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British Library Cataloguing in Publication Information Available
Library of Congress Cataloging-in-Publication Data
Names: Klosterman, Richard E., author. | Brooks, Kerry, author. | Drucker, Joshua, author.
Title: Planning support methods : urban and regional analysis and projection / Richard E. Klosterman, University of Akron, Kerry Brooks, Eastern Washington University, Joshua Drucker, University of Illinois at Chicago, Edward Feser, Oregon State University, Henry Renski, University of Massachusetts Amherst.
Description: Lanham : Rowman & Littlefield Publishing Group, Inc., [2019] | Includes bibliographical references and index. viewed.
Identifiers: LCCN 2018004652 (print) | LCCN 2018020506 (ebook) | ISBN 9781442220300 (ebook) | ISBN 9781442220287 (hardcover : alk. paper) | ISBN 9781442220294 (pbk. : alk. paper)
Subjects: LCSH: City planning—Mathematical models. | Land use—Planning—Mathematical models. | Information storage and retrieval systems—Land use. | Regional planning—Data processing.
LC record available at https://lccn.loc.gov/2018004652
∞ ™ The paper used in this publication meets the minimum requirements of American National Standard for Information Sciences—Permanence of Paper for Printed Library Materials, ANSI/NISO Z39.48-1992. Printed in the United States of America
List of Figures, Maps, and Tables xi
Preface xvii
1 Foundations 1
Planning and the Future 2
The Role of Assumptions 3
The Politics of Projecting the Future 3
The Promise of Scenario Planning 5
Planning and the Public 5
Planning for the Public 6
Planning with the Public 7
Planning Support Systems 8
Simple and Complex Models and Methods 9
Beyond Planning Support Systems 10
2 Welcome to Decatur 13
Background Information 13
Location 13 History 13
Population and Housing Information 15
Employment and Occupation Information 17
Land Uses 19
Decatur Block Groups 22
Land Use Information 23
Population Density 23
Racial and Ethnic Composition 25
Household Income 25
Population Pyramids 27
Lorenz Curves 31
Gini Coefficients 33
3 Trend Projection Methods 35
Concepts and Terminology 35
Estimates, Projections, and Forecasts 35
Projection Terminology 36
Trend and Share Projection Methods 39
Trend Projection Methods 40
Linear Curve 40
Identifying the Best Fitting Trend Curve 41
Geometric Curve 42
Parabolic Curve 45
Gompertz Curve 47
Using Trend Curves 48
Plotting the Observed Data 48
Plotting the Projected Values 51
Defining Growth Limits 51
Combining Projections 52
Evaluating Goodness of Fit Statistics 54
Evaluating the DeKalb County and Decatur Trend Projections 57
4 Share Projection Methods 61
Share Projections for One Area 61
Constant-Share Method 61
Shift-Share Method 62
Share-of-Change Method 63
Share-Trend Method 65
Comparing the Decatur Share Projections 65
Share Projections for Multiple Areas 66
Constant-Share Method 66
Shift-Share Method 68
Share-of-Change Method 70
Adjusted-Share-of-Change Method 72
Comparing the Decatur Block Group Share Projections 75
Evaluating the Share Projection Methods 76
5 Cohort-Component Methods 79
Demographic Trends 79
Mortality 79
Fertility 81
Migration 81
Cohort-Component Methods 83
Hamilton-Perry Method 84
Projecting the Female Population 84
Projecting the Male Population 86
Evaluating the Hamilton-Perry Method 89
Mortality Component 90
Life Tables 91
Computing One-Year Life Table Survival Rates 93
Computing Multiple-Year Life Table Survival Rates 94
Fertility Component 95
Computing Age-Specific Fertility Rates 95
Migration Component 97
Residual Net Migration Estimation Methods 98
Gross Migration Methods 105
DeKalb County Cohort-Component Projections 106
Projecting with Constant Rates 106
Projecting Survival and Fertility Rates 111
Projecting Net Migration Rates 114
Evaluating the DeKalb County Cohort-Component Projections 117
Evaluating the Projected Population Trends 119
Evaluating the Projected Population Change by Age and Sex 120
Evaluating the Components of Population Change 120
6 Economic Analysis Methods
Concepts and Definitions 125
125
Establishments, Firms, Sectors, and Industries 125
North American Industrial Classification System 126
Estimating Missing Values 126
Analysis Guidelines 129
Selecting an Appropriate Geographic Scale 130
Selecting an Analysis Period 130
Ensuring Consistency 131
Making Comparisons 133
Analyzing Decatur’s Local Economy 134
Decatur’s Aggregate Economy 134
Decatur’s Industry Composition 135
Decatur’s Industrial Specialization 137
Location Quotients 138
Decatur’s Occupations 144
Shift-Share Analysis 146
