25:["$","title",null,{"children":"Rockwood Identity Atlas 2025 | Chloe Bui | Observable"}] 49:T439,a1=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", caption: "Source: 2012-2023 US Census Bureau ACS 5 Year Estimates", y: { grid: true, label: "Population", domain: [0, 4500000], tickFormat: "s" } , x: { domain: [2011.5, 2024.5], ticks: ["2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021", "2022", "2023"], tickFormat: (d) => d } })4a:T456,a2= Plot.plot({ marks: [ Plot.ruleY([0]), Plot.lineY(race_4region, {x: "Year", y: "Logarithm (Total population)", stroke: "Region", strokeWidth: 3, tip : "x"}), Plot.dot(race_4region, { x: "Year", y: "Logarithm (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: "Logarithm (Total population)", text: d => d.Region, dx: 39, dy: 0, fill: "Region", fontSize: 12, fontWeight: "bold", }) ], title: "Logarithmic Scale of Population Trends", caption: "Source: 2012-2023 US Census Bureau ACS 5 Year Estimates", y: { grid: true, label: "Logarithm (Total Population)", domain: [10, 16], tickFormat: "s", } , x: { domain: [2011.5, 2024.5], ticks: ["2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021", "2022", "2023"], tickFormat: (d) => d } })4b:T4f8,html`For race distribution, Rockwood stands out as the most racially and ethnically diverse region among the four areas compared. In 2023, White residents made up just over 50% of Rockwood's population, far lower than East County (64%), West County (67%), and Oregon as a whole (72%). Hispanic or Latino residents accounted for 24.6% of Rockwood's population, significantly higher than all other regions, underscoring the area’s vibrant multicultural community.

Other racial groups, such as Black and Asian populations, also have a pronounced presence in Rockwood compared to its neighboring regions. Black residents represent 9.6% of Rockwood's population in 2023, nearly double the proportion in West County (5.6%) and far exceeding Oregon’s state average (1.8%). Similarly, Asian residents constitute 9.4% of Rockwood's population, compared to 4.4% statewide.

This diverse racial composition highlights Rockwood’s unique cultural fabric, positioning it as a vital focus for equitable resource allocation and culturally responsive community development. The region’s distinct demographic profile underscores the importance of addressing its needs in comparison to the broader, more homogenous populations of East and West County and the state.`4c:T56d,a4 = Plot.plot({ marks: [ Plot.ruleY([20]), 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", 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 } })4d:T50f,htl.html`Rockwood continues to demonstrate its position as a central hub of diversity, with more than half of its population consistently identifying as people of color since 2018. In 2023, 49.5% of Rockwood’s residents were people of color—far exceeding East County (35.9%), West County (32.6%), and Oregon overall (27.7%). This percentage, while slightly declining since its peak in 2019 (52.2%), underscores Rockwood's unique multicultural identity.

East County and West County have both seen gradual increases in diversity over the years, with East County rising from 25.4% in 2012 to 35.9% in 2023—a significant jump—while West County experienced a slower but steady increase from 27.4% to 32.6% in the same timeframe. Oregon as a whole has followed a similar upward trajectory, though at a lower rate, reflecting the state’s broader demographic shift toward greater racial and ethnic diversity.

Rockwood’s demographic composition highlights its role as the most diverse region in this analysis, making it a focal point for initiatives that embrace cultural inclusivity and address the unique needs of its diverse communities. The consistent growth of diversity in surrounding regions further emphasizes the broader trends of demographic change within Oregon.`4e:T4ce,a5= 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:T4d9,htl.html`In order to understand the Black community within Rockwood and its surrounding regions, the data reveals notable trends. Rockwood consistently maintains the highest percentage of Black-identifying residents among the four regions, peaking at 11% in 2017 before fluctuating. In 2023, Black residents accounted for 9.6% of Rockwood’s population, a significant share compared to East County (3.2%), West County (5.6%), and Oregon overall (1.8%).

This demographic highlights Rockwood as a vital hub for the Black community in the area, reflecting both its historical and contemporary significance as a diverse and inclusive region. While the percentage of Black residents in West County and East County has grown steadily but modestly over the years, Rockwood’s trends show greater variability, indicating potential shifts in housing, migration, or local socioeconomic conditions.

The unique concentration of Black residents in Rockwood underscores the importance of centering the voices and experiences of this community in any development or policy initiatives. By addressing the needs of its Black residents, Rockwood can continue to foster an inclusive and equitable environment that celebrates its diversity.`50:T4a2,htl.html`The fluctuations in the percentage of Black individuals across Rockwood’s census tracts from 2012 to 2023 reveal distinct demographic trends influenced by local dynamics. Tract 96.06 exhibited an initial increase in Black representation, peaking at 11.04% in 2019. However, it experienced a sharp decline to 4.11% by 2023. This decrease coincides with a rise in Asian populations, suggesting potential shifts in housing accessibility or affordability, as well as migration patterns within the region.

Tract 96.05 consistently maintained high Black representation compared to other tracts, peaking at 25.32% in 2023. This tract appears to be a key area of concentration for the Black population in Rockwood. However, fluctuations in other demographic groups, particularly Hispanic populations, indicate ongoing competition for housing and resources within the tract.

In contrast, Tract 96.03 and 98.01 consistently demonstrated lower levels of Black representation. Overall, the variations across these tracts reflect localized dynamics of migration, housing pressures, and community networks, illustrating the complex demographic landscape within Rockwood.`51:Ta7a,htl.html`Rockwood continues to stand out as a region with a significantly younger population compared to other areas analyzed, maintaining a vibrant and youthful demographic profile over the years. In 2023, the 0-9 age group accounted for 14.14% of Rockwood’s population, far surpassing East County (11.86%), West County (8.84%), and Oregon overall (10.53%). Despite a gradual decline from its peak of 16.51% in 2012, this age group remains a defining characteristic of Rockwood's population, highlighting the region’s appeal to families and its potential for a strong future workforce.

The 10-19 age group in Rockwood also continues to reflect its youthful demographic, representing 11.58% of the population in 2023. While this is lower than its 2017 peak of 16.25%, the percentage remains higher than in other regions, such as East County (12.95%), West County (9.83%), and Oregon overall (12.06%). Combined, these two younger age groups consistently represent over a quarter of Rockwood's population, reinforcing its status as a hub for younger generations.

Meanwhile, the 30-39 age group has steadily grown in prominence, reaching 16.11% in 2023—its highest percentage in the past decade—reflecting the influx of working-age adults. This trend mirrors East County, where the 30-39 age group has also grown steadily to 13.77% in 2023, and West County, where it has become the largest age group at 19.37%. Rockwood’s growth in this demographic underscores its potential for economic and community development driven by this core working-age population.

In contrast to its younger age profile, Rockwood’s older age groups, particularly 70-79 and 80 and over, remain significantly smaller in percentage compared to other regions. The 70-79 age group accounted for just 4.31% of the population in 2023, compared to 8.54% in Oregon and 6.6% in West County. Similarly, the 80 and over group was 3.11% in 2023, below the state average of 3.95%. This slower growth in older age cohorts highlights Rockwood’s comparatively lower rate of aging and its unique demographic trajectory.

Rockwood’s population trends highlight its role as a region with a dynamic and youthful demographic structure. The consistent presence of younger age groups positions Rockwood as an area with significant potential for growth and development, while its smaller older population presents opportunities to prioritize resources for its younger residents. As East County and West County also experience growth in working-age and older populations, Rockwood’s distinct profile underscores its unique position within Oregon’s broader demographic landscape.`52:T494,a8 = Plot.plot({ marks: [ Plot.ruleY([15]), Plot.lineY(age_1765_4region.filter((d )=> (d.Age_Group =="0-17")), {x: "Year", y: "Percentage", stroke: "Region", strokeWidth: 3, tip : "x"}), Plot.dot(age_1765_4region.filter((d )=> (d.Age_Group =="0-17")), { x: "Year", y: "Percentage", fill: "Region", r: 4 }), Plot.text(age_1765_4region.filter(d => d.Age_Group =="0-17" && d.Year === d3.max(age_1765_4region, 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", 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 } })53:T83d,htl.html`Rockwood maintains its position as a youth-centric region, with a consistently higher percentage of individuals aged 0-17 compared to East County, West County, and Oregon as a whole. In 2023, Rockwood's youth comprised 23.41% of its population, a slight rebound from its low of 22.35% in 2022, but still significantly lower than its peak of 28.44% in 2017. Despite this decline, Rockwood continues to surpass East County (22.84%), Oregon (20.2%), and West County (16.68%) in the proportion of youth.

The trend over the years shows a general decline in the percentage of youth in Rockwood, following a similar pattern in other regions, though the decline is more pronounced in Rockwood. Between 2012 and 2023, Rockwood’s youth population decreased by approximately 4 percentage points, reflecting broader demographic shifts and aging populations in the region.

East County, while trailing Rockwood, exhibits a steadier trend, maintaining a youth population share above 22% throughout the observed period. The percentage peaked at 25.54% in 2013 but has gradually decreased to 22.84% in 2023. This decline is less steep than that of Rockwood, suggesting a more stable demographic distribution in East County.

West County and Oregon, by contrast, have significantly lower youth percentages and show consistent declines over the years. West County’s youth proportion dropped from 19.12% in 2012 to 16.68% in 2023—a decline of over 2 percentage points. Oregon reflects a similar trend, with its youth population share decreasing from 22.53% in 2012 to 20.2% in 2023.

