Rediscovering Rural Appalachian Communities With Historical GIS 2010-Libre

southeastern geographer, 50(1) 2010: pp. 58–82

Rediscovering Rural Appalachian Communities

with Historical GIS

GEORGE TOWERS

Concord University

From the late 19th century until World War Two,

agrarian southern Appalachia was a patchwork

of small, close-knit farm communities. This his-

toric rural settlement pattern is locally recorded

in community case studies by ethnographers and

historical geographers but has not been mapped

systematically. This paper explores the hypothesis

that GIS analysis of historic topographic maps

adequately identifies the boundaries of bygone

southern Appalachian agricultural neighbor-

hoods. Using the ArcGIS cost allocation analysis

function, least cost regions are generated around

neighborhood nodes based on the energy cost of

foot travel relative to distance and slope. These

prospective agricultural neighborhoods closely

match ethnographers and historical geographers’

spatial descriptions. Mapping historic Appala-

chian agricultural neighborhoods provides an im-

portant basis for comparison with past and pres-

ent settlement patterns. The research method is

significant because it is easily replicated and may

be extended across southern Appalachia and the

past century.

Desde finales del siglo 19 hasta la Segunda

Guerra Mundial, el sur agrario de los Apalaches

era un mosaico de comunidades agrícolas pe-

queñas y muy unidas. Este patrón histórico de

asentamiento rural es registrado a nivel local en

estudios de etnógrafos y geógrafos históricos sobre

casos comunitarios, sinembargo no ha sido car-

tografiada de forma sistemática. Este trabajo ex-

plora la hipótesis de que el análisis mapas to-

pográficos históricos utilizando SIG identifica ad-

ecuadamente los límites pasados de los barrios

agrícolas del sur de los Apalaches. Utilizando la

función de análisis de asignación de costos de

ArcGIS, regiones de menor costo son generadas

alrededor de los nodos de los barrios, basadas en

el costo de la energía de viajes a pie con respecto a

distancia y pendiente. Estos vecindarios agrícolas

prospectos se asemejan a las descripciones espa-

ciales de los etnógrafos y los geógrafos históricos.

Cartografiar barrios agrícolas históricos en los

Apalaches provee una importante base para la

comparación de pasados y presentes patrones de

asentamiento. Este método de investigación es sig-

nificativo porque es fácilmente replicable y puede

ser empleado a través de los Apalaches del Sur y

del siglo pasado.

key words: historical GIS, Appalachia,

agricultural neighborhoods, topographic maps,

West Virginia, landscape, social history,

farming

introduction

This research assesses the hypothesis

that historical GIS (HGIS) may be used to

map an extinct and iconic American land-

scape: the southern Appalachian agricul-

tural neighborhoods of a century ago.

HGIS enables researchers to ask geograph-

ical questions of history and supports

its answers with maps and spatial analy-

Rediscovering Rural Appalachian Communities 59

sis. Over the last two decades, HGIS has

evolved from a research method to a well-

recognized interdisciplinary field of study

(Baker 2003; Colten et al. 2005; Gregory

and Healey 2007; Knowles 2008).

Occupying five or six square kilometers

each, agricultural neighborhoods of a few

dozen farm families ordered southern Ap-

palachia’s rural social landscape.

‘‘Preindustrial mountain society had

been based upon a system of small, in-

dependent family farms, clustered to-

gether in diffuse open-country

neighborhoods’’ (Eller 1982, p 194).

Neighborhoods, according to James S.

Brown, a leader in mid-20th century south-

ern Appalachian ethnography, are defined

by social solidarity, interdependence, and

a shared community of interests (1988).

Throughout the region, anthropologists

and sociologists reported that neighbor-

hood solidarity was cemented through

family ties and Protestant fundamentalism

while subsistence agriculture engendered

the neighborly interdependence that fos-

tered a community of interests (Pearsall

1959; Stephenson 1968; Kaplan 1971;

Beaver 1976; Photiadis 1980; Martin

1984). Ethnographers’ emphasis on social

organization led them to the label ‘‘kinship

neighborhoods.’’ The current research,

however, focuses on the cultural land-

scape and will instead use the term ‘‘agri-

cultural neighborhoods’’ to distinguish

this settlement pattern from other re-

gional rural communities like hamlets and

coal camps while retaining an emphasis on

local social integration.

Agricultural neighborhoods were a

passing phenomena, existing between the

Civil War and World War Two. Previously,

agricultural communities spread them-

selves over much more territory. For exam-

ple, early 19th century farm neighborhoods

in Tazewell County, Virginia of 25 to 30

households took up 25 to 65 square kilo-

meters (Mann 1995). By the late 1800s,

the labor demands of low technology sub-

sistence agriculture had sustained popula-

tion growth sufficient to crowd the coun-

tryside (Salstrom 1994; Billings and Blee

2000). Farms were subdivided among fam-

ily members. For instance, a re-visitation of

eastern Kentucky’s ‘‘Beech Creek’’ neigh-

borhood found that the three farms in the

area in 1850 had multiplied more than ten-

fold by 1942 (Billings and Blee 2000). Ag-

riculture also expanded to exhaust arable

land. Martin (1984) provides a case study

of this process in his description of Ken-

tucky farmers bringing the isolated Head

of Hollybush Hollow into agricultural pro-

duction in the early 1880s.

Coexisting with encroaching coal camps

in the first decades of the last century,

farm neighborhoods emptied out in the

1940s and 1950s. Farmers and their chil-

dren found factory jobs and the Great So-

ciety of the 1960s declared war on the ves-

tigial Appalachian culture of poverty (Eller

2008). A primarily residential presence—

rural sprawl—has since settled over the

old landscape of agricultural production.

Invaded, abandoned, and obscured, the

traditional agricultural neighborhood

has ‘‘disappeared from the map’’ (Howell

2003, p 122).

A case study of Summers County, West

Virginia assesses the hypothesis that HGIS

may identify the boundaries of historic

southern Appalachian agricultural neigh-

borhoods (see Figure 1). The primary data

for this study are century-old 1:62,500

scale U.S. Geological Survey (USGS) to-

pographic maps covering 15 minute quad-

Figure 1. Summers County, West Virginia and southern Appalachia.

Source for southern Appalachian boundaries: Salstrom 1994.


rangles of latitude and longitude. HGIS

methods are used to convert territory sur-

rounding neighborhood nodes—the coun-

try schools and hamlet centers shown on

the historic topographic maps—into po-

tential agricultural neighborhoods. HGIS

analysis compares the spatial arrange-

ment of the houses, country schools, and

churches in the prospective agricultural

neighborhoods with the consistent neigh-

borhood settlement patterns described in

ethnographic case studies made across

southern Appalachia. If corroborated by

evidence from ethnography and historical

quantitative data, the HGIS methodology

may be extended across the southern Ap-

palachian region. Subsequent research

may lead to construction of regional set-

tlement pattern datasets for comparative

temporal and spatial analysis.

study area

The Summers County portions of the

1912 Big Bend and Meadow Creek 15

minute USGS quadrangles serve as the

study area for historical and geographical

reasons (see Figure 2). Historically, these

quads were the first mapped in the Ap-

palachian plateau of southern West Vir-

ginia. They have been recently scanned

and georeferenced by the West Virginia

Department of Environmental Protection

and the West Virginia GIS Technical Cen-

ter (Dawson et al. 2007).