Projecting Decatur’s Economic Future 151
Trend Employment Projections 151
Constant-Share Employment Projections 152
Shift-Share Employment Projections 154
Share-of-Change Employment Projections 156
Share-Trend Employment Projections 157
Adjusted-Share Employment Projections 158
Evaluating the Decatur Employment Projections 159
Using Economic Projection Methods 162
Using Ready-Made Projections 162
Aggregation Level 162
Economic Analysis and Other Planning Information 163
Economic Futures and Scenario Planning 163
7 Spatial Analysis Methods 165
Concepts and Terminology 165
Geographic Information Systems 165
Maps, Layers, and Features 165
Feature Geometries 166
Map Scale 166
Map Accuracy and Metadata 167
Map Dimensions 168
Locating Spatial Features 168
Postal Addresses 168
Linear Referencing Systems 168
Cadasters 169
Geographic Coordinate Systems 169
Projected Coordinate Systems 169
Vector and Raster Data Models 170
Vector Data Model 170
Raster Data Model 171
Analyzing Spatial Data 171
Quantities, Categories, Ratios, and Ranks 171
Creating Classes 175
Choosing a Classification Scheme 180
Analyzing Attribute Data 180
Attribute Tables 180
Types of Attribute Tables 181
Attribute Measurement Scales 182
Boolean Queries 183
Venn Diagrams 183
Analyzing and Representing Spatial Relationships 186
Topology 186
Topology Models 187
Spatial Analysis Operations 188
Buffering 188
Overlays 189
Polygon-Polygon Overlays 189
Point–Polygon Overlays 191
Line–Polygon Overlays 192
Areal Interpolation 192
8 Land Suitability Analysis Methods 197
Map Overlay Method 197
Binary Selection Method 198
Binary Selection Analysis of Decatur Flood Risks 203
Ordinal Combination Method 206
Weighted Combination Method 210
Limitations of the Ordinal and Weighted Combination Methods 212
Ordinal and Weighted Combination Analyses of DeKalb County Vacant Parcels 214
DeKalb County’s Vacant Parcels 214
Suitability Factors 215
Ordinal Combination Analysis 216
Weighted Combination Analysis 223
Computing Standardized Suitability Scores 232
Other Land Suitability Analysis Methods 233
Fuzzy Overlay Methods 233
Land Supply Monitoring 234
Opportunities for Public Involvement 234
9 Using Planning Support Methods 237
Use Graphs and Charts 237
Types of Graphs and Charts 237
Using Graphs and Charts 240
Use Simple Graphs and Charts 243
Use Maps 245
Orienting Data Users 245
Displaying Analysis Information 245
Displaying Analysis Results 248
Use Appropriate Map Options 250
Document Assumptions 250
Compare to Projections by Other Organizations 252
Combine Different Information 252
Combining Trend and Cohort Component Projection Information 253
Combining Trend with Share Projection Information 253
Combining Population Projections with Housing Information 254
Combining Projections with Land Suitability Information 255
Support Other Applications 257
Land Use Planning 257
Allocating Growth to Small Areas 258
Use Scenarios 259
Appendix A: US Census Geography 261
Appendix B: American Community Survey 265
Appendix C: US Data Sources 271
Glossary 277
References 291
Index 297
Figures, Maps, and Tables
2.1 Population Pyramids for Bangladesh, Germany, and the United States, 2010
2.2 Georgia Population Pyramid, 2010
2.3 DeKalb County Population Pyramid, 2010
2.4 Decatur Population Pyramid, 2010
2.5 Sample Lorenz Curves
2.6 Household Wealth Lorenz Curves for Decatur, the State of Georgia, and the United States, 2010
3.1 Graphical Projection: DeKalb County Population, 2040
3.2
3.3
3.4
3.5
3.6 Alternate Forms of the Gompertz
3.7 DeKalb County Population, 1900–2010
3.8 Decatur Population, 1900–2010
3.9 Parabolic Trend Projections: DeKalb County, 1990–2010
3.10 Trend Projections: DeKalb County Population, 1990–2070
9.1 Observed and Projected Population by Age and Sex, Reduced Net Out-Migration Projection: DeKalb County, 2010 and 2040
9.2 Population and Housing Statistics: Decatur, 1970–2010
9.3 Atlanta Regional Commission and Trend Population Projections: Decatur, 2040
9.4 Atlanta Regional Commission and Trend Population Projections: DeKalb County, 2040
9.5 Projected Housing Units: Decatur, 2040
9.6 Projected Demand for New Housing Units: Decatur, 2040
9.7 Projected Residential Land Demand: DeKalb County, 2040
9.8 Weighted Combination Vacant Parcel Residential Suitability: DeKalb County, 2010
9.9 Observed and Projected Employment for Selected Employment Sectors: Decatur, 2013 and 2020
B.1 American Community Survey 2008–2012 Five-Year Population Estimates: Decatur, 2008–2012
Preface
