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<!doctype html>
<html>
<head>
<!-- Archiving banner -->
<div class="archived-banner">
<div class="banner-content">
<strong>Archived:</strong> This website is part of
<a href="https://escience.washington.edu/"><u>UW eScience Institute</u></a>'s
Web Archive. It is no longer maintained and may not meet current accessibility standards.
To request content in an accessible format, reach out to us by filling out our
<a href="https://escience.washington.edu/connect/contact-us/"><u>contact form</u></a>.
</div>
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<style>
.archived-banner {
background-color: #fff3cd; /* Light yellow background */
color: #856404; /* Dark yellow/brown text */
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padding: 15px; /* Spacing inside the box */
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</style>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, user-scalable=no">
<title>Equity Modeler</title>
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<body>
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<div class="col-lg-12 col-sm-12 test">
<!--
<a class="navbar-brand" href="http://www.dcactionforchildren.org/"><img src="assets/kidscount.jpg" alt="DC Action for Children Kids Count initiative"></a>
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Structural model
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How to use
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Project Description
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<h1 style="font-size: 30px !important">Equity Modeler</h1>
<p class="intro" style="font-size:18px !important;">Visualizing equity indicators and modeling their relationships to support positive change.</p>
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<!--<h4><strong>Four key policy insights from DC Action for Children are:</strong></h4>-->
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<!--<div id="narrative-row" class="row">
<div class="col-md-12">
<div class="col-md-3 col-lg-3 col-sm-6 col-xs-6">
<button data-filter="children_in_poverty_perc" type="button" class="btn btn-default btn-block one" data-rel="one">
<img class="hidden-xs" src="assets/button-poverty.jpg"/>
<h4>Children in Poverty</h4>
<span class="subhead visible-sm">Among 25% of the poorest neighborhoods, more than 1 in 2 children live in poverty.</span>
</button>
</div>
<div class="col-md-3 col-lg-3 col-sm-6 col-xs-6">
<button data-filter="single_mother_families_perc" type="button" class="btn btn-default btn-block two" data-rel="two">
<img class="hidden-xs" src="assets/button-singlemother.jpg"/>
<h4>Children in Single Mother Families</h4>
<span class="subhead visible-sm">Approximately 48% (almost 1 in 2) DC children live in families headed by single mothers.</span>
</button>
</div>
<div class="col-md-3 col-lg-3 col-sm-6 col-xs-6">
<button data-filter="reading_perc" type="button" class="btn btn-default btn-block three" data-rel="three">
<img class="hidden-xs" src="assets/button-reading.jpg"/>
<h4>Students Proficient in Reading</h4>
<span class="subhead visible-sm">40% of DC neighborhoods have less than half of their students scoring proficient in reading.</span>
</button>
</div>
<div class="col-md-3 col-lg-3 col-sm-6 col-xs-6">
<button data-filter="math_perc" type="button" class="btn btn-default btn-block four" data-rel="four">
<img class="hidden-xs" src="assets/button-math.jpg"/>
<h4>Students Proficient in Math</h4>
<span class="subhead visible-sm">Half of all DC neighborhoods have less than 55% of their students proficient in math.</span>
</button>
</div>
</div>
</div>-->
<div class="posrel" style="width: 100%; overflow-x: hidden;">
<div id="nav-panel" class="posabs">
<ul class="nav nav-pills nav-stacked layer-toggle-menu" style="text-align: center">
<li class="disabled" style="padding: 4px" display="inline-block">
<div class="btn-group" data-toggle="buttons">
<label class="btn btn-sm btn-warning">
<input type="radio" name="options" id="geom_nb" autocomplete="off" checked>Neighborhoods
</label>
<label class="btn btn-sm btn-warning">
<input type="radio" name="options" id="geom_tract" autocomplete="off">Tracts
</label>
<label class="btn btn-sm btn-warning">
<input type="radio" name="options" id="geom_bg" autocomplete="off">Block groups
</label>
</div>
</li>
<li>
<a id="no_neighborhood_data" href="#"><button type="button" class="btn btn-danger btn-sm" style="width:100%"><span class="glyphicon glyphicon-trash"></span> Clear selection</button></a></li>
</ul>
<div id="nav-panel-inner" class="posabs">
<h4 class="hidden-xs">Points of interest</h4>
<ul class="nav nav-pills nav-stacked schools-menu points-menu hidden-xs"></ul>
<ul class="nav nav-pills nav-stacked poi-menu points-menu hidden-xs"></ul>
<h4>Themes</h4>
<ul class="nav nav-pills nav-stacked layer-toggle-menu neighborhood-menu"></ul>
</div>
</div><!--/#nav-panel -->
<div id="narrative" class="panel panel-default">
