Hispanic or Latino Origin by Race)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1020,"value":"rockwood_race_1223 = await FileAttachment(\"rockwood_race_1223.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"rockwood_race_1223","database":"$undefined"},{"id":2042,"value":"viewof tab2 = Inputs.table(rockwood_race_1223.map(d => {\n return {Region: d.Region, Year: d.Year, Tract: d.Tract,\n 'Total Population': d['Total population'], Race: d.Race,\n 'Population Estimate': d['Population estimate'], Percentage: d.Percentage\n }}))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1025,"value":"a9=Plot.plot({\n marks: [\n Plot.barY(rockwood_race_1223.filter((d )=> (d.Race !=\"Some other race alone\" && d.Race !=\"Two or more races\")), {\n fx: \"Tract\",\n x: \"Year\",\n y: \"Population estimate\",\n fill: \"Race\",\n z: {value: d => d.variableOrder_race_rw}, // Use the factor for sorting\n order: \"variableOrder_race_rw\", // Ensure the stacking order follows the factor\n tip : \"X\",\n }),\n Plot.ruleY([0])\n ],\n color: {\n legend : true,\n domain: categoryOrder2,\n scheme: \"spectral\"\n },\n x: { axis: null },\n y: { grid: true, domain: [0,10000]},\n fx: { label: \"Tract\" },\n title: \"Population Composition in Rockwood by tract\",\n caption: \"Source: 2012-2023 US Census Bureau ACS 5 Year Estimates\"\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":27,"value":"a10 = md`# 2. Age`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":153,"value":"a11= md`**Data source**: US Census Bureau ACS 5-year estimate \nTable B01001 (Sex by Age)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1325,"value":"age_4region = await FileAttachment(\"age_4region@1.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"age_4region","database":"$undefined"},{"id":2054,"value":"viewof tab3 = Inputs.table(age_4region.map(d => {\n return {Region: d.Region, Year: d.Year, \n 'Total Population': d['Total Population'], 'Age Group': d['Age Group'],\n 'Population Estimate': d['Population Estimate'], Percentage: d.Percentage\n }}))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":34,"value":"a12=Plot.plot({\n marks: [\n Plot.barY(age_4region, {\n fx: \"Region\",\n x: \"Year\",\n y: \"Percentage\",\n fill: \"Age Group\",\n tip : \"x\",\n }),\n Plot.ruleY([0])\n ],\n color: {\n legend : true,\n scheme: \"spectral\"\n },\n x: { axis: null },\n y: { grid: null},\n fx: {label: null,\n domain: [\"Oregon\", \"West County\", \"East County\", \"Rockwood\"]},\n title: \"Age group distribution by Region\",\n caption: \"Source: 2012-2022 US Census Bureau ACS 5 Year Estimates\"\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":160,"value":"a13= html`Next, we will focus on two key groups: the population aged 17 and younger and the senior population aged 65 and older.
\n\nData source: US Census Bureau ACS 5-year estimate
\nTable B01001 (Sex by Age)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1335,"value":"age_1765_4region1 = await FileAttachment(\"age_1765_4region@1.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"age_1765_4region1","database":"$undefined"},{"id":2060,"value":"viewof tab4 = Inputs.table(age_1765_4region1.map(d => {\n return {Region: d.Region, Year: d.Year, \n 'Total Population': d['Total Population'], Age_Group: d.Age_Group,\n 'Population Estimate': d['Population Estimate'], Percentage: d.Percentage\n }}))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1341,"value":"$4f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1352,"value":"$50","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":165,"value":"a16= html`To analyze the changes in the percentage of the population aged 17 and younger in Rockwood, we will examine the shifts in each census tract of Rockwood from 2012 to 2023.
\n\nData source: US Census Bureau ACS 5-year estimate
\nTable B01001 (Sex by Age)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1370,"value":"rockwood_age_1223 = await FileAttachment(\"rockwood_age_1223.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"rockwood_age_1223","database":"$undefined"},{"id":2065,"value":"viewof tab5 = Inputs.table(rockwood_age_1223.map(d => {\n return {Region: d.Region, Year: d.Year, Tract: d.Tract,\n 'Total Population': d['Total Population'], Age_Group: d.Age_Group,\n 'Population Estimate': d['Population Estimate'], Percentage: d.Percentage\n }}))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":50,"value":"a17=Plot.plot({\n marks: [\n Plot.barY(rockwood_age_1223, {\n fx: \"Tract\",\n x: \"Year\",\n y: \"Population Estimate\",\n fill: \"Age_Group\",\n tip : \"X\",\n }),\n Plot.ruleY([0])\n ],\n color: {\n legend : true,\n scheme: \"spectral\"\n },\n x: { axis: null },\n y: { grid: true, domain: [0,10000]},\n fx: { label: \"Tract\" },\n title: \"Age Range Composition in Rockwood by tract\",\n caption: \"Source: 2012-2023 US Census Bureau ACS 5 Year Estimates\"\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":56,"value":"a18= md`# 3. Language`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":168,"value":"a19=md`**Data source**: US Census Bureau ACS 5-year estimate \nTable B16007 (Age by Language Spoken at Home for the Population 5 Years and Over)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1381,"value":"language_4region = await FileAttachment(\"language_4region@1.