An Examination of Typological Variation in Susquehanna Streamfront Towns by Greg Tenn

The Pennsylvania State University The Graduate School College of Arts and Architecture

An Examination of Typological Variation in Susquehanna Streamfront Towns A Masterâ&#x20AC;&#x2122;s Project Report in Landscape Architecture by Gregory A. Tenn

ÂŠ 2012 Gregory A. Tenn

Submitted in Partial Fulfillment of the Requirements for the Degree of

Master of Landscape Architecture

May 2012

The masterâ&#x20AC;&#x2122;s project of Gregory A. Tenn was reviewed and approved by the following:

Sean L. Burkholder Assistant Professor of Landscape Architecture Masterâ&#x20AC;&#x2122;s Project Advisor Christopher J. Duffy Professor of Civil and Environmental Engineering Thomas G. Yahner Associate Professor of Landscape Architecture

ABSTRACT

As physical manifestations of culture and society, the typological form of towns can provide clues to less tangible factors associated with their development. While the dynamic nature of stream-community relationships makes them inherently complex, a better understanding of the physical interface between town and stream can provide insight into design and planning that is responsive to a regionâ&#x20AC;&#x2122;s cultural landscapes. Largely shaped by its streams, the Susquehanna River Basin provides an opportunity to study how stream- and riverfront town typologies have developed. In particular, this study aims to examine how responses to a shared regional context have differentially influenced these physical relationships. In its initial phase, a research framework was developed that emphasized the important historic roles of county seats and a quantitative understanding of factors thought to contribute to their growth. A number of variables including transportation infrastructure, institutions of higher education, and flood recurrence rates, were examined. A subsequent qualitative examination of towns that utilized mapping exercises, historical map/document review, and first-hand observation was then conducted. This latter process was extremely important to the interpretation of the previous quantitative analyses and resulted in the development of generalizable streamfront town typologies. Ultimately, the combination of quantitative and qualitative analytical processes aided in understanding the influence of the regionâ&#x20AC;&#x2122;s extensive stream network on town development and can be used to provide insight into how waterfront design and planning initiatives might respond to the various economic, cultural, and environmental challenges facing the region.

TABLE OF CONTENTS LIST OF FIGURES....................................................................................................... vi LIST OF TABLES......................................................................................................... vii ACKNOWLEDGEMENTS.......................................................................................... viii 1. INTRODUCTION...................................................................................................... 1 2. A BRIEF OVERVIEW OF REGIONAL DEVELOPMENT............................................... 4 3. METHODS............................................................................................................. 10 3.1. Study Area & Sample Set................................................................................ 10 3.2. Quantitative Methods..................................................................................... 12 3.2.1. Town and county population growth............................................................. 14 3.2.2. Physical measures: area, perimeter, shape, & waterfront distance...................... 15 3.2.3. Hydrology: stream order, flood frequency, & flood area.................................... 16 3.2.4. Transportation systems............................................................................... 20 3.2.5. Natural resources....................................................................................... 22 3.2.6. Higher education....................................................................................... 23 3.2.7. Normalization & statistical correlations.......................................................... 24 3.2.8. Initial town categorization based on quantitative analysis................................. 25 3.3. Qualitative Methods....................................................................................... 25 3.3.1. Mapping................................................................................................... 25 3.3.2. Historic mapping....................................................................................... 26 4. RESULTS................................................................................................................ 29 4.1 Statistical Correlations................................................................................... 29 4.1.1. Town and county population growth............................................................. 29 4.1.2. Area......................................................................................................... 31 4.1.3. Transportation & higher education............................................................... 33 4.1.4. Natural resources....................................................................................... 36 4.1.5. Hydrologic measures................................................................................. 36 4.1.6. Initial town categorization ........................................................................... 38 4.2. Streamfront Town Typologies........................................................................ 39

4.2.1. Mapping................................................................................................... 39 4.2.2. Town typologies........................................................................................ 39 4.2.3. Historic mapping....................................................................................... 49

5. DISCUSSION.......................................................................................................... 55 5.1. County Seats.................................................................................................. 55 5.1.1. Sample set selection................................................................................... 55 5.1.2. A region of small towns............................................................................... 57 5.2. Indicators of Growth...................................................................................... 58 5.2.1. Transportation infrastructure and institutions of higher education..................... 58 5.2.2. Initial town classification............................................................................. 60 5.2.3. Natural resource score................................................................................ 60 5.2.4. Population growth rates.............................................................................. 62 5.2.5. Streams and floodplains.............................................................................. 63 5.2.6. Further quantitative measures...................................................................... 69 5.3. Streamfront Town Typologies......................................................................... 70 5.3.1. Frontage-towns ......................................................................................... 70 5.3.2. Confluence-towns...................................................................................... 74 5.3.3. Bisected-towns.......................................................................................... 76 5.3.4. Early-stream-towns.................................................................................... 78 5.3.5. Annexed-stream-towns............................................................................... 78 5.3.6. Flooding................................................................................................... 80 5.3.7. Potential Applications in Design & Planning................................................... 83 5.4. Susquehanna River Basin Cultural Region...................................................... 84 6. CONCLUSIONS...................................................................................................... 87 7. REFERENCES......................................................................................................... 90 APPENDICES............................................................................................................ 99 Appendix A: Comparing categorization methods................................................ 99 Appendix B: Counties and county seats within the Susquehanna River Basin......100 Appendix C: Data disc........................................................................................101

LIST OF FIGURES Figure 2-1 Susquehanna River Basin within the Chesapeake Bay Watershed............... 8 Figure 2-2 Major subbasins of the Susquehanna River Basin....................................... 9 Figure 3-1 Sample set selection: counties and county seats ...................................... 13 Figure 3-2 Flood frequency analysis ......................................................................... 19 Figure 3-3 Historic mapping utilizing GIS ................................................................. 27 Figure 4-1 Growth rate correlation charts.................................................................. 30 Figure 4-2 Quantitative measures for the Susquehanna River Basin ......................... 32 Figure 4-4 Mapping county seats.............................................................................. 40 Figure 4-5 Town typologies....................................................................................... 41 Figure 4-6 Quantitative measures for typologies ..................................................... 44 Frontage-towns.................................................................................................... 44 Confluence-towns................................................................................................ 45 Bisected-towns..................................................................................................... 46 Early-stream-towns.............................................................................................. 47 Annexed-stream-towns........................................................................................ 48 Figure 4-7 Historic maps .......................................................................................... 50 Lewistown, PA (1877)........................................................................................... 51 Lancaster, PA (1864)............................................................................................. 52 Laporte, PA (1934)................................................................................................ 53 Bellefonte, PA (1874)............................................................................................ 54 Figure 5-1 Alternative method for calculating floodplain area.................................. 68 Figure 5-2 Typological development diagrams......................................................... 71 Figure 5-3 Representative town maps........................................................................ 72 Frontage-town: Harrisburg, PA............................................................................. 72 Confluence-town: Lewistown, PA.......................................................................... 75 Bisected-towns: Bellefonte, PA.............................................................................. 77 Early-stream-town: Laporte, PA............................................................................ 79 Annexed-stream-town: Lancaster, PA.................................................................... 81

LIST OF TABLES Table 4-1 Correlation between tested variables....................................................... 34 Table 4-2 Correlation between individual town and county growth rates ................. 35

vii

ACKNOWLEDGEMENTS I would like to thank: Sean Burkholder for his patience and support, both as my faculty advisor and friend; Tom Yahner and Christopher Duffy for their insights; Karen Jensen –of the Donald W. Hamer Maps Library– for her willingness to help me find so many of the resources needed for this project; all those individuals in Lewistown, PA –especially Jillian Pry and family– for being such gracious hosts and guides, sharing their stories, and for piquing my interest in the region and its communities; and, finally, all of those family and friends who have supported me through it all.

viii

1. INTRODUCTION

The story of humanity is inextricably linked with access to fresh water. Though more than two-thirds of the Earth’s surface is covered by water, most of this is unusable in its present form or is not easily accessible in its present location. Fresh water makes up only two-and-one-half percent (2.5%) of all water on the planet, and the vast majority is contained in glaciers and ice caps or deep underground. While lakes and streams1 comprise only about one-third of one percent (0.33%) of this limited resource, these above-ground sources are those with which many of us are most familiar and provide the vast majority of the water we use (Gleick, 1993; United States Geological Survey [USGS], 2011). These palpable natural resources have long been sources of fresh water for drinking, agricultural development, and have facilitated industrial and commercial activities. They have also played more abstract and ambiguous roles in their roles as catalysts and purveyors of cultural development, as political boundaries, and as environmental liaisons between people and the natural world. With threats to fresh water resources abound, their importance to society worldwide has received extensive coverage (Linton, 2010; Tvedt & Coopey, 2010; Tvedt & Oestigaard, 2010; Zumerchik & Danver, 2010), and the intent of this examination was not to provide a detailed review or analysis of streams and their impact on human societal and cultural development. Rather, it provides a glimpse of a single region within the United States –the Susquehanna River Basin (the Basin)– whose streams have influenced human activity for thousands of years and, in some regards, have influenced culture and society throughout the United States (Zelinsky, 1977, 147). 1. Here, the term stream is utilized in the general sense to mean any flowing body of water. Streams may be distinguished semantically as rivers, brooks, etc. and described by a number of terms (e.g. perennial, ephemeral) that relate to their characteristic flow patterns (Langbein & Iseri, 1960; Strahler, 1957). Thus, the term stream– or streamfront–town encompasses the terms river– or riverfront–town. The converse is not, however, necessarily true, and not all stream– or streamfront–towns should be considered river– or riverfront–towns.

Despite their importance, the stream network of the Basin has, at times, confounded the region’s communities. Navigation has been difficult and flooding has been chronic. Utilitarian connections between communities and their stream(s) that were once widespread have waned over the last half century. And, for their long history of service, the streams themselves are now in danger. The Susquehanna River was recently named one of the country’s most imperiled (American Rivers, 2011). Undoubtedly, the relationship between the Basin’s streams and surrounding human environments is a dynamic one. Understanding the present states of these relationships and how they have developed is an important aspect in planning for a future that does not see the river as an obstacle to be overcome but, rather, as an asset that can, once again, help carry the region and its communities forward (Kretzmann & McKnight, 1993). While the work presented here does not intend to unveil fine grained intricacies of Basin communities’ perceptions or understanding of these prominent natural resources, it does begin to examine the formation of cultural ties to the streams of the Susquehanna River Basin by considering its manifestation in the development of the physical or built environment –within the region’s streamfront– or riverfront–towns. Many facets of town development within the Basin have been shared, and the idea that the region’s stream networks may in fact frame a broad cultural region that links its communities has remained an important and basic premise throughout the work. However, it was also hypothesized that variation in streamfront towns would likely be sufficient to enable their categorical distinction. These distinctions developed into a series of generalizable town typologies that were initially based upon the quantification of series of variables thought to be representative of or influential to town growth/development. Later, qualitative analysis, including various mapping exercises and analyses, became a necessary tool for framing the results of quantitative analysis in a manner that could be

translated into the final town typologies. Understanding similarities and variation between Susquehanna streamfront town typologies provides insight into historic streamfront development, the potential existence of the aforementioned cultural region, and valuable information pertaining to finer-scale design and planning decisions for a region of small towns and cities confronting numerous challenges related to their waterfronts.

2. A BRIEF OVERVIEW OF REGIONAL DEVELOPMENT

The Susquehanna River Basin encompasses nearly 28,000 square miles of portions of New York, Pennsylvania, and Maryland and contains over 49,000 miles of streams flowing toward the Chesapeake Bay (Figure 2-1). The majority of this (74%) is contained in Pennsylvania – a state with more streams than any other in the continental United States (Foundation for Pennsylvania Watersheds, 2011). Six major subbasins are defined by its major streams, the Chemung River, the Juniata River, and its namesake Susquehanna River - divided into North and West Branches and Middle and Lower Sections (Figure 2-2). The story of human settlement along the Basin’s streams begins during the pre-Columbian era. Evidence from numerous archaeological sites are indicative of the utilization of stream and floodplain resources for agricultural production, fishing, and harvesting of mollusks by Native American peoples such as the Susquehannock (Kent, 1993). European settlement of the interior reaches of the Basin remained limited until coastal communities, exploiting direct access to global trade routes, began to flourish (Jackson & Schultz, 1972b; Mancall, Rosenbloom, & Weiss, 2008). As individuals and groups began to move inland, access to fresh water and good agricultural soils likely provided greater impetus for their settlement than the streams’ collective ability to move people or goods. In general, navigation of inland waterways was no easy task prior to feats of engineering that facilitated the passage of larger vessels (O’Neill, 2006). Later, efforts to directly improve navigation on the Susquehanna by the Army Corps of Engineers were undertaken with mixed success (Macomb, 1890; Stimson, 1912). Today, the majority of the stream network is considered commercially unnavigable. Communities in the Basin grew as the success of coastal towns and cities began to demand reciprocal growth in the production of food and other commercial products from their hinterlands (Jackson & Schultz, 1972a). Agricultural

