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Monitoring Visitation in Georgia State Parks Using the System for Observing Play and Recreation in Communities (SOPARC)

Volume 30, Number 4pp. 21-37

Journal of Park and Recreation AdministrationWinter 2012

Jason W. WhitingLincoln R. LarsonGary T. Green

EXECUTIVE SUMMARY: As new demographic groups (e.g., racial/ethnic minorities) increasingly make up larger percentages of the population, state park PDQDJHUV�DUH�VHHNLQJ�QHZ�ZD\V�WR�HI¿FLHQWO\�PRQLWRU�WKHVH�FKDQJHV�DQG�VXVWDLQ�park visitation. Within this context, research is needed to identify strategies that help state park managers assess visitor use in order to adapt their services to meet the needs of a more diverse clientele. The System for Observing Play and Recreation in Communities (SOPARC) is one standardized, observation-based data collection strategy that could help to accomplish these goals. Although SOPARC was originally developed as a physical activity surveillance tool for municipal park settings, its use as a general park use monitoring tool on broader scales remains relatively unexplored. Using three Georgia state parks as a case study in summer 2010, this project was designed to (a) measure state park use patterns among various demographic groups using SOPARC; (b) supplement the SOPARC state park visitation data with information collected during intercept and exit surveys; and (c) explore the overall reliability, validity, and utility of SOPARC as a visitor assessment tool relative to two more conventional data collection strategies (e.g., intercept surveys and exit surveys) in state park settings. The ultimate goal of the study was to examine the feasibility of 623$5&�DV�DQ�HI¿FLHQW�DQG�HIIHFWLYH�WRRO�IRU�PRQLWRULQJ�VWDWH�SDUN�YLVLWDWLRQ��Three distinct data collection methods were compared: SOPARC (n = 18,525 individuals observed), intercept surveys (5,192 people surveyed), and exit surveys (1,113 vehicles surveyed). Data from the three methods revealed VLJQL¿FDQW� GHPRJUDSKLF� GLIIHUHQFHV� LQ� YLVLWRU� XVH� SDWWHUQV�� 623$5&� ZDV�especially useful for highlighting temporal and spatial variation in high-density day use areas. Intercept and exit surveys more effectively captured variables such as activity preferences, group size, and total time in park. Reliability and validity analyses indicated that SOPARC observations yielded visitor count data that was statistically similar to counts generated via the more conventional survey methods. Findings suggest that SOPARC is an effective tool for gathering baseline state park visitor data on demographics and general site use patterns. The administration of SOPARC, conducted in isolation or in conjunction with intercept and exit surveys, represents a versatile monitoring and assessment tool

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The population of the United States is changing rapidly. The U.S. Census projections indicate that, by 2050, current racial/ethnic minority groups may make up a majority of the population (Ennis, Rios-Vargas, & Albert, 2011; U.S. Census Bureau, 2010). In fact, over that period, the Hispanic population is expected to double in size, accounting for more than 30% of the total population (Pew Research Center, 2012). As these sociodemographic shifts occur, patterns of state park use are changing (Stodolska, Shinew, & Li, 2010). Many state parks were originally constructed and managed for traditional White visitors, a group that had always represented a majority of the constituent population (Byrne & Wolch, 2009; Washburn, 1978). Today, however, park management models that were historically VXI¿FLHQW�IRU�RQH�UDFLDO�HWKQLF�JURXS�DUH�QRZ�GDWHG�DQG�LQVXI¿FLHQW�WR�PHHW�WKH�QHHGV�RI�DQ�ever-increasing culturally diverse population.

0DQ\�VWDWH�SDUN�PDQDJHUV��DZDUH�RI�WKHVH�WUHQGV��DUH�VHHNLQJ�QHZ�ZD\V�WR�HI¿FLHQWO\�monitor changes in park visitation and identify visitor characteristics and park use patterns (Georgia Department of Natural Resources, 2008; Virginia Department of Conservation and Recreation, 2007). For example, in seeking to improve methods of collecting data on state park visitation, the Massachusetts Department of Conservation and Recreation (MA '&5��UHOLHG�RQ�UHVHDUFK�H[DPLQLQJ�WKH�HI¿FDF\�RI�GDWD�FROOHFWLRQ�PHWKRGV�XVHG�E\�VWDWH�DQG�national park agencies throughout the United States (Bezies, Calvetti, & Poppa, 2011). The 0$�'&5�VWXG\�IRXQG�WKH�HI¿FDF\�RI�GDWD�FROOHFWLRQ�VWUDWHJLHV�GLIIHUHG�GHSHQGLQJ�RQ�WKH�park settings (e.g., parks with controlled vehicular access vs. controlled pedestrian access). After examining observational, survey, and exit count methods, the study ultimately called IRU�PRUH�UHVHDUFK�WR�H[SORUH�WKH�HI¿FDF\�RI�GLIIHUHQW�YLVLWRU�PRQLWRULQJ�DSSURDFKHV�LQ�VWDWH�parks.

The need for improved data collection methods is exacerbated by the growing ¿QDQFLDO�FRQVWUDLQWV�WKDW�VWDWH�SDUNV�DFURVV�WKH�QDWLRQ�DUH�FXUUHQWO\�H[SHULHQFLQJ��%XGJHWDU\�challenges have already resulted in loss of park staff; degradation of park equipment and facilities; and declines in visitor satisfaction, return rates, and perceived service quality (Esprit & Smith, 2011; Siikamäki, 2012). Consequently, managers are feeling pressured to FUHDWH�SDUNV�WKDW�JHQHUDWH�UHYHQXH�DQG�EHFRPH�PRUH�VHOI�VXI¿FLHQW��1DWLRQDO�&RQIHUHQFH�of State Legislatures, 2010). Furthermore, state park visitation is often used as a key factor in determining annual park budgets (Cessford & Muhar, 2003), highlighting the critical OLQN�EHWZHHQ�YLVLWRU�DVVHVVPHQW�DQG�¿VFDO�VXVWDLQDELOLW\��7KH�QHHG�IRU�YDOLG�DQG�UHOLDEOH�data to help managers understand changing park visitation patterns has therefore become a vital component in the decision-making process (Siikamäki, 2012).

As managers develop an enhanced understanding of visitation trends, they may begin to identify critical resources that can help to meet the needs of diverse visitors. Managers could also determine what changes could be made to existing resources to increase park YLVLWDWLRQ� DQG� UHFUHDWLRQ� SDUWLFLSDWLRQ� RI� DOO� SUHVHQW� RU� SRWHQWLDO� XVHUV�� ,GHQWL¿FDWLRQ� RI�XVHU�SDWWHUQV��KRZHYHU��LV�GLI¿FXOW�IRU�PDQDJHUV�RSHUDWLQJ�RQ�OLPLWHG�EXGJHWV�ZLWK�UHGXFHG�

that could prove to be valuable to state park managers. Given the challenging economic climate facing parks across the United States, SOPARC can provide D� VLPSOH�� FRVW�HIIHFWLYH�� DQG� WLPH�HI¿FLHQW� VROXWLRQ� WR� YLVLWRU� XVH� PRQLWRULQJ�problems for state park managers with limited resources. KEYWORDS: Race/ethnicity, SOPARC, survey, visitor use

AUTHORS: Jason W. Whiting is with the Department of Recreation Administration, California State University, Fresno, 5310 N. Campus Dr. M/S PHS 125, Fresno, CA 93740, phone: (801) 895-6960, Email: jawhiting@csufresno.edu. Lincoln R. Larson is with the Department of Natural Resources, Cornell University. Gary T. Green is with the Warnell School of Forestry and Natural Resources, University of Georgia.

