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A discourse on suitability mapping methods with a selection process for recreation planning

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Title:
A discourse on suitability mapping methods with a selection process for recreation planning
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Colebank, M. Wayne
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English
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v, 123 leaves : illustrations, charts, maps ; 28 cm

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Subjects / Keywords:
Land use -- Planning ( lcsh )
Environmental mapping ( lcsh )
Environmental mapping ( fast )
Land use -- Planning ( fast )
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bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )

Notes

Bibliography:
Includes bibliographical references (leaves 93-96).
General Note:
Submitted in partial fulfillment of the requirements for the degree, Master of Landscape Architecture, College of Design and Planning.
Statement of Responsibility:
[by M. Wayne Colebank].

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University of Colorado Denver
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Auraria Library
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All applicable rights reserved by the source institution and holding location.
Resource Identifier:
15525226 ( OCLC )
ocm15525226
Classification:
LD1190.A77 1986 .C63 ( lcc )

Full Text
CoL£6AA/K
A DISCOURSE ON SUITABILITY MAPPING METHODS WITH A SELECTION PROCESS FOR RECREATION PLANNING


LO )|C) Q -a 77 /??£
M. WAYNE COLEBANK
HAS SUBMITTED THIS THESIS AS PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR A MASTER OF LANDSCAPE ARCHITECTURE DEGREE AT THE
UNIVERSITY OF COLORADO AT DENVER COLLEGE OF DESIGN AND PLANNING GRADUATE PROGRAM OF LANDSCAPE ARCHITECTURE
ACCEPTED:
Di rector
Jerry StratinsYAssociate Professor of Landscape Architecture
Randy Palmer, Landscape Architect WIRTH Environmental Services
Date

9


ACKNOWLEDGEMENTS
I would like to express my appreciation to the office of Phillip E. Flores Associates, Inc. for their assistance in the production of this document. I would also like to express my profound gratitude to my wife, Diane, for her contribution and support in this endeavor.


TABLE OF CONTENTS
Thesis Brief ............................................................ 1
Section 1.0 Introduction..................................................12
Section 2.0 Background....................................................14
Subsection 2.10 "Ideas on Suitability ................................ 14
Subsection 2.20 Recent Trends ........................................ 23
Subsection 2.30 Measurement .......................................... 29
Subsection 2.40 Methods for Generating Suitability Maps .............. 32
Subsection 2.41 Gestalt Method........................................33
Subsection 2.42 Ordinal Combination Method............................36
Subsection 2.43 Linear Combination Method ............................ 38
Subsection 2.44 Non-Linear Combination Method ..................... 40'
Subsection 2.45 Factor Combination Method ............................ 41
Subsection 2.46 Cluster Analysis Method .............................. 42
Subsection 2.47 Rules of Combination Method .......................... 42
Subsection 2.48 Hierarchical Combination Method ...................... 45
Subsection 2.50 Outdoor Recreation.................................... 46
Subsection 2.51 Recreation Classifications............................49
Section 3.0 Study Objectives..............................................52
Subsection 3.10 Hypotheses............................................53
Section 4.0 Study Process ............................................... 53
Subsection 4.10 Selected Land Uses..................................55
Subsection 4.20 Data Bases..........................................55
Section 5.0 Results.........................................................57
Subsection 5.10 Comparative Evaluation..............................57
Subsection 5.11 Gestalt Method......................................59
Subsection 5.12 Ordinal Combination Method..........................61
Subsection 5.13 Linear Combination Method ......................... 63
Subsection 5.14 Non-Linear Combination Method ..................... 64
Subsection 5.15 Factor Combination Method ......................... 65
Subsection 5.16 Cluster Analysis Method ........................... 66
Subsection 5.17 Rules of Combination Method ....................... 67
Subsection 5.18 Hierarchical Combination Method ................... 68
Subsection 5.19 Summary of Comparative Evaluation ................. 69
Subsection 5.20 Properties of Methods ............................. 74
Subsection 5.30 Suitability Purposes................................74
Subsection 5.40 Usefulness Level Requirements of the Suitability
Purposes..............................................79
Section 6.0 Interpretation................................................81
Subsection 6.10 Usefulness Level Recommendations......................81
Subsection 6.20 Suitability Methods Selection Process ................ 81


Section 7.0 Conclusions ................................................... 89
Bibliography .............................................................. 92
Appendix....................................................................97
Alabama Data......................................................... 98
Teller County Data...................................................109
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LIST OF FIGURES
No. Page.
1. Three Approaches to Suitability. 3
2. Factors to Consider when Selecting Suitability Methods. 3
3. Prescaled Process. 6
4. Postscaled Process. 7
5. Usefulness Ranking of Suitability Mapping Methods. 9
6. Interrelations of Biological and Cultural Systems. 13
7. Hills' Categories of Land Benchmarks. 17
8. Hills' and Vink's Conceptual Models of Suitability. 19
9. A Discussion Plan for the Planning Region Using the
Planner's Policy Preference Weights. 26
10. A Discussion Plan for the Planning Region Using the
Land Developers Preference Weights. 27
11. A Discussion Plan for the Planning Region Using the
Conservationist's Preference Weights. 28
12. Four Types of Measurement Scales. 31
13. Gestalt Method. 34
14. Ordinal Combination Method. 34
15. Ordinal Combination Method. 35
16. Linear Combination Method. 39
17. Factor Combination Method. 39
18. Rules of Combination Method. 43
19. Rules of Combination Method for Medford. 44
20. Hierarchical Combination Method. 44
21. Leisure time. 47
22. Demand for Recreation Activities in the U.S. 47
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No. Page.
23. Recreation Classes and Authority Level/Client Matrix. 52
24. Thesis Outline. 54
25. Advantages and Disadvantages of Prescaled and
Postscaled Methods. 58
26. Advantages and Disadvantages of Each Suitability
Method. 70
27. Advantages and Disadvantages of Each Suitability
Method. 71
28. Advantages and Disadvantages of Each Suitability
Method. 72
29. Summary of Suitability Methods. 73
30. Properties of the Suitability Methods. . 75
31. Authority Level/Client and Suitability Purposes
Correlation Matrix. 78
32. Usefulness Requirements of Suitability Purposes 80
33. Usefulness Level Recommendations for Suitability
Purposes. 82
34. Suitability Methods Selection Process. 83
35. Suitability Methods Selection Process. 84
36. Suitability Methods Selection Process. 85
37. Suitability Methods Selection Process. 86
38. Suitability Methods Selection Process. 87
39. Suitability Methods Selection Process. 88
40. Values for Ordinal Combination of Alabama Data 99
41. Map of Ordinal Combination of Alabama Data 100
42. Values for Linear Combination of Alabama Data 102
43. Map of Linear Combination of Alabama Data 103
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No. Page.
44. Values for Factor Combination of Alabama Data. 105
45. Map of Factor Combination of Alabama Data. 106
46. Values for Ordinal Combination of Teller County Data. 110
47. Values of Ordinal Combination of Teller County Data. Ill
48. Map of Ordinal Combination of Teller County Data. 112
49. Values for Linear Combination of Teller County Data. 114
50. Values for Linear Combination of Teller County Data. 115
51. Map of Linear Combination of Teller County Data. 116
52. Values for Factor Combination of Teller County Data. 118
53. Values for Factor Combination of Teller. County Data. 119
54. Map of Factor Combination of Teller County Data. 120
55. Map of Cells Common to Ordinal, Linear and Factor
Combination of Teller County Data. 122


THESIS BRIEF
Preface
What is suitability? How is it decided which suitability mapping method will be used for your study? Are all methods equally appropriate? What are the advantages and disadvantages of using one method versus another? What are the consequences of choosing one and not another? The prevalent assumption within the landscape architecture profession has been that all methods are equally suited for use regardless of the purpose and context of a study. This thesis takes issue with that assumption. It will attempt to demonstrate that some methods are more appropriate for planning, particularily recreation planning, than others based on an evaluation of the extent of data available, the ability of each method to handle data, the communicative quality of each method and the nature of the client/audience.
Background
Suitability mapping is an art, not a science. It relies on the ability of the planner to be able to predict the preference of a land use based on interrelationships of environmental and cultural factors. The point is, we know very little about these relationships. The science of ecology has yielded little tangeable information about the strengths and magnitude of these relationships or the ability to predict the results should one or several of the relationships be interrupted. However, landscape architects and planners, are obliged to include the principles of ecology to land use planning. The eminent ecologist Eugene Odum (1971) calls the application of ecological perspectives into planning "undoubtedly the most important


application of environmental science". Over the past decades, many wordly planners and designers have advocated the fundamental role of ecology within the landscape planning process. None more clearly and succinctly demonstrated this role than Ian McHarg with his concept of "ecological determinism". McHarg clearly demonstrated how the values of nature could be incorporated into the location, form and growth of development via suitability maps developed through the "overlay" method.
There are three general approaches to suitability (Figure 1). The first is represented by the United States Soil Conservation Service's (SCS) capability classifications. The McHarg method bases suggested land uses on the "intrinsic suitability" of natural factors associated with a specific land area. The Dutch method is characterized by the work of A.P.A. Vink.
It can be considered a hybridization of the McHarg and SCS methods although it possesses characteristics that they do not.
Of all the work done in the early suitability studies, the most important was the establishment of the terminology and definition of suitability and its associated concepts. In the early 1960's, Angus Hills struggled with the distinction between the true potential use of land (capability) and the immediate use of land (suitability) for all of Canada. He developed the working definitions of capability and suitability which have since been advanced by Vink.
In recent years there has been a trend towards the parametric (or quantitative) measurement of landscape data and away from the morphogenetic (landform based) descriptions used by Hills, McHarg and Vink. The reasons for this rest chiefly on the availability of more sophisticated and less costly sensing and computer technology. Computers can store and manipulate


SOIL CAPABILITY CLASSIFICATIONS
INTRINSIC SUITABILITY
LEWIS
HILLS
SOIL SUITABILITY
ACTUAL LAND SUITABILITY
. POTENTIAL LAND SUITABILITY
Figure 1. Three approaches to suitability.
experience
PLANNER
MONEY
CHOICE
OF
METHOD
PERSONAL
PREFERENCE
CLIENT
OUTPUT
DESIRED
METHODS
EXTENT OF DATA
Figure 2. Factors to consider when selecting suitability methods


far more data than humans and do it much, much quicker. Many of the analysis methods are based on mathmatical formulas and algorythyms; they too are the specialty of the computer.
This thesis attempts to enlighten professionals about how to choose the appropriate method for recreation planning at its various levels. There are a number of factors to consider when deciding on which method to choose (Figure 2). Time (restraints), money (budgeted in the contract), experience of the design staff, personal preferences of the designers, quantity and quality of the data, benefits of the methods themselves, the quality of study output desired and the nature of the client are very real considerations. This document assumes that the time, money, experience and preference of the design staff are variables that cannot be accounted for in this study. Based then, on the remaining considerations of data extent, the methods, desired output and client, a selection guide will be developed to outline the appropriate method(s) for various levels of recreation planning.
Land Suitability Mapping Methods
In 1977 Lewis Hopkins described eight methods (mostly McHarg's) used to generate land suitability maps. To be included as a suitability method, each must contain two items: (1) a procedure for identifying parcels of land that are homogeneous and (2) a procedure for rating each parcel for its suitability for a given land use. The methods identifited by Hopkins were:
METHOD
TYPE
Gestalt
Ordinal combination Linear combination Non-linear combination
Gestalt
Mathmatical


Factor combination Identification of Regions
Cluster analysis
Rules of combination Logical Combinations
Hiearchical combination
Upon closer inspection these various methods may be thought of as only two distinctly different types prescaled and postscaled (Figures 3 and 4). Prescaled methods are characterized by the assignment of values to each category for each factor before the values are combined. Postscaled methods assign rankings to combinations of categories after they have been combined. The chart below indicates the breakdown of methods into their respective prescale/postscale categories. The following text will briefly discuss each
method to indicate the advantages and disadvantages of each method.
PRESCALED
POSTSCALED
Ordinal combination Gestalt
Linear combination Factor combination
Non-linear combination Cluster analysis
Rules of combination Hierarchitcal combination
Ordinal combination utilitzes a simple data synthesis procedure in any of its forms. The concept of synthesis can easily be demonstrated by this method. Linear combinations is very efficient because it is usually peri'ormed on a computer. It is a statistically accepted method for arriving at weighted averages. Non-linear combination is more accurately a model, and as such, could be highly useful for predicting reactions between variables of different kinds. To be effective, this model requires ecological information not readily available to planners, except in isolated instances. Consequently, the non-linear combination model is seldom used.
Gestalt is a quick way to organize site data. It relies heavily on the experience of the planner to predict suitable lands. Little of available
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PRESCALED PROCESS
Figure 3. Prescaled process.


POSTSCALED PROCESS
1. DATA GATHERED 2. ALL POSSIBLE COMBINATIONS IDENTIFIED AND LISTED 3. COMBINATIONS RANKED f
/4A STEEP SLOPE POOR SOIL : 1 BAD VEGETATION STEEP SLOPE poor soil : L BAD VEGETATION
FLAT GOOD SOIL ; 2 GOOD VEGETATION FLAT GOOD SOIL H GOOD VEGETATION
4. PRELIMINARY ZONES PLOTTED 5. OVERRIDING RULES APPLIED 6. FINAL MAP GENERATED
/% / NO FLOOD PLAINS NO AQUIFER RECHARGE /a.

Figure 4. Postscaled process.
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site data is actually used which limits the validity of results. Factor combination allows the analyst/planner to see directly the combinations of data on a site. This method is very low in efficiency, requiring considerable time to develop all existing combinations and assigning a rating to them. Cluster analysis eliminates some of the problems of factor analysis, but not entirely, by liberalizing the data categories. In this case, generality may be so pervasive as to loose all meaning. Rules of combination offers the greatest latitude for planners. It operates on no mathmatical properties, only rules established by the planner. The rules must be specific and, by being so, are very explicit, allowing others the possiblity of replicating the study. Hierarchical combination is based on information not yet available. It, as well as the non-linear combination, would benefit greatly from more ecological information. Hierarchical combination can be of little consequence for developing suitability maps. Figure 5 shows in simplistic form the relative usefulness of each method based on its communicative qualities.
Four of these methods, ordinal, linear, and factor combination and rules of combination, were compared using a common data base to determine if they selected the same, or similar, areas of prime suitability for recreation. Ordinal and linear combination were replicated on a geographic information system. Factor combination and rules of combination results were inferred from manipulations on the same system. Based on the results, the advantages and disadvantages of each method, their data handling capabilities and their communicative qualitites were weighed against the requirements of suitability purposes at four levels of recreation planning to develop a selection process for the appropriate method".
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LEVEL 1
LEVEL 2
LEVEL 3
LAND USE SUITABILITY

LINEAR
COMBIN.
ORDINAL
COMBIN.
NON LINEAR COMBIN.
FACTOR
COMBIN.
CLUSTER
ANALYSIS
RULES OF COMBIN.
HIERARCH.
COMBIN.
GESTALT
increasing ----7N----
LEVELS OF
EXPLICITNESS,
SOPHISTICATION,
CONFIDENCE,
EFFICIENCY, A
REPRODUCIBILITY
decreasing
MATH £
7 METHODS /
, FACTOR <
> METHODS '

LOGICAL
METHODS

, GESTALT L
> *
, PRESCALE id
/ /
POSTSCALE

Figure 5. Usefulness ranking of suitability mapping methods


Results
The results of the comparative analysis were inconclusive about which method, or type of method, is the most accurate at selecting prime suitability locations. No strong tendencies developed even when tested on two different data bases. It may be stated that, in light, of the inconelusveness of these results, methods that allow the planner to assign rankings of the data directly, such as postscaled ones, would be considered more accurate.
Interpretation/Conclusions
The methodology utilized in this study involved taking available information (Hopkins') and combining it with new data generated to tackle a very common problem for landscape architects -- how to select a suitability method. Unfortunately the results of the study do not support the hypothesis that all methods reach the same conclusion. It does seem to indicate that rules of combination and factor combination, as well as other postscaled methods, would result in similar patterns when used by the same individual and assumptions. The prescaled mathmatical models of ordinal and linear combination produced the widest variations in patterns. This may well be attributed to the fact the prescaled methods require the analyst to singularly view the parts of the whole without benefit of seeing the interrelationships between them as in the postscale methods. This study suggest that some methods may be more appropriate for different purposes and most likely should be used in combinations with each other to achieve the best results.
This research does suppost the hypothesis that some methods are more
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appropriate for certain recreation purposes than others. Linear combaintion and rules of combination appear to have the broadest applicability over all recreation purposes. Rules has no limitations for any recreation level. Ordinal combination and factor combination are appropriate for the local purposes where simplicity and detailed data review is mandatory. These methods have limited usefulness for other purposes, i.e., county, where explicit synthesis proceudres are required. Non-linear and hierarchical combination, as well as cluster analysis and gestalt have very limited usefulness for any recreation purpose. Eachmethod has serious problems which prohibit usage.
This document does support the hypothesis that the appropriate method for recreation planning can be demonstrated. The suitability methods selection methodology process clearly outlines and identifies the appropriate method to choose based on factors such as, client, type of project, intended suitability purpose and data available to the planner/analyst. These variables have been incorporated into the selection process diagrams to assist other interested users in choosing the appropriate suitability method(s) for their particular circumstances.
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SECTION 1.0 INTRODUCTION
Landscape architects charge themselves with the responsibility of being "stewards of the land". One of the chief tasks of that responsibility is to evaluate land for its ability to withstand human influences (hopefully) without severe degradation. This is a complex task because it requires a thorough understanding of the biological and cultural systems as well as the consequences of their interactions (Figure 6). In reality, we know very little detailed knowledge of the functions of the land as a complete ecological system (Hammond 1984). Recognizing this, McHarg (1966) pleaded:
"...The burden of this paper is a request to natural scientists, particular!'ly ecologists, to provide the indespensable information which the artifiers require ecological inventories, explanation of natural processes and identification of their limiting factors, the attribution of value, the indicators of healthy and unhealthy environments and finally the degree of permissiveness or resistance to change implicit in natural processes. This ecological information is regarded as deterministic..."
The cultural systems are even more muddied.
In spite of the lack of meaningful data available to us, our responsibility is to clearly indicate land areas suitable for a given land use for the foreseeable future (+ 20 years). By doing so we are adding to the "supply side" of the supply-demand equation. The chief method of illustrating the "supply" avaliable is the land use suitablity map. It establishes the spatial parameters of suitable land. Suitability models show the spatial patterns of requirements, preferences or predictors of some activity (Anderson 1980). From these, land allocation decisions are made.
This thesis attempts to deal with the product of land use suitability maps and with how methods of suitability mapping are chosen. Much emphasis
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- -- dependence in direction of the dependent
reverse dependence of factors on the landscape is only shown for the free roaming organisms man and fauna, for which it is most evident
Figure 6.
Interrelations of biological
and cultural systems
(Vink 1983).
-13