milling operations became a ubiquitous component along the Basin’s streams (Pensack, 2011). With the advent of new technologies, streams would come to produce electricity both locally and on larger scales. Along the West Branch Susquehanna, in cities such as Williamsport and Lock Haven, PA, pine and hemlock forests were cleared to fuel precipitous growth in the lumbering industry (Cuff, Young, & Muller, 1989). Most prominently, the presence of coal and iron in the region helped facilitate the rise of industry in the 19th century. As the nation continued to expand westward, a growing demand for and need to move commercial resources became the driving force behind the advent of new transportation infrastructure. While the south vied for federal aid aimed at the development of their river transportation networks, the northeast successfully developed canal and, subsequently, rail systems that provided its coastal cities with links to the western frontier and, thus, a competitive commercial advantage (O’Neill, 2006). Interestingly, the fact that the Basin’s stream network was never ideally suited for navigation may have enhanced the roles of its hinterland. Where trade was once hindered, canals and railroads following streams overcame issues of navigability and facilitated the movement of goods to market. While manufacturing remained important into the third quarter of the 20th century (Zelinsky, 1962), today, many communities of the Susquehanna River Basin face serious economic challenges related to deindustrialization. Despite this, the Basin’s streams still provide a direct economic value of nearly seven-billion dollars (Susquehanna River Basin Commission [SRBC], 2006). In terms of water consumption, electrical generation comprises the largest component (SRBC, 2009) of this, while the food manufacturing sector contributes the highest number of employees and payroll (SRBC, 2006). Natural gas extraction from the underlying Marcellus Shale formation requires extensive use of freshwater resources and has

also contributed to economic gains in Pennsylvania (Considine, Watson, & Blumsack, 2011). Intergovernmental and community organizations working toward comprehensive planning for parts of the region are also approaching economic challenges in various ways. Near the confluence of the West and North Branches of the Susquehanna River, eleven counties have established the Susquehanna Economic Development Association-Council of Governments (SEDA-COG), an organization that provides assistance to communities and works to attract and retain business and industry in the region. The Mifflin County Industrial Development Corporation (MCIDC) plays a similar role along the Juniata River and has partnered with Downtown Lewistown Inc. and SEDA-COG in an effort to revitalize Lewistown’s commercial district (SEDA-COG, 2000). Cultural and recreation tourism have also been forwarded as potential means of economic stimulus in the region. Pennsylvania, New York, and Maryland have all established greenway programs that attempt to connect environmental, economic, cultural and recreational goals (Maryland Department of Natural Resources [MDNR], 2003; Parks & Trails New York [PTNY], 2011; Pennsylvania Department of Conservation and Natural Resources [PA DCNR], 2009). The Susquehanna Greenway Partnership (2001) works toward goals directly related to Susquehanna riverfront towns and aims to promote visitation by enhancing connectivity between towns and highlighting the Basin’s unique cultural and environmental resources. Similar efforts are being undertaken by organizations such as Historic Hudson River Towns Inc. in the Hudson River Valley and The Pennsylvania Environment Council (n.d.) in the Allegheny River Valley. According to Bowns & Stevenson (2010, 25), the influence of the Susquehanna River Basin’s streams has been a broad one, catalyzing the development of towns both “rich in material culture and defined by the confluence

of people and historic events that dictated the economic development of the region.â&#x20AC;? Today, community and governmental organizations are collaborating in efforts to enhance positive, mutually beneficial relationships between the streams and towns. Still, community-stream relationships are dynamic, and the precise roles that the Basinâ&#x20AC;&#x2122;s streams might play in the regionâ&#x20AC;&#x2122;s economic revitalization are still being sorted. Despite some inherent challenges, however, it seems apparent that retaining positions along the streams can play a positive role in redevelopment.

Figure 2-1 Susquehanna River Basin within the Chesapeake Bay Watershed The Susquehanna River Basin occupies portions of three states: Maryland, New York, and Pennsylvania. With approximately 49,000 miles of streams that drain approximately 27,500 square miles, water has played significant roles in the regionâ&#x20AC;&#x2122;s development.

Figure 2-2 Major subbasins of the Susquehanna River Basin The Susquehanna River flows 444 miles from Lake Otsego near Cooperstown, New York to the Chesapeake Bay. Its stream network is divided into the six major subbasins shown above (map adapted from USGS and Susquehanna River Basin Commission resources).

3. METHODS

3.1. Study Area & Sample Set

Describing a study area as a region can prove ambiguous. Often, dynamic environmental or cultural perceptions of space can make the definition of clear physical boundaries difficult. For instance, ecosystem and landscape approaches to analysis necessitate defining the scale of a system in which one is working (Allen & Hoekstra, 1992; Palka, 1995). The watershed scale is an interesting case, because it is a unit for which clear boundaries are readily distinguished â&#x20AC;&#x201C;one whose use has become convenient and widespread at various levels of government (Environmental Protection Agency [EPA], 2008). Utilizing the watershed concept for defining a study area, however, still poses some interesting questions. Grain and extent become important because of the numerous subunits that may be contained within the study area and because the study area itself may be a part of a larger whole. Additionally, while political boundaries sometimes follow or contain natural features (e.g. streams), they rarely correspond with larger environmental systems. The Susquehanna River Basin, for instance, falls within three state boundaries, overlaps sixty-seven counties, and contains over one-thousand smaller units of local governance (SRBC, 2007). Selection of the Susquehanna River Basin (as opposed to the entire Chesapeake Bay Watershed or a smaller subunit) and its streamfront towns for study was based primarily on the regionâ&#x20AC;&#x2122;s shared developmental history (section 2) and role that streams have played in that development. It is wholly expected, however, that physical and cultural variations over such a large area exist, and it was hypothesized that these variations would lend themselves to the identification of distinct physical stream-community relationships. While precedent exists for such regional distinctions between

communities, the Susquehanna River Basin has not, to the author’s knowledge, been specifically targeted for such a study. Cultural geographers such a Zelinsky (1977) described the Pennsylvania Culture Area based largely on elements of physical development (e.g. towns, streets, barns). A large portion of this area falls within the southern extent of the Susquehanna River Basin. On a broader scale, Zelinsky (1980) also identified numerous vernacular regions across the United States, based on the frequency of terms (e.g. Atlantic, Northern, New England) utilized by local commercial enterprises. Within portions of the Susquehanna River Basin, for instance, communities were associated with terms such as Eastern, Atlantic, and Middle Atlantic. Interestingly, an area of “places lacking regional identity” (Zelinsky, 1980, 13) was found to substantially overlap the Basin. It is notable, however, that terms associated with streams and rivers were not included in Zelinsky’s study, but were recently utilized in the cultural identification of River Towns in parts of the Mississippi River Basin (Rice & Urban, 2006). Detailed investigations of each streamfront community in the Susquehanna River Basin would be impossible over the course of an individual lifetime. Along the main stem of the Susquehanna River, alone, the Susquehanna Greenway Partnership identifies some seventy river towns in its greenway network. Thus, it was important to narrow the selection of streamfront towns for this study significantly but to do so in a manner that would provide a diverse and geographically broad sample set. In this regard, drivers of town development became an important factor in sample selection. During the early stages of settlement, existing and proposed towns alike fought to attract populations. Any competitive advantage was welcomed, and one such advantage was to become a seat of government. Such a title provided a level of stability, attracted residents, and, thus, enhanced economic development. Boorstin (1972, 11) wrote, “If a town could not be a state capital, the next best thing

was to be a county seat.” Contests over seat selections were not uncommon, as was the case when Juniata and Mifflin Counties (Pennsylvania) split, largely due to the chosen location of Mifflin County’s seat in Lewistown (Ellis & Hungerford, 1886). Rather fortuitously, selection of county seats as a sample set offered a desirable level of breadth in terms of cultural, environmental, and economic factors. Additionally, all seats in the study area are located adjacent to one or more streams of varying size. While there are sixty-seven counties that overlap the Basin, the sample set was further narrowed by only selecting the thirty-five seats whose majority of land is located within the Basin’s boundaries (Figure 3-1). Because no seats within Maryland met this criteria, Havre de Grace –located at the mouth of the Susquehanna River, in Harford County– became the lone exception, bringing the total sample set to thirty-six towns.

3.2. Quantitative Methods

The initial categorization of streamfront towns developed here was dependent upon a set of variables that were hypothesized to correlate with town and county population growth. Whenever practical, variables were analyzed as continuous over the course of the history of each town. For instance, rates of population change between decennial censuses, as opposed to total population, were examined, and flood recurrence rates were calculated using data sets that sometimes spanned more than a century. Many data sets were examined in ArcMap version 10.0 and ArcScene version 10.0 (Environmental Systems Resource Institute [ESRI], 2010). Numerical calculations, including statistical analyses, were completed in Microsoft Excel (2010). All variables were normalized to values ranging between 0.00 and 1.00 – representative of a percent of the maximum value observed over the entire sample set for that variable (section 3.2.7).

Figure 3-1 Sample set selection: counties and county seats County seats have played important economic and political roles and provide broad physical coverage over the entire Basin. Only those county seats that fall within the Basin (a total of 35) became part of the sample set. Havre de Grace, MD was the only non-county seat examined.

3.2.1. Town and county population growth

An attempt to collect information about all individuals in a U.S. decennial census did not occur until 1850. For many earlier censuses, the availability of town population data varies. Thus, population data was collected for the earliest available date for each individual town or county. In instances where either a county or its seat differed in their earliest available data point –as where a county seat formed prior to its corresponding county– the most recent data point took precedence. While some data was collected directly from digital copies of U.S. Census reports (U.S. Census Bureau), a large amount of data has been compiled and can be accessed in tabular form from the Minnesota Population Center (2011). Census population data was utilized to determine three primary measures – mean population growth rates between decennial censuses, correlations between town and county growth rates, and current town population density. Growth rates spanning consecutive decennial census reports were determined for each county and county seat as in equation 1. These rates were then averaged over the available data sets to determine mean growth rates (eq. 2). Correlations between each county’s and its seat’s mean growth rates were determined using Excel’s ‘correl’ function (see Section 3.2.7). Additionally, these town–county growth rate correlations were correlated with other town–county correlations to determine an overall growth � ��

� � ��correlation Population(1) �� rate for � the Basin. density for each town was determined as ��

the total number of persons �� per acre or square mile (eq. 3), utilizing data from the

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(1)

most recent (2010) census. Town area was determined as in section 3.2.2. � ��

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3.2.2. Physical measures: area, perimeter, shape, & waterfront distance

Town and county boundaries were available for download as shapefiles from the US Census Tigerline data clearinghouse (US Census bureau, 2010) and were utilized to determine town area measured in acres and square miles. All data was calculated as a ‘double’ format with a scale of 8 and precision of 2 (i.e. to the nearest hundredth). Area was subsequently utilized to calculate population density (see section 3.2.1). Perimeter� measures of town boundaries were necessary to determine ��

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(1)

the landscape ‘shape’ (S)��measure – the ratio of actual perimeter to the minimum � ��

bounding perimeter�for� an�area �� (eq. 4) that (1) is often utilized in landscape planning ��

(Leitao, Miller, Ahern, & McCarigal, 2006). Unlike perimeter-to-area ratios that can � ∑��

�� depending �� widely vary on the total�area�of a patch, the(2) ‘shape’ measure is size�

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∑ � ‘shape’ might also measure convolution independent. Here, it was hypothesized �� (2) � ��that

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of form that could provide clues to the influence of topography on town growth ��

�� (3) � and might also indicate sprawl. Perimeter for each town vector was automatically �� calculated in ArcMap, and measures were converted to miles. The minimum ��

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(3)

bounding perimeter was determined to be the minimum perimeter of a circle of the

�� 5). same area (eq.

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Stream distance (D) measure from the town center � is��a� of the distance (5) ��

to a defined stream or streams associated with the town (see below). Due to their

�� role the political center for center ��as was determined as a �� their given county, town(6) �

point defined by the county courthouse. (As it is not a county seat, Havre de Grace ��

(6) �� the was lone exception and was not �included in this analysis.) Courthouse locations ��

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were identified from individual county web pages, and addresses were subsequently located using online mapping resources such as Google Maps or Bing Maps. High resolution orthoimagery was then utilized to create point files for each courthouse location in ArcMap. If more than one courthouse location was identified (e.g. as when a courthouse had been moved or a historic courthouse still exists), the earliest known location was utilized. Here, relative distances from town to stream were deemed more important than a high level of precision, and, thus, more sophisticated spatial ‘near’ analytical methods were forgone in favor of measurements using the ‘measure’ tool in ArcMap. Distances were rounded to the nearest fifty feet. In addition, the direction which the courthouse entrance physically faces (aspect) relative to streamfront was noted but not utilized in analyses. Ultimately, the decision to make measurements to one stream over another (e.g. proximity of Bloomsburg PA to Fishing Creek instead of the Susquehanna River) was at the discretion of the author and based on qualitative analyses. However, where more than one prominent stream existed within or adjacent to a town boundary, measurements were made to each stream. In such cases, the average distance to these streams was determined. Distance to other prominent water bodies (e.g. Lake Otsego, Cooperstown NY) was not included in this analysis. 3.2.3. Hydrology: stream order, flood frequency, & flood area

Though a broad measure of hydrologic properties, it was anticipated that stream order might influence other measured variables. The Strahler Stream Order describes hierarchy of stream branching. As streams of the same order converge, they produce streams of a higher order. First order streams converge to produce a second order stream; second order streams produce a third order stream; and so on. Stream order is related to changes in the size of a stream, its drainage area, and a streams ecological function (Ward, D’Ambrosio, & Mecklenburg, 2008).

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�� section (2) � �� (see County seats were mapped 3.2.2.) using data from the �

U.S. Census Bureau (2010) and overlain with hydrologic data from the National

Hydrography Dataset (U.S. Geological Survey [USGS], U.S. Environmental Protection ��

�� [EPA] (3) �� 2004). Stream order for Pennsylvania Agency & USDA Forest Service, ��

streams can be found in a historic streams data set (Pennsylvania Department of Environmental Protection [DEP], 2004). A similar data set was not found for either ��

�� New York or � Maryland. In general, however, �� such (4)data sets have been replaced by

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the aforementioned national hydrography data set which does not list stream order.

