Seasonal Spatial Activity Patterns of Visitors with a Mobile Exercise Application at Seoraksan National Park, South Korea

sustainability

Article Seasonal Spatial Activity Patterns of Visitors with a Mobile Exercise Application at Seoraksan National Park, South Korea

Jinwon Kim 1,* ID , Brijesh Thapa 1 ID , Seongsoo Jang 2 and Eunjung Yang 1 1 Department of Tourism, Recreation and Sport Management, University of Florida, Gainesville, FL 32611-8208, USA; [email protected]fl.edu (B.T.); eunjung.yang@ufl.edu (E.Y.) 2 Cardiff Business School, College of Arts, Humanities & Social Sciences, Cardiff University, Aberconway Building, Colum Drive, Cardiff CF10 3EU, UK; [email protected] * Correspondence: jinwonkim@ufl.edu; Tel.: +1-352-294-1625

Received: 2 June 2018; Accepted: 27 June 2018; Published: 1 July 2018

Abstract: Visitors’ behavior in national parks can be inﬂuenced by seasonal variations in climate and preferred activities. Seasonality can produce different space consumption patterns, and impact visitor experience and natural resource use. The purpose of this study was to explore the seasonal spatial patterns of visitors’ activities using a mobile exercise application within the context of Seoraksan National Park in South Korea. A dataset composed of 5142 starting and ending points of 2639 activities (hiking and walking) created by 1206 mobile exercise application users (January–December 2015) were collected from a leading mobile exercise application operator. GIS-based spatial analytical techniques were used to analyze the spatial patterns of activity points across seasons and days (weekdays/weekends). Results indicated considerable seasonal and daily variations in activity distribution and hot spots (i.e., locations of potential congestion or crowding). The ﬁndings enable park managers to mitigate negative impacts to natural resources as well as enhance visitors’ experiences. Also, it allows potential visitors to decide when to visit certain sites via mobile application to ensure optimal conditions. Furthermore, the GPS-based exercise mobile application can be used as a new methodological approach to understand spatio-temporal patterns of visitors’ behavior within national parks and other natural protected areas.

Keywords: seasonality; mobile exercise application; GIS; spatial analytical techniques; visitors, national park

1. Introduction It is a global trend that natural protected areas, including national parks, have become major tourist attractions with eight billion annual visitors [1]. More specifically, national parks are valued by visitors due to the diverse recreation and tourism opportunities, as well as their natural and cultural resources [2,3]. With increasing demand and continual influx, park management agencies are faced with challenging options to develop more specific and measurable indicators that are central to management frameworks, and to ensure sustainable use that includes optimal visitor experience and resource protection (VERP) [4–7]. As noted by Manning [8], “indicators of quality are measurable, manageable variables that define the quality of visitor experiences and natural/cultural resources” (p. 93). Once standards and indicators of quality have been established, these can be monitored and managed within the scope of a park’s management plan to confirm that standards and indicators of quality are being preserved [9]. Tourism and recreational activities in national parks are seasonal phenomena due to climate variability that influences visitation [10–12]. Climate is an important factor in park-based tourism [6],

Sustainability 2018, 10, 2263; doi:10.3390/su10072263 www.mdpi.com/journal/sustainability Sustainability 2018, 10, 2263 2 of 21 as climate variability can influence physical resources that are considered tourist attractions [7]. Furthermore, visitors are very dependent on weather [13], as seasonal variation in conditions (e.g., temperature and wind) may affect activities and behaviors in national parks [14]. Seasonality can also have myriad negative impacts on the natural environment, as well as the visitor experience due to congestion or crowding issues, especially during the peak season [10,12,13]. Therefore, understanding seasonal patterns of park visitation is a prerequisite to forecast and manage park resources and enhance visitors’ experiences. As a result, several national park studies have examined the effects of seasonality on park visitation [10,11,14]. For example, Jones and Scott [10] studied seasonality and tourists’ visitation to Canada’s national parks, and identified expected increases due to an extended warm season, especially during the spring and fall seasons. Likewise, Scott et al. [11] also examined at Waterton Lakes National Park in Canada, and forecasted annual visitation to increase from 6% to 10% in the 2020s, and from 10% to 36% in the 2050s. In Australia, Hadwen et al. [14] identified key factors of seasonal visitation to 23 protected or natural parks across all six climate zones. They indicated that seasonal visits in equatorial, tropical, desert, grassland, and temperate zones were driven by climate, while visits to alpine and sub-alpine areas were influenced by natural and institutional factors (i.e., holiday periods). Different seasonal visitation to tourist destinations, including national parks, generates seasonal activity areas which are popular during the high and off-season [12]. Even during the high season, differences in visitations and relevant activity areas might occur between weekdays and weekends [15]. Additionally, seasonal activity areas are based on the distributions of visitors’ activities. Thus, an examination of the seasonal spatial patterns of visitors’ activities in national parks is a managerial necessity to effectively manage congestion and/or crowding that are essential to optimize visitors’ experience. Furthermore, park managers should be able to provide visitors with useful information about seasonal and daily activity areas, such as visit and/or avoidance of certain areas under specific temporal conditions. Prior studies have typically used global positioning system GPS-based tracking techniques to collect data on the spatio-temporal patterns of visitation in national parks and other tourist destinations [16–22]. Beeco et al. [16,17] used GPS tracking methods to assess the spatio-temporal patterns of visitor use, and identified the hot spots for runners, hikers, mountain bikers, and horseback riders in a local forest in Clemson, South Carolina, USA. Similarly, D’Antonio et al. [18] investigated the utility of GPS tracking methods to understand the spatio-temporal patterns of visitor use in various protected areas in the USA—Yosemite National Park, Bear Lake Corridor of Rocky Mountain National Park, and the Teton Range. Likewise, Hallo et al. [19] also noted the usefulness and functionality of GPS tacking methods, and assessed the spatial and temporal movement patterns of tourists in Sumter National Forest, USA. Lai et al. [20] used a GPS tracking method to assess visitors’ recreational tracking in Pokfulam County Park, Hong Kong. Similarly, Orellana et al. [21] used a GPS tracking method to analyze the spatio-temporal movement patterns of visitor flows in Dwingelderveld National Park, Netherlands. Collectively, these studies conclude that GPS-based tracking methods provide a more reliable, accurate, and precise dataset to describe visitor use patterns than traditional survey techniques. Although GPS-based tracking methods have enabled researchers and practitioners to understand the spatio-temporal dimensions of visitors’ behavior, previous studies have not captured seasonal and spatial variability for an extended period due to limited sample sizes and short data collection periods. Furthermore, due to the limited battery life and data storage of tracking devices, previous GPS-based methods have noted difficulties in tracking visitors’ behavior within a backcountry or multi-day trip setting [20]. However, the recent introduction of GPS-based mobile applications such as outdoor health and exercise application has overcome the sampling and time constraints of traditional tracking methods [23]. Essentially, mobile exercise applications are software programs that work on mobile devices such as smartphones and tablet computers, and are intended to assist individuals to exercise systematically [24,25]. Specifically, the accurate and rich spatio-temporal data extracted from Sustainability 2018, 10, 2263 3 of 21