is placed on the result of suitability methods without evaluation of how they are chosen or if they can predict suitable land areas. This thesis is a study of how to choose the appropriate suitability method based on their intrinsic qualities and accuracy.
SECTION 2.0 BACKGROUND
SUBSECTION 2.1 "IDEAS" ON SUITABILITY
Land use suitability mapping is a predictive model for land evaluation. Land use suitability mapping is "the process of estimating the potential of the land for one use or for several possible" (Young 1976), in terms of either the land's suitability or its capability (Hammond 1984).
These in turn may be defined as:
Land Capability: The fitness of a given tract of land to sustain a defined use. Differences in degree of capability are determined by the present state of the (limiting) attributes of the area (geology, soils, hydrology, etc.).
Land Suitability: The fitness of a given tract of land for a defined use. Diferences in the degree of suitability are determined by the relation, actual or anticipated, between the benefits (outputs) and the required inputs associated with the use of the land in question (Vink 1975).
These inputs and outputs for suitability include technology, labor and finances which are all time-dependent variables. A land capability analysis excludes such socio-economics. Capability analysis is an ecological evaluation of the land and the first step toward the social and economic development of it. Although in the final analysis land use and economics are inseparable, the first essential (ecological) step is to develop land


capability (Hammond 1984). In practice, generally, both bio-physical and socio-economic factors are present in most land capability/suitability studies, either implicitly by assumptions made about acceptable economic returns, levels of environmental degradation and so on, or explicitly by including these as parameters in the rating functions (McDonald 1984). Distinctions have been made in some studies (Hills, et. al. 1970, Hopkins 1977) between capability, suitability and feasibility, the latter two terms applying to rating procedures that provide details as to the specific technology and economic conditions under which use will occur. Capability refers to productivity under optimum management.
A. P. A. Vink who provided the definitions above has offered the most scholarly discussions of land use suitability. He defined land use as "a set of biological and technological human activities, engaged in for economic and social purposes" (1975). These activities are directed towards management and improvement of land resources. These land resources are phenomena of nature that can be described strictly in scientific terms and give no indications of how they could or should be used.
To judge land suitability for both land use and for land improvement, Vink suggests that a broader, systematic "land evaluation" is necessary.
Land evaluation is the process of collating and interpreting basic inventories of soil, vegetation, climate and other aspects of land in order to identify and make a first comparison of promising land use alternatives in simple socio-economic terms" (Brinkman and Smyth 1973). Land evaluation therefore bridges the gap between the physical and biological components of land use and its social and economic purposes. It uses sets of assumptions which are of value only during a specific period (i.e. + 2 years of the
-15-


study) and is always relatively ephemeral. Changing social and economic conditions or technical developments may alter these assumptions resulting 1n different suitabilities of the land. Land evaluation is often carried out in relative terms, due partly to insufficient availability of quantitative information, and partly as a first attempt of a complicated iterative (repetitious) procedure called "land suitability classification" (Brinkman and Smyth 1973). Land suitability classification is an appraisal and grouping (or the process of appraisal and grouping) of specific tracts (of land) in terms of their relative land suitability for a defined land use (based on specific social and economic assumptions)(Brinkman and Smyth 1973).
Vink (1975) states that suitability can be interpreted from land resources maps using two main set of assumptions:
1. Actual land suitability, i.e. "the suitability of land units for the use in question in their present condition without major land improvements". This is analogous to McHarg's intrinsic suitability (Steiner and Brooks 1981).
2. Potential land suitability, i.e. "the suitability of land units for the use in question at some future date after major land improvements have been effected where necessary" (Brinkman and Smyth 1973). Whether the major capital inputs required to effect major land improvement are considered as part of the suitability classifications above, ongoing (management) costs and expenditures for each must be considered for both as part of their evaluation.
Seminal to the discussion of application of suitability classification systems, is a further, detailed look at Angus Hills' System for Land
-16-


Classification of potential (land) use and management. It is a highly scientific approach to supply aspects of land inventory and analysis. Although Vink has provided clear elaboration of suitability methods for agriculture, Hills, along with McHarg, have provided the graphic format for landscape architects to present results of more comprehensive land use studies. They are the precursors of thought in this profession and subject of further attention.
Under Hills' system, the use potential for a unit is ranked under various management conditions and using separate value systems. Use Capability is ranked on an A-G scale of eleven values based on the inherent potential at the highest observed level of physiographic production under optimum mangagement conditions. A capability rating represents the natural capability of a unit to support, at the highest intensity possible, any given use. The rating is determined by measuring potential production of a unit under an assumed level of management. For example, the recreation capability rating of a mountain lake might be based on its present physical condition and that it will receive the best development, management and maintenance that can be provided.
Figure 7. Hills' categories of land benchmarks (Belknap 1967).
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Placing site types and phases (Hill's categories) into local capability
classes (classes A-G) for various activities requires a knowledge of the specific relationship between the unit's physical features and activity requirements. Each capability class indicates the specific capabilities on the basis of the intensity and quality of use, rather than the kind of use (Belknap, 1967). Although it is inferred that capability classes are expressed as levels of productivity, they are usually measured by the degree to which potential limitations in each site type or phase are absent, (the fewer dead tree stumps a lake has, the higher its productive rating for sail boating).
Because units of equal capability may have diversely different physical appearances or feature, Hills developed comparable capability classes to systematize the ratings procedure. He refers to these as "bench marks" which refer to examples or conditions in the field (Figure 7).
Use Suitability is the degree to which a unit, in its present condition, can respond to specific management practices. Suitability classes are based on the amount of work or investment required to bring a unit to its level of capability. This is measured in "degrees of effort" not by dollars. Degrees of effort may be the number of hours to cut timber or to plant seedlings, the amount of sediment dredging needed, or clearing for roads required to access a development site. The significance here is that two units may have an equal capability rating but because one may already contain improvements (e.g. roads) or existing management, they may require different degrees of effort to realize the same potential. There is little information on how Hills applied this information. Assumedly, he
-18-


would prepare itemized lists of improvements needed to be over come, then rank them ordinally from least to most limitations to be overcome.
Use Feasibility represents the relative advantage of managing or improving a unit, considering its capability and suitability for a specific use under the existing or projected socio-economic climate. Development potential is based on such items as closeness to a metropolitan center, travel costs, land prices and land management costs. If one unit is closer to a metropolitan area, it would be more accessable and therefore have a higher feasibility rating. There is little to demonstrate how this procedure is utilized.
LAND
UTILIZATION/
ECONOMIC
RETURN
LOW.
LOW.
INCREASE IN SUITABLE LANDS
-------M CAPABILITY,
y I REFLECTING
/ ............
^CAPABILITY,
}----FEASIBILITY2
FEASIBILITY,
LEVEL
SOCIETAL INFLUENCES
NOME, NONE 2
HIGH,
MANAGEMENT LEVEL/INPUT
Figure 8.
Hills' Vink's
Hills' and Vink's conceptual models of suitability.
The differences between Hill's and Vink's conceptual model of suitability is relatively little and basically evolutionary in nature. Hills, as the initial investigator into the area, visualized suitability as being bounded at the upper level by the land's capability for development (possesion of obstructive physical features) on one hand an its feasibility for development (the natural tendency of the marketplace if no input were made onto the land) on the other. Suitability is then the ability of the
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land, without major changes, to return a reasonable pre-determined level of profit or productivity based on determined level of input. Hills concept of capability, being limited by insurmountable physical features on the land, is a static one. The land's obstacles presented the ultimate limitation to man's desire to use it.
Vink has refined his concept. He states that suitability (read capability) is not static but is dynamic. External forces such as social climate, technology and political stability act on the capability to overcome physical constraints of the land. In the United States, for example, political stability over the last 200 years has allowed the development of technology to continue rapidly inrelative security. The technologic advancements made have been used to solve many problems related to use of the land. Before the development of large scale irrigation systems the capability for growing substantial wheat on the Kansas plains was relatively low. With advancements in irrigation technology, we are now capable of utilizing more of land in Kansas for growing wheat. Vink uses the example of an African country torn by civil strife. Because of the turmoil, money needed to develop technology or make use of the land is diverted to solving political problems. Consequently, these external forces affect capability and thereby affect suitability. The graphic display of Hills' and Vink's concepts is found in Figure 8.
Respect for natural processes is central to ecological determinism, a concept popularized by Ian McHarg (Anderson 1980). He has brought an increased awareness of environmental factors in regional landscape environmental factors in regional landscape analysis. He believes that "ecology provides the single indespensable basis for landscape architecture
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and regional planning" (McHarg 1967). The McHarg method bases suggested land uses on the intrinsic suitability" of natural factors associated with a specific land area. McHarg has summarized it as "the best areas for a potential land use at the convergence of all or most of the factors deemed propitious for the use, in the absence of all or most detrimental conditions" (Steiner and Brooks 1981). In contrast to the social and monetary values studied by social scientists, McHarg (and ecological determinism) place values on natural processes intrinsic, productivity, functional and negative. Intrinsic values of the landscape include beauty and educational value. Agriculture, forestry and mining are examples of productivity values. Functional values include water storage, purification and erosion control. Negative values are typically asociated with natural hazards such as earthquakes, floods, natural fires, etc.
A traditional analysis procedure for McHarg would be to inventory and map the natural and cultural features of the study area. Data is collected on the following eight catagories, which McHarg considers of primary importance:
1. Climate
2. Historical Geology
3. Physiography
4. Hydrology
5. Soi 1 s
6. Vegetation
7. Wil dli fe
8. Land Use (existing)
Next, the data is interpreted to reveal dominant prospective land uses


for each area in the study. The uses evaluated include agriculture, recreation, forestry, and urbanization. The result of this step is a series of maps locating economic minerals, unique sites, location of water resources and intrinsically suited uses. Then, each land area is evaluated on its potential for all possible uses. Each resource is value rated, and compatable and incompatible land uses are matrixed to measure degrees of compatability. These lead to a final step a combined suitability map.
The object of this final synthesis is to reveal the maximum conjunction of coexisting and compatible land uses that can be sustained by each area in the total study area. However, the report documents for these studies offer little information on exactly how the synthesis took place.
As can be seen, Vink, Hills and McHarg have made contributions to the field of landscape suitability analysis in different ways. Hills contributed an encompassing system and a clear enunciation of the issues. McHarg emphasized the value of the non-human environment. Vink clarified even further the terminology and issues involved with suitability studies. They all agree that even as close as the interpretive phase gets to the actual allocations (decision making) phase, they are not the same. McHarg points out that suitability maps are not development plans and by themselves are only one tool for deciding the allocation decisions. He states:
"This of course is not a plan. It merely shows the implications that land and its processes display for prospective development and its form. The plan can be developed only when there is adequate information on the nature of demand, its locational and resource character!'sties, the capacities to realize objectives and indeed, the social goals of the community" (McHarg 1969).
Hills demonstrates this philosophy by arriving at major and co-major uses for the same piece of ground without further detail. McHarg's end
-22-


product is usually a map with dominant, co-dominant and subordinate land uses established through the understanding of natural processes. McHarg and Hills demonstrate the attitude that all appropriate uses are co-equal and leave the final decision to public and private decision makers as they respond to additional current and future social, economic, site and environmental consideration (Anderson 1980).
SUBSECTION 2.2 RECENT TRENDS
In recent years there has been high impetus for quantitative methods for landscape analysis. Reasons for such impetus are varied. First there has been a substantial increase in detailed data .supplied by improved remote sensing technology. Consequently, interpretation techniques have had to cope (change) with the vast data received. The ability of computers to store and process vast quantities of data has improved while cost have gone down. State and federal legislation has called for improved methods to deal with these questions, reflecting a concern for environmental quality vis-a-vis environmental movement, while planning on increased development (growth) pressure.
Quantitative methods are characterized by Mabutt (1968) as employing the parametric approach which, he says, uses a precise definition of land, avoids subjectivity, allows comparisons of projects, allows consistency within projects and is suited to remote sensing and computing technology (Anderson 1980). The parametric approach is "the division and classification of land on the basis of selected attribute values" (Mabutt 1968). He believes that these methods are more "natural" than the "physiographic and morphogenetic" landscape units (i.e. Hills and McHarg) of
-23-


earlier methods. Fabos and Caswell (1977) point out that McHarg's recent work is moving in this direction, making recommendations on the basis of "quantitative composite evaluation". The chief benefits of quantitative methods and the parametric approach is not in the "natural" landscape units or a "scientific" basis, but in the explicit expression of the analysts criteria and value system (Anderson 1980).
Quantitative methods rely heavily on mathmatic models to explain the relationship between variables. This also allows systematic exposure of the process and ease of replicability. All mathmatic models are symbolic models and tend to be abstract and general. Many have the same format: v = f (x, ,y1 )
where v = some measure of performance of the real world phenomenon under study; the dependent variable
x^ a set of variables representing decisions or
environmental components that can be controlled
y, = a set of variables representing decisions or environmental components that cannot be controlled; an independent variable
f = the functional relationship between the independent variables x and y and the dependent variable (Anderson 1980)
For suitability this model can be shown:
Sjj F (ajj ajj 3jj )
where j = land use
i = site or spatial unit F = rating function (importance) a = site factors
S = suitability rating (McDonald 1984)
There have been a wide variety of factor-rating procedures have been used to specify *F* in multi-factor site suitability analyses. In most studies, these show some sophisticated notions of productivity, carrying
-24-


capacity, and environmental impact of specific land uses (McDonald 1984). Cost for development would also be considered for generating ratings.
It is difficult to make catagorical statements about the role of suitability mapping in planning since that role depends very much on the planning context of a study. In cases where planning commissions have requested a multi-use suitability analysis, a strong link between analysis and allocation phases would exist, e.g. Shamberg 1984. In other cases sutability mapping may become "new data" in the evaluation of land for several uses. Sometimes, it is not a consideration at all.
A novel benefit to quantitative data storage and analysis is the ability to manipulate environmental resource data, with values other than the analyst's. The resultant "maps" are then used as a discussion basis to resolve land use conflicts between competing and conflicting land uses proposed by different users. This can be demonstrated by the "discussion plans" generated during the Redland Shire (near Brishane, Australia) planning study reported by McDonald and Brown (1984)(Figure 9, 10 and 11). Planners, land developers and conservationists assigned their values for the location of conservation, recreation, horticulture and urban land uses based on their interpretation of the natural features. The resultant "discussion plans" were then used within the political process to resolve all conflicts. In Jefferson County, Colorado, a similar system is used to decide land use planning policies based on suitability maps generated from computerized landscape data of the county. The values of the County's planning staff, the County Commissioners, the public at large and/or land developers are interfaced with the land data to arrive at maps not unlike the "discussion plans" of Redland Shire. Decisions on land allocation are left to the
-25-





dnc/ 'd' tf,e 4*
^O^e'an.,


~

ip

, J'O/V^O/v P/

(Men? n pl
*na
^ ,> ,,
J98s>*io'>


political process.
SUBSECTION 2.3 MEASUREMENT
Significant as they are, the reasons for developing new suitability models are only as strong as the techniques used in these models. In this regard, rigor in proper data synthesis has never received due acclaim by landscape architects. Pre-quantitative methods were plagued by incorrectness of mathmatical computations (i.e. ordinal combinations). The same may be said of post quantitative methods if serious consideration is not given to proper data synthesis. Analysis of synthesis methods as described in project reports is non-existent, or mentioned and ignored (Brandes 1973). "Measurement" in its broadest definition means assigning numbers to things according to definite rules (Jacobs 1975). Measurement scaling is the basis for putting numbers on values. Landscape analysts have done poorly when it comes to abiding by the rules.
There are four measurement scales that are useful when numerically representing landscape characteristics and their values (Figure 12).
Nominal scales are the simplest. Here numbers serve only as names for characteristics. A map showing vegetation types is a nominal scale. No value judgements are made about the differences of groups indicated. This measurement type permits grouping. Other examples would be boy-girl, night-day, etc.
Ordinal scales involve ordering or ranking landscape character!'Stic to some ordered sequence without considering the interval between each rank or the zero point of the scale. Ordinal scales give more information than nominal scales because they are assigned a "more" or "better" ranking, but no other significant information can be determined. They do not indicate
-29-


how much more or how much better one characteristic is than another. Very few arithmatic functions operate on ordinal scaled data.
Interval scaling is the next most complex and reveals more information about the data. It also allows meaningful statements to be made about the differences in the magnitudes of properties of objects. Interval scales use a standard interval or unit of measure, i.e. time. Arithmatic functions such as addition and subtraction can be used in analysis. The key to identifying interval scaling is that it does not have a specified zero point.
The most complex scaling type is ratio scaling. Ratio scales have a constant, specified interval between points and a" fixed meaningful zero point. Measurements such as length, density, height above sea level, etc. are ratio scales. These values can be manipulated by the arithmetic functions of multiplication and division.
Landscape analysts should be knowledgeable of the limitations of each of these four scales so that the scales can be applied in appropriate ways to yield meaningful results. Unfortunately, data has been treated as if it were all interval or ratio scales. We are tempted to apply arithmetic functions not appropriate for the data.
Analysts need to consider the scaling of data at least twice during a study. The first time is when the data is collected then translated into maps. Vegetation, wildlife, land use and elevation maps are usually nominal data. No evaluation is made of their characteristics. Slope is ratio data. Soils, base geology, hydrology and climate may of either type.
The second time an analysts should be aware of scaled data is during synthesis. At this point values must be applied to factors by a rating
-30-


Scale Defining Realtions Examples
Nominal (a) Equivalence Class A = Class A Class A r Class B (a) Land-water classification of earths surface (b) Land-use classes (urban-rural)
Ordinal (a) Equivalence (b) Greater-less than A>B B Interval (a) Equivalence (b) Greater-less than (c) Ratio of any two intervals (assumed 0 value) (a) Temperature (0C. = 32F.) (b) Time (1980 Christian calendar = 1378 Arabic)
Ratio (a) Equivalence (b) Greater-less than (c) Ratio of any two intervals (d) Ratio of any two scale values (assumed true 0 value) (a) Density of population (b) Volume of stream discharge (c) Area of countries
Hill 111 il+
Joe Pete Tom Dick Harry
l f | | I r 1 t A A ! 1 A ^ t ! ! t A 1
fit 1 l 1 1 t 1 1 1 : t t t r 3 4 11*1 5
l~t 1 l 1 1 !' 1 1 I I -i Mtl
1:10 1:30 1:3- 1:38 1:41 1:50
1-H I 1 I I I I I 1 ] f il i 1 I t-i
oo ;o :: :s 3i 40
Figure 12. Four types of measurement scales (Anderson 1980).


scheme; then these ratings are manipulated. It is here where historically, incorrect arithmetic methods have been applied to determine results.
Diverse synthesis procedures have been used that may or may not be consistant with data at hand. It has been difficult to replicate the work of other analysts because this process of synthesis has been deleted from reports or implied to be always consistent. Even examination of the data can be of little value unless one is priviledged to the assumptions being brought into the study by the analyst. Mathmatical operations can imply the method of synthesis, but these, as well, are usually deleted from the study document.
SUBSECTION 2.4 METHODS FOR GENERATING SUITABILITY MAPS
Dr. Lewis Hopkins has defined the general purpose and character of land suitability maps. He has developed a taxonomy of eight methods used to develop land suitability maps and has referenced them to a common framework. The central issue dwelled upon was how cost or impact information can be manipulated and combined to generate suitability maps for land uses.
Although he states that suitability maps can be generated for natural hazards, vulnerability to impacts, or off-site impacts, these are usually preliminary steps in the development of suitability maps for the location of land uses. In as much as his definition states that a suitability map shows the spatial requirements, preferences or predictors of some activity, he prefers to focus his discussion on land uses. Suitability is assumed to include market, non-market and non-monetary costs and impacts.
The output of land suitability analysis is a set of maps one for each land use, that show the levels of suitability for each parcel
-32-


of land in the study. To do so, each method must contain (1) a procedure for identifying parcels of land that are homogeneous and (2) a procedure for rating each parcel for its suitability for each land use. The methods identified by Hopkins were:
Gestalt Gestalt
Ordi nal
Linear combination Mathmatic
Non-linear combination Factor combination
Identification of regions
Cluster analysis Rules of combination
Logical combination
Hierarchical combination
SUBSECTION 2.41 GESTALT METHOD
The essence of the gestalt (Figure 13) is that the homogeneous regions are determined by the single sensory attributevision. Aerial photographs, topographical maps and site visits are the chief tools. Individual factors are not considered. Perceptions on the relative usefulness of the site regions are made without consideration of a land use. In steps, sites are dissected into homogenous regions such as riparian areas, drainage ways, valley bottoms uplands, plateaus, etc. Step two, involves describing the suitabilities for each region in terms of problems and assets, opportunities and constraints, then a graphic code is developed to reflect the verbally stated suitabilities. A map is generated showing the suitabilities for each land use. One map contains one land use suitability.
It is probably the most used, whether mapped or envisioned. In many
-33-


Gestalt method
Siet 3 map tu Figure 13. Gestalt method (Hopkins 1977).
Ordinal combination method with numerical index
Facior 1 types map
Facto' 2 types map
Factor 1
Factor 2
Factor type R1 R2 R3 R4
Type * 2
Type B 3
Type C t
Type A 2
Type B 3
TypeC 1
Type D 2
Stap 2 rate each type of each facto' k>r each tand ute
Factor 1 auitabiirty map
Facto' 2 auitabiirty map
Composite auitabiirty map
Step 3 map ratings tor each land use one ee: of maps Step 4 overlay ample factor suitability maps to obtain com-*0 aach land use poste one map to* aach (and use
Figure 14.
Ordinal
combination method
(Hopkins 1977).