Thus, stream order for NY and MD streams was determined manually. For each town, only the highest order stream flowing through or adjacent to a town’s boundaries was ��

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noted. As with other variables, a stream�order score (S) was determined as a relative maximum (eq. 6).

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Flood frequency is often associated with commonly utilized terms such

� as 100-year floodplain, indicative of the annual probability of occurrence for a flood

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of a given size. A 100-year flood has a one-percent (1%) return probability in any

given year. Such measurements are determined using historic USGS stream gage data (USGS, 2012) and can be updated continuously, as new data is made available. ��

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If a gage did not exist within or immediately adjacent to a town’s boundaries, it was sometimes possible to derive flood frequency based on the existence of nearby �� gages. For example, in the case of Binghamton, NY, upstream and downstream gages

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provided sufficient data for analysis; flood frequency was determined for each gage and averaged to derive a flood frequency for the town.

As the threat of flooding �� is variable, USGS stream gage data was not

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equally available throughout the Basin. Sufficient data for analysis was defined as a

(8)

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(3)

minimum of ten concurrent years of annual peak stream flow data. Once collected, there exist a number of statistical methods for deriving flood frequency from stream flow data. However, the USGS �� recommends utilizing Log-Pearson Type III statistics ��

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(4)

(Oberg & Mades,1982; Oregon State University, 2005). This method is based on the statistical skew of data sets for which the product of analysis is series of points that plot flow (Q, cfs) against flood recurrence intervals in years. A regression equation ��

� ��curve (5) to derive the recurrence �� is then utilized that defines the best-fit logarithmic �� frequency for a given annual peak stream flow (Figure 3-2). Note that flood

frequency (f) and recurrence interval are reciprocals (eq. 7). A detailed resource describing the implementation�� of this method can be found on the Oregon State

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University Department of Civil, Construction, and Environmental Engineering web

page (Oregon State University, 2005). ��

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Here, flood frequency was not utilized to derive the 100- and 200-year

floodplains. Rather, frequencies were derived for minor, moderate, and major flood ��

��found � �� Administration’s (NOAA) stages on the National Oceanic and Atmospheric ��

(8)

National Weather Service website (2012). These stages are generally defined by flow volumes, but are also often given as an elevation above a specific datum associated ��

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�� the � �� are taken. � These flood stages do (9) � with USGS gage from which measurements not ��

necessarily correspond with the aforementioned 100- and 200-year floodplains and, generally, occur more frequently. Thus, they provide a more tangible sense of flood ��

��in �� risk a given area. ��

(10)

Flood frequency analysis can subsequently be utilized to determine the

proportion of town areas within floodplains. Flow volumes for flood stages can be translated into elevation and mapped in GIS software such as ArcMap (ESRI) through the use of digital elevation models (DEMs). These raster elevation data sets can then

Figure 3-2 Flood frequency analysis Flooding poses a substantial risk to human settlement in the Susquehanna River Basin. Above, logarithmic regression is utilized to determine flood stage recurrence based on peak annual stream flow data from USGS stream gages. Where possible, the proportion of a townâ&#x20AC;&#x2122;s area within a floodplain was also determined.

��

(5)

� using the raster calculator tool. Subsequent be redefined to represent flood stages,

conversion of raster data to vector format allows the user to easily calculate

floodplain area. These areas can be clipped to town boundaries to determine the ��

�� (6)stage � (FldA) for a given flood proportion of floodplain area (eq. 8). In Pennsylvania, �

the high (2m) resolution data necessary for this type of analysis is available through

the Pennsylvania Spatial Data Access (PASDA, 2011). Data for New York (CUGIR, �

�� (7) at lower (7.5min/30m) 2012) and Maryland (Towson University, 2011) was only found �� resolutions and should not be utilized for fine-grained topographic analyses.

��

(8)

Due to concerns about resolution of data sets and time constraints, FEMA-

�� derived flood hazard area database format) were utilized to determine �� (9) � files (vector ��

��

the FldA measure. These data sets were accessed from the Federal Emergency

Management Agency’s (FEMA) Map Service Center (2012) and placed directly into ��

�� ArcMap. Flood hazard areas � represented in (10) these data sets provide 100- and 200��

year floodplains. These were clipped to town boundaries and their physical area

determined. Unfortunately, the aforementioned NOAA-derived flood stages are not specifically delineated. Additionally, data was not available for all towns within the sample set –a scenario similar to that seen with USGS stream gage data. 3.2.4. Transportation systems

In his analysis of river town development in the upper Mississippi River basin, Mahony (1985, 320) argues that one must consider how a particular town “has interacted with and functioned within the regional system of towns of which it is a part” and proceeds to analyze towns as components of trade networks. In that regard, this examination of transportation infrastructure considered towns as entrepôts and examined connections made to and from individual towns to outside markets.

��

∑� ��

(2)

��

(3)

�� Within the Susquehanna River Basin, as with other regions in the United ��

��

(3)

States, innovations in transportation�� technology played a major role in development by spurring “economic development through the encouragement of new methods” ��

��

(4)

�� (Jackson & Schultz, 1972a, 6). An example of such a positive feedback loop can be ��

��

(4)

�� seen canal system –the impetus for which was largely provided by in Pennsylvania’s

the development of the Erie Canal in New York.

�� infrastructure (5) that have played �� Three (3) forms of � transportation � ��

crucial roles �� in the development�of�� the the towpath canal (5) �� Basin were considered: �

system; railroad infrastructure; and the modern highway system. For each type, all connections made from a town�� were scored equally (e.g. a rail line in two directions

(6)

��

� was given a score of two). If transportation �� infrastructure did not pass directly

�� (6) � through or was not immediately adjacent to a town boundary, the author’s discretion was form of transportation infrastructure, used to determine its inclusion. For each �

(7)

��

�� scores were normalized as a percent of the maximum score for that form. �

��

(7)

�� Normalizing these scores individually ensured that the importance of infrastructure

systems with inherently limited numbers of built connections (e.g. canal) would not

��

(8)

be completely overshadowed in importance by others. The�� sum of these scores ��

�� was, again, normalized, to represented an aggregate connectivity score (eq. 9) and derive a final transportation score (T) (eq. 10).

��

� �

��

(10)

� �

��

(9) (9)

(10)

Information on canal�systems was derived from a number of resources ��

(Cuff, Young, & Muller, 1989; Maryland State Archives, 2011; Sadowski Jr., 2010; Whitford, 1906a; Whitford, 1906b; Whitford, 1906c). The number of railroad

connections for a given location was taken as the sum of operating railroads in

(8)

1946. Carpenter (2003) describes that period as a significant, due to the intensified buildup and use of railroad infrastructure during World War II and its subsequent decline in use that followed. Highway scores are based on the number of U.S. Interstate and Highway System connections, derived from the National Highway Planning Network database (Federal Highway Administration, 2005). 3.2.5. Natural resources

The earliest major colonial cities were established on sites with ready access to oceanic trade routes and were rooted in commerce. As they grew, the “promising backcountry or hinterland” which they bordered became increasingly more important (Jackson & Schultz,1972b, 44). Attempting to account for the role of many streamfront towns in the provision of material goods to larger cities, a resource score (R) was developed to consider the availability of four regionally prominent natural resources: agricultural soils; lumber; coal; and natural gas. As in the determination of the transportation score (section 3.2.4), the resource score (R) was derived from an aggregate of individual scores (eq. 11 &�� 12). ��

��

� �

��

� �

��

� �

��

(11) (11)

(12)

��

�� (12) of influence of outlying regions would To ensure� that a certain degree ��

be considered in town growth, soil scores (s) (eq. 13) were determined by clipping ��

��Susquehanna � �1�� around the River Basin’s soil data set (SRBC, 2006b) to a five mile radius ∑ �� 1�� soil each town’s centroid. Based on the USDA Soil Series Descriptions (2010), each ∑ ��

type was classified as being used for primarily agricultural purposes and/or light

�� ∑�� as �1�� agriculture and pasture. Agricultural uses were weighted twice heavily as pasture. �� ∑�� Any given soil could receive a maximum score�� of three. Though it �1�� would likely prove

beneficial in future work, soil types were not weighted based on coverage area, and

�� ∑�� ∑��

�

(15) (15)

only a simple average of soil scores was taken as the final score for each town. Lumber, coal, and natural gas scores were derived from resource maps (Cuff, Young, & Muller, 1989, 95; Pennsylvania Department of Environmental Protection, 2012; The Pennsylvania State University, 2010). Maps were georeferenced into ArcMap and overlain with town boundaries. The lumber score (l) was derived from resource availability in 1880. Towns within the defined resource area received ��

��

� to produce � lumber received (11) �� within a � a�score of two, while those found county known ��

��

a score of one. There were two exceptions. For their substantial history in lumber

processing, the cities of Lock Haven and Williamsport, PA received scores of two �� despite being outside of resource �� mapped (12)areas. Coal and natural gas scores were ��

derived in a similar manner.

��

�� ∑ ��

��

�1��

��

�� 3.2.6. � �� Higher education � � ��

��

� �� ∑��have � �education �� 1�� role in Institutions of higher played an important

(11) (11)

shaping the landscape around �� them and have influenced many facets of town and

�� (12) � �� (12)within an approximately ten mile radius city life (Wiewel & Perry, 2008). Institutions �� ∑�� (15) of each town were located with Google Maps (Google, 2012) by applying the town

name and state followed by one of three terms in the software’s search function: �� 1�� ∑ �� university; college; or technical school. A normalized aggregate education (Ae) �� 1��score � � ∑ �� 1�� (eq. 16) was derived by applying a�� weight�� of two for each public and private four �� ∑�� 1�� ∑ � � �� 1�� 1�� ∑�� (15) �� ∑�� (15)

��

�

� ∑ ��∑ ��∑ �� 1�� ∑ �� ∑ � � ��∑ �� ∑� ��

��

�1�� 1��

��

�� ∑ �� ∑ ��

�1�� 1��

��

�� for�� tertiary � � � �1�� (t) � year (f) (eq. 14) and one point each institutions �institution � �� ∑� �� (1) � � � � � � � ��

��

(11) (11)

(eq. Tertiary institutions were which primarily a technical (11) ��15). � �� emphasize � �those ∑�� 1��

��

education and have shortened degree periods; examples include community

�

� �� ∑Schools �� (15) a single trade (e.g. colleges vocational � schools. which(12) emphasized � �� and � �� (12)

��

�� (15)(E) was then determined by �� ∑A phlebotomy) were not included. final education score �� (12) ��

normalizing the aggregate score (eq. 17). �

�

�� 1�� 1�� 1�� ∑ �� 1�� ∑ �� 1�� ∑ �� 1�� ∑ �� ∑�� 1�� 1�� 1�� ∑�� 1�� 3.2.7. Normalization & statistical correlations �� ∑comparison �� ∑�� In order to facilitate between(15) towns(15) and, �� later, town

�� typologies, all values derived variables were normalized between 0.00 �� ∑�� for (15) to scores � ∑ ��∑ ��∑ ��

�1��

∑ � � ��∑ �� scores represent percentages of the maximum observed ∑ � � ��∑ �� 1.00. and normalized � ∑These ��∑ ��∑ ��

� � � �1�� ∑�� ∑ � � ��∑ �� ∑ �� normalized �1�� 1�� set.�Eventually, � � � � value for that score over the entire sample scores were �� can be derived �1�� raw scores represented in charts from which (section 5.3). � � � �� 1�� Linear�� statistical correlations were determined for all examined �

�1�� 1�� 1�� table (4-1). Correlation coefficients (r) variables (eq. 18) and placed in a correlation � �� were for significance a two-tailed t-test (eq. 19). Correlation between �� tested � �� using�1�� individual town growth rates and their corresponding county are shown in table 4-2.

� � � � � �� ∑ ��∑ � ∑ ��∑ ��∑ ��∑ �� 1�� 1��

∑ � �� ∑ ��∑ �� ∑ ��∑ ��∑ �� ∑ �� ∑ �� ∑ ��∑ ��∑ ��

� � � � �� ∑ � ��∑ �� ∑ � ��∑ ��

� � ��

�

� � ��

�

��

�

��

�1��

�1�� 1�� 24

3.2.8. Initial town categorization based on quantitative analysis

Initial town categorization was based upon correlations between town and county growth (section 3.2.1) and an aggregate development score (D) (eq. 20) that included transportation and education scores (sections 3.2.4 and 3.2.6, respectively). Towns were first grouped by the significance of the correlation between their individual and county’s growth rates. Two groups –one exhibiting significant correlations and the other not– were formed. Within these groups, towns were then ranked and grouped based on their relative development scores. Groupings were based on relative values ranges of 0.00-0.15, 0.16-0.49, and 0.50-1.00.

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3.3. Qualitative Methods

3.3.1. Mapping

While quantitative analyses were useful in gaining a sense of certain variables responsible for town and county growth, conclusions regarding their influence on town form were difficult to derive. Thus, it was determined that an alternative and more visual approach to the examination of town growth was necessary. A number of variables, including many of those examined quantitatively, were mapped using ArcMap. Those features included town boundaries, hydrology (streams and floodplains), transportation infrastructure (existing rail lines, highways, and interstates), town centers (courthouses), and historic boundaries (section 3.3.2).

Topographic variation was visualized with DEMs in the manner described in section 3.2.3. It should be noted that visualization of railways did not correspond to the peak date (1943) utilized in the quantitative analysis, because such a historic data set does not exist. Adequate time to develop this and other data sets (e.g. Pennsylvaniaâ&#x20AC;&#x2122;s canal network) from historic documents was not available. Coupled with the utilization of online mapping services (e.g. Google Maps, Google Earth, Bing Maps), literature review, and historic document analysis, these maps were utilized to define basic, generalizable streamfront town typologies (section 4.2.2).