mobileSustainability exercise 2018 applications, 10, x FOR PEER enable REVIEW researchers to accurately determine visitors’ movement3 of patterns 22 across time and space, which compensates for the limitations in previous methods [26]. Duedetermine to the widespreadvisitors’ movement use of smartphones, patterns across GPS-based time and space,mobile which exercise compensates applications, for oncethe wea activatedk by individualpoints for usersprevious can method track apps [26 users’]. real outdoor activities and generate accurate time and location informationDue [ 23 to, 26the]. w Givenidespread the potential use of smartphone benefitss of, GPS a GPS-based-based mobile mobile exercise application, application tos, the once best of our knowledge,activated by thereindividual is a lack users of, can empirical track app research users’ real on itsoutdoor use and activities application, givingfor accurate park management.time and location information [23,26]. Given the potential benefits of a GPS-based mobile application, to the Therefore, this study aimed to explore the seasonal and daily spatial patterns of visitors’ activities that best of our knowledge, there is a lack of empirical research on its use and application for park were tracked and recorded by numerous mobile exercise application users at Seorakan National Park management. Therefore, this study aimed to explore the seasonal and daily spatial patterns of (SNP),visitors’ South activities Korea. The that findings were tracked of this and study recorded canassist by numerous park managers mobile exercise to better application understand users spatial variabilitywithin of the visitor context flow of Seorakan across seasons National and Park days, (SNP), which South further Korea. provides The findings additional of this comprehensive study can seasonalassist geographic park manage indicatorsrs to better to understand facilitate sustainablespatial variability park of management. visitor flow across seasons and days, which further provides additional comprehensive seasonal geographic indicators to facilitate 2. Materialssustainable and park Methods management.

2.1. Study2. Materials Area: Seoraksanand Methods National Park (SNP), South Korea SNP is the fifth established national park, and is located within Inje Gun, Yangyang Gun, 2.1. Study Area: Seoraksan National Park (SNP), South Korea Sokcho Si, and Goseong Gun in Gangwon Province, South Korea. SNP was chosen as the study area becauseSNP it is is the one fifth of established the most famous Korean andnational highly park visited; it is located parks, within and is Inje also Gun, a designated Yangyang UNESCOGun, Sokcho Si, and Goseong Gun in Gangwon Province, South Korea. SNP was chosen as the study area Biosphere Reserve [27]. The distributions of attractions and visitor facilities inclusive of parking because it is one of the most famous and highly visited parks, and is also a designated UNESCO lots, campgrounds, information centers, and toilets, are illustrated in Figure1. According to the Biosphere Reserve [27]. The distributions of attractions and visitor facilities, including parking lots, Koreancampgrounds, National Park information Service centers, [28], SNP and toilets attracted, are almostillustrated 4 millionin Figure visitors 1. According in 2015, to the to Korea experiencen a varietyNational of natural Park Service and cultural [28], the attractions. park attracted Such more a massive than 4 volumemillion visitors of visitation in 2015 causes, to experience congestion a and crowdingvariety that of natural not only and compromises cultural attractions. visitors’ Such quality a massive of experience, volume of butvisitation also creates causes environmentalcongestion impactsand (i.e., crowding soil erosion, that damagenot only to compromise vegetation/heritages visitors’ properties, quality of water experience, pollution, but and also increased creates fire frequency)environmental [27,28]. impacts Consequently, (i.e., soil maintainingerosion, damage sustainability to vegetation/heritage of park resources, properties, along water withpollution, ensuring visitors’and optimal increased experience, fire frequency are) [2 key7,28]. operational Consequently, priorities maintaining of managers. sustainability Hence, of park this resources study, can providealong managers with ensuring with visitors’ a guideline optimal to ex operateperience a, are more key effective operational management priorities of managers. system, and Hence, remedy this study of SNP can provide managers with a guideline to operate a more effective management resource damage from congestion and/or crowding at specific time periods (i.e., season and system, and remedy resource damage from congestion and/or crowding at specific time periods (i.e., weekdays/weekends) and locations. season and weekdays/weekends) and locations.

