ABSTRACT A SPACE PREFERENCE APPROACH TO THE DETERMINATION OF INDIVIDUAL CONTACT FIELDS IN THE SPATIAL DIFFUSION OF HARVESTORE SYSTEMS IN NORTHEAST IOWA by David James DeTemple The purpose of this research is to explore the im- plications of hypothesized relationship between spatial behavior and spatial diffusion of innovation processes. The focus of the research is on (1) the derivation of a rule of spatial behavior to account for movement from place to place in the spatial diffusion of rural innovations and (2) on the construction of a spatial diffusion simulation model employing the empirically derived rule of spatial be— havior. A basic premise in the conceptualization of the spatial diffusion of innovations is that adoption is primarily the result of a learning process, where an individual adopts an innovation as soon as he has accumulated sufficient infor— mation to overcome resistance to adopt. This premise im— plies that spatial diffusion theory should be concerned with those factors which relate to the spatial pattern of informa- tion flow. Thus, fundamental to modelling the spatial as- pects of innovation—adoption has been the manner in which David J. DeTemple information movement from one location to another has been explained. There are two information sources identified as being relevant to the learning-adoption process. The first source, mass media, is considered important in the initial introduc- tion of an innovation to an individual, but after awareness of the innovation, this source becomes less significant in persuading adoption. The second source, interpersonal con- tact with others who have either (1) previously adopted the innovation or who have (2) relevant information and are re- garded as reliable sources, is considered more significant in persuading final adoption. Thus, the research focuses exclusively on the spatial mechanisms of interpersonal con- tact. The transition mechanisms accounting for information movement from place to place have varied considerably from model to model. The view taken by many is that the intensity of information flow between individuals is a continuous func- tion of intervening distance; however, it is shown statisti- cally that for northeast Iowa distance is not as important a factor as previously assumed. The approach developed in this research is an attempt to clarify the spatial interaction mechanism which controls movement of innovation-adoption from one location to another. Two movement factors are hypothesized as controlling the David J. DeTemple flow of relevant information. The first movement factor is individual interaction with the central place system through which diffusion occurs. A rule of spatial behavior to account for individual interaction with the central place system is empirically derived by employing;the methodof paired—compari- sons. From consistent statements of choice by decision- makers residing at different locations a probabilistic be- havioral rule of preferred alternatives is obtained. This rule of spatial behavior is defined such that when applied to a distribution of central place alternatives it is capable of generating the probability of individual contact with each central place, or individual contact fields. The second movement factor is interpersonal contact within a central place. Not being able to discover the explicit structure of interpersonal contact, a simple random bias model is employed to model this movement factor. The model regards every individual that interacts with a central place as having an equal chance of contacting every other individual who interacts with that place. Thus, communication between individuals is hypothe- sized as being dependent on the probability of individual interaction with the central place system and on the prob- ability of interpersonal contact within a central place. Both movement factors are modelled separately, and linked together to provide the transition mechanisms in the spatial diffusion simulation model. David J. DeTemple The constructed simulation model is run and evaluated against the actual diffusion of Harvestore Systems (silos) in northeast Iowa. Visual and statistical analysis of actual and simulated patterns of diffusion show that both patterns could have been the result of the same real—world diffusion process. Based on evaluation criteria for judging the validity of a simulation model, it is concluded that the model is a plausible representation of the spatial diffusion process studied. The diffusion model is an improvement over previous models in that (1) it is sensitive to the Spatial structure of the central place system through which diffusion occurs; (2) distance is not regarded as an unchangeable force emanat— ing from all points equally in all directions, but is con- sidered as only one of several attributes of a spatial alter- native evaluated by a decision-maker; and (3) the exact resi- dential location of individual decision-makers is maintained. The behavioral approach and the alternative representation of the spatial diffusion process are the major contributions of this research. A SPACE PREFERENCE APPROACH TO THE DETERMINATION OF INDIVIDUAL CONTACT FIELDS IN THE SPATIAL DIFFUSION OF HARVESTORE SYSTEMS IN NORTHEAST IOWA by David James DeTemple A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Geography 1970 ACKNOWLEDGMENTS The writer is indebted to numerous people for con- tributions of time and patience during the progress of this study. The greatest debt is owed to my principal advisor, Dr. Gerard Rushton, now at the University of Iowa, for his guidance was essential to the successful completion of this dissertation. Especial thanks are due Dr. James 0. Wheeler, who was willing to take over the task of committee chairman after the departure of Dr. Rushton from Michigan State Univer— sity. Thanks are due Dr. Lawrence M. Sommers, chairman of the department of geography at Michigan State University for his patience and understanding, and for providing me the Opportunity to undertake this study; and Dr. Charles Wrigley director of the Computer Institute for Social Science Re- search for making the facilities of the institute and com— puter center available. During the research and writing, Cyrus W. Young contributed many suggestions. For his friendship and criticism I am grateful. The person most imposed upon has been my wife, Elaine. Her patience and encouragement has provided the incentive to complete this work. David J. DeTemple Bloomington, Indiana “I,“iii TABLE OF CONTENTS Page ACKNOWLEDGMENTS iii LIST OF TABLES Vi LIST OF FIGURES Vii LIST OF MAPS viii CHAPTER I. CONTEXT OF THE RESEARCH PROBLEM 1 Introduction 1 Conceptualization of the Spatial Diffusion of Innovation Processes 3 A Behavioral Aspect of Spatial Diffusion Theory 9 Statement of the Research Problem 12 II. THE TRANSITION MECHANISM IN THE SPATIAL DIFFUSION OF INNOVATION MODEL 15 The Neighborhood Effect 15 A Socio-economic Bias 21 A Random Bias 25 A Conceptual Model 25 III. A SPACE PREFERENCE DETERMINATION OF INDIVIDUAL CONTACT FIELDS 26 Revealed Space Preferences 26 A Rule of Spatial Behavior 28 Summary 55 IV. THE SPATIAL DIFFUSION OF HARVESTORE SYSTEMS IN NORTHEAST IOWA: THE MODEL 57 The Diffusion Model 57 The Study Area 58 Harvestore Systems 59 