Latest Contributions by LaurenRosenshein

Hi Arnau, My first guess as far as your NULL output is the fact that when the result of a computation is infinity or undefined, the result for nonshapefiles will be Null; for shapefiles the result will be -DBL_MAX = -1.7976931348623158e+308.  If your output is a featureclass, and the result is infinity, then the NULL would make sense.  The real question then becomes "Why are you getting results of Infinity".  One thing to keep in mind is that the data you are working with (i.e. continuous surfaces created from rasters, where presumably you have many neighboring points with identical or extremely similar values), you are very likely dealing with issues of local multicollinearity .  The type of data that you are working with is one clue that you have multicollinearity, and another clue is the Condition Numbers that are part of the GWR output.  In general, a condition number over 30 indicates issues with multicollinearity...and from looking at your images it would appear that many of your condition numbers are in the hundreds, some closer to the thousands.  This type of local multicollinearity indicates that your results are unstable.  To learn more about these issues, see the conceptual documentation on GWR, found here: How GWR Works .  One potential way to deal with this type of multicollinearity is to increase the cell size of your raster, which might have the effect of increasing the variation in both your dependent and independent variables.  The help states some additional methods: Try creating a thematic map for each explanatory variable. If the map reveals spatial clustering of identical values, consider removing those variables from the model or combining those variables with other explanatory variables to increase value variation. If, for example, you are modeling home values and have variables for both bedrooms and bathrooms, you may want to combine these to increase value variation or represent them as bathroom/bedroom square footage. Avoid using spatial regime dummy/binary variables, spatially clustering categorical/nominal variables, or variables with very few possible values when constructing GWR models. Hopefully this will help point you in the right direction.  The online documentation for GWR is a great resource, and may help you solve some of your problems! Lauren Rosenshein Geoprocessing Product Engineer Spatial Statistics ... View more

And don�??t forget�?� The most important thing to remember when you are going through these steps is that the goal of your analysis is to understand your data and use that understanding to contribute to solving problems and answering questions.  I am not going to lie�?�you may try a number of models, with and without transformed variables, explore several small study areas, analyze your coefficient surfaces, and still not find a good OLS model (again, I speak from experience!).  But, and this is a big but, you will still be contributing to the body of knowledge on the phenomenon that you are modeling!  If the model that you hypothesized would be a great predictor is not significant at all, that is incredibly important information.  If one of the variables that you thought would be important has a positive relationship in some areas and a negative relationship in other areas, that is important information.  It is true that you may ultimately want to move on to additional tools that will do other types of regression analysis (perhaps one that does not involve linear models, perhaps a spatial autoregressive model, the possibilities are endless).  The work that you do here, however, trying to find a good model using OLS, and using GWR to understand your data and improve your model, is valuable.  And speaking from experience�?�just writing up all of the work that you do and the valuable information that you have uncovered along the way will be more than enough to fill a thesis with meaningful content!  All the best, Lauren Rosenshein Geoprocessing Product Engineer ESRI | Redlands, CA ... View more