Figure 1. Study area. Figure 1. Study area. 2.2. Data Collection 2.2. Data Collection The GPS-based mobile application is regarded as an appropriate digital tracking technology Thethat can GPS-based offer highly mobile accurate application and successive is regarded information as an about appropriate time and digitalspace [2 tracking3,26]. This technology study that offer highly accurate and successive information about time and space [23,26]. This study used Sustainability 2018, 10, 2263 4 of 21 Sustainability 2018, 10, x FOR PEER REVIEW 4 of 22 aused depersonalized a depersonalized GPS-based GPS- mobilebased mobile application application dataset fromdataset “Tranggle”, from “Tranggle” which is, thethat most is the popular most outdoorpopular mobile outdoor exercise mobile application exercise (applicationhttps://www.tranggle.com (https://www.tranggle.com)) in South Korea. in The South depersonalized Korea. The datasetdepersonalized maintains datasetanonymity maintain of alls exercise anonymity application of all exercise users, andapplication the operator users, and provides the operator generalized provide data,s includinggeneralized activity data, type,including time, activity and the GPStype, coordinates time, and the of theGPS place coordinates in which of an the activity place occurs. in which Due an to theactivity numerous occurs locational. Due to the points numerous for each locat activity,ional two points points—starting for each activity, and ending two points GPS coordinates—of—starting and oneending activity GPS werecoordinates extracted—of from one the activity Tranggle were database. extracted The from starting the Tranggle and ending database. points The are thestarting most importantand ending locational points are information the most important for a specific locational hiking informatio trail [29].n Wefor assumea specific that hiking the startingtrail [29] point. We representsassume that the the point start ofing interest point represents for each activity, the point and of itsinterest ending for point each canactivity be similar, and its to ending the point point of origin—incan be similar the to case the of point a round of origin trip—or—in athe different case of point—ina round trip the— caseor a ofdifferent one-way point trip.— Mostin the startingcase of andone- endingway trip. points Most can starting be located and ending on trails points or across can be other located places. on trails or across other places. TheThe final final sample sample consisted consist ofed 2639of 26 activities39 activities and 5142 and starting 5142 andstarting ending and points—136 ending points ambiguous—136 outlierambiguous points outlier located points outside located theoutside study the study area werearea were excluded excluded from from the the initial initial 52785278 points points ((26392639 activitiesactivities ×× 22 points) points)—recorded—recorded by by 12 120606 participants participants from from 1 1 January January 2015 2015 to to 31 31 December December 2015. 2015. TheThe refinedrefined dataset dataset showed showed that that each each participant, participant, on average, on average, visited visit SNPed SNP 2.19 times 2.19 times during during the 1-year the period.1-year period. Most participants Most participants used the use Tranggled the Tranggle app for hiking app in (2489, SNP 94.3%), for hiking walking (2489, (106, 94.3%), 4.0%), walking jogging (19,(106, 0.7%), 4.0%), bicycling jogging (19, (18, 0.7%), 0.7%) andbicycling other (18, activities 0.7%) (7,and 0.3%). other activities (7, 0.3%). SuchSuch anan activityactivity dataset was exported into a point shape file file (.shp) in in a a geographic geographic information information systemsystem (GIS).(GIS). NichollsNicholls [[3300]] defineddefined a shape file file as a digi digitaltal vector vector storage storage format format for for the the geographical geographical representationrepresentation of aa layerlayer ofof spatialspatial data.data. Geographic Geographic data data,, such such as as park park boundaries boundaries and and airphoto airphoto, werewere downloadeddownloaded fromfrom Biz-GIS,Biz-GIS ( whichA corporati supportson that spatial supports analysis spatial and analysis GIS applications and GIS applications for business andfor business policy decisions and policy in Southdecisions Korea in South [31]. In Korea this study,[31]). In all this GIS study, point shapeall GIS files point were shape projected files were and displayedprojected and in the displayed Korean 1985 in the Katech Korean (TM128) 1985 Katech projection. (TM128) It should projection. be noted It should that the be use noted of GIS-based that the mappinguse of GIS and-based spatial mapping analysis and is an spatial evolving analysis tool to is understand an evolving visitors’ tool to spatial understand behaviors visitors’ in park spatial and protectedbehaviors areain park settings and protected [32]. area settings [32].

2.3.2.3. DataData Analysis InWhen order identifying to identify seasonal seasonal activity activity areas, areas, it it is is critical critical to to define deﬁne the the optimal optimal cell cell size size t too aggregate aggregate visitors’visitors’ activitiesactivitiesthat that occur occur within within a a large large park park [33 [33]]. This. This study study employed employed a 3a ×3 ×3 3 km km cell cell as as its its unit unit of analysisof analysis based based on Smallwoodon Smallwood et al.’s et al.’s [34 ][34] previous previous study. study. As aAs result, a result, the studythe study area area included included 69 cells. 69 Figurecells. Figure2 illustrates 2 illustrates the locations the locations of mountain of mountain trails, celltrails, numbers cell numbers and boundaries. and boundaries.

Figure 2. Cell number in SNP. Figure 2. Cell number in SNP. Sustainability 2018, 10, 2263 5 of 21 Sustainability 2018, 10, x FOR PEER REVIEW 5 of 22

Since the seasonal spatial patterns of visitors’ activities encompasses an intricate process Since the seasonal spatial patterns of visitors’ activities in SNP encompasses an intricate process that requires a sequence of activities, a methodology ﬂowchart for data analyses was formulated that requires a sequence of activities, a methodology flowchart for data analyses was formulated (see Figure3). (see Figure 3).

FigureFigure 3. 3.Methodology Methodology ﬂowchart. flowchart.