. Numerical overlays for soil suitability (top), land cover suitability (middle), and composite (bottom).
'/2/3
4/s, ^
lo,n, n-
I 5, 14,IS
Reclassified composite suitability map.
Figure 15. Ordinal combination method (Anderson 1980).


occasions, we have all used the gestalt method. That first site visitation where one is trying to get the "feel" of the place or "understanding the place" as McHarg calls it, forces us to decide what are suitable areas for development. It is an observation/evaluation/judgement process based on little knowledge other than perceptions. In some cases this is called "best professional judgement". The problem with this method is that few people have the knowledge and necessary experience to make valid interpretations and classification necessary in gestalt. Also, the process is difficult to express to those curious about how the evaluation process took place. Therefore, results are not easily to confirm and are suspected by decision makers.
SUBSECTION 2.42 ORDINAL COMBINATION METHOD
The ordinal combination method (Figure 14) is sometimes referred to the McHarg method because of its initial use on the Richmond Parkway Study (McHarg 1969). The first step of this method is to map, on a single map, each factor (e.g. soils, slope, vegetation) along with their types (soil categories, slope classes, vegetation types). Factors are the distinct elements which can be described about land, and types (or categories) are nominal labels for particular characteristies of these elements. Step two consists of developing a table and filling in the relative suitability rating for each land use for each factor and each type. This step of the procedure can be done with either gray tones, lines or numerical indexes.
The resultant maps are demonstrated in the same manner. The third step consists of making a suitability map for each land use for each factor.
Step four then consists of overlaying, for each land use, the suitability
-36-


maps of Individual factors. This composite map would then show the spatial pattern of levels of suitability for the given land use.
Demonstrating the ordinal combination by numerical index points to the crucial mistake and problem of this method. Step four involves addition of numbers from the ordinal scale. This addition is an invalid mathmatical operation (Brandes 1973). The mathmatic properties usually assumed here do not hold. The reason is that the indexes for each factor are different and akin to adding apples plus pears plus oranges. Nothing more can be said about them. McHarg, however, argues that concurrance of all the most suitable categories of all the factors must indicate a locale more suitable than one having the occurance of less than all the most suitable categories. He also uses this logic to identify the least suitable locale. This may be the case, but unfortunately the same logic cannot be used for the areas that do not exhibit all the most or least suitable categories. It may help identify those areas at either end of the spectrum but very little of the middle ground. One way to circumvent this problem would be to assign the numbers on the interval scale so that intervals will be equal. In that case, usual arithmetic operations (like addition) can be used.
Another problem of this method, because of the addition, is the assumption that each factor is independent. This method has no mechanism to incorporate synergisms or interdependence of factors. For example, a 5% slope might be good for housing. If that slope was situated on highly permeable soil over porous geology, and over a shallow aquifer area, it may be rated very low for its potential to pollute the aquifer if septic systems were used. Each factor, in itself, may have a good rating but together they consititute a danger to the environment. In this case suitability may be a
-37-


nonlinear and Inseparable function of the combination of factor types.
There are many examples of the ordinal combination method. It is probably the most practiced method. However, most exhibit the same additive assemption in arriving at suitabilities. Another example of the same assumption is shown in Figure 15. McHarg used ordinal combination in the landmark Staten Island Study of the late 1960's.
SUBSECTION 2.43 LINEAR COMBINATION METHOD
The most frequent way to respond to the difficulties of measurement assumptions of the ordinal combination method has been to add a weighting factor (Figure 16). The types within each factor, are rated on a separate interval scale (1-9). Then a multiplier, often called an importance weight, is assigned for each factor. The rating for each type are multiplied by the weight for each factor. The resultant rating for a region is the sum of multiplied ratings, or more commonly, the linear combination.
The effect of multiplying be the weights is to change the unit of measure of the ratings for each factor so that all ratings are on the same interval scale (i.e. dollars vs. dollars). Therefore, measurement for suitability can be made in the same scale for each type of each factor.
Once this is done, the information is then combined by the standard formula for weighted averages. That formula is:
Rating = w1 r, + w2r2 + wnrn wi + w2 + wn
There still remains a problem in suggesting that a system visualized as ordinal is being scaled by multiplication and addition. It must be assumed
-38-


LlneBt combination method
Facto' 1 types ma,'
Factor 2 type* mar
Facto' types Lend uses Rt r? R3 R4
Facto- 1 weight 3
Type A 2
Type B 3
lypt C 1
Facto' 2 weight 6
lype 2 a
Tycx B 3
Type C 1
Type D 2
Step 2 rate each type o each lacto' ana weigh each tacto to* each tana use
Step 3 map ratings k>f each tand use one aet o* maps to1 Step 4 overlay single-facto* auttab'iiiy maps to obtain con-
each iano use posrte one map tor each tand use
Figure 16.
Linear combination method (Hopkins 1977).
Factor combination method
Facto' 1 types map
Facto' 2 types map
Composite land types map
At 1 CA 1 BA
AC CC /BC
AD / CDT BD
AB l CB BP
Step 1 map data factors by type
Step 2 intersect tacto' types maps to obtain composne
Regions Land uses Rt R? R3 R4
AA 10 0 a a
AB 12 0 a a
AC 22 0 a a a
AD 14 0 a a
BA 40 0 a a
BP 22 0 a a
BC 36 0 a a
BD 200 a a
CA BO a a
CB 12 0 a a
CC 14 0 a a a
CD B0 a a
Step 3 rate tech region tor each land tree
Composite euitabiiny map
10 / e 1 40
22 1^14 /3f>
14 / 20
12 l 12 | 22
Step 4 map eunab ratings to* each land use one map to* each iano uer
"Figure 17.
Factor combination method
(Hopkins 1977).


then that 1 represents zero and signifies no amenities and all cost (Hopkins 1977). The rates are merely relative proportions among the units of measure in which the suitability within a factor was measured.
There are several alternatives for the rating procedure for each factor. These alternatives are, for this purpose, not significant because they resolve the measurement problem of ordinal combination method. The linear combination method does not, however, handle the interdependence of factors previously discussed in the ordinal combination method. Linear combination is the best method available in that benefits gained from using another method would be less than the cost. Even though interdependence does exist, not all factors are interdependent. Everything is not directly linked to something else in the same strength. Some caution should be used when trying to apply the linear combination method to all combinations of factors. Locally, Jefferson County uses the linear combination method to derive suitability maps used by the county for planning purposes.
SUBSECTION 2.44 NON-LINEAR METHOD
Interdependence of factors could be handled if the combination equation were not linear. If the appropriate relationships among factors were known (supplied by ecologists) and expressed in mathmatical functions, then the non-linear combination would be ideal. Instead of relying on the linear mathmatical model to predict suitability, data from a variety of sources would be plugged in and results computed systematically. Presently, this method is no more an idea than reality. It requires knowledge of the full range of relationships of factors and impacts between them. It is not a usable method at this time.
-40-


Most examples of non-linear equations in use now are restricted to simple predictive relationships. The non-linear combination method overcomes the problems of interdependence among factors, but so far it has not been operationally useful for generating land use suitability maps. Examples of this method would be runoff formulas and soil loss equations.
SUBSECTION 2.45 FACTOR COMBINATION METHOD
A slight modification of the gestalt method allows one to deal with interdependence among the factors. It is called the factor combination method (Figure 17). This gain is offset by a tremendous loss in efficiency compared to other methods such as linear and ordinal combination. The first step involves mapping the types of each factor. Step two consists of combining type maps for each of the factors to obtain a composite map of homogeneous regions. No rating is applied. This map lists all possible combination of types within the factors. Step three is the same as gestalt in that the vertical axis lists all combinations. Ratings are then applied to each set of combinations. This step is entirely subjective.
An obvious disadvantage and time consuming portion of this method is identifying the homogeneous regions and assigning the ratings for each combination. Hopkins calculates that if one dealt with ten factors having ten types each that there would be a potential for one billion homogeneous units (the product of multiplying all categories of all maps). By his experience only about 5% of these units actually occur on the site due to spatial corrleation of the site. Even at that, 50 million regions would exist in this example. The primary example of this method is the Plan for the Valleys by Wallace McHarg Associates (1964). Factor combination is best
-41-


for only a few factors.
SUBSECTION 2.46 CLUSTER ANALYSIS METHOD
Cluster analysis as used in the Rice Center (1974) study also identifies homogeneous regions by pairing the most similar sites or groups of sites, based on an index of similarity across the sets of factors. In this method criteria are liberalized to include less categorization. The clustering does reduce greatly the combinations of types to be considered but does not give explicitness such as with the factor combination method. Determining how and where parameters should be set is a judgemental assignment just as the assigning of suitability rankings. Hopkins reports that great care is required in interpretation while significant costs are needed for computation. Its usefulness has yet to be demonstrated.
SUBSECTION 2.47 RULES OF COMBINATION METHOD
Rules of combination (Figure 18) is a label for a class of methods which are a compromise between non-linear combination methods and the factor combination methods. Rules of combination assigns suitability ratings to sets of combinations of types (categories) rather than to single combinations. They are expressed in verbal logic rather than in terms of numbers and arithmetic. It is not necessary to evaluate each combination separately, nor is it necessary to find a precise mathmatical statement of the relationship among factors such as in the factor combination and non-linear combination methods. This method makes the process more explicit than the factor combination method and can deal with interdependence of factors.
Hopkins cites a clear example of this method given by Kiefer (1965).
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KEY TO RECREATION SUITABILITIES
USES SITE CHARACTERISTICS
Developed Uses partial forest cover
slight to moderate soils limitations
mostly 0-10% slopes proximate to reservoir
stable geology
not within drainageways not within inundation area
not critical wildlife habitat proximate to fishing proximate to existing facilities proximate to roads in less visually sensitive areas
areas with good views
fits with context
Dispersed Uses can also occur, with limitations, in areas:
with soils limitations greater than 10% slopes distant from reservoir with unstable geology
within drainageways
within critical wildlife habitat distant from facilities
distant from roads
within visually sensitive areas
Conservation Uses
severe soils limitations
greater than 20% slopes
geologic hazard areas
critical wildlife habitat
within drainageways
shoreline areas
visually sensitive areas
Figure 18. Rules of combination method (Flores Associates, Inc. 1985).
-43-


SUITABILITY CRITERIA: RECREATION COST SAVINGS (or Intensive Recreation
A. Concurrence of 4 acceptable factor* * Prime Suitability m
Concurrence of 3 acceptable factors * Secondary Suitability (2)
Concurrence of 2 acceptable factors * Terttarry suitability (3)
Presence of only 1 acceptable factor > Unsuitable
Factor
Maintenance Site Drainage Maintenance: Site Drainage and Lawns. Playgrounds etc. Maintenance: Lawns, Playgrounds, etc.
Maintenance: Lawns. Playgrounds. etc.
Acceptable Limit
Somewnat poorly drained soils
Min t-3 depth to the seasonal high water table
Concurrence of at least two of the following:
a. Moderate available soil mlsture
b. Fair nutrient retention
c. Moderate shrink-swell potential
Max. 100 tons/acre/year potential soil loss
B. Suitability categories derived from step A are modified by the following site factors:
Factor Location
Lack of gradient (Site Drainage cost)
Excessive run-off (Site Drainage cost)
inner Lowland: Plain;
Outer Lowland: Plain
see Footnote below
Suitability Mod.
1 becomes 2
2 becomes 3
3 becomes 4
1 becomes 2
2 becomes 3
3 becomes 4
4 becomes 5
FOOTNOTE Assumed acceptable limit is that no more than 5^ of site area is required to infiltrate within three hours the excess run*off generated over that site during the 10 year recurrent 24 hour stormmost intense hour.
Figure 19.
Rules of combination for Medford (Juneja 1974).
0
0
M
SUITABILITY
MAP
Figure 20. Hierarchical combination method (Brandes 1973).
44


After mapping the factors, he rates the types within the factors as equivalent to step two in Figure 17 (factor combination). Kiefer then states verbally that, as a general rule, the rating of the worst factor in a given region overrides the rating of all other factors. He then describes how to assign rating to sets of combinations. The combinations may be indicative of the presence of indicated factor types, parameters for factor types, or rules for combining ratings of factors.
Although McHarg's work is usually associated with the ordinal combination method described earlier, many of his studies are more accurately rules of combination. The Medford study (Figure 19) is a well documented example of this method. There are other examples, such as the Minneapolis-St.Paul study and the Skippack study.
The obvious advantage of using the rules of combination method is that ratings do not have to be applied for each possible combination of factors and types. This saves time and energy. In addition, the rules are explicit and subject to scrutiny by others. Interdependence of factors can be included in the criteria and handled.
SUBSECTION 2.48 HIERARCHICAL COMBINATION METHOD
This method can be visualized as a tree (Figure 20) where each of the numbered boxes represents a factor map (or its relevant types). Maps 1 and 4 are combined to produce Map A. Maps 5, 3 and 7 are combined to produce Map B. Maps 'A' and 'B' are combined to produce map C and so forth. Eventually a suitability map is achieved. In this approach, a combination of types is evaluated only once, rather than being evaluated each time it is a part of a combination. The theory is that strong interdependence will
-45-


remain throughout. Therefore, additions of independent factors will yield a more realistic picture of reality and therefore suitability.
Few studies have implemented this method. Most notable is the Honey Hills (Murray, et. al. 1971). That study was hampered by formating problems which resulted in limited validity.
SUBSECTION 2.5 OUTDOOR RECREATION
Webster's New World Dictionary, 1965 edition defines recreation as "refreshment in body or mind, as after work, by some form of play, amusement, or relaxation". Recreation is a subset of leisure, an often interchangeable, but not synonymus term. The simplest distinction is that leisure involves time while recreation connotes activity (Pigram 1983). Leisure can be demonstrated by Figure 21. Recreation, on the other hand is an activity entered into for pleasure and satisfaction during leisure time. Work is not recreation nor is attending to survival activities and other highly obliatory functions. Normally, no obligation or economic incentive can be associated with an activity for it to be considered as recreation.
Recreation activities that occur outside of closed buildings or structures are called outdoor recreation. Outdoor recreation is too often perceived as a product, resource, or other physical entity. It should be more appropriately viewed as a social service system in which programs are designed to meet human needs (Juhenville 1976). Kelley (1983) identifies three classifications of benefits from outdoor recreation. The personal benefits include enjoyment of participation, excitement, relaxation, escape, environmental appreciation, learning, tranquility and stimulation. As well, long term personal benefits such as self-enhancement, competence and renewal
-46-


LEISURE
Figure 21. Leisure time (Pigram 1983).
Participation in Outdoor Summer Recreation Activities.
Source: Gold, 1B80t 164
Figure 22. Demand for activities in the U.S.
recreational (Pigram 1983).


of both mind and body are realized. Societal benefits refer to enchancement of groups and communities, increased worker productivity, higher levels of public health and contribution to social cohesion. Economic benefits occur at various levels of the economy. These economic benefits include economic development, contribution of resources to the market and employment created in the production of goods and services. Others believe that dedicating land to recreational uses may save it from degradation that might occur if allocated to other uses.
The participation in and demand for recreational activity has spiralled upward over the last forty years (Pigram 1983)(Figure 22). The reasons for this are several. First, having rather swiftly moved from a frontier culture to an agricultural one, Americans are now adjusting to an urban culture dominated by man-made environment. The availability of more leisure time produced from an 8-to-5 work schedule, higher incomes from specialized jobs and more mobility due to better roads and highways have made the recreation cost, and the time and means of getting it, more palatable.
Urban densities and concomitant stresses require escape to refresh and rejuvinate the mind.
In Colorado, this demand is recognized by the State as a serious problem. The Colorado Land Use Commission in its report "A Land Use Program for Colorado" states that of three major land use challenges (to the State) in the environmental field, one is "to provide adequate and accessable recreational opportunities for its citizens". The report goes on to say that Colorado's particular situation is compounded by the fact that of Colorado's 24 million acres of recreational lands, 97 per cent is owned by the U.S. Federal government. As well, most of those recreation lands are
-48-


located on the West Slope while seven-eights of the states' population lives east of the mountains, more than half of them in the Denver Metropolitan Area.
Although there is a clear indication of greater demand for recreation across the U.S. and an anguishing dilemma in the State of Colorado, the extent of which recreation resources will be developed and used rests primarily on the concept of the land's carrying capacity. Carrying capacity is the most prevalent analysis barometer used in recreation planning. In essence, suitability analysis helps determine (delineate) the planning area's carrying capacity. William Catton (1982) has noted that "environments are finite; users and uses multiply and compete". Carrying capacity means the extent to which an environment can tolerate a given kind of use by a given type of user" (1982). For recreation planning and management, carrying capacity has been defined as the amount of use a recration area can sustain without deterioration of the experience provided or of the resource base (Steiner 1983). Lime and Stankey (1971) distinguished three aspects of outdoor recreational carrying capacity: management objectives, visitor attitudes, and impact on biophysical resources. These three aspects are viewed as interdependent, with no one being necessarily more important than others. Suitability analysis offers a framework to evaluate these aspects (Steiner 1983). More research is needed to clarify the relationship between suitability analysis and carrying capacity. This research would greatly enhance the effectiveness of resource planning and management.
SUBSECTION 2.51 RECREATION CLASSIFICATIONS
-49-