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VWDII��+HQFH��WKHUH�LV�D�JURZLQJ�QHHG�IRU�HI¿FLHQW�DQG�HIIHFWLYH�PRQLWRULQJ�DSSURDFKHV�WKDW�characterize state park visitation patterns across temporal and spatial scales.

Conventional data-collection strategies in state parks have included intercept and H[LW� VXUYH\V� DQG� PHFKDQLFDOO\� UHFRUGHG� YHKLFOH� FRXQWHUV� �'DUF\�� *ULI¿Q�� &ULOOH\�� �Schweinsberg, 2010). However, these measures are often unreliable and, when conducted in isolation, provide extremely limited information to park managers (Eagles, 2002). For example, exit counts alone do not provide managers with information regarding visitors’ preferences in terms of activities or site use. Intercept and exit surveys, while more robust than exit counts, require trained staff to dedicate considerable time and effort to obtain visitor samples that yield meaningful results (Vaske, 2008). With reduced budgets, these conventional monitoring approaches become very problematic.

However, one approach to visitor monitoring yields particular promise: the System for Observing Play and Recreation in Communities (SOPARC). Developed by McKenzie, Cohen, and Sehgal (2005), SOPARC was designed to “obtain observational data on the number of participants and their physical activity levels during physical activity…in community environments” (McKenzie, Cohen, Sehgal, Williamson, & Golinelli, 2006, p. 210). When using SOPARC, trained observers scan a predetermined area and code environmental setting characteristics (location, supervision, equipment, and free play vs. organized activity) as well as the physical activity levels (sedentary, walking, and vigorous) and demographic characteristics (age, gender, and race/ethnicity) of individuals. Physical activity and recreation researchers have used SOPARC extensively because of its utility as a systematic protocol for objectively measuring associations between physical activity and the area characteristics in community parks (Bocarro et al., 2009; McKenzie et al., 2006; Parra et al., 2010; Shores & West, 2008). Since 2005, numerous studies have implemented this versatile system to measure the differences in park-based physical activity by age, gender, race/ethnicity, and the level or intensity of individuals’ physical activity in a variety of settings (e.g., sport/athletic facilities, urban parks, neighborhood parks, rural areas, and preschools; Cohen et al., 2007; Cohen et al., 2006; Floyd, Spengler, Maddock, Gobster, & Suau, 2008; Larson, Whiting, & Green, 2010).

Although SOPARC has been primarily employed as a physical activity surveillance tool, some researchers have recently recognized the possibility of using SOPARC for managerial purposes beyond understanding physical activity trends. For example, Cohen et al. (2011) suggested:

Data from SOPARC [may] provide an indication as to when and where marketing and park programming might be introduced to increase park use or to potentially shift some of the use from the busiest times (or locations) to less used times (or locations). (p. 1122)

These management-based implications, while not focused directly on physical activity participation, pose an unexplored venue for the application of SOPARC. However, the feasibility of implementing SOPARC on larger scales (i.e., state parks) and for broader purposes (i.e., demographic assessments) remains relatively unexplored.

While state parks represent a much smaller percentage of total parks in the United States, they account for roughly one third of all outdoor recreation in the United States in terms of hours of per capita nature recreation (Siikamäki, 2012). In fact, when compared to national parks, state parks contain 16% of the total land area yet account for nearly three times as many visits (about 730 million visits per year; Walls, 2009). Understanding visitor demographics, activity choices, and basic visitation patterns is critical for state park managers seeking to encourage outdoor recreation participation among its larger constituency. In this regard, SOPARC may serve as an effective tool providing baseline YLVLWRU�GDWD�EH\RQG�SK\VLFDO�DFWLYLW\�SDUWLFLSDWLRQ��4XHVWLRQV�UHJDUGLQJ�623$5&¶V�HI¿FDF\�and utility is therefore of interest to state park managers who want to know more about basic visitor trends and demographics.

The use of SOPARC in state parks may provide many advantages compared to traditional data collection strategies. For example, as an observational tool, SOPARC

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can be less intrusive to visitors than other data collection strategies. Although SOPARC is somewhat labor intensive, it is typically more affordable and less time consuming than other survey methods particularly because large amounts of basic visitation data can be gathered in shorter amounts of time by staff conducting routine visits to park areas (McKenzie et al., 2006). The implementation of SOPARC may also improve the HI¿FDF\�RI� SDUN�EDVHG�GDWD� FROOHFWLRQ� DV� VWDII� FDQ� EH� GXDOO\� WUDLQHG� DV� HPSOR\HHV�ZLWK�regular responsibilities and as SOPARC observers. This type of employee crossover could generate a participatory approach to research and monitoring that fosters ownership of park management and facilitates the collection of qualitative and quantitative data that helps staff to better understand who their visitors are and their expected preferences.

'HVSLWH� WKHVH� EHQH¿WV�� WKH� SRWHQWLDO� YDOXH� RI� 623$5&� LQ� VWDWH� SDUN� PRQLWRULQJ�has not been extensively investigated. Furthermore, for a visitor monitoring strategy to function properly, it must yield information that is both reliable and valid (Vaske, 2008). In fact, reliability and validity are essential elements of monitoring efforts and should always be considered when attempting to evaluate data collection strategies.

This research used a case study of Georgia state parks to address three primary objectives, which were to (a) measure state park use patterns among various demographic groups using SOPARC; (b) supplement the SOPARC state park visitation data with information collected during intercept and exit surveys; and (c) explore the overall reliability, validity, and utility of SOPARC as a visitor assessment tool relative to the two more conventional data collection strategies (e.g., intercept surveys and exit surveys) in state park settings.

Method

Research SettingThree state parks in northern Georgia (Fort Mountain, Fort Yargo, and Red Top

Mountain) were selected for this study. All three parks are located within 75 miles of metro Atlanta. These sites were selected for two reasons. First, their physical characteristics and amenities were representative of many other state parks in the southern region. For example, these parks included facilities that offered an assortment of land- and water-based recreation activities such as picnicking, swimming, camping, hiking, cycling, and boating. Playgrounds and mini-golf courses were also available at each park, and all three parks contained historic sites with associated heritage interpretation facilities. Second, these parks featured high annual visitation rates and anecdotal reports from managers and DGPLQLVWUDWLYH�RI¿FLDOV�RI�KLJK�UDFLDO��HWKQLF��DQG�FXOWXUDO�GLYHUVLW\�DPRQJ�YLVLWRUV��%DVHG�on these characteristics, the three focal parks provided an ideal context to explore the HI¿FDF\�RI�623$5&�DV�D�VWDWH�SDUN�YLVLWRU�PRQLWRULQJ�WRRO���

Data Collection and InstrumentsThree data-collection strategies were used concurrently to record the recreational

participation and demographic characteristics of diverse park visitors. The SOPARC represented an innovative, objective, observation-based evaluation tool that could be employed in multiple park settings. Two more conventional data collection techniques, intercept and exit surveys, complemented SOPARC and provided a mechanism for comparing baseline visitor data.