Data analysis consisted of eight steps, which were implemented via ArcGIS (version 10.4.1., Data analysis consisted of eight steps, which were implemented via ArcGIS (version 10.4.1., ESRI Esri, Redland, NY, USA) and the ArcGIS Spatial Statistics Tool extension. All steps of seasonal Redlands, NY, USA) and the ArcGIS Spatial Statistics Tool extension. All steps of seasonal spatial spatial pattern analyses of visitors’ activities were also conducted during weekdays and weekends. pattern analyses of visitors’ activities were also conducted during weekdays and weekends. First, First, to determine the seasonal activity patterns of visitors, 5142 point shape files were categorized to determine the seasonal activity patterns of visitors, 5142 point shape files were categorized based based on season: spring (March–May), summer (June–August), fall (September–November), and on season: spring (March–May), summer (June–August), fall (September–November), and winter winter (December–February). Second, the categorized point shape files for each season were divided (December–February). Second, the categorized point shape files for each season were divided into into weekdays (Monday–Friday) and weekends (Saturday–Sunday) to determine weekdays (Monday–Friday) and weekends (Saturday–Sunday) to determine weekdays/weekends weekdays/weekends activity patterns of visitors. activity patterns of visitors. Third, the seasonal distribution of activity points was assessed. Specifically, the central Third, seasonal distribution of activity points was assessed. Specifically, the central tendency tendency (i.e., mean center and median center) and distributional trend (i.e., standard deviational (i.e., mean center and median center) and distributional trend (i.e., standard deviational ellipse) were ellipse) were measured. Spatial centrographic analysis and standard deviational ellipse analysis measured. Spatial centrographic analysis and standard deviational ellipse analysis were used to were used to measure and compare the seasonal mean centers, median centers, and standard measure and compare the seasonal mean centers, median centers, and standard deviational ellipses. deviational ellipses. Fourth, nearest neighbor analysis (NNA) was used to explore the spatial patterns of activity Fourth, nearest neighbor analysis (NNA) was used to explore the spatial patterns of activity points by calculating a nearest neighbor ratio (NNR). NNR is defined as the ratio of the observed mean points by calculating a nearest neighbor ratio (NNR). NNR is defined as the ratio of the observed distance to the expected mean distance between the features [35–37]. According to Wall et al. [38], mean distance to the expected mean distance between the features [35–37]. According to Wall et al. the point pattern reveals a clustered distribution when the value of NNR is less than 1. If the value [38], the point pattern reveals a clustered distribution when the value of NNR is less than 1. If the of NNR is greater than 1, the point pattern indicates a regular distribution. If the value of NNR is 1, value of NNR is greater than 1, the point pattern indicates a regular distribution. If the value of NNR the point pattern exhibits complete spatial randomness (CSR). is 1, the point pattern exhibits complete spatial randomness (CSR). Fifth, the study area with 69 cells (3 × 3 km) and all point shape files in each cell were aggregated. Fifth, the study area with 69 cells (3 × 3 km) and all point shape files in each cell were This step was a prerequisite for the subsequent spatial autocorrelation and hot spot analyses of seasonal aggregated. This step was a prerequisite for the subsequent spatial autocorrelation and hot spot activityanalyses areas. of seasonal Sixth, spatial activity autocorrelation areas. Sixth, analyses spatial via autocor globalrelation Moran’s analyses I statistic via were global used Moran’s to reveal I thestatistic seasonal were spatial used to patterns reveal ofthe activity seasonal areas. spatial Global patterns Moran’s of activity I statistic areas. has Global been commonly Moran’s I usedstatistic to has been commonly used to measure spatial clustering [39] based on Tobler’s First Law of Geography [40]. The global Moran’s I statistic is measured as follows:

N wij(xi−μ)(xj−μ) I = ∑i ∑j 2 , S0 = ∑i ∑j wij, S0 ∑i(xi−μ) Sustainability 2018, 10, 2263 6 of 21 measure spatial clustering [39] based on Tobler’s First Law of Geography [40]. The global Moran’s I statistic is measured as follows: N w (x − µ) x − µ = ij i j = I ∑ ∑ 2 ,S0 ∑ ∑ wij, S0 i j ∑i(xi − µ) i j where wij is the connectivity spatial weight between cell i and cell j (e.g., wij = 1 if cell i and cell j are adjacent; otherwise, wij = 0), xi is the number of activity points at cell i, xj is the number of activity points at cell j, µ is the average number of activity points, and N is the total number of cell units (in this study, n: 69). The range of global Moran’s I statistic lies between −1 and 1. A value of 1 exhibits a perfect positive autocorrelation, representing a pattern in which similar values occur in adjacent cells. A value of 0 exhibits CSR. A value of −1 exhibits a perfect negative autocorrelation, representing pattern in which high (or low) values are consistently located next to low (high) values [39,41,42]. ∗ ∗ Seventh, Getis-Ord Gi statistic (hereafter, Gi statistic) was employed to identify where the significant hot spots of seasonal activity were located in the study area. The hot spot analysis using ∗ the Gi statistic has been commonly used in urban studies, particularly to identify significant hot spots of traffic accidents [43] or crime [44]. This approach has also been recently applied in tourism research [45]. The analysis was also based on Tobler’s [40] First Law of Geography. As an index of ∗ local spatial autocorrelation, the Gi statistic was appropriate to identify cluster structures of high or ∗ low concentration [46]. A simple equation of the Gi statistic is noted as follows: n ω ∗ ∑j=1 ijxj Gi = n (1) ∑j=1 xj

∗ where Gi is the statistic that addresses the spatial dependence of the number of activity points in cell i of all n (n = 69) cells, xj is the number of activity points for each cell j, ωij is the weight value between ∗ cell i and cell j, and n is the total number of cells. The standardized Gi statistic generates z-scores that illustrates the number of activity points by cell with either high or low cluster value. This occurs ∗ spatially, and also measures statistical signiﬁcance. The Z (Gi ) statistic is noted as follows:

n n 2 ∑j=1 ωijxj − x ∑j=1 wij Z(G∗) = (2) i r 2 s n 2 n n ∑j=1 wij− ∑j=1 wij n−1

∗ The null hypothesis is CSR. If the Z(Gi ) value is positive, the high values’ distribution is clustered ∗ more spatially. In contrast, if the Z(Gi ) value is negative, the low values’ distribution is clustered more spatially [46]. Lastly, areas of potential congestion and crowding in SNP were identiﬁed.