Planning is looking backward at what we can learn from experience, looking around at what we can learn from observation, . . . looking forward to see where we are going, . . . choosing where we want to go and figuring out how we can get there.1
Quantitative methods have played a central role in planning education and practice since the middle of the twentieth century when planners’ vision of planning as an intuitive process of design was replaced by a new concept of planning as an applied science. Several texts have been published over the intervening years that introduced planning students and practitioners to an array of quantitative methods. The first widely used methods text, Walter Isard’s 760-page Methods of Regional Analysis (Isard et al. 1960), was published in 1960. Several planning methods texts were published in the 1970s but Urban Planning Analysis (Krueckeberg and Silvers 1974) dominated planning education in the United States. Only two quantitative methods texts for planning students and practitioners have been published in North America in the last thirty years: Community Analysis and Planning Techniques (Klosterman 1990) and Research Methods in Urban and Regional Planning (Wang and Hofe 2007).
This book describes and applies the most important and widely used urban and regional analysis and projection methods It updates and extends Community Analysis and Planning Techniques by including the trend, share, cohort-component, population, and employment analysis and projection methods covered in the earlier text. Recognizing the critical role that geographic information systems (GIS) play in planning education and practice, it reviews spatial analysis methods and describes land suitability methods. The book’s appendices describe data sources for applying the methods in the book, US census geography, and the US Bureau of the Census’s American Community Survey. More important, unlike recently published books such as Silva et al. (2015) and Lipovska and Stepenka (2016), the book applies the methods to an American city and county, demonstrating in detail how quantitative methods can be used to support community-based planning.
Planning Support Methods contains nine chapters and three appendices that describe and apply the quantitative planning methods at the core of planning education and practice. The book describes the methods’ underlying assumptions, advantages, and limitations; it also provides guidelines for using the methods and criteria for selecting the most acceptable projection (or projections) and extensive lists of references for further reading. The text can be used in graduate and undergraduate methods courses and by planning practitioners and others interested in understanding and using planning methods.
Chapter 1 lays out the intellectual foundations for using the methods described in the remainder of the book. It argues that planning is a forward-thinking profession that should promote citizen-centered planning with the public, which involves as many people as possible, as completely as possible, in the decisions that affect their lives. It also suggests that planners should use simple, understandable, and easy-to-use methods like the ones described in this book to support these efforts.
Chapter 2 describes the City of Decatur, Georgia, and DeKalb County, in which it is located. The chapter provides background information on Decatur and describes the city’s population and housing, employment and occupations, land uses, and block groups. The chapter uses population pyramids, Lorenz curves, and Gini coefficients to analyze and describe the city’s population and economy.
Chapter 3 introduces concepts and terms that will be used throughout the book. It then describes four trend curves—linear, geometric, parabolic, and Gompertz—that identify past trends in an area’s growth or decline and continue those trends to project the future. The chapter uses the trend curves to project Decatur and DeKalb County’s 2040 populations and describes procedures for using trend curves and identifying the most appropriate trend projections.
Chapter 4 describes share projection methods that project an area’s future population or employment, given projections for a larger area in which it is located. The constant-share, shift-share, share-of-change, and share-trend methods are used to project Decatur’s 2040 population. The constant-share, shift-share, share-of-change, and adjusted-share-of-change methods are used to project the 2040 population of Decatur’s thirteen block groups. The chapter concludes by evaluating the share projections for Decatur and its block groups.