Dominating the rural landscape de-

picted on the Big Bend and Meadow Creek

maps were diversified family farms. While

corn occupied half of the cultivated acre-

age, farmers also grew wheat, oats, and

hay and tended vegetable gardens and

fruit trees. Livestock included milk cows,

hogs and sheep (Unrau 1996). The farm

population of 11,008 was 82 percent of

the county’s rural total and 84 percent of

rural dwellings were farmhouses. In For-

est Hill, Jumping Branch, and Pipestem,

the three rural southern magisterial dis-

tricts without the railroad and without siz-

able unincorporated villages, more than

90 percent of people lived on farms (U.S.

Census 1913).

HGIS allows local case studies to be in-

tegrated with regional scale investigation,

offering the opportunity to assess whether

Summers County is representative of late

19th and early 20th century agricultural

southern Appalachia. Cunfer (2005) pro-

vides an example of this approach by sup-

porting his localized longitudinal case

studies of farming practices on the Great

Plains with a region-wide HGIS dataset

derived from agricultural censuses. The

regional boundaries shown in Figure 1

have found general agreement among his-

torians of southern Appalachia (Salstrom

1994; Williams 2001) and are the basis for

a county level HGIS dataset developed

from decadal census data that ranges from

1880 through 1940 and speaks to farm

size and farm density. Variables include

the number of acres per farm, the number

of farms per square mile, the percent of

county land in farms, the growth rate of

farms, and the rate of change in average

farm size.

For each southern Appalachian county,

these variables were standardized with z

scores (see Tables 1 and 2). This resulted

in seven z scores for the farm size and the

two farm density measures and six z scores

for the two rate of change variables. The

absolute values of z scores in each cate-

gory were then averaged to create a single

comparative index of each county’s corre-

spondence to regional norms. By this mea-

Figure 2. Study Area: The Summers County portions of the 1912

Big Bend and Meadow Creek Quadrangles.


Table 1. Agriculture in Summers County and Appalachia, 1880–1940.

1880 1890 1900 1910 1920 1930 1940

Mean farm size (ha): Regional mean 72.1 61.5 47.4 41.7 40.5 37.2 32.8

Mean farm size (ha): Summers County 68.4 57.5 41.7 38.9 39.3 39.3 34.4

Mean farm size (ha): Summers’ Z score –0.13 –0.19 –0.32 –0.21 –0.09 0.13 0.11

Farms/sq. km: Regional mean 1.12 1.30 1.71 1.87 1.83 1.77 2.03

Farms/sq. km: Summers County 1.10 1.39 1.98 2.17 2.12 2.06 2.34

Farms/sq. km: Summers’ Z score –0.04 0.15 0.43 0.43 0.42 0.39 0.34

Pct. of land in farms: Regional mean 72 72 73 71 67 60 60

Pct. of land in farms: Summers County 75 80 83 84 83 81 80

Pct. of land in farms: Summers’ Z score 0.18 0.44 0.51 0.65 0.80 1.04 1.02

Source: University of Virginia, 2004.

Table 2. Percent Decadal Change in Agriculture in Summers County and Appalachia, 1880–1940.

1880–

1890

1890–

1900

1900–

1910

1910–

1920

1920–

1930

1930–

1940

Total farms: regional mean 17 33 12 –2 –2 17

Total farms: Summers County 26 43 9 –2 –3 13

Total farms: Summers’ Z score 0.53 0.46 –0.08 –0.02 –0.05 –0.18

Mean farm size: regional mean –13 –22 –10 –3 –8 –12

Mean farm size: Summers County –16 –27 –7 1 0 –12

Mean farm size: Summers’ Z score –0.20 –0.46 0.22 0.36 0.61 –0.01


sure, Summers County ranked first among

the 43 West Virginia counties in Appala-

chia and third among all 175 Appalachian

counties whose boundaries remained un-

changed during the study period (see

Table 3). The territorial prevalence of ag-

riculture and the trajectory of agricultural

change in Summers County fits the re-

gion’s historical experience very closely.

data

The principle data for this research are

the building locations shown on the two

1912 USGS maps. Figure 3 shows detail

on the Big Bend map. The identification of

agricultural neighborhoods is predicated

upon the likelihood that nearly all uniden-

tified buildings in rural areas are farmers’

residences.

This assumption may be assessed with

manuscript records from the 1910 U.S.

Census. In 1910, census takers organized

people’s responses by dwelling. Their led-

gers record not only the people in each

dwelling, but also whether the dwelling

was a farm. Therefore, farm populations

and farm houses are readily tabulated

in absolute and relative terms from the

manuscript census.

64 george towers

Table 3. Summers County’s Agricultural Representativeness in Appalachia and

West Virginia, 1880–1940.

Category

Summers County’s

average absolute Z

score, 1880–1940

Summers County’s

rank among 175

Appalachian

counties

Summers County’s

rank among 43

Appalachian

counties in West

Virginia

Average farm size 0.17 6th 1st

Farms per square kilometer 0.31 20th 4th

Pct. of land in farms 0.66 80th 18th

Pct. change in number of farms 0.22 4th 2nd

Pct. change in average farm size 0.31 8th 1st

Average among categories 0.34 3rd 1st


The six magisterial districts served as

census enumeration districts, inviting

comparison between census figures and

map counts. Incomplete overlap between

the 1912 USGS maps and district bound-

aries limits evaluation to the Greenbrier,

Green Sulphur, and Forest Hill districts.

The 1912 USGS maps cover the entire

Greenbrier district, 90 percent of Green

Sulphur, and 83 percent of Forest Hill.

Within the Greenbrier district, assessment

is confined to the unincorporated land

outside the Hinton and Avis city limits.

The tenth of Green Sulphur shown on the

Clintonville quad of 1921 was sparsely

populated: of the 681 structures mapped

in the district as a whole, 96 percent are

on the 1912 Meadow Creek map. The one-

sixth of the Forest Hill district mapped in

1932 contains 15 percent of the district’s

structures. Larger, more populated por-

tions of the other three districts were

mapped after 1912.

Correspondence between unidentified

mapped buildings and census dwellings is

mediated by complications. There are two

compelling reasons to expect that mapped

buildings will outnumber dwellings. First,

not all mapped buildings were occupied

dwellings. For example, vacant houses

were mapped but would not have been

tallied by census takers.

Second, undercount afflicted censuses

of the late 1800s and early 1900s. An oft-

cited estimate of undercount in the 1910

Census is 6.5 percent (Robinson 1988).