The Basic Data 44 Summary 54 V. THE SPATIAL DIFFUSION OF HARVESTORE SYS- TEMS IN NORTHEAST IOWA: THE SIMULATION AND EVALUATION 55 iv VI. SELECTED APPENDIX APPENDIX APPENDIX APPENDIX APPENDIX APPENDIX APPENDIX APPENDIX The Simulation Runs Evaluation of the Diffusion Model Summary SUMMARY AND CONCLUSIONS Summary Conclusions BIBLIOGRAPHY A: Maps of the Actual and Simulated Diffusion of Harvestore Systems in Northeast Iowa, 1950—1967 A Central Place Hierarchy of Goods and Services Computer Programs with Notes on Programs Program TWOBY, Program ALTERN, Program SPACDIF Diffusion Data; 2,4D Weed Spray in Collins, Iowa; Harvestore Systems in Northeast Iowa Central Places in Northeast Iowa Space Preference Matrix Simulation 2: Spatial Diffusion of Harvestore Systems in Northeast Iowa A Brief Description of the Northeast Iowa Study Area Page 55 55 68 69 69 71 76 80 151 157 148 161 170 175 185 Table B—2 LIST OF TABLES Page 2 x 2 Comparative Time Trial Contingency Table to Test the Neighborhood Effect 17 Results from 2 x 2 Comparative Time Trial Contingency Test of Collins, Iowa 2,4D Diffusion Data 20 Central Place Alternatives and Locational Type Classification 55 Preference Data Matrix 55 Number of Farms Adopting Harvestore Systems 42 Scale Value for Locational Types 51 Cummulative Number of Adopters 56 Number of Adoptions Per Generation 57 Chi-Square Analysis Between Simulation 2 and the Actual Diffusion of Harvestore Systems in Northeast Iowa 62 70 Household Commodities 157 Factor Analysis of Commodity Similarity Matrix; Varimax Rotation-5 Factor Solution 158 vi Figure LIST OF FIGURES Distribution of Adopters of 2,4D Weed Spray, Collins, Iowa Definition of Locational Types Ratio of Actual to Potential AdOpters in 26 County Area of Northeast Iowa One-Dimensional Scale for Locational Types Space Preference Structure for Factor II. vii Page 19 29 47 52 55 Map \OCIJQOW 10 11 12 15 14 15 16 17 18 19 2O 21 LIST OF MAPS Northeast Iowa Study Area Distribution of 1000 Sample Farms in North— east Iowa Distribution of Central Places in Northeast Iowa Adoption of Harvestore Systems in Northeast Iowa, 1950 Distribution of Harvestore Systems in North- east Iowa, 1950 AdOption, 1951 Distribution, 1951 Adoption, 1952 Distribution, 1952 Adoption, 1955 Distribution, 1955 Adoption, 1954 Distribution, 1954 Adoption, 1955 Distribution, 1955 Adoption, 1956 Distribution, 1956 Adoption, 1957 Distribution, 1957 Adoption, 1958 Distribution, 1958 viii Page 58a 46a 48a 81 82 85 84 85 86 87 88 89 9O 91 92 95 94 95 96 97 98 Map 22 25 24 25 26 27 28 29 5o 51 52 55 54 55 56 57 58 59 4o 41 42 45 44 45 Adoption, 1959 Distribution, 1959 Adoption, 1960 Distribution, 1960 Adoption, 1961 Distribution, 1961 Adoption, 1962 Distribution, 1962 Adoption, 1965 Distribution, 1965 Adoption, 1964 Distribution, 1964 Adoption, 1965 Distribution, 1965 Adoption, 1966 Distribution, 1966 Adoption, 1967 Distribution, 1967 Adoption of Harvestore Systems in North- east Iowa, Simulation 2—Generation 1 Distribution of Harvestore Systems in Northeast Iowa, Simulation 2—Generation 1 Adoption, Generation 2 Distribution, Generation 2 Adoption, Generation 5 Distribution, Generation 5 Page 99 100 101 102 105 104 105 106 107 108 109 110 111 112 115 114 115 116 117 118 119 120 121 122 Map 46 4'7 48 49 5o 51 52 55 Adoption, Generation 4 Distribution, Generation Adoption, Generation 5 Distribution, Generation Adoption, Generation 6 Distribution, Generation Adoption, Generation 7 Distribution, Generation Page 125 124 125 126 127 128 129 150 CHAPTER I CONTEXT OF THE RESEARCH PROBLEM Introduction One of the fundamental concerns of human geography has been with the description and explanation of spatial patterns. In efforts to provide adequate explanation for rather complex spatial-temporal patterns, geographers have traditionally considered the spatial behavior of aggregate populations, and have regarded the spatial behavior of individuals as both unique and unpredictable.1 Some have felt that individual variations in space and time preferences are so great as to preclude any rationalization of individual spatial behavior.2 However, Hagerstrand's work in migration and in spatial dif— fusion of innovations has demonstrated the possibility of focusing geographic research at the level of the individual. His work has shown that even though the individual's exact decisions may not be precisely determined, the probability of making a range of decisions can be determined.5 1Richard L. Morrill and Forrest R. Pitts, "Marriage, Migration, and the Mean Information Field: A Study in Uniqueness and Generality," Annals of the Association of American Geographers, LVII (19675, p. -O2. 2Walter Isard, Location and Space Economy (Cambridge, Mass.: The M.I.T. Press, 1956), pp. 84-85. 5For the reader who is unacquainted with Hagerstrand's s atial diffusion of innovation research, see Torsten Hagerstrand, The Propagation of Innovation Waves, Lund Studies in Geography: Series B, 'Human Geography No. 4(Lund, Sweden: 1 Spatial diffusion has long been a subject of geographic inquiry, but Hagerstrand's pioneering work in the early 1950's on spatial diffusion of innovations provided the ini- tial stimulus for the development of a strong theoretical research traditionfL His spatial diffusion work was clearly an attempt to capture in a diffusion model the spatial struc— ture of the innovation-adoption process and characteristics of individual behavior in space. Since the highly complex processes preclude true analytic solutions, Monte Carlo simu- lation techniques were selected to model the processes which generate spatial patterns of innovation-adoption. The Gleerup, 1952); Torsten Hagerstrand, "Migration and Area," in Migration in Sweden: A Symposium, ed. by David Hannerberg, Torsten Hagerstrand, and Bruno Odeving, Lund Studies in Geog- raphy: Series B, Human Geography No.”15 (Lund, Sweden: Gleerup, 1957), pp. 25-158; Torsten Hagerstrand, "A Monte Carlo Approach to Diffusion," Archives of Europeennes De Sociologie, VI (1965), pp. 45-67; Torsten Hagerstrand, "Aspects of the Spatial Structure of Social Communication and the Dif— fusion of Information," Pa ers of the Regional Science Associ- ation, XVI (1966), pp. 27—42; TEESten Hfigerstrand, "On Monte Carlo Simulation of Diffusion," in Quantitative Geography, Part I: Economic and Cultural Topics, ed. by William L. Garrison and Duane F. Marble, Northwestern Universit , Depart- ment of Geography, Studies in Geography No. 15 (1967 , pp. 1-52; Torsten Hagerstrand, Innovation Diffusion 22.2 Spatial Process, translated by Allan Pred (CEicago: University of Chicago Press, 1967); Torsten Hagerstrand, "Quantitative Techniques for Analysis of the Spread of Information and Technology," in Education and Economic Development, ed. by C. A. Anderson and M. J. Bowman (Chicago: Aldine, 1965), pp. 244-280. 4Whenever the term "spatial diffusion” is used in this study, unless otherwise noted, reference is specifically to the "spatial diffusion of innovations". For a general review of spatial diffusion research in geography, see L. A. Brown and E. G. Moore, "Diffusion Research in Geography: A Per- spective," in Progress in Geography: International Reviews of Current Research, Vol. 1, ed. by Christopher Board, gt a1. (New‘York: St. Martin's Press, 1969), pp. 119-157. 