3. Results

3.1. Seasonal Spatial Patterns of Visitors’ Activities

3.1.1. Pattern of Visitors’ Activities The seasonal distribution of activity points in SNP is illustrated in Figure4. The largest number of activity points occurred in the fall season (September–November), while the smallest during the winter (December–February). More specifically, 696 (13.5%) of the 5142 activity points occurred in spring (March–May), 1625 (31.6%) in summer (June–August), 2286 (44.4%) in fall, and 535 (10.4%) in winter. These findings indicate the existence of seasonal effects of visitation in SNP (see Table1). Furthermore, the starting and ending points of all activities were identified across and outside trails. The results demonstrate that visitors’ activities may start and end on the trails—which could be closely monitored by park authorities—or outside the trails, which poses challenges. Sustainability 2018, 10, 2263 7 of 21 Sustainability 2018, 10, x FOR PEER REVIEW 7 of 22

Figure 4. Seasonal distribution of activity points in SNP. Figure 4. Seasonal distribution of activity points in SNP. Table 1. Seasonal number of activity points in SNP (N = 5142). Table 1. Seasonal number of activity points in SNP (N = 5142). Season Number of Activity Points (%) SpringSeason (March–May) Number of696 Activity (13.5% Points) (%) SSpringummer (March–May) (June–August) 16 69625 (13.5%)(31.6%) FallSummer (September (June–August)–November) 22 162586 (44.4 (31.6%)%) WFallinter (September–November) (December–February) 2286535 (10.4 (44.4%)%) Winter (December–February) 535 (10.4%) 3.1.2. Central Tendency of Visitors’ Activities 3.1.2. CentralThe mean Tendency and median of Visitors’ centers Activities for spring, summer, and fall were all located in cell 41 (i.e., mean centersThe meanfor spring and and median fall; centersmean and for median spring, centers summer, for andsummer fall) were and c allell located32 (i.e., median in cell 41 centers (i.e., meanfor centersspring for and spring fall) (see and Figure fall; mean 5). However and median, the mean centers and for median summer) centers and for cell w 32inter (i.e., were median in cell centers 42. forThese spring findings and fall) indicate (see Figure that 5 while). However, visitors’ the activit meanies and during median the w centersinter were for winter concentrated were in in cell the 42. Theseeast ﬁndingsern region, indicate they were that whilemainly visitors’ focused activities in the central during region the winter during were the concentratedspring, summer, inthe and eastern fall seasons. In addition, standard deviational ellipses indicated that all seasonal distribution of activity region, they were mainly focused in the central region during the spring, summer, and fall seasons. points had a similar directional trend. Essentially, visitors’ seasonal activities were concentrated In addition, standard deviational ellipses indicated that all seasonal distribution of activity points around the southwest axis in the northeast across all seasons. Table 2 reports that the largest area of had a similar directional trend. Essentially, visitors’ seasonal activities were concentrated around the standard deviational ellipse occurred in fall (44.01 sq mi), followed by those in summer (43.28 sq mi), southwest axis in the northeast across all seasons. Table2 reports that the largest area of standard spring (40.19 sq mi), and winter (22.82 sq mi). Thus, these results indicate the different seasonal deviationalspatial boundaries ellipse occurred of visitors’ in activities fall (44.01 with sq mi),in SNP. followed by those in summer (43.28 sq mi), spring (40.19 sq mi), and winter (22.82 sq mi). Thus, these results indicate the different seasonal spatial boundaries of visitors’ activities within SNP. Sustainability 2018, 10, 2263 8 of 21 Sustainability 2018, 10, x FOR PEER REVIEW 8 of 22

Figure 5. Seasonal central tendency and direction of activity points in SNP. Figure 5. Seasonal central tendency and direction of activity points in SNP. Table 2. Seasonal area of standard deviational ellipse in SNP. Table 2. Seasonal area of standard deviational ellipse in SNP. Season Area (Unit: sq mi) SpringSeason (March–May) Area (Unit:40.19 sq mi) SummerSpring (March–May) (June–August) 40.1943.28 FallSummer (September (June–August)–November) 43.2844.01 Fall (September–November) 44.01 Winter (December–February) 22.82 Winter (December–February) 22.82

3.1.3. Point Patterns of Visitors’ Activities 3.1.3. Point Patterns of Visitors’ Activities The results of NNA for all seasonal distribution of activity points are summarized in Table 3. The results of NNA for all seasonal distribution of activity points are summarized in Table3. The values of NNR for all seasons were less than 1, which confirmed that the distribution of all The values of NNR for all seasons were less than 1, which confirmed that the distribution of all seasonal seasonal activity points was significantly clustered. activity points was significantly clustered. Table 3. Summary of seasonal nearest neighbor analysis. Table 3. Summary of seasonal nearest neighbor analysis. Season Observed MD Expected MD NNR p-Value Clustered SpringSeason (March–May) Observed36.78 MD Expected320.05 MD NNR0.11 p-Value<0.01 ClusteredYes SummerSpring (March–May) (June–August) 36.7836.21 320.05238.50 0.110.15 <0.01<0.01 Yes FallSummer (September (June–August)–November) 36.2133.74 238.50204.30 0.150.16 <0.01<0.01 Yes FallWinter (September–November) (December–February) 33.7451.92 204.30344.32 0.160.15 <0.01<0.01 Yes Winter (December–February) 51.92 344.32 0.15 <0.01 Yes Note. MD: Medan distance; NNR: Nearest neighbor ratio. Note. MD: Medan distance; NNR: Nearest neighbor ratio. 3.1.4. Clustering of Activity Areas 3.1.4. Clustering of Activity Areas Table 4 provides the values of global Moran’s I for seasonal activity areas (cells) across the 69 cells.Table The4 globalprovides Moran’s the values I values of global for Moran’s all seasons I for seasonal were statistically activity areas signifi (cells)cant across (0.05 the and 69 cells. 0.1 Thesignificance global Moran’s levels) Iand values positive. for all These seasons findings were statistically indicate positive significant spatial (0.05 autocorrelation and 0.1 significance of seasonal levels) activity areas, implying a tendency toward the spatial clustering of seasonal activity areas in which Sustainability 2018, 10, 2263 9 of 21 and positive. These findings indicate positive spatial autocorrelation of seasonal activity areas that imply a tendency toward spatial clustering of seasonal activity areas whereby cells which exhibit high (or low)Sustainability levels 2018 of visitors’, 10, x FORactivity PEER REVIEW tend to be situated next to cells with similarly high (or low)9 of levels 22 of visitors’ activity. cells exhibiting high (or low) levels of visitors’ activity tend to be situated next to cells with similarly high (or Tablelow) levels 4. Global of visitors’ Moran’s activity. I values for spatial autocorrelation of seasonal activity areas.

