spatial implications of urban functional classification: a
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University of Nebraska at Omaha University of Nebraska at Omaha
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Student Work
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Spatial Implications of Urban Functional Classification: A Study of Spatial Implications of Urban Functional Classification: A Study of
Small Urban Places in the North-Central United States Small Urban Places in the North-Central United States
Tyler A. Van Meeteren University of Nebraska at Omaha
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Spatial Implications of Urban Functional Classification: A Study
of Small Urban Places in the North-Central United States
A Thesis
Presented to the
Department of Geography/Geology
and the
Faculty of the Graduate College
University of Nebraska
In Partial Fulfillment
of the Requirements for the Degree
Master of Arts
University of Nebraska at Omaha
by
Tyler A. Van Meeteren
May 2005
UMI Number: EP73236
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THESIS ACCEPTANCE
Acceptance for the faculty of the Graduate College, University of Nebraska, in partial fulfillment of the
Requirements for the degree Master of Arts, University of Nebraska at Omaha.
Committee
Chairperson
Date (X a /v J I J 4 j Q q o S
Spatial Implications of Urban Functional Classification: A Study
of Small Urban Places in the North-Central United States
ABSTRACT
Tyler A. Van Meeteren, M.A.
University of Nebraska, 2005
Advisor: Dr. Charles R. Gildersleeve
The idea that cities have diverse economic structures and social
characteristics is commonly understood. Many times these differences can be
traced to historical regional growth or resource availability. Recognition and
better understanding of these different types of cities requires their classification.
Classification is way to organize complex and diverse information in order to
create a better understanding of processes and relationships. One of the ways in
which geographers have classified cities, in terms of describing the external
relations, is called functional town classification. The simplest way of classifying
cites is to identify the distinctive role they play in the city system.
The purpose of this thesis is to examine the spatial distribution of
economic functions for the small urban places in the study area using a standard
classification method for urban geography, and by utilizing nearest neighbor
analysis. This study should produce spatial patterns of distribution based on the
site and situation of the place. There may also be a strong influence of function
based upon proximity to a larger urban area. The creation of a contemporary
taxonomy of the small urban places in the study area, and subsequent
understanding of the spatial distribution of dominant economic features should
provide the base for future investigation into small urban center relationships and
classification.
V
ACKNOWLEDGEMENTS
The completion of my Masters Degree in Geography would not have been
successful without the support from numerous people at the University of
Nebraska at Omaha. I would like to thank Dr. Charles Gildersleeve for
introducing me to the realm of urban geography. As the chair of my thesis
committee, Dr. Gildersleeve did a fantastic job molding the theoretical and
methodological foundations for my research. Dr. Gildersleeve’s enthusiasm for
geography was contagious. I would also like to thank Dr. Michael Peterson for
introducing me to cartography and geographic information systems (GIS). Jerry
Deichert from the Center for Public Affairs Research was instrumental in
organizing my census data for the thesis. Dr. Larry Stephens from the Math
Department offered suggestions for statistical analysis. I thank each of these
four gentlemen for their leadership and guidance.
Last but certainly not least, I thank my family for their support both
emotionally and financially as I completed my thesis. My parents Sheila and
Alan have provided unwavering support in my pursuit of a Masters degree. I
cannot forget to thank my brother, Bryant, and sister, Alissa, for being
exceptional siblings, even though I am not sure they thought I would ever get
done! Well, I did!
CONTENTS
THESIS ACCEPTANCE............................ ii
ABSTRACT....................... iii
ACKNOWLEGDEMENTS.......................................................................................... iv
CONTENTS..................................................................................................................vi
LIST OF ILLUSTRATIONS...................................................................................... viii
LIST OF TABLES......................................................................................................... x
Chapter
1. INTRODUCTION.................................................................. 1
Nature of Problem...............................................................................4
Research Objectives.......................................................................... 5
Hypotheses and Rationale......................................... 5
Significance of Research...................................................................8
Study A rea............... 9
Definition of Terms............................................................................10
Chapter Summary..................................................... ..................... . 11
2. LITERATURE REVIEW......................................................................... ..12
Traditional Functional Classifications.......................................... 13
A Guideline for Functional Classifications................................. 20
Multivariate Statistical Analysis.....................................................22
Chapter Summary..............................................................................25
vii
3.........................METHODOLOGY........ 27
Data Characteristics and Acquisition...................... 29
Data Organization..............................................................................30
Creating the Taxonomy....................................................................32
Creating the Classification Maps...................................................34
Nearest Neighbor Analysis............................................................. 36
Chapter Summary......................................... 38
4. ANALYSIS OF RESULTS.........................................................39
Descriptive Statistics....................................................................... 39
Functional Classification................................................................ 41
Classification Location Distribution............................................. 42
Nearest Neighbor Analysis............................................................. 67
Chapter Summary...............................................................................68
5. CONCLUSION.................................................................. 70
APPENDIX......................................................................................................76
A. City Percentages by Function (population sort)................... 76
B. City Percentages by Function (alphabetical sort).................85
C. Functional Classification........................................................... 94
BIBLIOGRAPHY.............................................................................................99
LIST OF ILLUSTRATIONS
Figure Page
1. Network of Interactions in Urban Geography......................................2
2. Study Area Location M ap..................................................................... 9
3. Chauncy Harris’ Functional Classes..................................................13
4. City Functions by Support.................................................................. 16
5. Methodological Model..........................................................................28
6. MSA Cities.............. 31
7. Eliminated Cities (population 2,500-10,000).................................... 32
8. ArcGIS 9 Query by Function Example.............................................. 35
9. Nearest Neighbor ArcGIS 9 Application Output....................... 37
10. Study Area Cities...............................................................................42
11. Mining Cities: by Standard Deviation Values .................... 44
12. Construction Cities: by Standard Deviation Values...................... 46
13. Manufacturing Cities: by Standard Deviation Values.................... 48
14. Wholesale Trade Cities: by Standard Deviation Values...............50
15. Retail Trade Cities: by Standard Deviation Values....................... 52
16. Transportation Cities: by Standard Deviation Values................... 54
17. Information Technology Cities: by Standard Deviation Values.... 56
18. Finance Cities: by Standard Deviation Values............................... 58
19. Professional Service Cities: by Standard Deviation Values 60
20. Personal Service Cities: by Standard Deviation Values...............62
ix
21. Public Administration Cities by Standard Deviation Values 64
22. Diversified Cities.......................... .66
LIST OF TABLES
Table Page
1. Population Information for Study Area.............................................. 10
2. Census Data: 13 Industry Groups........................... ....29
3. Thesis Service Classes..................................................................... 30
4. Mean and Standard Deviations by Function....................................40
5. Mining Standard Deviation Cities List...............................................43
6. Construction Standard Deviation Cities L is t....................................45
7. Manufacturing Standard Deviation Cities List..................................47
8. Wholesale Standard Deviation Cities List........................................ 49
9. Retail Standard Deviation Cities L is t................................................ 51
10. Transportation Standard Deviation Cities L is t..............................53
11. Information Technology Standard Deviation Cities L ist...............55
12. Finance Standard Deviation Cities List..........................................57
13. Professional Service Standard Deviation Cities List.................... 59
14. Personal Service Standard Deviation Cities L is t......................... 61
15. Public Administration Standard Deviation Cities L ist...................63
16. Diversified Cities L ist.......................................................................65
17. Nearest Neighbor Analysis Results............................................... 67
1
Chapter 1
Introduction
The study of urban geography brings several overlapping disciplines
including economics, political science, sociology, and history together to examine
the complex system of cities. To best understand the nature of urban geography,
two important approaches should be outlined. The first is to study the
relationships in the spatial distribution and dynamic movement of goods within
the cities, or a city system approach. The examination of interaction and
distribution patterns, or internal relations, within cities are looking into the city as
a system. The second approach, and the one this study employs, is the study of
spatial distribution of cities and the complex patterns of movement, and linkages,
or external relations that tie them together. (Yeates and Garner 1980) Urban
geography can merely be described as the study of cities as systems within the
framework of cities (Berry, 1964).
The relationships not only exist amongst
other urban areas, but also between cities and
the people living in that area. These complex
interactions are of significant interest to the
urban geographer. Figure 1 illustrates the
possible relationships of importance in urbanFigure 1: Network of interactions in urban
geography. The first possibility of investigation geography adapted from Ray Northam.,Urban Geography. (New York: Wiley & Sons, 1975), 3.
Place People
Landscape
Place People
2
(A) involves the associations between a place and its population. Another (B)
area of inquiry deals with the relationships linking different places. The
relationships (C) between people in more than one location can also be
researched. The final channel of study (D) includes the associations within one
place. (Northam 1976) Each of these interactions occur within the confines of
the landscape of the earth.
The study of city patterns began to pick up steam in the first half of the
20th century as N.S.B Gras (1922), Christaller (1933), Losch (1937), and Harris
and Ullman (1945) described the nature and origin of systematic variations in the
characteristics of urban places. These geographers set the framework for more
advanced statistical analysis that future generations could build upon.
The notion that cities have diverse economic structures and social
characteristics is commonly understood. Many times these differences can be
traced to historical regional growth or resource availability. Recognition and
better understanding of these different types of cities require their classification.
Classification is a way to organize complex and diverse information in order to
create a better understanding of processes and relationships. The relevance and
usefulness of classifications in geography is wide-spread throughout the
discipline. In urban geography, “generalizations can be made concerning a
single group comprised of like items, or one group can be compared and
contrasted with one or more other groups” (Northam 1975, 13). The idea that
cities differ in terms of economic functions and social characteristics has long
3
been known. Classification in geography is undertaken in an attempt to “search
reality for hypotheses...[and]...to structure reality to test specific hypotheses that
have already been formulated” (Yeates and Garner 1980, 95).
One of the ways in which geographers have classified cities, in terms of
describing the external relations, is called functional town classification. The
simplest way of classifying cities is to identify the distinctive role they play in the
city system. These schemes are qualitative in nature and are often highly
intuitive. Of the many classifications of this sort, a good example is that
undertaken by Aurousseau in 1921. Based on general observations, he
identified six types of cities based on the dominant economic functions they
perform: administration, defense, culture, production, communications, and
recreation. (Aurousseau 1921) Although it was noted that cities may perform a
combination of these general functions, it was common to find that one of them
dominated to indicate the major role a city plays in the organization of space.
A similar type of general classification was that proposed in 1945 by Harris
and Ullman, who recognized three general types of cities: (1) central places
performing a wide range of services for local hinterlands; (2) transportation cities
performing break-of-point and allied activities for larger regions; and (3)
specialized-function cities dominated by one activity, such as manufacturing, or
recreation, and serving a wider national market. (Harris and Ullman 1945)
The results of these classifications, when mapped, provide some useful
information about the patterns of functional specialization within the city system.
4
However, there is little gained from a simple organization of facts. There must be
a purpose in the classification system because spatial recognition cannot be the
only basis for scientific analysis. Other statistical procedures should be
undertaken to shed light on possible patterns that may be unseen on a two-
dimensional representation of the data.
Nature of Problem
We as humans are continually classifying everything from the rocks
beneath our feet to the stars beyond our reach. These taxonomic models are
continually being examined and studied with appropriate changes being made.
Almost all, however, have dealt with larger cities and not urban places with fewer
than 10,000 persons. By classifying these urban centers we may be able to see
patterns at a micro-scale that could possibly be used to address problems in
larger cities.
The nature of functional classification has evolved in the past century,
beginning with a qualitative approach by Aurousseau where general observations
were the basis. The majority of the functional classifications developed by
geographers across the United States were based on cities with more than
10.000 people. Only a small number of classified small cities and towns under
10.000 people, because of the apparent difficulty of processing the grouped
employment data for small cities. Now, with the availability of electronic data and
5
faster computers, it is plausible to work with the data as presented by the U.S.
Census Bureau.
Over time, many geographers made attempts to be more objective, and
this led to several different methods including multivariate statistics being
developed. However, no one method has proven to be completely accurate, as
all are trying to rationalize an extremely complex and dynamic system.
Research Objectives
There are two principal objectives for this thesis: 1) To create a
contemporary taxonomy of the small urban places (population 2,500-10,000) in
the study area using a standard classification method for urban geography. 2) To
discover and understand the spatial distribution of the dominant economic
functions of small cities in the study area.
