geographically-weighted-regression

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.

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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 H3
• native.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.

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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

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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.

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Reference Templates

Resource	Description
BQ Tutorial: Airbnb Listings Prices (GWR)	BigQuery step-by-step: Berlin Airbnb price vs bedrooms/bathrooms, H3 res 7, kring 3, Gaussian kernel
SF Tutorial: Airbnb Listings Prices (GWR)	Snowflake step-by-step: same analysis adapted for Snowflake

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.

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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