The Outdoor Recreation Resources Review Commission classifies all recreational resources into six major classes:
Class I High Density Recreation Areas. These areas are intensely developed, with high facility development, intended for mass usage and generally located within high population centers. There is a heavy impact on the natural systems. Usually, these would fall within municipal boundaries. Examples of this type would be concert halls, sports stadia, tot lots, mini-parks, playgrounds, play-fields, gardens, etc.
Class II General Outdoor Recreation Areas. These areas are moderate to substantially developed and provide for a wide variety of less crowded recreation opportunities. Facilities may be simple to elaborate and although the natural systems may be impacted, generally they are intact. These areas are generally found at the state and county level; they can be found at the municipal level via a strong park system. Examples for this type include camping, picnicking, fishing, water sports, nature walks and golf courses. This type is less user oriented with some emphasis on natural systems.
Class III Natural Environment Areas. These areas may contain some improvements such as access roads or trails and provide for recreational activities that emphasize the "outdoors" experience. These areas occur throughout the U.S. and constitute the largest class in terms of acreage. They may be associated with other related activities, i.e. grazing, timbering or mining even though their general natural characteristics remain unchanged. Often they adjoin unique natural and primitive areas (Classes IV and V) such as state parks and forests. Typical recreation activities include hiking, hunting, fishing, camping, picnicking, canoeing and
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sightseeing. Usually these areas are found under state and federal management.
Class IV Unique Natural Areas. These areas possess outstanding scenic splendor, unique natural wonder or scientific importance. Primarily they are areas of irreplacibility. The primary management objective of the state and national authorities is to preserve the integrity of the natural conditions by limiting the kind and amount of recreation uses.
Class V Primitive Areas. These roadless environments, with expansive solitude and unspoiled natural conditions offer refuge from civilization while giving mental and physical relaxation as well as inspiration to the visitor. The wild, natural, undeveloped characteristics of the primitive areas distinguish them from the other resources in this classification system. Recreational experience are limited to the "wilderness experience". Occassionally under state control, generally the primitive areas are under national management.
Class VI Historic and Cultural Sites. These are sites, buildings and objects associated with local, county, state or national history, tradition, or cultural heritage. They do not provide the usual outdoor recreation opportunities but are closely associated. The primary management objectives are restoration, rehabilitation and interpretation of the significance of the above to the public. These historic and cultural recreational features occur at all levels of management.
The six class levels of recreation and their general location are summarized in Figure 23. Natural resources have been condensed into one category for this study.
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OUTDOOR RECREATION CLASSIFICATIONS
AUTHORITY
LEVEL/
CLIENT
INTENSIVE GENERAL NATURAL HIST/CULT.
LOCAL
COUNTY
STATE
FEDERAL
Figure 23. Recreation classes and authority level/client matrix.
SECTION 3.0 STUDY OBJECTIVES
In 1977, Lewis Hopkins outlined eight methods for generating land suitability maps. He then went on to invalidate most all of these on the basis of improper data synthesis. This thesis will first attempt to determine if those methods invalidated by Hopkins can reach the same or similar spatial patterns when tested against a common data base. If they do, then the disqualification by Hopkins will be disproved. Although Hopkins' denunciation has been common knowledge for some time some of these methods are still in use today. The second objective of this thesis is to evaluate these methods for use in recreation planning at local, state, county and federal levels based on dimensions other than their algorythmic properties to arrive at a recommended method for each level and purpose. Finally, this thesis will develop a selection methodology to help others choose the appropriate method for suitability mapping based on their recreational planning purposes.
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SUBSECTION 3.1 HYPOTHESES
Based on the stated objectives of this study, the following hypotheses are proposed:
1. The choice of appropriate suitability methods for recreation planning can be demonstrated.
2. Suitability methods are not equally appropriate for all recreation planning purposes.
3. All suitability methods arrive at the same or similar spatial -
patterns.
SECTION 4.0 STUDY PROCESS
The methodology for this thesis broadly follows the process outlined in Figure 24. Certain procedural recycling loops necessary to accomplish the methodology have been left out to avoid confusing the reader. This process reflects the objectives previously defined, while at the same time, directing its focus.
First, suitability will be defined and several notable individuals who have dealt with suitability to a considerable extent (Vink, Hills and McHarg) will be expounded upcr.. Their contributions will be noted, and summary statements about suitability made. At that point, each of the methods indicated by Hopkins (1977) used to generate suitability maps will be discussed and displayed with Hopkins comments.
At this point, recreation, as a selected land use, will be applied to two different site data bases. Land suitability maps will be generated from these data bases. A comparative analysis of the methods tested with the two data bases will be done. Then the goals, emphasis and requirement of recreation planning at various levels will be enumerated. With the above
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Figure 24. Thesis outline.
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information, a careful match between recreation planning requirements and the method's abilities will lead to selecting the appropriate method at each level of planning. Once the ability to select an appropriate method for suitability mapping is clear, then this can be demonstrated.
SUBSECTION 4.1 SELECTED LAND USES
Recreation, specifically outdoor recreation, was chosen for this study for several reasons. First, it has a close affinity to the land and associated natural attributes. Granted, all land uses are related to the land but outdoor recreation can occur over the broadest range of land characteristics. Land uses such as agriculture cannot occur in mountainous terrain or within urban areas very readily. The same would hold true of forestry or housing. Second, outdoor recreation occurs at all levels of government. Be it in a neighborhood or in a national park, outdoor recreation serves the most diverse user interests of all major land uses. This diversity offers landscape analysts difficulties in selecting a suitability method appropriate to their requirements. The chief goal of this thesis is to remove some uncertainty from that selection process.
SUBSECTION 4.2 DATA BASES
Two differing data bases will be used for comparison of the suitability methods noted by Hopkins. Both are raster based, grid systems stored in the Prime 750 computer at the University of Colorado at Denver. Access and manipulation of this information is done thru the Map Analysis Package developed by Dana Tomlin (Yale). The first, called the Alabama data, has 1575 cells, 825' x 825'. The factors are varyingly appropriate for consideration. The catagories of some of the factors are, as well,
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varyingly appropriate. The second, called Teller County data, has 8,736 cells. The data is an accurate depiction of the data for Teller County, Colorado. The Teller County information was entered into the Map Analysis Package in the spring semester of 1984 by students of the Ecological Analysis class (Shamberg 1984). The data is all usable and very detailed. The assumptions and values used for rating and ranking the two data bases are described in the Appendix.
Via a computer terminal, the Map Analysis Package allows the user to make new maps from the existing maps, reassign values to existing data to create new maps and to prepare statistical analyses of the new maps.
Through a series of commands to the mainframe computer, the synthesis procedure used for the ordinal combination, linear combination, factor combination and rules of combination can be replicated. A slight modification is required to replicate the last two models. The modification is explained in detail in the Appendix.
The purpose here is not to advocate the use of computers. Each of these methods could have been evaluated with traditional map data. The reason why a computer analysis was selected for this study are: (1) the computer data was already existing for both data base areas, (2) the data bases are of a different depth and detail, (3) no mapped data base was available for other sites, and (4) a computer can replicate the processes used for mapped data, but at such an expedient rate that two data bases could be used for this study increasing familiarity to the methods and vailidity of statements.
'r
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SECTION 5.0 RESULTS
SUBSECTION 5.1 COMPARATIVE EVALUATION
Upon closer inspection, these various suitability mapping methods previously described may be thought of as only two distinctly different types prescaled and postscaled (Brandes 1973). Prescaled methods begin by collecting available data on all factors within the study area (Figure 3). Then a value, either number characters or colors, is assigned to each category for each factor. The values are then combined by mathmatics or overlays and the results are ranked. A land use map is generated based on the rankings and certain overriding rules are applied, i.e. exemption of land areas within flood plains. The final map is then completed.
Postscaled methods begin by gathering all available data on the site factors (Figure 4). All possible combinations of data are identified and listed. Rankings are assigned to these combinations and a preliminary map completed. Excluded areas identified by overriding rules are deleted and a final suitability map finished. The general advantages and disadvantages of the prescaled and postscaled methods are shown in Figure 25.
Hopkins has chosed to point out the weaknesses of each of the methods mentioned above. In turn, he has ruled out most of the predominant methods used by landscape architects to derive suitability maps. This thesis portends that the usefulness of these methods rests not on a singular dimension, but on a series of dimensions that together would suggest a proper utilization for varying purposes.
The critical criteria on which this thesis will evaluate each of the methods above are: explicitness, sophistication, confidence, efficiency and
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PRESCALED METHODS
ADVANTAGES:
1. REDUCES COMPLICATED DATA
2. EXPLICIT PLANNER VALUES
3. EFFICIENT DATA PROCESSING (COMPUTERS, CALCULATORS)
4. HANDLES SOPHISTICATED DATA
5. EASY TO REPLICATE
6. COMMUNICATES RATING PROCESS
DISADVANTAGES:
1. LOSS OF INFORMATION (DETAIL)
2. REQUIRES KNOWLEDGEABLE PLANNER/ANALYST
3. DIFFICULTY RANKING SUM OF VALUES
4. IGNORES SYNERGIES
POSTSCALED METHODS
ADVANTAGES:
1. PLANNER/ANALYST DIRECTLY ASSIGNS RANKINGS
2. HIGH CONFIDENCE LEVEL
3. RETAINS DATA DETAIL FARTHER INTO PROCESS
4. DATA QUALITY IS CONSIDERED
5. FLEXIBLE/ADAPTABLE
DISADVANTAGES:
1. INEXPLICIT RANKINGS
2. REQUIRES HIGHLY KNOWLEDGEABLE ANALYST/PLANNER
3. DIFFICULT TO REPLICATE
4. CAN BE INEFFICIENT
Figure 25. Advantages and disadvantages of prescaled and postscaled methods.
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reproducibility. These criteria were chosen because they represent the broader communicative contributions of these methods. To be effective, these methods must be able to impart information, other than results, to the listening audience. These criteria illuminate the effectiveness of doing so for each method. More detailed descriptions of these methods will be included with the critical criteria in Figure 30. These criteria evaluations will be matched against the requirements of suitability functions to arrive at the appropriate method for each.
The actual analysis of the ordinal, linear, factor and rules of combinations performed under this study is documented in the Appendix.
Those results are incorporated into Figure 30 under the category "verifiable". It was hoped that this information would be contributory to an overall confidence rating for each method. However, the results are inconclusive and of relatively little value in establishing the rating.
This does not mean that a confidence rating cannot be established using the criteria indicated in Figure 30. It only indicates that the confidence rating given is weaker without proof of a common conclusion by each of these methods.
SUBSECTION 5.11 GESTALT METHOD
The gestalt method was not replicated in this study with the data bases from Alabama and Teller County. The reason that it was not done is because these data bases were stored on a GIS (geographic information system).
There were no aerial photos, site visits or contour maps to make gestalt judgements from. This, then is one of the limitations of this method. In order to be used one has to be able to see or get a "feel" for the land
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being studied.
Gestalt is useful 1 as a first attempt at getting a handle on the site under consideration. It is in no way authoritative when practiced by someone unfamiliar with the nuances of a particular site. That is not to say that individuals intimately familiar with the land's qualities (i.e. a farmer) could not make fairly accurate gestalt judgements to support or refute suitability results obtained through another method.
Gestalt is a postscaled method. Postscaled means that the ranking for. suitability are given to the identified regions after they have been identified. Usually these regions are ranked on a ordinal scale through a criteria system that may or may not be made clear (to the audience).
Because gestalt relies on such a small portion of the information available to planners and analyst, its results can hardly be considered reliable. Chances are two individuals could not reach the same suitability ranking for a diverse site due to inexplicit criteria.
In terms of sophistication, gestalt is the lowest rated method. Very little data is "analyzed" and very few processing steps are taken. For small or less diverse sites this is an advantage. Very little time is spent actually analyzing the data and more time is spent establishing the suitability rankings. This gain is offset by the non-reproducibility of this method. Should another individual desire to recreate a gestalt study, little hard data would exist, site visits would be required, and no explicit system would exists to explain how data was synthesized. This method ranks low in almost all of the criteria of explicitness, sophistication, confidence, efficiency and reproducibility. Its applicability for use in solving significant land use suitability determination is also low.
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It is difficult to envision gestalt as being useful at any level of recreation planning except at the local level. Primarily at the local level, individuals may have the expertise to deal with a site's factors through long and continued exposure to it. Gestalt should remain as a first time attempt to organize site data. It cannot be considered as a good method to use.
SUBSECTION 5.12 ORDINAL COMBINATION METHOD
Ordinal combination is a prescaled method. Prescaled means that values are assigned to the types of all factors before they are synthesized. In this study an ordinal combination with numerical indexes was replicated on the Alabama and Teller County data bases. This information can be seen in the Appendix. Numerical indexes were used because the Map Analysis Package could handle that type of data manipulation.
Several observations are brought to mind from working with numerical ordinal combinations. First, gestalt or "professional opinion" is a major portion of this method. It begins with assigning values to the types of each factor and then surfaces when the synthesis is completed. Assigning a "5" (if a 1 thru 5 scale is used) to the best locations for recreation or a "1" to the worst locations is relatively easy. The judgemental difficulties arise when trying to distinguish among 1, 2, or 3. Finally, when the numbers are summed into the totals for each area, the limits of suitable areas are usually based on looking at a string of numbers that go from largest to smallest. In these cases, the determination is made on "best professional judgement".
Obviously the mathmatical properties used in this method, as noted by
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Hopkins, cast some doubt about the results gained by using it. This method has not fallen in disfavor because of this flaw, though. Its simplicity and straight foreward approach to data synthesis still draws a significant number of professionals to use it, as can be seen by the examples shown.
But regardless of its magnetism this method has a confidence rating of moderate. One way to look at this method is that it predicts with some accuracy land areas at the "high" and "low" ends of the spectrum. Between those ends, it can only "suggest" appropriate land areas and should not be taken literally.
When the value ratings of the analyst are included with the results, this method offers a clear and simple demonstration of how its results are obtained. Usually though, the analyst's values are left out of any documents or reports which leave the reader (or listener) at a loss. Acknowledging these difficulties, ordinal combination receives a high rating for explicitness.
Ordinal combination is not a complicated data synthesis process. It does imply that the analysist can make evaluations of the data and discern the ramifications of doing so. As in all prescale methods, emphasis is placed on the analyst's ability to understand sophisticated data and assign values accordingly. For this reason ordinal combination has a high sophistication rating. Also, data synthesis is a simple process under ordinal combination and its efficiency rating is high. Assuming that the analyst's values are explicitedly stated, this method could easily be reproduced.
Ordinal combination may have some usefulness in the lower levels of recreation planning. At the local level it may satisfy because of its
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explicitness and clear cut methodology. At the upper levels, this method falls short due to the improper mechanics involved. This basic flaw would not stand up to intense scrutiny.
SUBSECTION 5.13 LINEAR COMBINATION METHOD
Linear combination is a prescaled method. It was replicated on the data bases of Alabama and Teller County thru the Map Analysis Package. Some reservations about this method surfaced while trying to use it.
Just as in the ordinal combination method described earlier, professional judgement is required to assign the values. In the linear combination, not only are values assigned to the 'types of each factor but then an importance "weight" is then assigned to each factor. The pertinent question here is if there is only a certain level of confidence in the rates an weights assigned, what effect does the cumulative uncertainty have on the result? For example, Alonzo (1968) states that there is some merit to this mistrust. He wants us to imagine that we are argueing 'A' then 1B', 'B' then 1C', 'C' then 'D' and so forth. If we are 80% certain of each step in the chain, from the joint probably of the steps, it follows that we are less than 50% certain of where we stand after four steps. Linear combination may be considered as more inconclusive and more sophisticated but is may well be more fraught with cumulative error.
Not withstanding the above mistrust that has yet to be proven true, linear combination rates high for explicitness. Values that are assigned can be easily deduced from the mathmatical formula should they be provided with the results. This method also receives a high sophistication rating basically because of the unlimited number of variables that can be included
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in the formula and because of the skill required of the analyst to assign the values and weights.
The confidence rating is high for the linear combination method, in light of the above uneasiness, because the improper mathmatical properties that plague the ordinal combination method have been removed by using interval rather than ordinal ratings. The base formula for this method is a statistically accepted approach for weighted averages. There are no inappropriate mathmatical operations. Mathmatical methods in general have high efficieny ratings, this one is no exception to the rule. They benefit from the uses of technology (calculators, computers, etc.) which quickly processes mathmatical data. Because of this, the linear combination method can be quickly reproduced and replicated which implies a high replicability rating.
The linear combination method seems to have all the right qualities.
It is inclusive, properly done, and allows for easy replication. It is probably too complicated a method to use at the lower levels where another may serve just as well. Certainly it bodes well at the upper levels where more demands are placed on justification and explicit methodology.
SUBSECTION 5.14 NON-LINEAR COMBINATION METHOD
Non-linear combination is a prescale method. It was not replicated for this study. It has a high explicitness rating like most other mathmatic methods because of the expose of analyst's values. Non-linear combination also rates high in sophistication because the user is required to be so enlightened. Confidence in this method is very high. Its results are very accurate. However, because the information required to utlize the
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non-linear combination method is not yet available, this model can be very inefficient to try to implement. The efficiency rating is moderate. Like most mathmatic methods, the reproducibility of this method is high.
The non-linear combination method is an attempt to more fully predict the dynamic reactions between environmental factors. It is hampered by the lack of information, the same information that Ian McHarg pleaded for almost twenty years ago. Until such time that the dynamics between environmental factors is documented, then a mathmatical model that represents those relationships is developed, non-linear combination methods will be relegated to supplying incremental sub-portions of information about the environment.
Surely the non-linear combination method can contribute greatly to the information of other methods -- which is its relegated position at this time. As an overall methodology, non-linear would be hard pressed to claim abilities at any level of planning. Until further information is available, this method has little utility.
SUBSECTION 5.15 FACTOR COMBINATION METHOD
Factor combination is a postscaled method that was reproduced with some modifications for this report. It places high emphasis on professional discretion. Each combination of every factor type is mapped then ranked by the analyst. The number of possible combinations is phenomenal and each one must be given a separate ranking. Because of the time required to attend to all possibilities, the efficiency of this method is very low. By the same token, subtle distinctions between types of factors, for so many combinations, would be difficult to manage. Therefore, the confidence rating for this method is no more than moderate. It can be said however
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that a trained analyst should be able to discern the most suitable combination factors and the least suitable combination of factors. The difficulty occurs in the middle ranges.
Difficulties arise also when trying to define the criteria for arriving at suitability rating through this method. With all the possibilities, explicitness could, at best, be expected as moderate. A high sophistication rating is the result of the high requirements for the analyst to apply rating scales. With moderate explicitness and high sophistication, it would be impossible to rate the reproducibility at more than low.
Factor combination appears not to be significantly different than the non-linear combination method. Because factor combination allows one to see all the data in one particular cell or area, the analyst can make some prediction based on the combination of categories. If the non-linear method were sufficiently developed then it could feed into the analyst's value assignment by predicting the dynamics of the category combinations.
Factor combination, due to the limitations of possibilities calculable by the analyst, would seen to be more suitable at a small scale where smaller numbers of data need to be analyzed. Factor combination would be appropriate at all levels except the county where too many factors have to be considered.
SUBSECTION 5.16 CLUSTER ANALYSIS METHOD
Cluster analysis is a variation of the factor combination method and shares many of its traits. It is also a post-scaled method. The primary distiction between these two methods are that the criteria in cluster analysis are lessened to include variations within the data.


