GIS TECHNOLOGY IN ENVIRONMENTAL MANAGEMENT: a
Brief History, Trends and Probable Future
Joseph K. Berry
Phone: (970) 215-0825 –
Email: jberry@innovativegis.com
Web:
http://www.innovativegis.com/basis
[invited book chapter in Handbook of Global Environmental
Policy and Administration, edited by Soden and Steel, Marcel Dekker, 1999, ISBN: 0-8247-1989-1]
<Click here> for a
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INTRODUCTION
Environmental
management is inherently a spatial endeavor.
Its data are particularly complex as they require two descriptors;
namely the precise location of what is being described, as well as a clear
description of its physical characteristics.
For hundreds of years, explorers produced manually drafted maps which
served to link the “where is what” descriptors. With an emphasis on accurate location of
physical features, early maps helped explorers and navigators chart unexplored
territory.
Today, these early
attributes of maps have evolved from exploratory guides to physical space into
management tools for exploring spatial relationships. This new perspective marks a turning point in
the use of maps, setting the stage for a paradigm shift in environmental
planning and management— from one emphasizing physical descriptions of
geographic space, to one of interpreting mapped data and communicating
spatially-based decision factors. What
has changed is the purpose for which maps are used. Modern mapping systems provide a radically
different approach to addressing complex environmental issues. An understanding of the evolutionary stages
of the new technology, its current expression, and probable trends are
essential for today’s environmental policy-makers and administrators.
EVOLUTIONARY STAGES
Since the
1960's, the decision-making process has become increasingly quantitative, and
mathematical models have become commonplace.
Prior to the computerized map, most spatial analyses were severely
limited by their manual processing procedures.
Geographic information systems (GIS) technology provides the means for
both efficient handling of voluminous data and effective spatial analysis
capabilities (Carter 1989; Coppock and Rhind 1991). From this perspective, GIS is rooted in the
digital nature of the computerized map.
Computer Mapping
The early
1970's saw computer mapping automate
the map drafting process (Brown 1949; McHarg 1969; Steinitz et. al. 1976; Berry
and Ripple, 1994). The points, lines and
areas defining geographic features on a map are represented as an organized set
of X,Y coordinates. These data drive pen
plotters that can rapidly redraw the connections at a variety of colors,
scales, and projections. The map image,
itself, is the focus of this automated cartography.
The pioneering
work during this period established many of the underlying concepts and
procedures of modern GIS technology (Abler et. al. 1971; Muehrcke and Muehrcke
1980; Cuff and Matson 1982; Robertson et. al. 1982). An obvious advantage of computer mapping is
the ability to change a portion of a map and quickly redraft the entire
area. Updates to resource maps, such as
a forest fire burn, which previously took several days, can be done in a few
hours. The less obvious advantage is the
radical change in the format of mapped data— from analog inked lines on paper,
to digital values stored on disk.
Spatial Database Management
During the
early 1980's, the change in format and computer environment of mapped data was
utilized. Spatial database management systems (SDBMS) were developed that
linked computer mapping capabilities with traditional database management
capabilities (Burrough 1987; Sheppard 1991).
In these systems, identification numbers are assigned to each geographic
feature, such as a timber harvest unit or wildlife management parcel. For example, a user is able to point to any
location on a map and instantly retrieve information about that location. Alternatively, a user can specify a set of
conditions, such as a specific vegetation and soil combination, and all
locations meeting the criteria of the geographic search are displayed as a map.
During the
early development of GIS, two alternative data structures for encoding maps
were debated (Maffini 1987; Piwowar 1990; Pueker and Christman 1990). The vector
data model closely mimics the manual drafting process by representing map
features as a set of lines which, in turn, are stored as a series of X,Y
coordinates. An alternative structure,
termed raster, establishes an
imaginary reference grid over a project area, then stores resource information
for each cell in the grid. Early debates
in the GIS community attempted to determine the universally best data
structure. The relative advantages and
disadvantages of both were viewed in a competitive manner that failed to
recognize the overall strengths of a GIS approach encompassing both formats.
By the
mid-1980's, the general consensus within the GIS community was that the nature
of the data and the processing desired determine the appropriate data
structure. This realization of the
duality of mapped data structure had significant impact on geographic
information systems. From one perspective,
maps form sharp boundaries that are best represented as lines. Property ownership, power line right-of-ways,
and road networks are examples where the lines are real and the data are
certain. Other types of maps, such as
soils, ground water flows, and steep slopes, are abstract characterizations of
terrain conditions. The placement of
lines identifying these conditions are subject to judgment, statistical
analysis of field data, and broad classification of continuous spatial
distributions. From this perspective,
the sharp boundary implied by a line is artificial and the data itself is based
on expert opinion or probabilistic estimates.
