Lecture 10: An Overview of Spatial Database
Development: the project development approach
Introduction
This course is designed around three blocks of material: 1) database design; 2) GIS
database development; 3) GIS analysis and display. This lecture marks the beginning of
the second block of material which will concentrate on data acquisition, processing, and
integration. As an introduction to database development, today we will discuss the GIS
Project Development Approach (PDA). PDA is a formalized process aimed at
developing GIS projects. The three components of the PDA are
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Determining Project Objectives
Database Design
Database Development
Today’s lecture will look at each of these components but concentrate on database
development, providing an overview of the next 5 weeks.
Determining Project Objectives
Before developing a GIS application, the objective, or problem that the GIS will address
must be determined. Although selecting an objective seems like a no-brainer, GIS
projects often fail because this step is either skipped or mismanaged. This is especially
true if there are multiple opinions, or if the project is highly unstructured.
There are a number of methods available to help in determining project objectives. We
will look at two examples: Rich Picture, and Root
Definitions. The Rich Picture method uses a schematic
graphic of the issues necessary to determine a project
objective. The schematics include defined graphic
conventions to represent areas of conflict (crossed
swords), outside viewpoints (eyeballs), both official and
unofficial positions (a variety of text balloons), etc.
These schematics allow the presentation of the main
components of a problem, as well the interactions
between the main players. Rich pictures can be drawn
by individuals or by groups. Rich pictures tend to
represent a single view but at the same time allow for
multiple viewpoints. In the example to the left, the rich
picture presents a variety of problems, interactions, and
views of a proposal for a real estate GIS.
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Root definitions involve multiple statements submitted, negotiated, and edited until there
is a single, agreed upon statement. Sticking with the real estate example presented above,
the root definition of a real estate agent might be expressed in the following statement:
The GIS is a system to help maximize home sales; the root definition of the home buyer
as: The GIS is a system to help identify and rank possible homes; and the negotiated,
compromise root definition: The GIS is a system that identifies homes for sale that meet
the requirements of the buyers.
In addition to these, there are a number of other techniques, structures, and software tools
that can help identify the objective of a project.
Database Design
The second component of the GIS Project Development Approach is database design.
Having covered this topic ad nauseam during the first 9 lectures, we will pause only
briefly here to discuss design issues specific to the PDA.
Successful database design will create a
structure that allows the project
objective/s to be met. Given that the
combinations of objectives and available
data sets are unique, database design will
nearly always be unique as well. For
example, database design for projects
dealing with Forestry and Administrative
Boundaries are very different in
structure and components. Each is
designed to answer specific questions,
using specific data and specific analytic
techniques. This is not to say that these
designs are useful only within the
projects they were created for. They can
be used as templates for future products,
edited and changed to become new
designs suited to new, unique projects.
In addition to the project objective/s,
essential issues to consider when
beginning the design process are project constraints and nature of the final product.
Understanding what the final product will look like (analog/digital, journal article, web
site, etc.) will have a significant impact on project design. Constraints on the project are
also important. For example, data constraints may mean that your data is not adequate to
meet objectives; you may not have a large enough budget to support the design; the
people you’ve hired may not be trained adequately to carryout the development of the
design; etc.
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Database Development
Database development constitutes the majority of time and money spent on most GIS
projects. Because of its importance, the next several lectures will cover a variety of
database development topics, including:
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Data inventory
Data acquisition/automation
Data integration
Data transformation
Data conversion
As we do a quick survey of each of these today, you will notice that there is a lot of
overlap between these topics. For instance, data transformation will appear in
discussions of inventory and integration, as will data conversion. This indicates that
database development is not a strictly linear process, but goes forward and backwards in
order to develop a database that matches the objectives of a project.
Data Inventory
The data used in a spatial database can be both varied and extensive; knowing what you
have and don’t have is an important first step in database development. Data for a project
can range from physical to
analog to digital; from maps to
text to remotely sensed imagery;
from GIS to CAD to GPS; from
1:100000 to 1:500 to 1” = 1 mile;
from DMS to Conic to UTM;
from good to bad to indifferent.
The importance of a data
inventory cannot be over
estimated. Without an inventory
essential data could be
overlooked, duplicated, or used
inappropriately. Data inventories
should include information
about:
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Source
Format
Scale
Resolution
Projection
Automation type
Level of integration
Maintenance
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Data Acquisition/Automation
Data automation is the process of converting non-digital data to digital data. Automation
uses a variety of technologies, including digitizing tablets, scanners, GPS, and total
stations (below) to capture data directly or to convert data from analog to digital formats.
Traditionally automating data was the most common form of data acquisition and
dominated spatial database development. As readily available spatial datasets become
more widely available it has become less common but remains an important aspect of
project development. For students learning about GIS it is particularly valuable as a
pedagogic device for understanding vector data structures in a GIS. Beginning next week
in lab, you will be automating data in maps to GIS layers in a geodatabase.
Digitizing Tablet
Large Format Scanner
Using a GPS to Collect Data
Using a Total Station to Collect Data
Integration
Perhaps the main reason that GIS exist is their
ability to combine different datasets for analysis and
display. Before this can happen, integrating the
data layers is necessary. This integration takes a
variety of forms the most important of which are
geometry, attributes, projection, and format. Each
is important for analysis; in fact spatial analysis will
fail if one of these lacks integration. The easiest
way to describe this is by taking a quick look at
geometric, or vertical integration. Vertical
integration is registering features from different
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themes, but with common boundaries, to a common location. In the graphic above, state
boundaries do not match county boundaries. Using these data sets in an analysis of
shoreline development would create problems because each layer would produce a
different shoreline and thereby a different analytical outcome. In the same way attribute
integration is about making sure that attributes in different data sets, representing the
same information, are defined in the same way and that the values populating these
attributes come from the same domain.
Data Transformation
This aspect of database development
concerns projective transformation of
spatial data. That is, the
transformation of spherical data to a
flat surface. This process will distort
spatial data in terms of shape, area
distance, or direction. As seen in the
image to the left, different
projections produce different
distortions. Each has its own
strengths and weaknesses with some
being better suited to particular
analyses than others. Spatial analysis
should not and often cannot be
carried out on data in different projections. Because of this, it is important that spatial
data be normalized in terms of projective transformation.
Data Conversion
The final topic in our survey of database development is data conversion. This is the
process whereby data in one file format is converted to another file format, usually one
more suited to use in a particular GIS. Although many GIS software applications will
read data in other formats, this often limits the capabilities of the software for data
management or analysis. For example, ArcGIS will read AutoCAD files but the structure
of these files limits them in key areas such as topological awareness, data management,
and analysis. For these reasons, it is advisable to convert all data to a format that is fully
functional in your GIS application software.
Summary
This lecture used the components of the PDA to provide an introduction to the GIS
Project Development Approach. It concentrated on database development as an overview
of the material that will be covered in the coming weeks.
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