arcgis10回归分析教程.doc

Analyzing 911 response data using Regression This tutorial demonstrates how regression analysis has been implemented in ArcGIS, and explores some of the special considerations you’ll want to think about whenever you use regression with spatial data. Regression analysis allows you to model, examine, and explore spatial relationships, to better understand the factors behind observed spatial patterns, and to predict outcomes based on that understanding. Ordinary Least Squares regression (OLS) is a global regression method. Geographically Weighted Regression (GWR) is a local, spatial, regression method that allows the relationships you are modeling to vary across the study area. Both of these are located in the Spatial Statistics Tools - Modeling Spatial Relationships toolset: Before executing the tools and examining the results, let’s review some terminology: ? Dependent variable (Y): what you are trying to model or predict (residential burglary incidents, for example). ? Explanatory variables (X): variables you believe influence or help explain the dependent variable (like: income, the number of vandalism incidents, or households). ? Coefficients (β): values, computed by the regression tool, reflecting the relationship and strength of each explanatory variable to the dependent variable. ? Residuals (ε): the portion of the dependent variable that isn’t explained by the model; the model under and over predictions. The sign (+/-) associated with the coefficient (one for each explanatory variable) tells you whether the relationship is positive or negative. If you were modeling residential burglary and obtain a negative coefficient for the Income variable, for example, it would mean that as median incomes in a neighborhood go up, the number of residential burglaries goes down. Output from regression analysis can be a little overwhelming at first. It includes diagnostics and model performance indicators. All of these numbers should seem much less daunting once you complete