This era of
rapidly increasing demand for mapped data focused attention on data
availability, accuracy and standards, as well as data structure issues. Hardware vendors continued to improve digitizing
equipment, with manual digitizing tablets giving way to automated scanners at
many GIS facilities. A new industry for
map encoding and database design emerged and a marketplace for the sales of
digital map products emerged. Regional,
national and international organizations began addressing the necessary
standards for digital maps to insure compatibility among systems. This period saw GIS database development move
from being expensed as individual project costs to a corporate investment in a
comprehensive information resource.
GIS Modeling
As the
technology continued its evolution, the emphasis turned from descriptive
“geo-query” searches of existing databases to prescriptive analysis of mapped
data. For the most part, the earlier
eras of GIS concentrated on automating traditional mapping practices. If a user had to repeatedly overlay several
maps on a light-table, an analogous procedure was developed within the
GIS. Similarly, if repeated distance and
bearing calculations were needed, systems were programmed with a mathematical
solution. The result of this effort was
GIS functionality that mimicked the manual procedures in a user's daily
activities. The value of these systems
was the savings gained by automating tedious and repetitive operations.
By the
mid-1980's, the bulk of the geo-query operations were available in most GIS
systems and a comprehensive theory of spatial analysis began to emerge. The dominant feature of this theory is that
spatial information is represented numerically, rather than in analog fashion
as inked lines on a map. These digital
maps are frequently conceptualized as a set of "floating maps" with a
common registration, allowing the computer to "look" down and across
the stack of digital maps (Figure 1).
The spatial relationships of the data can be summarized (database
geo-queries) or mathematically manipulated (analytic processing). Because of the analog nature of traditional
map sheets, manual analytic techniques are limited in their quantitative
processing. Digital representation, on
the other hand, makes a wealth of quantitative (as well as qualitative)
processing possible. The application of
this new modeling theory to environmental management is revolutionary. Its application takes two forms— spatial statistics
and spatial analysis.
Geophysicists
have used spatial statistics for many
years to characterize the geographic distribution, or spatial pattern, of field
data (Ripley 1981; Meyers 1988; Cressie 1991 and 1993; Cressie and Ver Hoef
1993). The statistics describe the
spatial variation in the data, rather than assuming a typical response is
everywhere. For example, field
measurements of snow depth can be made at several plots within a
watershed. Traditionally, these data are
analyzed for a single value (the average depth) to characterize the
watershed. Spatial statistics, on the
other hand, uses both plot locations and the recorded measurements to generate
a map of relative snow-depth throughout the entire watershed.
More recently,
spatial statistics has evolved from descriptive, to predictive, to optimization
models. Precision farming, for example,
uses GIS modeling to investigate the spatial relationships between crop yield
and soil nutrients (
Traditional
“whole-field” management involves a similar analysis, except field averages are
used to derive a single application rate for the entire field. In highly variable fields, most areas receive
either too much or too little fertilizer.
Some farmers (encouraged by the chemical industry) hedge their bets on a
good crop by applying fertilizer at a higher rate in hopes of bringing up the
yield in the nutrient poor areas. The
result can be over-application on more than half the field. Precision farming, on the other hand, uses
“site-specific” management involving a “prescription map” derived by spatial
statistics and variable rate technology.
As a spray rig moves through the field, GPS locates its position on the
prescription map and the injected blend of nutrients is adjusted “on-the-fly.”
Many other
applications, from retail market forecasting to forest management, are using
spatial statistics to relate mapped variables.
The environmental sciences have a rich heritage in the quantitative
expression of their systems. Spatial
statistics provides a new set of tools for explaining spatially induced
variance— variations in geographic space rather than numeric space. From this perspective the floating maps in
Figure 1, represent the spatial distributions of mapped variables. In traditional mathematical terms, each map
is a “variable, “ each location is a “case,” and each map value is a
“measurement.” The GIS provides a
consistent spatial registration of the numbers.
The full impact of this map-ematical treatment of maps is yet to be
determined. The application of such
concepts as spatial correlation, statistical filters, map uncertainty and error
propagation await their translation from other fields.
