from the users of geographic information systems.
Somehow, these representational concepts are
considered “implementation details.” In this view, a
single real-world thing, such as the Mississippi River,
should be modeled as a single thing within the GIS.
Perhaps, behind the scenes, the system could
automatically use multiple representations for these
real-world things. If you ask “What is upstream?” it
could use a network representation of the river. If
you ask “What is the surface area of the water?” it
could use a polygon feature representation. If you
ask “What area does it drain?” it could use a surface
or terrain representation, and so on. While it may be
desirable to hide these concepts from some
consumers of geographic information, I believe that
a strong understanding of geographic data models
and representations is crucial to the correct design
and use of geographic information systems.
Geographic data models act as the lens or filter
through which we perceive and interpret the infinite
complexity of the real world. It is only in the context
of representations of the Mississippi River, for
example, that we can define specific properties,
behavior, or even its identity as a “thing of interest.”
Understanding geographic data model concepts is
central to knowing how to define and collect
geographic information. It is also crucial for correctly
interpreting the results derived from the analysis of
geographic information. This is similar to the role
that statistics and sampling theory play in the natural
sciences.
For the GIS specialist, this book serves as an
introduction to a new object-relational model for
representing features, spatial relationships between
features, and other thematic relationships. This new
model is significantly richer in its ability to represent
features with associated behavior, relationships, and
properties than the current coverage or shapefile
model. If you are already familiar with coverages,
shapefiles, and database tables, the new model is a
dramatic extension of concepts and capabilities with
which you are already familiar. Our goal in building
the new feature data model has been to move as
much specialized application logic (for example,
maintaining connectivity or relational integrity
between objects) as possible into the scope of the
data model itself. This allows more of the GIS
application to be defined using rules in the data
model, rather than custom application logic written
for each application. For other aspects of the data
model, which may already be familiar to the reader,
the specific jargon and concepts used in ArcInfo 8
(for topics like image data, as an example) are
clearly introduced and defined.
This book also connects the specialized world of
geographic information systems and the broader
world of object-relational databases. ArcInfo now
supports the direct use of standard relational
database technology as an integral part of the GIS.
This introduces some new concepts to the GIS
community. Topics such as transaction models for
simultaneous editing of a shared, seamless database
are described in detail. For the GIS specialist, this
provides a good introduction to standard database
concepts. For the database specialist, this book
serves as a good answer to the question “what is so
special about spatial?”
Working with geographic information systems is fun
for me because it serves to integrate concepts and
ideas from a variety of different disciplines—
geometry and networks from applied mathematics,
sampling and measurement theory from remote
sensing and physics, information modeling and
multiuser database issues from information
technology. In working with GIS, we get to integrate
all of this in a single, useful framework for building
real systems. This book presents that synthesis,
based on our work with ArcInfo 8. I hope you find
this book useful and stimulating as a basis for your
own work in geographic information systems.
Scott Morehouse
Director of Software Development
Environmental Systems Research Institute, Inc.
Redlands, California
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asdw0506072019-06-27挺好的一本书，挺好的