Hypotheses & Rationale
The five states of Iowa, Nebraska, South Dakota, North Dakota and
Minnesota should allow for a broad enough study area containing many
discernable spatial patterns of functionality. Based on previous studies, there
should be solid evidence supporting the three types of cities by support: central
places (a study by Brush in Wisconsin in 1953), special functions, and transport
cities. Based on results from other studies, retail centers and manufacturing
should be the most common functional class (Freestone et al. 2003; Harris 1943;
6
Nelson 1955). Mining, Transportation and Public Administration would likely be
some of, if not the least occurring functions due to the specialization and
necessity for resources or other special site requirements. The creation of the
taxonomy should result in finding many location specific examples, and more
than likely follow typical functional patterns. It is expected that solid evidence will
be found supporting the three types of cities by support: central places, special
functions, and transport cities. Research on the small urban places of the Great
Plains region in the late 1960’s by Charles Gildersleeve indicated that North and
South Dakota were primarily retail trade dominant. Nebraska was more
diversified, and not as trade oriented. Manufacturing has a greater influence in
the area east of the Missouri River including Iowa and portions of Minnesota.
(Gildersleeve 1969)
Using nearest neighbor analysis, spatial patterns should be found to assist
in understanding the distribution of functions throughout the region. When
attempting to understand spatial distributions and relationships, geographers
must realize that “the classification procedure that is adapted should produce
groups of towns about which the greatest number, most precise, and most
important statements can be made for the differentiating and accessory
characteristics” (Cline 1949, 82). This means that one cannot simply say that
group ‘X’ is located in area ‘Z’; we should be able to associate other
characteristics of towns in that group. With this in mind, classifications of towns
7
by function may possibly lead to generalizations about the location patterns and
the relationships with particular functions and their hinterlands.
The lack of significant data and interest has primarily been the reason for
the dearth of research on cities with less than 10,000 in population. With census
data more available today, it is possible to successfully complete this research.
Studies have occurred since the 1950’s on classifying the economic functions of
cities - at higher population centers (i.e., above 10,000). These study areas also
need to be readdressed since much has changed over the past half century, and
geographers ought to study the changes in city functionality over time. The
opportunity for a comparative temporal analysis of functionality will be achieved
through this research.
There must be a concentrated effort to not just report the results, but be
more scientific. “There is nothing inherently wrong with functional classifications
per se, yet without reference to the accessory characteristics, they have precious
little geographical relevance” (Smith 1965, 548). “The service classifications of
urban areas have often proved to be ends in themselves rather than points of
departure for further research” (Wilson 1962, 125). With this in mind, the overall
purpose of functional classifications in urban geography should be geared
towards gaining better understanding of the diverse and dynamic relationships
both vertical (function) and horizontal (countryside relationships) that make up
the true functionality of a city.
8
While analyzing the small urban center of Minnesota in 1959, John W.
Webb claimed that data are difficult to use for small urban centers. He
concluded the research by saying “work on a broad canvas should be
undertaken, despite the difficulties. Only in this way will the description of the
particular be clarified and general principles evolved” (Webb 1959, 72).
Significance of Research
Many studies conducted on classifying the economic functions of cities
were done several decades ago. These study areas should be readdressed
since much has changed over that time period. Freestone, Murphy and Jenner
recently updated a classic city classification of Australian towns, and many new
patterns and employment distributions were discovered. (Freestone et al. 2003)
Geographers also need to examine and re-evaluate the functional
changes in towns over time. Perhaps planners and city administrators can use
the results found in this study to assist them in planning the future economy in
their cities. This research will create a contemporary classification of small urban
places in the upper central United States. “This line of study might be likened to
one studying the human heart without regard for its role as a part of the
physiological system of the entire body. To study a single city without regard for
the whole urban system of which it is part is equally limited and short-sighted”
(Northam 1975, 99). There is a need to understand the small cities and towns, in
order to truly understand the larger, more complex system. This study is original
9
in the sense that it is a hybrid of the various studies done on functional city
classifications. The framework will be established for further research into
understanding the dynamic economic functionality of small urban centers.
Study Area
The area under investigation is a five state area making up the north-
central portion of the United States: Nebraska, Iowa, South Dakota, North Dakota
and Minnesota. These five states should allow for a broad enough study area
containing many discernable spatial patterns of functionality. The small urban
centers are also quite prominent in this region, which will assist in the overall
analysis of the character and nature of functional distribution (see Figure 2).
Five State Study Area
Figure 2: Five state study area location map.
10
Table 1 shows the 2000 population, land area (square miles), and
population density (persons per square mile) for each state in the study area.
When compared to the United States as a whole, the study area constitutes a
small percentage of the overall population and is generally quite rural.
State 2000 Population Area (sa mi mi) Pop. DensityIowa 2,926,324 55,869 52.4
Minnesota 4,919,479 79,610 61.8Nebraska 1,711,263 76,872 22.3
North Dakota 642,200 68,976 9.3South Dakota 754,844 75,885 9.9United States 281,421,906 3,537,438 79.6
Table 1: Population Figures for the five state study area.Source: United States Census Bureau, “http://factfinder.census.gov”
Definition of Terms
Geographic Information System (GIS): A System of computer software, hardware
and data. A GIS is used to help manipulate, analyze and present
information that is tied to a spatial location
Metropolitan Statistical Area: A city of 50,000 or more population or a U.S.
Census Bureau defined urbanized areas of 50,000 or more population and
smaller urban clusters of 10,000 to 49,999 population within a county or
adjacent counties.
Shapefile: A name for a layer in ArcGIS that contains location descriptions and
attribute information for the spatial features in a data set.
Site & Situation: Site is the physical location of a city, and a situation is the
influence of the surrounding area.
Small Urban Place: A city with a population of 2,500 to 10,000 inhabitants
11
Chapter Summary
The purpose of this thesis is to examine the spatial distribution of
economic functions for the small urban places in the study area using a standard
classification method for urban geography, and to discover and understand the
spatial distribution of the dominant economic functions of these places utilizing
nearest neighbor analysis.
This study should produce spatial patterns of distribution based on the site
and situation of the place. There may also be a strong influence of function
based upon proximity to a larger urban area. The creation of a contemporary
taxonomy of the small urban places in the study area, and subsequent
understanding of the spatial distribution of dominant economic features should
provide the base for future investigation into small urban center relationships and
classification.
Chapter 2 follows with a review of the literature on urban geography that
specifically addresses varying methods and theories of functional classification.
Chapter 3 discusses the methodological design, data collection, and analyses
performed in the research study, while chapters 4 and 5 provide an extensive
discussion of the results and conclusions of the research study.
12
Chapter 2
Literature Review
With respect to the discipline of geography as a whole, urban geography
is a relatively new field of study, and this has an impact on the quantity of
literature available for functional classification. The purpose behind each of
these studies is to find relationships in the spatial distribution of economic
functions in an attempt to better understand the incredibly complex urban
structure. The nature of functional classifications has changed throughout the
course of the last 100 years, with ever more concentrated efforts made to
produce more objective results. This had led to the application of various
statistical methods including multivariate statistical analysis in an attempt to
discover relationships within the dynamic urban system.
The literature on functional classification in urban geography presented in
this chapter follows this progression described above, with a focus on the
importance of understanding the roots of functional classification theory. The
chapter is divided into three sections: (1) traditional functional classifications, (2)
a guideline for functional classification analysis, and (3) multivariate statistical
analysis. The first part examines the foundation of functional classification
through the original architects of the discipline. The second section sets the
framework for a more scientific and replicable methodological design in city
classification. The final portion of the chapter discusses more recent
13
classifications accomplished with the application of multivariate statistical
analysis approaches including regression, factor analysis, and cluster analysis.
Traditional Functional Classifications
The idea that cities differ in function has long been understood, dating
back to the earliest time of city development Chauncy Harris’ (1943) A
Functional Classification o f Cities in the United States was the first to classify
cities in the U.S. by economic functions. This classification started a whole new
wave of urban geography in the mid 20th century. Many geographers used his
model as a base for future attempts at classifying and discovering spatial
distribution. Harris studied 1930 census data, including occupation and
employment figures. His classification included 984 cities of 25,000 or more
people and was based upon the activity of greatest importance in each city (see
Figure 3). Harris used the employment figures as the principal basis for
classification chart, while the occupation figures were used to supplement the
interpretation. Arbitrary class breaks of 74%, 60%, 50%, and 25% were used.
Harris then mapped the location of the cities
based on the category he calculated. He
concluded that the central-location theory was
exemplified by wholesale centers, and retail
centers. Mining and resort centers are based
heavily on materials or climate. Industrial cities Figure3: Functional classes used byChauncy Harris (1943)
Manufacturing Cities M ’ Subtype Manufacturing Cities M Subtype Retail Centers (R)Diversified Cities (D)Wholesale Centers (W) Transportation Centers (T) Mining Towns (S)University Towns (E)Resort and Retirement Towns (X )
14
have both location factors related to markets and raw materials. The
manufacturing belt was shown by the influence of power and labor supply.
(Harris 1943) The lasting impact of this article was that Harris attempted to
create a quantitative model that could be replicated in the future. He was able to
show a spatial pattern existed with his results, which led to further studies by
other geographers.
Howard Nelson published A Service Classification of American Cities, in
1955. Nelson used employment data in 24 industry groups for 897 urban
concentrations of 10,000 or more people. The data were then arbitrarily grouped
into nine major categories of service functions. For each industry group, the
average proportion of the labor force engaged in that activity was determined.
Most cities didn’t have average employment in a given industry; therefore, a
variation from the mean existed. This was done because Nelson wanted to
create a classification based on clearly stated statistical procedures. Nelson
used a more statistical method than his predecessors - standard deviation. He
used standard deviation to establish degrees of functional specialization in a
given industry group. Nelson calculated three standard deviations above the
mean of each industry group, since he was specifically concerned with higher
levels of employment. This would allow for a degree of emphasis inside the
overall functional specialization in a city. This research discovered many
instances of geographical patterns. Manufacturing was the most common of all
functions, with more than 1/5 of the 897 cities, and was located in the traditional
15
manufacturing belt of the country. (Nelson 1955) Retail trade tended to be
located more in the central portion of the country, and wasn’t present in the
region dominated by manufacturing. Nelson’s method was a multi-functional
approach, which is a stronger method of measuring economic levels than a
simple dominant classification.
A landmark article was written by Chauncy Harris and Edward Ullman in
1945 titled The Nature of Cities. The focus is on the support and internal
structure of cities. The concept emphasizing that the services the city provides
are based upon its hinterland. The service by which the city earns its livelihood
depends on the nature of the economy of the surrounding area. The land must
produce a surplus in order to support cities. This does not necessarily mean that
every city needs to be encompassed by a productive land, since a strategic
location may be more important. Three categories of support are discussed by
Harris & Ullman: (1) cities as central places, (2) transport cities, and (3)
specialized function cities. The first category describes cities as central places
performing comprehensive services for a surrounding area. Such cities tend to
have an even spatial distribution throughout a productive area (Figure 4a). This
is a common occurrence in the study area for this thesis, particularly in the state
of Iowa. Transport cities tend to perform break of bulk and other services along
major transportation routes including rail lines, roads, and seaways (Figure 4b).