Rockwood’s higher concentration of youth underscores its importance as a community for family-oriented policies and programs. The consistent decrease in youth populations across all regions may reflect broader trends such as declining birth rates or migration patterns favoring working-age or older individuals. However, Rockwood's youth-centric identity continues to make it a focal point for investments in education, childcare, and recreational facilities to support its younger population.`54:T593,htl.html`The analysis of youth ages 0-17 across census tracts in Rockwood reveals notable fluctuations over time. From 2012 to 2023, Tract 97.02 consistently holds the largest population of youth, reflecting its role as a focal point for families. In 2023, 26.81% of its population was under 17, maintaining a strong representation despite a slight decline from earlier years. Tract 96.06 shows a dynamic pattern, peaking at 29.03% in 2017 before gradually stabilizing to 21.68% in 2023, reflecting demographic shifts possibly influenced by housing or economic factors.

Tracts like 96.03 and 96.05 demonstrate contrasting trends. Tract 96.03 exhibits a relatively steady youth population, hovering around 25%-27%, while Tract 96.05 presents more variability, with youth percentages dropping in recent years from 27.82% in 2015 to 23.52% in 2023. Interestingly, Tract 96.04 experiences a pronounced decline in youth representation, particularly after 2020, reducing from 24.95% to 18.08% in 2023, potentially pointing to gentrification or migration patterns.

Overall, these trends highlight how individual tracts within Rockwood respond differently to urban, social, and economic changes. The sustained high percentages of youth in Tracts 97.02 and 96.06 suggest these areas remain crucial for family-oriented community planning, while declines in Tract 96.04 warrant closer examination of underlying factors.`55:T4b0,a10=Plot.plot({ marks: [ Plot.ruleY([8]), Plot.lineY(age_1765_4region.filter((d )=> (d.Age_Group =="65 and over")), {x: "Year", y: "Percentage", stroke: "Region", strokeWidth: 3, tip : "x"}), Plot.dot(age_1765_4region.filter((d )=> (d.Age_Group =="65 and over")), { x: "Year", y: "Percentage", fill: "Region", r: 4 }), Plot.text(age_1765_4region.filter(d => d.Age_Group =="65 and over" && d.Year === d3.max(age_1765_4region, 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 ", 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 } })56:T53d,htl.html`Rockwood has historically had a lower percentage of seniors (65 and over) compared to East County, West County, and Oregon overall. In 2023, seniors comprised 12.54% of Rockwood’s population, marking a significant increase from 9.36% in 2012. This reflects a steady upward trend in Rockwood's aging population, particularly in the last two years, with noticeable jumps from 11.42% in 2021 to 12.52% in 2022.

East County has consistently maintained a higher share of seniors compared to Rockwood, growing from 11% in 2012 to 14.98% in 2023. West County and Oregon demonstrate even higher senior populations, with West County increasing from 10.7% in 2012 to 14.37% in 2023, and Oregon showing the most dramatic growth, from 14.09% in 2012 to 18.63% in 2023.

This trend highlights the demographic aging occurring statewide, with Oregon showing the most rapid growth in its senior population. While Rockwood continues to be a region with a younger demographic focus, the increase in seniors suggests an emerging need for services and infrastructure that cater to aging residents. Compared to the state and neighboring regions, Rockwood’s smaller senior population may indicate a slower shift, but the steady rise points to a changing community composition that warrants attention to senior support and resources.`57:T570,htl.html`The analysis of language distribution highlights the linguistic diversity in Rockwood compared to other regions. In 2023, Rockwood exhibited the highest proportion of non-English speakers, with 39.56% of the population speaking languages other than English. Spanish is the dominant non-English language in Rockwood, representing 19.3% of its population, a stark contrast to West County (6.69%) and Oregon (8.95%). Additionally, Rockwood shows a notable presence of "Other Indo-European languages" (8.01%) and "Asian and Pacific Island languages" (7.73%), indicating a robust multicultural linguistic representation.

Over the years, the proportion of English speakers in Rockwood has declined from 63.43% in 2012 to 60.44% in 2023, reflecting a growing multilingual community. In contrast, West County and East County have maintained higher percentages of English speakers, around 82.75% and 76.44% respectively, showcasing less linguistic diversity than Rockwood.

These trends emphasize Rockwood's unique role as a hub for linguistic and cultural diversity within the region, making it a critical focus area for multilingual outreach and inclusive community initiatives. The sustained high proportion of Spanish and increasing representation of other language groups underscore the importance of tailored services and programs to cater to this diverse demographic.`58:T41f,a11b=Plot.plot({ marginLeft : 60, marks: [ Plot.ruleY([0]), Plot.lineY(fam_4region, {x: "Year", y: "Percentage", stroke: "Region", strokeWidth: 3, tip : "x"}), Plot.dot(fam_4region, { x: "Year", y: "Percentage", fill: "Region", r: 4 }), Plot.text(fam_4region.filter(d => d.Year === d3.max(fam_4region, d => d.Year)), { x: "Year", y: "Percentage", text: d => d.Region, dx: 39, dy: 0, fill: "Region", fontSize: 12, fontWeight: "bold"})], title: "Percent Households with Single Female Head of Household", caption: "Source: 2012-2023 US Census Bureau ACS 5 Year Estimates", y: { grid: true, domain: [0, 24], tickFormat: (d) => d + "%", ticks: [0,4,8,12, 16, 20, 24], } , x: { domain: [2011.5, 2024.5], ticks: ["2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021", "2022", "2023"], tickFormat: (d) => d } })59:T48b,a12=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 } })5a:T684,htl.html`The analysis of median household income trends reveals persistent disparities and varying growth rates across Oregon's regions. In 2023, Rockwood remained the region with the lowest median household income at $59,339, trailing significantly behind East County ($85,306), West County ($96,684), and Oregon’s state average ($80,426). However, Rockwood has experienced significant growth, especially since 2020, when its income increased from $47,164 to $59,339 in just three years, reflecting economic improvements, though the gap with wealthier regions remains substantial.

East County demonstrates consistent growth, starting at $55,768 in 2012 and reaching $85,306 in 2023. This rapid increase, particularly after 2019, highlights East County's strong economic development, surpassing the state average and showcasing its growing prosperity. Similarly, West County, the most affluent region, has maintained a leadership position in income levels, rising from $57,519 in 2012 to $96,684 in 2023. Its steady growth reflects the region's established economic stability and resources. The state average for Oregon shows a steady climb from $50,036 in 2012 to $80,426 in 2023, reflecting overall economic progress. However, this average conceals significant regional disparities, particularly the slower growth and lower income levels in Rockwood compared to the substantial increases in East and West County.

These trends underscore the need for targeted policies to address economic inequalities, particularly in regions like Rockwood, while continuing to support the upward momentum in East County and sustaining prosperity in West County.`5b:T68a,htl.html`The analysis of income range distribution highlights Rockwood as a region facing persistent economic challenges despite gradual improvements over the years. In 2023, Rockwood still had a significant concentration of households in lower income brackets, with 13.64% earning between $10,000 and $19,999 and 13.97% earning between $20,000 and $29,999. These figures represent some of the highest proportions of low-income households among the regions analyzed, showcasing the region’s economic struggles. However, there has been a modest increase in the percentage of households earning $100,000 or more, rising from a mere 0.61% in 2012 to 5.46% in 2023. This growth, although notable, remains significantly behind other regions like West County and East County.

Compared to Rockwood, other regions show more balanced or affluent income distributions. For instance, in West County, 22.47% of households earned $100,000 or more in 2023—a dramatic contrast to Rockwood. East County, though less affluent than West County, has also experienced steady economic improvement, with 10.68% of households earning $100,000 or more in 2023. Rockwood’s reliance on lower income brackets stands out as a stark contrast to these upward trends elsewhere.

Despite its challenges, Rockwood’s slow growth in higher income brackets signals a potential for economic improvement. However, this progress highlights the pressing need for targeted interventions to accelerate economic upliftment. Strategies such as job creation, education investment, and support for small businesses could help shift income distributions toward middle- and upper-income brackets.`5c:T57c,a14=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 "+ `${IncomeRange}`, caption: "Source: 2012-2023 US Census Bureau ACS 5 Year Estimates", y: { grid: true, tickFormat: (d) => d + "%", tickFontSize: 50 } , x: { domain: [2011.5, 2024.5], ticks: ["2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021", "2022", "2023"], tickFormat: (d) => d } })5d:T557,a16=Plot.plot({ marks: [ Plot.ruleY([30]), Plot.lineY(ten_4region.filter((d )=> (d[`Tenure type`] ==`${ten_type}`)), {x: "Year", y: "Percentage", stroke: "Region", strokeWidth: 3, tip : "x"}), Plot.dot(ten_4region.filter((d )=> (d[`Tenure type`] ==`${ten_type}`)), { x: "Year", y: "Percentage", fill: "Region", r: 4 // Radius of the dots }), Plot.text(ten_4region.filter(d => (d[`Tenure type`] ==`${ten_type}`) && d.Year === d3.max(ten_4region, d => d.Year)), { x: "Year", y: "Percentage", 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: " Percentage of "+`${ten_type}` +" Housing Units", caption: "Source: 2012-2023 US Census Bureau ACS 5 Year Estimates", y: { grid: true, domain: [30, 70], tickFormat: (d) => d + "%", //ticks: [25,30,35,40,45], //tickFontSize: 50 } , x: { domain: [2011.5, 2024.5], ticks: ["2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021", "2022", "2023"], tickFormat: (d) => d } })5e:T62f,htl.html`The percentage of owned housing units in Rockwood has shown improvement over time, despite some fluctuations. In 2012, homeownership in Rockwood was at 45.36%, reflecting a significant gap compared to other regions. The trend peaked in 2020, reaching its maximum of 53.56%, showcasing a positive shift toward increased homeownership. However, this progress has not been consistent, as the percentage slightly declined to 51.83% by 2023. Meanwhile, other regions, such as West County and East County, have remained relatively stable, maintaining consistently higher ownership rates throughout the period.

