Pick any neighborhood or group of census tracts in the US. See what residents spend in fourteen categories, what local stores sell in twelve of them, and how that compares with the national pattern.
Local store sales ÷ the sales the national pattern would predict from residents' spending. 1.0 is the national norm; below 1.0 means residents' spending likely leaves the area.
Annual, 2024 dollars. The spending gap is resident demand × (1 − pull factor): residents' spending the area would hold at the national norm but does not. Negative means the area draws in more than the norm.
Store counts use the sales gap, not the spending gap above: sales at the national norm minus local sales, because an average store's sales include all its customers, not only residents. Each divides that gap by what an average US benchmark store sells in that category alone (2022 Economic Census, restated in 2024 dollars); a supermarket's grocery sales, for example, leave out its pharmacy and household goods. A gap is a ceiling, not a forecast: some spending will always go online, near work or to destination stores.
Public data cannot match these to local sales: most medicine spending is paid through insurance at the pharmacy, and admissions are dominated by visitors. Vehicle purchases are left out entirely as a category no neighborhood captures.
A report you can share, the selection's figures, every tract in the state, or a link that reopens this selection.
This is a retail-gap estimate, not observed leakage. Local sales include shoppers who live elsewhere, and resident demand is modeled from national spending patterns by income. Neither side records where residents actually shop. That takes card data split by cardholder home and store location, which is the next layer (see the bottom of this page).
Households by income bracket in each tract (ACS 2020–2024 five-year, table B19001) times what US households at each income spend by category (BLS Consumer Expenditure Survey 2024), interpolated at each bracket's midpoint. The top bracket's mean is solved from aggregate household income (B19025), held between $200K and $1M. Gasoline uses 2022 survey spending at 2022 incomes, restated at 2024 pump prices. Gas, auto repair and vehicle purchases are scaled by the tract's vehicles per household (B25046) against what the survey shows at the same incomes, between 0.2 and 2.5 times.
Twelve categories are measured on both sides: groceries; full-service restaurants; quick-service restaurants, coffee and food trucks; alcohol, from stores and bars; tobacco and vaping; household supplies, garden and stationery; pets, toys, hobbies and books; personal care products and services; apparel, shoes and clothing care; furniture, home furnishings, appliances and computers; gasoline; and auto parts and repair. Medicines and entertainment admissions are demand only.
Stores sell across categories, so local sales are counted by what is sold, not by store type. Each store type's sales are split by what that kind of store sells nationally, from the 2022 Economic Census product lines: a supermarket's sales go mostly to groceries, partly to household supplies, personal care and alcohol, and its pharmacy counter to no category; a gas station's go to gasoline, groceries, tobacco and alcohol. Medicines and vitamins, prescription or over the counter, fall in no category, matching the demand-only medicines line; so do lines no category covers, such as sporting goods and used goods.
Full-service and quick-service restaurants split cleanly on the sales side. On the spending side, each tract's restaurant spending is split by the share that households at each income spend at full-service restaurants, from the BLS Consumer Expenditure Diary Survey 2024 public-use files: about 40%–45% for households under $100,000, 46%–48% from $100,000 to $200,000 and 52% above $200,000 (46% for all households; each income range's share has a standard error of 2–5 points). Food from vending machines and mobile vendors counts as quick-service. Employer and school cafeterias and catered events are left out when the share is measured, but their spending stays in restaurant demand and is split in the same proportion.
Each store type's jobs are multiplied by its 2022 Economic Census sales per employee, taken from the place, county, metro area, state or US, whichever is the smallest with enough stores (pharmacies always at the US rate), split into categories as above, and restated in 2024 dollars (gasoline at 2024 pump prices). Thirty store types are used, from supermarkets and convenience stores to salons, dry cleaners and food trucks; building-material stores, car dealers and online sellers are left out. The two estimates set every range on this page. Alaska uses LODES 2016 and Michigan 2021, the latest each state has published.
Across the US, store sales run 0.9 to 3.5 times modeled resident spending, depending on the category. Most run above it, because households under-report spending in the survey and stores also sell to businesses, commuters and visitors. Pets, toys and books and home furnishings run near or below it, because much of that spending goes online or to store types left out here. The pull factor divides local sales by resident demand times that national ratio, separately for each estimate, so 1.0 means an area sells what the national pattern predicts for its residents. The spending gap is resident demand times (1 − pull factor): residents' spending, in survey dollars, that local stores would hold at the national norm but do not. Because the survey under-reports some spending, it leans conservative. Store counts instead use the gap in store-sales terms (the predicted sales minus local sales), divided by a benchmark store's sales in that category.
For Chicago's South Shore (the City's 15 tracts), this page finds 25,043 households (CMAP's figure is 26,470) and $330M of resident demand in the five categories the hand-built South Shore baseline measured (the baseline has $352M from the same survey items; the difference comes from the household count, the baseline's assumption for top-bracket income, and its single car-ownership adjustment for the whole area where this page adjusts tract by tract). The baseline compared local sales with demand directly, found local sales at 63%–75% of demand and a gap of $88M–$129M. This page measures 12 categories against the national norm and splits store sales by what each kind of store sells, so its figures are larger and not directly comparable: a net spending gap of $232M–$263M, with pull factors of 0.50–0.57 for groceries, 0.05–0.12 for full-service dining, 0.22–0.45 for quick-service food and 0.12–0.14 for home furnishings. The two agree on where the biggest gaps are: restaurants and everyday goods (here groceries, household supplies, and pets, toys and books). Home furnishings, which the baseline could not measure, shows almost no local sales.
Who's On First (CC BY 4.0), compiled from: Atlanta Department of Planning and Community Development (CC BY 4.0); Baltimore Mayor's Office of Information Technology (CC BY 3.0); Washington Post neighborhood boundaries for Washington, DC (CC BY); Zolk Chicago Neighborhoods Map (CC BY 3.0 US); Azavea, Inc., Philadelphia neighborhoods (CC BY 3.0 US); Denver Community Planning and Development (CC BY 3.0); City of Cambridge GIS (CC BY); Quattroshapes, Foursquare (CC BY 2.0); Pediacities NYC neighborhoods (ODC-By 1.0); and, in the public domain or under CC0, Zetashapes, Mapzen, Mini Tenders, Burrito Justice, SanGIS/SANDAG, and the cities of Boston, Los Angeles, New Orleans, Oakland, Portland, San Francisco, San Jose, Santa Barbara, Seattle and St. Paul. Chicago community areas and neighborhoods: City of Chicago Data Portal. Boundaries are simplified and matched to census tracts here.