| name | geographically-weighted-regression |
| description | Builds Geographically Weighted Regression (GWR) workflows in CARTO. Triggers when the user mentions GWR, geographically weighted regression, spatially varying relationships, local regression, local coefficients, spatial regression, "what drives X in different areas", "why do prices vary spatially", "local factors affecting Y", varying coefficients, coefficient maps, spatial non-stationarity, or wants to model how the relationship between a dependent variable and predictors changes across geography. Produces per-cell regression coefficients that reveal how predictor importance shifts from place to place. |
Geographically Weighted Regression (GWR)
Builds CARTO Workflows that model spatially varying relationships between a dependent variable and one or more independent variables using GWR. Unlike global regression (one set of coefficients for the entire study area), GWR produces local coefficients per spatial unit, revealing how relationships change across space. Example: "bedrooms add $50k to price in downtown but only $20k in suburbs."
Prerequisites: Load build-carto-workflow for the full workflow development process, JSON structure, and validation commands.
Instructions
A GWR workflow follows this pipeline:
Source Data -> (Filter) -> Spatial Indexing (H3/Quadbin) -> Aggregation (dependent + independent vars per cell) -> GWR -> Save
Step 1: Load Source Data
Use native.gettablebyname. The input table must contain at least one numeric dependent variable and one or more numeric independent (predictor) variables.
Success: Node outputs a table with the necessary numeric columns.
Step 2: Filter (if needed)
Use native.wheresimplified or native.where to narrow the dataset (e.g. remove nulls from key columns, filter by category or date range).
Success: Output contains only rows with valid, non-null values for the dependent and all independent variables.
Step 3: Spatial Indexing
If the data is not already indexed, convert point geometries to spatial index cells:
native.h3frompoint for H3native.quadbinfromgeopoint for Quadbin
If the data already contains an H3 or Quadbin column (common for pre-aggregated datasets), skip this step.
Resolution guidance:
| Resolution | Cell size | Use case |
|---|
| H3 res 7 | ~5 km edge | City-level relationships |
| H3 res 8 | ~2 km edge | Neighborhood-level |
| H3 res 9 | ~500m edge | Street-level (needs dense data) |
Success: Every row has a spatial index column (e.g. h3).
Step 4: Aggregate per Cell
Use native.groupby to produce one row per cell with aggregated values for the dependent and all independent variables:
- Group by: the spatial index column (
h3)
- Aggregation:
price,avg,bedrooms,avg,bathrooms,avg (adapt to the actual columns)
The dependent variable should be aggregated with avg or sum depending on what makes sense. Independent variables are typically averaged.
Success: Output has exactly one row per unique cell, with numeric columns for the target and all predictors.
Step 5: Run GWR
Use native.gwr with:
| Input | Description | Default |
|---|
index_column | Column with H3/Quadbin indexes | h3 |
label_column | Target / dependent variable to model (must be numeric) | - |
features_columns | Predictor / independent variable columns (array of strings) | - |
kernel_function | Weighting function for neighbors | gaussian |
kring_distance | K-ring size (neighborhood radius in hops) | 3 |
fit_intercept | Whether to fit an intercept term | true |
Kernel options: gaussian (recommended -- smooth distance decay), uniform, triangular, quadratic, quartic.
K-ring size: Controls the neighborhood radius.
- Too small (1-2): noisy, unstable coefficients.
- Too large (5+): over-smoothed, approaches global regression.
- Start with
3 as a balanced default.
Success: Output contains per-cell columns: INDEX, INTERCEPT, one coefficient column per independent variable, R_SQUARED, and RESIDUAL.
Step 6: Save
Use native.saveastable to persist results. The spatial index column is directly visualizable in CARTO Builder -- style the map by coefficient columns to create coefficient maps showing spatial variation.
Success: Validated workflow that can be uploaded via carto workflows create.
Output Columns
| Column | Meaning |
|---|
INDEX | Spatial index cell ID (H3 or Quadbin) |
INTERCEPT | Local intercept term |
<variable_name> | Local coefficient for each independent variable |
R_SQUARED | Local model fit (0-1) -- higher = better local explanation |
RESIDUAL | Difference between observed and predicted value |
Gotchas
- The GWR component requires the Analytics Toolbox. Always run
carto workflows verify --connection <conn> to ensure the AT path is resolved. carto workflows validate is offline and cannot resolve AT location.
- The dependent variable must be continuous and numeric. Categorical targets need a different approach (e.g. classification).
- Cells with null values in ANY variable (dependent or independent) will be excluded from the model. Pre-filter or impute nulls before running GWR.
- Multicollinearity between independent variables degrades results. If two predictors are highly correlated (e.g.
bedrooms and total_rooms), drop one or combine them. Check correlation before including multiple similar variables.
- K-ring size matters significantly: too small = noisy, unstable coefficients; too large = over-smoothed results that approach a global regression. Start with
3 and adjust.
- On Snowflake, column names are uppercased. Use
H3, PRICE, R_SQUARED, etc. in expressions and references.
R_SQUARED per cell indicates local model fit. Very low values across many cells suggest important predictors are missing from the model.- The
features_columns input is an array of column names (e.g. ["bedrooms", "bathrooms"]), not a comma-separated string.
- The output column is named
INDEX, not the original spatial index column name. If joining back to original data, rename it with native.renamecolumn.
- Sparse data at high resolutions leads to unreliable coefficients. Ensure enough cells have data for all variables before choosing a high resolution.
Reference Templates
Workflow template (available in CARTO Workspace): "Applying Geographical Weighted Regression (GWR) to model the local spatial relationships in your data"
Builder use case: Analyzing Airbnb ratings in Los Angeles -- models overall_rating vs value_review, cleanliness, location, enriched with Data Observatory sociodemographics. Uses H3 res 7, kring 3, Gaussian kernel.
Common Variations
| Variant | How |
|---|
| Pre-aggregated data (already one row per cell) | Skip Steps 3-4, go directly to GWR |
| Enrich with Data Observatory | Add native.enrichgrid before GWR to include sociodemographic predictors |
| Coefficient comparison | Save results, then use Builder to style map by each coefficient column separately |
| Filter by model fit | Add native.where after GWR to keep only cells with R_SQUARED > 0.5 (or another threshold) |
| Combine with hotspot analysis | Run GWR first, then use residuals as input to Getis-Ord to find clusters of under/over-prediction |