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"language_4region","database":"$undefined"},{"id":2071,"value":"viewof tab6 = Inputs.table(language_4region.map(d => {\n return {Region: d.Region, Year: d.Year,\n 'Total Population': d['Total Population'], Language: d.Language,\n 'Population Estimate': d['Population Estimate'], Percentage: d.Percentage\n }}))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":67,"value":"a20=Plot.plot({\n marks: [\n Plot.barY(language_4region, {\n fx: \"Region\",\n x: \"Year\",\n y: \"Percentage\",\n fill: \"Language\",\n z: {value: d => d.variableOrder2}, // Use the factor for sorting\n order: \"variableOrder2\", // Ensure the stacking order follows the factor\n tip : \"x\",\n }),\n Plot.ruleY([0])\n ],\n color: {\n legend : true,\n domain: LanguageOrder, \n scheme: \"spectral\"\n },\n x: { axis: null },\n y: { grid: null,\n tickFormat: (d) => d + \"%\"\n },\n fx: {label: null,\n domain: [\"Oregon\", \"West County\", \"East County\", \"Rockwood\"]},\n title: \"Language group distribution by Region\",\n caption: \"Source: 2012-2023 US Census Bureau ACS 5 Year Estimates\"\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":70,"value":"a21 = md`# 4. Income`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":173,"value":"a22= md`**Data source**: US Census Bureau ACS 5-year estimate \nTable B19013 (Median Household Income)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1389,"value":"mhi_4region = await FileAttachment(\"mhi_4region@1.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"mhi_4region","database":"$undefined"},{"id":2073,"value":"viewof tab7 = Inputs.table(mhi_4region.map(d => {\n return {Region: d.Region, Year: d.Year,\n 'Average Median Household Income': d['Average Median Household Income']\n }}))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":76,"value":"$51","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1565,"value":"a24=html`The map below highlights that East County region, particularly Rockwood area, has lower Median Household Income than West County region.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1562,"value":"a25=html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":175,"value":"a26=html`Data source: US Census Bureau ACS 5-year estimate
\nTable B20001 (Sex by Earnings in the Past 12 Months (Inflation-adjusted Dollars) for the Population 16 Years and Over With Earnings in the Past 12 Months)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1402,"value":"income_4region = await FileAttachment(\"income_4region@2.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"income_4region","database":"$undefined"},{"id":2077,"value":"viewof tab8 = Inputs.table(income_4region.map(d => {\n return {Region: d.Region, Year: d.Year,\n 'Total population': d['Total population'], Gender: d.Gender,\n Income_Group: d.Income_Group, \n 'Population estimate': d['Population estimate'],\n Percentage: d.Percentage\n }}))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":90,"value":"viewof Gender = Inputs.select([\"male\", \"female\", \"both\"],\n {label: \"SELECT GENDER\", value : \"both\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":93,"value":"a27=Plot.plot({\n marks: [\n Plot.barY(\n income_4region.filter(d => d.Gender==`${Gender}`)\n , {\n fx: \"Region\",\n x: \"Year\",\n y: \"Percentage\",\n fill: \"Income_Group\",\n tip : \"X\",\n }),\n Plot.ruleY([0])\n ],\n color: {\n legend : true,\n domain : IncomeOrder,\n scheme: \"spectral\"\n },\n x: { axis: null },\n y: { grid: true,\n domain: [0,100],\n tickFormat: (d) => d + \"%\",\n },\n fx: { label: \"Region\",\n domain: [\"Oregon\", \"West County\", \"East County\", \"Rockwood\"]},\n title: \"Income Range Distribution in 4 regions for \"+`${Gender}`+\" gender\",\n caption: \"Source: 2012-2023 US Census Bureau ACS 5 Year Estimates\"\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":96,"value":"viewof Gender2 = Inputs.select([\"male\", \"female\", \"both\"],\n {label: \"SELECT GENDER\", value: \"both\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":102,"value":"viewof IncomeRange = Inputs.select([\"$4,999 or less\", \"$5,000 to $9,999\",\"$10,000 to $19,999\",\"$20,000 to $29,999\",\"$30,000 to $39,999\",\"$40,000 to $49,999\",\"$50,000 to $64,999\",\"$65,000 to $74,999\",\"$75,000 to $99,999\", \"$100,000 or more\"],\n {label: \"SELECT INCOME RANGE\", value : \"$50,000 to $64,999\" })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":106,"value":"$52","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":109,"value":"viewof Region = Inputs.select([\"Oregon\", \"West County\", \"East County\", \"Rockwood\"],\n {label: \"SELECT REGION\", value: \"Rockwood\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":99,"value":"viewof Year = Inputs.select([2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023],\n {label: \"SELECT YEAR\", value : 2023})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":112,"value":"a29=Plot.plot({\n marks: [\n Plot.barY(income_4region.filter((d )=> (d.Region==`${Region}` && d.Year==`${Year}`)), \n{ x: \"Gender\",\n y: \"Population estimate\",\n fx: \"Income_Group\",\n fill: \"Gender\",\n tip : \"X\",\n }),\n Plot.ruleY([0])\n ],\n color: {\n legend : true,\n scheme: \"Warm\"\n },\n x: { axis: null},\n fx: {domain: IncomeOrder,\n },\n marginLeft: 60, // Add left margin for better readability\n marginRight: 60, // Add right margin for better spacing\n width: 1100, // Increase chart width\n height: 600, // Increase chart height\n y: { grid: true},\n title: \"Income group distribution in \"+ `${Region}`+\"by gender in \"+ `${Year}`,\n caption: \"Source: \"+`${Year}` +\" US Census Bureau ACS 5 Year Estimates\"\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":201,"value":"a30 = md`**Data source**: US Census Bureau ACS 5-year estimate \nTable B25071 (Median Gross Rent as a Percentage of Household Income (Dollars))`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1434,"value":"rent_4region = await FileAttachment(\"rent_4region@1.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"rent_4region","database":"$undefined"},{"id":2085,"value":"viewof tab9 = Inputs.table(rent_4region.map(d => {\n return {Region: d.Region, Year: d.Year,\n 'Average Percent Income on rent': d['Average Percent Income on rent']\n }}))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":208,"value":"$53","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1571,"value":"a32=html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":247,"value":"a33= md`**Data source**: US Census Bureau ACS 5-year estimate \nTable B25070 (Gross Rent as a Percentage of Household Income)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1452,"value":"rentpt_4region = await FileAttachment(\"rentpt_4region@3.