Whether undercount was higher in the

countryside or in the city is debated by his-

torians. Most suggest that rural areas,

home to fewer transients and immigrants,

were better reported (Parkerson 1991;

King and Magnuson 1995). Others, how-

ever, draw a finer distinction, asserting

that cities and rural areas were both under-

enumerated relative to small towns (Win-

kle 1991). As their supervisors warned,

census takers could easily miss secluded

rural homes set back from main roads

(King and Magnuson 1995). Omissions of

this type are documented where census

manuscripts can be matched with the 1912

USGS maps. For example, Green Sulphur

Figure 3. Detail view of the area around the Low Gap School on the 1912 Big Bend Quadrangle.

66 george towers

Table 4. Dwelling Estimates

District

Census Dwellings

(Adjusted for 6.5% Undercount)

Mapped Dwellings

(Adjusted for 4.7% Vacancy)

Forest Hill 310 304

Greenbrier 256 257

Green Sulphur 608 618

Total 1,174 1,179

Table 5. Farmhouse Estimates

District

Census Farm Houses

(Adjusted for 6.5% Undercount)

Estimated Mapped Farm Houses

(Adjusted for 4.7% Vacancy)

Forest Hill 284 280

Greenbrier 201 219

Green Sulphur 522 516

Total 1,007 1,014

enumerator James E. Hensley made un-

usually detailed entries. He listed dwell-

ings by roads named for creeks or moun-

tains, features that enable identification

of matching roads on the Meadow Creek

map. While using the map to assign build-

ings to these roads is inherently imprecise,

there were clearly more mapped buildings

than enumerated dwellings along these

roads (U.S. Census 1910).

Adjusting for vacancy and undercount

allows for comparison of census dwellings

with mapped buildings. Housing vacancy

rates were first recorded by the U.S. Census

in 1940. These late rates are serviceable for

1910, however, because agricultural

neighborhoods in West Virginia did not dis-

solve until after World War Two (Photiadis

1980). Indeed, the Great Depression had

pushed many West Virginians back to sub-

sistence farming (Armentrout 1941;

Thomas 1998) and in Summers County the

number of farms peaked in 1940 at 2,168.

Given the crowded countryside, the 1940

vacancy rate of 4.7 percent for Summers

County was probably relatively low and

makes for conservative estimates of pre-

vious vacancy. The USGS maps show 1,237

unidentified rural structures in the three

comparable districts. Applying the 4.7 per-

cent vacancy rate produces an estimate of

1,179 occupied rural dwellings.

The 1910 census counted 1,102 rural

dwellings in the districts. As there is unre-

solved disagreement whether rural under-

count was exceptional, I applied the gen-

eral undercount estimate of 6.5 percent

which increases the dwelling total to

1,174. Table 4 shows that the remarkable

match between the adjusted total figures

is replicated within each district.

These comparisons may be extended to

farmhouses. To estimate how many un-

identified buildings were farm houses, I

first excluded buildings clustered together

in villages from my calculations. I then


multiplied the remainder by 92 percent,

the proportion of farm dwellings recorded

by the Census in the entirely rural Forest

Hill, Jumping Branch, and Pipestem dis-

tricts. Making adjustments for undercount

and vacancy generates a close fit between

census figures and map counts (see Table

5). Based on the results of these rough

comparisons, census data supports the use

of building locations on historic USGS

maps to study rural settlement patterns.

methodology and analysis

HGIS methods allow for the analysis of

historical settlement patterns with precise

modern topographic data. For example,

anthropologists and geographers use to-

pographic HGIS to analyze site selection

within settlement systems (Gragson and

Bolstad 2007; Hunter 2009). Of particular

relevance to the current research, archeol-

ogists employ HGIS to model past peoples’

movement across their landscapes with

cost allocation analysis (Wheatley and

Gillings 2002; Conolly and Lake 2006).

Topographic cost allocation analysis deter-

mines the cost of travel given the relative

impediments presented by slope, eleva-

tion, and aspect. Cost allocation analysis

may suggest likely locations for prehistoric

pathways (Bell and Lock 2000), or, it may

assign least cost regions to destinations

based on the incurrence of travel cost. In

this case, destinations will be country

schools and hamlet centers and the result-

ing least cost regions will be prospective

agricultural neighborhoods.

The application of topographic HGIS is

predicated upon ethnographers’ shared

conclusion that mountainous terrain ex-

erted a powerful influence on community

formation.

‘‘Neighborhoods develop among peo-

ple who have frequent and regular

contacts, and in this region topography

has helped to determine social rela-

tionships of the inhabitants and to

form neighborhood groupings’’

(Brown 1988, p 52).

While proximity brought people together,

rugged land created boundaries between

neighborhoods. Brown reports that as of

1942 all travel across the mountains was

on foot or horseback. Consequently, steep

slopes minimized contact between adja-

cent neighborhoods separated by ridges

(Brown 1988). This generalization is sup-

ported by subsequent ethnography and

oral history. For instance, Howell writes

that Tennessee mountain neighborhoods

‘‘were defined largely by the drainage sys-

tem’’ (2003, p 111); and, Martin notes

that mountains ‘‘continued to separate’’

Hollybush Hollow from adjacent settle-

ments throughout its history (1984, p 3).

In Summers County, early 20th century

cross-country transportation was equally

primitive. For example, in 1906 the 30

mile trip between Hinton and Princeton,

the seat of neighboring Mercer County,

took 10 hours by horse and carriage. The

county’s notoriously poor dirt roads were

not substantially improved until the late

1940s (Cottle 1997).

ESRI’s ArcGIS cost allocation analysis

function recreates the defining role of to-

pography on agricultural neighborhoods.

The cost surface is represented by the 30

meter raster cells of the USGS National El-

evation Dataset (NED). Each of the mil-

lions of cells in the NED is assigned an ele-

vation which allows for the calculation of

the slope across neighboring cells. Travel

cost is a function of distance and the en-

68 george towers

ergy cost of walking at slope. For each des-

tination cell representing a country school

or a hamlet center, a region is generated

from the surrounding cells for which the

travel cost to that destination is the least.

Hypothetically, these least cost regions ap-

proximate the boundaries of the southern

Appalachian agricultural neighborhoods

of a hundred years ago.

Hamlet centers and country schools

are destinations. Hamlets offered essential

commercial and social services to the sur-

rounding countryside. For example, a typi-

cal early 20th Century hamlet of 100 peo-

ple may have had a post office, a church, a

grocery store, a feed store, a mill, and a

school (Hart 1975). Assuming an average

household size of five or six, a dozen or

two houses would have complemented the

handful of community and commercial

buildings. Hamlets were named on the

1912 USGS maps. A central point within

each of the study area’s 36 named ham-

lets was digitized with ArcGIS. Consistent

with the premise that these were small ser-

vice centers, 33 of the 36 hamlets had post

offices in 1912 and two of the other three

had post offices that were closed before

1912 (Helbock 2004).