This highlights a positive but insufficient shift for Rockwood, which remains one of the lowest in homeownership rates compared to neighboring regions. The incremental progress in Rockwood is a step forward but underscores the challenges the community faces in achieving parity in housing equity and stability.

Homeownership is particularly important as it provides financial stability, acts as a hedge against real estate market fluctuations, and fosters long-term wealth-building opportunities for families. Without the robust policy protections that homeowners enjoy, renters remain vulnerable to the volatility of housing markets. This vulnerability is even more pronounced in Rockwood, where rental housing continues to dominate. Rising housing costs and shifts in market dynamics put many residents at risk of displacement, making targeted interventions and policies critical for ensuring equitable access to homeownership and housing stability.`5f:T4e8,Plot.plot({ marks: [ Plot.ruleY([30]), Plot.lineY(tenpop_4region.filter((d )=> (d[`Tenure type`] ==`${ten_type_pop}`)), {x: "Year", y: "Percentage", stroke: "Region", strokeWidth: 3, tip : "x"}), Plot.dot(tenpop_4region.filter((d )=> (d[`Tenure type`] ==`${ten_type_pop}`)), { x: "Year", y: "Percentage", fill: "Region", r: 4 // Radius of the dots }), Plot.text(tenpop_4region.filter(d => (d[`Tenure type`] ==`${ten_type_pop}`) && d.Year === d3.max(tenpop_4region, d => d.Year)), { x: "Year", y: "Percentage", text: d => d.Region, dx: 39, dy: 0, fill: "Region", fontSize: 12, fontWeight: "bold", }) ], title: " Percentage of Home "+`${ten_type_pop}` +"s", caption: "Source: 2012-2023 US Census Bureau ACS 5 Year Estimates", y: { grid: true, domain: [30, 70], tickFormat: (d) => d + "%", //ticks: [25,30,35,40,45], //tickFontSize: 50 } , x: { domain: [2011.5, 2024.5], ticks: ["2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021", "2022", "2023"], tickFormat: (d) => d } })60:T4f9,a18=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 + "%", ticks: [25,30,35,40,45], tickFontSize: 50 } , x: { domain: [2011.5, 2024.5], ticks: ["2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021", "2022", "2023"], tickFormat: (d) => d } })61:T4ee,htl.html`Rockwood demonstrates the highest burden of rent expenses relative to household income compared to other regions. In 2023, the average percentage of household income spent on rent in Rockwood stood at 39.7%, exceeding East County (34.3%), West County (31.9%), and Oregon as a whole (30.9%). Over the past decade, Rockwood's rent-to-income ratio consistently surpassed these regions, peaking at 42.5% in 2020, indicating a period of acute housing cost pressures.

From 2012 to 2023, Rockwood's rent-to-income ratio has shown greater volatility than other regions, reflecting shifts in rental market dynamics and economic conditions. The elevated percentages in Rockwood, consistently hovering around 40%, underscore the financial strain experienced by its residents, particularly when compared to West County, where the ratio has remained relatively stable and below 33%.

These trends highlight the challenges Rockwood faces in housing affordability, making it a critical area for interventions focused on affordable housing policies and economic support initiatives. Addressing the high proportion of income spent on rent in Rockwood is essential to alleviate financial burdens and ensure equitable living conditions for its residents.`62:T416,htl.html`Rockwood consistently exhibits the highest proportion of households spending 50% or more of their income on rent compared to other regions, reflecting severe rent burdens. In 2023, 36.94% of households in Rockwood fell into this category, significantly higher than West County (24.52%), East County (25.96%), and Oregon as a whole (24.64%). This trend has been a persistent challenge for Rockwood, with this proportion reaching its peak at 38.37% in 2020.

Furthermore, Rockwood demonstrates a relatively small percentage of households spending less than 20% of their income on rent. In 2023, only 14.83% of households in Rockwood spent less than 20% of their income on rent, significantly lower than the state average of 21.78% and West County's 22.39%.

The consistent high percentage of rent-burdened households in Rockwood underscores the urgent need for targeted interventions. Policies promoting affordable housing and rental assistance are critical to alleviating the financial strain faced by Rockwood residents.`63:T63b,htl.html`In Rockwood, household size distribution reveals a significant trend toward larger households, highlighting the ongoing demand for housing that can accommodate multiple residents. While single or two-person households are prevalent, larger household sizes—particularly those with 4, 5, or even 6 persons—constitute a considerable share of both owner-occupied and rental housing.

For owner-occupied housing in 2023, 3-person households accounted for 5.85%, 4-person households for 6.68%, and 5-person households for 4.86%, demonstrating the sustained need for homes suited to families or multi-generational living. Similarly, rental housing exhibits a similar trend, with 3-person households making up 9.49%, 4-person households 5.14%, and 5-person households 2.32% of all rental units. Notably, while these percentages are lower than single- and two-person households, they are substantial compared to the rest of the county.

This pattern reflects the unique socio-economic dynamics of Rockwood, where affordability pressures and cultural practices likely contribute to larger households. The higher prevalence of larger households, both in owned and rented housing, underscores a critical need for policies that support the development and preservation of family-friendly housing. This includes multi-bedroom units, affordable rental properties, and housing designs that accommodate extended family living arrangements. Failure to address this demand could exacerbate housing instability and displacement in a region already characterized by economic challenges.`64:T638,htl.html`Based on the enrollment trends in Rockwood, the region has consistently shown a higher proportion of school-aged children compared to other areas, with a notable emphasis on elementary and secondary education. Over the years, Rockwood has maintained a strong presence of students in grades 1 through 12, particularly in the middle school and high school levels. This suggests a relatively young population with a strong emphasis on primary and secondary education.

However, the enrollment data also reveals a declining trend in college undergraduate and graduate school participation in Rockwood compared to other regions. While high school enrollment remains robust, the transition to higher education is less pronounced, indicating potential barriers such as financial constraints, access to resources, or alternative career pathways that do not require post-secondary education.

When comparing Rockwood to East County, a similar trend emerges, but East County maintains a slightly higher percentage of individuals pursuing undergraduate and graduate degrees. Meanwhile, West County and Oregon as a whole display a markedly different pattern, with a stronger emphasis on college enrollment and graduate school participation. These regions have consistently shown a higher percentage of individuals in post-secondary education. This divergence highlights broader socioeconomic differences, where educational attainment in Rockwood may be influenced by systemic barriers or local economic conditions that prioritize workforce entry over extended academic pursuits.`65:T807,htl.html`Rockwood's education levels show a clear gap compared to other regions like East County, West County, and the state of Oregon as a whole. Rockwood stands out for having a significantly high percentage of its population categorized as "In school (No diploma)," indicating a relatively young community where many individuals are still in the process of completing their education. This contrasts with regions like West County, where a larger share of the population has already moved on to higher education or the workforce.

When it comes to college degrees and higher, Rockwood lags behind. Only 12.84% of residents had a bachelor's degree in 2023, compared to 17.02% in East County, 32.3% in West County, and 22.34% in Oregon. This shows that fewer people in Rockwood are able to continue their education beyond high school, likely due to financial struggles, lack of support, or limited access to higher education opportunities. Over the last decade, Rockwood has seen some improvement in this area—bachelor’s degree attainment rose from 9.02% in 2012 to 12.84% in 2023—but the progress has been slow compared to other regions.

Another concerning factor is the percentage of people with no schooling at all. In 2023, about 3.95% of Rockwood’s population fell into this category, which is much higher than in West County (1.57%) and Oregon overall (1.38%). This suggests that some people in Rockwood are missing out on basic education, which can limit their chances for stable jobs and financial security in the future.