3.3.2. Historic mapping

Based on the outcome of typological analysis, five towns representative of each described typology were selected for historic floodplain visualization. Historic maps for select towns and counties were obtained from digital sources or from Penn Stateâ&#x20AC;&#x2122;s Donald W. Hamer Maps or Special Collections Library and scanned at high resolution (300-450 dpi): Bellefonte (Pomeroy & Co., 1874); Harrisburg (Everts & Stewart, 1875); Lancaster (Bridgens, 1864); Laporte (USGenWeb, 2010); Lewistown (Pomeroy, Whitman & Co, 1877). Because of the rarity of many of these sources, their often fragile state, and lack of digital copies, such historic maps are not always made available for digitization. Maps were georeferenced in ArcMap, and their extent was utilized to clip corresponding DEMs. A shapefile based on historic town boundaries was also created and utilized in the aforementioned map analysis (section 3.3.1). Georeferenced map files were then imported into ArcScene where base elevation data was provided from the clipped DEM (figure 3-3). A vertical exaggeration of between 1.5 and 2.0 was utilized to highlight topographic variation in the images. FEMA-derived flood hazard areas were then overlain onto the historic

Figure 3-3 Historic mapping utilizing GIS Historic town boundaries and development were analyzed as part of the qualitative town assessment. Above, a map of Harrisburg, PA (Pomeroy, 1875) was georeferenced and visualized â&#x20AC;&#x201C;along with topographic informationâ&#x20AC;&#x201C; in ArcScene (Esri).

map documents. Base elevations and opacity of floodplain maps were adjusted to facilitate visualization. Knowles (2002) provides a good overview of techniques utilized in digitization and mapping of historic resources.

4. RESULTS

4.1 Statistical Correlations

Significance of correlation between basin-wide variables is shown in table 4-1. Correlations between individual town and county growth rates are shown in table 4-2. All correlations were tested for significance with a two-tailed t-test (see section 3.2.7). Varying levels of significance of correlation are distinguished in the table using a grey scale. In the following sections, discussion of observed relationships between two variables may be located in either section but is generally not duplicated. Some variables are grouped for clarity. 4.1.1. Town and county population growth

With the exception of Cooperstown, NY, growth rates for county seats in the sample were positive over their respective sampling periods (M=0.13, SD=0.08). All county growth rates were positive (M=0.08, SD=0.05). In contrast, more than half (n=20, 55%) of the towns and approximately one fifth (n=7, 19%) of the counties have exhibited population loss since 1950. As a whole, mean growth rates for counties and their seats correlated positively and significantly, t(34)= 2.728, p<0.01. Taken individually, town and county growth rates correlated significantly for more than half (n=21, 58%) of the samples (p<0.1) (table 4-2). It is worth noting that only a small proportion of correlations (n=6, 17%) were negative, and none of these were found to be significant at the above level. The growth rate charts in figure 4-1 provide examples of various degrees of correlation observed within the sample set. Both town and county growth rates exhibited significant correlations with a number of the tested variables (table 4-1). Interestingly, a significant positive correlation (p<0.05) was observed between town growth rate and stream order (S) but not for county growth rate. Additionally, both the education (E) and

Scranton & Lackawanna County, PA significant, highly positive correlation (d.f.=11, r=0.98, p<0.01)

Harrisburg & Dauphin County, PA significant, weakly positive correlation (d.f.=19, r=0.56, p<0.01)

Carlisle & Cumberland County, PA no growth rate correlation (d.f.=15, r=-0.09)

Figure 4-1 Growth rate correlation charts Overall, growth rate correlations were positive and significant for more than half of the sample set. No negative correlations were found to be significant. In recent decades, negative or slowing growth rates for some county seats (see Harrisburg above) have been observed.

transportation infrastructure (T) scores correlated to a significant degree (p<0.01) with both county and town growth rates. Other measured variables are described in the sections that follow. As previously described (section 3.2.7), all scores were normalized to facilitate comparisons. The graphic shown in figure 4-2 displays normalized means and standard deviations for each variable over the entire sample set and explains how to derive actual or raw values utilizing the chart. Similar charts were prepared for each typology (section 4.2.2) and for select towns (Appendix C). 4.1.2. Area

As of the 2010 census, population of county seats within the Basin varied widely (M=16,454, SD=18,923). The most populous seat was Scranton, PA (Lackawanna County) with a population of 76,089. The smallest was Laporte, PA (Sullivan County) with 316 individuals. A similar scenario was seen with counties (M=115,467, SD=119,446). York County, PA had the highest population (434,972), and Cameron County, PA had the lowest (5,085). The correlation between town and county population in 2010 was highly significant (p<0.001) (data not shown). Town area was also highly variable throughout the sample set (M=2553.34 acres, SD=2884.33). Scranton, PA represents the largest county seat (16,192 acres), and Mifflintown, PA (Juniata County) is the smallest at 90 acres. Interestingly, a townâ&#x20AC;&#x2122;s physical area was shown to correlate positively (p<0.05) with mean county growth rate but not with town growth rate. Transportation infrastructure (T) and education (E) scores also correlated highly (p<0.01) with town area. Population densities of the county seats (PopD) in 2010 were found to be relatively low and less variable (M=5.90 persons/acre, SD=3.22)21 than population or area measures. The highest population density was exhibited in York, PA (York County,12.91 persons per acre),32and the lowest was in Laporte (0.46 persons/ 2 In persons per square mile: (M=3780.67, SD=2062.19) 3 8264 persons per square mile

Figure 4-2 Quantitative measures for the Susquehanna River Basin In order to facilitate comparison between samples, quantified variables were normalized to values between 0.00 and 1.00. The chart above represents average scores from across the entire Susquehanna River Basin and describes how to derive raw values from the normalized ones.

acre).43 Population density was shown to correlate positively and significantly with numerous other variables. Mean county growth rate correlated more significantly with population density (p<0.02) than did mean town growth rate (p<0.05). The stream order score (S) also correlated positively (p<0.02) with population density, as did transportation infrastructure (p<0.02), and education (p<0.01) scores. While no significant correlation was found between population density and area, the shape measure (P) did correlate positively (p<0.05) with population density. Strong positive relationships (p<0.01) were exhibited between shape and mean county growth rate and the education score (E). To a lesser degree, stream distance (D) also appears to have been a positive driver of town shape (p<0.10). The resource score (R) correlated negatively with both population density (p<0.05) and shape (p<0.02). 4.1.3. Transportation & higher education

Significant positive correlations (p<0.01) between education (E) and transportation infrastructure (T) scores were revealed in the early stages of the study. Based on this result, a normalized aggregate score (T+E) that combined both variables was created and utilized in an initial attempt at town categorization (section 4.1.6, table 4-3, and figure 4-3). This score is listed in table 4-1 but was not utilized beyond that initial categorization. These two variables were also found to correlate significantly with numerous others (table 4-1). Mean town and county growth rates, town area, and population density all correlated positively (p<0.01) with the education score. To a lesser degree, the stream order score also correlated positively (p<0.05) with the education score. Transportation infrastructure correlated similarly with mean growth rates and area but to a lesser degree (p<0.02) with population density. It did not

4 295 persons per square mile

p < 0.01 0.02 0.05 0.10 0.20

Area ‐‐ ‐‐ 1.000 0.152 0.210 0.128 0.118 ‐0.043 ‐0.006 0.597 0.765 0.168 0.726

PopD ‐‐ ‐‐ ‐‐ 1.000 0.371 ‐0.090 0.422 0.070 0.035 0.488 0.422 ‐0.371 0.490

P ‐‐ ‐‐ ‐‐ ‐‐ 1.000 0.292 0.221 ‐0.012 ‐0.337 0.478 0.231 ‐0.402 0.388

D ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ 1.000 ‐0.390 0.418 ‐0.241 ‐0.007 ‐0.156 ‐0.126 ‐0.083

S ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ 1.000 0.033 0.561 0.303 0.527 ‐0.079 0.439

F ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ 1.000 ‐0.399 0.168 ‐0.102 0.092 0.052

Other Variables FldA ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ 1.000 ‐0.082 0.286 0.282 0.068

Correlation was tested using a two-tailed t-test. Though only those correlations exhibiting a significance level greater than 0.95 (p < 0.05) were considered significant, those above a significance level of 0.80 (p < 0.20) are also shown and may be referred to in the text when discussing potential trends.

Table 4-1 Correlation between tested variables

0.XXX 0.XXX 0.XXX 0.XXX

αp value 0.99 0.01 0.98 0.95 0.02 0.90 0.10 0.80 0.20

GC ‐‐ 1.000 0.375 0.393 0.505 ‐0.089 0.248 0.059 ‐0.375 0.628 0.560 ‐0.240 0.640

Growth Rates GT 1.000 0.645 0.213 0.325 0.183 ‐0.259 0.342 ‐0.011 0.154 0.465 0.449 0.091 0.491

confidence level 0.XXX 0.XXX 0.99 0.XXX 0.98 0.XXX 0.XXX 0.90 0.80

Mean Town Growth Rate (GT) Mean County Growth Rate (GC) Town Area (A) Population Density (PopD) Shape (P) Stream Distance (D) Stream Order (S) Flood Frequency (F) Flood Area (FldA) Education Score (E) Transportation Score (T) Resource Score (R) Aggregate (T+E)

Correlation Table E ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ 1.000 0.735 ‐0.145 0.939

T ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ 1.000 0.037 0.923

R ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ 1.000 ‐0.064

T+E ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ 1.000

Table 4-2 Correlation between individual town and county growth rates

Here, growth rate correlations are determined by making a direct comparison between decennial census data for individual counties and their respective county seat.

town

0.17 0.16 0.11 0.05 0.17 ‐0.02 0.08 0.09 0.07 0.06 0.08 0.09 0.12 0.21 0.28 0.11 0.16 0.21 0.12 0.05 0.06 0.14 0.19 0.28 0.31 0.19 0.04 0.17 0.05 0.12 0.08 0.04 0.13 0.08 0.11 0.16

Town

Havre de Grace City Binghamton City Elmira City Norwich City Cortland City Cooperstown Village Bath Town Owego Town Bedford Borough Hollidaysburg Borough Towanda Borough Emporium Borough Bellefonte Borough Clearfield Borough Lock Haven City Bloomsburg Town Carlisle Borough Harrisburg City Huntingdon Borough Mifflintown Borough Scranton City Lancaster City Lebanon City Wilkes‐Barre City Williamsport City Lewistown Borough Danville Borough Sunbury City New Bloomfield Township Middleburg Borough Laporte Borough Montrose Borough Wellsboro Borough Lewisburg Borough Tunkhannock Borough York City 0.18 0.13 0.08 0.02 0.05 0.02 0.03 0.05 0.05 0.13 0.03 0.03 0.13 0.12 0.09 0.09 0.13 0.14 0.04 0.04 0.09 0.13 0.12 0.15 0.11 0.08 0.02 0.10 0.06 0.07 0.02 0.03 0.04 0.05 0.07 0.15

county

mean growth rate 14 13 14 14 12 14 14 14 14 14 14 12 14 14 14 14 15 19 14 14 11 19 17 15 15 18 14 14 14 12 13 14 14 14 14 19

d.f. 0.25 0.66 0.75 ‐0.28 0.37 ‐0.35 ‐0.38 0.61 0.48 0.25 0.57 0.85 0.49 0.47 0.94 ‐0.2 ‐0.09 0.56 0.58 0.69 0.98 0.21 0.65 0.59 0.88 0.5 0.15 0.91 0.13 ‐0.44 0.68 0.53 0.56 ‐0.27 0.15 0.35

r 0.063 0.436 0.563 0.078 0.137 0.123 0.144 0.372 0.230 0.063 0.325 0.723 0.240 0.221 0.884 0.040 0.008 0.314 0.336 0.476 0.960 0.044 0.423 0.348 0.774 0.250 0.023 0.828 0.017 0.194 0.462 0.281 0.314 0.073 0.023 0.123

r^2 0.966 3.168 4.243 ‐1.091 1.380 ‐1.398 ‐1.537 2.880 2.047 0.966 2.596 5.590 2.103 1.992 10.309 ‐0.764 ‐0.350 2.946 2.664 3.567 16.333 0.936 3.527 2.830 7.176 2.449 0.568 8.212 0.491 ‐1.697 3.344 2.339 2.529 ‐1.049 0.568 1.629

value 1.761 1.770 1.761 1.761 1.782 1.761 1.761 1.761 1.761 1.761 1.761 1.782 1.761 1.761 1.761 1.761 1.753 1.729 1.761 1.761 1.796 1.729 1.740 1.753 1.753 1.734 1.761 1.761 1.761 1.782 1.770 1.761 1.761 1.761 1.761 1.729

0.05

0.1

0.9

0.XXX 0.XXX 0.XXX 0.XXX

2.624 2.650 2.624 2.624 2.681 2.624 2.624 2.624 2.624 2.624 2.624 2.681 2.624 2.624 2.624 2.624 2.602 2.539 2.624 2.624 2.718 2.539 2.567 2.602 2.602 2.552 2.624 2.624 2.624 2.681 2.650 2.624 2.624 2.624 2.624 2.539

0.01

0.02

0.98

confidence level 0.99 0.98 0.90 0.80

2.145 2.160 2.145 2.145 2.179 2.145 2.145 2.145 2.145 2.145 2.145 2.179 2.145 2.145 2.145 2.145 2.131 2.093 2.145 2.145 2.201 2.093 2.110 2.131 2.131 2.101 2.145 2.145 2.145 2.179 2.160 2.145 2.145 2.145 2.145 2.093

0.025

0.05

0.95

Critical values

α 0.01 0.02 0.10 0.20

2.977 3.012 2.977 2.977 3.055 2.977 2.977 2.977 2.977 2.977 2.977 3.055 2.977 2.977 2.977 2.977 2.947 2.861 2.977 2.977 3.106 2.861 2.898 2.947 2.947 2.878 2.977 2.977 2.977 3.055 3.012 2.977 2.977 2.977 2.977 2.861

0.005 α (1‐tail)

0.01 α (2‐tail)

0.99

correlate with the shape measure, but exhibited a stronger positive correlation (p<0.01) with stream order and a negative correlation (p<0.05) with stream distance. Raw transportation and education scores for each town have been made available in Appendix C. Overall, the number of transportation connections from sampled towns varied widely across the sample (M=9.55, SD=6.27). Canal connections were the least numerous (M=1.25, SD=1.08), whereas highway (M=3.44, SD=3.01) and rail (M=4.86, SD=3.26) connections were generally greater in number but also showed large variation. As described in section 3.2.4, normalizing canal (c), rail (r), and highway (h) scores prior to aggregating them was intended to minimize disparities in the number of raw connections that would have favored the influence of one system over another in the calculations. Variation in the number of institutions of higher education in and around county seats was also quite high (M=2.78, SD=2.93). The largest total number of institutions counted for any town was twelve (12), in Harrisburg, PA (Dauphin County). For the majority (n=22, 61%) of county seats, two or fewer institutions were counted. Nearly one fifth (n=7, 19%) of the towns had zero (0) institutions.