Table 4. Global Moran’s I values for spatial autocorrelation of seasonal activity areas. Season Global Moran’s I p-Value Clustered Spring (March–May)Season Global 0.06 Moran’s I <0.1p-Value Clustered Yes SummerSpring (June–August)(March–May) 0.210.06 <0.05<0.1 Yes FallSummer (September-November) (June–August) 0.190.21 <0.05<0.05 Yes WinterFall (September (December–February)-Norvember) 0.020.19 <0.1<0.05 Yes Winter (December–February) 0.02 <0.1 Yes 3.1.5. Activity Hot Spots 3.1.5. Activity Hot Spots The location and type of seasonal activity hot spots are illustrated in Figure6. The results are also reportedThe location in Table and5, which type of indicates seasonal changeactivity hot in the spot numbers in the ofSNP cells arethat illustrated exhibit in each Figure of the6. The seven results are also reported in Table 5, which indicates change in the number of cells that exhibit each of outcomes of hot spot analyses (hot spot [99%], hot spot [95%], hot spot [90%], cold spot [99%], cold spot the seven outcomes of hot spot analyses (hot spot [99%], hot spot [95%], hot spot [90%], cold spot [95%], cold spot [90%], and not statistically signiﬁcant). Interestingly, only hot spots were identiﬁed; [99%], cold spot [95%], cold spot [90%], and not statistically significant). Interestingly, only hot spots no coldwere spots identified existed.; no cold spots existed.

Figure 6. Spatial distribution of seasonal activity hot spots. Figure 6. Spatial distribution of seasonal activity hot spots. Table 5. Results of seasonal activity hot spot analysis. Table 5. Results of seasonal activity hot spot analysis. Frequency (%) Spatial Cluster z-Score CI (p-Value) Spring SummerFrequency Fall (%) Winter Type of Cluster >2.58CI (p-Value)99% (<0.01) 3 (4.3) 3 (4.3) 3 (4.3) 3 (4.3) Hot spot z-Score 1.96 ~ 2.58 95% (<0.5) Spring3 (4.3) Summer2 (2.9) 3 (4.3) Fall 2 (2.9) Winter (High clustering + High value) Hot spot >2.581.65 ~ 1.96 99% (<0.01)90% (<0.1) 3 (4.3)1 (1.4) 32 (4.3) (2.9) 2 (2.9) 3 (4.3) 1 (1.4) 3 (4.3) (High clustering +CSR 1.96 ~2.58−1.65 ~ 1.65 95% (<0.5)Not Significant 3 (4.3)62 (89.9) 262 (2.9) (89.9) 61 (88.4) 3 (4.3) 63 (91.3) 2 (2.9) −1.96 ~ −1.65 90% (<0.1) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) High value)Cold spot 1.65 ~1.96 90% (<0.1) 1 (1.4) 2 (2.9) 2 (2.9) 1 (1.4) −2.58 ~ −1.96 95% (<0.05) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) (HighCSR clustering + Low− 1.65value) ~1.65 Not Signiﬁcant 62 (89.9) 62 (89.9) 61 (88.4) 63 (91.3) Cold spot −1.96 ~−1.65<−2.58 90% (<0.1)99% (<0.01) 0 (0.0)0 (0.0) 00 (0.0) (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) (High clusteringTotal + (%) −2.58 ~−1.96 95% (<0.05) 0 (0.0)69 (100.0) 69 0 (0.0) (100.0) 69 (100.0) 0 (0.0) 69 (100.0) 0 (0.0) Low value) Note.< −CI:2.58 Confidence 99%Interval (<0.01); CSR: Complete 0 (0.0) Spatial Randomness 0 (0.0). 0 (0.0) 0 (0.0) Total (%) 69 (100.0) 69 (100.0) 69 (100.0) 69 (100.0) Specifically, 7 outNote. of CI: 69 Conﬁdence cells were Interval; statistically CSR: Complete significant Spatial inRandomness. spring, which indicated that 7 activity hot spots (99% [3], 95% [3], 90% [1]) existed during the spring season.

Sustainability 2018, 10, 2263 10 of 21

Specifically, 7 out of 69 cells were statistically significant in spring, which indicated that 7 activity hot spots (99% [3], 95% [3], 90% [1]) existed during the spring season. The detailed tourist attractions are identified in each cell area as follows:

• Cell 8: Oknyeo Waterfall, Sasigol Valley. • Cell 16: Osaek Spring, Geumgangmun, Jujeongol Valley, Yongso Waterfall. • Cell 23: Soseung Waterfall, Hangyeryeong Hill. • Cell 24: Seorak Waterfall, Dokjugol Valley. • Cell 42: Yangpok Waterfall. • Cell 52: Shinheungsa Valley, Gwongeumseong Peak, Geunganggul Cave, Waseondae Rock. • Cell 61: Heundeulbawi Rock, Ulsanbawi Rock.

During the summer season, 7 cells were identified as activity hot spots. The summer hot spots were similar to the spring hot spots, except for cell 16. Basically, cell 16 was replaced by cell 31: where Geoncheongol Valley, Gwittaegicheong Peak, and Baekun Waterfall are located. In the fall season, the number of significant cells increased to 8, with the addition of cell 6 to the 7 hot spots of the summer, or cell 31 to the 7 hot spots of the spring. Overall, this suggests that visitors’ activity areas during the fall become wider than those during the spring and summer seasons. Finally, during the winter, a total of 6 significant cells were identified and cells 23 and 31 were no longer significant.