Chapter 5 describes cohort-component projection methods that divide a population into age, sex, and racial or ethnic groups or cohorts and deal separately with the three components of population change—mortality, fertility, and migration. It describes past mortality, fertility, and migration trends for the United States. It projects Decatur’s 2040 population for ten-year age cohorts and DeKalb County’s 2040 population for five-year age cohorts, under different survival, fertility, and migration assumptions, and evaluates the projections.
Chapter 6 describes basic concepts and terms for understanding an area’s economy and provides economic analysis guidelines. It uses location quotients and shiftshare analysis to understand Decatur’s aggregate economy, industrial composition, and industrial specialization. The chapter uses the share projection methods described in chapter 4 to project the 2040 employment in Decatur’s major employment sectors. The chapter concludes with guidelines for using economic analysis and projection methods.
Chapter 7 provides an overview of the spatial analysis methods that underlie geographic information systems (GIS) applications. It describes: (1) fundamental spatial analysis concepts and terminology, (2) methods for locating spatial features, (3) the raster and vector data models, (4) methods for analyzing spatial and attribute data and representing spatial relationships, and (5) widely used spatial analysis methods.
Chapter 8 describes land suitability analysis methods that determine the suitability of one or more locations for one or more land uses. It introduces four widely used land suitability analysis methods—map overlay, binary selection, ordinal combination, and weighted combination. It uses the binary selection method to identify land parcels and buildings in Decatur’s flood zones. It uses the ordinal combination and weighted combination methods to identify vacant parcels in DeKalb County that are suitable for residential and commercial development, given a range of suitability assumptions. It concludes by describing procedures for computing standardized suitability scores and opportunities for public involvement.
The final chapter provides guidelines for using the planning support methods described in the preceding chapters. Seven guidelines are described and applied to the analyses and projections for Decatur and DeKalb County: (1) use graphs and charts, (2) use maps, (3) document assumptions, (4) compare to projections by other organizations, (5) combine different information, (6) support other applications, and (7) use scenarios.
Appendix A describes the US Bureau of the Census’s American Community Survey that replaced the US decennial population and housing census in 2010. Appendix B describes the geographic units and codes the US Bureau of the Census uses to collect and report its data. Appendix C describes free, online sources for data that can be used to apply the methods described in the book.
The book is a collaborative effort of the coauthors and other people who provided extremely helpful assistance and advice. Henry Renski coauthored chapter 5, Joshua Drucker and Edward Feser coauthored chapter 6, and Kerry Brooks coauthored chapter 8. Richard Klosterman coauthored these chapters and wrote the remainder of the book. Kerry Brooks and Richard Klosterman prepared the maps. Lewis D. Hopkins contributed greatly to the discussion of scenario planning in the final chapter and the remainder of the book. Terry Moore and Hyeon-Shic Shin made extremely helpful suggestions for improving large portions of the book. Any errors are the sole responsibility of the first author.
Terms in the glossary are printed in bold fonts when they first appear in the text. Additional resources for using this text and the methods it describes are available at PlanningSupport.org
Note
1 Isserman (2007, 195).
Foundations 1
Planning and predicting the future are essential human activities.1 Individuals and families consult weather forecasts to plan their weekend activities and rely on financial advisors to plan their financial futures. Public and private organizations hire consultants to help identify actions and investments that will benefit them in the future. Local, regional, state, and national governments hire planners and other public policy experts to help them deal with an uncertain and politically contentious future. Rapid urbanization has brought half the world’s population into cities and an array of public policy issues—climate change, housing affordability, and food security, to name a few—demand public attention. The increased interdependence, complexity, and uncertainty of a rapidly urbanizing world requires planning that helps public officials satisfy their diverse and vocal constituencies and assists private-sector actors to make and protect their investments.
Planners know that public sector planning is required to promote the collective interests of the community, consider the external effects of individual and group action, improve the information base for public and private action, and protect society’s most needy members (Klosterman 1985). They also realize that current actions affect future outcomes and that taking no action is an action in itself. Planning assumes that individuals, organizations, and communities can control their fates and act to achieve desired futures and avoid undesirable ones. Without planning, communities can only stumble blindly into a future they make no effort to shape. With planning, the future is the result of underlying trends and the public’s efforts to modify them.
However, planners also recognize the obstacles to public sector planning. Annual budgets and the short terms of elected officials focus public attention on issues immediately at hand. Property owners are understandably wary of public actions that may affect the value of their most precious assets and businesses, large and small, and place their particular interests above those of society at large. In the realm of political discourse, the rhetoric of protecting property rights and promoting individual freedom disparages government efforts to address the long-term collective interests of the community and protect the poor and powerless.