<a class="close-box" href="#"><img src="assets/close.png" /></a>
<div id="one" class="panel-body">
<h4 class="lead">Percentage of Children in Poverty</h4>
<p>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.</p>
</div>
<div id="two" class="panel-body">
<h4 class="lead">Percentage of Children in Single Mother Families</h4>
<p>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%).</p>
</div>
<div id="three" class="panel-body">
<h4 class="lead">Percentage of Students Proficient in Reading</h4>
<p>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.</p>
<p>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.</p>
</div>
<div id="four" class="panel-body">
<h4 class="lead">Percentage of Students Proficient in Math</h4>
<p>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.</p>
<p>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.</p>
</div>
</div> <!-- narrative cards here -->
<div id="main-container" class="posabs" style="position:relative;">
<div class="menu-toggle posabs btn-danger">
<strong class="closed">Choose a dataset</strong>
<strong class="open">Hide menu</strong>
</div>
<div id="content-wrapper" class="col-lg-12 col-md-12 col-sm-12">
<div id="content"></div> <!-- d3 viz written in here -->
<div class="panel panel-default" id="legend-panel">
<div id="legend-container" class="panel-body">
</div>
</div>
<div id="source">
<h3 id="sources" class="panel-title">Sources</h3>
<a id="source-title"></a>
</div>
<div id="zoom-toggle">
<input id="zoom-box" type="checkbox" checked></input> <label for="zoom-box">Enable Zoom</label>
</div>
</div>
</div><!-- /#main-container -->
<div id="details" class="panel panel-default">
<div class="panel-body">
<p class="lead">
<span id="visualized-metric"></span><br />
<span id="visualized-measure"></span>
</p>
<h4 class="neighborhood">Seattle, WA</h4>
<table id="pop-info" class="table table-hover">
<tr data-type="population_total">
<td>Population (total)</td>
<td class="count align-right"></td>
</tr>
<tr data-type="median_household_income">
<td>Household median income</td>
<td class="count align-right"></td>
</tr>
<tr data-type="perc_owner" class="sub-from-1">
<td>Percentage of renters</td>
<td class="count align-right"></td>
</tr>
</table>
<h4 class="chart"></h4>
<div class="chart"></div>
<h4 class="predict">Model Prediction <span class="label label-warning">Experimental</span></h4>
<div class="predict">
<p style="text-align:left;">Actual value: <span id="predict-actual-val" style="float:right;">0</span></p>
<p style="text-align:left;">Select new value: <span id="predict-slider-val" style="float:right;">0</span></p>
<div class="predict-slider">
<input class="predict-slider" type="range" value="3" min="1" max="5" ticks="1" onchange="checkPredictSlider()"/>
<datalist id="predict-ticks">
</datalist>
</div>
<p>Predictions:</p>
<table id="predict-info" class="table table-hover">
<tr data-type="fam_friendlnss">
<td>Family Housing</td>
<td class="count align-right"></td>
</tr>
<tr data-type="infrastructure">
<td>Mobility Infrastructure</td>
<td class="count align-right"></td>
</tr>
<tr data-type="conn">
<td>Connectivity</td>
<td class="count align-right"></td>
</tr>
<tr data-type="elem">
<td>Elem. School Well-being</td>
<td class="count align-right"></td>
</tr>
<tr data-type="upper">
<td>Upper School Well-being</td>
<td class="count align-right"></td>
</tr>
<tr data-type="health">
<td>Public Health</td>
<td class="count align-right"></td>
</tr>
<tr data-type="dev">
<td>Development</td>
<td class="count align-right"></td>
</tr>
<tr data-type="socio_status">
<td>Socio-economic</td>
<td class="count align-right"></td>
</tr>
</table>
</div>
</div>
</div> <!-- narrative cards here -->
<div id="schools_panel" class="panel panel-default hide">
<button class="btn-danger btn-xs btn-panel-close" id="btnPanelClose">X Close</button>
<div class="panel-body">
<div class="hide" id="school_data">
<h4 class="school-name"></h4>
<table class="table table-hover">
<tbody>
<tr>
<td>Enrollment</td>
<td class="school-enrollment">getDisplayValue(school.enroll_val, 'enroll_val')</td>
</tr>
<tr>
<td>Students per teacher</td>
<td class="school-spert">getDisplayValue(school.s_per_t, 's_per_t')</td>
</tr>
<tr>
<td>In-Seat attendance</td>
<td class="school-attendance">getDisplayValue(school.isa_perc, 'isa_perc')</td>
</tr>
<tr>
<td>Percent proficient at math</td>
<td class="school-math">getDisplayValue(school.math_perc, 'math_perc')</td>
</tr>
<tr>
<td>Percent proficient at reading</td>
<td class="school-reading">getDisplayValue(school.reading_perc, 'reading_perc')</td>
</tr>
<tr>
<td>Student perception of school climate</td>
<td class="school-stusat">getDisplayValue(school.stu_sat, 'stu_sat')</td>
</tr>
<tr>
<td>Family Satisfaction</td>
<td class="school-famsat">getDisplayValue(school.fam_sat, 'fam_sat')</td>
</tr>
<tr>
<td>Percent free/reduced lunches</td>
<td class="school-redlunch">getDisplayValue(school.red_lunch, 'red_lunch')</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
<div class="footer" style="position:relative;top:10px;">