The explicitness rating for this method is moderate. Even with the lesser number of evaluations to be made, cluster analysis would be difficult to express the limitations considered by the analyst. Cluster analysis is a sophisticated method and rates high in that category. This method rates low in confidence due to the variations within the units identified as suitable. Cluster analysis also rates low in efficiency due to time investment for the analyst. Reproducibility is likewise low due to the subjectiveness involved in the rating process and low explicitness.
Cluster analysis may be useful! at even more scales and levels than factor analysis because it resolves the problem of severe data analysis.
For recreation planning this method may have a special usefulness.
Recreation occurs over a broad variety of topographic regions. At a larger scale, cluster analysis could be used to predict more possible recreation lands easier than other methods.
SUBSECTION 5.17 RULES OF COMBINATION METHOD
Rules of combination is a post-scaled suitability method. It was reproduced as part of this study with some modifications as noted in +he description in a later section. The benefit of this method is that suitable areas are determined by a set of rules, as is implied in its name, established by the analyst. The concept here is that the analyst must be quite knowledgeable about the site factors to be able to explicitly state which parameters are important and which are not. The analyst can either state in sentence form the areas most suitable or can develop a criteria set not related to manipulation of the data. Irrespective of the form chosen, the analyst must be fully immersed in the site characteristics.
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The explicitness rating for the rules of combination method is high because it clearly outlines the criteria used by the analyst to arrive at decisions. The sophistication and confidence rating are both high because of the demands placed on the analyst and the ability to clearly demarcate areas of suitability. The efficiency rating is high, as well. This is because no time is lost through mathmatical manipulations and there are no limitations on the processing of data. Reproducibility is high due to the explicitness of the criteria.
Rules of combination is really a hybridization of gestalt and factor combination. The rules can be developed in preliminary portions of the study or after having some experience actually laoking at the data in a manner related to factor combination. In all three methods, the subjective values of the analyst are used to determine suitability ratings. In the rules method, those criteria are clearly spelled out.
Rules of combination would be possible at any level of recreation planning. Since it does not rely on mathmatical algorythms or evaluations of combinations of data it can easily be adopted for various purposes.
There seems to be no upper or lower limitations to this method.
SUBSECTION 5.18 HIERARCHICAL COMBINATION METHOD Hierarchical combination is also a postscale method. It was not replicated for this study. The essence of this method is that a hierarchy is established to govern the order in which factors are combined with factors. The concept is that the interdependency among factors is the criterion used to determine the combination of factors. For example, geology, hydrology, and soils maps may be combined to create a new map.
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Vegetation, wildlife, and slope may be combined to form another new map. These two new maps would then be combined for a third new map.
The explicitness rating for the hierarchical combination method is high because these hierarchical criteria would be spelled out in advance. Its sophistication level would also be high because of the demands on the analyst. Confidence and reproducibility ratings would be high because of the high explicitness and sophistication ratings. However, the efficiency rating is only moderate due to the necessity of information not yet available.
Hierarchical combination suffers from the same lack of information as the non-linear and factor combination methods. As with them, it will be of little value until that information is supplied and will be considered too specialized for use.
SUBSECTION 5.19 SUMMARY OF COMPARATIVE EVALUATION
In summary, the gestalt method is a poor method for use by anyone less than intimately familiar with a particular land area. Of the mathmatical methods, ordinal would hardly seem to be a proper selection due to its flaws. Non-linear combination is severely limited for usage due to shortage of proper information. Linear combination is acceptable although not complete alone. The factor methods are limited not by mathmatics but by the ability of the user to discern subtle differences. The rules methods have little problems except for hierarchical which requires the same data as several of the others above. The advantages and disadvantages of each of the methods can be found in Figures 26, 27 and 28. A summary of these methods can be found in Figure 29.
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GESTALT
ADVANTAGES disadvantages:
1. HIGHLY EFFICIENT 1. USES ONLY PORTIONS OF
AVAILABLE DATA
/ 2. INEXPLICIT VALUES
3. NOT REPRODUCIBLE
FACTOR COMBINATION
advantages: DISADVANTAGES:
1. PLANNER/ANALYST VIEWS ALL 1. VERY INEFFICIENT
POSSIBLE DATA COMBINATIONS 2. MODERATE EXPLICITNESS
3. REQUIRES HIGHLY KNOWLEDGEABLE PLANNER/ANALYST
4. HANDLES SMALL QUANTITY OF COMBINATIONS
CLUSTER ANALYSIS
ADVANTAGES: DISADVANTAGES:
1. IMPROVED EFFICIENCY 1. REQUIRES HIGHLY KNOWLEDGEABLE
2. FEWER COMBINATIONS PLANNER/ANALYST
3. PLANNER/ANALYST VIEWS ALL
POSSIBLE COMBINATIONS
Figure 26. Advantages and disadvantages of each suitability method.
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ORDINAL COMBINATION
advantages: DISADVANTAGES:
1. EXPLICIT PLANNER VALUES 1. IMPROPER DATA ASSUMPTIONS
2. SIMPLE DATA SYNTHESIS PROCESS 2. MODERATE CONFIDENCE RATING
3. HIGHLY EFFICIENT 3. DIFFICULTY RANKING SUM OF VALUES
4. EASILY REPRODUCED
LINEAR COMBINATION
ADVANTAGES: DISADVANTAGES:
1. ACCEPTED METHOD FOR WEIGHTED 1. REQUIRES HIGHLY KNOWLEDGEABLE
AVERAGES PLANNER/ANALYST
2. HIGHLY EFFICIENT 2. IGNORES SYNERGIES
3. HIGHLY REPRODUCIBLE
4. HANDLES SOPHISTICATED DATA
5. HIGH EXPLICITNESS
NON-LINEAR COMBINATION
advantages: disadvantages:
1. HIGHLY SOPHISTICATED DATA 1. USES ECOLOGICAL DATA NOT
2. HIGHLY EFFICIENT AVAILABLE
Figure 27. Advantages and disadvantages of each suitability method.


RULES OF COMBINATION
advantages: DISADVANTAGES:
1. HIGHLY EXPLICIT 1. REQUIRES HIGHLY KNOWLEDGEABLE
2. HIGHLY SOPHISTICATED PLANNER/ANALYST
3. HIGHLY REPRODUCIBLE
4. HIGH CONFIDENCE LEVEL
5. HIGHLY EFFICIENT
6. HIGHLY ADAPTABLE/FLEXIBLE
HIERARCHICAL COMBINATION
ADVANTAGES: disadvantages:
1. HIGH RATING IN ALL 1. USES ECOLOGICAL DATA NOT
COMMUNICATIVE CATEGORIES AVAILABLE
2. REQUIRES HIGHLY KNOWLEDGEABLE
PLANNER/ANALYST
Figure 28. Advantages and disadvantages of each suitability method.


PRESCALED METHODS
SOIL + SLOPE + VEG = SUM - > RANKING = ORDINAL
(4)SOIL + (3)SLOPE + (l)VEG = SUM - > RANKING = LINEAR
4 + 3 + 1
F(SOIL) x F(SLOPE) x F(VEG) = SUM - > RANKING = NON-LINEAR
POSTSCALED METHODS
PHOTO
AREA #82 10% SLOPE
HIGH PERMEABLE SOIL ASPEN VEGETATION
<10% SLOPE
MOD. TO HIGH PERMEABLE SOIL GRASSY TO DECIDUOUS VEGETATION
"AREAS WITH 10% SLOPE, MOD. TO HIGH PERMEABLE SOIL, GRASSY TO DECIDUOUS VEGETATION,
OUTSIDE FLOODPLAINS, GREATER THAN i MILE FROM HIGHWAYS"
SOILS, SLOPE AND VEGETATION ARE MOST SIGNIFICATLY RELATED FACTORS
= RANKING = GESTALT
= RANKING = FACTOR
= RANKING = CLUSTER
= RANKING = RULES
RANKING = HIERARCHY
Figure 29. Summary of suitability methods.
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SUBSECTION 5.2 PROPERTIES OF METHODS
Figure 30 is the graphic representation of the comparative evaluation discussed previously. The purpose for this graphic is to clearly summarize the properties of each suitability method. The most significant portion of this summary is the ratings of the critical criteria of explicitness, sophistication, confidence, efficiency, reproducibility and the summary Usefulness Levels 1, 2, or 3. The Usefulness Level is the basis of further evaluations. A more simplified form of this is shown in Figure 5. This graphic illustrates how the suitability methods rank for usefulness according to the critical criteria evaluations.
SUBSECTION 5.3 SUITABILITY PURPOSES
To facilitate the comparison of suitability methods, the purpose or function of the suitability mapping process is identified. There are several purposes for suitability which are mutually exclusive. These purposes can be generally identified as location, allocation, impact, hazards, resources and communication.
The location function involves the location of facilities, design concept development, and program compatibility. Commonly, a suitability study would indicate where on a particular site would be the best location for a facility. A study would be used as the guidance for further design concept development, or as a check for the compatibility of a proposed land use within the land area under consideration.
The allocation function for suitability identifies areas of preferred land uses, development of comprehensive plans and acquisition of other lands. In some cases, the actual use of land may change by making


COMMUNICATIVE QUALITIES
SUITABILITY METHODS
Y YES O ORDINAL
N NO IN INTERVAL L LOW R ~ RATIO M MODERATE NA NOT APPLICABLE H ~ HIGH 1 ~ INCONCLUSIVE GESTALT ORDINAL COMBINATION LINEAR COMBINATION NON-LINEAR COMBINATION FACTOR COMBINATION CLUSTER ANALYSIS RULES OF COMBINATION HIERARCHICAL COMBINATION
EXPLICITNESS ANALYST'S ASSUMPTIONS N N N N N N Y Y
ANALYSTS VALUES N Y Y Y Y Y Y Y
ANALYST'S WEIGHTS N NA Y Y NA NA NA NA
EXPLICITNESS L H H H M M H H
PRESCALE
POSTSCALE
SOPHISTICATION method GESTALT
MATHMATICAL
REGION IDENTIFICATION
LOGICAL COMBINATION
data SOPHISTICATED
INTERMEDIATE
GENERAL
USER SOPHISTICATION L H H H H H H H
SOPHISTICATION L H H H H H H H
CONFIDENCE SYNERGY OF FACTORS Y N N Y Y Y Y Y
CORRECT DATA MAN 1 PUL AT. NA N Y Y Y Y Y Y
CONSISTANT RESULTS N Y Y Y N N Y Y
VERIFIABLE N 1 1 Y 1 1 Y Y
CONFIDENCE L M H H M M H H
EFFIENCY MANUAL MANIPULATION
MACHINE MANIPULATION
USES AVAILABLE TECHNOL. N Y Y Y Y Y Y Y
USES AVAILABLE INFORM. N Y Y N N Y Y N
SPATIAL LIMITATIONS N N N N N N N N
DATA LIMITATIONS Y N N N N N N N
OUTPUT TYPE O 0 IN IN/R 0 0 0 0
EFFICIENCY M H H M L L H M
REPRODUCIBILITY TIME INVESTMENT L L M L H H L L
VALUE SUBSTITUTION N Y Y Y N N Y Y
REPRODUCIBILITY L H H H L L H H
USEFULNESS LEVEL 3 2 1 3 2 3 1 3
Figure 30. Properties of the suitability methods.
-75


judgements about which land uses are more appropriate and therefore the preferred land use to adopt (i.e. Hills' task in Canada). At larger scales suitability maps may lead to the adoption of results a comprehensive plan which would be enforceable by zoning regulations. Suitability studies may also be used by municipal entities to determine which lands would be appropriate for acquisition by annexation or purchase.
The impact function of suitability mapping is used to determine non-monetary impact assessment, performance criteria and economic evaluation for a given land area. Non-monetary impacts can be ascertained from suitability maps, although the negative biological effects of development cannot be quantitatively defined. Guidelines to mitigate expected negative effects of development are an important function of suitability mapping. Economic data translated from the spatial parameters of the suitability maps can be used for preliminary economic evaluations of a proposed land use.
The hazards function identifies hazards to individuals and society. These would include floods, geologic hazards and wildfire hazards. The resource function indicates the resources available to individuals and society such as minerals and historical/cultural features.
Suitability's communication function includes the demonstration of study logic, use for assistance to other planning entities, and use as an education "tool". By demonstrating the suitability's study logic, individuals not priviledged to the study details understand how decisions were made. Suitability studies of a larger scale can be useful to local entities as contributory to their planning process. At a county level suitability maps may by used as an educational "tool" for enlightening county citizens.
-76-


Each of these functions is applicable to planning in general, and recreational planning specifically, according to the type and level described in Figure 31. Figure 31 draws some conclusions about those relationships. This, by nature, is somewhat inexact because the purposes of suitability maps are not only dependent on the type of recreation class and its level of societal interface, but also to the specific charge (responsibility) given to the landscape analyst. We can assume that our generalities are specific enough to cover all requirements.
At the local level, it can be seen that the suitability's emphasis is on facility location, program compatiblity determination and concept (design) development. There is also a high correlation to a municipality's or special district's ability to acquire new lands via annexation. To some extent, economic evaluations are done by cities, as well as locating hazards and resources, due to their charge of protecting the health, safety and welfare of its citizens.
At the county level, it is easy to see how most responsibility for determining land uses within the state have been placed on the counties. In Colorado, all counties are required to develop a comprehensive plan and control land use via implementation of zoning regulations based on that plan. House Bill #1041 is an attempt by the State to offer guidelines to the counties on matters concerning mineral resources, geologic hazards, wildfire hazards and flood prone areas of State interest. Consequently, counties have the broadest political mandate in that they not only look out for the counties interest but for the State's as well. Therefore, the counties utilize suitability mapping functions to administer these charges through development of comprehensive plans, non-monetary impacts,
-77-


Figure 31. Authority level/client and suitabilty purposes correlation matrix.
-8 L~
AUTHORITY LEVEL/CLIENT
FEDERAL STATE COUNTY LOCAL
HIST/CULT NATURAL HIST/CULT NATURAL GENERAL INTENSIVE HIST/CULT NATURAL GENERAL | HIST/CULT GENERAL INTENSIVE
FACILITY LOCATION 1 LOCATION
PROGRAM COMPATIBILITY
CONCEPT DEVELOPMENT
PREFERRED USE ALLOCATION
COMPREHENSIVE PLAN
ANNEXATION
NON-MONETARY IMPACTS IMPACT
PERFORMANCE CRITERIA
ECONOMIC EVALUATION
HAZARD LOCATION HAZ.
RESOURCE LOCATION RES.
PLANNING ASSISTANCE COMMUNICATE
DEMONSTRATE LOGIC
EDUCATIONAL TOOL
SUITABILITY PURPOSES


performance criteria, economic evaluations, locations of hazards and resources, while contributing planning assistance to unincorporated entities within the county and communicating to the public at large.
At the state and federal levels, zoning of land, comprehensive plan development and regulation of the subdivisions of land are usually not done. They are charged with protecting the health safety and welfare of the public. Suitability analysis would then be geared to locate hazards and resources beyond providing the proper facitlity location. At the federal level, there is increased concern for annexation of land and determining the biophysical impacts of development. The state level of government generally does not focus on increasing its land size and, at this point, has no requirements for analyzing biophysical impacts.
SUBSECTION 5.4 USEFULNESS LEVEL REQUIREMENTS OF THE SUITABILITY PURPOSES
Each purpose of suitability mapping is best suited for a different Usefulness Level. Figure 31 shows the correlation between the recreation classes/government levels and the purposes of suitability. Suitability purposes are the discreet goals of the suitability mapping process identifiable by the planner or client. The purposes of suitability can be generally identified by the planner or client for their ability to locate facilities, determine allocation boundaries, derive impacts, indicate hazards and resources and communicate logic or results. Each of these purposes requires a different communicative quality which is shown in Figure 30 and summarized by the usefulness rating. In other words, the most sophisticated methods may not be appropriate when used as an education tool
-79-


Figure 32. Usefulness requirements of suitability purposes.
COMMUNICATIVE QUALITIES
33 n 2? r "i EFFIENCY CONFIDENCE 8 33 X i 1 5 z EXPLICITNESS V YES O ORDINAL N NO IN INTERVAL L LOW R RATIO M MOOERATE NA NOT APPLICABLE H HIGH 1 INCONCLUSIVE
data malhod
USEFULNESS LEVEL REPRODUCIBILITY s E m g 5 H C H o z TIME INVESTMENT EFFICIENCY OUTPUT TYPE DATA LIMITATIONS v> s H > f r* £ 5 3 o z USES AVAILABLE INFORM. a G 1 £ r m m 8 * E 0 1 z m C £ i c 3 z £ S' r* C > z I r- > d O z CONFIDENCE < in 5 2 m r- m CONSISTANT RESULTS 1 13 5 g £ c I o | SYNERGY OF FACTORS | SOPHISTICATION f GENERAL Z 3 c 3 > H m [ SOPHISTICATED [ LOGICAL COMBINATION | REGION IDENTIFICATION £ > £ > H o r [ GESTALT > m | PRESCALE | EXPLICITNESS | ANALYST'S WEIGHTS 1 ANALYST S VALUES Z P r- H 1/3 (/) c £ 3 5 z (/>
N) £ z z £ X FACILITY LOCATION LOCATION
ro r* £ z £ £ PROGRAM COMPATIBILITY
ro £ £ z £ X CONCEPT DEVELOPMENT
ro x £ z z z PREFERRED USE ALLOCATION
I z z z z COMPREHENSIVE PLAN
- z z z z z ANNEXATION
ro z £ z £ z NON-MONETARY IMPACTS IMPACT
ro z £ z £ X PERFORMANCE CRITERIA
ro z £ z £ X ECONOMIC EVALUATION
ro z £ z £ z HAZARD LOCATION ZVH
ro z £ z £ z RESOURCE LOCATION RES.
ro z £ z £ z PLANNING ASSISTANCE £ c z o > -1 m
u z £ z £ X DEMONSTRATE LOGIC
CO £ £ £ r z EDUCATIONAL TOOL
i
oo
0
1
SUITABILITY PURPOSES


for laypersons. Inexplicit methods are probably not going to be well received by federal planning authorities. Figure 32 establishes the minimally acceptable requirements of the Usefulness Levels mentioned above by the purposes of suitability. This information is then used to evaluate each method in Figures 34 through 39.
SECTION 6.0 INTREPRETATION
SUBSECTION 6.1 USEFULNESS LEVEL RECOMMENDATIONS
Having established the Usefulness Level intrinsic to each suitabiilty method and the requirement of suitability purposes, some conclusions can be drawn about how well they match. Based on the communicative requirements of each purpose, the advantages, disadvantages and the communicative qualities of each of the methods, a recommendation can be made about which level is appropriate for suitability for each purpose (Figure 33). These are the lowest allowable usefulness rating permitted in each category. Recommendations can then be made about which method is more appropriate at each level.
SUBSECTION 6.2 SUITABILITY METHODS SELECTION PROCESS In an attempt to more clearly express the information generated through this document, a methods selection process is shown in Figures 34 through 39. These figures show how the consideration of data is applied to the selection of a suitability method. In all cases, the methods chosen as appropriate would have a communicative rating in each of the categories of explicitness, sophistication, confidence, efficiency and reproducibility equal to, or greater than, the usefulness rating indicated for the
-81-


ua
c
-s
fD
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CO
CO fD ~h C=
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to
to
fD
<
fD
fD
O
O
fD
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OJ
O
=3
to
o
5
rt
o
cr
"O
c
T3
O
to
CD
to
AUTHORITY LEVEL/CLIENT
FEDERAL STATE COUNTY LOCAL
HIST/CULT NATURAL HIST/CULT NATURAL GENERAL INTENSIVE HIST/CULT NATURAL GENERAL HIST/CULT GENERAL INTENSIVE
- - mA -A - - - ro ro FACILITY LOCATION r~
- ro PROGRAM COMPATIBILITY n > H
ro ro mA ro CONCEPT DEVELOPMENT O z
-A -A PREFERRED USE ALLOCATION
A - _A COMPREHENSIVE PLAN
mA mA - - mA ANNEXATION
- mA ro ro IO * NON-MONETARY IMPACTS IMPACT
ro ro ro PERFORMANCE CRITERIA
-A mA A - ECONOMIC EVALUATION
ro ro IO ro IO ro ro ro ro ro ro ro HAZARD LOCATION X > N
ro ro ro ro ro ro ro ro ro ro RESOURCE LOCATION RES.
- - mA PLANNING ASSISTANCE COMMUNICATE
ro ro ro IO ro DEMONSTRATE LOGIC
ro ro ro EDUCATIONAL TOOL
CO
ro
SUITABILITY PURPOSES