These cities are often found in linear patterns because other smaller cities play a
supporting role along the transportation route. Specialized function cities perform
16
one service such as mining, manufacturing, or
recreation for large areas, and include several smaller
cities in the immediate surrounding area that support
the dominant function (Figure 4c). Commonly, cities are
a combination (see Figure 4d) of the above mentioned
factors with the relative importance varying from
location to location. (Harris and Ullman 1945)
Also discussed with detail were the internal
structures of cities including the concentric zone theory,
sector theory, and the multiple nuclei concept. The
importance of this article is that Harris and Ullman are
providing a strong base for further research in urban
geography, within a theoretical framework prescribed in
their research. (Harris and Ullman 1945)
A look into small towns was conducted by John
Brush in 1953 with The Hierarchy of Central Places in
Southwestern Wisconsin. This article examines the
importance of population on the ability to develop larger
trade areas. The influence and character of central
places were examined. Locational patterns developed
by C.J Galpin (1915), J.H Kolb (1946), and Christaller
(1933) are examined. Also, the traffic flow as an
Cities by Support
* ♦ * #
* • •
# * *
Figure 4a: Central Places
Figure 4b: Transport Cities
Figure 4c: Specialized Function Cities
Figure 4d:Comprehensive functional cities
17
influencing factor was mapped. Brush presents a solid application of central
place theory on small towns in Wisconsin. (Brush 1953)
Basic Concepts in the Analysis o f Small Urban Centers o f Minnesota in
1959 by John W. Webb examined the functional characteristics of cities using a
different methodology than previous geographers. Webb endorsed the standard
deviation method use by Howard Nelson (1955), L.L Powell (1953), and
Steigenga (1955) as a valid method of measuring specialization of service
functions. (Webb 1959)
Webb created a method that would account for a function’s importance to
a city relative to other cities. “The functional index,” where the percentage of the
employed population in a function is divided by the mean employment in all the
towns. Using the U.S. Census data of 1950, Webb created seven categories of
functions and calculated the functional index for each category of towns with
population 2,500-10,000, and also for populations 10,000-50,000. Webb also
attempted to create a system of measuring a town’s level of specialization or the
“specialization index.” Webb concluded by calling for more research on smaller
towns to be embarked upon in the future. (Webb 1959)
Functions and Occupational Structure o f Cities o f the American South, by
John Fraser Hart in 1955 is a functional classification system based upon Harris’
design of 1943. The purpose of the study was threefold: (1) to discover cities
whose function has changed since 1930, (2) to classify cities which have passed
the 10,000 population mark since 1930, and (3) analyze the distribution, size,
18
and occupational structure of cities within each functional category. The
geographic area examined is the U.S. Census’ definition of the South. (Hart
1955)
Hart’s study was based primarily on occupational data for the cities over
10,000 in population. This method leads to a mutually exclusive classification
based on the function of the city in terms of the people who live there and what
they do (similar to what is pursued in this thesis). Hart calculated the industry
data for the cities and determine the minimal, quartile, median, and upper decile
percentage for each age group. Manufacturing, retail trade, and personal
services were found to be the dominant functional service of cities in the south.
(Hart 1955)
An examination of small towns was undertaken in an article by Howard
Stafford in 1963 titled The Functional Bases o f Small Towns. Stafford claims that
theories developed for central places should hold true for the whole spectrum of
city size, from the largest to the smallest. The purpose of Stafford’s study was to
determine the functional bases for small towns in southern Illinois and compare
the results with similar studies throughout the region. His research was based on
Thomas’ Iowa study where data are attained for each town and values are
calculated for (1) total number of establishments, (2) total number of functions,
and (3) total number of functional units. (Thomas 1960) Stafford confirmed what
was generally understood that a relationship existed between population and the
three indices by applying simple correlation and regression analysis. The final
19
results of this study found that most towns were service centers. This was
consistent to what Berry and Garrison (Berry and Garrison 1958) discovered
since small towns simply do not have sufficient threshold populations or large
enough trade areas to support a specialized function. Stafford concludes that a
whole possible realm of research could be investigated by comparing the results
from many regions around the country in an attempt to create generalizations
with regards to economic functions in small towns. (Stafford 1963)
Howard Nelson followed up his classification of cities in the United States
in 1955 with an article titled Some Characteristics o f the Population of Cities in
Similar Service Classifications in 1957. With regards to concerns over the
relevance of classifications as simply a reference tool, Nelson claimed that
classifications should be utilized for further and more in-depth analysis of the
urban configuration. Analyses have been made of population change, education,
age, and labor force, but the main focus of Nelson’s research is to investigate
possible relationships amongst different functions. Nelson simply used the
classifications of U.S. cities as a basis for the study. (Nelson 1957)
It was evident through the research that variations in economic and social
qualities of American cities are related to the function or service classes to which
a city belongs. Nelson found that variations in the rate of change in population in
the 1940 to 1950 decade were strongly affected by a city’s leading function. One
example of this is that the population in cities classified under personal service
and professional service are increasing by more than twice the typical rate.
20
Contrast that with the population change in manufacturing where little to no
growth had occurred. (Nelson 1957)
Nelson also addressed the effects of regional location on social and
economic characteristics. The regional averages of population increase,
education, female labor force, male labor force, age, unemployment, and
average earnings were examined for the geographic regions of the Northeast,
North Central, South, and West. Nelson concluded that this research indicated a
relationship between the service class and regional location on the
characteristics of a city. According to the research, the characteristics of a region
generally affected people of all classes, ages, and gender. (Nelson 1957)
A Guideline for Functional Classification Analysis
The purpose of functional classifications is to identify the spatial
regularities in the distribution and structure or urban functions. Unfortunately,
according to Roberts H. T. Smith’s Method and Purpose in Functional Town
Classification, most studies lack a clear and specific objective. Most
classifications created ended up being ends to themselves instead of a
springboard for future research. Geographers also seem to be satisfied to simply
report their findings in broad geographic terms. The overwhelming majority of
classifications were be created by urban geographers in order to develop a new
methodology and simply display their results, rather than conducting a more
detailed analysis of the data. The primary purpose of Smith’s article is to review
21
several classification methods developed in the mid 20th century and point out
flaws and offer a blueprint on how to effectively conduct scientific research.
(Smith 1965)
The classification procedure that is used should produce groups of towns
about which the greatest number, most precise, and most important statements
can be made concerning differentiating and accessory characteristics.
Furthermore, to be justified on other than pedagogic grounds, any classification
should be relevant to a well defined problem. As a result, when towns are
classified according to function (the differentiating characteristic), we not only
want to say something about the function or combination of functions typical of
that group; knowledge of membership in any one group should automatically
carry with it knowledge of additional characteristics of the towns in that group.
Smith claims it is not difficult to deduce that there are at least two spatial
characteristics associated with town functions. First, since there is some spatial
order to the distribution of economic activities in general, we can then expect to
find distributional characteristics of towns in similar functional classes that are
abnormal to those classes. Second, given the notion that function implies a
relationship between a town and its hinterland, different functional classes should
be connected with different forms of hinterland areas. (Smith 1965) With this
thought process, classification of towns by function may lead to the formalization
of generalizations about location patterns of towns and the relationships between
22
towns with particular functions and their hinterlands, which is the essence of this
thesis.
Multivariate Statistical Analysis
Hart and Salisbury’s (1965) Population Change in Middle Western
Villages: A Statistical Approach analyzed population trends in villages (places
with incorporated status and populations less than 1,000 persons outside large
urban areas) in 1960 for a nine state area of the Midwest. It discusses the
process of regression analysis and the manipulation of data to obtain a linear
relationship between the dependent (percentage of population change) and
independent variables and the need for each variable to have a normal
distribution. Scattergrams are used to help identify linear or non-linear
relationships between variables. Data that do not conform to a normal
distribution should be normalized by use of logarithms or square roots. Upon
completion of the regression analysis, the residuals of regression (villages lying
outside the standard error band of the line of regression) were then mapped and
eventually analyzed by their distance from major population centers, which
became another independent variable in the analysis. (Hart and Salisbury 1965)
Hart and Salisbury’s research supports the idea that patterns of village
growth are too complex to be satisfactorily explained by any simple set of
statistical variables. Hart and Salisbury provide a strong argument for the
23
implementation of multivariate statistical analysis in urban geography, particularly
when examining population change.
Another article discussing the statistical approach was What is a Central
City in the United States? Applying a Statistical Technique for Developing
Taxonomies in 1998 by Edward Hill, John Brennan and Harold Wolman. This
article included a detailed outline of the methodological design using cluster
analysis to group cities in the United States. The purpose of the article was to
create and discuss a methodological design using cluster analysis to group U.S.
central cities, and then employ discriminant analysis to ascertain a statistical
based validity for the groups. Overall, the article provides a solid framework by
discussing a highly technical step-by-step application of multivariate statistical
analysis including several charts and graphs describing the results. (Hill et al.
1998)
The most recent study on functional classifications was conducted by
Robert Freestone, Peter Murphy, and Alan Jenner in 2003 titled The Functions of
Australian Towns, Revisited. This inter-temporal research aimed to create a
contemporary classification of towns in Australia using principal components
analysis and cluster analysis. This article argued for continued classification of
urban areas because functionality does change over time, and through their
research, several changes had occurred since the last classification in 1965.
This article will be used as justification for this thesis project. (Freestone et al.
2003)
24
Factor analysis using varimax rotation has been commonly used in
classification research because of the ability to identify the underlying structure of
complex data sets. However, in the study conducted by Freestone et al., a clear-
cut principal components analysis (PCA) with varimax rotation was selected.
PCA has the ability to “provide an informative, low dimensional representation of
the data” (Boloton and Krzanowski, 1999). PCA was primarily used in their study
as an intermediate step towards cluster analysis. (Freestone et al. 2003)
Cluster analysis techniques have become more prominent in taxonomic
studies. Freestone, et al, chose Ward’s Method because it had been used in
other comparable studies. An advantage of using Ward’s Method is not having
fixed entries where cases cannot be removed from a cluster even though the
cluster structure may change with each new case being introduced. (Freestone
et al. 2003).
The data used were inclusive of all recognized urban centers using the
1996 census data from the Australian Bureau of Statistics (ABS). The data
contained twelve 1-digit Australian and New Zealand Standard Industry
Classification codes for all 741 cities with a minimum population of 1,000 people.
The results of the research led to an updated economic classification of
Australian urban places. (Freestone et al. 2003).
Through cluster analysis, there were found to be thirteen distinct
groupings of urban places in Australia based on economic factors. A comparison
to Smith’s (1965) classification showed many notable differences including the
25
increase in overall population, the increase in the number of cities, and the
increased functional diversification of cities, among others. It was noted that
comparisons could indeed be made even though variations in methodologies
existed between the classifications conducted by Smith and Freestone, et al.
(Freestone et al. 2003)
Summary of Literature
Although the time-scale of urban geography is relatively short, the
development of methodological techniques and conceptual blueprints as regards
to how to generalize and understand the geographic relationships cities have
with one another is quite astonishing. Harris, Ullman, Nelson, and Hart set the
framework of functional classification as the original architects of the discipline.
Smith developed a methodological outline for a more scientific and replicable
methodological design in city classifications for the future. More recent
applications of multivariate statistical analysis created other avenues for scientific
inquiry to be obtained.
Over time, many geographers made attempts to be more objective, and
this led to several different methods being developed. However, no one method
has proven to be completely satisfactory, as all are trying to rationalize an
extremely complex and dynamic system. With this in mind, an attempt to better
understand the dynamic relationships both vertical (function) and horizontal
(countryside relationships) that make up the true functionality of a city is
26
exceptionally challenging. Therefore, the necessity of understanding the
foundation of functional classification theory and methodology is critical to the
urban geographer when undertaking the complex and diverse project of creating
a taxonomy and attempting to find subsequent relationships. .With these
thoughts in mind, this study continues with a discussion of the methodology
developed and utilized to answer the questions posed by this thesis
27
Chapter 3
Methodology
In discussing the role of geography within the scope of academic
research, Haring, Lounsbury, and Frazier state that “geography is the branch
largely concerned with the attainment of spatial knowledge, and is also
concerned with the identification, analysis, and interpretation of spatial
distributions of phenomena and their locational relationships as they occur on the
planet” (Haring et al. 1992, 5). The purpose of functional classifications is to
identify the spatial regularities in the distribution and structure or urban functions,
and this is consistent with the accepted role of geography in academia. The
steps explained in this chapter are in line with the two primary objectives for this
thesis: 1) To create, a contemporary taxonomy of the small urban places
(population 2,500-10,000) in the study area using a standard classification
method for urban geography. 2) To discover and explain the spatial distribution of
the dominant economic functions of small cities in the study area.
The chapter follows the steps shown in the methodological model as seen
in Figure 5. These stages include the acquisition of data, database organization,
and evaluation of the data by creating a modern taxonomy and applying nearest
neighbor analysis in order to establish spatial distribution patterns. The process
was partially adapted from previous functional classifications in urban geography
with minor alterations in classes.
28
Methodological Design
FunctionalClassification
Nearest Neighbor Analysis
DatabaseAssembly
Data Evaluation
SpatialRelationships
Data Acquisition
Conclusions
Figure 5: Methodological model applied to the study.
29
Data Characteristics and Acquisition
Industry data obtained from the 2000 U.S. Census was used for this thesis
project. “A common assumption in functional town classification is that the city’s
labor force is the best single indicator of the structure of the urban economy”
(Yeates and Garner 1980, 97). Going with tradition, the data used will be based
on the industry of working population in each small urban place in the study area.