A pilot test was conducted between Memorial Day and Labor Day in 2009. The purpose of the pilot test was to (a) evaluate the feasibility of data collection procedures DQG�SURWRFROV�DQG��E��WHVW�WKH�HI¿FDF\�RI�VXUYH\�LQVWUXPHQWV�DQG�FRGLQJ�VFKHPHV��'LOOPDQ��Smyth, & Christian, 2009). During this phase, researchers conducted preliminary SOPARC observations (N = 2,281 individuals observed in 23 sessions), intercept surveys (N = 805 surveys collected), and exit surveys (N = 189 vehicles surveyed) across all three focal parks.

During the summer of 2010 (late May to early September), more comprehensive GDWD�ZHUH�FROOHFWHG�XVLQJ�D�VDPSOLQJ�FDOHQGDU� WR�REWDLQ�D�VWUDWL¿HG�UDQGRP�VDPSOH� WKDW�

25

accounted for temporal and spatial variation in visitor activity. The calendar considered all available days and hours during the collection period by organizing four different categories: weekdays, Wednesdays (this was the only free admission day at Georgia state parks), weekend days, and holiday weekends (Memorial Day, Independence Day, and Labor Day). Then, parks were randomly assigned a priori to each category to ensure that researchers visited each park on at least three weekdays, at least two Wednesdays, at least six weekend days, and at least one holiday weekend. Extra trips were added near the end of WKH�VXPPHU�WR�PDNH�XS�IRU�GH¿FLWV�LQ�DQ\�FDWHJRU\�UHVXOWLQJ�IURP�XQIRUHVHHQ�VFKHGXOLQJ�FRQÀLFWV��$OWKRXJK� WKLV� VWUDWL¿FDWLRQ� V\VWHP� HQDEOHG� UHVHDUFKHUV� WR�PD[LPL]H� FRYHUDJH�across temporal and spatial scales, time constraints and travel-related challenges did not DOORZ� IRU� FRPSOHWH�FRYHUDJH�RI� DOO�GD\V�DW� HYHU\�SDUN� ORFDWLRQ��6SHFL¿F�GDWD�FROOHFWLRQ�methods and instruments are described in more detail below.

SOPARC. Prior to beginning data collection, authors consulted researchers with SOPARC implementation experience (M. Floyd, personal communication, February 2010; J. Bocarro, personal communication, March 2010). The SOPARC protocol and procedures manual developed by McKenzie and Cohen (2006) was also reviewed. Based on issues encountered in the pilot test (e.g., high visitor density, unique environmental variables such as beaches and trails, and elevated risk of double-counting individuals), the standard 623$5&�SURWRFROV�ZHUH�VOLJKWO\�PRGL¿HG�IRU�XVH�LQ�WKH�VWDWH�SDUN�VHWWLQJ��

)RU�H[DPSOH��623$5&�SDWK�FRGLQJ�IRUPV�ZHUH�FXVWRPL]HG�WR�UHÀHFW�WKH�XQLTXH�QDWXUH�of target zones and park activities found in state parks selected for the study. Coding forms similar to the Path Coding Form (McKenzie & Cohen, 2006, p. 13) were also used in place of observation zone counters in an effort to account for the high volume of visitors and systematically document the activity and demographic characteristics of each individual in WKH�WDUJHW�]RQH�DW�WKH�PRPHQW�KH�VKH�ZDV�REVHUYHG��2WKHU�PRGL¿FDWLRQV�LQFOXGHG�VORZO\�ZDONLQJ�WKH�OHQJWK�RI�D�WDUJHW�DUHD�LQVWHDG�RI�VWDQGLQJ�LQ�D�¿[HG�ORFDWLRQ�ZKLOH�FRQGXFWLQJ�observational scans. These changes were necessary due to the nature of highly crowded VWDWH�SDUN�WDUJHW�DUHDV��RIWHQ�FRQWDLQLQJ������SDUN�YLVLWRUV��WKDW�ZHUH�GLI¿FXOW�WR�GLYLGH�LQWR�subtarget areas, and the increased observer mobility reduced the risk of double-counting individuals in each zone.

7ZR�W\SHV�RI�WDUJHW�DUHDV�ZHUH�LGHQWL¿HG�GXULQJ�REVHUYDWLRQV��EHDFKHV�ZLWK�DGMDFHQW�picnic areas and trailheads. Pilot test exit survey data showed these areas were used by a majority of state park day users. Target areas in each park were then measured and mapped XVLQJ�VSHFL¿F�ODQG�PDUNHUV��H�J���IHQFH�OLQHV��ERGLHV�RI�ZDWHU��WUHHV��DQG�SHUPDQHQW�SLFQLF�tables) to establish observational boundaries. Observational scans were scheduled during four time intervals throughout the day (morning, 7:00 a.m. to 11:59 a.m.; early afternoon, 12:00 p.m. to 2:59 p.m.; late afternoon, 3:00 p.m. to 5:59 p.m.; and evening, 6:00 p.m. to 9:00 p.m.). Following the establishment of the SOPARC protocol, two observers completed multiple training sessions before the commencement of the pilot study. During WKHVH�WUDLQLQJ�VHVVLRQV��REVHUYHUV�EHFDPH�HI¿FLHQW�LQ�XVLQJ�REVHUYDWLRQDO�PDWHULDOV��H�J���synchronized wristwatches, clipboards, SOPARC recording forms, maps of target areas, and pencils) and in discriminating among various demographic characteristics of individual SDUN� XVHUV��2EVHUYHUV� DOVR� EHFDPH� IDPLOLDU�ZLWK� WKH� RSHUDWLRQDO� GH¿QLWLRQV� DQG� FRGLQJ�conventions associated with SOPARC administration.

In implementing SOPARC, trained observers used an approach adapted from McKenzie et al. (2006) that systematically scanned a target area (i.e., a mobile observation sweep moving from left to right) and recorded the gender, age (child, teen, adult, senior), race/ethnicity (White/Caucasian, Black/African American, Hispanic/Latino, Other), and physical activity level (sedentary, moderate, vigorous) of individuals in the target area. If visitors were physically active, the type of activity was also recorded. The results of SOPARC observations provided observers with a snapshot of visitor use patterns occurring in designated areas during a particular moment in time. Because this paper focuses VSHFL¿FDOO\�RQ�YLVLWRU�GHPRJUDSKLFV��WKH�SK\VLFDO�DFWLYLW\�FRGHV��D�FULWLFDO�FRPSRQHQW�RI�the original SOPARC protocol) are not discussed in depth here.