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"rentpt_4region","database":"$undefined"},{"id":2088,"value":"viewof tab10 = Inputs.table(rentpt_4region.map(d => {\n return {Region: d.Region, Year: d.Year,\n 'Total population': d['Total population'], Gender: d.Gender,\n Income_percentage: d.Income_percentage, \n 'Population estimate': d['Population estimate'],\n Percentage: d.Percentage\n }}))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":255,"value":"a34=Plot.plot({\n marks: [\n Plot.barY(rentpt_4region, {\n fx: \"Region\",\n x: \"Year\",\n y: \"Percentage\",\n fill: \"Income_percentage\",\n tip : \"x\",\n }),\n Plot.ruleY([0])\n ],\n color: {\n legend : true,\n domain : RentptOrder,\n scheme: \"spectral\"\n },\n x: { axis: null },\n y: { grid: null},\n fx: {label: null,\n domain: [\"Oregon\", \"West County\", \"East County\", \"Rockwood\"]},\n title: \"Percentage of Household Income for Gross Rent by Region\",\n caption: \"Source: 2012-2022 US Census Bureau ACS 5 Year Estimates\"\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":273,"value":"viewof IncomePct = Inputs.select([\"<10%\", \"10%-20%\",\"20%-30%\",\"30%-40%\",\"40%-50%\",\">=50%\",\"not computed\" ],\n {label: \"SELECT INCOME PERCENTAGE FOR GROSS RENT\", value: \">=50%\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":276,"value":"$54","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1727,"value":"a36= md`# 5. Housing`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1753,"value":"a37= md`**Data source**: US Census Bureau ACS 5-year estimate \nTable B25002 (Occupancy Status)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1735,"value":"occ_4region = await FileAttachment(\"occ_4region.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"occ_4region","database":"$undefined"},{"id":2092,"value":"viewof tab11 = Inputs.table(occ_4region.map(d => {\n return {Region: d.Region, Year: d.Year,\n 'Total unit': d['Total unit'],\n 'Occupancy type': d['Occupancy type'],\n 'Unit estimate': d['Unit estimate'],\n Percentage: d.Percentage\n }}))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1737,"value":"a38=Plot.plot({\n marks: [\n Plot.barY(occ_4region, {\n fx: \"Region\",\n x: \"Year\",\n y: \"Percentage\",\n fill: \"Occupancy type\",\n tip : \"X\",\n }),\n Plot.ruleY([0])\n ],\n color: {\n legend : true,\n scheme: \"spectral\"\n },\n x: { axis: null },\n y: { grid: true,\n domain : [0,100],\n tickFormat: (d) => d + \"%\",},\n fx: { label: null,\n domain: [\"Oregon\", \"West County\", \"East County\", \"Rockwood\"]},\n title: \"Percentage of Occupied and Vacant Housing Units by Region\",\n caption: \"Source: 2012-2023 US Census Bureau ACS 5 Year Estimates\"\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1743,"value":"viewof Year_occ = Inputs.select([2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023],\n {label: \"SELECT YEAR\", value : 2023})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1745,"value":"a39=Plot.plot({\n marginLeft : 60,\n marks: [\n Plot.barY(occ_4region.filter((d )=> (d.Year==`${Year_occ}`)), \n{ x: \"Occupancy type\",\n y: \"Unit estimate\",\n fx: \"Region\",\n fill: \"Occupancy type\",\n }),\n Plot.ruleY([0]), \n Plot.text(occ_4region.filter((d )=> (d.Year==`${Year_occ}`)), \n{ text: d => `${Number(d['Unit estimate']).toLocaleString()}`,\n x: \"Occupancy type\",\n y: \"Unit estimate\",\n fx: \"Region\", \n dy: -7,\n fill: \"black\"\n })],\n \n color: {\n legend : true,\n scheme: \"Warm\"\n },\n x: { axis: null},\n fx: {\n domain: [//\"Oregon\",\n \"West County\", \"East County\", \"Rockwood\"],\n label : null},\n y: { grid: true,\n domain: [0, 250000], \n ticks: [0, 50000, 100000, 150000, 200000, 250000],\n label: \"Housing Unit\"\n },\n title: \"Occupied and Vacant Housing Units by Region in \"+ `${Year_occ}`,\n caption: \"Source: \"+ `${Year_occ}` +\" US Census Bureau ACS 5 Year Estimates\"\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1756,"value":"a40= html`Next, we will focus on occupied housing units to analyze tenure, specifically examining how many units are owner-occupied versus rented.\n

\nData source: US Census Bureau ACS 5-year estimate
\nTable B25003 (Occupied Housing Units by Tenure)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1762,"value":"ten_4region = await FileAttachment(\"ten_4region.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"ten_4region","database":"$undefined"},{"id":2099,"value":"viewof tab12 = Inputs.table(ten_4region.map(d => {\n return {Region: d.Region, Year: d.Year,\n 'Total unit': d['Total unit'],\n 'Tenure type': d['Tenure type'],\n 'Unit estimate': d['Unit estimate'],\n Percentage: d.Percentage\n }}))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1764,"value":"a41=Plot.plot({\n marks: [\n Plot.barY(ten_4region, {\n fx: \"Region\",\n x: \"Year\",\n y: \"Percentage\",\n fill: \"Tenure type\",\n tip : \"X\",\n }),\n Plot.ruleY([0])\n ],\n color: {\n legend : true,\n scheme: \"spectral\"\n },\n x: { axis: null },\n y: { grid: true,\n domain : [0,100],\n tickFormat: (d) => d + \"%\",},\n fx: { label: null,\n domain: [\"Oregon\", \"West County\", \"East County\", \"Rockwood\"]},\n title: \"Percentage of owned vs rented occupied Housing Units by Region\",\n caption: \"Source: 2012-2023 US Census Bureau ACS 5 Year Estimates\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1770,"value":"viewof Year_ten = Inputs.select([2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023],\n {label: \"SELECT YEAR\", value : 2023})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1772,"value":"a42=Plot.plot({\n marginLeft : 60,\n marks: [\n Plot.barY(ten_4region.filter((d )=> (d.Year==`${Year_ten}`)), \n{ x: \"Tenure type\",\n y: \"Unit estimate\",\n fx: \"Region\",\n fill: \"Tenure type\",\n }),\n Plot.ruleY([0]),\n \n Plot.text(ten_4region.filter((d )=> (d.Year==`${Year_ten}`)), \n{ text: d => `${Number(d['Unit estimate']).toLocaleString()}`,\n x: \"Tenure type\",\n y: \"Unit estimate\",\n fx: \"Region\", \n dy: -7,\n fill: \"black\"\n })],\n \n color: {\n legend : true,\n scheme: \"Warm\"\n },\n x: { axis: null},\n fx: {\n domain: [//\"Oregon\",\n \"West County\", \"East County\", \"Rockwood\"],\n label : null},\n y: { grid: true,\n domain: [0, 120000], \n ticks: [0, 30000, 60000, 90000, 120000],\n label: \"Housing Unit\"\n },\n title: \"Owned and Rented Occupied Housing Units by Region in \"+ `${Year_ten}`,\n caption: \"Source: \"+ `${Year_ten}`+\" US Census Bureau ACS 5 Year Estimates\"\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1779,"value":"a43=html`Next, we will examine the total population living in occupied housing units, categorized by whether they own or rent their homes.\n

\nData source: US Census Bureau ACS 5-year estimate
\nTable B25008 (Total Population in Occupied Housing Units by Tenure)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1795,"value":"tenpop_4region = await FileAttachment(\"tenpop_4region@1.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"tenpop_4region","database":"$undefined"},{"id":2108,"value":"viewof tab13 = Inputs.table(tenpop_4region.map(d => {\n return {Region: d.Region, Year: d.Year,\n 'Total population': d['Total population'],\n 'Tenure type': d['Tenure type'],\n 'Population estimate': d['Population estimate'],\n Percentage: d.Percentage\n }}))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1790,"value":"a44=Plot.plot({\n marks: [\n Plot.barY(tenpop_4region, {\n fx: \"Region\",\n x: \"Year\",\n y: \"Percentage\",\n fill: \"Tenure type\",\n tip : \"X\",\n }),\n Plot.ruleY([0])\n ],\n color: {\n legend : true,\n scheme: \"spectral\"\n },\n x: { axis: null },\n y: { grid: true,\n domain : [0,100],\n tickFormat: (d) => d + \"%\",},\n fx: { label: \"Region\",\n domain: [\"Oregon\", \"West County\", \"East County\", \"Rockwood\"]},\n title: \"Tenure type by population percentage in 4 regions\",\n caption: \"Source: 2012-2023 US Census Bureau ACS 5 Year Estimates\"\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1800,"value":"viewof Year_tenpop = Inputs.select([2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023],\n {label: \"SELECT YEAR\", value : 2023})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1803,"value":"$55","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1810,"value":"a46= md`**Data source**: US Census Bureau ACS 5-year estimate \nTable B25009 (Tenure by Household Size)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1814,"value":"size_4region = await FileAttachment(\"size_4region.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"size_4region","database":"$undefined"},{"id":2111,"value":"viewof tab14 = Inputs.table(size_4region.map(d => {\n return {Region: d.Region, Year: d.Year,\n 'Total unit': d['Total unit'],\n Tenure_type: d.Tenure_type,\n 'Household size': d['Household size'],\n 'Unit estimate': d['Unit Estimate'],\n Percentage: d.Percentage\n }}))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1816,"value":"viewof TenureType = Inputs.select([\"own\",\"rent\"],\n {label: \"SELECT TENURE TYPE\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1818,"value":"a47=Plot.plot({\n marks: [\n Plot.barY(size_4region.filter(d => d.Tenure_type==`${TenureType}`), \n { fx: \"Region\",\n x: \"Year\",\n y: \"Percentage\",\n fill: \"Household size\",\n tip : \"X\"}),\n Plot.ruleY([0])],\n color: {\n legend : true,\n scheme: \"spectral\"\n },\n x: { axis: null },\n y: { grid: true,\n tickFormat: (d) => d + \"%\",\n domain: [0,70],\n ticks: [0,10 ,20 ,30 , 40, 50, 60, 70]\n },\n fx: { label: \"Region\",\n domain: [\"Oregon\", \"West County\", \"East County\", \"Rockwood\"]},\n title: \"Household size Distribution by region for \"+`${TenureType}`+\"ed type\",\n caption: \"Source: 2012-2023 US Census Bureau ACS 5 Year Estimates\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1855,"value":"viewof Year_tensize = Inputs.select([2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023],\n {label: \"SELECT YEAR\", value : 2023})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1857,"value":"a48= Plot.plot({\n marginLeft : 60,\n marks: [\n Plot.barY(size_4region.filter((d )=> (d.Tenure_type==`${TenureType}` && d.Year==`${Year_tensize}`)), \n{ x: \"Household size\",\n y: \"Unit Estimate\",\n fx: \"Region\",\n fill: \"Household size\",\n tip : \"X\",\n }),\n Plot.ruleY([0]),\n \n ],\n \n color: {\n legend : true,\n scheme: \"Warm\"\n },\n x: { axis: null},\n fx: {domain: [//\"Oregon\",\n \"West County\", \"East County\", \"Rockwood\"],\n label : null},\n y: { grid: true,\n domain: [0, 60000],\n ticks: [0, 10000, 20000, 30000, 40000, 50000, 60000]\n },\n title: \"Household size distribution of \"+ `${TenureType}` +\"ed type in \"+ `${Year_tensize}`,\n caption: \"Source: \"+ `${Year_tensize}` +\" US Census Bureau ACS 5 Year Estimates\"\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":117,"value":"# 6. Transportation","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":119,"value":"a48_2=md`**Data source**: US Census Bureau ACS 5-year estimate \nTable B08301 (Means of Transportation to Work)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1460,"value":"trans_4region = await FileAttachment(\"trans_4region@1.