Proceedings of the 2009 northeastern recreation research symposium

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Figure 3.—Examples of photographs used for scenic beauty assessment for point 16 (left is September, right is October). 2.4 Scenic Beauty Estimation Digital photographs of each study site were used to gather information on perceptions of scenic beauty. Photographs were taken from the same points with 360-degree coverage in September and October 2007; Figure 3 shows photographs taken from point 16 at the two diff erent times. Eighty-four photos were taken in September (early fall) of 2007, but due to ongoing construction work along the highway, only 64 photos were taken in October (late fall) 2007. Photographs taken at the sites were shown to 35 undergraduate students at West Virginia University. Research has shown that college students tend to make similar scenic beauty judgments as the general public (e.g., Daniel and Boster 1976, Buhyoff and Leuschner 1978, Anderson and Schroeder 1983). Th e students were asked to rank the scenic beauty of the photographs on a scale of 0 to 10, with 0 indicating the lowest scenic beauty and 10 indicating the highest. Th ey were told that the study was attempting to determine the public’s scenic perception of several landscapes. Participants were not told anything about the sites and the seasons in the photographs because past studies in the other disciplines have shown that people tend to rank familiar areas more highly than unfamiliar areas (Jackson et al. 1978, Hammitt 1979, Balling and Falk 1982). Research by Cook and Cable (1995) and Buhyoff et al. (1986) used a scenic beauty estimation method proposed by Daniel and Boster (1976). However, this study used the actual ratings assigned to the photographs to avoid further calculations and transformations of the scenic beauty indices. 2.5 Data Analysis Th e total area (in hectares) of all land cover types visible from each of the 16 points was calculated. Th e mean scenic beauty value was calculated for each of the points for September and October. T-tests were used to evaluate diff erences in the perceived scenic beauty of each point between early fall and late fall. Multiple linear regressions were used to model the relationship between the diff erent land cover types (explanatory variables) and scenic beauty (response variable) for each time period. 3.0 RESULTS AND DISCUSSION 3.1 Viewshed Calculation Point 9 had the largest viewshed area, followed by points 16, 11, 12, and 10, in that order (Table 1). Th ese were also the only points from which all seven land cover types were visible. Th e point with the smallest viewshed was point 3, followed by points 2, 1, and 4, in that order (Table 1). Figure 4 shows point 16’s GIS viewshed analysis with visible land cover types. 3.2 Scenic Beauty Estimation Th e average values of perceived scenic beauty ranged from 3.13 to 7.48 for September and from 3.98 to 7.93 for October. Th e average perceived scenic beauty values for all 16 points for the two time periods are presented in Figure 5. Except for points 4, 5, and 15, all points have higher values in October than in September. T-test results showed signifi cant diff erences between scenic beauty values for September and October (p-value
Table 1.—Viewshed results for all 16 points with qrea of visible land cover types in hectares Point Developed Agriculture Shrubland/ Woodland Forested Land Water Wetland Barren Land 1 3.60 0.09 0.00 245.73 0.00 0.00 0.00 249.42 2 0.18 0.09 0.00 236.72 0.00 0.00 0.00 236.99 3 0.00 0.18 0.00 228.89 0.00 0.00 0.00 229.07 4 0.00 0.18 0.00 260.40 0.00 0.00 0.09 260.67 5 0.00 0.00 0.00 343.96 0.00 0.36 0.00 344.32 6 0.00 0.00 0.00 708.10 0.00 0.18 0.00 708.28 7 0.00 19.63 3.24 879.36 0.00 0.09 0.00 902.32 8 0.00 11.71 13.06 3667.18 0.00 11.89 0.00 3703.84 9 3.69 1969.51 124.17 12,465.60 89.50 44.48 1.17 14,698.12 10 0.54 72.93 29.71 7298.44 0.81 3.51 1.71 7407.65 11 9.54 110.66 53.40 12,233.20 1.53 0.63 26.20 12,435.16 12 3.51 136.78 44.03 10,906.60 0.72 1.08 55.65 11,148.37 13 0.00 3.24 3.51 697.65 0.00 0.00 0.00 704.4 14 0.00 71.31 22.87 3914.08 0.18 5.49 0.27 4014.2 15 9.09 92.29 28.54 6323.63 0.00 1.44 6.75 6461.74 16 12.52 647.95 83.11 11,658.20 47.63 3.06 13.42 12,465.89 Figure 4.—GIS map showing viewshed from point 16. 3.3 Scenic Beauty Modeling Proceedings of the 2009 Northeastern Recreation Research Symposium GTR-NRS-P-66 Total Th e independent variables (i.e., the land cover types) considered for the regression had high levels of correlation. As a result, only the forest, agriculture, and nonvegetated land (combined barren land and developed area) variables were used. Tables 2 and 3 show the regression estimates of scenic beauty for each month. Th e regression for September was not signifi cant, but the regression for October was signifi cant at the 5 percent level of signifi cance, and the variables in the model explained 43 percent of the variation in scenic beauty values. Only the variable “forest” was signifi cant at the 5 percent level. Th e positive sign indicated that increasing the total forest area would increase the value of the scenic beauty in late fall. Furthermore, the negative sign of the nonvegetated areas (i.e., barren and developed) indicates an inverse relationship with scenic beauty values when other factors are held constant. Forest cover had a signifi cantly positive relationship with scenic beauty for October but not for September, implying that scenery in the area is more beautiful in October than in September. Although the area of land 184
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United States Department of Agricul
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PROCEEDINGS OF THE 2009 NORTHEASTER
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OUTDOOR RECREATION—TRAILS Experie
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GIS APPLICATIONS AND RECREATION RES
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diff erent points along the spectru
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Proceedings of the 2009 Northeaster
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amount of bare rock. Camel’s Hump
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Larson, M. 2004. A Passive Approach
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THE ROLE OF IMAGINATION IN EXPERIEN
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Table 1.—Special places surveys L
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For some people, being in their spe
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CLIMATE CHANGE AND RESOURCE PLANNIN
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large geographical area (Fig. 1). T
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Divers seen = 4.4 Figure 2.—Norm
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Table 4.—Most-specialized divers
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Sarah Young Human Dimensions of Mar
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3.0 RESULTS AND DISCUSSION Th e FRR
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Table 4.—Divers’ perception of
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YOUTH AND OUTDOOR RECREATION
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A qualitative research design was u
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containers, instead of plastic bags
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WALK, RIDE AND LEARN: STUDENTS’ D
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school. Because of the large sample
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Table 8.—Observation of built str
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“I’M TOO OLD TO GO OUTSIDE!”
