Reducing Child Poverty in California

Public Policy Institute of California / 2017 / Economics, Racial Equity

Reducing Child Poverty in California — A visualization tool built by Graphicacy for Public Policy Institute


Graphicacy partnered with the Public Policy Institute of California (PPIC) to create an interactive tool that would allow users to explore data showing how changes to housing costs, the minimum wage, and the social safety net could reduce child poverty statewide, as well as in individual counties.

Background and Challenge

The Public Policy Institute of California (PPIC) is a nonprofit, nonpartisan think tank whose mission is to inform and improve public policy in California through independent, objective, research. The focus for this project was on poverty among young children ages 0–5 using the California Poverty Measure, which – unlike the official poverty measure – accounts for variation in the cost of living and the cash value of safety net benefits.

A team of communications staff, researchers, and subject matter experts from PPIC came to Graphicacy for help in creating an embeddable dashboard with the goal of making a large amount of information easy to navigate and interpret. We needed to design and build a tool that would allow users to quickly scan the impact of multiple policy areas on the lives of real people.

Reducing Childhood Poverty in California Tool for Public Policy Institute of California

Opportunity and Solution

Taking a restricted, short-term view of likely policy outcomes, PPIC had found that lower housing costs and minimum wage increases could lower child poverty substantially while helping Californians across the income spectrum. The creation of this tool provided an interactive experience accessible to academics, researchers, journalists, and policymakers as well as general users.

One early design decision was to present a map and a ranking bar chart together, displaying the same information. Choropleth maps are good at showing geographic patterns, but bar charts and line charts are better at communicating the actual data values and the distribution of a dataset. We included a bar chart below the map to allow the user to easily see the distribution of the data and where a selected county ranks relative to other counties.

Another consideration was how to set the scales to ensure that the large differences between different policy scenarios would be apparent. For the map, there was a decision not to use quartiles or quintiles to ensure that the variation among counties was apparent in the colors on the map. For the bar chart, the y-axis remains the same as the user toggles between policy scenarios, so it’s readily apparent which policies have the largest impact on poverty.

Overall, five policy options to choose from, two toggle options for both income and poverty rate scenarios, and four different types of charting (map, donut charts, ranking bar chart, and table), provided users with a simple but highly flexible way to explore this dataset.

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