Crandi

Exploratory Regression Spatial Statistics Arcgis Pro Documentation

Leo Migdal
-
exploratory regression spatial statistics arcgis pro documentation

The feature class or feature layer containing the dependent and candidate explanatory variables to analyze. The numeric field containing the observed values you want to model using OLS. A list of fields to try as OLS model explanatory variables. A file containing spatial weights that define the spatial relationships among your input features. This file is used to assess spatial autocorrelation among regression residuals. You can use the Generate Spatial Weights Matrix File tool to create this.

When you do not provide a spatial weights matrix file, residuals are assessed for spatial autocorrelation based on each feature's 8 nearest neighbors. Note: The spatial weights matrix file is only used to analyze spatial structure in model residuals; it is not used to build or to calibrate any of the OLS models. Finding a properly specified OLS model can be difficult, especially when there are lots of potential explanatory variables you think might be important contributing factors to the variable you are trying to model (your... The Exploratory Regression tool can help. It is a data mining tool that will try all possible combinations of explanatory variables to see which models pass all of the necessary OLS diagnostics. By evaluating all possible combinations of the candidate explanatory variables, you greatly increase your chances of finding the best model to solve your problem or answer your question.

While Exploratory Regression is similar to Stepwise Regression (found in many statistical software packages), rather than only looking for models with high Adjusted R2 values, Exploratory Regression looks for models that meet all of... When you run the Exploratory Regression tool, you specify a minimum and maximum number of explanatory variables each model should contain, along with threshold criteria for Adjusted R2, coefficient p-values, Variance Inflation Factor (VIF)... Exploratory Regression runs OLS on every possible combination of the Candidate Explanatory Variables parameter values for models with at least the Minimum Number of Explanatory Variables parameter value and not more than the Maximum... Each model it tries is assessed against your Search Criteria parameter value. When it finds a model: It then runs the Spatial Autocorrelation (Global Moran’s I) tool on that model’s residuals.

If the spatial autocorrelation p-value is also larger than you specified in the tool’s search criteria (Minimum Acceptable Spatial Autocorrelation p-value parameter value), the model is listed as a passing model. The Exploratory Regression tool will also test regression residuals using the Spatial Autocorrelation tool for models with the three highest Adjusted R2 results. Models listed in the passing model section meet your specified search criteria. If you take the default values for the Maximum Coefficient p value Cutoff, Maximum VIF Value Cutoff, Minimum Acceptable Jarque Bera p value, and Minimum Acceptable Spatial Autocorrelation p value parameter values, your passing... A properly specified OLS model has the following properties: When you specify an Output Results Table parameter value, models that meet your Maximum VIF Value Cutoff parameter value and for which all explanatory variables meet the Maximum Coefficient p value Cutoff parameter value...

This table is helpful when you want to examine more than just those models included in the text report file. When you run the Exploratory Regression tool, the primary output is a report. The report is written as geoprocessing messages while the tool runs and can also be accessed from the project geoprocessing history. You can also output a table to help you further investigate the models that have been tested. One purpose of the report is to help you determine whether the candidate explanatory variables yield any properly specified OLS models. In the event that no models meet all of the criteria you specified when you launched the Exploratory Regression tool, the output will still reveal which variables are consistent predictors and help you determine...

Strategies for addressing problems associated with each of the diagnostics are provided in What they don't tell you about regression analysis and Regression analysis basics (see Common regression problems, consequences, and solutions). For more information about how to determine whether you have a properly specified OLS model, see Regression analysis basics. The Exploratory Regression tool report has five sections. Each section is described below. The first set of summaries in the output report is grouped by the number of explanatory variables in the tested models. If you specify 1 for the Minimum Number of Explanatory Variables parameter, and 5 for the Maximum Number of Explanatory Variables parameter, you will have five summary sections.

Each section lists the three models with the highest adjusted R2 values and all of the passing models. Each summary section also includes the diagnostic values for each listed model: corrected Akaike Information Criteria—AICc, Jarque-Bera p-value—JB, Koenker’s studentized Breusch-Pagan p-value—K(BP), the largest Variance Inflation Factor—VIF, and a measure of residual Spatial Autocorrelation... These summaries give you an estimate of how well your models are predicting (Adj R2), and whether any models pass all of the diagnostic criteria you specified. If you accepted all of the default search criteria (Minimum Acceptable Adj R Squared, Maximum Coefficient p-value Cutoff, Maximum VIF Value Cutoff, Minimum Acceptable Jarque Bera p-value, and Minimum Acceptable Spatial Autocorrelation p-value parameters),... If there aren’t any passing models, the rest of the output report still provides useful information about variable relationships and can help you make decisions about how to move forward. The Exploratory Regression Global Summary section is an important place to start, especially if you haven't found any passing models, because it shows you why none of the models are passing.

This section lists the five diagnostic tests and the percentage of models that passed each of those tests. If you don’t have any passing models, this summary can help you determine which diagnostic test is causing issues. The Spatial Statistics toolbox provides effective tools for quantifying spatial patterns. Using the Hot Spot Analysis tool, for example, you can ask questions like these: Each of the questions above asks "where?" The next logical question for the types of analyses above involves "why?" Tools in the Modeling Spatial Relationships toolset help you answer this second set of why questions.