While the young population suggests potential for future educational growth, the relatively low rates of college attainment indicate that structural barriers—whether financial, institutional, or social—may be limiting the transition from high school to post-secondary education. This trend underscores the need for continued investment in educational support systems that ensure students not only stay in school but also have the resources and encouragement to pursue higher degrees.`66:T4a4,a26=Plot.plot({ marks: [ Plot.ruleY([0]), Plot.lineY(edu_at_4region.filter((d )=> (d.Attainment ==`${edu_at}`)), {x: "Year", y: "Percentage", stroke: "Region", strokeWidth: 3, tip : "x"}), Plot.dot(edu_at_4region.filter((d )=> (d.Attainment ==`${edu_at}`)), { x: "Year", y: "Percentage", fill: "Region", r: 4 // Radius of the dots }), Plot.text(edu_at_4region.filter(d => (d.Attainment ==`${edu_at}`) && d.Year === d3.max(edu_at_4region, d => d.Year)), { x: "Year", y: "Percentage", text: d => d.Region, dx: 39, dy: 0, fill: "Region", fontSize: 12, fontWeight: "bold", }) ], title: " Percentage of "+`${edu_at}`, caption: "Source: 2012-2023 US Census Bureau ACS 5 Year Estimates", y: { grid: true, domain: [0, 50], tickFormat: (d) => d + "%", //ticks: [25,30,35,40,45] } , x: { domain: [2011.5, 2024.5], ticks: ["2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021", "2022", "2023"], tickFormat: (d) => d } })67:T4b8,a27b= Plot.plot({ marginLeft : 60, y: {label: "Median Household Income($)", grid: true, domain: [20000, 260000], //tickFormat: d => `$${d}M` }, x: {label: "Populations with Bachelor's degrees or higher(%)", grid: true, domain: [10, 100], tickFormat: (d) => d + "%", }, color: { legend: true, label: "Region", domain: ["West County", "East County", "Rockwood"], range: ["#3ca951", "#4269d0", "#ff725c"] }, title: "Education and Income Inequality in Multnomah County (2023): A Regional Gap Between Rockwood/East County and West County", caption: "Source: 2023 US Census Bureau ACS 5 Year Estimates", marks: [Plot.ruleY([20000]),Plot.ruleX([10]), Plot.dot(tract_edu_at_mhi_23, { x: "pct_bachelor", y: "avg_mhi", r: "Population", fill: d => d.Region === "West County" ? "#3ca951" : d.Region === "East County" ? "#4269d0" : "#ff725c", stroke: "black", tip : true, strokeWidth: 0.5, title: d => `Tract ${d.tract} \nBachelor's Degree: ${d.pct_bachelor}% \nMedian Household Income: $${d.avg_mhi.toLocaleString()} \nPopulation: ${d.Population.toLocaleString()}` }) ] })68:T5a1,a28=Plot.plot({ marks: [ Plot.ruleY([0]), Plot.lineY(incov_4region.filter((d )=> (d.Gender==`${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`]==`${age_range}`)), { x: "Year", y: "Percentage", fill: "Region", r: 4 // Radius of the dots }), ], title: " Percentage of "+ `${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, 2023.5], ticks: ["2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021", "2022", "2023"], tickFormat: (d) => d }, color: {legend: true, //domain: ["Rockwood", "East County", "West County", "Oregon"] }, })69:T5d6,a30=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" })] })6a:T463,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.

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.

The map below shows the latest data in Multnomah County in 2022. (We will provide all the maps via ArcGIS later) `6b: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"]})6c:T532,a31=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 => `${d.Percentage}%`, fontSize: 14, // Increase text size fontWeight: "bold", // Make text bold textAnchor: "middle", fill: (d) => (d.Percentage > 50 ? "white" : "black") }) ], style: { "text-anchor": "middle", "font-size": "15px", // Set font size for tick labels "font-weight": "bold", // Set font weight for axis labels "color": "black", // Optional: Set text color "tick-line-width": 0, // Remove tick lines (horizontal and vertical) "stroke-width": 0 } });6d:T6b7,a32=Plot.plot({ marks: [ Plot.ruleY([0]), Plot.lineY(snap_4region.filter((d )=> (d[`Public assistance status`] ==`${snap_type}`)), {x: "Year", y: "Percentage", stroke: "Region", strokeWidth: 3, tip : "x"}), Plot.dot(snap_4region.filter((d )=> (d[`Public assistance status`] ==`${snap_type}`)), { x: "Year", y: "Percentage", fill: "Region", r: 4 // Radius of the dots }), //Plot.text(snap_4region.filter(d => (d[`Public assistance status`] ==`${snap_type}`) && d.Year === d3.max(snap_4region, d => d.Year)), { // x: "Year", // y: "Percentage", // 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: snap_type === "With cash public assistance or Food Stamps/SNAP" ? "Percentage of Households With cash public assistance or Food Stamps/SNAP": "Percentage of Households with No cash public assistance or Food Stamps/SNAP", caption: "Source: 2012-2023 US Census Bureau ACS 5 Year Estimates", y: { grid: true, //domain: [0, 40], tickFormat: (d) => d + "%", //ticks: [25,30,35,40,45], //tickFontSize: 50 } , x: { domain: [2011.5, 2024], ticks: ["2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021", "2022", "2023"], tickFormat: (d) => d }, color: {legend: true, //domain: ["Rockwood", "East County", "West County", "Oregon"] }, })6e:T5b1,a33 = Plot.plot({ marginLeft : 60, title: snap_type === "With cash public assistance or Food Stamps/SNAP" ? "Percentage of Households With cash public assistance or Food Stamps/SNAP in "+`${Year_snap}`: "Percentage of Households with No cash public assistance or Food Stamps/SNAP in "+ `${Year_snap}`, caption: "Source: "+ `${Year_snap}` +" US Census Bureau ACS 5 Year Estimates", x: { padding: 0.5, // Adjust spacing between bars, smaller values mean thinner bars, domain: ["Oregon", "West County", "East County", "Rockwood"], label: null}, y: { grid: true, tickFormat: (d) => d + "%", label: "Percentage"}, marks: [ Plot.barY(snap_4region.filter((d )=> (d[`Public assistance status`] ==`${snap_type}` && d[`Year`]==`${Year_snap}`)), {x: "Region", y: "Percentage", fill: d => d.Region === "Oregon" ? "#efb118" : d.Region === "West County" ? "#3ca951" : d.Region === "East County" ? "#4269d0" : "#ff725c" }), Plot.ruleY([0]), Plot.text(snap_4region.filter((d )=> (d[`Public assistance status`] ==`${snap_type}` && d[`Year`]==`${Year_snap}`)), { text: d => `${d['Percentage']}%`, x: "Region", y: "Percentage", dy: 13, fontSize: 13, fontWeight: "bold", fill: "white" })] })6f:T482,htl.html`In Rockwood, how people get to work is quite different from other areas like East County, West County, and the rest of Oregon. While most people in Rockwood drive alone (61% in 2023), this is lower than in East County (71%) and Oregon overall (66%). This suggests that fewer people in Rockwood own a car or can afford to drive alone every day. Instead, many rely on carpooling (18%) and public transportation (7.6%), both of which are used more in Rockwood than in West County (7% and 7.3%, respectively). This likely reflects a need for more affordable commuting options. Over the years, public transit use in Rockwood has dropped from 14% in 2016 to around 7.6% in 2023, following a statewide trend as more jobs allow remote work. However, working from home is much less common in Rockwood (8.9%) compared to West County (25.5%) and East County (12.5%), likely because more Rockwood residents have in-person jobs in retail, food service, or manual labor. These differences show that Rockwood could benefit from better public transit options, support for affordable car ownership, and more job opportunities that allow flexible or remote work.`70:T567,htl.html` A large proportion of Rockwood residents experience longer commutes, with nearly 40% of workers spending 30 minutes or more commuting in 2023. This percentage is higher than in East County (35%) and West County (28%), where more people have jobs within shorter distances. While 27.7% of Rockwood commuters travel for 10-19 minutes, the share is still lower than in West County (30%) and Oregon overall (32.6%), suggesting that Rockwood workers may have fewer job opportunities nearby.

Additionally, Rockwood has a higher percentage of people with extreme commutes (60+ minutes) compared to West County and Oregon. Around 7% of Rockwood residents spend an hour or more commuting daily, compared to 5% in East County and only 3.6% in West County. This likely reflects a reliance on public transportation or carpooling, which can significantly extend travel times. While statewide and in wealthier regions like West County, more people work from home (17-25%), in Rockwood, remote work is much lower, meaning most residents are tied to in-person jobs.

Overall, Rockwood’s longer commutes and lower rates of remote work indicate a need for better local job access, improved transit efficiency, and affordable housing closer to employment hubs. Addressing these disparities could improve work-life balance and economic opportunities for Rockwood residents.`71:T4b3,a35=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 "+ `${Range}`, caption: "Source: 2012-2023 US Census Bureau ACS 5 Year Estimates", y: { grid: true, tickFormat: (d) => d + "%", tickFontSize: 50 } , x: { domain: [2011.5, 2024.5], ticks: ["2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021", "2022", "2023"], tickFormat: (d) => d } })72:T514,a36=Plot.plot({ marks: [ Plot.ruleY([0]), Plot.lineY(pov_4region.filter((d )=> (d.Gender==`${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, // domain: [0, 35], 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 } })73:T4f0,htl.html`Over the past decade, poverty rates have gone down across 4 regions. Rockwood, in particular, has seen big improvements, with the percentage of people living below the poverty line dropping from nearly 33% in 2015 to about 16% in 2023. However, despite this progress, Rockwood still has more people struggling financially compared to other areas.

One reason for this improvement is that the official poverty threshold has increased over time, adjusting for inflation and cost of living (you could find the information here: Poverty threshold). At the same time, incomes have also gone up, likely at a faster rate, which means fewer people are counted as living below the poverty line. While this looks like good news, it doesn’t mean everyone is financially stable - many people who are now above the poverty line may still have trouble making ends meet.

