Loading ...
Sorry, an error occurred while loading the content.
 

GEOSTATS: large datasets

Expand Messages
  • Robert K. Pace
    With regard to large datasets, I cannot comment on how to estimate spatial autoregressions in SAS. I have estimated a lattice model in S-PLUS (SAR) using 3,107
    Message 1 of 3 , Jun 4, 1998
      With regard to large datasets, I cannot comment on how to estimate spatial
      autoregressions in SAS. I have estimated a lattice model in S-PLUS (SAR)
      using 3,107 observations. This took around 20 minutes on a 200 Mhz Pentium
      Pro.

      I have published some papers about how to estimate large lattice models - I
      have dealt with as many as 200K+ observations using some of these
      techniques. I currently have two software packages I have written which I
      am distributing for free (with correspondingly little emphasis on GUI).

      I have a Fortran 90 based package with PC executable code. This relies on
      nearest neighbors for specifying the spatial relations. One can choose the
      number of neighbors and the weight given to them. To conduct maximum
      likelihood you need the log of the determinant of the variance-covariance
      matrix or its inverse. This package uses an approximation (which yields
      confidence intervals) for the log-determinant. We have handled matrices as
      large as 1M by 1M using this technique (see Barry and Pace, Linear Algebra
      and its Applications, forthcoming).

      Given the log of the determinant, we estimate via ML the model:
      Y=X*B1+S*X*B2+alpha*S*Y+e, where S is the n by n spatial weight matrix (we
      take the log-determinant of (I-alpha*S)). This corresponds to having
      separately spatially lagged independent and dependent variables. It takes
      under 10 seconds on a Pentium 233 MMX to find the neighbors, estimate the
      log-determinant, and compute the maximum likelihood estimates for the 3,107
      observation dataset. It yields profile likelihoods for the overall model
      and many submodels. There are separate profile likelihoods for the lower
      and upper bounds of the log-determinant and hence the model accounts for
      this source of uncertainty. Hence, one can conduct likelihood ratio tests
      easily.

      Compressed this package with an example and some documentation takes under
      1MB compressed and hence I can email it to whoever wishes to use it.

      I have a second more comprehensive (at least for lattice models) package
      written in Matlab. This does SAR, CAR, the model above, has Delaunay and
      nearest neighbor weight matrices, and simulation routines. This also has
      several example datasets and so forth. This package takes around 30MB and
      cannot be so easily sent. If anyone truly wants it right away, I can burn a
      CDROM and send it. However, I am planning on duplicating this on CDROM and
      sending out some copies at the end-of-the month for those without a
      pressing need.

      Anyone who is interested in receiving these please send a message to
      kpace@... and put "spatial package" as the first two words
      of the subject. I will send you either or both of these toolboxes.

      Kelley Pace
      Real Estate Research Institute
      E.J. Ourso College of Business
      Louisiana State University
      Baton Rouge, LA 70803

      email: kpace@...

      also

      email kelleypace@...

      phone: 504-388-6257
      --
      *To post a message to the list, send it to ai-geostats@....
      *As a general service to list users, please remember to post a summary
      of any useful responses to your questions.
      *To unsubscribe, send email to majordomo@... with no subject and
      "unsubscribe ai-geostats" in the message body.
      DO NOT SEND Subscribe/Unsubscribe requests to the list!
    Your message has been successfully submitted and would be delivered to recipients shortly.