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"trans_4region","database":"$undefined"},{"id":2117,"value":"viewof tab15 = Inputs.table(trans_4region.map(d => {\n return {Region: d.Region, Year: d.Year,\n 'Total population': d['Total population'],\n Transport_type: d.Transport_Type,\n 'Population estimate': d['Population estimate'],\n Percentage: d.Percentage\n }}))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":125,"value":"a49=Plot.plot({\n marks: [\n Plot.barY(trans_4region, {\n fx: \"Region\",\n x: \"Year\",\n y: \"Percentage\",\n fill: \"Transport_Type\",\n tip : \"x\",\n }),\n Plot.ruleY([0])\n ],\n color: {\n legend : true,\n domain : TransOrder,\n scheme: \"spectral\"\n },\n x: { axis: null },\n y: { grid: null},\n fx: {label: null,\n domain: [\"Oregon\", \"West County\", \"East County\", \"Rockwood\"]},\n title: \"Transport type distribution by Region\",\n caption: \"Source: 2012-2023 US Census Bureau ACS 5 Year Estimates\"\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":180,"value":"a50=md`**Data source**: US Census Bureau ACS 5-year estimate \nTable B08303 (Travel Time to Work)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1465,"value":"trans_time_4region = await FileAttachment(\"trans_time_4region@3.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"trans_time_4region","database":"$undefined"},{"id":2120,"value":"viewof tab16 = Inputs.table(trans_time_4region.map(d => {\n return {Region: d.Region, Year: d.Year,\n 'Total Population': d['Total Population'],\n Time_duration: d.Time_Duration,\n 'Population estimate': d['Population Estimate'],\n Percentage: d.Percentage\n }}))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":188,"value":"a51=Plot.plot({\n marks: [\n Plot.barY(trans_time_4region, {\n fx: \"Region\",\n x: \"Year\",\n y: \"Percentage\",\n fill: \"Time_Duration\",\n tip : \"x\",\n }),\n Plot.ruleY([0])\n ],\n color: {\n legend : true,\n //domain : TransOrder,\n scheme: \"spectral\"\n },\n x: { axis: null },\n y: { grid: null},\n fx: {label: null,\n domain: [\"Oregon\", \"West County\", \"East County\", \"Rockwood\"]},\n title: \"Time to work distribution by Region\",\n caption: \"Source: 2012-2023 US Census Bureau ACS 5 Year Estimates\"\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1826,"value":"a52= md`# 7. Education`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1831,"value":"a53= html`Data source: US Census Bureau ACS 5-year estimate
\nTable B14002 (Sex by School Enrollment by Level of School by Type of School for the Population 3 Years and Over)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1829,"value":"edu_4region = await FileAttachment(\"edu_4region.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"edu_4region","database":"$undefined"},{"id":2124,"value":"viewof tab17 = Inputs.table(edu_4region.map(d => {\n return {Region: d.Region, Year: d.Year,\n 'Total Population': d['Total Population'], Gender: d.Gender,\n 'School Type': d.School_Type, 'School Level': d.School_Level,\n 'Population Estimate': d['Population Estimate'],\n Percentage: d.Percentage\n }}))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1842,"value":"viewof Gender_edu = Inputs.select([\"male\", \"female\", \"both\"],\n {label: \"SELECT GENDER\", value : \"both\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1844,"value":"viewof school_type = Inputs.select([\"both\", \"public\", \"private\"],\n {label: \"SELECT SCHOOL TYPE\", value : \"both\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1851,"value":"school_order = [\"Preschool\", \"Kindergarten\", \"Grade 1 to 4\", \"Grade 5 to 8\",\"Grade 9 to 12\", \"College Undergraduate\", \"Graduate or Professional school\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1846,"value":"a54=Plot.plot({\n marks: [\n Plot.barY(edu_4region.filter(d => d.Gender==`${Gender_edu}` && d.School_Type==`${school_type}`), \n { fx: \"Region\",\n x: \"Year\",\n y: \"Percentage\",\n fill: \"School_Level\",\n tip : \"X\"}),\n Plot.ruleY([0])],\n color: {\n legend : true,\n domain : school_order,\n scheme: \"spectral\"\n },\n x: { axis: null },\n y: { grid: true,\n tickFormat: (d) => d + \"%\",\n domain: [0,30],\n ticks: [0,5,10,15 ,20,25,30]\n },\n fx: { label: \"Region\",\n domain: [\"Oregon\", \"West County\", \"East County\", \"Rockwood\"]},\n title: \"Percentage of School Level Enrollment by region\",\n caption: \"Source: 2012-2023 US Census Bureau ACS 5 Year Estimates\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1867,"value":"viewof Region_edu = Inputs.select([\"Oregon\", \"West County\", \"East County\", \"Rockwood\"],\n {label: \"SELECT REGION\", value: \"Rockwood\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1870,"value":"viewof Year_edu = Inputs.select([2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023],\n {label: \"SELECT YEAR\", value : 2023})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1872,"value":"a55= Plot.plot({\n marks: [\n Plot.barY(edu_4region.filter((d )=> (d.Region==`${Region_edu}` && d.Year==`${Year_edu}` && d.Gender==\"both\")), \n{ x: \"School_Type\",\n y: \"Population Estimate\",\n fx: \"School_Level\",\n fill: \"School_Type\",\n tip : \"X\",\n }),\n Plot.ruleY([0])\n ],\n color: {\n legend : true,\n scheme: \"Warm\"\n },\n x: { axis: null},\n fx: {domain: school_order,\n },\n marginLeft: 60, // Add left margin for better readability\n marginRight: 60, // Add right margin for better spacing\n width: 1100, // Increase chart width\n height: 600, // Increase chart height\n y: { grid: true},\n title: \"School Level distribution in \"+ `${Region_edu}`+\" in \"+ `${Year_edu}`,\n caption: \"Source: \"+`${Year_edu}` +\" US Census Bureau ACS 5 Year Estimates\"\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":551,"value":"a56= md`# 8. Poverty`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":685,"value":"a57=html`Data source: US Census Bureau ACS 5-year estimate
\nTable C17002 (Ratio of Income to Poverty Level in the Past 12 Months)
\nThe Census Bureau computes 48 different poverty income levels, based mostly on family size, and, for 1-2 person households, separate numbers for senior citizens. For more details, including links to the specific poverty levels, see How the Census Bureau Measures Poverty.