26

7KURXJKRXW�WKH�623$5&�¿HOG�VHVVLRQV��UHOLDELOLW\�GDWD�ZHUH�FROOHFWHG�E\�DGKHULQJ�WR�the protocol established by McKenzie et al. (2006) using interobserver agreements for the contextual variables of age, race/ethnicity, and gender. During these sessions, observers simultaneously scanned target areas to test independent judgments of the coding variables. 5HVXOWV�ZHUH�WKHQ�FRPSDUHG�EHWZHHQ�REVHUYHUV�WR�¿QH�WXQH�DQG�LQFUHDVH�WKH�FRQVLVWHQF\�of recorded observations and coding. Intercoder reliability was assessed by examining multiple, joint, and separate coding session results.

Intercept surveys. Self-administered intercept surveys available in English and Spanish were conducted in the SOPARC target areas and nearby campgrounds (n = 5,192 surveys collected across 115 sessions). In these areas, bilingual researchers approached every third park visitor aged 18 or older and inquired if they would be willing to complete a brief survey about state park use. Visitors were presented the survey with a clipboard and pencil and were told the researcher would return within approximately 15 minutes to collect the completed survey. When collecting surveys, researchers thanked visitors for their time and answered any questions regarding survey content. Across all parks and day use areas, researchers collected an average of 25 surveys per hour. The intercept survey response rate across all state parks and target areas was 91.5%.

The surveys contained questions about visitors’ participation during their visit and their sociodemographics. Visitors were also asked an array of physical activity-related questions adapted from existing instruments (e.g., Walker et al., 2009; Centers for Disease Control and Prevention, 2009), but physical activity results are not discussed in this paper.

Exit surveys. Exit surveys were used because they allow for monitoring of all park visitors leaving through the primary exit points (i.e., not just visitors in target areas), while obtaining more detailed information about visitors’ length of stay and activity choices than mechanical vehicle counters (English, Kocis, Zarnoch, & Arnold, 2001). Exit surveys were conducted at the primary exit points in each park. Although two parks (Fort Yargo and 5HG�7RS�0RXQWDLQ��FRQWDLQHG�VHFRQGDU\�H[LW�SRLQWV��WKH�YROXPH�RI�WUDI¿F�OHDYLQJ�WKURXJK�these exits was minimal. Exit surveys were conducted in sessions that lasted 30 minutes during four time intervals (morning, 7:00 a.m. to 11:59 a.m.; early afternoon, 12:00 p.m. to 2:59 p.m.; late afternoon, 3:00 p.m. to 5:59 p.m.; and evening, 6:00 p.m. to 9:00 p.m.). With exit surveys, every third vehicle passing through the exit point was stopped and visitors were asked questions about their visit such as “How long have you been at the park today?” and “What was the main activity during your visit?” While conducting the surveys, researchers documented the gender and race/ethnicity of vehicle occupants. These VXUYH\V�ODVWHG�DSSUR[LPDWHO\���±���VHFRQGV�VR�DV�WR�QRW�LPSHGH�WKH�ÀRZ�RI�WUDI¿F�OHDYLQJ��Exit survey responses were documented using a data collection form, a clipboard, and a handheld mechanical counter.

Data AnalysisState park visitation data were analyzed using chi-square tests (for categorical data)

and analysis of variance (for continuous data) to examine demographic group differences. Reliability of SOPARC was assessed using intraclass correlations for count data from paired observers. Validity of SOPARC was examined by comparing observed ratios (with ����FRQ¿GHQFH�LQWHUYDOV��RI�LQGLYLGXDOV�LQ�YDULRXV�GHPRJUDSKLF�JURXSV�WR�WKRVH�REWDLQHG�through the intercept surveys and exit surveys.

Results5HVXOWV�DUH�SUHVHQWHG�DFFRUGLQJ�WR�WKH�SULPDU\�UHVHDUFK�REMHFWLYHV��7KH�¿UVW�VHFWLRQ�

examines general visitation data collected via SOPARC and highlights prominent demographic trends. The second section supplements the SOPARC visitation data with LQIRUPDWLRQ� FROOHFWHG� LQ� WKH� LQWHUFHSW� DQG� H[LW� VXUYH\V�� 7KH� ¿QDO� VHFWLRQ� H[DPLQHV� WKH�reliability of SOPARC in a state park setting and the validity of SOPARC relative to the RWKHU�GDWD�FROOHFWLRQ�VWUDWHJLHV��$OO�PHDQV�DUH�SUHVHQWHG�ZLWK�����FRQ¿GHQFH�LQWHUYDOV�

27

State Park Use PatternsSOPARC. Researchers used SOPARC to count 18,525 park visitors during 217

separate observation sessions in target areas of the three Georgia state parks. A total of 76.6 hours was spent conducting SOPARC observations, with an average observation session length of 8.9 minutes (excluding trailhead observations, which were always 30 minutes). Session length ranged from 1 to 60 minutes, and visitor counts during the sessions ranged from zero to 501. On average, SOPARC data collection allowed researchers to count 516 people per researcher hour.

A factorial ANOVA model examined differences in total visitors counted per observation session with respect to day of the week (i.e., weekday, free Wednesdays, Saturday, Sunday, and holiday) and time of the day (morning, early afternoon, late DIWHUQRRQ��HYHQLQJ���6LJQL¿FDQW�GLIIHUHQFHV�LQ�YLVLWRU�FRXQWV�ZHUH�REVHUYHG�IRU�WLPH�RI�WKH�day, F(3, 75) = 10.4, p����������Ș2� �������,QWHUDFWLRQV�ZHUH�QRW�VWDWLVWLFDOO\�VLJQL¿FDQW��Observations showed that visitor numbers across the three parks were highest in target areas on weekends and holidays. The late afternoon period (3:00 p.m. to 6:00 p.m.), in particular, seemed to correspond with a peak in visitors.

Results indicated that relatively more Whites (50.9%) were seen than Latinos (36.1%) across all target areas in all parks. Although Whites accounted for a vast majority of all visitors observed at trailheads (82.2%), they accounted for less than half of observed visitors in the beach areas (47.0%; Figure 1). Visitation to target areas among racial/ethnic JURXSV�DOVR�GLIIHUHG�E\�GD\V�RI�WKH�ZHHN��Ȥ2(9, 16464) = 1482.2, p < 0.001, Cramer’s V = 0.17. Whites were more likely to visit state parks on weekdays and Saturdays. Most African Americans, Latinos, and “Others” visited the beaches on weekends, and a majority of these weekend visitors came on Sundays (Figure 2). These observations also showed that target area visitation patterns among racial/ethnic groups differed by time of the day, Ȥ2(9, 16464) = 727.1, p < 0.001, Cramer’s V = 0.12. Whites were more likely to come to the beaches earlier, and African Americans and Latinos were more likely to stay later (Figure 3). Whites were more likely to visit state parks on weekdays and Saturdays. Most African Americans, Latinos, and Others visited the beaches on weekends, and a majority of these weekend visitors came on Sundays.

Although SOPARC observations provided a clear measure of spatial (by park zone) and temporal (by day of week and time of day) distribution of park visitors and associated demographic differences, they did not yield other information that park managers generally consider to be useful.

Figure 1. Visitors Observed in Different Areas of State Parks in Georgia, Summer 2010 (by Race/Ethnicity) Note. Proportions represent pooled sample across all three parks, days of the week, and times of the day.