<small class="col-md-10 col-lg-10 col-sm-10 col-md-offset-1 col-lg-offset-1 col-sm-offset-1" style="text-align:center;clear:both;">
<a href="http://www.dcactionforchildren.org/dc-kids-count-data-tools-acknowledgements">Credits & acknowledgements</a> | <a href="http://www.dcactionforchildren.org/dc-kids-count-data-tools-methodology" target="_blank">Data & Methodology</a> | <a href="http://www.dcactionforchildren.org/dc-kids-count-data-tools" target="_blank">Revisit version 2012</a>
</small>
<div class="clearfix"></div>
<div style="text-align:center;margin:20px 0;clear:both;"><iframe src="http://ghbtns.com/github-btn.html?user=DCActionforChildren&repo=dcaction&type=fork&count=true" allowtransparency="true" frameborder="0" scrolling="0" width="95" height="20"></iframe></div>
</div>
<div class="modal fade" id="myModal" tabindex="-1" role="dialog" aria-labelledby="myModalLabel" aria-hidden="true">
<div class="modal-dialog">
<div class="modal-content">
<div class="modal-header">
<button type="button" class="close" data-dismiss="modal" aria-hidden="true">×</button>
<h2 class="modal-title" id="myModalLabel">Project description</h2>
</div>
<div id="how-to" class="modal-body">
<p>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:
<ul>
<li>In the <i>Points of Interest</i> section, users can examine key points of interest in the city and find out more about each one; for example, a public school's performance, or the size and amenities of a city park.</li>
<li>A range of <i>Equity Indicators</i> are grouped thematically so that users can explore how different combinations of indicators interact with one another and how they play out spatially across the city.</li>
<li>Along with the interactive tool, the UW team developed a <i>structural equation model</i> that tests the correlation among each themed set of indicators. Results of the model are fed back into the tool to allow users to explore the relative impact of the analysis on neighborhoods across the city.</li>
<li>As a prototype, we have enabled predictive abilities for four indicators, allowing user inputs to adjust a quantity in the model.</li>
</ul>
</p>
<!--
<div class="row">
<div class="col-lg-12">
<div class="one" style="position:relative;">
<h3>Click menu, select data layer</h3>
</div>
</div>
<div class="col-lg-12">
<div class="two" style="position:relative;">
<h3>Select school points for data</h3>
</div>
</div>
<div class="col-lg-12">
<div class="three" style="position:relative;">
<h3>Click neighborhood to freeze data</h3>
</div>
</div>
-->
</div>
</div>
<div class="modal-footer">
<button type="button" class="btn btn-default" data-dismiss="modal">Close</button>
</div>
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</div>
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<div class="modal fade" id="myModal2" tabindex="-1" role="dialog" aria-labelledby="myModalLabel" aria-hidden="true">
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<div class="modal-header">
<button type="button" class="close" data-dismiss="modal" aria-hidden="true">×</button>
<h2 class="modal-title" id="myModalLabel">How to use this tool</h2>
</div>
<div id="how-to" class="modal-body">
<p>
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.
<br><br>
Next select one of the equity-related themes to explore. Under each theme you will find an <b>Overall score</b> 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.
<br><br>
To better understand how areas compare relative to others, use the <b>Histogram</b> feature. When a dataset is selected, hover over an area to see where it falls in the spectrum.
<br><br>
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.
<br><br>
Explore all the themes to understand the distribution of equity in Seattle!
</p>
<!--
<div class="row">
<div class="col-lg-12">
<div class="one" style="position:relative;">
<h3>Click menu, select data layer</h3>
</div>
</div>
<div class="col-lg-12">
<div class="two" style="position:relative;">
<h3>Select school points for data</h3>
</div>
</div>
<div class="col-lg-12">
<div class="three" style="position:relative;">
<h3>Click neighborhood to freeze data</h3>
</div>
</div>
-->
</div>
</div>
<div class="modal-footer">
<button type="button" class="btn btn-default" data-dismiss="modal">Close</button>
</div>
</div>
</div>
</div>
<div class="modal fade" id="myModal3" tabindex="-1" role="dialog" aria-labelledby="myModalLabel" aria-hidden="true">
<div class="modal-dialog">
<div class="modal-content">
<div class="modal-header">
<button type="button" class="close" data-dismiss="modal" aria-hidden="true">×</button>
<h2 class="modal-title" id="myModalLabel">The structural equation model</h2>
</div>
<div id="how-to" class="modal-body">
<p>
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:
<IMG src="https://farm5.staticflickr.com/4409/36127609110_a8fe2463d9_b.jpg" ALT="some text" WIDTH=549 HEIGHT=463>
<br><br>
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.
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