3.2. Seasonal Spatial Patterns of Visitors’ Activities during Weekdays and Weekends

3.2.1. Visitors’ Activities during Weekdays and Weekends The distribution of activity points was obtained via sub-division of the temporal unit from season to two daily formats: weekdays (Monday–Friday) and weekends (Saturday–Sunday). The seasonal distribution of activity points during weekdays and weekends is summarized in Table6. Visitors’ activities were concentrated largely during weekends for all four seasons. From a daily perspective, among the 5142 activity points, the largest number of activity points (1316, or 25.5%) were observed during fall weekends, while the smallest number (180, or 3.5%) were identified for spring weekdays. Specifically, 516 (74.2%) of 696 activity points were observed during spring weekends, 1157 (71.2%) of 1625 during the summer, 1316 (57.5%) of 2286 for fall, and 318 (59.5%) of 535 for the winter season. These findings indicate that, although the weekend effects of visitation were illustrated through each season, they were more pronounced during the spring and summer than for the fall and winter.

Table 6. Number of activity points during weekdays and weekends.

Season Day Number of Activity Points (%) Total (%) Weekdays 180 (25.8%) 696 Spring (March–May) Weekends 516 (74.2%) (100.0) Weekdays 468 (28.8%) 1625 Summer (June–August) Weekends 1157 (71.2%) (100.0) Weekdays 970 (42.5%) 2286 Fall (September–November) Weekends 1316 (57.5%) (100.0) Weekdays 217 (40.5%) 535 Winter (December–February) Weekends 318 (59.5%) (100.0)

3.2.2. Central Tendency of Visitors’ Activities during Weekdays and Weekends Figure7 illustrates that the seasonal central tendency of activity points varies during weekdays and weekends. For weekdays, the mean and median centers for spring, summer, and fall were all located in cell 41 (i.e., mean centers for spring, summer, and fall; median center for summer) and cell 32 (i.e., median centers for spring and fall). But, the mean and median centers for the winter were in Sustainability 2018, 10, 2263 11 of 21

Sustainability 2018, 10, x FOR PEER REVIEW 11 of 22 cell 42. These findings indicate that while visitors’ activities during the spring, summer, and fall were were concentrated in the central region, they were mostly focused in the east region during the concentrated in the central region, they were mostly focused in the east region during the winter. winter. For weekends, the mean and median centers for summer and fall were all located in cell 41 For weekends, the mean and median centers for summer and fall were all located in cell 41 (i.e., (i.e., mean centers for summer and fall) and cell 32 (i.e., median centers for summer and fall). But, mean centers for summer and fall) and cell 32 (i.e., median centers for summer and fall). But, the the median centers for spring and winter were in cell 42 (i.e., median center for spring) and cell 33 median centers for spring and winter were in cell 42 (i.e., median center for spring) and cell 33 (i.e., (i.e., median center for winter). These findings indicate that while visitors’ activities in the summer median center for winter). These findings indicate that while visitors’ activities in the summer and andfall fall occurred occurred in the in thecentral central region, region, they were they werefocused focused in the east in the region east during region the during spring the and spring winter. and winter.In addition, In addition, seasonal seasonal standard standard deviational deviational ellipses ellipsesduring weekdays during weekdays and weekends and weekends denoted that denoted all thatseasonal all seasonal distribution distribution of activity of activity points had points a similar had adirectional similar directional trend. Specifically, trend. Specifically, visitors’ seasonal visitors’ seasonalactivities activities were concentrated were concentrated around the around southwest the southwest axis in the axis northeast. in the northeast.

Figure 7. Central tendency and direction of activity points during weekdays and weekends. Figure 7. Central tendency and direction of activity points during weekdays and weekends.

Areas of seasonal standard deviational ellipses during weekdays and weekends are summarizedAreas of seasonal in Table standard7. The largest deviational area of standard ellipses duringdeviational weekdays ellipse andoccurred weekends during are weekdays summarized in in Tablethe fall,7. Thewhile largest the smallest area of standardduring weekdays deviational during ellipse the occurredwinter season. during For weekdays weekdays, in thethe fall,areas while of theseasonal smallest standard during weekdays deviational during ellipses the were winter 34.03 season. sq mi For(spring), weekdays, 44.38 sq the mi areas (summer), of seasonal 45.45 standard sq mi deviational(fall), and ellipses 22.15 sq were mi (winter). 34.03 sq miFor (spring), weekends, 44.38 the sq areas mi (summer), of seasonal 45.45 standard sq mi deviational(fall), and 22.15 ellipses sq mi (winter).were 41.41 For weekends,sq mi (spring), the areas42.19 sq of seasonalmi (summer), standard 40.86 deviationalsq mi (fall), ellipsesand 23.01 were sq mi 41.41 (winter). sq mi (spring),These 42.19findings sq mi als (summer),o indicate 40.86 the sq different mi (fall), seasonal and 23.01 spatial sq mi boundaries (winter). These of visitors’ ﬁndings activities also indicate during the differentweekdays seasonal and spatialweekends. boundaries Specifically, of visitors’ visitors’ activities activities during tend weekdays to be spatially and weekends. distributed Speciﬁcally, during visitors’weekdays activities in summer tend to and be fall, spatially and weekends distributed in duringspring and weekdays winter. in summer and fall, and weekends in spring and winter. Table 7. Area of standard deviational ellipse across season and day.

