Throughout the course of history, the use of the term “equity” has ebbed and flowed in frequency. Recently, equity has become quite a buzzword. Why? Advancing equity across the United States is a large focus within President Biden’s Build Back Better framework. The purpose of this is to empower individuals, and make sure everyone has access to the important resources they need (like healthcare and transportation) no matter their location, race, wealth, etc.
Currently, there is a wide gap in the accessibility of necessary resources between high- and low-income neighborhoods and urban vs. rural areas, as well as between communities of differing ethnicities. Having limited access to resources, like high speed internet, transportation and, yes, even trees, isn’t just a minor daily inconvenience – it’s a huge issue that can affect people’s success, quality of life and wellbeing.
So, how do we begin closing the gap? How can we discover which specific regions and demographics have equity and which do not? How do we analyze and visualize this data once we have it? We can do so by using location data, spatial analysis and opportunity index scores to determine what and where access is currently equitable vs. what access is lacking.
Tree equity refers to the idea that everyone, regardless of the neighborhood, should have access to the climate, health and economic benefits that city trees provide. Unfortunately, key findings, like this op-ed and data visualization story by the New York Times, shows there is disparity in the distribution of trees in wealthy vs. low income areas across the entire United States. When diving deeper into the impact of this disproportionate canopy coverage between these communities, research has found that areas with a lesser amount of trees have greater levels of air pollution and higher temperatures; and the individuals who live within these communities have a higher risk of experiencing mental and physical health issues.
As the conversation continues to grow louder, an increasing number of states at the city-level are putting more budget toward researching tree equity and making adjustments according to their findings.
For instance, using CARTO, the Healthy Regions + Policy Lab (HeRoP) at the University of Chicago ingested and integrated housing, transportation and hardship insights data from the CDC and surface temperature data from NASA to provide evidence-based research that showcased the impact of tree equity in Chicago and how it affects vulnerable communities across the city. Thanks to the help of a custom-built CARTO dashboard, HeRop was able to pull all of this data into a central location which allowed them to more easily analyze natural factors (i.e. tree coverage’s correlation to health) and social factors (i.e. traffic volume) across different communities.
Exciting news: HeRop’s findings encouraged other organizations to create explorable map interfaces with complex spatial data, and resulted in the Chicago Mayor’s Office passing a budget that allocated $46 million in spending to help plant 75,000 trees across the city over the next few years.
The goal of transportation equity is to ensure that everyone in the community has access to affordable transportation with an additional focus on equal distribution of transportation resources, benefits, programs, safety measures and infrastructure. In addition to reviewing public transportation options, transportation equity under the lens of traffic safety equity helps cities understand where and why certain areas have more traffic accidents as well as determine where they should invest more budget toward additional programs, like installing more traffic lights, stop signs and crosswalks.
For example, research shows that lower-income neighborhoods experience more than twice as many pedestrian fatalities as higher income neighborhoods and cars typically take 32% longer to yield for African American pedestrians than white pedestrians. When cities are able to map this data and layer on additional datasets, they can review and visualize all of the variables that may be contributing to such examples that showcase a lack of equity so they know which areas to begin improving.
Leveraging the CARTO for React library and index dashboard, a state’s DOT team can layer and join multiple disparate data sources or “indicators” (like investment per capita, bike/car crashes) using standard geography (ie. block group or a grid) into a single score for an area (0-100) to give overall insights into how different areas compare. Additionally, within the dashboard there are widgets for the different data points that you can toggle on and off which make it easy to drill down on analysis and reveal certain insights through different demographic lenses.
Equity of opportunity serves to ensure individuals, regardless of race or means, have the right kind of access to opportunities and resources like healthcare, transportation, housing, education, income, childcare and food. Access to opportunity not only depends on physical proximity, but also the ability to attain the skills and experience needed to fully utilize certain opportunities.
In order to get a pulse-check on opportunity equity levels across different communities, cities can analyze first-party data or look through the lens of third-party data types, like demographic, sociodemographic, human mobility and points-of-interest. This approach can not only help cities visualize inequalities in existing systems, but also understand their ongoing impacts throughout communities.
For example, to better understand opportunity across neighborhoods and to create a plan for equitable growth, the City of Seattle could use CARTO and geospatial attributes to explore and visualize the current state of equity in the city on both macro- and micro-levels. After analyzing this data, Seattle (as well as other cities) can leverage collective resources to create communities of opportunity for everyone, regardless of race or means.
In 2019, the Atlas of Inequality (a joint project between MIT Media Lab’s Human Dynamics Group and the Department of Mathematics at Universidad Carlos Ill de Madrid) used CARTO’s platform and anonymous aggregated location data from Cuebiq to examine behavioral patterns and investigate segregation in cities to understand the inequality of income opportunity.
Using the anonymized data, the team determined home locations as the most frequent location between 8 PM and 4 AM; they then estimated income using the median household income level of the surrounding census block for those home locations. By matching these datasets with billions of visits to thousands of locations (think coffee shops, movie theaters, stores, restaurants, etc.) across the Boston Metro Area, they were able to create a diversity index, visualizing segregation and inequality of income opportunity as a function of how many visitors are coming from four distinct income buckets.
The project’s CARTO dashboard has a built-in interactive category widget which enables users to filter by location categories, allowing them to look at inequality in specific places, such as restaurants or art venues; there’s also a button that allows for on-the-fly styling by category selection and the histogram to the top right-hand side of the map shows distribution of the segregation percentage across all locations currently visualized.
When using a user-friendly location intelligence platform to conduct spatial analysis with your chosen datasets, it’s much easier to study and identify different types of equity across various communities. Now that you have a better understanding of how you can use CARTO and spatial analytics to visualize the distribution of resources, you can start mapping out a strategy to help determine and close equity gaps.
If you have any questions or you’re interested in learning more about how CARTO can help you, feel free to reach out here here to schedule a live demo with one of our geospatial experts.
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