# Data sources Open data sources used to build the neighborhood profiles and similarity scores. LookAlike is built entirely from freely available, open data. Every data source is keyless, has no authentication requirement, and is refreshed with pinned vintages recorded in the separate `LookAlike-data-pipeline` repository. ## Open data sources Source Vintage What it provides Access License **ACS (Census Bureau)** 2023 5-year Summary File 16 ACS-derived demographic & socioeconomic features (income, education, age, race/ethnicity, housing, commute, poverty) aggregated to census tracts Keyless bulk download: `acsdt5y2023-.dat` files from Census Bureau Public domain **TIGER/Line (Census Bureau)** 2024 Tract boundary polygons, centroids, and land-area measurements Per-state shapefiles from Census Bureau Public domain **Overture Maps Places** 2026-06-17.0 ~15.4 million U.S. points of interest (restaurants, grocery stores, cafés, shopping) grouped into densities per km² Public GeoParquet on S3, queried with DuckDB (no full download required; spatial predicate pushdown reads only relevant data) CDLA-Permissive 2.0 **CBSA delineation (Census Bureau)** OMB 2023 383 Metropolitan Statistical Areas in the continental US; maps counties to metros for the metro-focus feature Keyless `.xlsx` download from Census Bureau Public domain **Brand store locations** OpenStreetMap current For each of the 11 built-in brands: store coordinates queried by brand:wikidata ID and exact name Overpass API (two public endpoints, no key required) ODbL (OpenStreetMap License) **Basemap** OpenStreetMap current Raster background tiles (dark style, no labels or only labels) for visual context CARTO’s no-token dark basemap built on OpenStreetMap © CARTO, © OpenStreetMap contributors ## Data caveats Understand these constraints when interpreting results: - **ACS lag and sampling error**: The ACS 2023 5-year estimates reflect data collected 2019–2023. Neighborhood demographics can shift. Small census tracts (especially rural ones) have larger margins of error. - **OpenStreetMap store coverage**: OSM is crowdsourced and comprehensive for major chains, but not exhaustive. Store counts reflect OSM coverage at query time, not the true store universe. A brand with fewer mapped stores will have a narrower, potentially less representative archetype. - **Point-of-interest coverage**: Overture Maps POI density depends on contributor coverage. Urban and commercial areas are well-covered; rural areas, small retailers, and informal commercial activity may be under-represented. - **Feature set and weighting are interpretive choices**: The 21 features and their grouping into 6 themes are designed to be interpretable and balanced, not to capture all possible neighborhood variation. A different feature set would produce different archetypes and rankings. Trust the explanation (why a tract matches) over the exact percentile rank. ## Key numbers - **Scoreable tracts**: 83,008 continental-US census tracts across all 50 states (excluding Alaska, Hawaii, and territories). - **Built-in brands**: 11 brands ranging from 96 stores (H Mart) to 11,136 stores (Dollar General) as of build time (after the 150 m dedup and 1 km US-containment). - **Model features**: 16 ACS-derived features plus 5 Overture retail-density measures, for 21 engineered features in total. - **Metro areas**: 383 Census-defined Metropolitan Statistical Areas for geographic focus. ## Accessing the data All raw data is downloaded and cached inside the data-pipeline repository. The pipeline writes processed assets to `dist/releases//` and publishes them to R2: - `features.f32` — compact float32 feature arrays per tract (21 features per row) - `sims.u8` — pre-computed within-brand similarity percentiles per tract - `centroids.f32` — tract centroids (latitude, longitude) - `rel.u8` — low-ACS-reliability flag per tract - `meta.json` — brand profiles and archetypes, feature descriptions, per-brand `dist_ref` distance quantiles, and coherence - `stores/.json` — store coordinates for each brand - `metros.json` — metro-area definitions and summaries - PMTiles vector tileset — tract polygons and boundaries for the map The per-brand candidate JSON is generated under `data/out` for the offline consistency test and is not part of the web bundle; the browser rebuilds the ranked list from the binaries. The browser loads these national assets and scores live using JavaScript. Authenticated customer uploads are stored separately from the open national release in private R2 and client-scoped D1 records. All source URLs and endpoints are listed and versioned in `LookAlike-data-pipeline/pipeline/config.py`. If the relevant cache is cleared, the pipeline downloads them again from those canonical sources. ## Provenance The data pipeline is maintained in its own repository. Every transformation is logged: which tables were fetched, how many rows were kept, what cells were extracted, and how features were engineered. The choice to use only free, open data is intentional. It makes the tool reproducible, auditable, and accessible — no proprietary foot-traffic data, no purchased mailing lists, no commercial geospatial databases. The trade-off is that resemblance to open-data neighborhoods is a weak predictor of sales or success. See [the method](https://docs.lookalikeiq.com/method/how-it-works/) for how we validate and honestly frame this limitation. Canonical URL: https://docs.lookalikeiq.com/method/data-sources/