Within the study area, the cluster of

some twenty structures at Green Sulphur

Springs is a representative hamlet. Farm

families from the surrounding area regu-

larly traveled to Green Sulphur Springs to

trade at the store and attend church (New-

comb 2008). Smaller hamlets in the study

area also served as central places and shift

size expectations downward. For example,

True, which lay at the confluence of the

Bluestone and New Rivers until it was

flooded by the construction of the Blue-

stone Dam in 1948, was a tiny hamlet of-

fering commercial services and river ac-

cess to communities up Pipestem Creek

and on adjacent Tallery Mountain. One

hundred years ago, True ran a kilometer

or two along the south bank of the Blue-

stone. Only five structures, however, in-

cluding a mill, store and post office,

formed the hamlet at the mouth of Pipe-

stem Creek. Four additional structures,

presumably farmhouses, were located

along the floodplain in the True vicin-

ity (Summers County Historical Society

1984; Sanders 1992). Similarly, Warford

was a New River hamlet comprised of a

four building cluster that included a coun-

try store, post office, and a blacksmith

shop (Sanders 1992). Like True, a half

dozen residences were scattered through

the surrounding neighborhood.

Country schools were community nodes

for the agricultural neighborhoods that

filled the countryside between hamlets.

Agricultural neighborhoods, as discussed

above, were small kinship-based commu-

nities. A representative example from the

study area is the River Ridge neighbor-

hood. River Ridge rises sharply between

Pipestem Creek and the New River. The

Lane, Keaton, Farley, and Pettrey families

established a tightly knit neighborhood on

the ridge in the early 1800s after valley

land was taken. The population pressure

representative of the region certainly ap-

plied to River Ridge: an early family of

Lanes included 15 children and a late 19th

century Keaton fathered a dozen with two

wives. Community buildings, Ridge School

(see Figure 4) and a log church, were

built in the 1870s in a central location

(Summers County Historical Society 1984;

Sanders 1992).

Peaking in the first decades of the last

century, country schools were an expedi-

ent means of providing mandated public


Figure 4. Ridge School.

education to rural residents in the pre-

auto era. In 1913, the year after the Big

Bend and Meadow Creek maps were pub-

lished, America’s 212,000 one room rural

schools enrolled more than half of the na-

tion’s schoolchildren (Gulliford 1996).

In West Virginia, the number of school-

houses more than doubled from 2,142 in

1880 to 4,819 in 1905 (Ambler 1951). In

Summers County, the number of country

schools grew from 16 in 1871 to 119 in

1890 to 160 by 1908 (Miller 1908).

Country schools were loci of functional

regions in two important ways. First, they

were locally administered. Upon state-

hood in 1863, West Virginia established

a highly localized hierarchy of country

school administration. From the county

scale, administrative space was divided

among the magisterial districts which

were in turn divided into school districts

containing a single country school (Fer-

guson 1950; Ambler 1951; Trent 1960).

In this way, each agricultural neighbor-

hood was formally recognized as a func-

tional region.

Second, country schools were central

locations for neighborhood activities and

symbolized neighborhood identity. As the

only public property belonging to the typi-

cal rural neighborhood, schools housed

not only classes but also a variety of com-

munity events including elections and

entertainment (Dunne 1977; Reynolds

1999). Neighboring focused on the school

and the school came to symbolize the com-

munity (DeYoung and Lawrence 1995;

Howell 2003). For example, James New

70 george towers

comb, who attended Summers County’s

Red Spring country school in the 1920s,

vividly recalls the pie suppers and cake-

walks that gathered his neighbors at

the school on Friday evenings (Newcomb

2008). In short, ‘‘The schools housed the

activities that joined people into a com-

munity, and the identity of rural com-

munities became inextricably linked with

their schools’’ (Gulliford 1996, p 35).

Consistent with their function as com-

munity centers and in order to minimize

children’s walk to class, schools were cen-

trally located within agricultural neigh-

borhoods. Anecdotally, Newcomb relates

that the Red Spring School was sited so

that community members were no more

than a mile (1.6 km) walk from the school

(2008). A 1929 West Virginia Department

of Education survey of 29 rural school dis-

tricts, including one in Summers County,

found that in almost two-thirds of the dis-

tricts, more than 70 percent of students

lived within 1.6 kilometers of their school.

Conversely, in 87 percent of the districts,

less than 20 percent of students lived more

than 2.4 kilometers from school (Holy

1929). Centrally located and regularly dis-

tributed, country school locations com-

prise a spatial catalog of functional nodes

required for the GIS analysis. The 74

schools located in the countryside away

from named places serve as potential

neighborhood nodes.

Country churches also organized agri-

cultural neighborhoods. Ethnographers

attest that neighborhood churches were

an important element of community or-

ganization (Stephenson 1968; Photiadis

1980). In some neighborhoods, congrega-

tions met in schoolhouses (Brown 1988);

in others, freestanding churches occu-

pied central community locations (Beaver

1976). Twenty-five churches appear on

the USGS maps in the study area. Of these,

22 are in hamlets or near a school and

were not considered as unique nodes.

Three churches, however, were alone

amidst linear settlement patterns along

streams and were included as destinations

in the cost allocation analysis.

Two assumptions derived from the

above discussion support the importance

given to walking at slope in constructing

the cost surface. First, since schools

were sited within walking distance of stu-

dents, people regularly walked to these

destinations. Second, following ethnog-

raphers’ reports, steep slopes bounded

communities. The cost surface recreates

neighborhood boundaries by attaching a

high energy cost to traversing steep slopes

on foot.

From NED elevation data, I generated a

raster layer measuring slope as the per-

centage of vertical rise over horizontal

run. To create a walking energy cost sur-

face from slope, I departed from archeolo-

gists’ convention of using a physics-based

trigonometrical formula (Bell and Lock

2000) and instead borrowed the following

experimentally-based equation from ap-

plied physiology that gauges the amount

of energy expended by walking relative

to the percentage of slope (Minetti et al.

2002):

Cwi = 280.5i 5 – 58.7i 4 – 76.8i 3 +

51.9i 2 + 19.6i + 2.5 (1)

where Cw is joule / (body weight in kilo-

grams * meters traveled) and i is percent

slope. As 4,184 joules equal one kilo-

calorie (kcal) and assuming an average

body weight of 60 kilograms, the follow-

ing equation converts this formula into a

surface of caloric expenditure:


kcal = (Cwi * 60 * meters traveled) /

4184 (2)

The cost surface was modified to allow the

study area’s three swift rivers to bound

neighborhoods. Sections of the Bluestone,

Greenbrier, and New that were mapped as

two dimensional features on the 1912 to-

pographic maps were digitized as poly-

gons interrupted at bridge locations. As-

signing the rivers an insurmountably high

value of 100,000 calories turned the river

polygons into neighborhood barriers.

Figures 3 and 5 show how the analy-

sis converts topography to least cost zones.