AUTHORITY
LEVEL/CLIENT-
RECREATION
--CLASS----
SUITABILITY PURPOSE-
-----APPROPRIATE METHOD
____________DATA___________
LQ/LD* LQ/HD HQ/LD HQ/HO
FACILITY LOCATION 1-----[FACTOR MfACTHMORDInT-{RULES 1
fRULES~MRULES HRULES HLINEAR (
ANNEXATION

HAZARD LOCATION 1-----{FACTOR hfFACTORHRULFS'H RULES 1
FACILITY LOCATION
PROGRAM COMPATABILITY b
CONCFPT DEVELOPMENT }-
ANNEXATION
HAZARD LOCATION
>
RESOURCE LOCATION
DEMONSTRATE LOGIC
FACILITY LOCATION
CONCEPT DEVELOPMENT
ANNEXATION
-{FACTOR (-{FACTOR HRULES t{RULES |
-{factor hfactor[Gordin Hordin 1
-{FACTOR 'MFACTQRj-foMFHftUlES 1
-{rules'Hrules {-TruTeTHlinear'I
ECONOMIC EVALUATION |------------{RULES {fRUl~ES {-fRL?LlES~l{LI NEAR 1
(factor {-{factor{-(ordin'Mrules 1
-(FACTOR'{-{FACTORl-IORDIN (-{RULES'" 1
-{orpin Mordin {-{MdTnV-{rules 1
HRULES (n^UirES (-{RULES {{LINEAR 1
PROGRAM COMPATABILITY 1------{RULES HRULES~(-{ RULES {-{RULES {
------{RULES "|fRULES HTTuTTsl{LINEAR )
I-----{RULES ({RULES^ (-fRULTTHL INEAR {
HAZARD LOCATION 1------{FACTOR (-{FACTOR^{ORDIN'l-lRULES (
DEMONSTRATE LOGIC |-----------(ORPIN (~{ORDTn~|-{ORDINT(RULES |
*L = LOW; H ^ HIGH; Q = QUANTITY; D = DIVERSITY
Figure 34. Suitability methods selection process.


AUTHORITY
-LEVEL/CLIENT-
RECREATION
--CLASS-
SUITABILITY PURPOSE-
*L = LOW; Q = QUANTITY; H = HIGH; D = DIVERSITY
Figure 35. Suitability methods selection process.
-APPROPRIATE METHOD
______________DATA____________
LQ/LD* LQ/HD HQ/LD HQ/HD
jRULES h-fRULESh-fRULES][LINEAR) Irules I[rUles]I rules]|L I NEAR]
I FACTORMrULEST(RULESHL INEAR
IfactorMordinIirules)(Mis
[RULES 1TrULEST{RULES |[LINEAR
factor!TqrdinI(rules!Trules
rFACTORMMplNl|Mts[Irules ) irules iTrulTs]I rules ||l i near I
I FACTOR]TORDTnI{RULES)IRULES | ORPIN |[MdTnI-^RULHHRULES ]


AUTHORITY
-LEVEL/CLIENT-
RECREATION
"'CLASS
SUITABILITY PURPOSE-
-APPROPRIATE METHOD-
______________DATA_____________
LQ/LD* LQ/HD HQ/LD HQ/HD
COUNTY
PREFERRED USE
COMPREHENSIVE PLAN
NON-MONETARY IMPACT
PERFORMANCE CRITERIA
ECONOMIC EVALUATION
HAZARD LOCATION
RESOURCE LOCATION
PLANNING ASSISTANCE
DEMONSTRATE LOGIC
EDUCATIONAL TOOL
RULES ITrULEsI[RULES'!(LINEAR
H RULES iTrULES"!TrUITEsII LINEAR]
-I factor!Trules I[rulesI1l INEARl
H factor![qrdTnI(rUlesTI rules 1
-1 RULES M RULES MMIsH LI NEAR
FACTOR h-fORDINT-fRULESlI RULES
-! factor MordinI[rules Mrules
[RULES 1TrULES~][RULES 1I LINEAR 1
factorITordinI[rules!(rules !
ORPIN |fORDIN 1iRULESl[RULES
*L = LOW; Q = QUANTITY; H = HIGH; D = DIVERSITY
Figure 36. Suitability methods selection process.


AUTHORITY RECREATION
-LEVEL/CLIENT----------CLASS---------------SUITABILITY PURPOSE------------APPROPRIATE METHOD-
_____________DATA_____________
LQ/LD* LQ/HD HQ/LD HQ/HD
[COUNTY
-fHI ST/CULT.
PREFERRED USE
1
COMPREHENSIVE PLAN |-
NON-MONETARY IMPACT
PERFORMANCE CRITERIA
ECONOMIC EVALUATION
HAZARD LOCATION
RESOURCE LOCATION
PLANNING ASSISTANCE
DEMONSTRATE LOGIC
EDUCATIONAL TOOL

[RULES 1fRULEST{RULES 1(LINEAR! RULES"TrIJLES)TfUiUEST[LINEAR |
factor MordTnHrules'Hlinear |
FACTORl-lORDINH RULES HRULES 1
RULES lTrules}~[rules![linear!
4fACTOR MORPIN h-fRULESlHRULES |
[FACTOR [[ORPIN MMlES1~4MIs
rules ^TrlTDesI-|^T3I7es~1[linear
ORPIN 1fORPTiTlj RULES |[RULES
ORPIN 1fORDlrT}[RULES [-[rules 1
I
00
cr>
I
*L = LOW; Q = QUANTITY; H = HIGH; D = DIVERSITY Figure 37. Suitability methods selection process.


AUTHORITY
-LEVEL/CLIENT-
RECREATION
- CLASS---
-SUITABILITY PURPOSE-
APPROPRIATE METHOD
_____________DATA_____________
LQ/LD* LQ/HD HQ/LD HQ/HD
(STATE
4RULES KRULESHRULESHRULES
Ifactor || orpin I(rule's!(linear]
\ FACTORHMDlNl-fMES]-| RULES | \ FACTOR HTiRDlFHRULES}-!RULES ~|
| RULES I|^1JL~ES~|TrUTES]rLINEAR | FACTOR ([~QR^TN~jTrIjL~ES~](RULES |
FACTOR KORDTFTH RULES}-[RULES
FACTOR H ORPIN](RULE~s1(RULES 1
rules hTMTsMMTsjfrules |
( FACTOR j-TORDTTTKMIsH RULES I ( FACTOR t^MDINVTRULlsHm I RULES (-f~RUTES~)[RULES]TRULE S~
\ FACTOR 1{ ORDIN}fRDLESV-fRULES 1
j FACTOR }-f ORDIN1I RULES I| RULES 1
*L = LOW; Q = QUANTITY; H = HIGH; D = DIVERSITY
I
CO
I
Figure 38. Suitability methods selection process.


AUTHORITY
-LEVEL/CLIENT-
RECREATION
--CLASS'
-SUITABILITY PURPOSE-
APPROPRIATE METHOD
_____________DATA_____________
LQ/LD* LQ/HD HQ/LD HQ/HD
[FEDERAL
FACILITY LOCATION \-
ANNEXATION
>
NON-MONETARY IMPACT
HAZARD LOCATION
RESOURCE LOCATION |-
FACILITY LOCATION l
ANNEXATION
>
NON-MONETARY IMPACT \-
HAZARD LOCATION
RESOURCE LOCATION |-
-[rules "Hrules 1-fMIsT--{linear t
-[RULES tTRLJLES~ifRULESl[LINEAR 1
rules HRulesHrulesHlinear
-[FACTOR ][QRDIN~1{RULES{RULES 1 factor 1TordTTTI|ruTTes~|{RULES I
^rules HMTsl[MIsl[linear]
RULES 1[RULES jTRUTTES")[LINEAR] RULES 1[RULES-]TrUlESI[lINEAR'I
F
[FACTOR'HORDIN j-fHLEST--[RULES [
factor][orpin |[rules![rules '1
I
00
00
I
*L = LOW; Q = QUANTITY; H = HIGH; D = DIVERSITY Figure 39. Suitability methods selection process.


suitability purpose chosen. With this graphic diagram, a landscape analyst can quickly determine which method(s) will serve the purposes and levels where they are working.
By first determining the authority level, then the recreation class, suitability purpose and data availability, the appropriate suitability method can be found under the "Appropriate Method" category.
SECTION 7.0 CONCLUSIONS
This thesis has attempted to shed light on a subject previously given
little attention. It has provided new information on the methods of
suitability mapping results and supplied a systematic justification for
recommending the appropriate method for recreation planning at its various
levels. This thesis has also established a methodology for others to follow
%
in selecting an appropriate method for their purposes.
Hopkins invalidated most of these methods based on improper algorythmic operations. The premise of this thesis is that if all suitability mapping methods reached the same spatial conclusion, he invalidation based on a single aspect of these methods is in itself improper. Given that this hypothesis was not proven true, these methods could be useful under certain circumstances due more to other qualities that they possess than to improper mathmatics.
The methodology used in this study involved taking available information (Hopkins) and combining it with new data generated to tackle a very common problem for landscape architects how to select a suitability method. Unfortunately, the results of the study do not support the final hypothesis of this thesis, that all methods reach the same conclusion. It
-89-


does seem to indicate that rules of combination and factor combination, as well as other postscaled methods would result in similar patterns when used by the same individual and the same assumptions. The prescaled mathmatical models of ordinal and linear combination produced the widest variations of patterns. This may well be attributed to the fact that prescaled methods require the analyst to singularity view the parts of the whole without benefit of seeing the interrelationships between them as in the postscale methods. In conclusion, this data suggest that some methods may be more appropriate for different purposes and most likely should be used in combinations with each other to achieve the best results.
Uncertain as we may be about the accuracy of each method to predict most suitable locations, we can state our conclusions on the second hypothesis of this thesis. Rules of combination appears to be usable at any scale of recreation planning. It uses no mathmatical processes, only explicit statements about what consititute suitable lands. Factor combination allows the analyst to view the data will intense scrutiny. This causes severe problems with data handling and explicitness of the criteria used. It could be used at all scales though its uses should be restricted to use where data combinations are few. Linear combination provides the ability to handle more data more efficiently. Its role would be properly at the broader level of county and not at the local level where it may be too sophisticated. Because the linear combination method does not account for interdependencies, it would best be used in combination with rules of combination. Ordinal combination, even with its algorythmic improprieties, may be useful at the local level where explicitness is high and the concept can be grasped by the public. If used with rules of combination, the
-90-


ordinal combination method may be applicable at higher levels. Cluster analysis would have some merit at the state and federal levels. Generalizations, such as are made in this method would be of little value at the local levels. Hierarchical and non-linear combinations are too specialized to be of any use now for suitability mapping. Their function can best be used to add input to other methods. Gestalt can be useful at any level of recreation planning to develop organization. It could be useful at the local level if done by knowledgeable individuals.
In the suitability methods selection methodology, this study has supported the first hypothesis by demonstrating that the choice of appropriate suitability methods for recreation planning can be made and made systematically. The selection methodology represents the findings developed through this thesis and presents interested users a single procedure for determining which method(s) is most appropriate.
In the end, a central question is provoked by this thesis. That question is "Do these methods really predict the most suitable lands for development where no environmental denegration occurs?" A good test would be to evaluate physical development that has occured on land deemed suitable through each of these methods. If it were found through measuring environmental qualities that one method protects the environment better or did not predict the most suitable lands, then this would be quite pursuasive for disqualifying some suitability methods. A task like that would be gargantuan. It also would be quite informative.
-91-


BIBLIOGRAPHY


BIBLIOGRAPHY
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APPENDIX


ALABAMA DATA
DATA MANIPULATION
At this point it is time to look at the methods of generating suitability maps that were compared. It was apparent that not all methods outlined by Hopkins are applicable for replication. Cluster analysis, while highly advantageous, would be of little information to us. Hierarchical combination required information currently unavailable. Future studies will undoubtably gravitate towards this method as data becomes available on the strengths of interdependence. Non-linear combination suffers from the same lack of credible evidence as hierarchical combination. Gestalt required visual resources not available in this study. Therefore, the methods remaining for replication were ordinal, linear and factor combination and rules of combination.
Before the data base could be used, it was necessary to scan the data base for redundance and duplication. The following were the factors deemed appropriate for consideration in arriving at the Alabama data base suitabilities.
Water Aspect
Housing Relief
Roads 1971 Map
Forest Landuse
Utility Location
Slope
ORDINAL COMBINATION OF THE ALABAMA DATA
To "overlay" the appropriate maps listed above, it was necessary to assign an ordinal ranking to the categories of each map. A "0" was assigned to categories that were the worst locations for outdoor recreation. A "5"
-98-


VALUES FOR ORDINAL COMBINATION
Factor: Forest Factor: Slope
Catagories: Forested = 3 Catagories: wr~ = S>
Non-forested = 3 1 5% = 5
6 10% = S
Factor: Roads 11 15% = 3
Catagories: Roaded = o 16 20% = 2
Non-roaded = 5 21 25% = o
26 30% = o
Factor: Housinq 31 35% = a>
Catagories: 0 units/cell = -5 36 40% =
1 unit/cell = 3
2 units/cell = o Factor: 1971 Map
3 units/cell = O Catagories: Surrounding Area = a
4 units/cell = o Residential = £>
5 units/cell = o Commercial =
Communication = o
Factor: Location Urban = o
Catagories: This place = & Crop/pasture = ^f
That place = a * Orchards = 1
Headquarters = <£? Forest = 3
Open space = e Water = 3
Factor: Water Factor: Aspect
Catagories: Dryland = 5 Catagories: North = o
Pond or lakes = 4- Northeast = o
March = o East = 3
Creek = o Southeast = 5
Stream = o South = 5
River = o Southwest = 3
West = 5
Factor: Utility Northwest = 5
Catagories: Nw powerline = o Horizontal =
Central powerline = c>
NE powerline = c>
Railroad = o
Factor: Land use
Catagories: Undeveloped = 3
Commerce = c
Institutional = a
Recreation = &
Mining = o
Factor: Relief
Catagories: >590 feet = 5
^ 590 feet = c>
Figure 40. Values for ordinal combination of Alabama data.
-99-
MJUJIVJI UWOO 0 0 o b $ 0 o 00 0 0 MVsUUIt \n


DISPLAY LAY3
1
+++ OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO + <-+ OOOOQOOOOl 1 l I 1 ] J I 1 122222222223333333333444444444433333333336666 -*+ + 1234 36 73*701234 367370 12343670*70 1234067370 1 2343670*7012343670-70 123
001
002
003
004 003 006 007
ooe
007 OlO Ol 1 012
013
014 013 016
017
018 017 020 021 022
023
024 023
. RRRRR. RR H ######## R RRR # R MR RRRR RR RR RRRRRRRR* **##** ((-4
RRRRRRR
it#. .
. R. RR
. r. r *. RR. R RRRRR, RR* RRRRRR K HH-il rrrrrh r It'S rrrr a it*
. RRRRRlfR-R. RRRRRR RRR R RR RRRR RR
.... RR RRR. .
. RR. .
RR RR RR ?. R H RR
RRR HRR
RRRRRr. R R RRRRR. R. RR RRR F. RR H RR F.
krrrrcirr. R
RRRR. RU. R R RRRR. RRRR RRRR. RR. R RR. RR. .
. RRR *
......R. RR
RRR RRR RR, R RR. RRRRRRR RRRRRRR RRR RRRRR. HR R RR RRR R. RRR
RRR.
RRR. R a R RR. RRRRR RRRRRRRR R. RRR. RRRR RRRRRRRRR K RRR RR RRR RR RRR H RR R RRRRR RRRR
R. RR . RRRRR. RRR*. RRRR....*. RRRRRR RRR
R. . RR. RRR. RRRRRR. HR H. R. RRRRRRRR
RR. RRRR. RRRR . RRRRRRRRRRRRRR RRRR. RRRRR
RRR. R(1RR K RRR OlJa RRR RR. RHROHRRHRR. R R. RRRRR RR RRRRRrjlJU*. U # RR. RRRDfl ORGRR* R. H R RRRR RRR. RRRRRRRRQRRORR. n RRRRRDO*ORRR***RR*R. RRR.
RRR RRRRROROR RORIIRRRRUIJRRRRRRRRR. R. RRR. RR.
. R. R. RRRR. o. RRR. ROOOC)RRRR*R. RR RRHRRRRR
......RRRHH. RRRQRRRR DO***#**. HR RRR. *R
RRRR. . . H, R. RORORDRRHRR RORRROH R H H RRRRRRRR
RR R RRH RR RR. R RRRR. RRR R RRH .
R
.HR .
RR. .
RR RHi
R R
. RR.
R.
. RRH RRR.
RRR. H RRR RDRRRRRRRRRR. RR. RR. RR R. RQRR. R. RDR. HR R. R RRfJORO. RRR. RR*. RRR. RRORDR. DRRRRR. RRRRR.
RRH jt RR fl R RR H R
. RR R
R RR RR R RR
HRUl.iRRDRRRRRKR. H OR RK .
. RIJH RRQRO*. R-H RRRU* R| J R R .
. R RQOORHRR* RRRRR. URU RORROOIDRRRR R. RRR. RRRRRrR R. ORORRHHRO. RRO. RR3RRRR.
H RRRR. RR. RR RRR RR RR RRRR R.
. RR R* RRRRR RRRRR RRR RR
R. K M RRR RRRRRR.RR RR J. RRRRR R RR. R. R RR R. RR H RR RRR RRR RRRRRRRRRRR
R RRH RRRRRR. ORRR. RRH RR. RRRR R R RRRRRR RR RRRDR*U*R. R. RRRRRRRR RR RRRRRRRR. RRRQRR. R. RR. RRR RR. R RRR. RRRRHRRH. RRRRR. R RRRRRRRR. R.
. RRR. . -R. RRRRR RRRRRRRRRRR
. RRRRRR. R R. RRRRR. RRRRRR
+++ COOOOOOOOOOOOGOOGOOOOOOOGOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO + + OOOOOOOOOJ 11111111 122222222223333333333444444444433333333336666 +< + 12343673701234 3673701234367070123406737012343678701234367670123
LAY5
a 0 87 CELLS 5. 57.
+ OUOOGaauOU 3 Prime suitability 74 CELLS *1. 7/1
# it H- -ft it -if -ft ft ft * 4 10/7 CELLS 68. 4%
-* 5 337 CELLS 21. 4%
. 1
i

o
0
1
Figure 41. Map of ordinal combination of Alabama data.
+-+
+++
001
002
003
004 003 006
007
008 007 OlO Ol 1 0 12
01 3 014 O l 3 016
017
018 017 020 021 022 023
02 1 023
+++
++-I


was assigned to categories most suitable for outdoor recreation. Figure 40 shows the values assigned in this study. By using the ADD command of the Map Analysis Package, all the maps were overlaid to arrive at a total score (sum of values). In this study, the sum of values ranged from a high of 47 to a low of 21 per cell. The following chart displays the sum of values and suitability ranking.
Sum of Values Number of Cel
47 = 50
Prime Suitability 46 = 14
45 = 10
44 = 212
43 = 141
High/Moderate 42 = 117
Suitability 41 = 172
40 = 126
39 = 147
38 = 162
37 = 57
Low/Moderate 36 = 95
Suitability 35 = 76
34 = 57
33 = 52
32 = 19
31 = 21
30 = 13
Low Suitability 29 = 15
28 = 11
27 = 2
26 = 3
25 = 1
23 = 1
21 = 1
Figure 41 displays the location of the prime sutability cells for the
ordinal combination method.
LINEAR COMBINATION with the ALABAMA DATA
The linear combination method utilized the AVERAGE command of the Map