4.1.4. Natural resources

Both raw and normalized resource scores for agricultural soils (s), lumber (l), coal (c), and natural gas (g) are provided in the accompanying data disc (Appendix C). In its current form, the natural resource score showed almost no correlation with the majority of variables except for those previously mentioned (section 4.1.2) â&#x20AC;&#x201C;shape (P) and population density (PopD). 4.1.5. Hydrologic measures

All county seats within the Basin contained at least one stream within or adjacent to their town boundaries. The precise number for each town was not determined. A stream order score was determined for the largest stream flowing

through or adjacent to each town. Over the entire sample set, this number was found to be relatively large (M=5.67, S=2.34) and correlated positively and significantly with town growth rates (p<0.05), population density (p<0.02), floodplain area (p<0.02), education (p<0.10), and transportation infrastructure (p<0.01). As described in section 3.2.2, the stream distance measure (D) was taken as that average distance from the county courthouse to selected adjacent streams. The average distance to streams was found to be just over one quarter (1/4) of a mile (M=1469ft, SD=1186), with a maximum average distance of 4675 feet at Carlisle, PA (Cumberland County). As previously noted (section 4.1.2), stream distance correlated positively with town shape. Additionally, significant negative correlation (p<0.05) was observed between stream distance and stream order, indicating that town centers tended to be nearer to larger streams than to smaller ones. Stream distance also correlated positively with (p<0.05) with flood frequency. In all, sufficient USGS stream gage data to calculate flood recurrence frequency rates was collected for approximately half (n=22, 55%) of the sample set. Flood frequency was determined utilizing the NOAA-derived moderate flood stage. For this stage, the mean flood recurrence rate for the entire Basin was found to be 0.18 (SD=0.09), corresponding to a return period of approximately five-andone-half years. With the exception of stream distance noted above, flood recurrence rates did not correlate strongly with other variables. While there appears to be a negative correlation between flood frequency and the proportion of town area within a floodplain, the significance of this correlation was not strong, t(10) = 1.372, p<0.20. Another interesting finding was the significant positive correlation, t(4) = 2.776, p<0.05, between flood frequency and the mean growth rate of county seats within New York State.

Similar to flood frequency data, calculating the proportion of floodplain area

(FldA) for towns was limited by the availability of FEMA database files. In total,

data was available for a more than half of the sample set (n=24, 66%) â&#x20AC;&#x201C;a larger proportion than was available for flood frequency analysis. Unfortunately, these data sets did not fully overlap, and only twelve towns (33%) had data for both variables. Proportion of town area within the FEMA-derived 100- and 200-year floodplains was high (M=0.34, SD= 0.23). The largest proportion was found in the city of Sunbury, PA (Northumberland County), with greater than 80% of its approximately 1320 acres within the 200-year floodplain. Carlisle, PA presently has the smallest proportion (5%) of its area within the 200-year floodplain. Relative to flood frequency, flood area appears to correlate more closely with other variables examined during typological analysis. A negative correlation, t(22)=1.717, p<0.10, exists between flood area and mean county growth rate, while a significant positive correlation, t(22)=2.819, p<0.02, exists between flood area and stream order. Other variables exhibited less significant correlations: a negative correlation with town shape, t(22)=1.312, p<0.20; a negative correlation with flood frequency (above); and positive correlations transportation and resource scores t(22)=1.312, p<0.20. 4.1.6. Initial town categorization

Due to their strong correlations, transportation and education scores were combined into a single aggregate score (T+E) utilized in an early attempt to categorize streamfront towns. Based on this score and on the correlation between town and county growth, towns were ultimately separated into eight groups (Appendix A). The distinction between significant and insignificant population growth rate correlations conveniently bisected the sample set into two groups of eighteen. The second distinction was made based on the aggregate transportation and education scores and further subdivided these groups into two groups of four.

4.2. Streamfront Town Typologies

4.2.1. Mapping

Where possible, variables analyzed quantitatively were also visualized using a combination of geographic information systems (GIS) and rendering software (Adobe Photoshop CS5). Final maps included digital elevation models (DEMs), streams and other water bodies, 100- and 200-year floodplains, current and historic town boundaries (where available), and existing transportation infrastructure (highways, interstates, and railways). While not analyzed quantitatively, topographic variation became extremely important for typological categorization (section 5.3). Figure 4-4 shows a representative sample of the maps produced. 4.2.2. Town typologies

Through first hand visits, examination of various mapping resources, historic documents, the creation of original town maps, and quantitative analyses (section 4.1), five town typologies were described: frontage-towns, confluence-towns, bisected-towns, early-stream-towns, and annexed-stream-towns (figure 4-5). The confluence-town type represents the largest group with sixteen towns; the frontagetown type contains eight; the bisected-town-type has five; the early-stream-town type has six; and the annexed-stream-town type is represented by a single sample, Lancaster, PA (Lancaster County). Descriptions of each typology can be found in section 5.3. As was completed for individual towns, variables were analyzed over typological groups. As described in figure 4-2, all data was normalized to values between 0.00 and 1.00 and subsequently placed in data charts (figure 4-6). All values provided therein represent proportions of the maximum values observed for each typology. This data is also provided in numeric form for all samples and sample groups (i.e. typologies) in the disc that accompanies this volume (Appendix C).

Figure 4-4 Mapping county seats Town maps, such as that for Harrisburg (above), were created to visualize specific variables and became revelatory in understanding typological distinctions. Information shown includes hydrology, topography, transportation infrastructure, and town boundaries.

Figure 4-5 Town typologies Based on both quantitative and qualitative assessments, five stream- or riverfront town typologies within the Basin were described: frontage-towns; confluence-towns; bisected-towns; early-streamtowns; and annexed-stream-towns.

With the exception of the early-stream-town type, mean town growth rates were similar between typologies and exhibited similar levels of variability. Early-stream-towns tended to have lower town growth rates (M=0.26, SD=0.23), lower county growth rates (M=0.26, SD=0.20), and the smallest total population sizes (M=0.07, SD=0.13). Confluence-town county growth rates (M=0.36, SD=0.18) tended to be greater than those of early-stream-towns but lower than other groups. Correlations between town and county growth rates was lowest amongst the frontage- (M=0.23, SD=0.35) and annexed-stream-town (M=0.22) types. Overall, population was spread between town typologies rather evenly. However, a high degree of variability was exhibited by each group. As noted above, early-stream-towns tended to have the lowest populations. Population densities were also similar between typologies but exhibited less variability. Lancaster, the only annexed-stream-town, has one of the highest relative population densities (0.99) but also had the highest relative shape measure (1.00). All other typologies were relatively similar in terms of their shape. Though, early-stream-towns tended to have the lowest shape measures. Transportation scores (M=0.11, SD=0.13) and education scores (M=0.10, SD=0.10) for early-stream-towns were low relative to other groups, with the exception of the confluence-town education score (M=0.19, SD=0.19). Conversely, the early-stream-town resource score (M=0.60, SD=0.27) was amongst the highest. Frontage-towns (M=0.48, SD=0.27) and the annexed-stream-town (M=0.20) exhibited the lowest resource scores. Hydrologic conditions varied more sharply amongst typologies relative to other variables. The average stream order score for the early-streamtowns (M=0.21, SD=0.10) was significantly lower than other town typologies. With the exception of early-stream-towns, flood gage data was available for at least one (1) town of each type. Data was available for nearly 88% (n=14) of the confluence-

towns and 50% (n=4) of the frontage-towns. Only a single sample was analyzed for each of the annexed-stream- and bisected-town typologies. Both frontage-towns (M=0.50, SD=0.19) and confluence-towns (M=0.48, SD=0.27) also exhibited similar flood recurrence rates. Due to the nature of this measure, the lack of available data for a given town or typology speaks to the immediate threat of flooding in certain typologies over others (see section 5.2.4). FEMA-derived floodplain data was available for a greater number of towns than flood gage data and was more evenly distributed throughout typologies. Still, early-stream-towns were represented by only two samples. Overall, the confluence-town-type had the highest proportion of area within a floodplain (M=0.59, SD=0.24) and contained many of the highest values observed within the sample set. Frontage-towns had the second highest proportion of floodplain area (M=0.31, SD=0.23). Early-stream-towns had the lowest average FldA scores (M=0.15, SD=0.08). The average distance to streams also appears to vary by typology. Bisected- (M=0.17, SD=0.06) and frontage-towns (M=0.18, SD=0.14) were most proximal to their streams. (Here, the lower the relative scores represent increased proximity.) On average, confluence-towns were also closer (M=0.26, SD=0.23) than either early-stream- (M=0.36, SD=0.21) or annexed-towns (M=0.83).

Figure 4-6 Quantitative measures for typologies The following charts represent normalized values derived from samples from each typology. The normalized values in each chart can be compared to the SRB as a whole, other typologies, and individual towns. Figure 4-2 describes how to derive raw values from the normalized ones shown.

Frontage-towns

Confluence-towns

Bisected-towns

Early-stream-towns

Annexed-stream-towns

note: only one annexed-stream-town was described (Lancaster, PA) and, therefore, standard deviations were unable to be calculated

4.2.3. Historic mapping

The ability to find and scan historic town and county maps was limited by availability of online resources and to those found within Penn State library collections. Due to their often fragile state and the cost associated with professional scanning services, not all maps could be scanned prior to project completion. Thus, georeferencing and three-dimensional visualization of historic maps was conducted for only small subset of the towns examined. One town was mapped for each typology described: Harrisburg, PA (frontage-town); Lewistown, PA (confluencetown); Bellefonte, PA (bisected-town); Laporte, PA (early-stream-town); and Lancaster, PA (annexed-stream-town). Historic maps for these five (5) towns were successfully georeferenced and visualized in conjunction with FEMA-derived floodplains (figure 4-7). Historic town boundaries were also placed in town maps. In some instances, historic boundaries were scaled down slightly, in order to aid in their visualization against current boundaries.

Figure 4-7 Historic maps Coupled with high resolution (2m) digital elevation models and FEMA database files, historic maps became a useful tool for visualizing town development relative to floodplains. Above, Harrisburgâ&#x20AC;&#x2122;s capital area is shown. Note the location of important political infrastructure outside of the floodplain. (map source: Everts & Stewart, 1875)

Lewistown, PA (1877) At the confluence of the Juniata River and Kishacoquillas (Kish) Creek, portions of Lewistown are highly susceptible to flooding. Note how topography has constrained development toward the top of the map. (map source: Pomeroy, Whitman & Co., 1877)

The borough of Lancaster developed at some distance from its neighboring Conestoga River (note the distinctive Pennsylvania diamond at town center). Though the floodplain is not large, floods along the Conestoga reach the moderate flood stage approximately once in every four (4) years. (map source: Bridgens, 1864)

Lancaster, PA (1864)

An earthen dam was responsible for the creation of Lake Mokoma in 1888. The impetus for its construction southeast of Laporte had apparently little to do with flooding or access to water. Rather, it was intended to attract tourists that would have easier access to the area due to the arrival of a new rail line. (map source: USGS 1934)

Laporte, PA (1934)

Elevated areas to the east (map right) of the Spring Creek contain many of the townâ&#x20AC;&#x2122;s residences, as well as the prominently situated court house. Industrial uses and rail lines are seen in the low lying areas to the west. (map source: Pomeroy, A., & Co., Philadelphia, 1874)

Bellefonte, PA (1874)

5. DISCUSSION

The results of this work have been an enhanced understanding of factors contributing to the growth of county seats within the Susquehanna River Basin and, more importantly, the development of a conceptual framework that speaks to the development of Susquehanna streamfront towns in general. This framework has taken the form of typological distinctions that provide insight into shared facets of regional history, the potential existence of a unique cultural region built upon the regionâ&#x20AC;&#x2122;s stream network, and more localized physical and cultural streamcommunity relationships that can be utilized in waterfront planning and design that acknowledges both the opportunities and challenges of coexisting with these prominent natural resources.