It is important to remember that people like planning. They prepare plans for their personal lives, for the organizations to which they belong, and for the companies that employ them. They care deeply about their neighborhoods and communities and are concerned about the future they are leaving their children and grandchildren. Given an opportunity, people turn out night after night to express their concerns and hopes for a better future. Arbitrary-seeming regulations and the lack of a meaningful role in shaping their world are what people understandably resist. Planning is inherently about the future: it is a process of identifying and implementing actions and policies in the present that will make the future better. Unfortunately, planners have all too often abandoned their traditional role of helping communities consider what the future may—and should—be. Instead, many planners focus their efforts largely on pragmatic problem-solving and more immediate concerns of community development, code enforcement, and public/private development.
Planners’ neglect of the future has several causes. The press of daily job requirements and the desire for job security understandably focus planners’ attention on short-term concerns with clearly identified results. Today’s political environment
often makes idealism, visioning, and inspiration seem naively unrealistic. And the widespread adoption of social science methods has greatly enhanced planners’ ability to understand the past and present but provides little assistance in helping communities engage in their collective futures.
Planning and the Future
You spend your life walking backwards because you see the past not the future; that’s why we trip.2
All predictions and projections are fictions about a future that has not occurred. Individuals and organizations cannot accurately predict their long-term futures, and there is no reason to assume that planners can foresee an unknowable future better than the public they serve. No model or method can determine what a community’s future will be. At best, they can only provide explicit procedures for determining the implications that can be drawn from limited information about the past and the present, and plausible assumptions about the future. To ask for more is to require the impossible.
Planners’ attempts to deal with the future are particularly difficult because they are often required to prepare long-term projections for small areas to satisfy the requirements of other agencies and support the needs of local clients. Short-term projections for small areas are relatively easy to prepare because demographic and economic changes over a two- or three-year period are generally small. Long-term projections for large areas are often not difficult to prepare because growth or decline in some subareas is often offset by countervailing changes in others. However, it is extremely difficult to project long-term changes for small areas where new residential developments and employment centers or changes in local land-use policies can lead to substantial population and employment changes over relatively short periods. Predicting the future is also more challenging in smaller communities where budgets and staff are limited, and the consequences of the projections are clearer to interested parties.
Planners’ projections are also more difficult because they are generally prepared with information that is out of date, estimated, or collected at the wrong level of spatial and sectoral aggregation. Predictions in the natural sciences are based on welldeveloped bodies of theory and carefully controlled experiments that are impossible in the social sphere. Well-funded business analysts use large quantities of reliable data and sophisticated methods to predict short-term inventory and sales changes. As a result, it is not at all surprising that planners’ projections for the long-term future of cities and neighborhoods inevitably prove to be wrong.
The difficulty of accurately projecting the future does not mean that projections are not useful or important. The question isn’t whether projections turn out to be accurate but whether they provide information that helps inform the policy-making process, facilitate community understanding, and help the public deal with an unknown future. If the projected future is undesirable, planning is required to prevent the “most likely” future from occurring. If the future is uncertain, communities should recognize this fact and plan for dealing with an unknown future.
While the information available for considering the future is often bad, that is, incomplete and inaccurate, it is also the best available data. Planners’ models and methods must accommodate the data available in a particular time and place and use these data as best they can. They can update their projections and improve their plans if more current and accurate information becomes available later.
The Role of Assumptions
Any projection model or method is loaded with assumptions.3 Projecting the future by continuing past trends assumes that these trends will continue or will vary in a known way. But finding relationships that fit the past does not guarantee that they will apply in the future. Simple extrapolations of past trends into the future ignore many factors that may change in the future, including planners’ efforts to identify and implement policies that support desired futures and avoid undesirable ones. Implementing planning methods in a computer model does not eliminate the need to make assumptions and choices. Representing a complex reality in a computer model inevitably requires choices in specifying causal relationships, selecting input variables, and adjusting model outputs so that they are “reasonable” to modelers and their clients. All projections are built on hypotheses and assumptions that become increasingly unlikely the farther out the projection goes. Computer-based models and methods do not reduce the need to make assumptions; they only hide them in computer algorithms that are inaccessible outsiders.