Therefore, while the data shows positive change, Rockwood continues to face bigger economic challenges than East or West County. 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2025","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":924,"value":"html`These tables and charts below show the demographic, economic and social data of Rockwood compare to other regions, including: Oregon, West County (refers to all the census tracts west of 162nd Avenue in Multnomah County) and East County (refers to all the census tracts east of 162nd 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":"$undefined"},{"id":29,"value":"# 1. Race","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":136,"value":"**Data source**: US Census Bureau ACS 5-year estimate \nTable B03002 (Hispanic or Latino Origin by Race)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1898,"value":"race_4region = await FileAttachment(\"race_4region.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"race_4region","database":"$undefined"},{"id":938,"value":"$49","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3822,"value":"html`In the first graph, it is pretty difficult to see the total population changes in regions like Rockwood and East County, where the population is significantly lower than in West County or Oregon as a whole. Therefore, we apply a logarithmic transformation of total population to get a more proportional comparison of trends across four regions.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3813,"value":"$4a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3825,"value":"htl.html`Rockwood’s population has experienced ups and downs from 2012 to 2023, showing more volatility than other regions. In the early years, the population grew gradually, peaking at 33,240 in 2021 before declining slightly to 32,103 in 2023. In contrast, East County has grown at a slower but steadier pace, increasing from 113,801 in 2012 to 124,516 in 2023. West County has also seen consistent growth but with some recent fluctuations, rising from 574,142 in 2012 to 626,321 in 2023, though growth has slowed since 2020. At the state level, Oregon’s population has grown the most consistently, increasing from 3,750,575 in 2012 to 4,141,889 in 2023.
This suggests that while Rockwood has grown, its population trends have been less stable compared to other areas. Factors like housing affordability and economic mobility may be contributing to frequent population shifts.\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":952,"value":"a3 = Plot.plot({\n marks: [\n Plot.barY(race_4region.filter((d )=> (d.Race !=\"People of color\")), \n {fx: \"Region\",\n x: \"Year\",\n y: \"Percentage\",\n fill: \"Race\",\n tip : \"X\",\n }),\n Plot.ruleY([0])\n ],\n color: {\n legend : true,\n domain: categoryOrder2,\n scheme: \"spectral\"\n },\n x: { axis: null },\n y: { grid: true,\n domain:[0,100],\n tickFormat: (d) => d + \"%\",\n ticks: [0,20,40,60,80, 100]},\n fx: { label: null,\n domain: [\"Oregon\", \"West County\", \"East County\", \"Rockwood\"]},\n title: \"Race distribution (2012-2023)\",\n caption: \"Source: 2012-2023 US Census Bureau ACS 5 Year Estimates\"\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2067,"value":"$4b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":978,"value":"$4c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2072,"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":2075,"value":"$4f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":141,"value":"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 2022, noting that from 2020 onward, census tracts 97.02 was seperated into 97.03 and 97.04.
\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":"$undefined"},{"id":1904,"value":"a6= Plot.plot({\n marks: [\n Plot.barY(rockwood_race_1223, {\n fx: \"Tract\",\n x: \"Year\",\n y: \"Population estimate\",\n fill: \"Race\",\n z: {value: d => d.variableOrder_race_rw}, // Use the factor for sorting\n order: \"variableOrder_race_rw\", // Ensure the stacking order follows the factor\n tip : \"X\",\n }),\n Plot.ruleY([0])\n ],\n color: {\n legend : true,\n domain: categoryOrder2,\n scheme: \"spectral\"\n },\n x: { axis: null },\n y: { grid: true, domain: [0,10000]},\n fx: { label: \"Tract\" },\n title: \"Rockword Race Composition by tract (2012-2023)\",\n caption: \"Source: 2012-2023 US Census Bureau ACS 5 Year Estimates\"\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2078,"value":"$50","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":27,"value":"# 2. Age","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":153,"value":"**Data source**: US Census Bureau ACS 5-year estimate \nTable B01001 (Sex by Age)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1910,"value":"age_4region = await FileAttachment(\"age_4region.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"age_4region","database":"$undefined"},{"id":34,"value":"a7 = 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 (2012-2023)\",\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":2082,"value":"$51","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":160,"value":"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":"$undefined"},{"id":1918,"value":"age_1765_4region = await FileAttachment(\"age_1765_4region.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"age_1765_4region","database":"$undefined"},{"id":1341,"value":"$52","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2085,"value":"$53","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":165,"value":"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":"$undefined"},{"id":1927,"value":"rockwood_age_1223 = await FileAttachment(\"rockwood_age_1223@1.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"rockwood_age_1223","database":"$undefined"},{"id":50,"value":"a9= Plot.plot({\n marks: [\n Plot.barY(rockwood_age_1223, {\n fx: \"Tract\",\n x: \"Year\",\n y: \"Population estimate\",\n fill: \"Age_Group\",\n tip : \"X\",\n }),\n Plot.ruleY([0])\n ],\n color: {\n legend : true,\n scheme: \"spectral\"\n },\n x: { axis: null },\n y: { grid: true, domain: [0,10000]},\n fx: { label: \"Tract\" },\n title: \"Age Range Composition in Rockwood by Tract\",\n caption: \"Source: 2012-2023 US Census Bureau ACS 5 Year Estimates\"\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2093,"value":"$54","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1352,"value":"$55","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2090,"value":"$56","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":56,"value":"# 3. Language","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":168,"value":"**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":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1936,"value":"language_4region = await FileAttachment(\"language_4region.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":67,"value":"a11=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 (2012-2023)\",\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":2111,"value":"$57","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":4153,"value":"## Foreign-born population in Rockwood","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":4156,"value":"rw_pob_central_americas = await FileAttachment(\"rw_pob_central_americas.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"rw_pob_central_americas","database":"$undefined"},{"id":4158,"value":"ca=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: \"Foreign-born population from Central America 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":"$undefined"},{"id":4162,"value":"rw_pob_asian = await FileAttachment(\"rw_pob_asian.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"rw_pob_asian","database":"$undefined"},{"id":4165,"value":"as=Plot.waffleY(rw_pob_asian, Plot.groupZ({y: \"count\"}, \n {fill: \"country\", sort: \"country\", unit: 10})).plot(\n { y: { axis: null }, \n height: 400, \n title: \"Foreign-born population from Asia in Rockwood (2023)\",\n color: {legend: true}})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":4170,"value":"rw_pob_eu = await FileAttachment(\"rw_pob_eu.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"rw_pob_eu","database":"$undefined"},{"id":4173,"value":"eu=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: \"Foreign-born population from Europe 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":"$undefined"},{"id":4178,"value":"rw_pob_af = await FileAttachment(\"rw_pob_af.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"rw_pob_af","database":"$undefined"},{"id":4181,"value":"af=Plot.waffleY(rw_pob_af, Plot.groupZ({y: \"count\"}, \n {fill: \"country\", sort: \"country\", unit: 10})).plot(\n { y: { axis: null }, \n height: 300, \n title: \"Foreign-born population from Africa in Rockwood (2023)\",\n color: {legend: true}})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":4073,"value":"# 4. Family","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":4076,"value":"**Data source**: US Census Bureau ACS 5-year estimate \nTable B11001 (Household Type)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":4082,"value":"fam_4region = await FileAttachment(\"fam_4region.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"fam_4region","database":"$undefined"},{"id":4087,"value":"$58","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":70,"value":"# 5. Income","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":173,"value":"**Data source**: US Census Bureau ACS 5-year estimate \nTable B19013 (Median Household Income)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1941,"value":"mhi_4region = await FileAttachment(\"mhi_4region.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":76,"value":"$59","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2114,"value":"$5a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1565,"value":"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":"$undefined"},{"id":1562,"value":"map1=html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":175,"value":"html`Next, we will analyze the Income Range Distribution.