5 simulation model was designed as a pseudo-experiment in real space, and an analog for abstract decision-making processes.5 As Hagerstrand notes:6 "The simulation technique makes it possible to create imagined societies of different structure, to endow individuals with various behavior proba- bilities and rules of action, and finally to let random numbers infuse life into the system." Conceptualization of the Spatial Diffusion pf Innovation Processes Hagerstrand's conceptualization of the spatial diffusion processes are most explicit in his simulation models. These models consider specific empirical examples--the spread of agricultural innovations through a rural landscape. Innovation Adoption as'g Learning Process The basic premise in Hagerstrand's conceptualization is that the adoption of an innovation is primarily the result of a learning process, where an individual adopts an innovation as soon as he has accumulated sufficient information to over- come resistance to adopt. This premise implies that spatial diffusion theory should be concerned with those factors which relate to the spatial pattern of information flow, e.g., the -characteristics which influence the spatial pattern of com- munication and resistances to adopt and the relationship 5J. Wolpert and D. Zillmann, "The Sequential Expansion of a Decision Model in a Spatial Context," Environment and Planning, I (1969), p. 91. 6Hagerstrand, "Quantitative Techniques," p. 266. 4 between exposure to relevant information and the reduction of resistances to adOpt.7 Information Factors Hagerstrand identifies two information sources relevant to the individual's learning-adoption process. The first source, mass media, is considered significant in the initial introduction of an innovation to an individual, but after awareness of the innovation, this source becomes less signi- ficant in persuading adOption. The second source, inter- personal contact with others who have either (1) previously adOpted the innovation or who have (2) relevant information and are regarded as reliable sources, is considered more significant in persuading final adoption.8 Hence, Hagerstrand focuses his simulation model exclusively on the mechanisms of interpersonal contact. The Neighborhood Effect Hagerstrand hypothesizes that the destination of personal messages depends on the configuration of an individual's network of interpersonal contact, and that this network is 7Lawrence A. Brown, "Diffusion Dynamics: A Review and Revision of the Quantitative Theory of the Spatial Diffusion of Innovation,” (unpublished Ph. D. dissertation, Northwestern University, 1966), pp. 7- 10; Hagerstrand, Innovation Diffusion .gs a Spatial Process, pp. 158-140. 8For a brief review of the significance of interpersonal contact in the learning-adOption process, see Everett M. Rogers, Diffusion of Innovation (New York: The Free Press, 1962), pp. 158- 140. dependent on the presence of various barriers. Initial focus is primarily on the spatial ramification of physical barriers which impede contact, such as lakes, rivers, and mountains, and on geographical distance which separates potential communicants. This distance factor plays a major role in Hagerstrand's diffusion model and has been termed the neighborhood effect. A Hierarchypf Networ 8.2: Communication Hagerstrand, also, recognized the importance of hierarchy of networks of communication:9 "As a demonstration and entirely arbitrary, we can make three groups Operating in international, regional, and local ranges. Some individuals are wholly bound to the local plane, others operate on the regional and local plane, and still others operate more or less on all three." At the local level innovations Spread through a communication network linking individuals directly to one another through interpersonal contact. However, at the regional level a different network of communication comes into play, one tied closely to the Spatial pattern of linkages between central places. AS Hagerstrand notes, diffusion over a landscape of central places tends to follow the structure of the central place hierarch . Urban places tend to adopt certain innovations before rural; and larger, relatively more 9Hagerstrand, "0n the Monte Carlo Simulation of Dif- fusion,” p. 8. important places at greater distances tend to adopt before smaller places that are nearby. Hagerstrand observes that:10 "The urban hierarchy canalizes the course of dif- fusion. In addition to the influence from a neighboring center on the neighboring districts we find Short circuits to more important places at greater distance." Brown has suggested that diffusion may be viewed at two levels, local and regional, and that "these two levels may be super- imposed to provide a more comprehensive picture of diffusion within a large region——in other words, among central places and then to individual farmers."ll Market Factors In identifying patterns of diffusion of commercial and manufactured items not adequately explained by spatial dif- fusion theory, Brown postulated that the deviations may be the result of (1) marketing decisions by distributors and (2) the shopping trip behavior of potential adopters. These additional factors have been termed market factors, as opposed to the previously identified information factors.l2 Market factors are important in determining the hier— archical pattern of diffusion through a central place land- scape. In the case of a dispersed farm population, consumers are not residing in central places. Therefore, their shopping loHagerstrand, The PrOpagation pf Innovation Waves; Brown, "Diffusion Dynamics,fipp. 55—42. 11 Brown and Moore, "Diffusion Research," p. 125. 12Brown, "Diffusion Dynamics," pp. 2-4, 42-49. trip behavior strongly influences both the frequency and the set of central places with which they interact. The type of innovation and the distribution of the propagators of that innovation determine the set of central places through which relevant information circulates. Thus the central place sys- tem is extremely important in focusing the Spatial pattern of innovation diffusion. Modelling the Spatial Diffusion Process One of the challenges for diffusion research has been to combine individual behavior with the structure of the spatial system to deve10p process theories from which Spatial diffusion patterns can be deduced. Hagerstrand's research goal was to Simulate the Spatial diffusion process and eventually make predictions achievable.13 Unfortunately, even though information factors, market factors, and the central place system were recognized as basic elements of the spatial diffusion process, Hagerstrand was only able to incorporate a portion of his conceptualization into a diffusion model. In part, the reason the model included only a portion of his conceptualization of the diffusion process was that . the nature of many of the basic relationships, such as that of the central place hierarchy, Simply were not known. Geographic diffusion studies following the Hagerstrand approach are either concerned with refinements of the original 15Hagerstrand, ”0n Monte Carlo Simulation of Diffusion,” p. 7. simulation model14 or focus upon the processes which generate the observed Spatial pattern of innovation—adoption. These latter studies