Spatial analysis, on the other
hand, has a rapidly growing number of current resource and environmental
applications (Ripple 1987; Maguire et. al. 1991a; Goodchild et. al. 1993;
Ripple 1994) . For example, a forest
manager can characterize timber supply by considering the relative skidding and
log-hauling accessibility of harvesting parcels. Wildlife managers can consider such factors
as proximity to roads and relative housing density to map human activity and
incorporate this information into habitat delineation. Landscape planners can generate visual
exposure maps for alternative sites for a proposed facility to sensitive
viewing locations, such as recreational areas and scenic overlooks. Soil scientists can identify areas with high
sediment loading potential based on proximity to streams and intervening
terrain slope, vegetative cover and soil type.
Similarly, groundwater and atmospheric scientists can simulate the
complex movement of a release as it responses to environmental factors
affecting its flow through geographic space.
Just as spatial
statistics has been developed by extending concepts of conventional statistics,
a mathematics supporting spatial analysis has evolved (Uwin 1981; Berry 1987a;
Goodchild 1987; Ripple 1989; Johnson 1990; Maguire et. al. 1991b) . This "map algebra" uses sequential
processing of spatial operators to perform complex map analyses (Berry, 1987b;
Tomlin, 1990). It is similar to
traditional algebra in that primitive operations (e.g., add, subtract,
exponentiate) are logically
sequenced on
variables to form equations.
Figure 2. Percent
Change Map. Algebraic equations, such as
percent change, can be evaluated using entire maps as variables.
However in map
algebra, entire maps composed of thousands or millions of numbers represent the
variables of the spatial equation.
For example,
the change in lead concentrations in an aquifer can be estimated by evaluating
the algebraic expression
%change = ((new value - old value) / old
value) * 100
using the
average values for two time periods.
Map algebra replaces the simple averages with spatially interpolated
maps based on the same sets of field data used to calculate the averages. The %change
equation is evaluated at each map location, resulting in a map percent change
(Figure 2).
Areas of
unusual change can be identified by the standard normal variable (SNV)
expression
SNV = ((new value - %change_average) / %change_standard_deviation) * 100
This normalizes
the map of changes in lead concentration, with areas of statistically unusual
increase having SNV values over 100.
These potentially hazardous areas can be overlaid on demographic maps to
determine the level of environmental risk.
Most of the
traditional mathematical capabilities, plus an extensive set of advanced map
processing operations, are available in modern GIS software. You can add, subtract, multiply, divide,
exponentiate, root, log, cosine, differentiate and even integrate maps. After all, maps in a GIS are just an
organized sets of numbers. However, with
this “map-ematics,” the spatial coincidence and juxtapositioning of values
among and within maps create new operations, such as effective distance,
optimal path routing, visual exposure density and landscape diversity, shape
and pattern.
For example,
distance is traditionally defined as “the shortest straight line between two
points.” Both a ruler (analog tool) and
the Pythagorean theorem (mathematical tool) adhere to this strict
definition. The simple definition of
distance is rarely sufficient for most environmental applications. Often “…between two points” must be expanded
to “…among set of points” to account for proximity, such as buffers around
streams. And “…straight line” needs to
be expanded to “…not necessarily straight lines,” as nothing in the real world
moves in a straight line (even light bends in the atmosphere). In a GIS, the concept of movement replaces
distance by introducing the location of absolute and relative barriers into the
calculations (Muller 1982; Elridge and Jones 1991). An effective butterfly buffer “reaches” out
around a stream capturing an appropriate amount of butterfly habitat (function
of vegetation cover and slope/aspect), instead of simply reaching out a fixed
number of feet regardless of habitat.
Another example
of advanced spatial analysis tools involves landscape analysis. The ability to quantify landscape structure
is a prerequisite to the study of landscape function and change. For this reason considerable emphasis has
been placed on the development of landscape metrics (Turner, 1990; McGarigal
and Marks 1995). Many of these
relationships are derived through analysis of the shape, pattern and
arrangement of landscape elements spatially depicted as patches (individual
polygons), classes of related patches (polygons of the same type/condition),
and entire landscape mosaics (all polygons).
The convexity index compares each patch’s perimeter to its area, with an
increase in perimeter per unit area indicating more irregularly shaped
parcels. The mean proximity index
indicates the average distance between the patches within a class as a measure
of the relative dispersion. The fractal
dimension of a landscape assesses the proportion of edge versus interior of all
patches, summarizing whether the mosaic is primarily composed of simple shapes
(circle or square like) or complex shapes with convoluted, plane-filling
perimeters. These, plus a myriad of
other landscape indices, can be used to track the fragmentation induced by
timber harvesting and relate the changes to impacts on wildlife habitat.
This GIS
modeling “toolbox” is rapidly expanding.