% o

f Vis

itors

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Intercept surveys and exit surveys. Intercept surveys (N = 5,192 surveys collected during 116 survey sessions) and exit surveys (N = 1,113 vehicles surveyed during 139 sessions) served to supplement the SOPARC data. Intercept surveys consisted of 206 researcher hours, with an average length of 1.8 hours per survey collection session (range �����WR�����KRXUV���7KH�QXPEHU�RI�VXUYH\V�FROOHFWHG�GXULQJ�HDFK�VHVVLRQ�UDQJHG�IURP�¿YH�to 153, with an average collection rate of 24.9 surveys per researcher hour. Each exit survey session was 30 minutes, resulting in a total of 69.5 researcher hours. The number of cars counted during each session (researches counted every third vehicle) ranged from zero to 29 (zero to 103 vehicle occupants), with an average of 16.0 cars and 48.1 visitors surveyed per hour. Together, these methods provided more expansive data regarding variables such as visitors’ activity preferences, group size, and total time in park. For example, intercept survey results showed that females (58.6%) frequented day use areas more than males (41.4%). The intercept surveys showed that Whites (51.7%) and Latinos (30.9%) represented the largest proportion of visitors observed across day use areas in all parks. A majority of respondents across all three state parks participated in picnicking/cookouts (68.5%) and swimming (61.3%). Maintained outdoor areas (playgrounds, picnic areas, beaches, etc.) were more important to visitors than developed outdoor areas and facilities �VSRUW�¿HOGV�FRXUWV��UHVWURRPV��YLVLWRUV�FHQWHUV��HWF���RU�QDWXUDO�DUHDV��IRUHVWV��KLNLQJ�WUDLOV��etc.). Natural areas were more strongly preferred by Hispanic/Latinos and Whites than any other racial/ethnic group of state park visitors.

Results also indicated that beach activities were more popular among younger respondents and minorities. Traditional nature-based activities such as camping, hiking/walking, and wildlife photography were most common among males, older individuals (aged 60+), and Whites. The mean group size for state park visitors (excluding large groups or special events with more than 30 people) in day use areas was 7.4 ± 0.20 people. About ����RI�GD\�XVH�JURXSV�KDG�WZR�RU�IHZHU�SHRSOH������RI�GD\�XVH�JURXSV�KDG�¿YH�RU�IHZHU�people, and 20% of day use groups had 10 or more people. Group size in day use areas was related to respondents’ race/ethnicity, F(5, 3072) = 50.7, p����������Ș2 = 0.08. Latinos (9.36 ± 0.40 visitors per group), Asians (9.15 ± 1.25), and African Americans (8.74 ± 0.88) tended to recreate more often in groups with more individuals than Whites did (5.98 ± 0.22). Intercept surveys also showed that state park visitors spent an average of 5.07 ± 0.16 hours in the park. Total time in park differed by race/ethnicity, F(4, 737) = 13.1, p < 0.001, Ș2� �������ZLWK�:KLWHV� VSHQGLQJ� VLJQL¿FDQWO\� OHVV� WLPH� WKDQ� LQGLYLGXDOV� LQ�RWKHU� UDFLDO�ethnic groups. On average, Latinos spent the longest amount of time in the park during day use visits (M = 5.71, SD = 2.28).

Figure 2. Distribution of Visitors in Georgia State Park Day Use Areas Observed During Weekend Days, Summer 2010 (by Race/Ethnicity) Note. Proportions represent pooled sample across all three parks and times of the day.

29

([LW� VXUYH\V� UHYHDOHG� DQ� DYHUDJH� FRXQW� RI� ����� �� ����� �PHDQ� �� ���� FRQ¿GHQFH�interval) passengers per vehicle leaving state parks. Data suggested that cars and people were leaving parks more on weekends and holidays (63%) in the late afternoon or evening (65%). According to exit surveys for visitor data across all parks (n = 3198), approximately 18.8% of visitors spent 1 hour or less in the park and 38.2% of visitors spent 4 hours or more in the park (M = 3.36 ± 0.05 hours).

Exit surveys also indicated that swimming and beach activities were the most popular activities at all parks (49.1%), followed by picnics and cookouts (26.1%). Results also showed that 70.7% of vehicles surveyed had visited target areas during their visit (n = 1,020). When weighted by the total number of people in each car, exit surveys showed that 78.8% of people in the parks visited target areas (n = 3,202). Exit survey data collection did not allow for detailed analysis of park use patterns by demographic group primarily because many vehicle occupant groups included multiple genders, ages, and race/ethnicities.

Reliability & Validity of SOPARC in a State Park SettingReliability. In this study, SOPARC reliability estimates were calculated using

procedures outlined by McKenzie and Cohen (2006). To assess reliability, two researchers simultaneously performed independent observations in the same target areas during both the 2009 pilot test and the primary 2010 study. During the 2010 study, observers conducted 13 paired observation sessions, observing a total of 2,827 individuals across the three focal parks. Following previous research (McKenzie & Cohen, 2006), a series of single and average measures intraclass correlations were then calculated to determine the reliability of quantitative visitor count data on four different variables: (a) total number RI�YLVLWRUV���E��UDFH�HWKQLFLW\���F��DJH��DQG��G��JHQGHU��7DEOH�����1HDUO\�DOO�FRHI¿FLHQWV�PHW�acceptable criteria for reliability assessment, with the possible exception of individuals in the senior age group during the pilot test. This discrepancy was resolved by reviewing self-reported age data on random samples of intercept surveys and linking them to SOPARC observations of these same survey participants. This “ground truthing” strategy resulted in higher correlations among paired observers’ counts of seniors during the 2010 summer. Additional measures to ensure that paired observers were estimating park visitors’ race correctly were conducted throughout data collection (i.e., comparing observers’ SOPARC race codes with self-reported intercept survey data—a strategy identical to the one outlined for seniors above). After observers conducted a SOPARC assessment that included estimations of individuals’ race/ethnicity, they distributed an intercept survey to randomly selected individuals and compared their estimations to the respondents’ self-categorization. Analysis of these comparisons revealed that the SOPARC observer’s estimation of race/ethnicity were correct 97.3% of the time.

Validity. Historically, the validity of the SOPARC protocol has been tested within the context of physical activity and energy expenditure equivalents (McKenzie & Cohen, 2006). This study sought to explore a different element of validity: the accuracy with which SOPARC could be used to characterize general park visitation patterns by demographic group. Drawing upon the principle of convergent validity, demographic ratios obtained via SOPARC observations were compared to results obtained via other data collection methods. For SOPARC to be valid it should produce numbers and patterns similar to those obtained via the other data collection strategies, assuming each strategy effectively sampled a random subset of visitors in a particular park zone.

30

Results demonstrated how triangulation can be used to assess validity and identify potential sampling bias associated with certain data collection methods. Overall, demographic ratios focused on visitors using major day use areas (e.g., beaches and picnic DUHDV��REWDLQHG�YLD�623$5&�ZHUH�VLPLODU��EDVHG�RQ�RYHUODS�RI�����FRQ¿GHQFH�LQWHUYDOV��to those obtained using other intercept and exit surveys in all three of the parks studied (Table 2). These similarities suggest SOPARC counts can provide valid approximations RI�SDUN�XVH�DFURVV�GHPRJUDSKLF�JURXSV��+RZHYHU��D�IHZ�VWDWLVWLFDOO\�VLJQL¿FDQW�GHYLDWLRQV�were evident. For example, SOPARC counts tended to detect a higher proportion of Latino visitors and a lower proportion of Other visitors. Females tended to complete intercept surveys more often than males did. Exit surveys yielded a higher proportion of adults than SOPARC counts did. Age data for intercept surveys was not incorporated because children (i.e., individuals under age 18) did not take the intercept survey. Implications of this SOPARC validity analysis are outlined in the Discussion section.