Some people call out the official poverty income levels as being very low, pointing out that many people who are not officially living in poverty still have a hard time making ends meet. The table referring to \"ratio of income to poverty level\" can help to get a broader count of people experiencing economic hardship. The column \"Range\" in the table below indicates that ratio.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1475,"value":"rpov_4region = await FileAttachment(\"rpov_4region@1.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"rpov_4region","database":"$undefined"},{"id":2129,"value":"viewof tab18 = Inputs.table(rpov_4region.map(d => {\n return {Region: d.Region, Year: d.Year,\n 'Total Population': d['Total Population'], Range: d.Range,\n 'Population Estimate': d.Estimate,\n Percentage: d.Percentage\n }}))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":562,"value":"a58=Plot.plot({\n marks: [\n Plot.barY(rpov_4region, {\n fx: \"Region\",\n x: \"Year\",\n y: \"Percentage\",\n fill: \"Range\",\n tip : \"x\",\n }),\n Plot.ruleY([0])\n ],\n color: {\n legend : true,\n domain : RpovOrder,\n scheme: \"spectral\"\n },\n x: { axis: null },\n y: { grid: null},\n fx: {label: null,\n domain: [\"Oregon\", \"West County\", \"East County\", \"Rockwood\"]},\n title: \"Ratio Range of Income to Poverty Level in the Past 12 Months by Region\",\n caption: \"Source: 2012-2023 US Census Bureau ACS 5 Year Estimates\"\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":569,"value":"viewof Range = Inputs.select([\"Under 0.50\", \"0.50 to 0.99\",\"1.00 to 1.24\",\"1.25 to 1.49\",\"1.50 to 1.84\",\"1.85 to 1.99\",\"2.00 and over\"],\n {label: \"SELECT RATIO OF INCOME TO POVERTY LEVEL\", value: \"2.00 and over\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":579,"value":"$56","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1578,"value":"a60=html`This map below explores the percentage of the population below the poverty level in Multnomah County in 2023.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1575,"value":"a61=html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":618,"value":"a62=md`**Data source**: US Census Bureau ACS 5-year estimate
\nTable B17001 (Poverty Status in the Past 12 Months by Sex by Age)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1484,"value":"pov_4region = await FileAttachment(\"pov_4region@1.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"pov_4region","database":"$undefined"},{"id":2131,"value":"viewof tab19 = Inputs.table(pov_4region.map(d => {\n return {Region: d.Region, Year: d.Year,\n 'Total Population': d['Total Population'], \n Gender: d.Gender, 'Poverty Status': d['Poverty status'], \n 'Population Estimate': d.Estimate,\n Percentage: d.Percentage\n }}))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":626,"value":"viewof Gender_pov = Inputs.select([\"both\", \"male\",\"female\"],\n {label: \"SELECT GENDER\", value: \"both\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":628,"value":"viewof pov_status = Inputs.select([\"below poverty level\", \"at or above poverty level\"],\n {label: \"SELECT POVERTY STATUS\", value: \"below poverty level\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":631,"value":"$57","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":660,"value":"a64=html`The chart highlights a significant decline in the percentage of individuals living below the poverty level in the Rockwood area starting in 2016. To better understand this trend, the next step is to analyze changes at the census tract level during this period, uncovering the underlying story of this improvement.
Data source: US Census Bureau ACS 5-year estimate
Table B17001 (Poverty Status in the Past 12 Months by Sex by Age)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1491,"value":"rockwood_pov_1223 = await FileAttachment(\"rockwood_pov_1223.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"rockwood_pov_1223","database":"$undefined"},{"id":671,"value":"a65=Plot.plot({\n marks: [\n Plot.barY(rockwood_pov_1223, {\n fx: \"Tract\",\n x: \"Year\",\n y: \"Population estimate\",\n fill: \"Poverty status\",\n tip : \"X\",\n }),\n Plot.ruleY([0])\n ],\n color: {\n legend : true,\n scheme: \"spectral\"\n },\n x: { axis: null },\n y: { grid: true},\n fx: { label: \"Tract\" },\n title: \"Poverty level Status in Rockwood by tract\",\n caption: \"Source: 2012-2023 US Census Bureau ACS 5 Year Estimates\"\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":701,"value":"a66=html`This chart reveals notable improvements between 2012 and 2022. The most significant reductions in the population below the poverty level occurred in tracts 97.02, 96.04, and 98.01, which drove the overall decline for the area. In contrast, tract 96.03 exhibited a minor upward trend in its poverty levels, diverging from the broader pattern of improvement. This data underscores the varying socioeconomic dynamics across Rockwood and highlights key areas where poverty reduction efforts have been most effective.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":709,"value":"a67=html`The next section provides an in-depth analysis of the percentage of individuals living below the poverty level, categorized by \nrace.