Other geographical classification studies have also used the industrial census
data (Harris, 1943; Webb, 1959; Nelson, 1955, Hart, 1955, Freestone et al.,
2003). The data set was obtained in electronic form via the U.S. Census Bureau
online at http://www.census.gov. Information was only collected for cities with
populations between 2,500 and 10,000 were collected. The data contained the
number of employed persons in each urban place, and are divided into 13 major
categories. The data were then broken down into more specific industries on
several occasions (see Table 2).
1 INDUSTRY EM PLO YED A ckley ,Io w a
A ckw orth ,Io w a
A d a ir,Io w a
11
2 Total 793 40 3933 Male: 43D 21 196 -4 Agriculture, forestry, fishing and hunting, and mining: 38 3 205 Agriculture, forestry, fishinq and hunting 38 3 20
*Mining □ □ 0
Construction 42 2 36 ■■■H . Manufacturing ........ ......................... ......... 112 7 34y W holesale trade ............................... 31 0 2213 Retail trade 51 E 13
| :11 Transportation and"warehousing, and utilities: □12
i11aci □ .13 Utilities 014 Information 015 F'nance, insuranceI Tedl estdfd add rehfdl and leSdihq' 016 Finance and insurance □17 ReaT estate and rental and Teasing □13 Professional, scientific, m anagement, administrative, and w aste management services: 20 □13 Professional, scientific, arid technical services 15 02U Management of companies and enterprises 0 o ::2122
Administrative and support a n d ’wasfa management services 5 oEducational, health'and social services: .4 8 3 24
S i Educational services3-r —
0 2'24 Health care and social assistance 15 325 y i i I I 16 10
Arts, entertainment, and recreation 2 o2 / Accommodation arid food services 14 in28 Either services (except public administration) 1329 Public administration 14JJ
H < ► w \s h e ir l 1 / Sheet? / S1*et3 J \ * ) j ►JrTable 2: The census data acquired breaks into 13 main categories, as are the sub-categories. The data included both male and female employment figures listed separately. Only the male data are shown here.
30
Database Organization
A vital and often times overlooked component of a thesis is the
organization of data so an effective and accurate assessment can be completed.
The initial step taken to accomplish the first objective was to group the 13
industrial categories into services classes for the new taxonomy. Using previous
models (Harris 1943 and Nelson 1955) and with consultation of the thesis
committee, eleven classes were chosen for this study (see Table 3). The
employment by industry data from the census is by place of residence, not place
of work. It is important to note the omission of agriculture, forestry, fishing and
hunting in this classification since these people are most likely performing
activities in the countryside, and this would not be considered an economic
function of the city. Also, the combination of educational, health and social
services with professional scientific, management, administrative and waste
management services was done because these occupations are considered to
be "professional" in nature.
Census Classification by Industry Groups Thesis Taxonomy Symbol
Agriculture, forestry, fishing and hunting.................................................................. ... OmittedMining................................................................................................................................ .. Mining MiConstruction.................................................................................................................... ... Construction cManufacturing................................................................................................................. .. Manufacturing MfWholesale trade.............................................................................................................. .. Wholesale WRetail trade...................................................................................................................... .. Retail RTransportation and warehousing, and utilities........................................................ .. Transportation TInformation...................................................................................................................... .. Information Technology IFinance, insurance, real estate and rental and leasing....................................... .. Finance FProfessional, scientific, management, administrative & waste management.. .. Professional Service PfEducational, health and social services:................................................................... .. Professional Service PfArts, entertainment, recreation, accommodation and food services.................. .. Personal Service PsOther services (except public administration)......................................................... .. Personal Service PsPublic administration..................................................................................................... Public Administration Pa
Table 3: The service classes for the taxonomy are shown on the right and the U.S census industry erouDS from which the data were collected are on the left.
31
Of the 280 cities in the study area, many were in close proximity of
Metropolitan Statistical Areas (MSAs). Within the study area there were 18
MSAs including Omaha, Sioux City, Waterloo-Cedar Falls, Dubuque, Cedar
Rapids, Davenport, Iowa City, Des Moines, Duluth-Superior, St. Cloud,
Minneapolis-St. Paul, Rochester, Fargo-Moorhead, Grand Forks, Lincoln,
Bismarck, Sioux Falls and Rapid City, (see Figure 7) To alleviate the influence of
these larger cities, all cities within the 2,500 to 10,000 population range that were
contained within contiguous urbanized area of the MSA cities were excluded
from the study. This led to a subtraction of 49 cities mostly in the Minneapolis-St.
Paul metropolitan area (see Figure 8). The remaining 231 cities were then
organized by the number of employed persons for each of the eleven classes
(see APPENDIX A for cities sorted by population, and APPENDIX B for cities
sorted alphabetically).
Metropolitan Statistical Areas: Cities Over 50,000 People
Omal
Figure 7: The MSA cities within the thesis study area.
32
Urban Places Removed
Figure 8: The 49 cities removed from the study because of their location inside of the contiguous area o f a MSA city. (34 in MN, 10 in IA, 3 in SD, 2 in NE, 0 in ND)
Creating the Taxonomy
Various methods have been developed and tested throughout the past
century, and no single method has been determined to be the best. When
determining a method to use for this thesis, it is important to consider the overall
objectives of the study. The purpose of this classification is to compare the
economic functions of towns within the specified population range in one
particular geographic region. With this in mind, the standard deviation method
developed by Howard Nelson provides an approach that works well for this study
because the degrees of variation lead to a classification of multi-functionality and
gives a solid relative comparison of these cities. Furthermore, for the purpose of
33
creating a classification that is both understandable and replicable, the standard
deviation method works well.
Standard deviations from the mean of each function were calculated for
each of the eleven categories. There are three degrees of variation from the
average following the standard deviation breaks. Subjective selection of class
breaks has been eliminated by the implementation of an accepted statistical tool
such as standard deviation. With regards to the taxonomy, any city over +1 SD
from the mean value in manufacturing will be given a Mf1 rating. Over +2 SD’s
receives a Mf2 rating and + 3 SD or more gets a Mf3 rating. This approach
delivers a simple rating that is easily understood. The biased formula for
standard deviation was used for this study:
l i e * - ) a
Where: X - Sample arithmetic mean
n - Sample size
Xi = ith Observation of the variable X
23^ = Summation of all X{ values in the samplei- 1
When applied to the 231 remaining cities in the study area, the method
described is not mutually exclusive because there is a possibility that a city can
exceed the requirements (i.e., + 1SD or more) in more than one service category.
34
There is also a possibility that some cities will not rank high enough in any of the
eleven service categories. These cities are placed into a “diversified” group in
the taxonomy, thus the classification has a total of twelve categories.
Creating the Classification Maps
In order to visualize the spatial distribution within a two-dimensional
framework, the results of the classification needed to be mapped. There were
multiple methods for compiling city location data to be implemented into a GIS
mapping program. Since the cities were located within a five state area, it was
most logical to use a dataset that included all the states for consistency. ESRI, a
leading distributor of GIS software and data, provides a dataset that includes all
cities in the United States. The 231 cities in the survey were selected from the
ESRI data set using a query search in ArcGIS 9. A new shapefile was created to
be used for adding standard deviation values for mapping purposes. In order to
create the maps of economic functionality, an operation called a "join" was
completed. A join simply combines the data from two databases through a
specified field name, in this case, the city name. However, when dealing with
multiple states, often times a city name was found more than once. These
duplicate names such as Glenwood (Iowa and Minnesota) created an invalid join
because the data were combined due to the lack of a unique value for each city.
An alternate naming method was established where city names were sorted
35
alphabetically and an "ID" number was established for each city. This eliminated
any problems with duplicate city names.
Once the city location and standard deviation classification datasets were
joined together, the mapping of the twelve functions was completed. Each of the
twelve economic functions was mapped by using the query search in ArcGIS. A
query search allows for the selection of values (cities) based up the attribute
data. In this case, each city was given a value of 0. 1, 2, or 3 for each economic
function in the classification The 0 was a null value, and the 1, 2, and 3
indicated the amount of standard deviations above the mean. A visual
representation of this process is show below in Figure 9.
$ tp * © m » ^□ & u a : jt % §ax io « j<+> fu&mJi 3 ;J? n o *?
$ h«|
JE □ Manufacturing
B □ Transportation
E3 O ConsdueSao
0 O WhdeKfe G S3
S 2 g g g B j
l i _ J
li •*; /
;h r —j. * ' *•. . Jt)
if " • ’I M< It - V-
J Selected Attribu tes ol 2 3 1 Cities —. l O f x i
j IOC NT j UKBAN PLAC j ST_1 J 28M.POP Mi M M l W j R T 1 f PC PSiRA♦ 16 tHie- Fourcbe ;4 £65 2 | l 0 0 1 P 0 9 0 1 0
34 Csnion :5D 3110 0 1 0 0 1 0 0 1 0 0 0165 ffetfliew SD 2897 0 0 0 a t 0 0 0 1 0 3182 Spearfirh St> <JE0& 0 11 0 0 1 0 0 0 0 2 0193 Slbrgis SO 6442 1 0 0 a i 0 0 0 tl 1 0226 wsmer ;S» 3137 0 9 0 o H 0 0 9 0 1 0
7222 0 <1 0 0 1 0 0 0 0 1 0125.M4X4AFB ND 7699 0 <1 0 a 1 :0 0 0 2 0 238 Chadron NE 5634 0 u 0 0 2 C 0 0 1 0 020;B«woo MN 3376 0 0 0 0 1 if u 3 3 & 0
147 Ogatela ;nc 4939 0 0 0 a 12 0 0 0 0 f> 0
< if*Recod n | < | _J0 JMJ At jH>H°c1pd %Ssxxds (32 out ol 231 Selected} 0p*icn-
Figure 9: Example of selecting cities in ArcGIS 9 based on Standard Deviation values in Retail Trade.
36
Nearest Neighbor Analysis
Essentially geography is concerned with distributions in space and one the
most important distributions the geographer has to consider is that of human
settlement. A primary objective of many geographic studies that begin with
locations of a variable on a dot map is to determine the form of the pattern of
points. The nature of the point pattern can reveal information about the process
that produced the geographic results. (McGrew and Monroe 1993) General
descriptions have been used in previous functional classifications that include
described patterns as "dense" or "sparse." Devising a more precise
mathematical description of areal distributions is needed to produce objective
results. (Hammond and McCullagh 1975)
Urban geographers are interested in using a method of analysis that
discerns objectively between clustered and dispersed spatial distributions, and
also distinguishes between degrees of clustering or dispersal. (Yeates 1974)
Nearest Neighbor Analysis is a common procedure for determining the spatial
arrangement of a pattern of points within a study area. The distance of each
point to its closest neighbor is measured, and the average nearest neighbor
distance for all points is determined. This method quantitatively defines a scale
which measures the degree of departure of an observed spatial distribution from
a theoretical random distribution. (Silk 1979) The maximum departure at one
end of the scale is absolute clustering, where all points are at the same place.
The other end is absolute uniformity, where all points are equidistant from other
37
points. Basically, there are three benchmarks: absolute clustering, absolute
randomness, and absolute dispersal. The index ranges from 0, indicating
clustering, to 2.15, indicating maximum dispersion. The index value, normally
written as R, is calculated by dividing the measured mean distance between
neaiest neighbor points in a given area, by the mean distance to be expected
from a similar number of points randomly distributed in the same area.
(Hammond & McCullagh 1975)
Nearest Neighbor Analys s was performed on each economic function of
the classification using a Visual Basic application in ArcGIS (Sawada 2002) The
program performed basic Nearest Neighbor Analysis (Clark and Evans 1954)
and provided summary statistics of the point distribution for each function. An
example output of the application for construction is shown below in Figure 10.
o r
f i t (p t yen Insnt ifciecuan I « * jvrdow
i r Vertonia'.ion * S5t If? R *ta r£ lM n up * - J C«8Sel»'t«e * >S3 '■%_ ’u
c^a/sSi % 8 X i n pTo,1% S72 . u :|:2. a v? » a lia iA n ^ s t - .. * i
ts te c * j *• 6 * ‘ P :■ " • ] : r “ 3 x o 1 £ 3 : a ;| m * ■?& i a £3 ® e : 4m € t t t i O # 4 » ;
fe * Layers□ Export jCUtpUt
■s □ Mring selection « □ 231 jObes
- 0 E585g§3♦- □
3 C l France
& □ MWng ♦
3 D Manufacturing •
5 Q lr/oT«ch46 D flcwoMlJjerv
- □ Prof_Serv
- O PubtcAdfnin♦3 □•
.5? D Tiansixrtatiwi 4S. Q Variesate4
& 0 studyn
PfepjeyJ " S ^ c e j Sefecbonj
Construction ____ » !
ftxnda ry in wrbidi to taiculaca the rxiex^ “ 3 1• f jt Chass* to P&u&xr- u r t -------- •{
a?* it*&jFer Szx Show extent as a graph*
I & pofcrgon ^ boundary C* none
r * Add cbtances & OTDs to feature table?