5.1. County Seats

5.1.1. Sample set selection

Given the tremendous number of individual communities that exist within the Susquehanna River Basin, examining each and every one in detail would be a nearly impossible task in an individual lifetime. Thus, county seats whose physical boundaries largely fell within the Basin were selected for analysis, and the sample set was ultimately reduced to thirty-six towns/cities (section 3.1). With the exception of Havre de Grace, MD, each is its countyâ&#x20AC;&#x2122;s seat. In general, utilization of county seats for this study proved fortuitous. Their dispersal throughout the Basin provided a diverse sample set in terms of the varied environmental conditions that define geographic regions within the Basin. Additionally, they provided variation in terms of physical areas and population sizes (sections 4.1.2) and seemed to be good barometers of growth within the Basin. Taken as a whole, population growth of the sample set correlated quite positively

with population growth throughout the Basin. Perhaps more importantly for this inquiry was the fact that the present boundaries of each county seat were found to exist either on or adjacent to one or more streams. However, it should be noted that there exist certain concerns regarding the selection of county seats in terms of their ability to represent towns within the Basin. Principle in this is the idea that the seats may be more similar to each other than the many hundreds of other communities within the Basin â&#x20AC;&#x201C;a distinct possibility considering their important political and economic roles relative to surrounding communities. The numerous similarities observed in some of the collected data may also support this hypothesis. Additionally, the largely consistent placement of seats along streams of high order may be indicative of the importance of the water itself in their success and may indicate that other communities were not so fortunate in their position. In this last scenario, however, it should be noted that many county seats existed on lower order streams and that other communities within the same counties are located on larger ones. Greater historical insights are necessary to define the precise reasons why each community was chosen as the county seat. Addressing these concerns would likely require an examination of a certain number of non-county seats within the Basin. While this task has yet to be realized, the course of this study has led to the qualitative examination of numerous historic atlases and maps that contain numerous communities other than those described here. After examining the form of various streamfront communities, many were noted to be similar to those streamfront town typologies described here. Whether future analysis serves to bolster current findings or are a cause for their reconsideration, it can only serve to enhance the understanding of streamfront development and stream-community relationships.

5.1.2. A region of small towns

The Susquehanna River Basin is largely a rural region comprised of small towns and cities. While metropolitan regions may contain rather sizeable populations, municipal boundaries have generally remained small. This is counter to the situation often observed in the southern and western parts of the United States, where land around developing urban areas was more abundant and readily available for incorporation. In some instances, the physical environment within the Basin has played a large role in defining and limiting the expansion of towns. Despite their relatively small size, however, county seats have remained very important to the growth of their surrounding areas â&#x20AC;&#x201C;something exhibited by the positive correlations between growth rates that were seen with even the smallest county seats. Scranton, PA (Lackawanna County) for instance exhibited a nearly oneto-one positive correlation, t(11)=3.106, p<0.01, with its countyâ&#x20AC;&#x2122;s growth. Only a few towns exhibited negative growth rate correlations with their respective county. However, no such negative correlations were shown to be statistically significant. This finding might have been different if only the last half century of population data was considered. Many town populations would likely show signs of decline or a stagnant population, while communities outside of their boundaries exhibited growth. The previously noted inability of towns and cities to grow in terms of physical area likely has a negative impact on other factors related to their success. If towns that were once centers of industry and commerce are unable to retain existing populations or attract new ones, they face declining tax revenues, a limited resource pool, and large tracts of land that require economic repurposing (e.g. abandoned mine lands, industrial brown fields). Their role as the county seat enables them to remain relevant and retain a significant workforce, but many town employees might

not reside within the town itself. (In Pennsylvania, local taxes are paid to the borough or township in which one resides.) Though it is an oversimplified assessment of its present situation, Harrisburg (Dauphin County), Pennsylvaniaâ&#x20AC;&#x2122;s capital city, provides a recent example of urban decline that is surrounded by a relatively prosperous hinterland. While the move was opposed by both the mayor and governor, Harrisburgâ&#x20AC;&#x2122;s city council filed for bankruptcy in 2011.

5.2. Indicators of Growth

For practical purposes, population growth was utilized as the primary indicator of town growth. Though measures of physical area were considered, the creation of a historic boundaries database from which to derive town areas would have required an enormous effort. As previously noted, (section 4.2.1) this effort would have been made more challenging by limitations associated with finding, interpreting, and digitizing historic map documents. Population data, on the other hand, is far more accessible over the desired time periods. Unfortunately, the plausible scenario that population growth does not correspond directly with town growth was confirmed by the lack of correlation between the two factors and the positive correlation found between town area and population density (section 4.1.2). As previously noted (section 5.1.2), town area was likely limited by other factors. Despite this, population growth rates still provided an indicator of development within and around the towns, as well as social and economic changes (e.g. expansion of transportation infrastructure) that may have been the impetus for or are otherwise associated with accommodating such growth. 5.2.1. Transportation infrastructure and institutions of higher education

Many sources point to the positive influence of transportation infrastructure and higher education on town growth and development, and this analysis of the Susquehanna River Basin did not find evidence to the contrary. Both

the transportation and education scores correlated positively with each other, with population growth rates, and with numerous other variables (section 4.1). While the links between transportation infrastructure, higher education, and growth may seem fairly intuitive, their interrelationships are often complex. Transportation infrastructure was largely developed in response to needs in industry and commerce, but it also acted to catalyze growth in these sectors. Higher education acted similarly. For instance, Bellefonte, PA was incorporated as a borough nearly sixty (60) years prior to the founding of the nearby Agricultural College of Pennsylvania.51 Both rail and canal networks connected the borough to the West Branch Susquehanna River. Yet, while the surrounding areas (including the borough of State College and Centre County, PA) have grown significantly, the population of Bellefonte has declined. Additionally, two nearby interstate highways (I-99 and I-80) now provide easy access from the borough to the nearby university. With relatively little development surrounding Bellefonte, the borough has been able to extend its boundaries (figure 5-2), and low density residential development can been seen approaching the new interstate. In contrast, Harrisburg, PA is surrounded by the largest number of institutions of higher education in the sample set and has also been at the crossroads of a large number of transportation systems. While a few of these institutions of higher education (including its largest â&#x20AC;&#x201C;the Harrisburg Area Community College) are found within city boundaries, the vast majority are located outside of the city along major highways. As previously noted (5.1.2), Harrisburg struggles to sustain positive economic growth, while surroundings regions are relatively prosperous.

5 The college was chartered by the commonwealth of Pennsylvania in 1855 and constructed as the stateâ&#x20AC;&#x2122;s first and only land grant institution in 1863. The school would be known as The Pennsylvania State College until its final name change in 1953 when it came to be known as The Pennsylvania State University.

5.2.2. Initial town classification

In some ways, the initial town categorization based on the significance of correlations between town and county growth rates and the aggregate transportation and education scores appeared logical. For instance, some of the largest cities â&#x20AC;&#x201C;Scranton, PA; Wilkes-Barre, PA (Luzerne County); Harrisburg, PA; and Binghamton, NY (Broome County)â&#x20AC;&#x201C; were categorized together. At the opposite end of the spectrum, Lancaster, PA and York, PA also comprised their own group. Both are found on opposite sides of the Susquehanna River, in predominantly agricultural areas, and face challenges in regards to sprawling development in their counties. Many of the smallest county seats were also grouped together. While transportation and education scores became important factors in the final typological distinctions and this ordering system provided insights in that regard, it did not foster an understanding of the formative processes of town form nor of the relationship between town and stream. Ultimately, these classifications were discarded. 5.2.3. Natural resource score

The development of a natural resource score (R) was an attempt to examine the influence of one of the key underlying reasons for town development. After all, transportation infrastructure largely developed to move goods from areas of production to areas of distribution and, finally, to market. With a few exceptions, the resource score, as developed here, exhibited low correlation with most other variables (section 4.1.4).6 1 6 The smallest values for population density, town shape, area, and population size were associated with the early-stream-town typology (section 5.3). Interestingly, a negative correlation between the natural resource score and two of these variables (population density and shape) was observed. In that regard, it is worth noting that these towns are often located distal to major streams/rivers, other large towns, and toward the fringes of the Basin. In many instances, their reason for establishment was largely based on the extraction of a particular resource, and, in that regard, such a negative correlation (i.e. high resource scores) would seem logical.

It is unlikely that no correlation between natural resource development and town and/or county growth exist. In a region largely established to provide resources to cities such as Baltimore, Philadelphia, and New York, it is difficult to imagine that agricultural soils, coal and/or natural gas, and lumber would not have played some role in growth. It is more likely that no simple correlation exists and that this score was simply too broad or imprecise to represent the true complexity of such a variable. Derived values were based, essentially, on a yes-or-no system of measure that generally resulted in there being very little variation in resource scores between towns. After normalization, most scores for a particular resource were 0.00, 0.50, or 1.00. Accounting for the existence of agricultural soils proved to be the lone exception, because there was more variation in soil groups in and around towns. However, even this data could have been improved by applying an weight for area. Additionally, it is clear that not all of the regionâ&#x20AC;&#x2122;s prominent natural resources (e.g. iron, limestone) were accounted for. While ensuring that the majority of variation in resource extraction and production is accounted for may ultimately improve this score, it may not necessarily provide a complete solution. It is possible that, in some instances, an areaâ&#x20AC;&#x2122;s resource base may have been economically fruitful without being equally diverse. Relatively short-lived periods of resource exploitation were not uncommon to the Basin or any other region. Williamsport and Lock Haven were well regarded for and leaders in the processing of lumber; Wilkes-Barre for its anthracite coal; and Lancaster for its fertile agricultural fields. Though they bore the success associated with extraction of certain resources, many towns and cities today struggle to cope with diminishing returns. Ultimately, it is believed that the conceptualization of the resource score was, in and of itself, valuable. However, the means of quantification should

be reconsidered. Future analyses, for instance, might involve a more detailed investigation into the economic activity of the region and its towns. 5.2.4. Population growth rates

Having found that town and county growth rates within the Susquehanna River Basin correlate to such a high degree, one might presume that both measures would correlate equally with other variables. This turned out to not be the case. In particular, mean county growth rates correlate significantly with town area, population density, and shape measures; whereas, town growth rates do not. The disparities in these measures might be explained by previously noted concepts (sections 5.1.2 and 5.2.1) regarding limitations on physical town growth observed in the region. The significance of correlation between town growth rates and population density was relatively high but was still lower (p < 0.10) than that between population density and county growth rate (p < 0.02). To a certain extent, positive rates of both town and county growth would elicit increases in population density within the town. This, of course, would be attenuated by the ability of the town itself to grow in size, and, to a point, both town and county growth rates would positively influence town area. A rapidly increasing county population might also provide impetus for the town to spread into its surroundings, leading to a potential increase in the shape measure (see below). However, county growth would eventually cease to have influence on town area, as exhibited by its relatively low –though still significant– correlation with town area. Generally, measures of sprawl are complex and involve a number of variables that are tailored specifically to the form of a place. While the town shape measure was intended to consider sprawling form, it is truly only a measure of the compactness of form and should not be regarded as a definitive measure of sprawl (Tsai, 2005; Parent, Civco, & Angel, 2009). As previously noted, ‘shape’ correlated

with county growth rate and may indicate that regional growth induced the physical expansion of town form. Lancaster, PA, for instance, has the highest shape measure of any town in the sample set and a corresponding tendril-like form. Its physical area is also larger than average. Still, the town itself has one of the highest populations and the second highest population density (8216 per/sqmi, only slightly below that of York, PA at 8264 persons/sqmi) â&#x20AC;&#x201C; a trend that was observed throughout the Basin with the significant and positive correlation between shape and population density. Without considering greater context, these results are deceptive. From these measures alone, it would be difficult to conclude that a city such as Lancaster exhibits sprawl. Yet, it is important to remember that these results say nothing of the area surrounding the towns themselves. In the case of Lancaster, there is a lack of correlation between town and county growth rates, and it would be interesting to consider how the surrounding region fares in terms of its relationship between population density and the shape measure. Additionally, town boundaries appear to have been heavily influenced by topography of the more mountainous regions that dominate much of the Basin, and towns whose growth followed stream valleys should also exhibit higher shape measures than others (section 5.3). 5.2.5. Streams and floodplains

Hydrologic analysis considered four variables: stream order (S), flood frequency (f), proportion of town area within a floodplain (fldA), and the distance from a townâ&#x20AC;&#x2122;s center to nearby prominent streams (D). Overall, results seem to indicate that town development tended to favor locations along larger streams. Additionally, the flood risk indicators used here support the idea that, while flooding did not necessarily have a negative impact on town growth, it was not disregarded completely in terms of development. The importance of streams to development, in general, is bolstered by the positive relationship observed between stream order and population-related

variables (town growth rate and population density). Stream distance provided further indication of the influence of streams on town growth. A negative correlation between it and town growth rate indicates that towns proximal to these water sources grew more quickly than others. As noted for the early-stream-town typology (section 5.3.4), many of the smaller towns in the sample set are more distal from their streams. Other variables provide insight into why such stream-community relationships might exists. In many instances, streams represented the most efficient routes through various terrains, and it seems logical that transportation infrastructure would be positively influenced by proximity to streams. While this was a necessity for canal routes, major rail routes would also follow stream networks. For lack of eligible undeveloped land around towns, many modern highways also follow the low lying valleys created by streams. Even prior to major improvements in transportation infrastructure, this proximity to streams was necessitated by commercial and industrial development (section 2). Unfortunately, in many instances, these types of development have been detrimental to their adjacent streams and to the potential interactions between them and neighboring communities. The location of transportation infrastructure, thus, became an important consideration in the distinction between town typologies (section 5.3). As indicated by the regionâ&#x20AC;&#x2122;s long history of flooding, proximity to streams has also posed significant, albeit known, risks to streamfront towns. While large streams were important for growth-inducing infrastructure development, the larger streamfront towns also tend to have the largest proportion of area within a floodplain.71 Coinciding with the correlation between transportation infrastructure and stream distance, map analysis indicated that the much of this flood-prone development was in industrial or infrastructure use. Interestingly, while flood frequency did not influence most examined variables, towns with shorter flood return 7 Transportation infrastructure and the proportion of floodplain area exhibited a weak (p<0.20) correlation.

periods also tended to locate their court houses farther from the streams. Whether these locations were outside of floodplains was not determined, because not all floodplain maps were available. However, just as distance should not be taken as insurance against flood damage, proximity should not be utilized to indicate certainty of flood risk. Pennsylvaniaâ&#x20AC;&#x2122;s state capital complex in Harrisburg is located within site of the Susquehanna River. Yet, it has been built along a high point outside of the 200year floodplain (figure 5-2). While flood frequency and proportion of floodplain area did not seem to influence town growth rate, the impact of flooding on these towns should not be understated. Flooding occurs in various forms and is an annual occurrence in the Basin that causes hundreds of millions of dollars in damage annually (SRBC, 2006). Flood events such as those related to Tropical Storm Agnes (1972) and, most recently, Tropical Storm Lee (2011) provide prominent reminders of the potential negative impacts of flooding on streamfront development. In that regard, it is interesting to note the negative correlation between flood frequency for county seats and their respective county growth rates. This phenomenon may hint at the influence of the county seats on surrounding areas. Despite the inevitability of flooding, private industrial and commercial development within flood prone landscapes continued because of the potential for substantial returns on investment associated with those activities. As long as it was warranted economically, development would return after a flood event. However, this process would likely not have been instantaneous, and the time needed to reestablish these economic and political centers would have a negative impact on the rest of the county. This hypothesis could be tested by examining the growth rates of other non-county seats in relation to flood frequency. A decline in growth rate for these towns due to flooding would support the notion that the county seats played such an important role.