\nData 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":"$undefined"},{"id":1946,"value":"income_4region = await FileAttachment(\"income_4region.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"income_4region","database":"$undefined"},{"id":90,"value":"viewof Gender = Inputs.select([\"male\", \"female\", \"both\"],\n {label: \"SELECT GENDER\", value : \"both\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":93,"value":"a13=Plot.plot({\n marks: [\n Plot.barY(\n income_4region.filter(d => d.Gender==`${Gender}`)\n , {\n fx: \"Region\",\n x: \"Year\",\n y: \"Percentage\",\n fill: \"Income_Group\",\n tip : \"X\",\n }),\n Plot.ruleY([0])\n ],\n color: {\n legend : true,\n domain : IncomeOrder,\n scheme: \"spectral\"\n },\n x: { axis: null },\n y: { grid: true,\n domain: [0,100],\n tickFormat: (d) => d + \"%\",\n },\n fx: { label: \"Region\",\n domain: [\"Oregon\", \"West County\", \"East County\", \"Rockwood\"]},\n title: \"Income Range Distribution for \"+`${Gender}`+\" gender (2012-2023)\",\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":2117,"value":"$5b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3852,"value":"htl.html`We can also explore the graph below to see the trend in each income range over time:`","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":"$5c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3857,"value":"htl.html`We can also look at the income distribution by gender in each region for a specific year: `","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":"$undefined"},{"id":112,"value":"a15=Plot.plot({\n marks: [\n Plot.barY(income_4region.filter((d )=> (d.Region==`${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":"$undefined"},{"id":3987,"value":"# 6. Jobs","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":3994,"value":"html`Data source: US Census Bureau ACS 5-year estimate
\nTable C24030 ( Sex by Industry for the Civilian Employed Population 16 Years and Over)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3990,"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":3997,"value":"viewof Region_industry = Inputs.select([\"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":"$undefined"},{"id":4000,"value":"viewof Year_industry = Inputs.select([2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023],\n {label: \"SELECT YEAR\", value : 2023})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":4003,"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":"$undefined"},{"id":4006,"value":"a15b=Plot.plot({\n marginLeft: 300,\n x: { axis: null, domain: [0,16], },\n y: { label: null },\n title: \"Shares of Jobs by industry in \"+ `${Region_industry}`+\" in \"+ `${Year_industry}`,\n caption: \"Source: \"+`${Year_industry}` +\" US Census Bureau ACS 5 Year Estimates\",\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":4011,"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":4019,"value":"viewof Year_industry2 = 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":"$undefined"},{"id":4014,"value":"a15c=Plot.plot({marginLeft: 280,\n label: null,\n title: \"Difference in Employment by Industry (compare to West County)\"+\" in \"+ `${Year_industry2}`,\n caption: \"Source: \"+`${Year_industry2}` +\" US Census Bureau ACS 5 Year Estimates\",\n x: {\n axis: \"top\",\n domain: [-10, 6],\n //label: \"Employment Difference by Industry\",\n labelAnchor: \"center\",\n tickFormat: \"+\",\n tickFormat: (d) => d + \"%\"\n //percent: true\n },\n y: { axis: null}, \n color: {\n legend: true,\n domain: [\"East County\", \"Rockwood\"], \n range: [\"#4269d0\", \"#ff725c\"]\n },\n marks: [\n Plot.barX(mc_industry_compare.filter((d )=> (d.Year==`${Year_industry2}`)), {\n x: \"Difference\",\n fy: \"Industry\",\n fill: d => d.Comparison_Region === \"East County\" ? \"#4269d0\" : \"#ff725c\",\n y: \"Comparison_Region\",\n tip : \"Y\",\n sort: { fy: \"x\" }\n }),\n\n Plot.ruleX([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1727,"value":"# 7. Housing","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1753,"value":"**Data source**: US Census Bureau ACS 5-year estimate \nTable B25002 (Occupancy Status)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2020,"value":"occ_4region = await FileAttachment(\"occ_4region@1.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"occ_4region","database":"$undefined"},{"id":1737,"value":"Plot.plot({\n marks: [\n Plot.barY(occ_4region, {\n fx: \"Region\",\n x: \"Year\",\n y: \"Percentage\",\n fill: \"Occupancy type\",\n tip : \"X\",\n }),\n Plot.ruleY([0])\n ],\n color: {\n legend : true,\n scheme: \"spectral\"\n },\n x: { axis: null },\n y: { grid: true,\n domain : [0,100],\n tickFormat: (d) => d + \"%\",},\n fx: { label: null,\n domain: [\"Oregon\", \"West County\", \"East County\", \"Rockwood\"]},\n title: \"Percentage of Occupied and Vacant Housing Units by Region\",\n caption: \"Source: 2012-2023 US Census Bureau ACS 5 Year Estimates\"\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$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":"$undefined"},{"id":1745,"value":"Plot.plot({\n marginLeft : 60,\n marks: [\n Plot.barY(occ_4region.filter((d )=> (d.Year==`${Year_occ}`)), \n{ x: \"Occupancy type\",\n y: \"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['Estimate']).toLocaleString()}`,\n x: \"Occupancy type\",\n y: \"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, 300000], \n ticks: [0, 50000, 100000, 150000, 200000, 250000, 300000],\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":"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":"$undefined"},{"id":2026,"value":"ten_4region = await FileAttachment(\"ten_4region@1.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"ten_4region","database":"$undefined"},{"id":2156,"value":"viewof ten_type = Inputs.select([\"Owned\",\"Rented\"],\n {label: \"SELECT TYPE\", value : \"Owned\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2138,"value":"$5d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2127,"value":"$5e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3867,"value":"htl.html`The 2 charts below show the Owned versus Rented Housing by percent or total units in a specific year: `","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":2130,"value":"a17=Plot.plot({\n marginLeft : 60,\n marks: [\n Plot.barY(ten_4region.filter((d )=> (d.Year==`${Year_ten}`)), \n{ x: \"Tenure type\",\n y: \"Percentage\",\n fx: \"Region\",\n fill: \"Tenure type\",\n }),\n Plot.ruleY([0]),\n \n Plot.text(ten_4region.filter((d )=> (d.Year==`${Year_ten}`)), \n{ text: d => `${d['Percentage']}%`,\n x: \"Tenure type\",\n y: \"Percentage\",\n fx: \"Region\", \n dy: 13,\n fontSize: 13,\n fontWeight: \"bold\",\n fill: \"red\"\n })],\n \n color: {\n legend : true,\n scheme: \"Warm\"\n },\n x: { axis: null},\n fx: {\n domain: [\"Oregon\",\"West County\", \"East County\", \"Rockwood\"],\n label : null},\n y: { grid: true,\n // domain: [0, 160000], \n //ticks: [0, 30000, 60000, 90000, 120000, 160000],\n label: \"% Housing Unit\"\n },\n title: \"Percentage of Owned versus Rented Housing Units in \"+ `${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":1772,"value":"Plot.plot({\n marginLeft : 60,\n marks: [\n Plot.barY(ten_4region.filter((d )=> (d.Year==`${Year_ten}`)), \n{ x: \"Tenure type\",\n y: \"Estimate\",\n fx: \"Region\",\n fill: \"Tenure type\",\n }),\n Plot.ruleY([0]),\n \n Plot.text(ten_4region.filter((d )=> (d.Year==`${Year_ten}`)), \n{ text: d => `${Number(d['Estimate']).toLocaleString()}`,\n x: \"Tenure type\",\n y: \"Estimate\",\n fx: \"Region\", \n dy: -7,\n fill: \"black\"\n })],\n \n color: {\n legend : true,\n scheme: \"Warm\"\n },\n x: { axis: null},\n fx: {\n domain: [//\"Oregon\",\n \"West County\", \"East County\", \"Rockwood\"],\n label : null},\n y: { grid: true,\n domain: [0, 160000], \n ticks: [0, 30000, 60000, 90000, 120000, 160000],\n label: \"Housing Unit\"\n },\n title: \"Owned and Rented Occupied Housing Units by Region in \"+ `${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":"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":"$undefined"},{"id":2035,"value":"tenpop_4region = await FileAttachment(\"tenpop_4region.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"tenpop_4region","database":"$undefined"},{"id":2166,"value":"viewof ten_type_pop = Inputs.select([\"Owner\",\"Renter\"],\n {label: \"SELECT TYPE\", value : \"Owner\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2168,"value":"$5f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2183,"value":"htl.html`The population living in owner-occupied housing units in Rockwood has shown significant changes over time, reflecting a positive trend. In 2012, the percentage of home owners was 45.5%, indicating that less than half of the population owned their homes. This trend gradually improved over the years, reaching its peak in 2020, with 55.67% of the population living in owner-occupied housing. However, following this peak, the percentage decreased slightly in subsequent years, settling at 54.94% in 2023. While this still represents an improvement compared to earlier years, it suggests that the region has yet to achieve sustained growth in homeownership.