DiscussionPrevious research suggests that activity participation patterns differ by visitor

demographics in many outdoor settings, but few studies have used different methods of data collection to triangulate results (Richmond, Hayward, Gahagan, Field, & Heisler, 2006; Sandersonl et al., 2003; Spengler et al., 2011). Even fewer studies have employed this strategy to assess visitation in a state park setting. Results from this study indicate many commonalities as well as marked differences in state park use patterns among gender, age, and racial/ethnic groups captured by different data collection approaches (e.g., SOPARC, intercept surveys, and exit surveys). The following discussion examines the implications of these results for state park managers.

31

Table 1 Summary of Intraclass Correlations Depicting Reliability of Paired SOPARC Observer Counts During 2009 and 2010 Data Collection Periods

2009 SOPARC Reliability MeasuresA

2010 SOPARC Reliability MeasuresB

r

Single Measures

ICC

Average Measures

ICC

r

Single Measures

ICC

Average Measures

ICC Total

Visitors 0.997 0.989 0.995 0.990 0.990 0.990

Race White

0.992

0.985

0.993

0.995

0.998

0.998

African American

0.992 0.992 0.966 0.986 0.992 0.992

Latino 0.982 0.968 0.984 0.988 0.988 0.988 Others 0.998 0.993 0.997 0.979 0.962 0.962 Age Child

0.960

0.939

0.968

0.969

0.985

0.985

Teen 0.912 0.908 0.952 0.888 0.942 0.942 Adult 0.964 0.963 0.981 0.995 0.997 0.997 Senior 0.388 0.225 0.371 0.927 0.970 0.970 Gender Male

0.987

0.981

0.990

0.992

0.996

0.996

Female 0.999 0.992 0.996 0.996 0.998 0.998

A - Regularly paired observation sessions (N = 11) accounted for 2,192 individuals during the 2009 pilot study

B - Regularly paired observation sessions (N = 13) accounted for 2,827 individuals during the 2010 study

Table 2 Validity of SOPARC Visitor Demographic Data in Beaches and Picnic Areas Compared to Estimates Obtained Through Intercept and Exit Survey Data in Three Georgia State Parks, Summer 2010 Gender Ratios

(with 95% CI) Age Ratios

(with 95% CI) Park & Tool Female Male Child Adult FMa SOPARC Intercept Exit

54.8 ± 1.7 60.4 ± 3.0 49.1 ± 4.3

45.2 ± 1.7 39.6 ± 3.0 50.9 ± 4.3

53.1 ± 1.7

42.4 ± 4.2

46.9 ± 1.7

57.6 ± 4.2

FYb SOPARC

53.2 ± 1.3

46.8 ± 1.3

48.3 ± 1.3

51.7 ± 1.3

Table 1

Summary of Intraclass Correlations Depicting Reliability of Paired SOPARC Observer Counts During 2009 and 2010 Data Collection

31

State Park Use Patterns: Demographic Differences'DWD�FROOHFWHG� LQ�*HRUJLD�VWDWH�SDUNV�FRQ¿UPHG� WKDW� VLJQL¿FDQW�GLIIHUHQFHV� LQ�SDUN�

use patterns exist among distinct demographic groups (Leslie, Cerin, & Kremer, 2010; Reed & Price, 2012). This study helped to clarify some of those differences. For instance, data showed that Hispanics currently represent a disproportionately large segment of state park visitors relative to their overall representation in the larger population. This result highlights the increasingly important role that state parks and other public lands can play in the outdoor recreation pursuits of Hispanics (Brown, Perkins, & Brown, 2003). The SOPARC observations also revealed marked differences in site use by time of day and day of the week, with a larger proportion of White visitors coming on weekdays and weekend mornings and a larger proportion of racial/ethnic minority visitors coming weekend afternoons. Racial/ethnic minorities also visited parks in larger groups and stayed PXFK�ORQJHU�WKDQ�:KLWH�YLVLWRUV��+RZHYHU��DGGLWLRQDO�UHVHDUFK�LV�QHHGHG�WR�FRQ¿UP�WKHVH�patterns at other locations and to identify factors that drive these observed patterns. Racial

Table 2

Validity of SOPARC Visitor Demographic Data in Beaches and Picnic Areas Compared to Estimates Obtained Through Intercept and Exit Survey Data in Three Georgia State Parks, Summer 2010

32

Table 2 Validity of SOPARC Visitor Demographic Data in Beaches and Picnic Areas Compared to Estimates Obtained Through Intercept and Exit Survey Data in Three Georgia State Parks, Summer 2010 Gender Ratios

(with 95% CI) Age Ratios

(with 95% CI) Park & Tool Female Male Child Adult FMa SOPARC Intercept Exit

54.8 ± 1.7 60.4 ± 3.0 49.1 ± 4.3

45.2 ± 1.7 39.6 ± 3.0 50.9 ± 4.3

53.1 ± 1.7

42.4 ± 4.2

46.9 ± 1.7

57.6 ± 4.2

FYb SOPARC Intercept Exit

53.2 ± 1.3 56.3 ± 2.5 50.1 ± 3.2

46.8 ± 1.3 43.7 ± 2.5 49.9 ± 3.2

48.3 ± 1.3

40.9 ± 3.2

51.7 ± 1.3

59.1 ± 3.2

RTMc SOPARC Intercept Exit

52.6 ± 1.1 59.9 ± 2.6 50.7 ± 3.0

47.4 ± 1.1 40.1 ± 2.6 49.3 ± 3.0

53.3 ± 1.1

44.8 ± 3.0

46.7 ± 1.1

55.2 ± 3.0

Race/Ethnicity Ratios

(with 95% CI) Park & Tool White Black Latino Other FMa SOPARC Intercept Exit

62.9 ± 1.6 70.7 ± 2.8 71.4 ± 2.8

1.3 ± 0.4 1.2 ± 0.7 1.1 ± 0.9

33.6 ± 1.6 23.4 ± 2.6 26.0 ± 3.8

2.1 ± 0.5 4.8 ± 1.3 1.5 ± 1.0

FYb SOPARC Intercept Exit

37.7 ± 1.3 41.7 ± 2.5 36.2 ± 3.1

13.1 ± 0.9 14.1 ± 1.8 17.7 ± 2.5

44.5 ± 1.3 34.3 ± 2.4 36.1 ± 3.1

4.7 ± 0.6 9.8 ± 1.5

10.0 ± 1.9 RTMc SOPARC Intercept Exit

47.0 ± 1.1 48.5 ± 2.6 60.1 ± 2.9

10.3 ± 0.7 9.5 ± 1.5 8.4 ± 1.7

39.3 ± 1.1 36.5 ± 2.5 24.0 ± 2.6

3.4 ± 0.4 5.5 ± 1.2 7.5 ± 1.6

Note. Statistically significant differences among ratios are highlighted by bold font. aSample sizes for FM: SOPARC = 3,460, Intercept = 1,043, Exit = 524 bSample sizes for FY: SOPARC = 7,313, Intercept = 1,426, Exit = 1,068 cSample sizes for RTM: SOPARC = 5,691, Intercept = 1,503, Exit = 931

32

and ethnic differences were also prominent across park zones. State park beaches and picnic areas tended to be very diverse, whereas trailheads were almost exclusively white. Using this type of information, park managers in Georgia and other states could begin to develop management frameworks that respond to current conditions while adapting to meet the future needs or expectations of an increasingly diverse clientele. A key question then becomes “What is the most effective way to obtain this information on visitor use?”