A detailed discussion of all of the statistical and analysis tools is
beyond the scope of this chapter. It
suffices to note that GIS technology is not simply automating traditional
environmental approaches, but radically changing environmental science. It is not just a faster mapper, nor merely an
easier entry to traditional databases.
Its new tools and modeling approach to environmental information combine
to extend record-keeping systems and decision-making models into effective decision
support systems (Parent and Church 1989; Densham 1991; Pereira and Duckstein
1993).
Spatial Reasoning and Dialogue
The 1990's are
building on the cognitive basis, as well as the databases, of current
geographic information systems. GIS is at
a threshold that is pushing beyond mapping, management, and modeling, to spatial reasoning and dialogue. In the past, analysis models have focused on
management options that are technically optimal— the scientific solution. Yet in reality, there is another set of
perspectives that must be considered— the social solution. It is this final sieve of management
alternatives that most often confounds resource and environmental
decision-making. It uses elusive
measures, such as human values, attitudes, beliefs, judgment, trust and
understanding. These are not the usual
quantitative measures amenable to computer algorithms and traditional
decision-making models.
The step from
technically feasible to socially acceptable options is not so much an increase in
scientific and econometric modeling, as it is communication (Calkins 1991;
Epstein 1991; King and Kraemer 1993; Medyckyj-Scott and Hernshaw 1993). Basic to effective communication is
involvement of interested parties throughout the decision-making process. This new participatory environment has two
main elements— consensus building and conflict resolution. Consensus
building involves technically-driven communication and occurs during the
alternative formulation phase. It
involves the resource specialist's translation of the various considerations
identified by a decision team into a spatial model. Once completed, the model is executed under a
wide variety of conditions and the differences in outcome are noted.
From this
perspective, a single map rendering of a environmental plan is not the
objective. It is how the plan changes as
the different scenarios are tried that becomes information for
decision-making. "What if avoidance
of visual exposure is more important than avoidance of steep slopes in siting a
new haul road? Where does the proposed
route change, if at all?" Answers
to such analytic queries focus attention on the effects of differing
perspectives. Often, seemingly divergent
philosophical views result in only slightly different map views. This realization, coupled with active
involvement in the decision-making process, often leads to group consensus.
If consensus is
not obtained, conflict resolution is
necessary. Such socially-driven
communication occurs during the decision formulation phase. It involves the creation of a "conflicts
map" which compares the outcomes from two or more competing uses. Each management parcel is assigned a numeric
code describing the conflict over the location.
A parcel might be identified as ideal for a wildlife preserve, a
campground and a timber harvest. As
these alternatives are mutually exclusive, a single use must be assigned. The assignment, however, involves a holistic
perspective that simultaneously considers the assignments of all other locations
in a project area.
Traditional
scientific approaches are rarely effective in addressing the holistic problem
of conflict resolution. Most are
deterministic models, involve a succession, or cascade, of individual parcel
assignments. The final result is
strongly biased by the ordering of parcel consideration, mathematical
assumptions and the assignment of discrete model parameters. Even if a scientific solution is reached, it
is viewed with suspicion by the layperson.
Modern resource information systems provide an alternative approach
involving human rationalization and tradeoffs.
This process involves statements like, "If you let me harvest this
parcel, I will let you set aside that one as a wildlife preserve." The statement is followed by a persuasive
argument and group discussion. The
dialogue is far from a mathematical optimization, but often closer to an
effective decision. It uses the
information system to focus discussion away from broad philosophical positions,
to a specific project area and its unique distribution of conditions and
potential uses.
THE CURRENT FRONTIER
The elements
for computer mapping and spatial database management are in place, and the
supporting databases are rapidly coming on-line. The emerging concepts and procedures
supporting GIS modeling and spatial reasoning/dialogue are being refined and
extended by the technologists. There are
a growing number of good texts (Star and Estes 1990; Berry 1993; Korte 1993;
Berry 1995b; Douglas 1995) and college courses on GIS technology are becoming
part of most land-related curricula.
What seems to be lacking is a new spatial paradigm among the user
communities. Many are frustrated by the
inherent complexity of the new technology.
Others are confused by new approaches beyond those that simply automate
existing procedures. Fundamental to the
educational renaissance demanded by GIS is a clear understanding of the
questions it can address.
Seven Basic Questions
Seven basic
questions encompassing most GIS applications are identified in Table 1. The questions are progressively ordered from
inventory-related (data) to analysis-related (understanding) as identified by
their function and approach. The most
basic question, "Can you map that?" is where GIS
began over thirty years ago— automated
cartography. A large proportion of
GIS applications still involve the updating and timely output of map
products. As an alternative to a room
full of draftspersons and drafting pens, the digital map has a clear edge. Applications responding to this question are
easily identified in an organization and the "payoffs" in
productivity apparent. Most often, these
mapping applications are restatements of current inventory-related activities.