Data source: US Census Bureau ACS 5-year estimate
Table B17001A/B17001B/B17001C/B17001D/B17001E/B17001F/B17001G (Poverty Status in the Past 12 Months by Sex by Age of each race)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1497,"value":"pov_race_4region = await FileAttachment(\"pov_race_4region@1.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"pov_race_4region","database":"$undefined"},{"id":720,"value":"a68=Plot.plot({\n marks: [\n Plot.barY(pov_race_4region, {\n fx: \"Region\",\n x: \"Year\",\n y: \"Percentage\",\n fill: \"Race\",\n tip : \"X\",\n }),\n Plot.ruleY([0])\n ],\n color: {\n legend : true,\n domain: RaceOrder, // Ensuring legend follows the same order\n scheme: \"spectral\"\n },\n x: { axis: null },\n y: { grid: true},\n fx: {label: null,\n domain: [\"Oregon\", \"West County\", \"East County\", \"Rockwood\"]},\n title: \"Percentage of Poverty Population within each race\",\n caption: \"Source: 2012-2023 US Census Bureau ACS 5 Year Estimates\"\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":730,"value":"viewof Region2 = Inputs.select([\"Oregon\", \"West County\", \"East County\", \"Rockwood\"],\n {label: \"SELECT REGION\", value: \"Rockwood\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":738,"value":"a69=Plot.plot({\n marks: [\n Plot.barY(pov_race_4region.filter((d )=> (d.Region==`${Region2}` )), {\n fx: \"Year\",\n x: \"Race\",\n y: \"Poverty_population\",\n fill: \"Race\",\n tip : \"X\",\n }),\n Plot.ruleY([0])\n ],\n color: {\n legend : true,\n domain: RaceOrder,\n scheme: \"spectral\"\n },\n x: { axis: null, domain: RaceOrder },\n y: { grid: true},\n marginLeft: 60, // Add left margin for better readability\n marginRight: 60, // Add right margin for better spacing\n width: 1000, // Increase chart width\n height: 600, // Increase chart height\n title: \"Poverty Population by race in \"+`${Region2}`,\n caption: \"Source: 2012-2023 US Census Bureau ACS 5 Year Estimates\"\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":769,"value":"a70= md`# 9. Health`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":771,"value":"a71= md`**Data source**: US Census Bureau ACS 5-year estimate
\nTable B27001 (Health Insurance Coverage Status by Sex by Age)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1549,"value":"incov_4region = await FileAttachment(\"incov_4region@1.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"incov_4region","database":"$undefined"},{"id":778,"value":"viewof Gender_incov = Inputs.select([\"both\", \"male\",\"female\"],\n {label: \"SELECT GENDER\", value: \"both\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":780,"value":"viewof Incov = Inputs.select([\"yes\", \"no\"],\n {label: \"SELECT INSURANCE COVERAGE\", value: \"yes\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":782,"value":"viewof age_range = Inputs.select([\"18 years or below\", \"19 to 64 years\",\"65 years or above\",\"all ages\"],\n {label: \"SELECT AGE RANGE\", value: \"all ages\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":784,"value":"$58","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":812,"value":"a73= Plot.plot({\n marks: [\n Plot.barY(incov_4region.filter((d )=> (d.Gender==\"both\" && d[`Insurance coverage`]==\"yes\" && d[`Age Range`] !=\"all ages\")), {\n fx: \"Region\",\n x: \"Year\",\n y: \"Percentage\",\n fill: \"Age Range\",\n tip : \"X\",\n }),\n Plot.ruleY([0])\n ],\n color: {\n legend : true,\n //domain: RaceOrder, // Ensuring legend follows the same order\n scheme: \"spectral\"\n },\n x: { axis: null },\n y: { domain: [0,100],\n grid: true,\n tickFormat: (d) => d + \"%\"} ,\n fx: {label: null,\n domain: [\"Oregon\", \"West County\", \"East County\", \"Rockwood\"]},\n title: \"Percentage of Health Insurance Coverage by Age Range\",\n caption: \"Source: 2012-2023 US Census Bureau ACS 5 Year Estimates\"\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":822,"value":"viewof Year_incov = Inputs.select([2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023],\n {label: \"SELECT YEAR\", value : 2023})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":824,"value":"viewof Incov2 = Inputs.select([\"yes\", \"no\"],\n {label: \"SELECT INSURANCE COVERAGE STATUS\", value: \"no\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":826,"value":"$59","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1586,"value":"a75= md`## PLACES: Local Data for Better Health`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1599,"value":"a76=html` PLACES provides health and health-related data using small-area estimation for counties, incorporated and census designated places, census tracts, and ZIP Code Tabulation Areas (ZCTAs) across the United States. This project, which started in 2015, is a partnership between CDC, the Robert Wood Johnson Foundation (RWJF), and CDC Foundation. \n

PLACES helps state and local health officials, policy makers, community planner, and nonprofit groups better understand the health of their communities by identifying: Local health problems and priority health risk behaviors for action, Communities with health disparities to help prioritize investments in areas with the greatest need, Overlapping health and social needs in an area and Areas that need access to resources and services to support basic health.\n

The map below shows the latest data in Multnomah County in 2022.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1588,"value":"viewof topic = Inputs.select([\"Health Outcomes\", \"Prevention\"],\n {label: \"SELECT TOPIC\", value : \"Health Outcomes\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1590,"value":"viewof subtopic = Inputs.select(\n topic === \"Health Outcomes\" ? [\"Arthritis\", \"Asthma\", \"High Blood Pressure\"] :\n topic === \"2023\" ? [\"health\", \"transport\"] :\n [\"job\", \"house\"], \n {label: \"SELECT SUB-TOPIC\"}\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1593,"value":"a77= html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1613,"value":"mc_health_3region = await FileAttachment(\"mc_health_3region.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"mc_health_3region","database":"$undefined"},{"id":1615,"value":"viewof topic1 = Inputs.select([\"Health Outcomes\", \"Prevention\", \"Disability\", \"Health Risk Behaviors\", \"Health Status\"],\n {label: \"SELECT CATEGORY\", value : \"Health Outcomes\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1617,"value":"$5a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1717,"value":"$5b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":909,"value":"# 10. Life Expectancy","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":915,"value":"html`Life expectancy at birth is the average number of years a newborn is expected to live if mortality rates at the time of birth remain the same. The data below shows estimated life expectancy at birth for the period 2010-2015 based on The U.S. Small-area Life Expectancy Estimates Project (USALEEP).