have been successful in identifying critical elements relevant to diffusion in a Specific study area. However, in modelling diffusion processes many of these studies have applied the structure of Hagerstrand's Simula- tion model directly to their own problem without appropriate modifications.15 The result has been that relatively little l4Refinements of Hagerstrand's original Monte Carlo Simu— lation model have focused on (1) experimentation with various mathematical distance-decay functions (see, Richard L. Morrill, "The Distribution of Migration Distances," Papers 2£.ERE Regional Science Association, XI (1965), pp. 75-84; Morrill and’PittS, "Uniqueness and Generality," pp. 401—422), (2) derivation of both biased and unbiased mean information fields (see, Duane F. Marble and John D. Nysteun, "An Approach to the Direct Measurement of Community Mean Information Fields," Pa ers pf the Regional Science Association, XI (1965), pp. 99— 108; Morrill and Pitts, ”Uniqueness and Generality," pp. 401— 422; Lawrence A. Brown, Eric G. Moore, and William Moultrie, TRANSMAP: A Program for Planar Transformation of Point Dis— tributions, Ohio State University, Department of_Geography, Discussion Paper No. 5, pp. 26; Forrest R. Pitts, MIFCAL and NONCEL: Two Computer Programs for the Generalization p£_ph§_ Hagerstrand Model :2 ap Irregular Lattice, Northwestern University, De artment of Geography, Technical Paper No. 25 (1967), pp. 55 , and (5) the construction of computer programs (see, Forrest R. Pitts, "Problems in Computer Simulation of Diffusion," Pa ers of the Regional Science Association, XI (1965), pp. Ill-I22?_Forrest R. Pitts, HAGER III and HAGER IV: Two Monte Carlo Computer Programs for the Study p£_Spatial Diffusion Problems, Northwestern University, Department of GEography, Research Report No. 12 (1965), pp. 42; Pitts MIFCAL and NONCEL; Brown, Moore, and Moultrie, TRANSMAP . 15For examples where the Hagerstrand model has been applied see, Leonard W. Bowden, Diffusionqu the Decision pg Irrigate, University of Chicago, Department of Geography, Research Paper No. 97 (1965), pp. 89-120; and Burton 0. Witthuhn, "The Spatial Integration of Uganda as Shown by the Diffusion of Postal Agencies, 1900—1965," The East Lakes Geographer, IV (1968), pp. 5-20. insight has been gained in either understanding individual Spatial behavior or explaining general spatial diffusion 16 processes. A_Behavioral Aspect prSpatial Diffusion Theory Many existing theories in human geography, including spatial diffusion theory, have at least implicit behavioral assumptions in their structure. The spatial patterns of the diffusion of phenomena, ideas, and techniques through a region are spatial expression of many individual decisions. The basic geographic elements of distance, direction, and Spatial variation are evident in diffusion patterns. But if the processes which generate diffusion patterns are to be explained, then notions of human decision—making must be incorporated into geographic diffusion theory.17 As King has noted:18 ". . . existing theoretical statements in geog- raphy appear weak on at least two accounts. First, it usually is the case with statements that the basic Spatial structure appears as given, rather than as a logical consequence of theory. . . . A second weakness . . . is that the behavioral 16Brown and Moore, "Diffusion Research," pp. 145-144. 17David Harvey, "Conceptual and Measurement Problems in the Cognitive—Behavioral Approach to Location Theory," in Behavioral Problems ip_Geo ra h : A Symposium, ed. by Kevin R? COX and Reginald G. Golledge, Northwestern Univer- sity, Department of Geography, Studies in Geography No. 17 (1969), p- 55- 18Leslie J. King, "The Analysis of Spatial Form and Its Relation to Geographic Theory " Annals of the Association pf_American Geographers, LIX (19695, ppT_595é595. 1O underpinnings of these statements have seldom been made explicit . . . much of geographical analysis has been pursued on highly aggregative levels with considerable emphasis upon techniques and too little attention upon possible behavioral mechanisms." Thus, to understand processes that evolve Spatial pat- terns, concern should be for building geographic theory and models on the basis of postulates regarding human behavior. One approach to the Search for relevant behavioral postulates relates parameters describing actual behavior patterns in an area to specified spatial structures in the same area. Hager— strand's use of the mean information field is an excellent example of this type of approach. The parameters of the information field are based upon interaction data for the area under study. The parameters are place dependent, in that they are tied directly to the Spatial structure of the system for which they are calibrated and say little about the characteristics of parameters for different places or spatial systems.19 This form of description of overt behavior is no more a process type of explanation than is the descrip— tion of the diffusion pattern itself.20 A second approach to the search for relevant behavioral postulates consists of a description of behavioral processes irrespective of the spatial system in which the behaviors are 19Kevin R. Cox and Reginald G. Golledge, "Editorial Introduction: Behavioral Models in Geography," in Behavioral Problems ip Geography, pp. 2-5. 2OLeslie Curry, "Central Places in the Random Spatial Economy," Journal pf Regional Science, VII (Supplement, 1967), p. 219. 11 found. This approach involves a search for postulates or rules of Spatial choice, movement, and interactions which are place independent of the Spatial system in which they operate. In support of this type of approach Curry argues that:21 "A postulate on Spatial behavior Should not directly describe the behavior occurring within a central place system, since it is obvious that the system can then be directly derived without providing any insight. The behavior postulate must allow a central place system to be erected on it in a suf— ficiently indirect manner that a measure of initial surprise is occasioned by the results, and this postulate must still describe behavior after the system has been derived." Moreover, Rushton states that:22 " . . . the essential feature of a useful postulate is that it should describe the rules by which alter- native locations are evaluated and choices conse— quently made. This procedure we may call spatial behavior, reserving the term 'behavior in space' for the description of the actual Spatial choices made in a particular system. Since behavior in Space is in part determined by the particular Spatial system in which it has been observed, it is not admissable as a behavioral postulate in any theory. In Short, such behavior is not independent of the particular system in which it has been studied. 0n the other hand, a postulate which describes the rules of spatial behavior is capable of generating a variety of behavior patterns in space as the system . . . to which the rules are applied, is allowed to change." Thus, postulates of spatial behavior should mirror individual decisions and be able to deduce "behavior in Space" where each individual decision-maker, encompassed in his own 21Curry, "Central Places," p. 219. 