Re: [Openspace] Digest for openspace-list@googlegroups.com - 1 update in 1 topic

[Robert Schwartz]

Hi Marshall,

I enjoyed your post. When and where can I sign up for your virtual course. I'm a practitioner of GWR but a bit out of practice since ArcGIS Pro includes now GWR, hot spot analysis, etc. I've tipped--toed into R but gave up.

Best,

Bob Schwartz

Mount Holyoke College

On 10/28/2021 6:59 PM, openspa...@googlegroups.com wrote:

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• Spatial regression when the y variable is point data and some of the x values are polygons - 1 Update

Spatial regression when the y variable is point data and some of the x values are polygons

Marshall Feldman <marshf...@gmail.com>: Oct 28 12:21PM -0400 Dear Stephen,   I don't know who your audience is or your purposes for making these estimates.   With these caveats aside, I'd advise starting with the simplest estimates you can. I suspect in your case this would be assigning polygon values to each business and then running a logit/probit estimate. Even then you'd have to account for spatial autocorrelation, but I think even the importance of this would depend on the sizes of your polygons and the spatial pattern of your businesses.   You can also get carried away with resampling methods and the like, largely because there may be no analytical solutions for your estimating statistics. But ask yourself what difference this would make.   Even commonly used OLS estimators are "BLUE" (Best Linear Unbiased Estimators), and the preference for linearity is due to a combination of analytical tractability and limitations on 20th-century computing power. In other words, mathematically arbitrary, pragmatic concessions.   In my experience, such considerations as seeking the best possible estimator is often a case of the juice not being worth the squeeze: i.e., the substantive interpretation of the more complicated results does not differ from the simple, but mathematically flawed, results. This is especially true if your intended audience is unfamiliar with the math and you have no understandable way to explain your methods. With logit models one can display logistic curves, but other methods may have nothing similar.   Additionally, with logit models, I most often have found little difference between them and simpler methods. I always run OLS or GLS models first; typically the substantive interpretation of the OLS model results are identical to the GLS and MLE-logit models' results. If not, then this is another, methodological finding regarding under what circumstances the simpler-but-wrong method leads to substantively wrong results.   I'm not saying don't attempt something more complicated: just start with baby steps. If you go further, I think Miguel's suggestions about point processes is a good one.   But if you go to such trouble, be cautious with R. Years ago I estimated a GLM in S but was suspicious of the results: they were too close to the OLS estimates. So I took a textbook GLM model and estimated it. The results differed from the textbook's. Thankfully, S (and R) allows access to the source code, and I by inspecting it I found a bug. After fixing it, both the textbook model and my original were correct. I'm currently working to modify an existing R package to analyze timeseries data. You may have to do something similar.   I'd add that, IIRC, David Birch found the half-life of small businesses to be about five years. There are so many considerations like this (e.g., spatio-temporal status of the macro economy, firm niches in industrial sectors, etc.) that you may run out of degrees of freedom by the time you take all relevant considerations into account. Starting with simpler models would reveal this before you waste time with more complex modeling strategies.   Good luck!   Marshall Feldman Emeritus Professor of Urban Studies and Labor Research The University of Rhode Island     On 10/26/21 3:15 PM, Stephen Clark wrote:

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