\n\nIn comparison to other regions like West County and East County, Rockwood continues to have one of the lowest percentages of owner-occupied housing units. For example, West County consistently maintained ownership rates above 58%, while East County hovered around 60%.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3873,"value":"htl.html`The 2 charts below show the Owners versus Renters by percent or total population in a specific year:`","pinned":false,"hidden":false,"mode":"ojs","author":"$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":"$undefined"},{"id":2177,"value":"Plot.plot({\n marginLeft : 60,\n marks: [\n Plot.barY(tenpop_4region.filter((d )=> (d.Year==`${Year_tenpop}`)), \n{ x: \"Tenure type\",\n y: \"Percentage\",\n fx: \"Region\",\n fill: \"Tenure type\",\n }),\n Plot.ruleY([0]),\n \n Plot.text(tenpop_4region.filter((d )=> (d.Year==`${Year_tenpop}`)), \n{ text: d => `${d['Percentage']}%`,\n x: \"Tenure type\",\n y: \"Percentage\",\n fx: \"Region\", \n dy: 13,\n fontSize: 13,\n fontWeight: \"bold\",\n fill: \"red\"\n })],\n \n color: {\n legend : true,\n scheme: \"Warm\"\n },\n x: { axis: null},\n fx: {\n domain: [\"Oregon\",\"West County\", \"East County\", \"Rockwood\"],\n label : null},\n y: { grid: true,\n // domain: [0, 160000], \n //ticks: [0, 30000, 60000, 90000, 120000, 160000],\n label: \"% population\"\n },\n title: \"Percentage of Owners versus Renters in \"+ `${Year_tenpop}`,\n caption: \"Source: \"+ `${Year_tenpop}`+\" 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":"$undefined"},{"id":1803,"value":"Plot.plot({\n marginLeft : 60,\n marks: [\n Plot.barY(tenpop_4region.filter((d )=> (d.Year==`${Year_tenpop}`)), \n { x: \"Tenure type\",\n y: \"Estimate\",\n fx: \"Region\",\n fill: \"Tenure type\",\n }),\n Plot.ruleY([0]),\n \n Plot.text(tenpop_4region.filter((d )=> (d.Year==`${Year_tenpop}`)), \n { text: d => `${Number(d['Estimate']).toLocaleString()}`,\n x: \"Tenure type\",\n y: \"Estimate\",\n fx: \"Region\", \n dy: -7,\n fill: \"black\"\n })],\n \n color: {\n legend : true,\n scheme: \"Warm\"},\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, 400000], \n ticks: [0, 50000, 100000, 150000, 200000, 250000, 300000, 350000, 400000],\n label: \"Population\"\n },\n title: \"Owner and Renter Population by Region in \"+ `${Year_tenpop}`,\n caption: \"Source: \"+ `${Year_tenpop}`+\" 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":"$undefined"},{"id":3877,"value":"htl.html`Next, we will examine the Average Percentage of Household Income on rent.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":201,"value":"**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":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1955,"value":"rent_4region = await FileAttachment(\"rent_4region.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":208,"value":"$60","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2120,"value":"$61","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3882,"value":"htl.html`The map below illustrates the percentage of household income on rent in Multnomah County by census tract in 2023. It highlights two primary regions: West County and East County, with the Rockwood area outlined in green.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1571,"value":"map2=html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3885,"value":"htl.html`To better understand the housing cost burden, we will examine the Distribution of the Percentage of Household Income spend on rent from 2012 to 2023. `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":247,"value":"**Data source**: US Census Bureau ACS 5-year estimate \nTable B25070 (Gross Rent as a Percentage of Household Income)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1961,"value":"rentpt_4region = await FileAttachment(\"rentpt_4region.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"rentpt_4region","database":"$undefined"},{"id":255,"value":"a19=Plot.plot({\n marks: [\n Plot.barY(rentpt_4region, {\n fx: \"Region\",\n x: \"Year\",\n y: \"Percentage\",\n fill: \"Income_percentage\",\n tip : \"x\",\n }),\n Plot.ruleY([0])\n ],\n color: {\n legend : true,\n domain : RentptOrder,\n scheme: \"spectral\"\n },\n x: { axis: null },\n y: { grid: null},\n fx: {label: null,\n domain: [\"Oregon\", \"West County\", \"East County\", \"Rockwood\"]},\n title: \"Percentage of Household Income for Gross Rent\",\n caption: \"Source: 2012-2023 US Census Bureau ACS 5 Year Estimates\"\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2124,"value":"$62","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3891,"value":"htl.html`We can also explore the graph below to see the trend in each income percentage on rent over time:`","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":"a20=Plot.plot({\n marks: [\n Plot.ruleY([0]),\n Plot.lineY(rentpt_4region.filter((d )=> (d.Income_percentage==`${IncomePct}` ))\n , {x: \"Year\", y: \"Percentage\", \n stroke: \"Region\", \n strokeWidth: 3,\n tip : \"x\"}),\n Plot.dot(rentpt_4region.filter((d )=> (d.Income_percentage==`${IncomePct}`)), {\n x: \"Year\", \n y: \"Percentage\", \n fill: \"Region\",\n r: 4 \n })\n ],\n title: \" Percentage of Households spending \"+ `${IncomePct}`+\n \" Income on Gross Rent\",\n caption: \"Source: 2012-2023 US Census Bureau ACS 5 Year Estimates\",\n y: {\n grid: true,\n tickFormat: (d) => d + \"%\",\n domain: [0, 40]\n } ,\n color: {legend: true, \n //domain: [\"Rockwood\", \"East County\", \"West County\", \"Oregon\"]\n },\n x: {\n domain: [2011.5, 2024],\n ticks: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", \"2021\", \"2022\", \"2023\"],\n tickFormat: (d) => d \n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3894,"value":"htl.html`Next, we will examine the Household size distribution for both owned or rented tenure type.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1810,"value":"**Data source**: US Census Bureau ACS 5-year estimate \nTable B25009 (Tenure by Household Size)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2043,"value":"size_4region = await FileAttachment(\"size_4region@1.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"size_4region","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":"a21=Plot.plot({\n marks: [\n Plot.barY(size_4region.filter(d => d.Tenure_type==`${TenureType}`), \n { fx: \"Region\",\n x: \"Year\",\n y: \"Percentage\",\n fill: \"Household size\",\n tip : \"X\"}),\n Plot.ruleY([0])],\n color: {\n legend : true,\n scheme: \"spectral\"\n },\n x: { axis: null },\n y: { grid: true,\n tickFormat: (d) => d + \"%\",\n domain: [0,70],\n ticks: [0,10 ,20 ,30 , 40, 50, 60, 70]\n },\n fx: { label: \"Region\",\n domain: [\"Oregon\", \"West County\", \"East County\", \"Rockwood\"]},\n title: \"Household size distribution for \"+`${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":2440,"value":"$63","pinned":false,"hidden":false,"mode":"ojs","author":"$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":"$undefined"},{"id":1857,"value":"a22=Plot.plot({\n marginLeft : 60,\n marks: [\n Plot.barY(size_4region.filter((d )=> (d.Tenure_type==`${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":"$undefined"},{"id":1826,"value":"# 8. Education","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2443,"value":"## School Attainment","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":3898,"value":"htl.html`(information about people currently enrolled in school, from nursery school through graduate programs)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1831,"value":"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":"$undefined"},{"id":2013,"value":"edu_4region = await FileAttachment(\"edu_4region@1.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"edu_4region","database":"$undefined"},{"id":1842,"value":"viewof Gender_edu = Inputs.select([\"male\", \"female\", \"both\"],\n {label: \"SELECT GENDER\", value : \"both\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$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":"$undefined"},{"id":1846,"value":"a23=Plot.plot({\n marks: [\n Plot.barY(edu_4region.filter(d => d.Gender==`${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":"a24=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":3904,"value":"$64","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2445,"value":"## Educational Attainment\n(the highest level of education for persons at least 18 years old)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2447,"value":"html`Data source: US Census Bureau ACS 5-year estimate
\nTable B15003 (Educational Attaintment for the population 25 years and overs)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3909,"value":"edu_at_4region = await FileAttachment(\"edu_at_4region@1.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"edu_at_4region","database":"$undefined"},{"id":2465,"value":"a25=Plot.plot({\n marks: [\n Plot.barY(edu_at_4region, \n { fx: \"Region\",\n x: \"Year\",\n y: \"Percentage\",\n fill: \"Attainment\",\n tip : \"X\"}),\n Plot.ruleY([0])],\n color: {\n legend : true,\n domain : edu_order,\n scheme: \"spectral\"\n },\n x: { axis: null },\n y: { grid: true,\n tickFormat: (d) => d + \"%\",\n //domain: [0,30],\n //ticks: [0,5,10,15 ,20,25,30]\n },\n fx: { label: \"Region\",\n domain: [\"Oregon\", \"West County\", \"East County\", \"Rockwood\"]},\n title: \"Percentage of Education Attainment Types\",\n caption: \"Source: 2012-2023 US Census Bureau ACS 5 Year Estimates\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3918,"value":"$65","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3921,"value":"htl.html`We can explore the trend of each education attainment level over time:`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2472,"value":"viewof edu_at = Inputs.select([\"Highschool Diploma/GED\", \"Associate's degree\", \"Bachelor's degree\", \"Master's degree\",\"Professional school degree\", \"Doctorate degree\", \"No schooling completed\", \"In school (No diploma)\"],\n {label: \"SELECT DEGREE\", value: \"Bachelor's degree\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2476,"value":"$66","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3925,"value":"htl.html`We can also categorize education attainment levels into 2 main types: No college dgree and College degree or higher and compare among regions.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2665,"value":"edu_at_4region_sum = await FileAttachment(\"edu_at_4region_sum@1.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"edu_at_4region_sum","database":"$undefined"},{"id":2653,"value":"viewof Year_edu_at = Inputs.select([2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023],\n {label: \"SELECT YEAR\", value : 2023})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2655,"value":"a27=Plot.plot({\n marginLeft : 60,\n marks: [\n Plot.barY(edu_at_4region_sum.filter((d )=> (d.Year==`${Year_edu_at}`)), \n{ x: \"Type\",\n y: \"Percentage\",\n fx: \"Region\",\n fill: \"Type\",\n }),\n Plot.ruleY([0]),\n \n Plot.text(edu_at_4region_sum.filter((d )=> (d.Year==`${Year_edu_at}`)), \n{ text: d => `${d['Percentage']}%`,\n x: \"Type\",\n y: \"Percentage\",\n fx: \"Region\", \n dy: 13,\n fontSize: 13,\n fontWeight: \"bold\",\n fill: \"red\"\n })],\n \n color: {\n legend : true,\n scheme: \"Warm\"\n },\n x: { axis: null},\n fx: {\n domain: [\"Oregon\",\"West County\", \"East County\", \"Rockwood\"],\n label : null},\n y: { grid: true,\n // domain: [0, 160000], \n //ticks: [0, 30000, 60000, 90000, 120000, 160000],\n label: \"% population\"\n },\n title: \"Percentage of population with versus without College degree in \"+ `${Year_edu_at}`,\n caption: \"Source: \"+ `${Year_edu_at}`+\" US Census Bureau ACS 5 Year Estimates\"\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":4194,"value":"tract_edu_at_mhi_23 = await FileAttachment(\"tract_edu_at_mhi_23.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"tract_edu_at_mhi_23","database":"$undefined"},{"id":4197,"value":"$67","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":769,"value":"# 9. Health","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":771,"value":"**Data source**: US Census Bureau ACS 5-year estimate
\nTable B27001 (Health Insurance Coverage Status by Sex by Age)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1995,"value":"incov_4region = await FileAttachment(\"incov_4region.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":"$68","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":812,"value":"a29=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":"$undefined"},{"id":3930,"value":"htl.html`Rockwood has seen a steady improvement in health insurance coverage, reaching 90% in 2023, a significant increase from 75% in 2012. However, it still lags behind West County (95%) and Oregon overall (94%). A key factor in Rockwood’s coverage pattern is its relatively high percentage of insured children (18 years or below), reaching 23% in 2023. However, Rockwood’s coverage rate for working-age adults (19 to 64 years) remains lower (54%) compared to West County (63%) and Oregon (54%). The elderly population (65 years and above) in Rockwood also has lower coverage (12.27%) compared to Oregon (18.41%) and West County (14.01%), reflecting gaps in Medicare enrollment or supplemental insurance participation. Overall, while Rockwood has made considerable progress in closing the health insurance gap, challenges persist in ensuring broader access, particularly for working-age adults and seniors.`","pinned":false,"hidden":false,"mode":"ojs","author":"$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":"$69","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3935,"value":"htl.html`Next, we will take a look at local healthcare data provided by 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":"$undefined"},{"id":1586,"value":"## PLACES: Local Data for Better Health","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1599,"value":"$6a","pinned":false,"hidden":false,"mode":"ojs","author":"$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":"map4 = html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2003,"value":"mc_health_3region = await FileAttachment(\"mc_health_3region@1.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$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":"$6b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1717,"value":"$6c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3941,"value":"htl.html`Note: The percentage is calculated based on the total population aged 18 and older.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3945,"value":"htl.html`In healthcare outcomes, Rockwood exhibits some of the highest prevalence rates for chronic health conditions in Multnomah County. Obesity (33%) is particularly concerning, higher than East County (31%) and West County (28%), indicating potential lifestyle, dietary, and socioeconomic challenges that may contribute to higher rates of related conditions like diabetes and heart disease. Similarly, depression (28%) affects over a quarter of the population aged 18 and older in Rockwood, suggesting mental health challenges that may be exacerbated by socioeconomic stressors. Notably, arthritis (25%) and diabetes (11%) are more prevalent in Rockwood than in West County (22% and 8%, respectively), suggesting long-term impacts of lifestyle, healthcare access, or preventive care disparities.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":4032,"value":"# 10. Food Access","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":4038,"value":"html`Data source: US Census Bureau ACS 5-year estimate
\nTable B19058 (Public Assistance Income or Food Stamps/SNAP in the Past 12 Months for Households)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":4035,"value":"snap_4region = await FileAttachment(\"snap_4region.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"snap_4region","database":"$undefined"},{"id":4041,"value":"viewof snap_type = Inputs.select([\"With cash public assistance or Food Stamps/SNAP\",\"No cash public assistance or Food Stamps/SNAP\"],\n {label: \"SELECT STATUS\", value : \"With cash public assistance or Food Stamps/SNAP\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":4044,"value":"$6d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":4057,"value":"viewof Year_snap = 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":4060,"value":"$6e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":117,"value":"# 11. Transportation","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":119,"value":"**Data source**: US Census Bureau ACS 5-year estimate \nTable B08301 (Means of Transportation to Work)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1966,"value":"trans_4region = await FileAttachment(\"trans_4region.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"trans_4region","database":"$undefined"},{"id":125,"value":"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":3950,"value":"$6f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3955,"value":"htl.html`Next, we will examine the travel time to work.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":180,"value":"**Data source**: US Census Bureau ACS 5-year estimate \nTable B08303 (Travel Time to Work)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1971,"value":"trans_time_4region = await FileAttachment(\"trans_time_4region.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"trans_time_4region","database":"$undefined"},{"id":188,"value":"Plot.plot({\n marks: [\n Plot.barY(trans_time_4region, {\n fx: \"Region\",\n x: \"Year\",\n y: \"Percentage\",\n fill: \"Time_Duration\",\n tip : \"x\",\n }),\n Plot.ruleY([0])\n ],\n color: {\n legend : true,\n //domain : TransOrder,\n scheme: \"spectral\"\n },\n x: { axis: null },\n y: { grid: null},\n fx: {label: null,\n domain: [\"Oregon\", \"West County\", \"East County\", \"Rockwood\"]},\n title: \"Time to work distribution by Region\",\n caption: \"Source: 2012-2023 US Census Bureau ACS 5 Year Estimates\"\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3958,"value":"$70","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":551,"value":"# 12. Poverty","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":685,"value":"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":"$undefined"},{"id":2462,"value":"rpov_4region = await FileAttachment(\"rpov_4region.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"rpov_4region","database":"$undefined"},{"id":562,"value":"a34=Plot.plot({\n marks: [\n Plot.barY(rpov_4region, {\n fx: \"Region\",\n x: \"Year\",\n y: \"Percentage\",\n fill: \"Range\",\n tip : \"x\",\n }),\n Plot.ruleY([0])\n ],\n color: {\n legend : true,\n domain : RpovOrder,\n scheme: \"spectral\"\n },\n x: { axis: null },\n y: { grid: null},\n fx: {label: null,\n domain: [\"Oregon\", \"West County\", \"East County\", \"Rockwood\"]},\n title: \"Ratio Range of Income to Poverty Level in the Past 12 Months\",\n caption: \"Source: 2012-2023 US Census Bureau ACS 5 Year Estimates\"\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3964,"value":"htl.html`Over the years, Rockwood has seen noticeable economic improvement, with a growing share of residents earning more than twice the poverty level. This trend aligns with broader regional progress in Oregon, East County, and West County, reflecting an overall rise in income levels. However, despite these gains, Rockwood continues to lag behind. A significantly larger portion of its population still struggles with low incomes, with a higher percentage of residents earning below twice the poverty level compared to other regions. While East County and West County have made strides in reducing economic hardship, Rockwood remains more vulnerable, highlighting persistent barriers to financial stability. These challenges suggest that while progress is being made, Rockwood requires continued targeted support—such as workforce development and economic investment—to bridge the income gap and ensure more sustainable growth for its residents.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3967,"value":"htl.html`We can also explore this chart below to see the trend of each ratio of Income to Poverty Level over time:`","pinned":false,"hidden":false,"mode":"ojs","author":"$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":"$71","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1578,"value":"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":"$undefined"},{"id":1575,"value":"map5=html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":618,"value":"**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":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1983,"value":"pov_4region = await FileAttachment(\"pov_4region.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":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":"$72","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3971,"value":"$73","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":660,"value":"html`To better understand the significant decline in the percentage of individuals living below the poverty level in the Rockwood area starting in 2016, the next step is to analyze changes at the census tract level during this period, uncovering the underlying story of this improvement.\n