I r.~il OototeSfaohics jResi>s..............VARIABLE CORRECTED NN Index .87189 Z 1,31106Avg.dbt. 76614.06062 Exp. art}. 90164.79545 SD G610.26774Area. 9525925939? Perm. 4606455.343#p U . 35
,531573614,0608262487,773727260,3164952592533804.!4606455.34335
Add&Oo«n? Save ResultsAboUt One
*174.25 1880.04 Mes
Figure 10: Example output of the Nearest Neighbor Application in ArcGIS 9.
38
Chapter Summary
Once again, the primary focus of creating a functional classification is to
identify the spatial relationships and the distribution of specific urban functions.
This chapter followed the methodological model formulated around the two
objectives of the thesis. The process was adapted from previous functional
classifications within the accepted framework of urban geography.
Staying consistent with previous studies concerning functional
classifications, the occupational data obtained from the 2000 U.S. Census was
used. Only cities with populations between 2,500 and 10,000, and not contained
within the contiguous urbanized area of a MSA city, were collected.
When determining a method to use for this thesis, it is important to
consider the overall objectives of the study. The purpose of this classification is
to compare the economic functions of towns within the specified population range
in one particular geographic region and to discover spatial relationships. The
standard deviation method developed provides an approach that allows a multi
functional classification, and provides a firm relative assessment of these cities.
The mapping of the classification by economic functions provides a unique
insight of the spatial distribution of the cities. Nearest Neighbor analysis is a
valid statistical tool for determining spatial distribution in a two-dimensional
space.
39
Chapter 4
Analysis of Results
In this chapter an explanation is given for the results of the functional
classification. The first step is to present details of the descriptive statistics for
this study and make comparisons with previous studies. The second section
includes a detailed discussion of the spatial distribution of each service class
within the new functional classification. The final segment is dedicated to the
exploration of nearest neighbor analysis results.
Descriptive Statistics
The purpose of this classification is to compare the economic functions of
towns within the specified population range in one particular geographic region.
Keeping this in mind, the standard deviation method provides an approach that
works well for this study because the degrees of variation lead to a classification
of multi-functionality and gives a solid relative comparison of these cities.
Standard deviations from the mean of each function were calculated for
each of the eleven categories. There are three degrees of variation from the
average following the standard deviation breaks. With regards to the new
taxonomy, any city over +1 SD from the mean value in mining will be given a Mil
rating, +2 SD’s receives a Mi2 rating, and + 3 SD or more gets a Mi3 rating. This
approach delivers a simple rating that is easily understood.
40
The five state study area provided 231 cities of population 2,500 to 10,000
that were not contained within the contiguous area of city with a population of at
least 50,000. There were 91 cities in Minnesota, 84 in Iowa, 31 in Nebraska, 17
in South Dakota, and 8 in North Dakota. The average population for the cities in
the study area was 4,829.6, and the average employment per city was 2,334.8.
Standard deviations from the mean were calculated for each of the eleven
categories as discussed in detail in chapter 3. The results are shown below in
Table 4. When examining the averages per function, several numbers stick out
including the rather high portion of people engaged in professional service
industries and manufacturing, and to some extent personal service. The
importance of services that provide for the needs of the surrounding countryside
is quite evident when reviewing the results.
Several intriguing similarities and differences can be found while
comparing the average employment per function in this classification with
previous studies conducted by Nelson (1955), Atchley (1967), and Webb (1959).
Function Symbol Mean (%) SD (%) +1 SD (%) +2 SD (%) +3 SD (%)
Mining Mi 0.54 2.01 2.55 4.56 6.57
Construction C 6.41 2.20 8.61 10.81 13.01
Manufacturing Mf 17.49 8.32 25.81 34.13 42.45
Wholesale W 3.14 1.73 4.87 6.6 8.33
Retail R 12.76 2.84 15.6 18.44 21.28
Transportation T 4.81 2.78 7.59 10.37 13.15
Information Technology I 2.20 1.18 3.38 4.56 5.74
Finance F 5.17 2.87 8.04 10.91 13.78
Professional Service Pf 28.76 5.57 34.33 39.9 45.47
Personal Service Ps 12.25 4.0 16.25 20.25 24.25
Public Administration Pa 4.11 2.85 6.96 9.81 12.66
Table 4: Mean and Standard Deviation values for each function.
41
Most of the functions were relatively consistent, especially public administration,
which was between four and five percent in each of the four studies.
Manufacturing in this study was similar to Webb, but much less than the national
studies by Nelson and Atchley. Professional Service industries made up an
average of almost 29 percent in this study, compared to 11 percent (Nelson),
14.7 percent (Atchley), and 16.9 percent (Webb).
Functional Classification
The creation of a modern functional classification of small towns is the
primary objective of the thesis. The cities were classified using the standard
deviation results for the eleven economic classes. Of the 231 cities, 45 did not
meet the criteria established to be +1 SD in any of the eleven functions. These
45 cities are grouped together in the diversified group, meaning that they are not
unusually high in any single function. There were 107 cities that qualified with
only one function, 63 cities had two functions at least +1 SD, 14 cities reached
three functions, and two cities actually had four functions of at least +1 SD or
above (Belle Fourche, SD and Elkhorn, NE). Cities located in North and South
Dakota had a high degree of multi-functionality. In fact, 22 of the 25 (88%) cities
within those two states had at least two functions with a minimum of +1 SD. The
opposite was true in Iowa, where only 23 of the 84 (27%) cities had multi
functionality. The complete results of the taxonomy are located in APPENDIX C.
42
City Classification Spatial Distribution
With the second objective in mind, the following section includes a
detailed discussion of the spatial distribution for each of the eleven service
functions, plus diversified cities Focus will be placed on the explanation of site
and situation, and other possible factors that could explain the reasoning for
nclusion within a particular function. The location of the 231 cities in the study
area is shown below in Figure 11. Notice the relatively even dispersion within the
corn belt of Iowa and Minnesota and the general bareness in the Dakotas and
the sand hills of Nebraska.
231 Study Area Cities: Population 2,500 - 10,000
Figure 11: 231 cities in the study area not contained within the contiguous are of a MSA city.
43
Mining Cities
There are few cities in the study area where mining is considered a
significant economic function, (see Table 5) Mining can be viewed as an optimal
example of site and situation because this activity only exists where the presence
of highly localized natural resources are found. The two distinct clusters of
mining activities are located in the iron ore region of northeast Minnesota, and in
the Black Hills of South Dakota (see Figure 12). There are however, a few
isolated locations in Beulah, North Dakota, Milbank, South Dakota, and Kimball,
Nebraska. Mining is the only economic activity that is not reported in every city.
Mining activities include sand and gravel pits, coal and metal mining, oil and gas
extraction, and limestone quarries. Interestingly, the areas with high levels of
mining also tend to have significant levels of personal service activities. Such
can be understood because of the location of these cities in more of a
comparative wilderness with rugged topography, and timber where vacationers
and sportsmen would also be found in elevated quantities.
City State Function % + SD
Sturgis SD 2.98 + 1 SD
Milbank SD 3.53 + 1 SD
Two Harbors MN 3.57 + 1 SD
Kimball NE 3.79 + 1 SD
Belle Fourche SD 6.02 + 2 S D
Ely MN 7.44 + 3 SD
Eveleth MN 10.05 + 3 SD
Lead SD 10.92 + 3 SD
Virginia MN 11.38 + 3 SD
Beulah ND 12.34 + 3 SD
Mountain Iron MN 12.66 + 3 SD
Chisholm MN 13.56 + 3 SD
Table 5: Cities above 1, 2, and 3 SD from the mean in mining.
44
Mining Cities
r~
© +3 Standard Deviations
o +2 Standard Deviations
* + 1 Standard Deviation
Figure 12: Mining cities above 1, 2, and 3 SD from the mean.
45
Construction Cities
In terms of the number of cities in a particular function, the 35 cities
classified in the construction category are second only to manufacturing (see
Table 6). The average employment of 6.4 percent is not especially high, but is
higher than six other classes. Construction cities are found to be located near
larger cities, transportation routes, or manufacturing cities (see Figure 13). There
are 16 cities in Minnesota, nine in Iowa, six in Nebraska, three in South Dakota,
and only one in North Dakota. By examining the map, there are two clusters of
construction cities around Minneapolis/St. Paul and Omaha where heavy
expansion of suburbia is occurring. There are relatively few cities in this class in
North and South Dakota, and west of the Omaha area. In Iowa there is a
reasonably even distribution of construction cities throughout the state.
City State Function % + SDValley City ND 8.56 + 1 SDGering NE 8.71 + 1 SDGrimes IA 8.84 + 1 SDSpirit Lake IA 8.89 + 1 SDElkhorn NE 8.89 + 2 SDMontgomery MN 8.90 + 3 SDForest Lake MN 8.90 + 3 SDDe Witt IA 8.97 + 3 SDPlainview MN 9.07 + 3 SDMobridge SD 9.07 + 3 SDDilworth MN 9.17 + 3 SDMaquoketa (A 9.25 + 3 SDO’Neil NE 9.31 + 1 SDBig Lake MN 9.36 + 1 SDGlenwood IA 9.43 + 1 SDCokato MN 9.51 + 1 SDCherokee IA 9.52 + 1 SDCanton SD 9.69 + 1 SD
City State Function % + SDWahoo NE 9.70 + 1 SDSt. Charles MN 9.73 + 1 SDBlair NE 9.73 + 1 SDIowa Falls IA 9.85 + 1 SDVinton IA 10.18 + 1 SDNorth Branch MN 10.29 + 1 SDMora MN 10.35 + 1 SDBelle Fourche SD 10.35 + 1 SDAlbia IA 10.65 + 1 SDGrant MN 10.68 + 1 SDBelle Plaine MN 11.08 + 2 SDBreckenridge MN 11.19 + 2 SDAnnandale MN 11.48 + 2 SDZimmerman MN 11.74 + 2 SDPiattsmouth NE 13.01 + 3 SDBecker MN 13.23 + 3 SDSt. Francis MN 15.34 + 3 SD
Table 6: Cities above 1, 2, and 3 SD from the mean in construction.
46
Construction Cities
® +3 Standard Deviations
+ 2 Standard Deviations
• + 1 Standard Deviation
Figure 13: Construction cities above 1, 2, and 3 SD from the mean.
47
Manufacturing Cities
The number of cities with significant amounts (+1 SD or more) of
manufacturing is higher than any other category. Manufacturing tends to be an
important part of the economic structure in these urban places where an average
of 17.5 percent work in the industry (see Table 7). Most of the 38 classified cities
are part of the traditional manufacturing belt that spans from the northeast coast
of the United States to roughly the middle of Minnesota and Iowa (see Figure
14). The location of manufacturing cites also has a tendency to follow major
routes of transportation such as Interstate 35 through central Iowa and
Minnesota. Schuyler, Nebraska was the only city to receive a rating of + 3 SD in
manufacturing. North and South Dakota failed to register a single city in the
category. Of the 38 cities, 24 (63%) were specialized, meaning no other
economic function was significant.
City State Function % + SDCokato MN 26.04 + 1 SDWest Point NE 26.14 + 1 SDMount Pleasant IA 26.25 + 1 SDMelrose MN 26.37 + 1 SDBelmond IA 26.61 + 2 SDOsage IA 26.68 + 3 SDHumboldt IA 26.80 + 3 SDCold Spring MN 26.92 + 3 SDBig Lake MN 27.32 + 3 SDLong Prairie MN 28.01 + 3 SDFairbury NE 28.19 + 3 SDCenterville IA 28.24 + 3 SDWilton IA 28.61 + 1 SDBelle Plaine IA 29.28 + 1 SDCamanche IA 29.37 + 1 SDLake City MN 29.64 + 1 SDGarner IA 29.78 + 1 SDWebster City IA 29.92 + 1 SDZimmerman MN 30.01 + 1 SD
City State Function % + SDCrete NE 30.13 + 1 SDNorwood Young MN 30.24 + 1 SDPella IA 30.29 + 1 SDSibley IA 30.31 + 1 SDLitchfield MN 30.46 + 1 SDMarengo IA 30.51 + 1 SDMontgomery MN 30.66 + 1 SDPrinceton MN 30.77 + 1 SDGlencoe MN 31.80 + 1 SDDenison IA 32.28 + 1 SDGoodview MN 33.13 + 1 SDCozad NE 34.02 + 1 SDWaseca MN 34.03 + 1 SDSt. James MN 34.95 + 2 SDForest City IA 35.16 + 2 SDLe Sueur MN 35.32 + 2 SDRoseau MN 36.41 + 2 S DWest Liberty IA 41.66 + 2 SDSchuyler NE 46.10 + 3 SD
Table 7: Cities above 1, 2, and 3 SD from the mean in manufacturing.