22Gerard Rushton, "Analysis of Spatial Behavior by Revealed Space Preferences " Annals pf_the Association pf American Geographers, LIX (19695, p. 592. 12 Spatial environment, reaches decisions which maximize some 25 satisfaction or preference function. Statement pf the Research Problem The primary purpose of this study is to pursue the implications of the hypothesized relationships between spatial behavior and Spatial processes that appear to have been present in virtually every conceptualization of spatial diffusion processes. The focus of the research is on (1) the derivation of a rule of Spatial behavior to account for move- ment from one location to another in the spatial diffusion of rural innovations24 and (2) on the construction of a Spatial diffusion simulation model employing the empirically derived rule of Spatial behavior. The proposed model is an improve- ment over previous diffusion models in that (1) it is sensi— tive to the Spatial structure of the central place system through which diffusion occurs; (2) distance is not regarded as an unchangeable force emanating from all points equally in all directions, but is considered as one of Several char— acteristics of a Spatial alternative to be evaluated by decision-makers; and (5) the exact residential location of the individual decision—maker is maintained. 23Harvey, "Conceptual and Measurement Problems,” p. 56. 24A rule of Spatial behavior is defined so as to. describe behavioral processes irrespective of the Spatial structure of the system in which behaviors are found. 15 The first objective of this study (Chapter II) is to clarify the role of movement in spatial diffusion of innova- tion models. In this chapter a simple conceptual model is proposed that offers an alternative to transition mechanisms25 proposed in previous diffusion models. The model considers both individual interaction with the central place system and interpersonel contact at the central place as important determinants of the spatial pattern of innovation adoption. Both determinants can be modelled separately and then linked together to account for movement. The next objective of the study (Chapter III) is to model individual interaction with the central place system by defining a procedure for deriving a rule of Spatial behavior. The Spatial behavioral rule when applied against a set of alternative central places will give the probability of individual contact with each central place. This indi- vidual contact field is defined such that, given the location of a decision-maker and the locations of alternative central places, the behavioral rule can generate the probability of the decision-maker interacting with each central place. Finally, the third objective is to incorporate aspects of existing diffusion theory, central place theory, and behaviorally determined individual contact fields into a spatial diffusion of innovation model. In Chapter IV the 25In construction of spatial diffusion models the transition mechanism is the modelling approach employed to account for movement from one location to another. l4 simulation model is constructed and in Chapter V it is run and evaluated against the actual diffusion of Harvestore Systems in northeast Iowa (See Map 1).26 Chapter VI in- cludes a brief summary and critique of the research and proposals for future research. 26The Harvestore System, a Special type of farm silo manufactured by A. 0. Smith Harvestore Products, Inc., is a unique feed-crop storage innovation in that it does three things of which no other S110 is capable; (1) it resists corrosion from feed acids, (2) it provides maximum rotec- tion from oxygen to preserve feed nutrients, and (5 it un- loads from the bottom. CHAPTER II THE TRANSITION MECHANISM IN THE SPATIAL DIFFUSION OF INNOVATION MODEL The Neighborhood Effect Fundamental to modelling the spatial aspects of the in— novation—adoption processes has been the manner in which move— ment from place to place has been explained. The transition mechanisms accounting for movement have varied considerably from model to model.1 The view taken by Hagerstrand is that the intensity of movement is a continuous function of geog— raphic distance. This particular transition mechanism has been termed the neighborhood effect and has been widely ac- cepted as a basic premiseh 4420:4004 «.5 29:27.30 m onsmflm Ana—E: 230». Oh woz03em HM <38. .mnz .ai 59 the consumer behavior data (Iowa) and the 2,4D weed spray 1 data (Collins, Iowa). a Harvestore Systems The Harvestore System is a unique feed-crop storage system that has a number of advantages over ordinary farm silos. A serious problem with feed-crop storage in ordinary Silos iS that up to one—fourth Of the feed—crop is lost through oxidation. Atmospheric temperature changes cause gases inside silos to expand and contract. This action exerts pressure on the silo structure which can not be compensated for without allowing air to enter and contact the feed-crop. The major advantage of the Harvestore System is that it can be sealed air—tight to reduce feed—crop loss through Oxidation. The Harvestore structure iS constructed of glass- fused-to—steel plates that are impervious to air. Inside the structure pressure absorbing gas—bags vented to the out— Side compensate for changes in atmospheric temperature and pressure. With a rise in outside temperature gases inside the structure expand and push air out of the breather bags. With a fall in outside temperature gases inside contract and the breather bags are filled with air. Thus, the system, by controlling in—and—out air flow, compensates for pressure changes inside the Harvestore structure without allowing air to contact the feed—crop. laSee Appendix H for a more complete description of the study area. 40 The obvious advantage to adopting a Harvestore System iS the Significant reduction in feed-crop loss through oxi- dation. But the system also gives the farmer greater flexi- bility in cropping and harvesting, and allows him to increase both the quantity and quality of animal feed. Feed-crops can be harvested early when moisture and protein content are high and stored in the Harvestore structure without the worry or cost of drying. Double-cropping with a winter crop and an early spring harvest is a possibility that allows the farmer to get an extra crop per year off the same acreage. Harvestore structures have automatic unloading from the bottom, therefore it is not necessary to unload the structure before refilling. Ordinary Silos load and unload from the top, thus they must be emptied before refilling. With a Harvestore System a farmer can realize a savings in labor costs since harvesting takes less time, much of the heavy labor is eliminated with automatic equipment, and crops need not all be harvested at once but may be harvested when the farmer has the available labor. The first Harvestore System recorded in northeast Iowa was installed in 1949. The initial structure was located on a farm ten miles southeast of Waterloo (see Maps 4 and 5 in Appendix A). From 1950 through 1967 there was a general in— crease in the number of systems adopted per year so that by the end of 1967 there were Harvestore Systems on 595 farms in northeast Iowa. The number of farms adopting and 41 cummulative number adopted from 1950 through 1967 are re- corded in