__________________________________________________________
Table 1. BASIC QUESTIONS GIS CAN ADDRESS
There are seven types of questions addressed by
GIS technology. The first three are
inventory-related; the latter four are analysis-related investigating the
interrelationships among mapped data beyond simple spatial coincidence.
_______________________________________________________
Questions
involving "Where is what?" exploit the linkage between the digital
map and database management technology.
These questions are usually restatements of current practices as
well. They can get a group, however, to
extend their thinking to geographic searches involving coincidence of data they
had not thought possible. The nature and
frequency of this type of question provide valuable insight into system
design. For example, if most
applications require interactive map queries based on a common database from a
disperse set of offices, a centralized GIS provides consistency and control
over the shared data. However, if the
queries are localized and turnaround is less demanding, a distributed GIS might
suffice. The conditions surrounding the
first two questions are the primary determinants of the character and design of
the GIS implemented in an organization.
The remaining questions determine the breadth and sophistication of its
applications. They also pose increasing
demands on the education and computer proficiency of its users.
The third type
of question, "Where has it changed?" involves temporal analysis. These questions mark the transition from inventory-related
data searches to packaging information for generating plans and policies. Such questions usually come from managers and
planners, whereas the previous types of questions support day-to-day operations. A graphic portrayal of changes in geographic
space, whether it is product sales or lead concentrations in well water,
affords a new perspective on existing data.
The concept of "painting" data which is normally viewed as
tables might initially be a bit uncomfortable— it is where GIS evolves from
simply automating current practices to providing new tools.
"What
relationships exit?" questions play heavily on the GIS toolbox of
analytic operations. "Where are the steep areas?", "Can you see
the proposed power plant from over there?", "How far is the town from
the contamination spill?", and "Is vegetation cover more diverse
here, or over there?" are a few examples of this type of question. Whereas the earlier types involved query and
repackaging of base data, spatial relationship questions involve derived
information. Uncovering of these
questions within an organization is a bit like the eternal question— “Did the
chicken or the egg come first?" If
users are unaware of the different things a GIS can do differently, chances are
they are not going to ask it to do anything different. Considerable training and education in
spatial reasoning approaches are needed to fully develop GIS solutions to these
questions. Their solution, however, is
vital to the treatise of the remaining two types of questions.
Suitability
models spring from questions of "Where is it best?" Often these questions are the end products of
planning and are the direct expression of goals and objectives. The problem is that spatial considerations
historically are viewed as input to the decision process— not part of the
"thruput." Potential GIS users
tend to specify the composition (base and derived maps) of "data
sandwiches" (map layers) which adorn the walls during discussion. The idea of using GIS modeling as an active
ingredient in the discussion is totally foreign. Suitability questions usually require the
gentle coaxing of the “visceral visions” locked in the minds of the
decision-makers. They require an
articulation of various interpretations of characteristics and conditions and
how they relate within the context of the decision at hand.
"What
effects what?" questions involve system models— the realm of the
scientist and engineer. In a manner of speaking,
a system model is like an organic chemist's view of a concoction of interacting
substances, whereas a suitability model is analogous to simply a recipe for a
cake. Whereas suitability models tend to
incorporate expert opinion, a system model usually employs the tracking of
"cause and effect" through empirically derived relationships. The primary hurdle in addressing these
applications is the thought that GIS simply provides spatial summaries for input
and colorful maps of model output. The
last 100 years have been spent developing techniques that best aggregate
spatial complexity, such as stratified random sampling and the calculation of
the average to represent a set of field samples. The idea that GIS modeling retains spatial
specificity throughout the analysis process and responds to spatial
autocorrelation of field data is a challenging one.
"What
if...?" questions involve the iterative processing of suitability
or system modeling. For suitability
models, they provide an understanding of different perspectives on a project—
“What if visual impact is the most important consideration, or if road access
is the most important; where would it be best for development?" For system models, they provide an
understanding of uncertain or special conditions— “What if there was a 2-inch
rainstorm, or if the ground was saturated; would the surface runoff require a
larger culvert?"
In determining
what GIS can do, the first impulse is to automate current procedures. Direct translation of these procedures is
sufficient for the first few types of questions. As GIS moves beyond mapping to the
application modeling required addressing the latter questions, attention is
increasingly focused on the considerations embedded in the derivation of the
"final" map. The map itself is
valuable, but the thinking behind its creation provides the real insights for
decision-making. From this perspective,
the model becomes even more useful than the graphic output.