Of course, not all towns share in the problem of flooding equally â&#x20AC;&#x201C;a prominent consideration utilized in typological distinctions. In the Basin, topographic variation, it seems, plays a major role in this. For certain streamfront town typologies (section 5.3), their mountainous surroundings and higher levels of topographic variation likely contributed to the weak correlation (p<0.2) between shape and floodplain area â&#x20AC;&#x201C;something supported by examination of DEMs for towns and surrounding areas. As noted in section 5.2.3, topography likely constrained physical town growth, necessitating more streamfront and floodplain development. Lewistown (figure 5-2) provides a good example of this, especially when one considers the substantial flood damages that the town and surrounding areas have incurred over the last 200 years. Though incomplete, these hydrologic measures provide an interesting basis for future examinations. As described in section 3.2.2, sample sizes were limited by available data from existing USGS flood gages and from digital FEMAderived flood insurance rate map databases. The limited correspondence between these data sets likely contributed to the limited correlation between them and with other data sets. It is unlikely that stream gages will be implemented in less flood prone locations, and certain of these data points will never exist. In some instances, stream gages have only recently been implemented and simply require time for data collection. Though FEMA-derived 100- and 200-year floodplains encompass the NOAA-derived flood stages utilized for flood frequency calculations, adjusting the floodplain area data sets to correspond with those finer-grained flood stages should also increase accuracy of these results. The process for resampling floodplain areas based on specific flood stages is relatively straightforward and was conducted for one town (Lewistown) as part of a separate project (figure 5-1). Flow rates associated with flood stages can be converted to elevations above datum, and digital elevation models can

be utilized to create vector or raster files that represent total areas beneath these elevations. Again, the challenge here lies in the number of towns for which stream gage data is available or sufficient. It is possible, however, to expand these data sets by considering nearby town data, as was done here for Binghamton. For instance, Milesburg, PA (Centre County) is located at the confluence of Spring Creek and Bald Eagle Creek, just downstream of Bellefonte. Here, flood stage data for Bellefonte was not calculated because only the Milesburg gage was available. Stages for towns such as Binghamton were calculated due to the presence of both downstream and upstream gages. However, it is feasible that a method sufficient for determining flood stages from a single downstream or upstream gage could be contrived. Additionally, flood frequency calculations are based upon cumulative data that is impacted by infrastructure development and other factors such as climate change. In order to maintain their accuracy, it will be necessary to revisit these calculations periodically. Fortunately, USGS stream gages have a long history of federal funding and are likely continue to receive support due to their role in the regionâ&#x20AC;&#x2122;s Flood Forecast and Warning System (SRBC, 2007). As previously noted, certain townâ&#x20AC;&#x2122;s gages (e.g. Bloomsburg, PA) simply require more time to collect sufficient data points for analysis.

Lewistown, PA

below flood stage + 10’ + 20’ + 30’

Figure 5-1 Alternative method for calculating floodplain area Flood stages are associated with elevations above a certain datum and can be visualized in GIS software such as ArcMap (Esri). From this information, the total area falling within each flood stage can be determined. The elevations above (Lewistown, PA) were determined for a separate project and do no correspond with NOAA-derived flood stages.

5.2.6. Further quantitative measures

The possibilities for further analyses are innumerable, and many strategies for enhancement of the study have been noted throughout previous sections. One of the most notable of these is the desire to measure topographic variation. Understanding topography was imperative for typological distinctions (section 5.3), because it relates to town form and many of the flooding- or streamrelated measures. Quantifying topography can be accomplished utilizing GIS by measuring surface roughness. However, a large number of such measures exist and are largely dependent upon the specific questions being asked. Here, a measure was considered that would assess change from a base elevation or datum over a defined area around each town. The digital elevation models produced for Pennsylvania, for instance, were comprised of a total of sixty-four high resolution (2m) raster files that were stitched together and could be translated into an elevation database. From these elevation data points, a standard deviation from the datum could then be determined. Other measures were also considered and not previously noted. Wagon trails and turnpikes were some of the earliest forms of major transportation infrastructure in the region, and their influence could be considered in the transportation score. While providing insights into the development of specific towns and regions, it is unlikely that the addition of this variable would have a major impact on the transportation score. A measure of land use and land use change within floodplains would be interesting as well. However, such a variable could be highly complex and would likely require detailed investigations into historic resources. As has been noted, such investigations pose challenges regarding the availability and accessibility of data. The â&#x20AC;&#x2DC;stream distanceâ&#x20AC;&#x2122; measure could also be enhanced by utilizing â&#x20AC;&#x2DC;near

analysis.â&#x20AC;&#x2122; This would utilize GIS software to determine distance measures between selected feature classes such as those used to represent a town center and streams (3.2.3). Depending on the data sets created, it would be a relatively simple task to determine proximity from a town center to streams of a certain order or those exhibiting any number of hydrologic characteristics. 5.3. Streamfront Town Typologies

The analysis of quantified variables revealed many similarities amongst groups within the sample set and within the region as a whole, bolstering the notion that the Susquehanna River Basin may comprise a cohesive cultural region framed by its network of streams (see section 5.4). Despite their many similarities, sufficient differences were noted through the coupling of quantitative and qualitative analyses such that a total of five Susquehanna streamfront town typologies were described: frontage-towns; confluence-towns; bisected-towns; early-stream-towns; and annexedstream towns. These distinctions largely rested upon a few major variables (i.e. topographic and hydrologic variation). Conceptual diagrams for each typology (figure 5-2) outline possible scenarios for their formation, while town maps (figure 5-3) provide representative examples. 5.3.1. Frontage-towns

Though smaller tributaries are often present around them, frontagetowns are often found along higher order streams and are oriented toward their largest adjacent stream. Physical growth is generally less restricted by topography, because these towns are often found in larger, wider valleys. Despite this, town boundaries tend to track streams linearly, likely contributing to higher relative shape values. Unlike bisected-towns, town boundaries do not completely cross these large streams (figure 5-2). The precise reasons why town boundaries remained on only one side of these streams was not examined. However, it is thought that stream size may

Figure 5-2 Typological development diagrams Typologies were intended to be generalizable to towns throughout the Susquehanna River Basin. The above illustrations represent proposed formative processes for each and can be referenced to descriptions found in sections 5.3.1-5.3.5. For clarity, one example has been enlarged.

Figure 5-3 Representative town maps

Frontage-town: Harrisburg, PA Unlike other typologies, topography has generally been less restrictive on town form. Towns developed within close proximity to streams and tend to follow them linearly. This close relationship has also left waterfronts less impeded by heavier forms of development.

have played a role. In Harrisburg, for instance (figure 5-3), the Susquehanna River reaches nearly a mile wide, and the town was once a prominent ferry crossing â&#x20AC;&#x201C; hence its former name, Harris Ferry. Additionally, the impetus for development on the opposite bank may not have come until a later date. Mifflin, PA (Juniata County) is as a small town directly opposite of the county seat, Mifflintown, along the Juniata River. Once known as Patterson, the town was established in the 1840s in response to the passage of the Pennsylvania Railroad along the riverâ&#x20AC;&#x2122;s west bank. For a time, the town would even surpass its neighbor in terms of population. Though Juniata County was not established until 1831, Mifflintown was laid out in 1791, many decades before the railroad boom that elicited the incorporation of Patterson. Having close functional relationships with them, frontage-town centers can be found relatively near their streams. However, closer examination might reveal that important political infrastructure is likely located outside of the floodplains. Additionally, because towns were built within close proximity to the large streams, transportation infrastructure was often developed farther away from them. Despite this, frontage-towns often exhibit the highest transportation scores. Infrastructure tends to be located along historic or contemporary town boundaries or on the opposite side of stream. As was noted in the case of Mifflin and Mifflintown, this latter scenario may have served as an impetus for major new development. Due to relatively low flow conditions that are often associated with larger order streams, industrial and commercial development was also situated around smaller tributaries. This, coupled with the development of transportation infrastructure away from major streamfronts, has left fewer obstructions to and potentially more opportunities for the furtherance of stream-community interactions today.

5.3.2. Confluence-towns

As their name implies, confluence-towns sit at the confluence of two or more major streams that help establish physical boundaries or otherwise influence town form. Town orientation is not necessarily directed toward the largest of these streams â&#x20AC;&#x201C;important when considering the influence of local stream-community relationships on design and planning initiatives. Additionally, restrictions on the availability of flat land have led to the creation of dynamic town forms that follow terrain and hydrologic systems. With the exception of the coastal plain and piedmont, the majority of geographic regions within the Susquehanna River Basin are largely mountainous. Topography and hydrology are related through the impact of erosion on the creation of landscapes accessible to development. Towns are often situated where streams have carved out niches or valleys which were deemed more suitable than their surroundings for development. With one exception (Lewisburg, PA) all confluencetowns were also found in largely mountainous terrain and limited in growth by that terrain. Though their average flood frequencies were not significantly different than other typologies, the topography surrounding these towns makes this statistic deceptive. Intuitively, the restrictive terrain limits potential for development outside of floodplains. In fact, the towns with the highest flood frequencies and highest proportion of floodplain area are found in this group. They also provided the majority of the samples from which to calculate these measures. The placement of infrastructure along stream fronts is commonplace in confluence-towns. Especially in more mountainous regions, both large merging streams provided sufficient flow for various uses. Thus, even though transportation scores are on par with frontage-towns, the location of this infrastructure is often

Confluence-town: Lewistown, PA Found at the confluence of two or more major streams, town form has been largely limited by topography. Though flood risk is high, floodplains represent the largest flat areas and saw prodigious industrial and infrastructural development.

more proximal to adjacent streams. Depending on the nature of the development, distinct opportunities and constraints exist for present streamfront development. For instance, highway and/or interstate infrastructure may largely block access to the water and will require innovative design measures to overcome. Even if such measures exist, approval and funding for such projects may be difficult to come by. Alternatively, opportunities for reutilizing now out-of-service rail or canal corridors are more abundant, and many are being incorporated into streamside canal and rail trails for recreation. Abandoned industrial and commercial buildings and brownfields also provide redevelopment opportunities but must also take into account the high flood risk. 5.3.3. Bisected-towns

The form of bisected-towns is largely influenced by a single stream and unique in that a townâ&#x20AC;&#x2122;s boundaries overlap both stream banks. In some ways, bisected-towns might be thought of as a hybrid between frontage- and confluencetowns. Bisected-towns are often constrained by topography but differ from confluence-towns in that their stream valleys are often wider, allowing more room for development. Additionally, streams are generally found meandering through the center of the valleys, creating a bisected appearance. Wider valleys and the ability to develop on both sides of a stream create more linear stream frontage and produce less dynamic forms. Like frontage towns, town centers have developed in close proximity to streams. Unfortunately, only a single town provided flood frequency data. While this may imply a reduced flood risk, data for at least two towns was not collected for other reasons: the presence of a large dam installation (Clearfield, PA); or because only a downstream stream gage exists (Bellefonte, PA). Transportation and industrial infrastructure generally occur along one stream bank, providing a separation of land uses that parallels the development of towns on opposite streams banks of the frontage-towns.

Bisected-towns: Bellefonte, PA Bisection of town boundaries by a stream is a relatively rare occurrence in this sample set. With time and with sufficient room to expand, some towns have outgrown this form. Bisected-towns share opportunities and constraints with both frontage- and confluence-town types.