Table 5 (Also, see Maps 4-59 in Appendix A).2 The Harvestore System is an innovation in the produc— tion of feed for dairy cattle, beef cattle, and hogs and might have spread more rapidly in northeast Iowa, but the cost of construction and the need for additional mechanized equipment impeded adoption. The large scale financing needed to in- stall a Harvestore System requires that a farmer make a sub— stantial financial commitment in adopting a new system Of feed—crop production and storage. The Diffusion Pattern There are several observable trends in the spatial pattern of acceptance of Harvestore Systems in the northeast Iowa study area. The earliest trend is the development of a cluster of adopters south of Waterloo (See Maps 4-21 in Appendix A). The Waterloo cluster iS most pronounced in the early 1950's; in 1952 and 1955 nearly half of all systems in 2Each farm that adOpted a Harvestore System and the years of adoption is listed in Appendix D. This information was Obtained from Mr. Robert Lyons at A. 0. Smith Harvestore Products, Inc., Arlington Heights, Illinois. The exact 10- cations of farms adOpting the systems were verified by the local dealers; Iowa Structures, Cedar Falls, Iowa, and Sky- line Harvestore, Nashua, Iowa. The diffusion of Harvestore Systems in northeast Iowa is plotted on Maps 4—59 in Appendix A. The even numbered maps record the location of each farm adopting the system in a particular year and the odd numbered maps record the loca— tion of all farms that have adopted the system up to the end of a particular year. 42 TABLE 5 NUMBER OF FARMS ADOPTING HARVESTORE SYSTEMS Year New Adopters Total 1950 6 7 1951 14 21 1952 21 42 1955 16 58 1954 18 76 1955 15 89 1956 9 98 1957 ~ 7 105 1958 28 155 1959 25 158 1960 11 169 1961 42 211 1962 24 255 1965 12 247 1964 19 266 1965 22 288 1966 57 545 1967 50 595 use were in this cluster. By 1955 adOption had tended to move away from this cluster. A second trend is the development of a tight cluster of adopters west of Dubuque (See Maps 16-51 in Appendix A). Initial growth of this mode of adopters was slow until 1958. From 1958 through 1960 nearly half of all new systems adOpted in the study area were installed in this cluster. After 1960 acceptance of the innovation tended to expand away from the Dubuque cluster. A third identifiable pattern was the general tendency for adOption of Harvestore Systems to move from south to 45 north. Throughout the study period there had been scattered growth in the number of systems adopted in the northern half of the study area. In the early 1950's there were a number of systems adOpted in the northern half of the area, but from 1957 through 1962 very few systems were installed in the north area. However, after 1962 there has been a tendency for the proportion of adopters to increase. By 1967 the majority of Harvestore Systems being adopted were in the northern half of the study area (See maps 54-59 in Appendix A). The three trends identified account for a majority of the Harvestore Systems adopted. The development of each of the three trends corresponds to peak years in the number of systems adopted. The Waterloo cluster developed early in the study period and accounts for a large proportion of the adoptions in the peak years of 1952 and 1955 (See Table 5). In the late 1950's the Dubuque cluster accounts almost totally for the number of adoptions in 1958 and 1959. Finally, the general trend for adoption to move from south to north corresponds with the increase in number of adop— tions in 1966 and 1967. In addition to the three previously identified trends is an Observed general diffusion of adoption of the innova— tion into an area south of Mason City and west of Waterloo along the western boundary of the northeast Iowa study area. The pattern in the 1950's begins as a slow diffusion of 44 acceptance of the innovation spreading from the east, but from 1957 through 1959 a number Of adoptions occur south of Mason City which appear independent of the westward dif- fusion pattern (See Maps 18-25). What is apparent in the development of the spatial diffusion pattern in northeast Iowa is that when a cluster Of adopters reaches some minimum threshold Size, the adop- tion rate increases. The adoption rate remains high in the cluster until all of the most innovative potential adopters have accepted Harvestore Systems, and then the rate decreases. With both the Waterloo and Dubuque clusters the adOption rate remained high for three or four years. The Basic Data Before the simulation may be run the diffusion model needs the following information: 1. The number and location of all potential adopters. 2. The number and location Of all initial adOpterS. 5. The behavioral rule used to derive individual con- tact fields (paired-comparisons matrix Of prefer- red locational types). 4. The location and population of all central places in the study area which decision-makers consider as possible alternatives. The Population 9: Initial and Potential Adopters To insure that simulation runs are not spatially biased care must be taken in selecting the distribution of potential adopters. In northeast Iowa there are over 40,000 farms. 45 Thus assuming that the Operator of each farm could adopt a Harvestore System there are over 40,000 potential adOpters in the study area. Analytically, this number Of potential adopters is more than the diffusion model can handle. There- fore, the number must be reduced to something less than the total. Both the number and the location of potential adopters can bias simulation runs. It is Obvious if the sample of potential adopters considered in the diffusion model is not an unbiased sample of the total population of potential adopters that the resulting simulation patterns will be Spatially biased. Also, simulation patterns will be Spati— ally biased if the sample is not sufficiently large. For example, if in a Simulation run 999 out of 1000 potential adopters accept an innovation, then the resulting Spatial pattern Of adopters is highly predictable.5 In fact, the results of the simulation are determined by the distribu- tion Of potential adOpters; no other mechanism in the diffu— sion model plays an important part in determining the spatial pattern. 5When 999 out Of a sample of 1000 potential adopters accept in a simulation run it iS clear that the sample is not large enough. Only 1000 different spatial patterns can occur: N! _ 10001 _ r! (N—r)! ‘ 555T‘IT ‘ 1000 Each of the spatial patterns iS almost exactly the same as all the others. 46 Since there are 595 adopters of the actual innova- tion in the study area, the sample of potential adopters must be significantly larger than this number. Arbitrarily, a stratified random sample of 1000 farms is drawn as the set of potential adopters for the diffusion simulation model (see Map 2). By stratifying the sample an unbiased estimate Of the spatial distribution of the population of potential adopters is Obtained; and 1000 farms are considered a suffic— iently large sample for the number of actual adopters.4 In none of the twenty—six counties in the study area does the number of Harvestore Systems accepted exceed the number of sample potential adopters (see Figure 5). The initial Set of 21 adopters selected for the Simu- lation model correspond to the 1951 distribution of Harvestore Systems (see Map 7 in Appendix A). This distribution allows the model sufficient number of initial adopters to Simulate the spatial pattern of innovation-adOption in a minimum number of generations. 