Visualizing equity indicators and modeling their relationships to support positive change.
The overall percentage of children in poverty in DC is approximately 30%, which equals about 30,000 children. Poverty is also concentrated in certain neighborhoods: Approximately 25% of the poorest neighborhoods have more than 1 in 2 children in poverty and 60% of all DC children who live in poverty. More than 35% of all DC children in poverty live in four neighborhoods: (1) Congress Heights, Bellevue, Washington Highlands; (2) Douglas, Shipley Terrace;(3) Columbia Heights, Mt. Pleasant, Pleasant Plains, Park View; and (4) Deanwood, Burrville, Grant Park, Lincoln Heights, Fairmont Heights. Many of these high poverty neighborhoods are overwhelmingly (more than 90%) black.
Approximately 48% (almost 1 in 2) DC children live in families headed by single mothers. Approximately 30% of DC neighborhoods have more than 2 in 3 children living in households headed by single mothers and account for more than half of all children living in single mother households. Six Clusters with the highest number and more than 3 in 4 children living in single mother headed households are: Mayfair, Hillbrook, Mahaning Heights (92%), Historic Anacostia (86%), Douglas, Shipley Terrace (83%), Woodland/Fort Stanton, Garfield Heights, Knox Hill (81%), Congress Heights, Bellevue, Washington Highlands (80%), and Sheridan, Barry Farm, Buena Vista (78%).
The 2013 DC CAS results in reading for neighborhoods by student’s residence indicates that 40% of DC neighborhoods have less than half of their students scoring proficient in reading. Half of all DC neighborhoods have less than 53% of their students proficient in reading. The highest performing three neighborhoods, which are clustered in Northwest DC, have 90% or more of their students proficient in reading. Friendship Heights, American University Park, Tenleytown neighborhood in NW DC has 94% of its students scoring proficient in reading while Historic Anacostia neighborhood in SE DC has 35% of its students scoring proficient in reading.
Additionally, a simplistic correlational analysis revealed that on average the higher the child poverty rate in the neighborhood, the lower the percentage of students proficient in reading. The median poverty rate was 52% for the ten neighborhoods with the lowest percentage of students proficient in reading in DC.
The 2013 DC CAS results in math for neighborhoods by student’s residence indicates that in 1 of 3 DC neighborhoods, less than half of the students are proficient in math. Overall, half of all DC neighborhoods have less than 55% of their students proficient in math. The highest performing four neighborhoods, which are clustered in Northwest DC have 90% or more of their students scoring proficient in Math. Friendship Heights, American University Park, Tenleytown neighborhood in NW DC has 94% of its students scoring proficient in math while Historic Anacostia neighborhood in SE DC has 40% of its students scoring proficient in math.
Additionally, a simplistic correlational analysis revealed that on average the higher the child poverty rate in the neighborhood, the lower the percentage of proficient students in math. The median poverty rate was 52% for the ten neighborhoods with the lowest percentage of students proficient in math in DC.
Population (total) | |
Household median income | |
Percentage of renters |
Actual value: 0
Select new value: 0
Predictions:
Family Housing | |
Mobility Infrastructure | |
Connectivity | |
Elem. School Well-being | |
Upper School Well-being | |
Public Health | |
Development | |
Socio-economic |
Enrollment | getDisplayValue(school.enroll_val, 'enroll_val') |
Students per teacher | getDisplayValue(school.s_per_t, 's_per_t') |
In-Seat attendance | getDisplayValue(school.isa_perc, 'isa_perc') |
Percent proficient at math | getDisplayValue(school.math_perc, 'math_perc') |
Percent proficient at reading | getDisplayValue(school.reading_perc, 'reading_perc') |
Student perception of school climate | getDisplayValue(school.stu_sat, 'stu_sat') |
Family Satisfaction | getDisplayValue(school.fam_sat, 'fam_sat') |
Percent free/reduced lunches | getDisplayValue(school.red_lunch, 'red_lunch') |
This interactive mapping tool allows users to examine equity in Seattle across multiple scales including block groups, Census tracts, and neighborhoods. Users can explore and analyze equity in multiple ways:
Welcome to a quick overview of the visualization tool and its features. You can get started by opening the side menu and selecting the level of granularity you wish to view; select either the neighborhood, census tract or block group level.
Next select one of the equity-related themes to explore. Under each theme you will find an Overall score as well as the individual indicators that make up a theme. The overall score provides summary information of how well each area performs in a specific theme, and is calculated using a structural equation model. See the structural model tab for more information.
To better understand how areas compare relative to others, use the Histogram feature. When a dataset is selected, hover over an area to see where it falls in the spectrum.
You may also choose to display points of interest such as libraries, food banks, public health centers, and schools. Clicking on a school provides additional information about school performance.
Explore all the themes to understand the distribution of equity in Seattle!
A structural equation model is used to understand relationships between unmeasurable quantities, such as school well-being, and measurable quantities, such as students' reading proficiency.
The idea is that school well-being is an underlying feature of a neighborhood, which we observe through the measurable quantities. Additionally, unmeasurable quantities may be interrelated. For example, socioeconomic well-being is something we expect to affect many other neighborhood features.
By speficying this relationship between themes and measurable indicators, the model will predict an overall score for each theme variable. Our structural model is specified by the following diagram:
Measurable indicators are represented by rectangles, and unmeasurable themes are represented by ovals. Green arrows indicate that an indicator is positively correlated with a theme, while red indicates negative correlation. For example, socioeconomic well-being is postively correlated with median house value, and negatively correlated with percentage of the population with no health insurance. Blue arrows indicate relationships among themes, for example, upper-school well-being depends on both socioeconomic well-being and elementary school well-being.
The goodness of fit measures (CFI .92, RMSEA .067) indicate that this model successfully captures the data structure.
Use the side navigation bar to visualize theme scores across seattle.