GIS Modeling Approach and Structure
Consider the
simple model outlined in the accompanying figure (Figure 3). It identifies the suitable areas for a
residential development considering basic engineering and aesthetic
factors. Like any other model it is a
generalized statement, or abstraction, of the important considerations in a
real-world situation. It is
representative of one of the most common GIS modeling types— a suitability
model. First, note that the model is
depicted as a flowchart with boxes indicating maps, and lines indicating GIS
processing. It is read from left to
right. For example, the top line tells
us that a map of elevation (ELEV) is used to derive a map of relative steepness
(SLOPE), which in turn, is interpreted for slopes that are better for a campground
(S-PREF).
Figure 3. Development
Suitability Model. Flow chart of GIS
processing determining the best areas for a development as gently sloped, near
roads, near water, with good views of water and a westerly aspect.
Next, note that
the flowchart has been subdivided into compartments by dotted horizontal and
vertical lines. The horizontal lines
identify separate sub-models expressing suitability criteria— the best
locations for the campground are 1) on gently sloped terrain, 2) near existing
roads, 3) near flowing water, 4) with good views of water, and 5) westerly
oriented. The first two criteria reflect
engineering preferences, whereas the latter three identify aesthetic
considerations. The criteria depicted in
the flowchart are linked to a sequence of GIS commands (termed a command macro) which are the domain of the GIS
specialist. The linkage between the
flowchart and the macro is discussed latter; for now concentrate on the model’s
overall structure. The vertical lines
indicate increasing levels of abstraction.
The left-most primary maps section identifies the
base maps needed for the application. In
most instances, this category defines maps of physical features described
through field surveys— elevation, roads and water. They are inventories of the landscape, and
are accepted as fact.
The next group
is termed derived maps. Like
primary maps, they are facts, however these descriptors are difficult to
collect and encode, so the computer is used to derive them. For example, slope can be measured with an
Abney hand level, but it is impractical to collect this information for all of
the 2,500 quarter-hectare locations depicted in the project area. Similarly, the distance to roads can be
measured by a survey crew, but it is just too difficult. Note that these first two levels of model
abstraction are concrete descriptions of the landscape. The accuracy of both primary and derived maps
can be empirically verified simply by taking the maps to the field and
measuring.
The next two levels,
however, are an entirely different matter.
It is at this juncture that GIS modeling is moved from fact to
judgment—from the description of the landscape (fact) to the prescription of a
proposed land use (judgment). The interpreted
maps are the result of assessing landscape factors in terms of an
intended use. This involves assigning a
relative "goodness value" to each map condition. For example, gentle slopes are preferred
locations for campgrounds. However, if
proposed ski trails were under consideration, steeper slopes would be
preferred. It is imperative that a
common goodness scale is used for all of the interpreted maps. Interpreting maps is like a professor's grading
of several exams during an academic term.
Each test (vis. primary or derived map) is graded. As you would expect, some students (vis. map
locations) score well on a particular exam, while others receive low
marks.
The final suitability
map is a composite of the set of interpreted maps, similar to averaging
individual test scores to form an overall semester grade. In the figure, the lower map inset identifies
the best overall scores for locating a development, and is computed as the
simple average of the five individual preference maps. However, what if the concern for good views
(V-PREF map) was considered ten times more important in siting the campground
than the other preferences? The upper
map inset depicts the weighted average of the preference maps showing that the
good locations, under this scenario, are severely cut back to just a few areas
in the western portion of the study area.
But what if gentle slopes (S-PREF map) were considered more
important? Or proximity to water (W-PREF
map)? Where are best locations under
these scenarios? Are there any
consistently good locations?
The ability to
interact with the derivation of a prescriptive map is what distinguishes GIS
modeling from the computer mapping and spatial database management activities
of the earlier eras. Actually, there are
three types of model modifications that can be made— weighting, calibration and
structural. Weighting modifications
affect the combining of the interpreted maps into an overall suitability map,
as described above. Calibration modifications affect the assignment of the individual
"goodness ratings." For
example, a different set of ranges defining slope “goodness” might be assigned,
and its impact on the best locations noted.
Weighting and
calibration simulations are easy and straight forward— edit a model parameter
then resubmit the macro and note the changes in the suitability map. Through repeated model simulation, valuable
insight is gained into the spatial sensitivity of a proposed plan to the
decision criteria. Structural modifications, on the other hand, reflect changes in
model logic by introducing new criteria.