VALUES FOR LINEAR COMBINATION
Factor: Forest 62O Factor: Z'' C! s O uo
Catagories: Forested = 1 Catagories: 0% = 4
Non-forested = 2. 1 5% = 4
6 10% = 4
Factor: Roads £40 11 15% = 3
Catagories: Roaded = 1 16 20% = 2
Non-roaded = 4- 21 25% = I
26 30% = 1
Factor: Housinq 64} 31 35% = 1
Catagories: 0 units/cell = 3 36 40% = 1
1 unit/cell = 2-
2 units/cell = 1 Factor: 1971 Map CO
3 units/cell = 1 Catagories: Surrounding Area = 3
4 units/cell = 1 Residential = f
5 units/cell = 1 Commercial = 1
Communication = l
Factor: Location f2} Urban = 1
Catagories: This place = 1 Crop/pasture = 1
That place = 1 Orchards = 1
Headquarters = 1 Forest = Z
Open space = -f Water = 2-
Factor: Water 63} Factor: Aspect 6l}>
Catagories: Dryland = 3 Catagories: North = l
Pond or lakes = 2. Northeast = t
March = 1 East = Z
Creek = 1 Southeast = 2.
Stream - i South = 2.
River = 1 Southwest = z
West = 2
Factor: Utility 64^ Northwest = Z
Catagories: NW powerline = 1 Horizontal = 1
Central powerline = I
NE powerline = 1
Rai1 road = t
Factor: Land use 6*?}
Catagories: Undeveloped = 4-
Commerce = 1
Institutional = i
Recreation = 5
Mining = I
Factor: Relief (0
Catagories: >590 feet = 3
^ 590 feet = l
Figure 42. Values for linear combination of Alabama data.
----- M---------------N ^ ---cm M ("1 r\l Cm c\|


I
CO
o
t-+4-
M--1-
CEO
bilO
£20 T 20 020 T O ii!0 /. T 0 9 S O u J O n 1 O
t:io c i o
T If) O l O AGO
aoo
zoo
GOO
too
t:oo
200
TOO
F+ + )+ + 4 + 4
*eqep euipqB[v J.0 uo.LqeuLquioo jbsull .jo dew 'Zi? 0jn6i-|
i
/ V./ STTJ:> VCf-;! ' K- if i( B |; *9 BB B T
0 02 5T13r> ^?TC /fqL[LqeqLns buiuj r.- Of '00000000 u
o>_n EET OAB/9C bCH 1068/*?G 1-C5 106Z9tibEr'T060/9Gbf..S 1 068/ 9Gl-EE106l3/9£feES I + 4 + 9999Ci C C C C C C C C £ b b b b b b b b b b £ t. L t. L: /1. K C". E t222:22 i I I i I L t i I 1 OuOC'OOOOO -i f k
OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOCOOOOOt'MOOOOOOOCrt lOOOOOOOOoOOOOOOO 4+4-
ooou-ornMi-a*** **n*** o b*p* onrj? i*t*nohonou--*oo*nr-ao-n-no c.20
bihioh x on xoo 4 v- .4 x bor h? n t-t n nbri K4 uo s (Hi x v nrs rinxr x o b11
Gif II Bit 4 it M 4B it 3. 'r 0 J?- if 0- > H % B -jj-
Q?t 4 Bit BB s it a sOtS-il Bit s Bit flil-l| 111! J Bif > B j)r--- a Bif X B tl a B --1
* ***<**. *## b if- a O 4 x- it a *- ri
I? It 4 I* V- ft It a it- a -. - 1 J l a v-a i? 'I
I'JM ## BB a Bit Bit 4 BB * Bit a B it
CJ 41 a V- a B B a Bil a- t-J a ?r -?S- a* B It a * !>
v I X ij- It a- B It a B -a- a-)(-'{- 4 (I c- ir II- a B'!
0.4 x a b a -:t a b it 4 s ;? a ;> -:t x b -:t a
OB a it -JI- a -Bat -a- >r Ir a v if a- it it -X >t it a- -it it D-t ft it -it a it it 1 tt il a-' t a it it I .) -B- -it- a -it !i
it B 4 it it tf il it a- v- it a- at It a- it it X it it a B -It
it O a it B a it it a -it it v !- :i a-L) it a B It t Bit
CI it it it it ta it it a it it a it It a- it if a- it it a it it
flit a it it a it it a it It a- It It a itif -a nfliiil-'r Brit it aa 4- it it *## it It BBB X 1)00 Bit
it It *a- it it 4 S- it 4 Bit 4 -it < a ft If I ] ) f 1 f") it II
3 v- if rj i l l G- } T i K N > 1 a t.lt 1) I. i) <-
9 BCICIlIB 1.) ): -1 If ;f .0-1. Obi I s ( !t 1 <
> C- if a " v *< r#- -t- a r, i -
3 -Jf 3 f !! 9 if *1 V if ir f r- :f :j i- 3- ir
# !U if r- ;f T f |i ft a *: f :f % ir* V
3 -it ft If :l /. 3 ir : * ft v i' -ir > ii 3 i'
3 > 1 !! !r t ,1 t t. -:: r- : ft ft ift :s a I-
v tr-:r o' Yr if II "if 3 *f Ji ft- ii :r ft ifIf ft i- ii 3 V-
ft V* *| ti Ji* - ;r If : > -!* !| !
3 ir ^ if :1 * 1' ir ! if iV ft 3 Jf 3 >>
- y- 3 ;; ; i :i ; * i r r -r : t{ ?f ft V
> r-; 3 i >!- :r 9 V V 3- r > v- **f 3 i * i il
5# *f 3 Ir if 3 ^ *- ;i i- : 1 ift rf 1 j* n |i
k- if n- r* tf 3 < ? >1 ii ft* v ft- v- f 3 if ir -ft if
tr > :'r 3- If ?f ; > *f : ft *f v- Jf 3 W- M o %>
3 if 3 If :r 3 if if 3 i- : 3- if fr if-!f */ If if b if
*i r ;? if '!f .. ; > n* ft : :i ft K :! ft 3- {ft
* #-:t i *f - if ;f I? >- -> 3- J.'- Iv 3 -T- Jt i -if
3 jf -:f e ^ jf 5 U If if }* 3 1. a V if
(I li t) if 41* ri .fli 3 ito3 UClO 1*20
Jl 3 it if s' L it a it i 4f|| lOttC.lS FI 2 O
I- -a- irl.it i J yr a it It a-1- It 4 it it 3 al20
if a it If i i tt n it It- 3 it It 3 it -B at 12 O
.r it S Oil a itlf a l.)ll)l )-B|t fl iJc.O
" a at 1.1 a Itir a OOi. If ICICI Bit a A T O It a- it It n l- I! f J| ILlOi lCIX it it a- fi T O a it a oi M JClOfl Bit 4 Brito / T 0
t it t" t" i lit LtCii liriitll.ilt 4* i? I ti
B a B ittlO+X ill 11)30* BB CIO i -- :ti IflCii)in ji 11. ititOOit w- HO >< > B it vQlr non 4 Bit +Bit rt- £1 f) i> *i- it (i m k ;nn s cut 4 21 o
X- a B -t i'j it + a L i 0 a- it It if B-B- a- I 10 *- a B ft a I" -t a Bit a-ltlf-BltOi i IJ i O a B it it B .+ it If a B it G It O A GO I'- -a BfJ a It it a if It .a BitClClCIO POO
*t a b it o b nor ini j oi i o o o o /. o o
:i a it it -- ill il il nlOCli IQOiflJ 900
BBXOit 3 Bit 4 Bit a {fit X-BniiOOK-it + iHf a Bit = Irlt t itlf-- BB a If it a it it a it it 4 It lirill ClOnGit S GitO GOO it Clfjlt ft Bit 4 Bit 3- It 3 it it X LIB a B it B Bit i B B 3- if if It a Bit a Bit 9 Bit a B f. a Bl' i iOGI it jG a ClOOGOO bOO
oonoo 3 OB Qbx BfiOBOOitCl H GCiOOt IGbii O t-l inciii = Bit a Bit s til l il OI.I x OCIOOI ICiOGOOGODUO FIOO OClOOnOBitbob 3 BriOOitUifdaOl isOlltlBif ;;OB j bi. i -- b ii r- r. .1 i 1 li.iOit a x-l I t Bi li lOi ltJOGOOClO 200 BfJ bo BOOO+OBB Btf*ObOObOOOa itl 101 HI9 iH' a bk bii 9 (jij|j it a ttx 000000*0BOO a 100
F.2 T 068/9CbC2 T 0&9/9C bCS l OAF!/ ^0 l>F.2 i 9999CwCCCCGCGGbbbbbb b b b bCFOFF.iOFFit.Of..i/n/i OOOOOOGOOOOGOOOOOOOOOOOOOOOOOOOOviOOOiJtiO
b F2 211 A e / r! G b r. 210 A G / 9 G b C 2 T 22222 I I 1 f t1 I lTOOOOOOOOO 01 > G O) o 000 O O i j O (>0 OOOOOOOOO
+ + 4-4-4 + + 4 +
T
CTJ^IAV AVldSia


Analysis Package. Although in this method the values assigned to categories was not done in an ordinal fashion, where 5 was >4>3>2>1. Instead the values were assigned where 5 was five times more significant than 1, or 2 is twice as significant as 1 (interval scale). A weighting was assigned to each factor to indicate its importance. Figure 42 shows the rating and weighting used for this study.
No summation of values was given by the computer. It only distinguished two categories as being different. These can be seen in Figure 43.
FACTOR COMBINATION with ALABAMA DATA
The factor combination method was not directly replicatable through the Map Analysis Package. Each individual cell cannot be scanned in a single operation for the categories of each factor within it, as was the way factor combination was done. Even if it could have been done for this data base, there would have been a potential of 16,796,160 factor combinations to assess!! Hopkins estimated that only 5% of the potentials do exist on a site. That would still mean 839,808 combinations.
Another way of looking at the problem would be to say that the prime suitability locations would be where, out of all the possible combinations that occur, only the best or highest categories of each factor occur in the same cell. Without being able to look all 1575 cells individually, was there any way to determine which cells contain all of the best categories? The simple solution was to assign a value to the best category in each map while assigning "0" to the lesser categories. By overlaying the map values via the ADD command, the cells with totals matching the highest possible sum
-104-


VALUES FOR FACTOR COMBINATION
Factor: Forest Factor: Slope
Catagories: Forested - C> Catagories: 0% = B>
Non-forested = & 1 5% = B
6 10% = E>
Factor: Roads 11 15% = O
Catagories: Roaded = o 16 20% - G
Non-roaded = 5 21 25% = G
26 30% = o
Factor: Housing 31 35% = o
Catagori es: 0 units/cell = £? 36 40% = G
1 unit/cell = 5
2 units/cell = o Factor: 1971 Map
3 units/cell = o Catagories: Surrounding Area =
4 units/cell = o Residential =
5 units/cell =o Commercial =
Communication =
Factor: Location Urban =
Catagories: This place = c> Crop/pasture =
That place = o Orchards =
Headquarters = o Forest =
Open space = B Water s
Factor: Water Factor: Aspect
Catagories: Dryland = 5 Catagories: North = g
Pond or lakes = £ Northeast = G>
March = East = ^
Creek = o Southeast = B
Stream = o South
River = ^ Southwest ^B
* West = ^
Factor: Utility Northwest = 5
Catagories: Nw powerline = c Horizontal = ^
Central powerline = c?
NE powerline - G
- Railroad = o
Factor: Land use
Catagories: Undeveloped = ^
Commerce = o
Institutional = &
Recreation = ^
Mining = G
Factor: Relief
Catagories: >590 feet = 5
^ 590 feet = o
Figure 44. Values for factor combination of Alabama data.
_i nt;_
Q 0


1
DISPLAY F 1 C13
+++ ooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooo +++ +++ ooooooooo i mmui rn!.vi?S2r?:-;33333333:i3 4 44 *14 q 4 44:.*3t>533r>6666 +++ 1 a3-q 6 ?u?o i 234da'/byo 1 2j^i nanjvo 1 r:j 1 :>£7fci?o 1 £3436/evo 1234367a?o123 +
001 =r = =r a. . ft H5? 0 ii f 3 3 a*-.--. . St 002 ssvs-Jt-svs: A ****. It :*? r.r-s. sr -M 3- *=.-= SB* ***. *s=as-1t-*-5SKi; s=rru: 002
003 = *=::= c - CHt -if-R on-a-- an i* -* - it 3 =w. :==** ***. a*ass|J, - 003
003 a h-iji.tihi P -R--V . - a li -S 5 r. 'fi" S !j R? i; i ->'3'3HtS5-ir-.==SM-st= -frR-it-a- rsss. . .r= . = U 000 *. =. . = 0 S=OL'i ---(] fi = H>- Mi! -if - ST - IllJtJM . ,r-=-;-t. asssJHHi *st#saa.a<-Jm-'ir> sr#3*n=. . 85 005
006 zsssssssas = KIHO --Ril-RUl IM ( IR e i-M!3<( ii s-- It it=:K.vas H = S -Dfi iHt O-ir-iUJs = 006
007 = rr=:=: = -R?i OR ? =S (J . (ID* P IJ-S-- un-- if st Has 3 s***#* . s= 007
008 sssssssv. l.l 'f = SB. ^000 30-^ *- . OOP
oo*? = . si - iji* a =s =ss^:s:sj:sss-.*a-nt;iUIHS-==0K-S-U. =. . 009
01 0 ... : f!- Ol 1 . Ii ~ . 3 i! (J -V-.-.SS nr-- = 3=-~-it-=!-it Oit S *lJ =~Q. =-. -ft- ;s 01 1
P 12 :: sr.:?s SK sn :: a sr. ^t. ~ **. it- it-S ~. . :s 012
013 . . . -h -S-fr-K-S no iH;- s ii-o it. it -M--S--M-. R # RP. ssif-OSt-it-a-O*. = 013
014 = . = :=. wo O'* !I 1t MO-it i- rr it M Rit. rjit-sa-jsniUHt-OiiU8 - Ol 4
n i r* - = r = 9 -C :-s s ltit-8 B3S-f -K-R ** (JKK. SS. SS . 015
61 6 = =r =. u >> Si.)" " :s:s it*. -3 i!R-3 *'fl it -RiTit-R -** . n= VS O i 6
017 ... = rs:=** R - . -P-. r.:s=.- . . . ~ if=. -Ofi RUOIJ-'-RHO-fi-....- . 017
0 1 B . :* '1 ~ if, = :t := . r:rss li "! !. ---ns It R -- it- R=u = -. - :-s=s 013
Ol1? =: - H-UOUit- R-Rit-RU. . i.-.S= Ol 9
020 . =s. . . 53 . . s: = " "linoRitR-mt*. . . vs 020
021 . =. 6 *. .H. if A. = - s - - ROa R -S->H 3 3 =s=:=. =-.,: vs 021
022 = =** as: m M- Mq < i if *1=7 M R it-R (J t> it, . -. !32. 02.2
023 =s =T M = 3 -: Tr. = R H R- RR IJ = . = = . =- = . 023
024 =-- = . a- 4 ss.vrr.--n-s.s-: J a r. i'=--nno:---. --=. =. --=. =5. --r sbct.ss 024
025 K S, . =. . s =s sr;s; -,=i. fr -H "(Jit R"fi -if-|J. =-. =-. it-~= Si ==. 020
+ i + 0000000000000000(i0000000 0 <100000(>00000() 0 0 000000(.10 0 00000000000000 +*M
+++ OOOOOOOOOl 1 ] 1 1 1 ] 1 l J y22nS'2;?a:2;?3T33333333444fl4D536666 +t"t -*+ + 1 2345673901234 5-L7B90123406 739012343678901234 3678701234367BVO123 +++
4- FTCB t 4- 25 C t2. L. i- \ 4- *.* A i-J f*
h = -ss 512 CULLS 32. 32
h OUaGUDCJLlfiU 3 Prime suitability 101 CULLS 6. 47.
* tt it- it-it it it it it it-if- 431 CiTLL.S . c' 7. 47
*h 5 306 CELLS 32. 17
l
Figure 45. Map of factor combination of Alabama data.
0
cn
1


had all of the best categories in them. Figure 44 illustrates the values assigned to demonstrate this technique.
For this technique the highest possible sum was 50 (5 x 10 maps). However, no cells possessed all of the best categories of all maps as can be seen in the following chart.
Sum of Values Number of Cells
Prime Suitability 45 101
High Suitability 40 431
35 512
30 368
25 138
20 24
15 1
The suitability rankings were then determined with prime suitability given to cells containing 9 of 10 possible "best" categories. The map representing the results of this designation is shown in Figure 45. Even by altering this method, one cannot be sure which 9 of the 10 factors were found within this group. It may not be a homogeneous group.
RULES OF COMBINATION with ALABAMA DATA
In this method the criteria were expressed verbally. For the Alabama Data base, the criteria would be expressed by:
"Prime suitability areas would be those cells with 0 10% slope, unroaded, unforested, without power lines or train tracks, having one or less housing units per cell, located in recreation land use designations, possessing dry land or lakes higher than 590 feet with south and southeast aspects."
In this case the rules described coincide with the rating scheme in the factor combination method previously described. Therefore the resultant map for this method would be the same. Had the criteria been relaxed and less
-107-


selective, then significant differences would have resulted between the patterns of these two methods.
RESULTS OF ANALYSIS ON ALABAMA DATA
It appears from the results of the analysis on the Alabama data base that there was little similarity among the maps created by ordinal, linear and factor combination methods. There can be some justification for saying that factor combination and rules of combination maps would be similar for land units rating very high or very low. At some point factor combination would begin to include marginally suited lands because of the indiscrete parameters resulting from subtle difference in combinations. Thereby including more or less land areas indiscriminately.
This one analysis may not be a true indictor of the results stated above. Should the results on the Teller County data base indicate the same then these statements may be considered true.