Figure 3 is the portion of the 1912 Big

Bend Quadrangle immediately surround-

ing a node, Low Gap School. Figure 5 over-

lays the semi-transparent cost surface

on the topographic map and shows the

boundaries (in white) of the Low Gap

School least cost zone. As darker shades

indicate greater cost, Figure 5 shows how

slopes impart higher travel cost and how

zone boundaries tend to follow high-cost

steep slopes. The overlay suggests that

Low Gap School, at a low spot, or ‘‘gap’’

along Wolf Creek Mountain (the letters

‘‘WOLF CR’’ are splined to follow the ridge-

line on the map), was the focal point for a

ridgetop agricultural neighborhood. The

cost allocation analysis generated an ini-

tial least cost rural zone around each of

the 111 country schools, hamlet centers,

and country churches that were digitized

as point features. Of these, 32 were trun-

cated by the study area boundaries and

were removed from further analysis. Of

the remaining 79, 24 are hamlets orga-

nized around hamlet centers; the 53 cen-

tered on country schools and the 2 based

on churches are presumed to be agricul-

tural neighborhoods.

results and discussion

The following discussion of these zones’

spatial qualities is based on the preceding

demonstration that agricultural neighbor-

hoods in Summers County are representa-

tive of those throughout southern Appala-

chia. As presented above, anthropologists

and sociologists found great commonal-

ity amongst agricultural neighborhoods

across the region and local histories of

communities like River Ridge match ex-

pectations from ethnography. The preced-

ing census data analysis provides quan-

titative evidence that Summers County

agriculture was emblematic of the region.

With the establishment of the study area’s

representativeness, the hypothesis that

HGIS analysis reveals the boundaries of

historic southern Appalachian agricultural

neighborhoods may be examined.

I compared the zones’ spatial charac-

teristics—building counts, geographic

size, and building density—with those re-

ported for southern Appalachian agri-

cultural neighborhoods by ethnographers

and historical geographers. Ethnogra-

phers’ estimates of residences per south-

ern Appalachian agricultural neighbor-

hood range from 11 to 60 (Pearsall 1959;

Matthews 1965; Stephenson 1968; Beaver

1976; Martin 1984). Mid-century ethnog-

raphy, however, did not involve formal car-

tography and ethnographers made only

passing notice of the spatiality of neigh-

borhood settlement patterns. On the other

hand, Wilhelm’s historical geography of

Virginia’s Shenandoah National Park is

unique for its attention to the spatial de-

tail of southern Appalachian agricultural

neighborhoods. Although Wilhelm’s work

was in the Blue Ridge physiographic prov-

ince instead of the Appalachian Plateau

Figure 5. Cost surface analysis and least cost region around the Low Gap School.


province that dominates the study area,

he is confident that ‘‘the geometric pat-

terns of settlement, much more difficult to

change [than other aspects of material

culture], became prototypes for the rest of

the Mountain South’’ (1978, p 206). His

meticulous diagrams indicate that neigh-

borhoods contained between 11 and 49

farms, closely corresponding to ethno-

graphic reports (Wilhelm 1978). For ex-

ample, Brown’s Beech Creek census of 164

people in 31 houses and Martin’s Holly-

bush Hollow count of 150 people living on

30 farms agree and are representative

(Martin 1984; Brown 1988).

The agricultural neighborhoods de-

rived from the HGIS analysis performed

here return representative building

counts. The 55 agricultural neighbor-

hoods averaged 17 structures and 43

neighborhoods (78 percent) were within

Wilhelm’s range of 11 to 49 homes. The

remaining twelve zones were smaller, con-

taining 10 or fewer buildings. A two-

structure zone that contained a single

structure and a schoolhouse was reallo-

cated to adjacent zones, leaving 54 agri-

cultural neighborhoods. The other 11

small zones contained 4 and 9 farmhouses

around a country school, enough for sev-

eral dozen relatives to form a kinship

neighborhood. Figure 6 displays the final

54 agricultural neighborhoods and 24

hamlets within the study area. The empty

buffer inside the study area and partially

surrounding these 78 shaded regions was

originally occupied by the 32 least cost

zones that crossed the study area bounda-

ries. Figure 6 also serves a reference map

showing the places named in the above

discussion.

The secondary literature suggests that

there should be little difference between

agricultural neighborhoods and hamlets

in absolute terms of structures and geo-

graphic size. Hamlets and their surround-

ing communities in the study area aver-

aged 20 structures. Large hamlets, like

Green Sulphur Springs, had around 50

structures within their vicinities and the

smallest, like True, had a half dozen.

Multiplication of Wilhelm’s range of 11

to 49 farmhouses per neighborhood by the

average 1910 Appalachian farm size of 42

hectares, suggests that agricultural neigh-

borhoods should have ranged in size from

462 to 2,058 hectares. By 1940, farms

averaged 33 hectares, lowering the range

to between 363 and 1,617 hectares. Gen-

erally consistent with these calcula-

tions, the 54 zones centered on schools

and churches averaged 606 hectares of po-

tential neighborhood area with a median

of 579 hectares and ranged from 250 to

1,445 hectares.

Unlike size measures, density ratios di-

rectly address the contrast between dis-

persed neighborhoods of farmsteads and

clustered hamlets. Historical researchers

concur that regardless of geomorphologi-

cal setting, farms were dispersed within

neighborhoods. In linear hollows, farm

houses spread about 800 meters apart

along streams (Wilhelm 1978; Brown

1988). In fan-shaped hollows, farmsteads

at headwaters and stream confluences

were 150 meters from one another. In

coves, several dwellings clustered at the

stream outlet and the rest were dispersed

around the basin’s periphery. Ridge settle-

ment was linear with about 150 meters

separating farmers’ residences (Wilhelm

1978). These observations establish a

range of 150 and 800 meters between

farms.

Calculating dispersion based on small

Figure 6. Agricultural neighborhoods and hamlets derived by HGIS analysis.


and large Summers County farm sizes

leads to nearly identical figures. From

1880 to 1930, about 90 percent of Sum-

mers County farms occupied between 8

and 202 hectares. Farmsteads centered

within evenly dispersed very small eight

hectare farms will be 160 meters apart;

those on 202 hectare farms will be 802

meters apart.

HGIS analysis converted these dis-

tances into four density categories. Two

are non-agricultural—‘‘commercial’’ and

‘‘vacant’’—and two correspond to farming

—‘‘general agricultural density’’ and ‘‘ar-

chetypal agricultural density.’’ Maximum

density expectations for agriculture derive

from a hypothetical area divided into very

small farms of eight hectares. A 40 hectare

search area centered on each raster cell

accommodates 5 very small farms. There-

fore, 6 or more structures within the

search area suggest that land use is ‘‘com-

mercial’’ and typical of a hamlet. The mini-

mum agricultural density is that of an

area exclusively occupied by very large

202 hectare farms with their farmsteads

spaced 800 meters apart. Land further

than 800 meters from a structure is not

likely to be farmland and is classified as

‘‘vacant.’’ Only 2 percent of the study area,

however, was this remote and two-thirds

of the least cost zones did not contain any

‘‘vacant’’ land.