They involve modifications in the structure of the flowchart and
additional programming code to the command macro. For example, a group of decision-makers might
decide that forested areas are better for a development than open terrain. To introduce the new criterion, a new
sequence of primary, derived and interpreted maps must be added to the
"aesthetics" compartment of the model reflecting the group’s preference. It is this dynamic interaction with maps and
the derivation of new perspectives on a plan that characterize spatial
reasoning and dialogue.
GIS IN
By their
nature, all land use plans contain (or imply) a map. The issue is determining "what should go
where," and as noted above there is a lot of thinking that goes into a
final map recommendation (Berry and Berry 1988; Gimblett 1990). One cannot simply geo-query a database for
the recommendation any more than it can arm a survey crew with a "land
use-ometer" to measure the potential throughout a project area. The logic behind a land use model and its
interpretation by different groups are the basic elements leading to an
effective decision. During the
deliberations, an individual map is merely one rendering of the thought
process.
The potential
of "interactive" GIS modeling extends far beyond its technical
implementation. It promises to radically
alter the decision-making environment itself.
A "case study" might help in making this claim. The study uses three separate spatial models
for allocating alternative land uses of conservation, research and residential
development. In the study, GIS modeling
is used in consensus building and conflict resolution to derive the
"best" combination of competing uses of the landscape.
The study takes
place on the western tip of
A map of accessibility
to existing roads and the coastline formed the basis of the Conservation Areas Model. In determining access, the slope of the
intervening terrain is considered. The
“slope-weighted proximity” from the roads and from the coastline was used. In these calculations, areas that appear
geographically near a road may actually be considered inaccessible if there are
steep intervening slopes. For example,
the coastline might be a “stone's throw away” from the road, but if it lands at
the foot of a cliff it is effectively inaccessible for recreation. The two maps of weighted proximity were
combined into an overall map of accessibility.
The final step of the model involved interpreting relative access into
conservation uses (Figure 4). Recreation
was identified for those areas near both roads and the coast. Intermediate access areas were designated for
limited use, such as hiking. Areas
effectively far from roads were designated as preservation areas.
Figure 4.
Conservation Areas Map. Maps of
relative accessibility to roads and the coastline formed the basis for locating
various conservation uses.
Figure 5. Research
Areas Map. Three watersheds were chosen
as research areas as they are relatively large and contain a diversity of landscape
characteristics.
The
characterization of the Research Areas
Model first used an elevation map to identify individual watersheds. The set of all watersheds was narrowed to
just three based on scientists' preferences that they require relatively large
and wholly contained areas for their research (Figure 5). A sub-model used the prevailing current to
identify coastal areas influenced by each of the three terrestrial research
areas.
The Development Areas Model determined the
“best” locations for residential development.
The model structure used is nearly identical to that of the development
suitability model described in the section above. Engineering, aesthetic, and legal factors
were considered. As before, the
engineering and aesthetic considerations were treated independently, as
relative rankings. An overall ranking
was assigned as the weighted average of the five preference factors. Legal
constraints, on the other hand, were treated as critical factors. For example, an area within the 100meter
set-back was considered unacceptable, regardless of its aesthetic or
engineering rankings.
Figure 6 shows
a composite map containing the simple arithmetic average of the five separate
preference maps used to determine development suitability. The constrained and undesirable locations are
shown as white. Note that approximately
half of the land area is ranked as “Acceptable” or better (gradient of darker
tones). In averaging the five preference
maps, all criteria were considered equally important at this step.
Figure 6.
Development Areas Map (Simple Average). The best areas for development
were first determined through equal consideration of the five criteria.
The analysis
was extended to generate a series of weighted suitability maps. Several sets of weights were tried. The group finally decided on
·
view preference times 10 (Most Important)
·
coast proximity times 8
·
road proximity times 3
·
aspect preference times 2, and
·
slope preference times 1 (Least Important).
The resulting
map of the weighted averaging is presented in Figure 7. Note that a smaller portion of the land is
ranked as “Acceptable” or better. Also
note the spatial distribution of these prime areas are localized to distinct
clusters.
Figure 7. Development
Areas Map (Weighted Average). Weighed
averaging of the maps expressing the five criteria narrowed the acceptable
areas for development, reflecting the relative preferences of the group.
The group of decision-makers
were actively involved in development of all three of the individual models—
conservation, research and development.
While looking over the shoulder of the GIS specialist, they saw their
concerns translated into map images.