The core assumptions about the future cannot be derived from a method or model. A model is only a vehicle for tracing the implications of the core assumptions chosen independently from, and prior to, the model or method that implements them. When the core assumptions are valid, the choice of methodology is either secondary or obvious. And when they are wrong, no level of model sophistication can generate good predictions from bad assumptions, an example of “garbage in, garbage out.”
Projections prepared in a wide range of areas have routinely proven to be wrong, often embarrassingly so. Some of the most dramatic projection errors are the result of “assumption drag”—the assumption that current conditions and past trends will continue, despite recent changes. It is often difficult to know in advance whether changes in past trends represent fundamental (structural) changes or short-lived “quirks” and cyclical changes that will soon disappear. As a result, the most important—and difficult—question planners must ask is how will change itself change: What current trends are likely to change? How will they change? And when will they change?
The Politics of Projecting the Future
Planners, elected officials, and the public often assume—or at least claim—that planning models and methods are unbiased and politically neutral tools used by technical experts to objectively identify and evaluate public policies and action.4 The appearance of authoritativeness is reinforced by the value- and politically neutral language of technical objectivity used to prepare, use, and evaluate planning information, analyses, and proposals. The choices planners must inevitably make to operationalize their models and apply their methods are similarly portrayed as purely technical judgments that do not promote the interests of some groups over others.
Portraying projections as the objective outcomes of value-neutral analysis allows analysts to maintain their professional identities as politically neutral experts. Advocates of particular policy positions gain support for their positions by relying on supposedly unbiased technical analyses that support their positions. Politicians who choose between conflicting policy proposals can accept projections that correspond to their preconceptions and ignore those that do not. All three sets of actors—analysts, advocates, and politicians—gain by pretending that projections are objective scientific statements and not political arguments for a particular position.
In fact, value-neutral technical expertise in the preparation and use of projections is more apparent than real. Projections are rarely produced in a vacuum. Instead,
they are prepared for a client—a government agency, an organization, a private firm, or an interest group—who often has a vested interest in the projected numbers. Elected officials and developers generally prefer projections that show continued population and economic growth that will attract new residents, businesses, and public investments. Environmental groups often view rapid growth as a problem that threatens natural systems and community livability. At the local level, landowners and businesses prefer projections that direct growth toward—or away from—particular locations. In all these cases, the clients for whom the projections are prepared will often request—and even demand—projections that support policies and actions that benefit them.
Planners’ models and methods are political in a bureaucratic sense because control of information is a powerful organizational resource that increases the power of technical experts and technically sophisticated groups. They are also useful in organizational settings for postponing difficult political choices, justifying decisions that have been made on other grounds, and adding the sheen of technological sophistication to public policy making. Consultants are hired not only for their expertise but also to provide someone to blame if things go wrong. Elected officials use planners’ analyses and projections like drunks use a light post: for support, not enlightenment.
Planning analyses and projections are based not on universally accepted bodies of theory and uncontested facts, but rather on incomplete data, partial hypotheses, and assumptions about what the future will—and should—be. As a result, the preparation and use of planning models and methods necessarily involves numerous choices about selecting data, applying computational procedures, and analyzing, presenting, and distributing the analysis results. These choices are inherently political because they can help shape the analysis results, the perception of problems, and the definition of potential solutions.
Projections are inherently political because they can be either self-fulfilling or self-defeating. They can be self-fulfilling because the fact that an area is predicted to grow may stimulate growth by attracting people and firms seeking new opportunities and by providing the public infrastructure that allows growth to occur. Conversely, areas where decline is assumed will often do so because people and firms are reluctant to migrate to or invest in areas where their prospects appear to be poor. Projections can also be self-defeating by stimulating action that prevents an undesired future from occurring.
The accuracy of a projection can only be determined when the forecast date arrives. This makes them useful for political purposes because they can serve their intended purpose even though they may ultimately prove to be wrong. Planners can justify a major facility investment by exaggerating the demand for it and underestimating its costs. If the investment is made and the projections prove to be false, the political purpose has been achieved and planners can avoid responsibility by citing factors that were outside their control when the projections were prepared.
Planners should not only recognize that their analysis, projections, and plans are inherently political but should also see them as reflecting underlying assumptions that may support the competing perspectives and interests of contending parties. Viewed in this way, the future is not a single grand vision or the inevitable consequence of past trends but rather an object of public deliberation. Planning models and methods can play an essential role in this deliberation by testing a range of projections and policy proposals derived from the preferences and assumptions of different segments of the population.