I characterize land at ‘‘general agri-

cultural’’ density levels as follows: the min-

imum density is a single house within

800 meters; the maximum density is five

houses within the surrounding 40 hect-

ares. A narrower density sub-category, ‘‘ar-

chetypal agricultural,’’ corresponds to a

landscape of evenly spaced 40 hectare

farms, the average farm size in the county

from 1900 to 1930. Allowing for slightly

uneven spacing expands expectations by a

farmstead on either side, or an ‘‘archetypal

agricultural’’ density range from zero to

two farms within the search area.

Two expectations follow from the estab-

lishment of these density categories. First,

an overwhelming majority of the land

within zones assumed to be agricultural

neighborhoods should be at ‘‘archetypal

agricultural’’ densities. Second, even those

zones assumed to be hamlets should be

primarily farmland but should also contain

a relatively greater minority share of ‘‘com-

mercial’’ density. Remember that as in the

cases of Green Sulphur Springs, True, and

Warford described above, farms fringed

hamlets’ tiny commercial cores, leading to

the expectation that agricultural densities

predominated within hamlet zones.

Table 6 shows that in 61 percent of the

agricultural neighborhoods, at least 90

percent of land is at ‘‘archetypal agricul-

tural’’ densities. In more than 80 percent

of these zones, as shown in Table 7, there

is no ‘‘commercial’’ land. The second ex-

pectation finds support from Table 7 in

that 58 percent of the hamlets contain

‘‘commercial’’ land.

An equally meaningful measure is the

density surrounding the set of house loca-

tions within each zone. This metric in-

dicates whether houses are located in

agricultural settings. The above density

categories are adapted to this purpose

by simply subtracting one—the house

in question—from the number of houses

within the search area. Therefore, houses

situated amidst ‘‘archetypal agricultural’’

densities will have, at most, a single neigh-

bor within the search area and houses in

‘‘commercial’’ settings will have five or

more neighbors.

The expectations for this measure are

76 george towers

Table 6. Archetypal agricultural density.

Percent of archetypal agricultural land

39–49% 50–79% 80–89% 90–95% 96–100% Total

Agricultural neighborhoods, N, (%) 0

(0%)

7

(13%)

14

(26%)

20

(37%)

13

(24%)

54

(100%)

Hamlets, N, (%) 2

(8%)

6

(25%)

8

(33%)

7

(29%)

1

(4%)

24

(100%)

Table 7. Commercial density.

Percent of commercial land

0% 1–5% 6–15% 16–44% Total

Agricultural neighborhoods, N, (%) 44

(81%)

10

(19%)

0

(0%)

0

(0%)

54

(100%)

Hamlets, N, (%) 10

(42%)

6

(25%)

7

(29%)

1

(4%)

24

(100%)

straightforward: more houses in hamlets

should be in ‘‘commercial’’ settings and

more houses in agricultural neighbor-

hoods should be in areas of ‘‘archetypal

agricultural’’ density. These expectations

are borne out by a variety of calculations.

In agricultural neighborhoods, 2 of every

3 houses (596 of 903) are in ‘‘archetypal

agricultural’’ settings and only 1 in 50 (15

of 903) are in ‘‘commercial’’ areas. Forty-

nine of 54 agricultural neighborhoods (91

percent) do not contain any houses in

‘‘commercial’’ areas. In and around ham-

lets, houses in ‘‘archetypal agricultural’’

settings fall to 41 percent of the total while

those in ‘‘commercial’’ areas increase to 28

percent. Of the 793 houses in ‘‘archetypal

agricultural’’ settings, three-fourths are in

agricultural neighborhoods; of the 151

houses in ‘‘commercial’’ areas, nine-tenths

are in hamlets.

This consistency within zones in terms

of size and density is a function of the

even spacing of community nodes and the

uniformity of farmhouse density. The reg-

ularly dispersed pattern of community

nodes has less than a one percent like-

lihood of occurring randomly according

to nearest neighbor analysis. Farmhouse

density is also constant: 89 percent of the

land in the 78 zones is at ‘‘archetypal agri-

cultural’’ densities. Because both catego-

ries of point features are evenly spaced

across the landscape, zones are certain to

contain homogenous settlement patterns.

The equivalencies between farmhouse

density and zone sizes with those sug-

gested by census data and ethnographic

observations assure these patterns’ fidelity

to expectations from secondary sources. In

other words, the above analysis merely

translates the organizational logic of agri-


cultural neighborhood settlement patterns

into numerical terms.

conclusions

This research presents an HGIS meth-

odology that reliably locates the Appala-

chian agricultural neighborhoods of a cen-

tury ago. The least cost zones generated

by HGIS analysis of cultural features re-

corded on early topographic maps share

the spatial signature of the early 20th cen-

tury southern Appalachian agricultural

neighborhoods described by ethnogra-

phers and cultural geographers.

The methodology presented here is also

significant for its replicability. The primary

data sources, geo-referenced historic to-

pographic maps and modern topographic

coverages, are freely downloadable for

HGIS analysis. The principal analytical

method, cost allocation analysis, is a stan-

dard, transparent GIS tool that requires

only modest GIS proficiency.

Supported by the regional representa-

tiveness of the study area, the method may

be applied by scholars across the social

and environmental sciences to reconstruct

historic southern Appalachian rural social

space and extend our understanding of

the region’s historical geography and con-

temporary cultural landscape. For exam-

ple, in archeology the inventorying of his-

toric maps to analyze past settlement

patterns is a fundamental HGIS applica-

tion (Harris 2002; Armstrong et al. 2008).

As students of the southern Appalachian

countryside attest, once ubiquitous major

landscape artifacts like the log cabins and

company houses represented on old topo-

graphic maps are rapidly vanishing (Re-

hder 2004; DellaMea 2009). Archeolo-

gists may find this topographic HGIS

method useful as they search for and inte-

grate the remaining traces of material cul-

ture representing 19th century southern

Appalachian society.

Historical geographers might use this

research method to explore Francaviglia’s

observation that ‘‘one of the greatest vi-

sual contrasts in our culture occurs as one

crosses the line from agriculture to min-

ing’’ (1991, p 5). This passage resonates

with Figure 7, which shows structures on

a panel of contemporaneous USGS topo-

graphic maps. The coal camps around

Winona and those strung between Gentry

and Backus comprised the eastern flank of

Fayette County’s New River coalfield and

stand out from the surrounding farm-

lands. For southern Appalachia, the juxta-

position of these two landscapes is a dual-

ism that defines the region’s history. The

methodologies presented here invite in-

quiry not only into how the coalfield-

countryside boundary shifted over time

and space, but also may inform questions

about the complementarity of these settle-

ment patterns.