These tools include Ordinary Least Squares (OLS) regression and Geographically Weighted Regression. Regression analysis allows you to model, examine, and explore spatial relationships and can help explain the factors behind observed spatial patterns. You may want to understand why people are persistently dying young in certain regions of the country or what factors contribute to higher than expected rates of diabetes. By modeling spatial relationships, however, regression analysis can also be used for prediction. Modeling the factors that contribute to college graduation rates, for example, enables you to make predictions about upcoming workforce skills and resources. You might also use regression to predict rainfall or air quality in cases where interpolation is insufficient due to a scarcity of monitoring stations (for example, rain gauges are often lacking along mountain ridges...

OLS is the best known of all regression techniques. It is also the proper starting point for all spatial regression analyses. It provides a global model of the variable or process you are trying to understand or predict (early death/rainfall); it creates a single regression equation to represent that process. Geographically weighted regression (GWR) is one of several spatial regression techniques, increasingly used in geography and other disciplines. GWR provides a local model of the variable or process you are trying to understand/predict by fitting a regression equation to every feature in the dataset. When used properly, these methods provide powerful and reliable statistics for examining and estimating linear relationships.

The input features containing fields of the explanatory and dependent variables that will be used in a prediction model. The input fields of the explanatory and dependent variables that will be used in a prediction model. The output features that will contain fields of the spatial components that can be used as additional explanatory variables in a prediction model. Specifies whether all fields will be copied from the input features to the output feature class. A list of input SWM files (.swm) that will be used as candidates for the SWM that will be used to create the spatial component explanatory variables. If no files are provided, the tool will test 28 different neighborhoods.

The Spatial Statistics toolbox contains statistical tools for analyzing spatial distributions, patterns, processes, and relationships. While there may be similarities between spatial and nonspatial (traditional) statistics in terms of concepts and objectives, spatial statistics are unique in that they were developed specifically for use with geographic data. Unlike traditional nonspatial statistical methods, they incorporate space (proximity, area, connectivity, and/or other spatial relationships) directly into their mathematics. The tools in the Spatial Statistics toolbox allow you to summarize the salient characteristics of a spatial distribution (determine the mean center or overarching directional trend, for example), identify statistically significant spatial clusters (hot... In addition, for those tools written with Python, the source code is available for you to learn from, and modify, extend, and share these and other analysis tools with others. When using shapefiles, keep in mind that they cannot store null values.

Tools or other procedures that create shapefiles from nonshapefile inputs may store or interpret null values as zero. In some cases, nulls are stored as very large negative values in shapefiles. This can lead to unexpected results. See Geoprocessing considerations for shapefile output for more information. These tools evaluate whether features, or the values associated with features, form a clustered, dispersed, or random spatial pattern. These tools assess the sensitivity of an analysis to different types of uncertainty by comparing the original analysis results to results from simulated data.

The Exploratory Regression tool evaluates all possible combinations of the input candidate explanatory variables, looking for OLS models that best explain the dependent variable within the context of user-specified criteria. You can access the results of this tool (including the optional report file) from the Results window. If you disable background processing, results will also be written to the Progress dialog box. Learn more about how Exploratory Regression works The primary output for this tool is a report file which is written to the Results window. Right-clicking on the Messages entry in the Results window and selecting View will display the Exploratory Regression summary report in a Message dialog box.

This tool will optionally create a text file report summarizing results. This report file will be added to the table of contents (TOC) and may be viewed in ArcMap by right-clicking on it and selecting Open.

People Also Search

The Feature Class Or Feature Layer Containing The Dependent And

The feature class or feature layer containing the dependent and candidate explanatory variables to analyze. The numeric field containing the observed values you want to model using OLS. A list of fields to try as OLS model explanatory variables. A file containing spatial weights that define the spatial relationships among your input features. This file is used to assess spatial autocorrelation amo...

When You Do Not Provide A Spatial Weights Matrix File,

When you do not provide a spatial weights matrix file, residuals are assessed for spatial autocorrelation based on each feature's 8 nearest neighbors. Note: The spatial weights matrix file is only used to analyze spatial structure in model residuals; it is not used to build or to calibrate any of the OLS models. Finding a properly specified OLS model can be difficult, especially when there are lot...

While Exploratory Regression Is Similar To Stepwise Regression (found In

While Exploratory Regression is similar to Stepwise Regression (found in many statistical software packages), rather than only looking for models with high Adjusted R2 values, Exploratory Regression looks for models that meet all of... When you run the Exploratory Regression tool, you specify a minimum and maximum number of explanatory variables each model should contain, along with threshold crit...

If The Spatial Autocorrelation P-value Is Also Larger Than You

If the spatial autocorrelation p-value is also larger than you specified in the tool’s search criteria (Minimum Acceptable Spatial Autocorrelation p-value parameter value), the model is listed as a passing model. The Exploratory Regression tool will also test regression residuals using the Spatial Autocorrelation tool for models with the three highest Adjusted R2 results. Models listed in the pass...

This Table Is Helpful When You Want To Examine More

This table is helpful when you want to examine more than just those models included in the text report file. When you run the Exploratory Regression tool, the primary output is a report. The report is written as geoprocessing messages while the tool runs and can also be accessed from the project geoprocessing history. You can also output a table to help you further investigate the models that have...