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Mean Eco-Affinity Score 5.0 4.5 4.0
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children. In: Klenosky, David B., F
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Cherie LeBlanc Fisher U.S Forest Se
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Table 1.—Comparative statistics f
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did some fi shing himself at each s
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On the other hand, some personal kn
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Illinois Department of Natural Reso
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Consumer behavior researchers have
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50 percent of the households were d
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y respondents indicated that these
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a bivariate approach (instead of th
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HERITAGE TOURISM DEVELOPMENT IN RUR
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Short cruise season. Th e river fre
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Lynda J. Sperazza, Ph.D. Recreation
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2.3 Leisure Participation Values Re
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Dychtwald, K. 1999. Age power: How
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With all research, the research que
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comprehensive. Cross (2005) even ar
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Stillwater, OK: Oklahoma State Univ
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EXPERIENCE USE HISTORY AND ITS RELA
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Table 1.—Comparison of experience
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5.0 DISCUSSION As mentioned previou
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HUMAN DIMENSIONS OF WILDLIFE
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2.0 METHODS Data were collected at
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0 buses 4 buses 8 buses 12 buses Fi
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4.0 DISCUSSION Visitor surveys and
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LEISURE AND HEALTH
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Second, in a world where clean drin
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Table 1.—Importance of different
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Countries also need to be careful a
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Proceedings of the 2009 Northeaster
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Table 1.—Informant profi les Info
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do something and we might think it
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Th ese relational interactions enab
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EXPLORING ELEMENTS THAT INFLUENCE S
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trails, and asking others to stay o
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time/fi nances” in dune protectio
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In summary, this study explored the
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Jinhee Jun Texas A&M University jju
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Table 1.—Sample demographics Indi
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Grayson 1995). Th e fi t of this mo
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Stets, J.E.; Burke, P.J. 2003. A so
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Jiaying Lu Texas A& M University Ji
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Table 1.—Demographic and particip
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Page 149 and 150: Manzo, L.C. 2005. For better or wor
Page 151 and 152: NORMATIVE TOLERANCES FOR SCUBA DIVE
Page 153 and 154: neutral point. Bubbles at the top o
Page 155 and 156: Table 1.—Snorkeler evaluations of
Page 157 and 158: Davis, D.; Tisdell, C. 1995. Recrea
Page 159 and 160: William Valliere University of Verm
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Page 163 and 164: Manning, R. 2007. Parks and carryin
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Page 167 and 168: Figure 1.—Pedestrian walkway LOS
Page 169 and 170: Acceptability 4 3 2 1 0 -1 -2 -3 -4
Page 171 and 172: TOURISM TRENDS AND CHALLENGES
Page 173 and 174: the rankings of 12 attendance motiv
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Page 179 and 180: PLACE MEANINGS
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Page 199 and 200: EUH Place Satisfaction Figure 1.—
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Page 203 and 204: Jőreskog, K.G.; Sőrbom, D. 2004.
Page 205 and 206: OUTDOOR RECREATION II
Page 207 and 208: Th e primary objective of the propo
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Page 211 and 212: Rodney B. Warnick Tom Stevens Unive
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Page 221 and 222: RECREATION TRAILS IN MAINE AND NEW
Page 223 and 224: Table 2.—Average cross-sectional
Page 225 and 226: Table 5.—The percentage of stream
Page 227 and 228: Demrow, C.C. 2002. Maine trails man
Page 229 and 230: Turner, J.M. 2002. From woodcraft t
Page 231 and 232: Chad P. Dawson, Professor State Uni
Page 233 and 234: 80 70 60 50 40 30 20 10 0 Very diss
Page 235 and 236: Table 4.—The number of campers in
Page 237 and 238: Fuller, C.; Dawson, C. 1999. A comp
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Smee, A.; Brennan, M. 2000. Electro
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Table 1.—Characteristics of parti
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Table 3.—Results of discriminant
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5.0 CITATIONS Babbie, E. 2001. Th e
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Bernardita Silva Graduate Assistant
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Table 1.—Expenditures by 2008 Ame
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6.0 CITATIONS Della Bitta, A.; Lond
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the economic impact of travel, and
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the air show. Th e second most popu
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not displace other local spending.
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6.0 CITATIONS Adams, P.D.; Parmente
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MANAGEMENT AND USE OF PARK, RECREAT
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which suggests that diff erent type
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Table 2.—Between-groups ANOVA to
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forest settings (means = 4.4 and 4.
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LEISURE RESEARCH
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communities together, and a decline
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Table 2.—Life satisfaction scale
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onds within a social group is a mec
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“I JUST WANTED TO GET AWAY”: AN
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Th e importance of “getting away
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Gerlach, J. 1989. Spring Break at P
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www.nrs.fs.fed.us The U.S. Departme
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