For historical sociologists and social

geographers, topographic HGIS analysis

might address the social and economic dif-

ferences long observed between valley and

ridge communities. Early on, environmen-

tal advantages found socioeconomic ex-

pression. Valleys offering access to water

and good farmland were settled first and

supported the region’s leading rural com-

munities (Wilhelm 1978). Ridge commu-

nities, physiographically denied these

amenities, were afflicted by the notorious

southern Appalachian ‘‘culture of poverty’’

(Weller 1965; Gallaher 1974). Determin-

ing whether the topography of socioeco-

Figure 7. Structures shown on the Big Bend, Meadow Creek, and Winona quadrangles.


nomic status persists or has been reversed

with rural sprawl as suggested in recent

Canadian research (Paquette and Domon

2001) will contribute to our understand-

ing of contemporary Appalachia.

Finally, for geographers, planners, and

landscape ecologists studying ‘‘rural

sprawl,’’ the low density settlement pattern

that encircles many small towns and flanks

rural roadways (Daniels 1999), topo-

graphic HGIS provides important context.

Like metropolitan sprawl, rural sprawl is

lamented for its encroachment on farm-

land and wilderness, its infrastructural de-

mands, and its centrifugal effects on com-

munity (Daniels 1999; Reeder et al. 2001).

While GIS-based assessment of sprawl’s

costs is a burgeoning research area, it is

typically made on the basis of relatively

recent changes (Hasse and Lathrop 2003;

Burchell et al. 2005; Wolman et al. 2005).

Topographic HGIS puts recent landscape

change in historical perspective, allowing

for a richer assessment of rural sprawl’s

environmental impact.

The digitally driven ‘‘democratization

of cartography’’ empowers diverse scholar-

ship with GIS (Slocum et al. 2009). Be-

yond the reconstruction of southern Ap-

palachian agricultural neighborhoods, the

goal of this study is to invite others to

put topographic HGIS to their research

purposes.

acknowledegment

The author thanks the editors and anony-

mous reviewers for their very helpful suggestions.

references

Ambler, C.H. 1951. A History of Education in

West Virginia: From Early Colonial Times to

1949. Huntington, WV: Standard Printing

and Publishing Company.

Armentrout, W.W. 1941. The Low-Income Farm

Situation in West Virginia As We Know It. In

Bulletin 299: Proceedings of the 1940

Conference on Low-Income Farms, 12–16.

Morgantown: Agricultural Experiment

Station, West Virginia University.

Armstrong, D.V., M.W. Hauser, D.W. Knight,

and S. Lenik. 2008. Maps, Matricals, and

Material Remains: An Archeological GIS of

Late-Eighteenth-Century Historic Sites on

St. John, Danish West Indies. In Archaeology

and Geoinformatics: Case Studies from

the Caribbean, ed. B.A. Reid, 99–126.

Tuscaloosa: University of Alabama Press.

Baker, A.R.H. 2003. Geography and History:

Bridging the Divide. Cambridge: Cambridge

University.

Beaver, P.D. 1976. Symbols and Social

Organization in an Appalachian Mountain

Community. Ph.D. Dissertation, Department

of Anthropology, Duke University.

Bell, T., and G. Lock. 2000. Topographic and

Cultural Influences on Walking the

Ridgeway in Later Prehistoric Times. In

Beyond the Map: Archaeology and Spatial

Technologies, ed. G. Lock, 85–100.

Amsterdam: IOS Press.

Billings, D.B., and Blee, K.M. 2000. The Road to

Poverty: The Making of Wealth and Hardship

in Appalachia. Cambridge: Cambridge

University Press.

Brown, J.S. 1988. Beech Creek: A Study of a

Kentucky Mountain Neighborhood. Berea,

KY: Berea College Press.

Burchell, R.W., Downs, A., McCann, B., and

Mukherji, S. 2005. Sprawl Costs: Economic

Impacts of Unchecked Development.

Washington: Island Press.

Colten, C.E., P.J. Hugill, T. Young, and K.M.

Morin. 2005. Historical Geography. In

Geography in America at the Dawn of the 21st

Century, eds. G.L. Gaille and C.J. Willmott,

149–163. Oxford: Oxford University.

80 george towers

Conolly, J., and LakeM. 2006. Geographical

Information Systems in Archaeology.

Cambridge: Cambridge University.

Cottle, R.K. 1997. Cemetery Siting in the

Bluestone Reservation Area, Summers

County, West Virginia: 1750–1997. M.S. Thesis,

Department of Geography, Virginia

Polytechnic Institute and State University.

Cunfer, G. 2005. On the Great Plains:

Agriculture and Environment. College

Station: Texas A&M University Press.

Daniels, T. 1999. What to Do About Rural

Sprawl? Paper presented at the American

Planning Association Conference, Seattle.

Dawson, A., Donaldson, K., Elmes, A., and

Gormont, J. 2007. WV Historical Geospatial

Products. Morgantown: West Virginia GIS

Technical Center.

DellaMea, C. 2009. Southern West Virginia

Coalfields. Accessed 31 July 2009 at http://

www.coalcampusa.com/sowv/index.html.

DeYoung, A.J., and Lawrence, B.K. 1995. On

Hoosiers, Yankees, and Mountaineers. Phi

Delta Kappan 77(2):104–112.

Dunn, D. 1988. Cades Cove: The Life and Death

of a Southern Appalachian Community,

1818–1937. Knoxville: University of

Tennessee Press.

Dunne, F. 1977. Choosing Smallness: An

Examination of the Small School

Experience in Rural America. In Education

in Rural America, ed. J.P, Sher, 81–124.

Boulder, CO: Westview Press.

Eller, R.D. 1982. Miners, Millhands, and

Mountaineers: Industrialization of the

Appalachian South, 1880–1930. Knoxville:

University of Tennessee Press.

———. 2008. Uneven Ground: Appalachia since

1945. Lexington: University Press of

Kentucky.

Ferguson, L.M. 1950. The Educational

Development of Mercer County. M.A. Thesis,

Department of Education, Marshall College.

Francaviglia, R.V. 1991. Hard Places: Reading

the Landscape of America’s Historic Mining

Districts. Iowa City: University of Iowa

Press.

Gallaher, A., Jr. 1974. The Community as a

Setting for Change in Southern Appalachia.

In Appalachia: Its People, Heritage, and

Problems, ed. F.S. Riddel, 291–304.

Dubuque, IA: Kendall/Hunt Publishing

Company.

Gragson, T.L., and Bolstad, P.V. 2007. A Local

Analysis of Early-Eighteenth-Century

Cherokee Settlement. Social Science History

31(3):435–468.

Gregory, I.N., and Healey, R.G. 2007. Historical

GIS: Structuring, Mapping and Analysing

Geographies of the Past. Progress in Human

Geography 31(5):638–653.

Gulliford, A. 1996. America’s Country Schools,

3rd Edition. Niwot, CO: University Press of

Colorado.

Harris, T.M. 2002. GIS in Archaeology. In Past

Time, Past Place: GIS for History, ed. A.K.

Knowles, 131–142. Redlands, CA: ESRI

Press.

Hart, J.F. 1975. The Look of the Land.

Englewood Cliffs, NJ: Prentice-Hall, Inc.

