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95Re: GEOSTATS: large dataset in geographic coordinates

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  • Alok K. Bohara
    Feb 11, 1997
    • 0 Attachment
      Robert,

      I would be interested in receiving them too. Thanks.

      Alok Bohara
      Department of Economics
      University of New Mexico
      Albuquerque, NM 87131



      On 11 Feb 1997, Robert K. Pace wrote:

      > Date: 11 Feb 97 02:37:09 EST
      > From: Robert K. Pace <73132.1000@...>
      > To: GEOSTATS <ai-geostats@...>
      > Subject: Re: GEOSTATS: large dataset in geographic coordinates
      >
      > Tim,
      >
      > I have been running some large spatial autoregressions of up to 73,000
      > observations using maximum likelihood. I would be happy to send you some of my
      > forthcoming articles on this topic (many with Ron Barry). These also look at
      > kriging with many observations.
      >
      > You often don't need to know all the distances, just the distances to the
      > observations which have a direct effect upon the observation itself. Why spend
      > your computational time looking at distances to observations 100 miles away if
      > all that matters occurs within 1 mile? I have one program in Matlab which can
      > come up with a spatial weight matrix for a 73,000 observation dataset overnight.
      > I suspect the state-of-the-art in this regard is Anselin and Smirnov's (?)
      > linked list method which they published in the Journal of Regional Science in
      > 1996. All of these methods only store the information of interest, which greatly
      > reduces storage requirements.
      >
      > Many multivariate techniques have spatial variants. I am most familiar with
      > regression, if that suits your tastes.
      >
      > Kelley Pace
      > U of Alaska
      >
      > $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
      > From: "Timothy H. Keitt" <tkeitt@...>
      > To: ai-geostats@...
      > Subject: GEOSTATS: large dataset in geographic coordinates
      > Sender: owner-ai-geostats@...
      > Precedence: bulk
      > X-Administrivia-to: majordomo@...
      > X-comment: send subscribe/unsubscribe requests to
      > ai-geostats-request@...
      >
      > Hi,
      >
      > I would like some advise on tools for doing spatial analysis on two
      > large, multivariate spatial datasets. (I've checked several of the
      > packages listed in the ai-geostats home page, but its not clear if they
      > will do what I want.) Both of the datasets are in the form of text
      > files and the spatial locations are given in geographic (lat-lon)
      > coordinates. There are more than 3,000 points in each set and the
      > coverage is most of North America. I would like to do an analysis of
      > spatial autocorrelation on both datasets, and an analysis of their
      > cross-correlation, i.e., to test the hypothesis that one of the
      > datasets is influencing the other.
      >
      > The main stumbling block has been calculating the distances among the
      > lat-lon coordinate pairs. I have been using a combination of PERL and
      > the "geod" program from the PROJ.4 distribution. Unfortunately,
      > "geod" is written in such a way that it is extremely difficult to call
      > repeatedly from within PERL. (If someone could provide documentation
      > for the PROJ.4 library routines, I would consider encapsulating them
      > in a perl module.) I can't just dump all the pair-wise comparisons
      > and then run them through "geod" because it would require at least 2GB
      > to store the intermediate data. (Probably more, I ran out of
      > memory/disk space long before it finished.)
      >
      > So here are a couple of questions:
      >
      > 1) If I only need +/-1km precision in my distances, is there an
      > alternative to PROJ.4, i.e., a simple formula for calculating the
      > geodesic?
      >
      > 2) Can I transform the data points into some Cartesian coordinate
      > system and then use simple linear distances? PROJ.4 has many planar
      > projections, but its not clear to me that distances wouldn't become
      > distorted over an area the size of North America.
      >
      > 3) Are there any (free Unix ;-) geostats programs that work in
      > geographic coordinates and can process large data sets?
      >
      > Finally, are there standard methods to test for spatial dependence of
      > one dataset on another?
      >
      > Thanks,
      > Tim
      >
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