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Environmental Systems Research Institute, Inc.
Modeling Our World
The ESRI Guide to Geodatabase Design
ISBN 1-879102-62-5
Preface
All geographic information systems (GIS) are built
using formal models that describe how things are
located in space. A formal model is an abstract and
well-defined system of concepts. It defines the
vocabulary that we can use to describe and reason
about things. A geographic data model defines the
vocabulary for describing and reasoning about the
things that are located on the earth. Geographic data
models serve as the foundation on which all
geographic information systems are built.
We are all familiar with one model for geographic
information—the map. A map is a scale model of
reality that we build, using a set of conventions and
rules (for example, map projections, line symbols,
text). Once we construct a map, we can use it to
answer questions about the reality it represents. For
example, how far is it from Los Angeles to San
Diego? Or, what cities lie along the Mississippi River?
The map model also serves as a tool for
communicating facts about geography visually: Is the
terrain rough? Which way is north? In fact, when we
see a map, we often understand things that might not
even occur to us as specific questions.
Maps work because we know the “rules” of
conventional map reading: blue lines are rivers,
North is toward the top of the page, and so on. In a
similar way, geographic data models define their
own set of concepts and relationships, which must
be understood before you can expect to create or
interpret your own data model. These concepts
relate to how you can represent geographic
information in a computer system, rather than, as in
the map example, on paper.
In Modeling Our World, Michael Zeiler has written
an excellent primer for understanding the various
models used to represent geographic information in
ArcInfo™ 8 software. He presents, using
straightforward text and excellent illustrations, the
concepts and vocabulary employed in the design,
implementation, and use of the ArcInfo 8 geographic
database. In addition to explaining the ArcInfo data
model (objects, features, surfaces, networks, images,
and so forth) in detail, Michael also provides good
insight into how to use this framework to design
useful information models that fit your particular
needs.
This book serves a variety of different purposes. For
the geographer or scientist, it defines a conceptual
context for representing geographic information. For
the GIS specialist, it serves as a guidebook in
designing and using geographic databases. Finally, it
introduces database concepts to a geographic
audience, and geographic concepts to the database
specialist.
ArcInfo 8 defines a unified framework for
representing geographic information in a database.
Several different generic data models are supported
within this framework:
• cell-based or raster representation
• object-based or feature-based representation
• network or graph-element representation
• finite-element or TIN representation
Each of these generic models has its own vocabulary
used to define and reason about geographic
information. When we decide to represent roads,
rivers, terrain, or any sort of phenomena in a GIS,
we need to decide exactly how we define
information in terms of these generic models. As
chapter 1 points out, there are many ways that
information can be modeled in a GIS. The
representation you choose for the data model will
affect how you sample and measure geographic
information, how you display it visually, and which
relationships between elements can be represented,
as well as query and analysis operations that can be
applied to the information.
Some have asserted that we should hide
representational models for geographic information
(features, geometry, rasters, surfaces, and so on)
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
