health

Transportation barriers (adults)

CDC PLACES (model-based small-area estimates) 2022–2023 Higher = more need
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Why this matters

Transportation barriers measures the share of adults who, in the past year, lacked reliable transportation to get to work, medical appointments, the grocery store, or daily errands. On this platform these are modeled estimates that blend survey and population data, so they are best read as the level in each year, not as year-to-year change (the CDC does not support using them as a trend).

When getting around is unreliable, people miss medical care, job shifts, and chances to buy fresh food, so this measure ties directly to health, employment, and food access. It complements the share of households with no vehicle, capturing people who may have a car that is unreliable or who depend on limited transit.

In Charlotte, this is the practical question behind the mobility story: can a family in the crescent reach jobs in the southeast wedge or the airport-area warehouses? It connects to transit access, the LYNX Blue Line, and the city’s "corridors of opportunity."

About the Charlotte region

This explorer covers the 14-county, two-state Charlotte region (11 North Carolina and 3 South Carolina counties). Many of these indicators connect to a defining regional story: a landmark Harvard study ranked Charlotte last, 50th of the 50 largest U.S. metros for economic mobility (a child raised in the poorest fifth had about a 4.4% chance of reaching the top fifth), a finding that launched the region's Leading on Opportunity agenda. Updated 2024 data show Charlotte has since climbed to 38th.

Opportunity here is also unevenly mapped. An affluent “wedge” fanning southeast from uptown holds a large majority of the city's wealth on under a quarter of its land, while a ”crescent” to the north, west, and east carries higher poverty, a legacy of redlining, exclusionary zoning, and highway routing that recurs across the race, income, health, tree-canopy, and school maps in this tool. No single indicator is good or bad on its own; together they describe how the region sorted opportunity.

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About the data

Model-based small-area estimates from the CDC PLACES project (2022–2023), which uses BRFSS survey data and multilevel regression & poststratification to estimate adult prevalence for every Census tract. Each estimate has a confidence interval; values with high uncertainty are flagged.

Source: CDC PLACES (model-based small-area estimates).