... From: Gedas Vaitkus To: Sent: Monday, June 05, 2000 9:40 AM Subject: Re: GEOSTATS: philosophical question ...
Message 1 of 1
, Jun 5, 2000
----- Original Message -----
From: Gedas Vaitkus <seabird@...>
Sent: Monday, June 05, 2000 9:40 AM
Subject: Re: GEOSTATS: philosophical question
> Hi there,
> A good question, really. I was always taught, that GIS is kind of a
> model of the reality, but the more I work in GIS, the more I understand,
> that the overall quality and "performance" of a GIS-based model first of
> depends on it's "resolution" (=scale of data capture), the quality of
> information source (there are many maps of insufficient quality, from
> GIS data is produced), as well as topological quality of GIS data
> data" has almost no use in GIS analysis). Therefore, in most cases one
> admit, that the map he produces in a GIS is just a nice picture.
> When we talk about "a model", we have in mind "modelling". Modelling on a
> typical GIS data is rather limited (one can cut, overlay certain types of
> objects, measure distances, create buffers, query certain types of
> data/objects, etc.). To be able to perform real modelling on spatial data,
> one needs to have a regularly spaced attribute data set related to
> type of spatially referenced "geodetic markers", which usually are points
> polygons. In fact, here we are talking about a collection of grids, made
> from geostatistically processed raw data. Therefore, we can conclude here,
> that geostatistical approach is the best for data analysis and modelling,
> whereas GIS approach provides strong basis for cartographic presentation
> the data/modelling product combined with base maps of relevant type. And
> this is what most of the professionals do every day. This means a lot of
> work, in fact - a double work.
> For the last 2-3 years I am working on a new concept - a high-resolution
> pseudo-raster in GIS, which includes topologically perfect high-resolution
> coverage in GIS, which allows to link and perform any kind of digital
> manipulation on external datasets, produced not only from geostatistically
> processed raw data, but also from imagery and even "standard" GIS data.
> works as a large collection of "normal" geostatistical grids under a
> standard "cover" of GIS coverages. Working this way, if resolution of the
> pseudo-raster coverage is high enough (here you need lots of computing
> on powerful hardware), I get rid at least of problems, related with GIS
> topology, spatial resolution and do not bother about limitations of
> mathematical modelling on my data. The only thind to do is get high
> data (both field sampling and GIS). The best GIS environment (for me) to
> this is MapInfo Pro.
> You can see some earlier examples of this approach on the BaltSAS web page
> Gedas Vaitkus
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