\nData 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":"$undefined"},{"id":1491,"value":"rockwood_pov_1223 = await FileAttachment(\"rockwood_pov_1223.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"rockwood_pov_1223","database":"$undefined"},{"id":671,"value":"a37=Plot.plot({\n marks: [\n Plot.barY(rockwood_pov_1223, {\n fx: \"Tract\",\n x: \"Year\",\n y: \"Population estimate\",\n fill: \"Poverty status\",\n tip : \"X\",\n }),\n Plot.ruleY([0])\n ],\n color: {\n legend : true,\n scheme: \"spectral\"\n },\n x: { axis: null },\n y: { grid: true,\n domain: [0, 10000]},\n fx: { label: \"Tract\" },\n title: \"Poverty level Status in Rockwood by tract\",\n caption: \"Source: 2012-2023 US Census Bureau ACS 5 Year Estimates\"\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":701,"value":"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":"$undefined"},{"id":709,"value":"html`The next section provides an in-depth analysis of the percentage of individuals living below the poverty level, categorized by \nrace.
Data source: US Census Bureau ACS 5-year estimate
Table B17001A/B17001B/B17001C/B17001D/B17001E/B17001F/B17001G (Poverty Status in the Past 12 Months by Sex by Age of each race)`","pinned":false,"hidden":false,"mode":"ojs","author":"$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":"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":"a39=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":"$undefined"},{"id":909,"value":"# 13. Life Expectancy","pinned":false,"hidden":false,"mode":"md","author":"$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":2008,"value":"mc_le_2015 = await FileAttachment(\"mc_le_2015@1.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":"map6=html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":864,"value":"# 14. Social Vulnerability","pinned":false,"hidden":false,"mode":"md","author":"$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). A higher Social Vulnerability Index (SVI) score indicates that a community is more vulnerable. It suggests that the area faces greater challenges in preparing for, responding to, and recovering from disasters or public health emergencies.

\nThe map below highlights that East County, particularly the Rockwood region, has a higher 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":"$undefined"},{"id":872,"value":"map7=html``","pinned":false,"hidden":false,"mode":"ojs","author":"$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\", \"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":"$undefined"},{"id":950,"value":"race_4region.forEach(d => {d.variableOrder_race = categoryOrder2.indexOf(d.Race)});","pinned":false,"hidden":false,"mode":"ojs","author":"$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":false,"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":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":269,"value":"rentpt_4region.forEach(d => {d.VariableOrder5 = RentptOrder.indexOf(d.Income_percentage)});","pinned":false,"hidden":false,"mode":"ojs","author":"$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`]==`${Incov}` && d[`Age 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school\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2467,"value":"edu_order = [\"No schooling completed\", \"In school (No diploma)\",\"Highschool Diploma/GED\", \"Associate's degree\", \"Bachelor's degree\", \"Master's degree\",\"Professional school degree\", \"Doctorate degree\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W74","stdlib":"1"}} 24:null