48
Manufacturing Cities
• +3 Standard Deviations
© +2 Standard Deviations
■ + 1 Standard Deviation
Figure 14: Manufacturing cities above 1, 2, and 3 SD from the mean.
49
Wholesale Trade Cities
The distribution of wholesale cities follows conventional central place
theory where the most significant places (+3 SD) are evenly spaced with smaller,
supportive cities found in between (see Figure 15). Wholesaling activities include
the sale of commodities in large quantities for retailers and the assembly and
sale of merchandise. In this region, farm equipment sales are a significant
industry of wholesale trade. These cities are generally located where specialized
forms of agricultural produce must be assembled, packaged, and marketed.
(Hart 1955) Access to transportation is also a high priority for wholesaling. Also,
there are no cities with +3 SD located within 30 miles of MSA cities. The average
amount of people working in wholesale trade is relatively small at only 3.1
percent. Even cities with a substantial amount (see Table 8) within the function
are typically multifunctional in this region.
City State Function % + SD City State Function % + SDPark Rapids MN 4.99 + 1 SD Monticello IA 5.91 + 1 SDLa Crescent MN 4.99 + 1 SD Waconia MN 5.95 + 1 SDDyersville IA 5.09 + 1 SD Goodview MN 5.96 + 1 SDElkhorn NE 5.12 + 1 SD Glenwood MN 6.40 + 1 SDWaukee IA 5.15 + 1 SD Sheldon IA 6.51 + 1 SDAdel IA 5.28 + 1 SD Chisago City MN 6.75 + 2 SDWyoming MN 5.37 + 1 SD Mountain Iron MN 7.07 + 2 SDMadison SD 5.40 + 1 SD West Union IA 7.25 + 2 SDRugby ND 5.52 + 1 SD Iowa Falls IA 8.37 + 3 SDPleasant Hill IA 5.55 + 1 SD Milbank SD 8.40 + 3 SDWadena MN 5.62 + 1 SD O’Neil NE 8.80 + 3 SDDilworth MN 5.69 + 1 SD Harlan IA 10.24 + 3 SDCannon Falls MN 5.69 + 1 SD Chariton IA 14.20 + 3 SDVictoria MN 5.90 + 1 SD
Table 8: Cities above 1, 2, and 3 SD from the mean in wholesale trade.
50
Wholesale Trade Cit es
" M '
+ 3 Standard Deviations
© +2 Standard Deviations
• + 1 Standard Deviation
Figure 15: Wholesale Trade cities above 1, 2, and 3 SD from the mean.
51
Retail Trade Centers
One of the most dispersed functions in the classification are the retail
trade cities. These small urban places are responsible for providing goods to the
surrounding agricultural population. It could be said that these cities are the
backbone of rural America, particularly in this particular region of the country.
Cities average 12.76 percent of the workforces in this category. Within the study
area, half of the retail cities are specialized due to the high level of employment
in this function, (see Table 8). In the Black Hills region, cities are providing
merchandise targeting the tourist flow (see Figure 16). Waite Park, Minnesota,
just west of Minneapolis is a large shopping area. Sidney Nebraska, with an
incredible 29.06 percent engaged in retail trade, is home to sportsmen's
superstore Cabela's. Other locations are more dispersed and far away from
larger cities, signifying their role in supplying the hinterland.
City State Function % + SD
Eveleth MN 15.63 + 1 SDMonticello MN 15.74 + 1 SDTipton IA 15.75 + 1 SDSpearfish SD 15.76 + 1 SDJordan MN 15.88 + 2 SDWadena MN 15.99 + 3 SDMinot AFB ND 16.06 + 3 SDSt. Charles MN 16.08 + 3 SDRedfield SD 16.11 + 3 SDStory City IA 16.18 + 3 SDWayne NE 16.21 + 3 SDEast Grand Forks MN 16.23 + 3 SDCanton SD 16.26 + 1 SDWinterset IA 16.26 + 1 SDBelle Fourche SD 16.32 + 1 SDWaukon IA 16.54 + 1 SD
City State Function % + SD
McCook NE 16.61 + 1 SDThief River Falls MN 16.69 + 1 SDWinner SD 16.96 + 1 SDNew Hampton IA 17.00 + 1 SDRed Oak IA 17.04 + 1 SDAlexandria MN 17.11 + 1 SDWindom MN 17.13 + 1 SDChariton IA 17.85 + 1 SDSturgis SD 18.02 + 1 SDDevils Lake ND 18.03 + 1 SDBenson MN 18.32 + 1 SDChadron NE 18.45 + 2 S DOgallala NE 18.83 + 2 SDShenandoah IA 19.23 + 2 SDWaite Park MN 21.35 + 3 SDSidney NE 29.06 + 3 SD
Table 9: Cities above 1, 2, and 3 SD from the mean in retail trade.
52
Retail Trade Cities
V
0 + 3 Standard Deviations
+ 2 Standard Deviations
• + 1 Standard Deviation
Figure 16: Retail Trade cities above 1, 2, and 3 SD from the mean.
53
Transportation Cities
Another example of site and situation, to a lesser degree than mining, is
that of transportation. Access to large scale routes of transportation such as
interstates, railways, or waterways is of critical importance. Only 16 cities
reached at least +1 SD from the mean, similar to mining (see Table 10).
Typically these cities are found in linear patterns or in groups because the
smaller cities play a supporting role along a transportation route. This sort of
pattern can be seen in western and extreme southeastern Nebraska (see Figure
17). Oftentimes, cities classified as transportation area also found in another
category such as manufacturing, construction, or mining. This category also
includes utility based industries like the nuclear power plant in Auburn, and the
coal factories associated with Beulah and Nebraska City. The importance of
transporting materials across the region from the east to west by railroad and
interstate highway is quite evident when examining the amount of transportation
cities in Nebraska. In fact, there just as many cities in this category from
Minnesota, Iowa, South Dakota, and North Dakota combined as there are in
Nebraska.
City State Function % + SD
Valentine NE 7.65 + 1 SDSibley IA 7.71 + 1 SDHot Springs SD 7.90 + 1 SDBrandon SD 7.96 + 1 SDNebraska City NE 8.04 + 1 SDDavid City NE 8.19 + 1 SDChisholm MN 8.22 + 1 SDBecker MN 8.28 + 1 SD
City State Function % + SD
Clarion IA 9.73 + 1 SDKimball NE 10.13 + 1 SDGering NE 10.46 + 2 SDEagle Grove IA 10.59 + 2 SDFalls City NE 10.95 + 2 SDBeulah ND 19.26 + 3 SDAuburn NE 22.17 + 3 SDAlliance NE 27.15 + 3 SD
Table 10: Cities above 1, 2, and 3 SD from the mean in transportation.
54
Transportation Cities
• +3 Standard Deviations
© +2 Standard Deviations
° + 1 Standard Deviation
Figure 17: Transportation cities above 1,2, and 3 SD from the mean.
55
Information Technology Cities
A new category to the classification is that of information technology.
Most of the previous functional studies on cities in the United States either
occurred before the computer age or simply grouped communications and
transportation together in one class. Industries in this category include
newspaper publishing, radio and television broadcasting, libraries, data
processing services, software publishing, and other telecommunication services.
The average of 2.7 percent is second lowest only to mining, but there were 29
cities with at least +1 SD from the mean (see Table 11). The most intriguing
discovery in this service class was the distribution of cities. It is generally thought
that information technology jobs are only located in or around a larger city, but
this is not the case. A wide spatial distribution of cities, both close and far from
larger cities, are found (see Figure 18). There are no information technology
cities in North and South Dakota or west of the 98th meridian in Nebraska.
City State Function % + SD City State Function % + SDGrand Rapids MN 3.46 + 1 SD Jackson MN 4.28 + 1 SDWilliamsburg IA 3.49 + 1 SD Norwalk IA 4.40 + 1 SDAfton MN 3.54 + 1 SD Falls City NE 4.56 + 2 SDAppleton MN 3.57 + 1 SD Sauk Centre MN 4.70 + 2 SDWaconia MN 3.57 + 1 SD Winterset IA 4.74 + 2 SDMontevideo MN 3.68 + 1 SD Wayne NE 4.83 + 2 SDLong Prairie MN 3.74 + 1 SD Belmond IA 4.89 + 2 SDCarlisle IA 3.79 + 1 SD Vinton IA 5.03 + 2 SDVermillion SD 3.79 + 1 SD Elkhorn NE 5.08 + 2 SDOnawa IA 3.89 + 1 SD Grinnell IA 5.11 + 2 SDMonticello IA 3.90 + 1 SD Perham MN 5.26 + 2 SDPleasant Hill IA 4.13 + 1 SD Waseca MN 5.86 + 2 SDGrimes IA 4.13 + 1 SD Blair NE 7.23 + 2 SDCambridge MN 4.14 + 1 SD Fairfield IA 7.72 + 3 SDFairbury NE 4.27 + 1 SD
Table 11: Cities above 1, 2, and 3 SD from the mean in information technology.
56
Information Technology Cities
® +3 Standard Deviations
© +2 Standard Deviations
• + 1 Standard Deviation
Figure 18: Information technology cities above 1, 2, and 3 SD from the mean.
57
Finance Cities
Cities included within this category are related to finance, insurance, real
estate, and rental and leasing. Only 5.17 percent of the total employment is in
the finance class. Previous studies conducted in the United States have found
that a considerable amount of the largest cities in the country boast high levels of
banking and finance. Typically, this function is not going to be found in excessive
amounts in smaller cities. Within the study area, the city of Des Moines, Iowa, is
considered an insurance and financial center. A majority of the cities in this
category are from the state of Iowa. In fact, eight of the nine highest averages
come from the Hawkeye state (see Table 12). The spatial distribution of these
cities tends to be clustered around the Des Moines metropolitan area (see Figure
19). Proximity to a larger city can be seen as the rule with cities of this class
found around Sioux Falls, Minneapolis/St. Paul, Rapid City, and Omaha. One
obvious exception is that of International Falls, Minnesota, located along the
border with Canada.
City State Function % + SDCold Spring MN 8.18 + 1 SDEllsworth AFB SD 8.33 + 1 SDGering NE 8.37 + 1 SDVictoria MN 8.40 + 1 SDWyoming MN 8.52 + 1 SDInternational Falls MN 8.80 + 1 SDWaconia MN 8.83 + 1 SDCanton SD 9.13 + 1 SDForest Lake MN 9.24 + 1 SDLuverne MN 9.26 + 1 SDMilbank SD 9.93 + 1 SDDe Witt IA 10.21 + 1 SD
City State Function % + SDWaverly IA 10.30 + 1 SDDell Rapids SD 10.53 + 1 SDElkhorn NE 11.10 + 2 SDAdel IA 12.53 + 2 SDWinterset IA 13.08 + 2 SDMissouri Valley IA 14.60 + 3 SDCarlisle IA 14.72 + 3 SDPleasant Hill IA 14.86 + 3 SDBrandon SD 17.12 + 3 SDNorwalk IA 17.26 + 3 SDGrimes IA 19.25 + 3 SDWaukee IA 19.99 + 3 SD
Table 12: Cities above 1, 2, and 3 SD from the mean in finance.
58
Finance Cities
M
+ 3 Standard Deviations
+ 2 Standard Deviations
+ 1 Standard Deviation
Figure 19: Finance cities above 1, 2, and 3 SD from the mean.
