From 92fc25f6b5a47f1e414ef819b7bed8ff24e5c33f Mon Sep 17 00:00:00 2001 From: Andy Eschbacher Date: Thu, 5 Jan 2017 16:18:08 -0500 Subject: [PATCH] placeholders for desciptions fix references --- doc/21_gwr.md | 59 +++++++++++++++++++++++++++++++++++++++++++++------ 1 file changed, 53 insertions(+), 6 deletions(-) diff --git a/doc/21_gwr.md b/doc/21_gwr.md index 7869db1..057fcaf 100644 --- a/doc/21_gwr.md +++ b/doc/21_gwr.md @@ -1,8 +1,57 @@ ## Regression -### Geographically weighted regression +### Predictive geographically weighted regression (GWR) -Can currently estimate Gaussian, Poisson, and logistic models (built on a GLM framework). GWR object prepares model input. Fit method performs estimation and returns a GWR Results object. +-- add description here + +#### Arguments + +| Name | Type | Description | +|------|------|-------------| +| subquery | text | SQL query that expose the data to be analyzed (e.g., `SELECT * FROM regression_inputs`). This query must have the geometry column name (see the optional `geom_col` for default), the id column name (see `id_col`), dependent and independent column names. | +| dep_var | text | name of the dependent variable in the regression model | +| ind_vars | text[] | Text array of independent used in the model to describe the dependent variable | +| bw (optional) | numeric | bandwidth value consisting of either a distance or N nearest neighbors. Defaults to calculate an optimal bandwidth. | +| fixed (optional) | boolean | True for distance based kernel function and False for adaptive (nearest neighbor) kernel function (default). Defaults to false. | +| kernel | text | Type of kernel function used to weight observations. One of gaussian, bisquare (default), or exponential. | + + +#### Returns + +| Column Name | Type | Description | +|-------------|------|-------------| +| coeffs | JSON | JSON object with parameter estimates for each of the dependent variables. The keys of the JSON object are the dependent variables, with values corresponding to the parameter estimate. | +| stand_errs | JSON | Standard errors for each of the dependent variables. The keys of the JSON object are the dependent variables, with values corresponding to the respective standard errors. | +| t_vals | JSON | T-values for each of the dependent variables. The keys of the JSON object are the dependent variable names, with values corresponding to the respective t-value. | +| predicted | numeric | predicted value of y | +| residuals | numeric | residuals of the response | +| r_squared | numeric | R-squared for the parameter fit | +| bandwidth | numeric | bandwidth value consisting of either a distance or N nearest neighbors | +| rowid | int | row id of the original row | + + +#### Example Usage + +```sql +SELECT + g.cartodb_id, + g.the_geom, + g.the_geom_webmercator, + (gwr.coeffs->>'pctblack')::numeric as coeff_pctblack, + (gwr.coeffs->>'pctrural')::numeric as coeff_pctrural, + (gwr.coeffs->>'pcteld')::numeric as coeff_pcteld, + (gwr.coeffs->>'pctpov')::numeric as coeff_pctpov, + gwr.residuals +FROM cdb_crankshaft.CDB_GWR('select * from g_utm'::text, 'pctbach'::text, Array['pctblack', 'pctrural', 'pcteld', 'pctpov']) As gwr +JOIN g_utm as g +on g.cartodb_id = gwr.rowid +``` + +Note: See [PostgreSQL syntax for parsing JSON objects](https://www.postgresql.org/docs/9.5/static/functions-json.html). + +### Descriptive geographically weighted regression + +-- add description here #### Arguments @@ -52,8 +101,6 @@ Note: See [PostgreSQL syntax for parsing JSON objects](https://www.postgresql.or ## Advanced reading -I think it would be good to have some of the early papers and "the GWR book" as the most base references. - * Fotheringham, A. Stewart, Chris Brunsdon, and Martin Charlton. 2002. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. John Wiley & Sons. * Brunsdon, Chris, A. Stewart Fotheringham, and Martin E. Charlton. 1996. "Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity." Geographical Analysis 28 (4): 281–98. @@ -72,6 +119,6 @@ I think it would be good to have some of the early papers and "the GWR book" as * Gilbert, Angela, and Jayajit Chakraborty. 2011. "Using Geographically Weighted Regression for Environmental Justice Analysis: Cumulative Cancer Risks from Air Toxics in Florida." Social Science Research 40 (1): 273–86. doi:10.1016/j.ssresearch.2010.08.006. -Ali, Kamar, Mark D. Partridge, and M. Rose Olfert. 2007. "Can Geographically Weighted Regressions Improve Regional Analysis and Policy Making?" International Regional Science Review 30 (3): 300–329. doi:10.1177/0160017607301609. +* Ali, Kamar, Mark D. Partridge, and M. Rose Olfert. 2007. "Can Geographically Weighted Regressions Improve Regional Analysis and Policy Making?" International Regional Science Review 30 (3): 300–329. doi:10.1177/0160017607301609. -Lu, Binbin, Martin Charlton, and A. Stewart Fotheringhama. 2011. "Geographically Weighted Regression Using a Non-Euclidean Distance Metric with a Study on London House Price Data." Procedia Environmental Sciences, Spatial Statistics 2011: Mapping Global Change, 7: 92–97. doi:10.1016/j.proenv.2011.07.017. +* Lu, Binbin, Martin Charlton, and A. Stewart Fotheringhama. 2011. "Geographically Weighted Regression Using a Non-Euclidean Distance Metric with a Study on London House Price Data." Procedia Environmental Sciences, Spatial Statistics 2011: Mapping Global Change, 7: 92–97. doi:10.1016/j.proenv.2011.07.017.