5.3.4. Early-stream-towns

Many towns in the Susquehanna River Basin sprouted in mountainous areas near headwater or lower order streams. Headwater streams in flatter areas of piedmont and coastal plain exist, but these were generally collocated with county seats. As exhibited by the lowest transportation scores, physical areas, and other measures of growth in the sample set, access to the towns has remained relatively limited. In some instances, town centers are proximal to streams and appear organized by the streams themselves. Other streams appear to have no influence on town organization. However, in a number of cases, existing or man-made features such as lakes have increased the visibility and influence of a town’s waterfront and provide opportunities for waterfront activities. Towns such as Laporte, PA (Sullivan County) and Cooperstown, NY (Otsego County) are well known for these water bodies and gain economic incentives from them via tourism. For early-stream-towns, flooding is generally not a major concern. While there is little data from which to draw this conclusion, the concept is supported by the sheer lack of available data related to flood plains and flood frequency, as well as the positive correlation between stream order and proportion of flood area. 5.3.5. Annexed-stream-towns

Only one county seat (Lancaster, PA) falls within the annexed-streamtown typology. Certainly, there are towns within the Susquehanna Basin with little or no association with nearby streams. In other instances, physical town growth has caused a figurative ‘annexation’ of the stream into town boundaries. The fact that so few county seats exhibit this status supports the important role of streams on development within the Basin. Without more samples, it is difficult to draw conclusions about annexed-

Early-stream-town: Laporte, PA Often the smallest and least populated towns of the sample set, early-stream-towns are associated with headwater streams. Recreational usage is likely to dominate community-stream relationships, especially where infrastructure projects (e.g. dams) exist.

stream-towns. However, it is hypothesized that towns of this type are likely found in less mountainous regions (e.g. piedmont or coastal plan) where development has not been greatly inhibited by topography that would restrict development nearer to floodplains. With an abundance of flat land, major rail networks would not have needed to follow streams and might be organized in a seemingly more haphazard manner. Industrial and commercial uses would still have likely developed near streams and may have created an incentive for the expansion of town boundaries toward them. Likely, flooding is not a major concern for the majority of the town. While flood frequency may be high due to the nature of the stream itself, the proportion of area within a floodplain would be low unless the annexation process was significant. 5.3.6. Flooding

Generally speaking, the problem of flooding is pervasive throughout the Susquehanna River Basin and was, in fact, one of the reasons for consideration of the entire Basin as a study area. Causes of flooding throughout the region tend to vary based on environmental factors, but those factors which cause much of the moderate and major flooding are often widespread (Shank, 1968; USGS, 1980). For this same reason, catastrophic flood events (e.g. Tropical Storm Agnes, 1972) are often witnessed over very large areas within the Basin. Thus, it was not entirely unexpected that variation in flood frequency was not significant between the floodprone typologies. Essentially, frontage-, confluence- and bisected-towns were found to carry a higher flood risk than that observed with either early-stream- or annexedstream-towns. This is likely due to their increased topographic variation, as well as their hydrologic characteristics (e.g. near major stream confluence points). These three typologies also appear to have closer ties to their streams and may represent a subgroup of streamfront towns.

Annexed-stream-town: Lancaster, PA Here, stream proximity is believed to be the product of physical town growth rather than initial placement. Thus, community-stream relationships may be weaker than in other town types. The fact that only a single representative sample was identified may indicate strong regional ties to streams.

While many flood-related variables correlated with other variables to a lesser degree than anticipated, it is believed that this was largely due to the lack of available data for many of the towns. However, in and of itself, this lack of data was informative to typological distinctions. Due to a lack of gaging stations, flood frequency could not be calculated for any of the early-stream-towns. This lack of stations implies that these towns are not areas of flood concern. On the other hand, all but two confluence-towns had stream gages. With towns increasingly considering the status of their streamcommunity relationships, floodplain analysis becomes increasingly important in design and planning. Flood frequency is widely considered the measure by which flood risk is determined. However, flood frequency generally emphasizes designing and planning for the largest and most severe floods. Additionally, relative to measures of floodplain use, flood frequency was found to be a poor historic indicator of examined variables related to town growth and development. While these low frequency severe flood events are clearly those that remain within the minds of residents, traditional infrastructural means of mitigating the impact of these events may not necessarily be the most beneficial to the communities moving forward. With towns beginning to reconsider their relationships with their streams, a balance must be struck between understanding and mitigating flood risk, land uses planning within and along floodplains, developing innovative design solutions, and responding to the desire to reconnect with these historically, culturally, and environmentally significant resources. Finally, it is also worth noting that flood frequencies for county seats within New York State showed a strong positive correlation, t(4)=2.776, p<0.05, with town growth rates (Appendix C). While the implications of these result were not considered here, it may be interesting to consider how flood hazard mitigation infrastructure may have acted as a positive driver of growth in flood-prone areas.

5.3.7. Potential Applications in Design & Planning

Communities within the Susquehanna River Basin are presently faced with a number of shared economic and environmental challenges. The threat of flooding is an annual one; economic restructuring after an era of deindustrialization is still occurring; and, for many towns, sprawling development has made it difficult to maintain a sufficient tax base. Additionally, most communities remain politically segregated but often lack the resources to confront these challenges individually. As a region of small towns and cities largely settled around streams and with limited or no room for growth, streamfronts and floodplains become an important aspect of town planning. Historically this land has represented an economic resource for the entire town, providing areas for transportation infrastructure, commercial and industrial development, and agriculture. Though not disregarded, the threat of flooding in these areas was counterbalanced by the potential for economic gain. Today, however, many of these types of land uses have been abandoned, leaving neglected landscapes that block the potential for interaction with these streams. In other instances, they have been replaced by modern land uses (e.g. highways, flood hazard mitigation infrastructure) intended to â&#x20AC;&#x2DC;protectâ&#x20AC;&#x2122; communities from streams and rivers but that also maintain a disconnect between the community and their streams. Though not specifically responding to the concerns of any one streamfront community, typological forms provide a generalizable set of information from which more fine-grained analyses and approaches to design and planning can branch. From the description of streamfront town typologies, a number of opportunities and constraints related to waterfront development can be derived. While many reference the physical form of these towns along their streamfront, others are based on less tangible facets of development such as demographic

changes. A number of these concepts were noted in the above typological descriptions (sections 5.3.1â&#x20AC;&#x201C;5.3.6) and figure 5-3. 5.4. Susquehanna River Basin Cultural Region

Existing cultural ties to the streams of the Susquehanna River Basin are a potentially powerful agent in many facets of regional redevelopment. While this work did not set out to definitively define such cultural ties, they were always implicit in the identification of the physical relationships utilized to define the regionâ&#x20AC;&#x2122;s streamfront town typologies. Ultimately, this analysis strengthened the notion that a cohesive cultural region framed by the streams of the Susquehanna River Basin might exist. Previous work in the realm of cultural geography identified cultural regions that overlap and surround the Basin (section 2). It is interesting to note that, in at least one such study, areas surrounding the Basin appeared to lack a cohesive cultural identity.81 The methods considered place names throughout the United States to identify vernacular regions and were more recently used to identify river towns along portions of the Mississippi (Rice & Urban, 2006). Unlike this latter study of Mississippi river towns, the role of streams was conspicuously omitted from the noted earlier studies. It is believed that many of the findings outlined in this study provide evidence that warrant a reexamination of the Susquehanna River Basin. Over 80% of communities within the Basin contain area within a floodplain, and this study revealed that all county court houses were located within approximately one mile of a body of water. The majority of county seats were also located near a stream of high order â&#x20AC;&#x201C;a factor shown to positively correlate with town growth rates and population densities. Towns next to larger streams were more likely to be larger and nearer to these streams relative to those towns near smaller ones. Additionally, floodplain, flood frequency, and historic map analyses began to 8 This should not be misconstrued as meaning that the area lacks culture.

point toward relationships between town development and flood risk. The fact that quantitative analyses revealed many similarities between towns is a testament to their shared history and, perhaps, cultural associations. It is interesting to note that some of the strongest distinctions between typologies came from differences in hydrologic characteristics. Thus, for their apparently closer relationships with water, the confluence-, frontage-, and bisected-town typologies appear more similar to each other than to the remaining typologies. While these correlations lend credence to the idea of the importance of water on a regional scale, they do not necessarily indicate the precise nature of stream-community relationships. In that regard, a greater understanding of local context is required. For instance, the major streams adjacent to a town might incorrectly be considered those which have been most influential in town development. However, in some instances,92 it appears that smaller streams were as or more important to physical town development and the development of a sense of place in the local community. Time spent by the author in Lewistown, PA and its adjacent communities revealed strong local ties to Kishacoquillas Creek and Jacks Creeks â&#x20AC;&#x201C;both tributaries of the more physically prominent Juniata River. A similar situation seems to exist with regard to Fishing Creek, as opposed to the Susquehanna River, in Bloomsburg, PA. Despite differences in the specific nature of community-stream relationships, the author believes that further research will show that streams have played a significant role in the development of the vast majority of towns within the Susquehanna River Basin. Perusal through historic county atlases quickly reveals numerous examples of towns that might be categorized in one of the named typologies. The true challenge will be to define the regionâ&#x20AC;&#x2122;s cultural relationships to water and to understand how certain physical interactions with streams have played 9 Especially, in confluence towns.

a role in the formation of these cultural ties. Methods for posing these questions are numerous, and, as noted above, such a study might include the consideration of a stream- or rivertown vernacular region within the Basin. It might also be beneficial to consider investigation into regional town aesthetics and aesthetic preferences of residents. Regardless of the methods, however, the streamfront typologies noted here can be a useful framework for both identifying similarities and understanding differences in stream-community relationships throughout the region.

6. CONCLUSIONS

While the Susquehanna River Basin is a region of great environmental and cultural diversity, many of its stories are inextricably linked to the streams that comprise the watershed. The Basin’s cities, towns, communities, and people share a history of nature and economy that has both impressed with feats of innovation and influence and has been burdened with sharp declines and hardships. As manifestations of culture and society, the region’s towns become the physical imprints of this heritage. In his study of the Pennsylvania Town, Zelinsky (1977, 128) noted that, of all human constructs, the town is “the most profusely charged with cultural signals…[providing] major clues to regional or national cultural identity.” It has been with this in mind that this investigation into regional patterns of development within the Susquehanna River Basin has proceeded. Intending to reveal connections between the development of town form and nearby streams, variables were identified and examined quantitatively for their relationships with town growth and with each other. Not surprisingly, transportation infrastructure and higher education –both regarded as having potentially strong influences on development patterns– correlated highly with regional growth in counties and their seats of government. The positive influence of the streams on growth was also shown. Unexpectedly, the examination of variables related to riverine flooding within the region bore less clear results. Despite the potential to bolster the thoroughness of data collection in this area, it was surprising that the well documented and chronic problem of flooding seems to have had relatively little historic influence in certain aspects of town planning. Qualitative analysis of maps and other historic documents resulted in the formation of an analytical framework that offered a solution to the interpretation of quantitative data. Ultimately, five streamfront town typologies were described: frontage-towns, confluence-towns, bisected-towns, early–stream-towns, and annexed–

stream-towns. Distinctions were made based on numerous factors but relied primarily on connections made between topography, hydrology, and the placement of various town features (e.g. courthouses) that influence present interactions with streams. Simultaneously, similarities found between typologies bolster the idea that the Basin’s communities share or have shared many facets of development and hints at the potential existence of a cultural region framed by the region’s extensive stream network. Though discussed only in a broad sense, the applications of typological descriptions toward design and planning are an important consideration, especially as many communities reconsider their stream-community relationships as a means of catalyzing broader revitalization efforts. That some communities are founding and actively participating in regional development programs that recognize mutual opportunities and challenges associated with their waterfronts only lends credibility to these efforts. Typological distinctions might provide insight into regional facets of culture and environment prior to implementing finer grained design solutions. They might also be utilized to consider the feasibility of previously implemented strategies in towns which share typological features. Finally, it is important to note that these typologies –like the towns from which they were derived– are neither intended to be completely distinct nor static. It is expected that, no matter the degree to which data is collected or analyzed, overlap and change will be inherent to this type of analysis. When one considers the large number of streamfront communities within the Basin, the interpretation of Susquehanna streamfront towns presented here was based upon what could be considered a relatively small –though broad– sample set. While thoroughness was paramount, many factors could not be examined in the time allotted, and many that were could not be examined to the desired extent. However, it is believed that the general premises under which typological distinctions were made are valid, and

further analyses will only serve to enhance an understanding of both historic and contemporary interfaces between town and stream in the Susquehanna River Basin.

7. REFERENCES

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Appendix B: Counties and county seats within the Susquehanna River Basin

County MD Harford NY Broome Chemung Chenango Cortland Ostego Steuben Tioga PA Bedford Blair Bradford Cameron Centre Clearfield Clinton

County Seat Havre de Grace * Binghamton Elmira Norwich Cortland Cooperstown Bath Owego Bedford Hollidaysburg Towanda Emporium Bellefonte Clearfield Lock Haven

Columbia Cumberland Dauphin Huntingdon Juniata Lackawanna Lancaster Lebanon Luzerne Lycoming Mifflin Montour Northumberland Perry Snyder Sullivan Susquehanna Tioga

Bloomsburg Carlisle Harrisburg Huntingdon Mifflintown Scranton Lancaster Lebanon Wilkes-Barre Williamsport Lewistown Danville Sunbury Bloomfield Middleburg Laporte Montrose Wellsboro

Union Wyoming York

Lewisburg Tunkhannock York

* Bel Air is the county seat for Harford County but falls largely outside of the Susquehanna River Basin and was not sampled in this study.

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Appendix C: Data disc

Contents

1. Masterâ&#x20AC;&#x2122;s project presentation poster (PDF) 2. ASLA 2012 Student Award submission (PDF) 3. Raw data and data tables (Excel spreadsheet) 4. Selected town charts (PDF) 5. Project digital copy (PDF)

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