59
Professional Service Cities
The category of professional services comprises the highest average of
any class by a considerable amount. Included in the professional service group
are accountants, payroll services, legal services, scientific and technical
management, advertising, consulting, educational services, and health care
services. The 30 cities in this class all exhibit a substantial amount of average
employment ranging from 34 percent to almost 48 percent (see Table 13). Many
of these cities are college towns like Grinnell, Orange City, Sioux Center,
Chadron, Mount Vernon, Vermillion, and Decorah. The distribution of these cities
is widespread and occurs in every state, providing the fundamental educational
and health services for the immediate surrounding region (see Figure 20). North
and South Dakota have a particularly high proportion of cities in this class. Five
of the eight cities in North Dakota, and five of seventeen in South Dakota are
classified as professional service cities. Also, all ten cities in the Dakotas are
multi-functional.
City State Function % + SD City State Function % + SDPlainview MN 34.67 + 1 SD Stewartville MN 38.56 + 1 SDEmmetsburg IA 35.62 + 1 SD Grand Forks AFB ND 38.61 + 1 SDRedfield SD 35.93 + 1 SD Sisseton SD 38.99 + 1 SDHot Springs SD 36.22 + 1 SD Seward NE 39.08 + 1 SDCrookston MN 36.28 + 1 SD Sioux Center IA 39.76 + 1 SDGrinnell IA 36.54 + 1 SD Glenwood IA 39.77 + 1 SDOrange City IA 37.04 + 1 SD Vermillion SD 40.57 + 2 SDGrafton ND 37.05 + 1 SD Waverly IA 40.96 + 2 SDValley City ND 37.45 + 1 SD Byron MN 41.25 + 2 SDRugby ND 37.47 + 1 SD Pine Ridge SD 42.90 + 2 SDFairfield IA 37.56 + 1 SD Minot AFB ND 44.27 + 2 SDBaxter MN 37.79 + 1 SD Mount Vernon IA 44.29 + 2 SDChadron NE 37.89 + 1 SD St. Peter MN 45.94 + 3 SDLa Crescent MN 38.42 + 1 SD Morris MN 46.95 + 3 SDSt. Joseph MN 38.45 + 1 SD Decorah IA 47.90 + 3 SD
Table 13: Cities above 1, 2, and 3 SD from the mean in professional services.
60
Professional Sen/ ce Cities
c ~ \
+ 3 Standard Deviations
+ 2 Standard Deviations
+ 1 Standard Deviation
Figure 20: Professional service cities above 1, 2, and 3 SD from the mean.
61
Personal Service Cities
Personal service is another function that is widely distributed throughout
the study area, but each state has a different set of circumstances. The average
employment of 12.25 percent is the fourth highest of the eleven functions. Of the
28 cities in this group, ten are in South Dakota, nine in Minnesota, five in Iowa,
and only two each in North Dakota and Nebraska (see Table 14). Cities in this
category are usually found in areas that attract a large flow of people. The tourist
area of the Black Hills is a prime example where five cities, including the largest
in the class, Lead, are located (see Figure 21). This region offers a multitude of
functions that fit into this class consisting of motels, restaurants, bars, gift shops,
sight-seeing, and gambling. The second and third highest cities in personal
service, Tama and Toledo, Iowa, are located only a few miles from one another.
The Meskwaki Casino and entertainment center provides a substantial amount of
employment for these two cities. Many cities in North Dakota are also classified
as professional service cities. There is no overlap of classes in any other state.
City State Function % + SD City State Function % + SDGranite Falls MN 16.34 + 1 SD Mtnden NE 17.57 + 1 SDEly MN 16.63 + 1 SD Chisholm MN 17.79 + 1 SDDetroit Lakes MN 16.70 + 1 SD Valentine NE 17.97 + 1 SDBelle Fourche SD 16.79 + 1 SD Virginia MN 18.33 + 1 SDDevils Lake ND 16.80 + 1 SD Winner SD 18.37 + 1 SDOnawa IA 16.82 + 1 SD Sisseton SD 19.86 + 1 SDSpirit Lake IA 16.85 + 1 SD Mobridge SD 20.33 + 2 SDOsceola IA 16.90 + 1 SD Mora MN 20.84 + 2 SDGrand Forks AFB ND 17.36 + 1 SD Pine City MN 21.28 + 2 SDEllsworth AFB SD 17.37 + 1 SD Spearfish SD 23.28 + 2 SDSturgis SD 17.39 + 1 SD Pine Ridge SD 23.89 + 2 SDVermillion SD 17.40 + 1 SD Tama IA 25.82 + 3 SDRedwood Falls MN 17.50 + 1 SD Toledo IA 29.85 + 3 SDGrand Rapids MN 17.52 + 1 SD Lead SD 39.31 + 3 SD
Table 14: Cities above 1, 2, and 3 SD from the mean in personal services.
62
Personal Serv ice Cities
■-------- V -^ p .
0 + 3 Standard Deviations
o +2 Standard Deviations
- + 1 Standard Deviation
Figure 21: Personal service cities above 15 2, and 3 SD from the mean.
63
Public Administration Cities
Cities in this study area providing public administration services are almost
always going to be political centers or military installations. The overall average
employment in the study area is relatively low at only 4.11 percent, but many
cities in this category have significant levels (see Table 15). In other words,
much like mining, a city is either fairly low or quite high in public administration.
Unlike mining though, the location of these cities is not based on the proximity to
a natural resource. The spatial distribution of these cities is quite dispersed (see
Figure 22). The three air force bases of Minot, Ellsworth, and Grand Forks are all
at least +2 SD from the mean. Pine Ridge, South Dakota, is a significant political
center for the Lakota people, and is home to federal government sponsored
Bureau of Indian Affairs. Anamosa, Iowa, is home to a state penitentiary. Other
cities are local seats of government. All seven cities in North and South Dakota
classified as public administration also fall into the professional or personal
service class. Only half of the cities in Iowa and Minnesota are multi-functional.
City State Function % + SD City State Function % + SD
Eldora IA 6.96 + 1 SD Toledo IA 8.25 + 1 SDWahoo NE 6.99 + 1 SD Anamosa IA 9.75 + 1 SDWabasha MN 7.05 + 1 SD Minot AFB ND 12.46 + 2 SDSisseton SD 7.19 + 1 SD Redfield SD 13.52 + 3 SDOlivia MN 7.22 + 1 SD Grand Forks AFB ND 15.23 + 3 SDWest Union IA 7.41 + 1 SD Appleton MN 20.50 + 3 SDGrafton ND 7.45 + 1 SD Pine Ridge SD 22.13 + 3 SDClarinda IA 7.70 + 1 SD Ellsworth AFB SD 23.80 + 3 SD
Table 15: Cities above 1, 2, and 3 SD from the mean in public administration.
64
Public Administration Cities
+ 3 Standard Deviations
+ 2 Standard Deviations
+ 1 Standard Deviation
Figure 22: Public administration cities above 1,2, and 3 SD from the mean.
65
Diversified Cities
Of the 231 cities within the study area of this functional classification, there
are 45 cities that did not reach at least +1 SD in any of the eleven services
classes (see Table 16). Iowa alone had 22 of the cities, and Minnesota was
second with 16. Nebraska has six cities in the category, North Dakota has one,
and South Dakota contains zero. The location of these cities tends to follow the
traditional cornbelt throughout Iowa, southern Minnesota, and through south-
central Nebraska (see Figure 19). These cities serve important roles in the local
economy despite not having a significant amount of employment in one of the
eleven classes. The spacing of these cities is quite even in Iowa and southern
Minnesota.
City State City State City State
Wahpeton ND Oak Park Heights MN Estherville IAAurora NE Pipestone MN Grundy Center IABroken Bow NE Sartell MN Hampton IACentral City NE Sleepy Eye MN Independence IAGothenburg NE Spring Valley MN Jefferson IAHoldrege NE Staples MN Knoxville IAYork NE Watertown MN Le Mars IABayport MN Zumbrota MN Manchester IABlue Earth MN Algona IA Nevada IACaledonia MN Atlantic IA Oelwein IAKasson MN Bloomfield IA Perry IALindstrom MN Charles City IA Rock Rapids IALittle Falls MN Clear Lake IA Rock Valley IAMilaca MN Cresco IA Washington IANew Prague MN Creston IA West Burlington IA
Table 16: Diversified cities.
66
Diversified Cities
Figure 23: Diversified cities.
67
Nearest Neighbor Analysis
Many geographers utilize nearest neighbor analysis as a valid statistical
tool for determining spatial distribution in a two-dimensional space. The
maximum departure at one end of the scale is absolute clustering, where all
points are at the same place. The other end is absolute dispersal, where all
points are equidistant from other points. The index ranges from 0, indicating
clustering, to 2.15, indicating maximum dispersion.
The nearest neighbor results are shown below in Table 17. The columns
contain the index value (r value), average distance calculated in miles (Ave.
Dist), the expected average distance for the number of points randomly placed in
a study area (Exp.Ave.Dist), standard deviation (S.D.), the study area in square
miles (Area) and the number of cities per function (# of points). Overall, the point
distribution of each function, except retail, was random tending toward clustering.
Function R Value Ave. Dist (mi) Exp.Ave.Dist (mi) S.D. (mi) Area (mi2) # of PointsAll Cities 0.95 19.6 20.6 0.8 367,798 231
Mi 0.67 68.8 102.6 17.6 367,798 12C 0.87 48.8 56.0 5.5 367,798 35Mf 0.67 35.8 53.5 5 367,798 38W 0.77 49.7 64.6 7.2 367,798 27R 1.02 60.3 58.8 6 367,798 32T 0.85 74.3 86.8 12.8 367,798 161 0.92 . 38.7 62.1 6.7 367,798 29F 0.67 46.4 69.0 8.2 367,798 24Pf 0.78 48.0 61.0 6.5 367,798 30Ps 0.8 51.0 63.3 7 367,798 28Pa 0.83 72.0 86.8 12.8 367,798 16D 0.64 31.3 48.8 4.2 367,798 45
Table 17: Nearest Neighbor Analysis results for each economic function.
68
Retail was random and slightly leaning towards uniformity. The average distance
between cities in the transportation class was the highest at 74.3 miles.
Diversified cities were the closest together at an average of 31.3 miles.
However, those cities were generally clustered towards the southeastern region
of the study area.
Summary of Results
It must be noted again that the purpose of functional classifications is to
identify the spatial regularities in the distribution and structure or urban functions.
This chapter provided an explanation of the results produced by the creation of
the contemporary functional classification of cities in the study area. Compared
to previous studies on city classification, many service categories were
consistent regarding percent of workers. Examples of this are public
administration, wholesaling, transportation, and to a certain extent mining. Other
economic classes such as personal and professional services were significantly
higher in this study than previous research had found in other geographic areas,
and city size. There was a noticeable divide in functions from the agricultural
portions of Iowa, Minnesota and eastern Nebraska to the rest of the study area of
western Nebraska, North Dakota, and South Dakota.
Urban geographers are interested in describing the pattern of points within
a specified study area. With this in mind, the utilization of nearest neighbor
analysis, a method of analysis that distinguishes objectively between clustered
69
and dispersed spatial distributions was used. The results showed that most of
the spatial distribution was random, with a tendency towards clustering for every
function except retail, which was random tending towards uniformity. The results
of the nearest neighbor analysis demonstrate a degree of spatial distribution of a
two-dimensional distribution. It is important to reiterate that these cities provide
basic connections between the dispersed agricultural populations and the
agglomerated urban populations. For the most part, such direct connections as
do exist are through the goods and services which are provided in these small
towns for the agricultural population surrounding them.
70
Chapter 5
Conclusion
It is commonly understood that cities have diverse economic structures
and social characteristics. Many times these differences can be traced to
historical regional growth or resource availability. Recognition and better
understanding of these different types of cities results from their classification.
Classification is one way to organize complex and diverse information in order to
create a better understanding of processes and relationships. The relevance and
usefulness of classifications in geography is wide-spread throughout the
discipline. In urban geography, “generalizations can be made concerning a
single group comprised of like items, or one group can be compared and
contrasted with one or more other groups” (Northam 1975, 13).
Location also has been an important dimension in the study of systems of
cities. The activities and characteristics of a local community are thought to be
influenced not only by its immediate locality, but also by its ecological position
with respect to other centers of various sizes. Given the exchange relationships
between cities, and the economics of transportation and communication,
geographic location is an important aspect of this ecological position. (Fuguitt
and Field 1972) The small town is of academic interest because it represents the
lower end of the central place continuum. Any generalizations, theories, or laws
developed for central places should hold true for larger cities as well as smaller
cities. (Stafford 1963)
71
Harris, Ulfman, Nelson, and Hart set the framework of functional
classification as the original architects of the discipline. Smith developed a
methodological outline for a more scientific and replicable methodological design
in city classifications for the future. More recent applications of multivariate
statistical analysis created other avenues for scientific inquiry to be obtained.