Comparing Data Collection StrategiesFor many years, state park managers have relied on vehicle counts and standard

survey procedures to gather information about visitors. While these techniques are indeed useful, integration of a new monitoring framework might help to reduce the time, money, and effort required to conduct reliable and valid visitor assessments. SOPARC represents a promising solution that might help managers accomplish this goal. This study explored WKH�VSHFL¿F�SRWHQWLDO�RI�623$5&�DV�D�YHUVDWLOH�YLVLWRU�DVVHVVPHQW�WRRO�LQ�VWDWH�SDUN�VHWWLQJV�with a particular emphasis on assessing visitor diversity and general activity patterns. While previous research has implemented SOPARC in the United States and Latin America as a SK\VLFDO�DFWLYLW\�VXUYHLOODQFH�VWUDWHJ\��WKLV�VWXG\�LV�DPRQJ�WKH�¿UVW�WR�XVH�WKH�SURWRFRO�DV�D�management tool for gathering patterns on baseline visitor use data. Comparisons between data obtained through SOPARC and two other data collection methods (e.g., intercept surveys and exit surveys) highlighted advantages and disadvantages associated with each data collection approach and provided new insight regarding the utility of the three tools for state park managers (Table 3).

Results indicated that, of the three methods examined, SOPARC may be the most effective tool for quickly and accurately gathering baseline data on visitor use patterns across spatial and temporal scales, effectively answering the question of who is doing what, where, and when? McKenzie et al. (2006) explained that, unlike other visitor assessment tools that rely heavily on individual subjects and self-reported values, SOPARC uses “direct observation, focuses on group behavior, and its unit of analysis is a target area, not an individual” (p. 7). As a result, the implementation of this tool in state parks that have large public open areas becomes useful as individual visitors arrive and depart at different times. As an added bonus, SOPARC assessments produce basic information about physical activity levels at various park sites. Other SOPARC advantages include its objective nature, which documents overt behavior and not visitor-reported information common in many other monitoring approaches that may be subject to participant recall bias. SOPARC is also noninvasive, and it does not require observers to interact with or disturb people at the park.

SOPARC was also a reliable instrument for measuring visitor demographics in certain target areas. Interobserver agreement scores for nearly all visitor demographic characteristics recorded were high (R����������7KH�RQO\�H[FHSWLRQ�ZDV�FRXQWV�RI�VHQLRUV�in the pilot test, where low correlations were likely due to the small sample size. This LVVXH�ZDV�TXLFNO\�UHFWL¿HG�LQ�WKH�PRUH�FRPSUHKHQVLYH�GDWD�FROOHFWLRQ�LQ�������ZKHUH�PRUH�rigorous training protocols were applied to facilitate individual categorization. Research implementing SOPARC in the future could increase reliability by adhering to the training protocol established by McKenzie and Cohen (2006). The protocol not only provides the logistics of SOPARC, but also assists in reaching consistent, grounded observations of park visitors through activities such as facial recognition exercises contained in the SOPARC training DVD. These measures prepare observers to identify the differences, similarities, and nuances of racial/ethnic and age-related characteristics. When evaluating reliability, researchers should consider the environmental characteristics of target areas within a park. 7DUJHW�]RQHV�VKRXOG�FRQWDLQ�SXUSRVHIXOO\�VHOHFWHG��ZHOO�GH¿QHG�SDUDPHWHUV�DQG�ERXQGDULHV�to ensure the congruency of observation sessions within and between parks.

33

Table 3

Strengths and Weaknesses of Various State Park Visitor Use Monitoring Strategies

SOPARC Intercept Surveys Exit Surveys

Strengths - Objective measure of behavior - Avoid bias of self- report and recall - Easy to implement - Noninvasive to visitors - Yields large amounts of baseline data in a relatively short time period

Weaknesses

Time andEffort

- Potential for detailed visitor data (preferences, demographics, scales, etc.) - Possible meaningful visitor responses and perceptions

- Accounts for all park visitors - More accurate length of stay and activity information

- Depth of information limited - Requires observer KRXUV�DQG�VSHFL¿F�training- Only covers certain park areas - Potential for duplicate counts

- Requires creation of valid survey instrument- Costs associated with administration- Must have willing participants

- Depth of information limited- Short data collection window - Potential for duplicate counts - Relies on visitor recall

- Average 516 people counted per researcher hour

- Average 24.9 surveys collected per researcher hour

- Average 16.0 cars sampled (48.1 people sampled) per researcher hour

7KH�YDOLGLW\�DQDO\VLV�UHYHDOHG�IHZ�VWDWLVWLFDOO\�VLJQL¿FDQW�GLIIHUHQFHV�LQ�WKH�RYHUDOO�ratios of visitors counted by race/ethnicity, gender, and age using each of the data collection methods. However, the few differences that did emerge should be noted. Compared to other data collection methods, the objective SOPARC counts resulted in higher percentages of children and Latino visitors. These numbers suggest that, in Georgia state parks, more conventional visitor monitoring methods might underestimate the number of children and Latino visitors using park resources. In all three parks, the ratio of females calculated through intercept surveys was higher than that for the other data collection strategies. Despite random allocation of surveys, females might be more likely than males to complete a survey, creating an apparent gender gap. Exit surveys theoretically provide the best estimates of park use because all visitors using various park resources must exit through the same point. However, exit survey protocols used in this study may have failed to capture families of day users who stayed at beaches and picnic areas until after dark. Researcher observations indicated that many of these families were Hispanic/Latino. Overall, though the most accurate visitor monitoring should probably incorporate multiple approaches, these validity comparisons suggest that SOPARC is at least comparable to conventional survey approaches when it comes to determining visitation patterns and trends in state parks.

Of course, as with any monitoring framework, disadvantages of SOPARC also exist. Compared to other sampling techniques, moment-in-time observations limit the depth of information gained. For example, intercept surveys can yield more detailed information

34

about visitor perceptions, motivations, preferences, as well as self-reported estimates of site use over longer temporal scales and pointed feedback. Similarly, exit surveys can provide a more holistic look at overall visitation time and activity participation across an entire park (not just target areas) following a visit.

Despite these issues, the SOPARC protocol holds one important advantage that is especially relevant to current state park management. State park managers can use the SOPARC approach to train their employees, in short periods of time and with minimal costs, how to implement a basic visitor monitoring protocol in a variety of outdoor settings. This advantage alone makes SOPARC an appealing alternative to conventional methods that require more intense effort and increased costs.