USALEEP is a joint effort of the National Association for Public Health Statistics and Information Systems, the Centers for Disease Control and Prevention’s National Center for Health Statistics, and the Robert Wood Johnson Foundation. The report is based on state death records and population estimates from the U.S. Bureau of the Census.
Source: Centers for Disease Control and Prevention (CDC) `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":912,"value":"mc_le_2015 = await FileAttachment(\"mc_le_2015.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"mc_le_2015","database":"$undefined"},{"id":906,"value":"html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":864,"value":"# 11. Social Vulnerability","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":869,"value":"html`Social vulnerability refers to the demographic and socioeconomic factors (such as poverty, lack of access to transportation, and crowded housing) that adversely affect communities that encounter hazards and other community-level stressors. These stressors can include natural or human-caused disasters (such as tornadoes or chemical spills) or disease outbreaks (such as COVID-19).

The map below highlights that East County, particularly the Rockwood region, has a higher Social Vulnerability Index (SVI), indicating greater vulnerability in these areas.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":872,"value":"html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":293,"value":"# Appendix","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":946,"value":"categoryOrder2 = [ \"Black alone\", \"Asian alone\", \"Native Hawaiian and Other Pacific Islander alone\", \"American Indian and Alaska Native alone\", \"Hispanic or Latino\", \"White alone\"];","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":950,"value":"race_4region.forEach(d => {d.variableOrder_race = categoryOrder2.indexOf(d.Race)});","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1029,"value":"rockwood_race_1223.forEach(d => {d.variableOrder_race_rw = categoryOrder2.indexOf(d.Race)});","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":18,"value":"categoryOrder = [\"White\", \"Hispanic or Latino\", \"American Indian and Alaska Native\", \"Native Hawaiian and Pacific Islander\", \"Asian\", \"Black\"].reverse();","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":61,"value":"LanguageOrder = [\"English\", \"Spanish\", \"Other Indo-European languages\", \"Asian and Pacific Island languages\", \"Other\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":64,"value":"language_4region.forEach(d => {d.VariableOrder2 = LanguageOrder.indexOf(d.Lang_Group)});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":82,"value":"IncomeOrder = [\"$4,999 or less\", \"$5,000 to $9,999\",\"$10,000 to $19,999\",\"$20,000 to $29,999\",\"$30,000 to $39,999\",\"$40,000 to $49,999\",\"$50,000 to $64,999\",\"$65,000 to $74,999\",\"$75,000 to $99,999\",\"$100,000 or more\" ]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":85,"value":"income_4region.forEach(d => {d.VariableOrder3 = IncomeOrder.indexOf(d.Income_Group)});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":88,"value":"income_4region_new = income_4region.filter((d )=> (d.Gender==`${Gender2}` && d.Income_Group==`${IncomeRange}`))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":267,"value":"RentptOrder = [\"<10%\", \"10%-20%\",\"20%-30%\",\"30%-40%\",\"40%-50%\",\">=50%\",\"not computed\" ]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":269,"value":"rentpt_4region.forEach(d => {d.VariableOrder5 = RentptOrder.indexOf(d.Income_percentage)});","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":127,"value":"TransOrder = [\"drove alone\", \"carpool\",\"public\",\"taxicab\",\"motorcycle\",\"bike\",\"walk\",\"others\",\"work from home\" ]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":130,"value":"trans_4region.forEach(d => {d.VariableOrder4 = TransOrder.indexOf(d.Transport_Type)});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":284,"value":"rentpt_4region_new = rentpt_4region.filter((d )=> (d.Income_percentage==`${IncomePct}` ))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":565,"value":"RpovOrder = [\"Under 0.50\", \"0.50 to 0.99\",\"1.00 to 1.24\",\"1.25 to 1.49\",\"1.50 to 1.84\",\"1.85 to 1.99\",\"2.00 and over\" ] ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":582,"value":"rpov_4region_new = rpov_4region.filter((d )=> (d.Range==`${Range}` ))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":636,"value":"pov_4region_new = pov_4region.filter((d )=> (d.Gender==`${Gender_pov}` && d[`Poverty status`]==`${pov_status}`))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":717,"value":"RaceOrder = [\"White alone\", \"Black or African American alone\", \"American Indian and Alaska Native alone\", \"Asian alone\", \"Native Hawaiian and Other Pacific Islander alone\", \"Some Other Race alone\", \"Two or More races\"];","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":787,"value":"incov_4region_new = incov_4region.filter((d )=> (d.Gender==`${Gender_incov}` && d[`Insurance coverage`]==`${Incov}` && d[`Age Range`]==`${age_range}`))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1631,"value":" filteredData = mc_health_3region.filter(\n d => d.Category === topic1 && checkbox.includes(d.Sub_category)\n);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W5c","stdlib":"1"}} 24:null