The purpose behind each of these studies is to find relationships in the spatial
distribution of economic functions in an attempt to better understand the
incredibly complex urban structure.
Within the scope of academic research, “geography is the branch largely
concerned with the attainment of spatial knowledge, and is also concerned with
the identification, analysis, and interpretation of spatial distributions of
phenomena and their locational relationships as they occur on the planet”
(Haring et al. 1992, 5). The purpose of functional classifications is to identify the
spatial regularities in the distribution and structure or urban functions, and this is
consistent with the accepted role of geography in academia. There are two
primary objectives for this thesis: 1) To create a contemporary taxonomy of the
small urban places (population 2,500-10,000) in the study area using a standard
classification method for urban geography. 2) To discover and explain the spatial
distribution of the dominant economic functions of small cities in the study area.
Any system of classification should provide a vehicle for efficient
communication, a set of definitions, and a system of relationships among these
definitions. Each label in the classification system should convey the greatest
72
possible meaning in the fewest possible symbols. The categories should be
precisely defined, and overlapping should be eliminated wherever possible. The
goals of any such system are to allow the investigator to compare groups of cities
by type and allow him to reduce hundreds of cities into some kind of order.
(Atchley 1967)
Staying consistent with previous studies concerning functional
classifications, the occupational data obtained from the 2000 U.S. Census were
used. Only cities with populations between 2,500 and 10,000, and not contained
within the contiguous urbanized area of a MSA city were examined.
When determining a method to use for this thesis, it is important to
consider the overall objectives of the study. The purpose of this classification is
to compare the economic functions of towns within the specified population range
in one particular geographic region and to discover spatial relationships. The
standard deviation method developed provides an approach that allows a multi
functional classification, and provides a firm, relative assessment of these cities.
The mapping of the classification by economic functions provides a unique
insight into the spatial distribution of the cities. Nearest neighbor analysis is an
applicable statistical tool for determining spatial distribution in a two-dimensional
space.
It must be noted again that the purpose of functional classifications is to
identify the spatial regularities in the distribution and structure or urban functions.
This chapter provided an explanation of the results produced by the creation of
73
the contemporary functional classification of cities in the study area. Compared
to previous studies on city classification, many service categories were
consistent regarding the amount of workers. Examples of this are public
administration, wholesaling, transportation, and to a certain extent mining. Other
economic classes such as personal and professional services were significantly
higher in this study than previous research had found in other geographic areas,
and city size. A noticeable divide was formed with functions from the agricultural
portions of Iowa, Minnesota and eastern Nebraska to the rest of the study area of
western Nebraska, North Dakota, and South Dakota.
Urban geographers are interested in describing the pattern of points within
a specified study area. The utilization of nearest neighbor analysis provides a
method of analysis that distinguishes objectively between clustered and
dispersed spatial distributions. (Berry 1958) The results illustrate that most of the
spatial distribution was random, with a tendency towards clustering for every
function except retail, which was random tending towards uniformity. The results
of the nearest neighbor analysis demonstrate a degree of spatial distribution of a
two-dimensional distribution. It is important to reiterate that these cities provide
basic connections between the dispersed agricultural populations and the
agglomerated urban populations. For the most part, such direct connections as
do exist are through the goods and services which are provided in these small
towns for the agricultural population surrounding them.
74
The present study, in conjunction with those that have preceded it, lends
empirical support to Brush’s statement that “small towns and villages in
agricultural areas of Anglo-America exist mainly because of their function as
central places for the exchange of goods and services, each for its local farm
trade area” (Brush 1953, 380). By building one similar study upon another in
different areas, progress is made toward valid generalizations concerning the
economic functioning of central places, thus making precise prediction more
possible. (Stafford 1963)
These small places provide basic connections between the dispersed
agricultural populations and the agglomerated urban populations. For the most
part, such direct connections that do exist are through the goods and services
which are provided in these small towns for the agricultural population
surrounding them. Second, even if small towns do not fulfill their role of providing
goods and services for a dispersed farm population, the fact remains that these
small places exist and that economic activities are performed in them just as they
are in larger places. (Thomas 1960)
This thesis establishes the framework for further research into
understanding the economic functionality of small urban places. Future research
could investigate various issues including temporal studies, because
geographers should examine functional changes and spatial distribution as the
urban construct evolves. Another aspect that should be carefully examined is the
changes in population for cities in a particular region or service class. Other
75
forms of multivariate statistical analyses, such as cluster analysis or regression
analysis, could be used to locate groups of cities with similar economic
structures. Many plausible avenues can be utilized in order to discover and
understand this diverse and complex system, but it is crucial to employ a method
that strictly follows the research objective(s) of a particular study.
76
APPENDIX A
City Employment Data and Percentages by Function (population sort)
77
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38
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3.7%
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85
APPENDIX B
City Employment Data and Percentages by Function (alpha sort)
86
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94
APPENDIX C
Functional Classification
95
Functional Classification
KEY
Function Plus 1SD Plus 2SD Plus 3SD
Mining............................. .......M i......... .......M i2.................Mi3Construction............ ...... .......C .......... ....... C2...... ........C3Manufacturing................ .......M f......... ....... M f2....... ........Mf3Wholesale Trade ............ .......W .......... .......W 2 .................W3Retail Trade.................... ....... R .... . ........R2......... ........R3Transportation............... ........T ......... ...... T2 ........ ........T3Information Technology. ......... I .............. .........12......... .........13Finance........................... ........F .......... ........F2......... ........F3Professional Service...... .......Pf.......... ...... Pf2 ........ ........Pf 3Personal Service........... .......P s ........ ..... . Ps2...... .......Ps3Public Administration..... .......P a ........ ...... Pa2........ ...... Pa 3Diversified....................... .......D
DDDC FPf3MfWT2PaPfD13 Pf Mf Mf C Pf C I F3 12 Pf D
Iowa Clear Lake............ :...............Cresco............. ......................
Adel.... ............................. ..... W F2 Creston..................................
A lb ia................................. ..... C De W itt...................................
Algona.............................. .... D Decorah.................................Anamosa......................... ..... Pa Denison.................................A tlantic............................. ......D Dyersville........... ...................Belle Plaine..................... ..... Mf Eagle G rove.........................Belmond.......................... ......Mf 12 Eldora................... .................Bloomfield............ .......... ..... D Emmetsburg.........................
Camanche....................... ..... Mf Estherville..............................Carlisle.... ........................ ..... 1 F3 Fairfield.................... .............Centerville....................... ..... Mf Forest C ity............. ...............Chariton ........................ ..... W3 R Garner...................................Charles C ity .................... ......D Glenwood..................... ........Cherokee........................ ..... C Grimes........................... .......Clarinda............................ ..... Pa Grinnell............... ....... ..........Clarion.............................. ..... T Grundy Center......................
96
Hampton .......................DHarlan ..............................W3Humboldt............................... MfIndependence ............... DIowa Falls ..... .......... ......C W3Jefferson............................... DKnoxville................................ DLe Mars ........................DManchester...........................DMaquoketa............................CMarengo................................ MfMissouri Valley..................... F3Monticello..............................W IMount Pleasant....................MfMount Vernon....................... Pf2Nevada.................................. DNew Hampton ................... ...RNorwalk............................ ..I F3Oelwein................................. DOnawa................................... I PsOrange City...........................PfO sage................................... MfOsceola................. ...... ......... PsPella............. ............ .............MfPerry......................................MfPleasant H ill W I F3Red Oak................................ RRock Rapids................ ......... DRock Valley...........................DSheldon..................................WShenandoah ..................R2Sib ley.....................................Mf TSioux Center........................ PfSpirit Lake.............................C PsStory C ity ..............................R
Tam a .............. ............. Ps3Tipton ....................................RToledo........................ . Ps3 PaVinton......................................C 12Washington...........................DW aukee.................................. W F3Waukon .......................RW averly...................................F1 Pf2Webster City. ......... MfWest Burlington..................... DWest Liberty..................... Mf2West Union.............................W2 PaWilliamsburg...........................LW ilton.....................................MfWinterset............................... R 12 F2
Minnesota
Afton....................................... IAlexandria...................... ...... RAnnandale..............................C2Appleton........................ ....... I Pa3Baxter................................ PfBayport................................... DBecker............. ......................C3 TBelle P la ine............................C2Benson .............................RBig Lake................................. C MfBlue Earth...............................DBreckenridge ....................... C2Byron......................... ............PfCaledonia...................... ....... DCambridge.............................ICannon Falls......................... WChisago C ity.......................... W2
97
Chisholm................................Mi3 T PsCokato................................ ... C MfCold Spring ........................MfCrookston.............................. PfDetroit Lakes.........................PsDil worth................................. C WEast Grand Forks.................RE ly .......................................... Mi PsEveleth.................................. Mi3 RForest Lake...........................C FGlencoe................................. MfGlenwood.............................. WGoodview..................... ........ Mf WGrand Rapids............... ........ I PsGranite Falls..........................PfGrant......................................CInternational Falls................. .FJackson................................. IJordan....................................RKasson.................................. DLa Crescent ............. W PfLake C ity............................... MfLe Sueur......................... ......Mf2Lindstrom ..............................DLitchfield................................ MfLittle Falls ......................... DLong Prairie............................Mf ILu verne............... .................. FMelrose................................. MfMilaca. .......................... DMontevideo...........................IMontgomery..........................C MfMonticello..............................RMora.......................................C Ps2Morris.....................................Pf3
Mountain Iron ................ Mi3 W2New Prague.............. ........... DNorth Bcanch.........................CNorwood Young America MfOak Park Heights................ DOlivia .................................. PaPark Rapids.................... ...... WPerham................................ . 12Pine C ity .............. ....... Ps2Pipestone..............................DPlainview................................C PfPrinceton......................... ..... MfRedwood Falls..................... . PsRoseau........................ ..........Mf2Sartell ........... .............DSauk Centre ............. . 12Sleepy Eye ....................DSpring Valley............... . DSt. Charles............................ C RSt. Francis........................... C3St. Jam es..................... ........ MfSt. Joseph.............................PfSt. Peter........................ ....... Pf3Staples...................................DStewartville................. ... P FThief River Falls................. RTwo Harbors........................ . MiV ictoria ........................W FV irg in ia.............................. Mi3 PsW abasha..................... . PaW aconia................................ W I FWadena............................ . W RWaite Park..................... . R3W aseca....................... ......... Mf 1Watertown D
98
Windom...............................RW yoming.............................W FZimmerman....................... . C2 MfZumbrota. ....................D
Nebraska
Alliance ....................T3Auburn ......................T3Aurora.................................DBlair..................................... C 12Broken Bow........................ DCentral C ity .....................DChadron ......................R2 PfCpzad............. ....................Mf FC rete...................................MfDavid City .................. TElkhorn................... ............C W 12 F2Fairbury .......................Mf IFalls C ity .............................T2 12Gering.................................C T2 FGothenburg ............. DHoldrege .....................DKim ball................................Mi TMcCook...............................RM inden................................PsNebraska City.....................TOgallala.............................. R2O 'N eil.................................. C W3Plattsmouth C3Schuyler..............................Mf3Seward .................. PfS idney.................................R3Valentine .................. T PsWahoo.................................C Pa
W ayne R 12West Point.............................. MfYork................................. ...... D
North Dakota
Beulah.............................. Mi3 T3Devils Lake...................... R PsGrafton ....................... Pf PaGrand Forks AFB Pf Ps Pa3Minot AFB.........................R Pf2 Pa2Rugby............................... W PfValley C ity C PfWahpeton.........................D
South Dakota
Belle Fourche..................Mi2 C R PsBrandon............. ....... ......T F3Canton.............................. C R FDell Rapids...................... FEllsworth AFB..................F Ps Pa3Hot Springs...................... T PfLead .............................Mi3 Ps3Madison..................... . WMilbank............................. Mi W3 FMobridge..........................C Ps2Pine Ridge........................Pf2 Ps2 Pa3Redfield............................R Pf Pa3Sisseton ..................Pf Ps PaSpearfish......................... R Ps2Sturgis .....................Mi PsVermillion..........................I Pf2 PsWinner...................... .......R Ps
99
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