4The possible number of different spatial patterns that can result from a simulation where 595 out of 1000 potential adopters accept an innovation is almost infinite, 1000! 595! 6051 46a u 394 .. / .\ ”LAB n. K / \ I\l WONG onrq .6541..an a . Don—5.0 T after ./| 5.90 ... K. H. .. H. . a. H I 4 fl. NUMBER OF COUNTIES 47 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 RATIO OF ACTUAL TO POTENTIAL ADOPTERS IN 26 COUNTY AREA OF NORTHEAST IOWA Figure 5 48 The Behavioral Rule Two data sets are used to generate the paired-comparisons matrix of preferred locational types.5 The first set of data describes the consumer behavior for a random sample Of dispersed farm households in Iowa. Identified in the data are the central places patronized and the total dollar value of expenditures on selected household commodities.6 The second data set is the location and 1960 population of all Iowa central places (see Map 5).7 These two data sets form the basis from which the behavioral rule is empirically calibrated.8 Available Spatial Alternatives The distribution of central places within 48 miles of an individual defines all of his alternative Opportunities 5The paired—comparison matrix Of preferred locational types is listed in Appendix F. The locational types used to generate the matrix are the same as defined in Figure 1. 6The type and number Of household commodities used to define the behavioral rule is fundamental to the structure of the probabilities. For a listing Of the 20 commodities selected and the reason for selections, see Appendix B. 7This data was collected in the Spring of 1961 as part of a survey of expenditures and sales by persons living in rural Iowa. The survey was conducted by the Iowa State University Statistical Laboratory for the Iowa College- Community Research Center. For further description of this survey and the data collected, see Appendix A in Gerard Rushton, Spatial Pattern pi GroceryPurchaseS‘Ey the Iowa Rural Population, University of Iowa, Bureau of Business and Economic Research, Studies in Business and Economics, New Series NO. 9 (1966), pp. 105-109. 8The behavioral rule was used to generate individual contact fields for each household in the Iowa sample. The individual contact fields were successful in predicting the most preferred central place for greater than 65% Of the sample. 48a m as... OLGA. mmU/qnd JZESZmu LO 0:43m44u4w4 HQ <3qu m Z c.8728 5nd MHHEflfO \ \rJ 49 9 for central place interaction. For every sample farm in the study area there are well over 200 central places within 48 miles. Thus, it is obvious that a decision—maker is unable to perceive all of his alternatives and to evaluate each one. The farther away and the smaller the central place, the more likely the individual is to ignore it as an alternative. Preferred Locational Types Theoretically the decision-maker has access to a broad range of locational types; typically only some limited por— tion of the alternatives are relevant and applicable to his decision behavior.10 In Iowa greater than 99 Per cent of all dollars spent on the selected household commodities are Spent at five or fewer central places. In most cases the five cen— tral places are the five with which the individual has the highest probability of interacting according to the behav— ioral rule. This tends to indicate that decision-makers perceive their first five preferred locational type central places as the complete set of relevant alternatives. To model interaction with the central place system, it is necessary to only consider a decision-makers first five preferred alternatives. Thus, the individual contact 9The name, location, and 1960 population of all central places in northeast Iowa are listed in Appendix H. Also, see Map 2. 10Julian Wolpert, "Behavioral Aspects of the Decision to Migrate," Papers pi the Rpgional Science Association, XV (1965), p. 161. 50 field need be defined for only five central places. Identi- fication of preferred alternatives is accomplished first by scaling the information contained in the paired—comparisons matrix to Obtain a one-dimensional ranking of all locational types. Then by comparing the preference ranking to the list of locational types available to the decision—maker, the five preferred central places can be identified. A ranking of locational types by preferences is found by scaling the information contained in the paired—comparison 11 matrix of revealed Space preferences. The scaling technique 12 Table 6 shows used is an algorithm developed by Kruskal. the computed scale values and rankings on the first dimension. The stress value for the first dimension equals 0.554. In Figure 4, locational types are plotted on one dimension. The negative scale values are most preferred and the positive scale values are least preferred. In Figure 5, the scale is Shown as isolines. The isolines represent a trade—Off be— tween population Size and distance to a central place; the same variables used to define locational types. This sur- face is called an indifference surface of spatial choice and infers that a decision-maker would be indifferent between any two central places located along one of the isolines. The 11For a more complete discussion of scaling of loca- tional types, see Gerard Rushton, "The Scaling of Locational Preferences," in Cox and Golledge, Behavioral Problems 1p Geography, pp. 197—227. 12J. B. Kruskal, "Non-Metric Multi—Dimensional Scaling: A Numerical Method," Psychometrika, XXIX (1964), pp. 115-129. 51 preferred central place lies on the highest point on the surface (upper left). TABLE 6 SCALE VALUES FOR THE LOCATIONAL TYPES Locational Scale Rank Locational Scale Rank Types Value Types Value 1 -0.821 15 25 -l.522 5 2 -0.272 20 26 -1.026 9 5 0.285 29 27 -0.622 16 4 0.821 56 28 —0.204 22 5 0.819 55 29 0.566 50 6 1.221 44 50 0.611 55 7 1.461 45 51 1.150 42 8 2.052 48 52 1.965 47 9 —1.l81 7 55 —l.6l5 2 10 -0.702 14 54 -l.541 5 11 -0.094 .24 55 -0.989 10 12 0.416 52 56 —O.521 17 15 0.928 58 57 -0.165 25 14 0.766 54 58 0.105 27 15 1.201 45 59 0.547 51 16 1.065 40 40 0.894 57 17 -1.557 6 41 -1.762 1 18 -0.951 11 42 —l.465 4 19 —0.264 21 45 —1.l55 8 20 -0.012 26 44 -O.895 12 21 0.204 28 45 -0.652 15 22 1.656 46 46 —0.584 19 25 1.075 41 47 -0.426 18 24 1.017 59 48 —0.07O 25 52 cummmuwma hmb 1.4203300... mom uddom 44,55sz3 mzo o; n. o of: 0.! owmmuuwma Pmoz 0.: QNI m omdmflm __ «0.405, 3.0.. manhunapm muzmawmmmm mugm ill! .. I I I. O 32...