Hasse, J., and Lathrop, R.G. 2003. A Housing-

Unit-Level Approach to Characterizing

Residential Sprawl. Photogrammetric

Engineering & Remote Sensing 69(9):1021–

1030.

Helbock, R.W. 2004. United States Post Offices,

Volume VI—The Mid-Atlantic. Scappoose,

OR: La Posta Publications.

Holy, T.C. 1929. Survey of Education in West

Virginia, Volume III: School Buildings.

Charleston: West Virginia State Department

of Education.

Howell, B.J. 2003. Folklife along the Big South

Fork of the Cumberland River. Knoxville:

University of Tennessee Press.

Hunter, R.W. 2009. People, Sheep, and Landscape


Change in Colonial Mexico: The Sixteenth-

Century Transformation of the Valle Del

Mezquital. Ph.D. Dissertation, Department

of Geography and Anthropology, Louisiana

State University.

Kaplan, B.H. 1971. Blue Ridge: An Appalachian

Community in Transition. West Virginia

University Bulletin 71:(7/2).

King, M.L., and Magnuson, D.L. 1995.

Perspectives on Historical U.S. Census

Undercounts. Social Science History

19(4):455–466.

Knowles, A.K. 2008. GIS and History. In Placing

History: How Maps, Spatial Data, and GIS

Are Changing Historical Scholarship, eds.

A.K. Knowles and A. Hillier, 1–26.

Redlands, CA: ESRI Press.

Mann, R. 1995. Diversity in the Antebellum

Appalachian South: Four Farm Communities

in Tazewell County, Virginia. In Appalachia

in the Making: The Mountain South in the

Nineteenth Century, eds. M.B. Pudup, D.B.

Billings, and A.L. Waller, 132–162. Chapel

Hill: University of North Carolina Press.

Martin, C.E. 1984. Hollybush: Folk Building

and Social Change in an Appalachian

Community. Knoxville: University of

Tennessee Press.

Matthews, E.M. 1965. Neighbor and Kin: Life in

a Tennessee Ridge Community. Nashville:

Vanderbilt University Press.

Miller, J.H. 1908. History of Summers County,

West Virginia from the Earliest Settlement to

the Present Time. Hinton, WV: James H.

Miller.

Minetti, A.E., Moia, C., Roi, G.S., Susta, D., and

Ferretti, G. 2002. Energy Cost of Walking

and Running at Extreme Uphill and

Downhill Slopes. Journal of Applied

Physiology 93:1039–1046.

Newcomb, J.C. 2008. Interview with W.J.

Daniels and author. Lynco, WV,

December 30.

Paquette, S., and Domon, G. 2001. Trends in

Rural Landscape Development and

Sociodemographic Recomposition in

Southern Quebec (Canada). Landscape and

Urban Planning 55:215–238.

Parkerson, D.H. 1991. Comments on the

Underenumeration of the U.S. Census,

1850-1880. Social Science History

15(4):509–515.

Pearsall, M. 1959. Little Smoky Ridge: The

Natural History of a Southern Appalachian

Neighborhood. Birmingham: University of

Alabama Press.

Photiadis, J.D. 1980. The Changing Rural

Appalachian Community and Low-Income

Family: Implications for Community

Development. West Virginia University

Bulletin 80(9/7).

Reeder, R., Brown, D., and McReynolds, K.

2001. Rural Sprawl: Problems and Policies

in Eight Rural Counties. The Small City and

Regional Community: Proceedings of the

2000 Conference 14:199–206.

Rehder, J.B. 2004. Appalachian Folkways.

Baltimore: Johns Hopkins University Press.

Reynolds, D.R. 1999. There Goes the

Neighborhood: Rural School Consolidation at

the Grass Roots in Early Twentieth-Century

Iowa. Iowa City: University of Iowa Press.

Robinson, J.G. 1988. Perspectives on the

Completeness of Coverage of Population in

the United States Decennial Censuses.

Paper presented at the Annual Meeting of

the Population Association of America.

Salstrom, P. 1994. Appalachia’s Path to

Dependency: Rethinking a Region’s Economic

History, 1730–1940. Lexington: University

Press of Kentucky.

Sanders, W. 1992. A New River Heritage,

Volume II. Parsons, WV: McClain Printing

Company.

Slocum, T.A., R.B. McMaster, F.C. Kessler, and

H.H. Howard. 2009. Thematic Cartography

82 george towers

and Visualization, 3rd Edition. Upper Saddle

River, NJ: Pearson/Prentice Hall.

Stephenson, J.B. 1968. Shiloh: A Mountain

Community. Lexington: University of

Kentucky Press.

Summers County Historical Society. 1984. The

History of Summers County, West Virginia.

Marceline, MO: Walsworth Press.

Thomas, J.B. 1998. An Appalachian New Deal:

West Virginia in the Great Depression.

Lexington: University Press of Kentucky.

Trent, W.W. 1960. Mountaineer Education: A

Story of Education in West Virginia 1885–

1957. Charleston, WV: Jarrett Printing

Company.

United States Census. 1910. Thirteenth Census

of the United States, 1910—Population,

Microfilm Roll 1694 Pocahontas, Putnam

and Summers Counties, West Virginia.

Washington: U.S. Census.

United States Census. 1913. Thirteenth Census

of the United States: 1910—Volume 7:

Agriculture: Reports by States, Nebraska—

Wyoming, Alaska, Hawaii, Puerto Rico.

Washington: U.S. Census.

University of Virginia, Geospatial and

Statistical Data Center. 2004. Historical

Census Browser. http://fisher.lib.virginia

.edu/collections/stats/histcensus/index

.html.

Unrau, H.D. 1996. New River Gorge National

River, West Virginia: Special History

Study/Historic Context Study. Washington:

National Park Service, U.S. Department of

the Interior.

Weller, J.E. 1965. Yesterday’s People: Life in

Contemporary Appalachia. Lexington:

University of Kentucky Press.

Wheatley, D., and Gillings, M. 2002. Spatial

Technology and Archaeology: The

Archaeological Applications of GIS. New

York: CRC Press.

Wilhelm, G., Jr. 1978. Folk Settlements in the

Blue Ridge Mountains. Appalachian Journal

5(2):204–245.

Williams, J.A. 2001. Appalachia: A History.

Chapel Hill: University of North Carolina

Press.

Winkle, K. 1991. The U.S. Census as a Source in

Political History. Social Science History

15(4):565–577.

Wolman, H., Galster, G., Hanson, R.,

Ratcliffe, M., Furdell, K., and Sarzynski, A.

2005. The Fundamental Challenge in

Measuring Sprawl: Which Land Should Be

Considered? Professional Geographer

57(1):94–105.

george towers is a Professor of Geography

and Associate Academic Dean at Concord

University, Athens, WV 24712. Email:

[email protected]. His research interests

involve the human geography of Appalachia.

Documents

Rediscovering Rural Appalachian Communities With Historical GIS 2010-Libre