They discussed whether their assumptions made sense. Debate surrounded the "weights and
calibrations" of the models. They
saw the sensitivity of each model to changes in its parameters. In short, they became involved and understood
the map analysis taking place. The
approach is radically different from viewing a "solution" map with
just a few alternatives developed by a sequestered set of GIS specialists. It enables decision-makers to be just that—
decision-makers, not choice-choosers constrained to a few pre-defined
alternatives. The involvement of
decision-makers in the analysis process contributes to consensus building. At this
stage, the group reached consensus on the three independent land use
possibilities.
The three
analyses, however, determined the best use of the project area considering the
possibilities in a unilateral manner.
What about areas common to two or more of the maps? These areas of conflict are where the
decision-makers need to focus their attention.
Three basic approaches are used in GIS-based conflict resolution— hierarchical dominance, compatible use and
tradeoff. Hierarchical dominance assumes certain land uses are more important
and, therefore, supersede all other potential uses. Compatible
use, on the other hand, identifies harmonious uses and can assign more than
one to a single location. Tradeoff recognizes mutually exclusive
uses and attempts to identify the most appropriate land use for each
location. Effective land use decisions
involve elements of all three of these approaches.
From a map
processing perspective, the hierarchical approach is easily expressed in a
quantitative manner and results in a deterministic solution. Once the political system has identified a
superseding use it is relatively easy to map these areas and simply assign the
dominant use. Similarly, compatible use
is technically easy from a map analysis context, though often difficult from a
policy context. When compatible uses can
be identified, both uses are assigned to all areas with the joint condition.
Most conflict,
however, arises when potential uses for a location are justifiable and
incompatible. In these instances,
quantitative solutions to the allocation of land use are difficult, if not
impossible, to implement. The complex
interaction of the spatial frequency and juxtapositioning of several competing
uses is still most effectively dealt with by human intervention. GIS technology assists decision-making by
deriving a map that indicates the set of alternative uses vying for each
location. Once in this graphic form,
decision-makers can assess the patterns of conflicting uses and determine land
use allocations. Also, GIS can aid in
these deliberations by comparing different allocation scenarios and identifying
the areas of change.
In the case
study, the Hierarchical Dominance approach was tried, but resulted in total
failure. At the onset, the group was
uncomfortable with identifying one land use as always being better than
another. However, the approach was
demonstrated by identifying development as least favored, recreation next, and
the researchers' favorite watershed taking final precedence. The resulting map was unanimously rejected as
it contained very little area for development, and what areas were available,
were scattered into disjointed parcels.
It graphically illustrated that even when decision-makers are able to
find agreement in “policy space,” it is frequently muddled in the complex
reality of geographic space.
The alternative
approaches of compatible use and tradeoff faired better. Both approaches depend on generating a map
indicating all of the competing land uses for each location— a comprehensive conflicts map. Figure 8 is such a map considering the
Conservation Areas, Research Areas and Development Areas maps. Note that most of the area is without
conflict (lightest tone). In the absence
of the spatial guidance in a conflicts map, the group had a tendency to assume
that every square inch of the project area was in conflict. In the presence of the conflicts map, however,
their attention was immediately focused on the unique patterns of actual
conflict.
Figure 8.
Conflicts Map. The Conservation
Areas, Research Areas and Development Areas maps were overlaid to identify
locations of conflict which are deemed best for two or more uses.
First, the
areas of actual conflict were reviewed for compatibility. For example, it was suggested that research
areas could support limited use hiking trails, and both activities were
assigned to those locations. However,
most of the conflicts were real and had to be resolved "the hard
way." Figure 9 presents the group's “best” allocation of land use. Dialogue and group dynamics dominated the
tradeoff process. As in all discussions,
individual personalities, persuasiveness, rational arguments and facts affected
the collective opinion. The easiest
assignment was the recreation area in the lower portion of the figure as this
use dominated the area.
The next
break-through was an agreement that the top and bottom research areas should
remain intact. In part, this made sense
to the group as these areas had significantly less conflict than the central
watershed. It was decided that all
development should be contained within the central watershed. Structures would
be constrained to the approximately twenty contiguous hectares identified as
best for development, which was consistent with the island's policy of
encouraging “cluster” development. The
legally constrained area between the development cluster and the coast would be
for the exclusive use of the residents.
The adjoining
research areas would provide additional buffering and open space, thereby
enhancing the value of the development.
In fact, it was pointed out that this arrangement provided a third
research setting to investigate development, with the two research watersheds
serving as control. Finally, the
remaining small “salt and pepper” parcels were absorbed by their surrounding
'limited or preservation use' areas.