- 2304 o4 623w... on P 55 .. ooom . ooov f 88 NOIlVlndOd NMOI. (0961) 54 Summary In this chapter a transition mechanism accounting for both individual interaction with the central place system and interpersonal contact within a central place has been incorporated into the rules of a diffusion Simulation model. The transition mechanism links the individual contact field construct with a Simple random bias model to account for place to place movement in the diffusion process. The behavioral rule and the parameters of the model have been defined so that the model can be run through a num- ber of simulations. In the following chapter a number of Simulations are performed, and the diffusion model is evaluated against the actual diffusion of Harvestore Systems. CHAPTER V THE SPATIAL DIFFUSION 0F HARVESTORE SYSTEMS IN NORTHEAST IOWA: THE SIMULATION AND EVALUATION The Simulation Runs Ten Simulation runs are performed to compare with the actual diffusion of Harvestore Systems.l Each Simulation is run through seven generations. See Tables 7 and 8 for the results of the ten Simulation runs.2 Evaluation pi the Diffusion Model Validation is the process Of determining how well a model replicates the properties of the real-world system under study. Evaluation of the validity of a Monte Carlo diffusion model is a difficult process. Since the Monte Carlo method depends on sampling from a probability distri- bution, each run through the model may produce a wide range of results even though the underlying spatial process iS 1The ten Simulations are run using Program SPACDIF listed in Appendix C. The number of simulations is restricted to ten because of the time limitations on the CDC 6500 com— puter. 2Simulation 2 is also mapped, see Maps 40—55. This Simulation was chosen to map because it corresponds closely to the mean for all ten simulations and appears to be what might be called an average Simulation. If it had been pos- sible all ten simulations would have been mapped. 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The study area includes 26 counties in the northeast corner of the State of Iowa and covers an area of 15,256 square miles. The area extends from south of Cedar Rapids 114 miles north to the Minnesota border and from west of Ames 180 miles east to the Mississippi River (see Map 1). Even though in this study Northeast Iowa is considered to be a homogeneous agricultural region with little variation in either physical character or agricultural land use, diversity does exist. Relative to variations in the physical and agri— cultural landscape in other regions of the United States, especially in contrast to the differences between arid moun— tains and irrigated valleys in the West, the differences are more subtle.1 Northeast Iowa is an agricultural region with rich soil, good climate, and a favorable topography. The surface of the area is an undulating plain dissected by several tributaries of the Mississippi River that flow in broad parallel valleys 1Neil E. Salisbury, "Agricultural Productivity and the Physical Resource Base of Iowa," Iowa Business Digest, XXXI (1960), p. 27. 184 185 bordered by valley bluffs with rock outcrops.2 The roughest terrain in the area lies along the Mississippi River where glacial deposits are thin or have long been stripped from the hillsides by erosion.5 The soils in the area are the product of thick loess de- posits which have been leached and are less fertile than the prairie soils, but sufficiently good to produce high yields. The best soils are in the southern portion of the area and as one moves north, especially into the Driftless Area along the Mississippi River, the so ls tend to be thinner, lighter, and less fertile.4 In an area as small as Northeast Iowa the climate does not vary significantly from one portion to another. The aver— age annual precipitation varies from BO to 56 inches with most occuring during the growing season. The warmest month, July, has a mean temperature of 74°F in the southern portion of the area and 72°F in the northern portion. From north to south there is less than a five day difference in the length of the growing season.5 The variations in the heat and mois— ture resource in Northeast Iowa are such that climatic condi- tions do not place limits upon midlatitude grain (particularily 2John H. Garland (ed.), The North American Midwest (New York: John Wiley, 1955), p. 105. 5Salisbury, "Agricultural Productivity," p. 29. 4Garland, The North American Midwest, pp. 104—105, 147. 5U.s., Department of Agriculture, Yearbook of Agriculture, 1941 (Washington, D.C.: Government Printing DFfIEe, 1941). pp. 862-872. 186 corn) production.6 Agricultural productivity varies spatially from north to south in Northeast Iowa. Higher productivity per acre occurs in the southern portion of the region than in either the north or the northeast. Climatic resources of heat and moisture have little influence on the spatial pattern of agricultural productivity. Topography apparently has the greatest influence on agricultural productivity; flat land generally being more conducive to high agricultural produc- tivity than rough, dissected land. When soil characteris— tics are taken into account with terrain differences, most of the variation in agricultural productivity in Northeast Iowa can be explained.7 Northeast Iowa is a dairy region with both hog and beef cattle production being an important part of the rural economy. Most of the crops harvested are feed crops which are largely fed to livestock on the farm. The dominant crops are corn, oats, and hay. Corn is the most important feed crop har- vested in the region and is used as a grain to fatten both hogs and beef cattle for meat production and as silage which is a high quality, moist feed for dairy cattle.8 6Salisbury, "Agricultural Productivity," p. 28. 7Salisbury, "Agricultural Productivity," p. 31. 8Garland, The North American Midwest, p. 146. 187 On most farms in the area livestock production is diversified with varying emphasis on dairy cattle, hogs, and beef cattle. Diversification in livestock production allows a farmer to spread his work load over a period of time and to reduce the risk as far as farm prices are con— cerned. With agricultural productivity being greater in the southern part of the area, particularly corn production, there is a tendency for hog production to be relatively more im- portant in diversified livestock Operations in the south. The difference in emphasis on hog production between the northern and southern portions of the area is a matter of degree rather than a difference in the type of farming. The distribution of rural settlement, farm ownership, and standard of living tend to correspond with variations in agricultural productivity. Except for variations in rural population density along river valleys, in the Driftless Area along the eastern edge of the study area, and near larger urban centers most of the area has from 25 to 55 persons per square mile. There is a general tendency for rural population density and standard of living to be higher in the south and to decrease towards the north.9 Farm size varies very little throughout the area but because of the high capital investment required in dairy operations the 9U.S. Department of Commerce, Bureau of the Census, County and City Data Book, 1962 (Washington, D.C.: Govern- ment PrintingIOffice, 1962), pp. 112-151. 188 proportion of farmers owning their own farm is slightly higher in the northern area.lO Even though the variation in agricultural productivity is not very great in Northeast Iowa, it is the key to under— standing most of the economic diversity of the region. Relative to variation in physical and agricultural character in other regions in the United States, Northeast Iowa is a fairly homogeneous area. lOCounty and City Data Book, 1962, pp. 112—151. "11111111111111111111111'1111111111“