Most studies using SOPARC have provided instruction to observers during a 1 to 2 day workshop (McKenzie et al., 2006; Parra et al., 2010; Shores & West, 2008). Though actual observational sessions may last 30 minutes to 1 hour for highly visited target areas (e.g., 300+ visitors), the time spent on data collection is minimal compared to traditional visitor assessment tools (Table 3). For instance, research suggests that “selecting as few as ���WR����KRXUV�D�ZHHN�>IRU@�PHDVXUHPHQW�DSSHDUV�VXI¿FLHQWO\�UREXVW�IRU�HVWLPDWLQJ����KRXUV�of park use…over a week’s time” (Cohen et al., 2011, p. 1121). This relatively minor time commitment and substantial return on effort invested may cause many state park managers WR�¿QG�623$5&�LQFUHDVLQJO\�DWWUDFWLYH�DV�D�YLVLWRU�DVVHVVPHQW�WRRO��)XUWKHUPRUH��WUDLQHG�park staff can engage in observation sessions throughout the day while adhering to other park employee duties before or after observational sessions. This frequent exposure to KLJK�XVH�DUHDV�DOVR�SURGXFHV�WKH�DGGHG�EHQH¿W�RI�HQFRXUDJLQJ�YLVLELOLW\�DQG�LQWHUDFWLRQV�between park staff and visitors. These increased interactions could help promote increased recreation participation, particularly among racial/ethnic visitors (Stanis, Schneider, Chavez, & Shinew, 2009).

LimitationsThis research has some limitations that need to be acknowledged. As mentioned

previously, the reliability and validity of using SOPARC as a management tool was partially established through the efforts involving two trained observers and two separate data collection techniques. The correlation analyses of paired observations along with the comparison of SOPARC with intercept and exit survey data required additional resources than simply having one trained observer working. While combined methods served to increase the reliability of demographic estimations, they required additional resources.

Another limitation of using SOPARC is the limited number of variables observers can record simultaneously during a scanning period. This limitation was evident on high visitation days when several hundred people were active in a single observation area. During these high volume days, two visitors could possibly be observed and counted more than once. As a result, the need for interrated reliability measures and—circumstance permitting—division of target areas into subtarget areas, as described by McKenzie and Cohen (2006), should be considered.

Additionally, the race/ethnicity category used in SOPARC is inherently based on physical appearance. Because these race codes are static in nature, they fail to account for visitors’ ethnicity and associated properties and nuances of culture. In other words, placing visitors into socially constructed categories may be stereotyping certain racial groups that hold different social backgrounds but share the same skin color (e.g., darker skinned individuals categorized as Black could be Latinos of Dominican, Cuban, or Puerto Rican descent). It is therefore suggested that managers implementing SOPARC recognize this constraint and restrict inferences regarding visitor use to coarse assessments of demographic trends, which are still very useful in a management and marketing context.

Finally, while SOPARC may account for visitors in observation zones, it cannot account for all visitors in all park zones. Consequently, some park visitors may be omitted. Although exit survey data from this study suggested that the majority of visitors (about 80%) frequented day use areas where SOPARC observations were conducted, some

35

visitors were not accounted for. The baseline demographics of these unobserved visitors may have been different from the sampled population. Furthermore, most analyses in this study were conducted using pooled data from all three focal parks to illustrate general WUHQGV��&RQGLWLRQV�DQG�YLVLWRU�XVH�SDWWHUQV�DW�VSHFL¿F�VLWHV�YDU\��DQG�PDQDJHUV�PD\�ZDQW�WR�consider how these inferences apply to their own unique park setting.

Implications for ManagersResults suggest that park managers (particularly state park managers) should consider

using SOPARC as a stand-alone method to provide a basic understanding of visitor trends and site use (e.g., visitor counts by gender, age, and race/ethnicity). Based on this study, the 623$5&�DSSURDFK�PD\�EH�HVSHFLDOO\�EHQH¿FLDO�IRU�JHQHUDWLQJ�D�TXLFN�HVWLPDWH�RI�YLVLWRU�activity in high-density day use areas (e.g., beaches, picnic areas) common to many popular state parks across the country. This is not to say that SOPARC should completely supplant RWKHU�PHWKRGV�WKDW�FRQWLQXH�WR�SURYLGH�PDQ\�EHQH¿WV�RXWOLQHG�DERYH��EXW�UDWKHU�LW�FDQ�EH�XVHG�WR�HI¿FLHQWO\�DQG�HIIHFWLYHO\�WR�FROOHFW�EDVHOLQH�GDWD�WKDW�DOORZV�PDQDJHUV�WR�GHYHORS�a better understanding of their visitors and their visitation patterns. Using this information, managers may be able to inform future data collection strategies and improve their parks by repositioning facilities and programming efforts to increase park use or disperse areas of crowding to areas of lower impact.

%HIRUH� LPSOHPHQWLQJ� 623$5&�� VWDWH� SDUN� PDQDJHUV� ZRXOG� EHQH¿W� IURP� WUDLQLQJ�sessions for all staff conducting observations. Details of training options and suggestions have been provided in this paper and other studies, and the protocols are continuously adapted and updated by Active Living Research (2012). As this study demonstrates, having D�VWDII� WKDW� LV�FRPSHWHQW� LQ�DGPLQLVWHULQJ�623$5&�FDQ�EHQH¿W�PDQDJHPHQW�LQ�VHFXULQJ�SDUN�YLVLWRU�GDWD��0DQDJHUV�PD\�DOVR�¿QG�YDOXH�LQ�FROODERUDWLQJ�ZLWK�ORFDO�RUJDQL]DWLRQV�and agencies to compare SOPARC data to municipal parks in similar geographic settings. For example, obtaining overviews of visitors to nearby parks will help to establish standards that expose park use trends and highlight demographic groups that are over- or XQGHUUHSUHVHQWHG�DW�YDULRXV�VLWHV��6WDWH�SDUN�V\VWHPV�PD\�DOVR�EHQH¿W�IURP�SDUWQHUVKLSV�ZLWK� XQLYHUVLWLHV� DV� UHVHDUFK� SDUWQHUV� WR� VXSSO\� ¿HOG� VWDII� WR� IXUWKHU� H[DPLQH� YLVLWRU�patterns. As visitor demographics change, visitor assessment strategies should also evolve WR�KHOS�PDQDJHUV�UHVSRQG�WR�¿VFDO�FRQVWUDLQWV�ZKLOH�VLPXOWDQHRXVO\�DGGUHVVLQJ�WKH�QHHGV�and preferences of a diversifying clientele. As this study shows, the implementation of innovative monitoring strategies such as SOPARC could help to expedite that process.

ReferencesActive Living Research. (2012). Active Living Research tools and measures: SOPARC -

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Bocarro, J., Floyd, M., Moore, R., Baran, P., Danninger, T., Smith, W., & Cosco, N. (2009). Adaptation of the System for Observing Physical Activity and Recreation in Communities (SOPARC) to assess age groupings of children. Journal of Physical Activity & Health, 6(6), 699–707.

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