TELLER COUNTY DATA BASE
DATA MANIPULATION
The following maps were used as the data base for Teller County for
comparison of the ordinal, linear, factor and rules of combinations methods:
Wildlife Vegetation
Soil Aspect
Visual Roads
Topo Zoning
Water Land Use
ORDINAL COMBINATION OF TELLER COUNTY DATA BASE
A 'O' to '5' ordinal scale was used to rate the categories of each map
of the data base. A '5' was assigned to categories that were the best
location for a recreation field. A 'O' was assigned to the worst
categories. Categories rating between these two extremes were assigned a
'1', '2', '3' or '4'. Figure 46 and 47 show the rating used in this test.
The sum of the categories of these maps ranged from a high of 43 to a low of
4. The following chart displays the sums of values and the suitability
rankings assigned to them.
Sum of Values Number of Cells
Prime
Suitability
43 =
41 = 5
40 = 16
39 = 14
38 = 23
37 = 41
36 = 35
35 = 43
34 = 52
33 = 101
32 = 44
31 = 124
30 = 261
-109-


VALUES FOR ORDINAL COMBINATION
Factor:
Categories
Factor: Categori es
Factor: Categori es
Factor: Categori es
Figure
Wildlife
Not i n county = c>
Low = 4
Moderate = £5-
High = o
Urban = C>
Vegetati on
Not in county = c>
Alpine slopes =o
Riparian bogs = o
Spruce/fi r = o
Limber/bristlecone = c> Douglas fir =o
Pinyon/.iuniper =0
Factor:
Categories:
Factor:
Categories:
Factor:
Water
Land
1st order stream 2nd order stream 3rd order stream Intermi ttent stream
Lakes/reservoi rs Roads
Not in county
Non-roads
Roads
Visual
G
o
o
a
a>
Mountain shrub = 5 Categories: Not in county = o
Mountain meadow = S Douglas fir/
Aspen = 2 Ponderosa pine = 4-
Ponderosa = 2 Woodland park =
Ponderosa pine/ Limber/bristle-
Douglas fir = I cone pine = o
Limber/bristle- C. Creek/Victor = o
cone/Aspen = £> Pi nyon/juni per = 3
Douglas fir/ Ponderosa pine = 5
mountain meadow = z Factor: Landuse
Aspen/Douglas fir = o Categories: Public lands = G
Spruce-fir/Dougl as National forest = o
fi r = o National monu-
Pinyon juniper/ ment = a
mountain shrub = o State park = o
Urban = o BLM land Agriculture/ = o
Soil ranching = o
Not in county = o Rural subdi-
Cryob-rock = 4 vision = &
R-o-cry-cry = 2 Industry = o
Cryoboralfs = 2 Mi ni ng - o
Rock outcrop = a Reservoi rs = o
Cry-cry = 4- Ski areas = o
Juget-rock = 2 Roads = o
Urban = o Urban = o
Topo Factor: Aspect
6800 8000 feet = ^ Categories: N, NW, NE Aspect = o
8000 13,040 feet = o Others = e>
46. Values for ordinal combination of Teller County data.
-110-
s>\ns


ORDINAL COMBINATION
FACTOR:
Categories:
Zoning
Not in county = o
RI-M = 3
Forest Service = &
A-l = O
R-l = ^
Campground = o
Mobile home park/
campground - o
Mobile home park = Z.
C-l = &
PUD = &
R-2 = &
M-l = &
Historic park = O
National park = o
National park
buffer
Figure 47. Values for ordinal combination of Teller County data.


OISllflT OkOlNTC
1
OoGgOuGOQuO JOgOOOOOuOjOOOutuOu'tuOgOG''
** cv:j0g0g?mi 11111*24.22221223" **
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7s*jU Me7c9'j14!
U'tu9g00''u?u00033TJ34l4.,4ft*44443535355555e6e6d666&6/7777777/79$j*.} 'J1*3-5o 7eJ1*3*56789 j1*3*5799U1 2Jft5o7o9un 343&7g9g1 c 3*
321
uu 3 O04 GjS
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313
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017 015 1 9
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026
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032
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til
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3:*7
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353
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Sit
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Jo 0
001
002 Go 3
Go 4
Os 5 0 o 6
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1 u 2 1 j3
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ft ft ftftft * a ft ft ftftftgftftftftftftftft ft ft ft
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U] I
J 1 4
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ft ft a ft COO J *
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a a a * a * *o*ocooo
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a ft a a ft a * a Qa"** a ft ftaftaftQO a a a a a a
ft a a a* ft a a
a a a a a a a a ^aft v*ft a aaf)Q aa a a a a a a a ft
a ft a a a 0 a a a a*u* a ft a a
a 0 , a ^ ft ft a a* *000*** ** ft ft a a a ft a a a a
a ftQufl'J a a
c a a * a a a a a a a a a a a ft a a g 0 * a a a ft a a a
a ft a a Jft ft ft *aQa* a ft a a ft a ft
ft ftftftftftftftft ftftft ** ft * ft
a ft ft a a a a a a a a a a ft a * ft a a** a a
3ga a a a a a ft ft *0 ftft *0
a * a a a ftftQrt a ft ftftftft ftftftftftftftft ft a a a ** a * a
a a a * * a a a ft ft * J*0* c a a a ftftftft *** * a a
a a a a y ft a a a ft ft a ft J ft a a
a a a Qg ft 0 * ft ft ft ft ft ft ft a
ft ft a ag ft gOg a a a a a a a a ***0* * ft ft * aft a a ft a a a a a
ft a a aa* ft Qft ft ft ftft * a ft ft a a aa a ft
a a a a a a a , ft a a ft a ft a ft a a ft a a ft
ft ft a a a a a a a a C a ft ft. ftftft ft* ** ft a a a a a a ft
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ft * a ftftftft ftft a a a a ft ft ft a r
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ft ft ft ft ft ft *
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C 4 1
c*2 043 0 4* 04>
04 6 04/ j4 0 4 y UV,
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U*4
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u5,
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07ft
0< > 07e
07/
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J7v
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gli
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092
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U5 j
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1
i^ft
C JGJOjOuOvOoO JnvCO')g?wC j^g*'Lng'vj^w'>0'lC^j^oCCOCGonoOOrOOOOOOOOOnuOO?g9uQjOu2ij'?g2jOu^U GgJur'g0wGI111l11111*.2fc2224:2.2jl33j3j3j,ft4ft4ft*ft4.435355535356So63S6*>c*>7'/7/7/7/^c*oac l 2 34537o9(J123fto73 9'jl2T.,= 67a9j12 3*<37i90i:3ft567a9j12l4567o9u143ft5d769k;W3ft5d73 9gW3ft
OK OX *11 c
0 0u* ?u!T*jIL!Tr 4550 cuts 52.H
0 uOuCUOO 00 3 P41*6 SUIT462LIT 502 C5LIS 5.7X
ft 4 nOJ'KiTf Suitla I 36*4 C ll s 42.21
1
Figure 48. Map of ordinal combination of Teller County data.
112


29
28
27
26
Moderate 25
Suitability 24
23 22 21
___________________20
19
18
17
16
15
14
13
12
11
10
9
8
7
6
5
4
Figure 48 displays the locati ordinal combination method.
187
38
261
300
448
510
574
358
405
406 604 409 317 358
1726
304
37
111
131
53
76
2
7
4
2
3
of the prime suitability cells for the
LINEAR COMBINATION OF TELLER COUNTY DATA
The categories of each map were rated on an interval scale of '1' to '9, then each factor was assigned a weighting of 1' through '5' (Figures 49 and 50). The following chart shows the ratings and weightings used for this test generated by the computer.
-113-


VALUES FOR LINEAR COMBINATION
Factor:
Categories:
Factor: Categori es:
Factor:
Categories:
Factor:
Categories:
Wildlife
Not in county
Low
Moderate
High
Urban
Vegetation Not in county Alpine slopes Riparian bogs Spruce/fi r Limber/bri stlecone Douglas fir Pinyon/juni per
= a =& = ^ = 3 = 1
= o
= l = I = I = I = I
Factor: Categories:
Factor:
Categories:
Factor:
Water
Land
1st order stream 2nd order stream 3rd order stream Intermi ttent stream
Lakes/reservoi rs Roads
Not in county
Non-roads
Roads
Visual
= *\ = I
= I
Mountain shrub = Cp Categories: Not in county = a
Mountain meadow - B Douglas fir/
Aspen = 5 Ponderosa pine = 7
Ponderosa = 4 Woodland park = l
Ponderosa pine/ Limber/bristle-
Douglas fir = l cone pine = 4
Limber/bristle- C. Creek/Victor = 1
cone/Aspen = I Pinyon/juniper = e>
Douglas fir/ Ponderosa pine =
mountain meadow = 3 Factor: Landuse
Aspen/Douglas fir = 1 Categories: Public lands = (
Spruce-fir/Douglas National forest = i
fir = 1 National monu-
Pinyon juniper/ , ment = /
mountain shrub = 1 State park = i
Urban = 1 BLM land = i
Agriculture/
Soil ranching = i
Not in county = o Rural subdi-
Cryob-rock = 3 vi si on = *7
R-o-cry-cry = 4 Industry = l
Cryoboralfs = Mi ni ng = t
Rock outcrop = i Reservoirs = I
Cry-cry = e Ski areas = I
Juget-rock = 4 Roads = 1
Urban = i Urban = 1
Topo Factor: Aspect
6800 8000 feet = i Categories: N, NW, NE Aspect = 1
8000 13,040 feet = 2. Others
Figure 49. Values for linear combination of Teller County data.
-114-
Q


VALUES FOR LINEAR COMBINATION
FACTOR: Zoning
Categories: Not in county RI-M
Forest Service
A-l
R-l
Campground Mobile home park/ campground Mobile home park C-l PUD R-2 M-l
Historic park National park National park buffer
= a
= i
= i
= i
=
= i
= i
= 3 = I = 5
= 7
= i = i
= i
Figure 50. Values for linear combination of Teller County data.


1
00
UIS^LAT LlHcMTl
** 1<:J57j7u1.!J*5o7e<.,W1<.'ii>7e,>jUJ*S7S90W3*5l>7li9UH5*57901 3U 3U
Ou 5
006 Ou7 Jo 3 0j9
0*
*Q
4
0 *0*
012 0*00* J 00OJ ** non * *
013 0 OJO 0 cO *000*
01 4 o o e 0 0 0 0 * *u n *
015 JO 0 d o o * o* **
J 1 6 31 7 Ij 0 no O^o* * * *
013 *c* 00* 0 OuOU *0lC0un**
Olv OoOw *w* OuOUOdw* *0
0*0 OdC Od?C * ny * *
041
82$ 0*4 0*5 0*6 04? 0*3 04* 3a0 001 004 33 3 Oik
Kl
Sis
009
J40
341
0-2
0-3
046
0 0 000*0 0* 00 0*J***
0*
uO**.** 000**
* *00 *
00 *00* *
0*
0
0
000
00
Ow>
0 0^0* 0*0*0d0j*
oO
0*5 * oc * *
0*6 **
047 *0*00 c*** Ou
0-a o CO
0,9 o
050 * * U 00*
0 5 1 * 0 c
052 * Ou* 00
053
054 05 5
056
057
815
OoO C o 1 Jo 2 0o3 Ook Go 5
35!?
m
0 72 073
3/? 0/6 077 0/3 C 79 Oa J
001
O'*
uOO
00
oo*
Oo 0* *
00* ouo** dO o *o* dOoc 00 0 000 0 u* 00 0*
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**00 *
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o o o o j r o o w o
OoOwC
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104
* *

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88
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01 1 314 u1i Glut i
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01 5
c1 y
J?d
02 I
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025 02o 0 27 02a o?y
030 d 3 1 Ct4 U 3 5 O-
0*5
0^3
u! 7 0 3 6 u* y
J4 J
u4i
04* 0 4 o J 4 04 j v 4 o U-7 c4 U4y 05u 051
05* 055 d54 J 5 5 0 5o 057
8?5
03 d 0*1 064 36 3 0*, 06 j 06a 04/ 0*0 0 6 y u-G 071 07* c7a 074 07a u 7a 0 7 7 0 7 j J7, u*0
031 d 32 0^5
**
J5 0 ? o J / d 3 o 0* 09 J d1
O3*
d-Ui d o
G*/
O^o C 9*
ITj
1?1
10*
103
10,
* J0 3d3d0dgy;g;?,Odiw7-^oojCd^^c2,jT--0'./'oooojOo?jrc''L'c?o'iooo'iorort3r''jocr'on;tL'ic3dOoo.c
JjJj. w'dd1lTli111lf**2**<2*2j!j35,j3j?,4,4,4,,,*>555355555o6a6o6o509/777/7/7/783a?a
* 1*3,5u7aoui*345673-jl4,*.5o7a?,01*7,*o7j601*3,5c7s9o1 34567o90l*3,5 67oOji<^*c73a:jl4t4 *
LlNfcA4TC
0 pojs SuITaalirrT 7315 CLt 5 3?.31
OuOyOOOJOo 3 p3i*e su:t*8il:t 33S C3LLS 3.9X
4 ^CwfOlT* Sella r 1 i3 C ¥ LL S 1 5. ¥X
Figure 51. Map of linear combination of Teller County data.
116


Computer Values Number of Cells
7 s ~T~
Prime 6 = 65
Suitability 5 = 269
Moderate 4 = HI
3 = 2102
2 = 1502
1 = 1802
0 = 1608
Figure 51 shows the location of prime suitability cells for the linear combination method.
FACTOR COMBINATION WITH TELLER COUNTY DATA
This method was replicated by assigning a '1' to the best categories and a '0' to all other categories of each map. Figures 52 and 53 show the ratings used for this method. Since ten maps were used in the test '10' would be a maximum score possible by any cell. The following chart shows the range of scores an the suitability rankings determined through this test.
Sum of Values Number of Cells
~8~ = 18
Prime 7 = 121
Suitability 6 = 570
Moderate 5 = 1459
4 = 2280
3 = 3592
2 = 651
1 = 45
Figure 54 shows the location of prime suitability cells for the factor combination method.
RULES OF COMBINATION ON TELLER COUNTY DATA
Because the rules of combination established under this method were the
-117-


VALUES FOR FACTOR COMBINATION
Factor: Wildlife
Categories: Not in county = o
Low = o
Moderate = 1
High = o
Urban = c>
Factor: Vegetation
Categori es: Not in county = c>
Alpine slopes = a
Riparian bogs = O
Spruce/fi r = c>
Limber/bri stl econe = o
Douglas fir = G
Pinyon/juni per = a
Mountain shrub = 1
Mountain meadow = 1
Aspen = o
Ponderosa Ponderosa pine/ = o
Douglas fir Limber/bristle- = o
cone/Aspen Douglas fir/ = o
mountain meadow = o
Aspen/Douglas fir Spruce-fi r/Dougl as = G
fi r Pinyon juniper/ - c>
mountain shrub * = o
Urban = G
Factor: Soil
Categories: Not in county = O
- Cryob-rock = 1
R-o-cry-cry = O
Cryoboral fs = o
Rock outcrop = o
Cry-cry = 1
Juget-rock = o
Urban = o
Factor: Topo
Categories: 6800 8000 feet = J
8000 13,040 feet = o
Figure 52. Values of factor combi
Factor: Water
Categories: Land = 1
1st order stream =0
2nd order stream = C>
3rd order stream =g
Intermittent
stream = G
Lakes/reservoi rs = G
Factor: Roads
Categori es: Not in county = G
Non-roads = 1
Roads = O
Factor: Visual
Categories: Not in county Douglas fir/ = O
Ponderosa pine = 1
Woodland park Limber/bri stl e- = G
cone pine = O
C. Creek/Victor = O
Pi nyon/juni per = O
Ponderosa pine = 1
Factor: Landuse
Categories: Public lands = O
National forest National monu- = G
ment = G
State park = G
BLM land Agriculture/ = O
ranchi ng Rural subdi- = G
vision = 1
Industry = G
Mi ni ng = a
Reservoirs = e>
Ski areas = a
Roads = o
Urban = a
Factor: Aspect
Categories: N, NW, NE Aspect = o
Others = /
ion of Teller County data.
-118-
OOO


VALUES FOR FACTOR COMBINATION
FACTOR:
Categories:
Zoning
Not in county = c>
RI-M = a
Forest Service = o
A-l - o
R-l = i
Campground = o
Mobile home park/
campground - o
Mobile home park = o
C-l - o
PUD = 1
R-2 = O
M-l - o
Historic park = o
National park = o
National park
buffer - o
Figure 53. Values for factor combination of Teller County data.


OlSrlAT MCUKL
0o00 Ou;- W 3i
5o79*. W3%5o7euU 3**5 67e9ol2 3*5 o790125454769y1 2**.56 749012545d769j1< US07 jOj^O *
-------- ... . . .. . a5o9&
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901 9u2 003 0U4 Ou5 006 Qu 7 003
8!l
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0 I 4
815
01 7 015
818 0*1 022 U*3 4 025
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032 0 33
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036 03 7 233
0*00 0 **0**
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Figure 54. Map of factor combination of Teller County data.
120


same or equivalent to those in factor combination, it was expected that rules of combination would arrive at a common conclusion with it. However, this method would have required several iterations of data manipulation to eventually get to this conclusion. One would expect a more severe retriction of criteria limit (must contain all best ratings) as a first attempt. Considering that no cell contained all of the best features from each factor, several liberalizing steps would surely take place. With only 139 (of + 8,700) cells having at least 7 out of 10 possible "best" categories within them, the criteria for this study was lowered to include cells with 6 of the 10 best categories. The high number of cells (570) having at least these 6 categories skewed the results of this method. Rules of combination designated 709 cells as being "prime suitability". These cells are shown in Figure 54, the same as factor combination.
RESULTS OF ANALYSIS ON TELLER COUNTY DATA
Statistical evaluations unavailable for the Alabama data offerred more clarity on the results of the Teller County data. Between the linear, ordinal and factor methods, there were 263 cells commonly ranked as prime suitability (Figure 55). Linear combination ranked 338 cells as prime suitability of which 78% of those were common to all methods. Ordinal combination ranked 502 cells as prime suitability of which 52% of those were common to all methods. Factor combination identified 709 cells as prime suitability, 37% of those were common to all methods.
This does appear to indicate that the linear combination method was more accurate in choosing cells that were common to the results of the other methods. However, this could be severly impacted if the criteria used in
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Figure 55. Map of cells common to ordinal, linear and factor combinations of Teller County data.
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the factor combination method were more stringent, allowing only cells with 7 or 8 of the best categories, instead of the 6 permitted during this study. These results offerred no proof that either the prescaled or postscaled methods were more adept at selecting the prime cells. Further analysis using a modified version of the factor combination results may significantly effect these conclusions.