Table 7. Area ofSeason standard deviational ellipseDay acrossArea season (sq and mi) day. Weekdays 34.03 Spring (March–May) SeasonWeekends Day Area41.41 (sq mi) WeekdaysWeekdays 44.38 34.03 SpringSummer (March–May) (June–August) WeekendsWeekends 42.19 41.41 Weekdays 44.38 Summer (June–August) Weekdays 45.45 Fall (September–November) Weekends 42.19 WeekdaysWeekends 40.86 45.45 Fall (September–November) Weekdays 22.15 Winter (December–February) Weekends 40.86 WeekdaysWeekends 23.01 22.15 Winter (December–February) Weekends 23.01 3.2.3. Point Patterns of Visitors’ Activities during Weekdays and Weekends 3.2.3. PointThe Patternsresults of of NNA Visitors’ for Activities all seasonal during distribution Weekdayss of and activity Weekends points during weekdays and weekendsThe results are of summariz NNA fored all in seasonal Table 8 distributions. The values of of activity NNR for points all seasons during weekdays during weekdays and weekends and are summarized in Table8. The values of NNR for all seasons during weekdays and weekends were Sustainability 2018, 10, 2263 12 of 21 less than 1, which implied that the distribution of all seasonal activities during weekdays and weekends were signiﬁcantly clustered.

Table 8. Summary of nearest neighbor analysis during weekdays and weekends.

Season Day Observed MD Expected MD NNR p-Value Clustered Weekdays 78.00 541.21 0.14 <0.01 Yes Spring Weekends 40.38 360.17 0.11 <0.01 Yes Weekdays 61.86 380.11 0.16 <0.01 Yes Summer Weekends 45.67 280.79 0.16 <0.01 Yes Weekdays 40.05 300.54 0.13 <0.01 Yes Fall Weekends 46.73 225.98 0.20 <0.01 Yes Weekdays 119.92 484.17 0.24 <0.01 Yes Winter Weekends 53.89 416.10 0.12 <0.01 Yes Note. MD: Medan distance; NNR: Nearest neighbor ratio.

3.2.4. Clustering of Activity Areas during Weekdays and Weekends Table9 summarizes that the values of global Moran’s I for seasonal activity areas during weekdays and weekends across the 69 cells in the SNP. The global Moran’s I values for all seasons during weekdays and weekends were statistically signiﬁcant (0.05 level: weekdays/weekends for summer and fall; 0.1 level: weekdays/weekends for spring and winter). These ﬁndings indicate positive spatial autocorrelation of activity areas across season and day. That is, there existed a tendency toward the spatial clustering of seasonal activity areas during weekdays and weekends in which cells that exhibited high (or low) levels of visitors’ activity intensity tended to be situated next to cells with similar high (or low) levels of visitors’ activity intensity.

Table 9. Global Moran’s I values for spatial autocorrelation of activity areas during weekdays and weekends.

Season Day Global Moran’s I p-Value Clustered Weekdays 0.04 <0.1 Yes Spring (March–May) Weekends 0.05 <0.1 Yes Weekdays 0.15 <0.05 Yes Summer (June–August) Weekends 0.24 <0.05 Yes Weekdays 0.13 <0.05 Yes Fall (September–November) Weekends 0.21 <0.05 Yes Weekdays 0.01 <0.1 Yes Winter (December–February) Weekends 0.02 <0.1 Yes

3.2.5. Activity Hot Spots during Weekdays and Weekends The location and type of seasonal activity hot spots during weekdays and weekends are illustrated and summarized in Figure8 and Table 10, respectively. The number of significant activity hot spots during weekends were typically greater than those of weekdays hot spots. The findings indicated that weekend visitors were located densely in more areas. Although there were some differences in the location of activity hot spots between weekdays and weekends for all four seasons, the results were similar to those of the seasonal activity hot spots. Seasonal activity hot spots during weekdays and weekends were also identified (see Table 11). Cells 24, 42, 52, and 61 were the most visited hot spot areas both across the four seasons and during weekdays and weekends, while cell 63 was visited mostly during summer weekdays only. Sustainability 2018, 10, 2263 13 of 21 Sustainability 2018, 10, x FOR PEER REVIEW 13 of 22

Figure 8. Spatial distribution of activity hot spots during weekdays and weekends. Figure 8. Spatial distribution of activity hot spots during weekdays and weekends. Table 10. Results of activity hot spot analysis during weekdays and weekends. Table 10. Results of activity hot spot analysis during weekdays and weekends. Frequency (%) CI SC z-Score Spring SummerFrequency (%)Fall Winter (p-Value) SC z-Score CI (p-Value) WDSpring WE WD Summer WE WD FallWE WD WinterWE >2.58 99% (<0.01) 3 (4.3) 3 (4.3) 3 (4.3) 4 (5.7) 3 (4.3) 3 (4.3) 4 (5.7) 6 (8.6) Hot Spot WD WE WD WE WD WE WD WE 1.96~2.58 95% (<0.5) 2 (2.9) 1 (1.4) 2 (2.9) 2 (2.9) 3 (4.3) 3 (4.3) 1 (1.4) 0 (0.0) (HC+HV) >2.58 99% (<0.01) 3 (4.3) 3 (4.3) 3 (4.3) 4 (5.7) 3 (4.3) 3 (4.3) 4 (5.7) 6 (8.6) Hot Spot 1.65~1.96 90% (<0.1) 0 (0.0) 3 (4.3) 1 (1.4) 1 (1.4) 2 (2.9) 1 (1.4) 1 (1.4) 0 (0.0) 1.96~2.58 95% (<0.5) 2 (2.9) 1 (1.4) 2 (2.9) 2 (2.9) 3 (4.3) 3 (4.3) 1 (1.4) 0 (0.0) (HC+HV) 64 62 63 62 61 62 63 63 CSR 1.65~1.96−1.65~1.6590% (<0.1)NS 0 (0.0) 3 (4.3) 1 (1.4) 1 (1.4) 2 (2.9) 1 (1.4) 1 (1.4) 0 (0.0) 64(92.7) 62(89.9) (91.3)63 (89.9)62 (88.4)61 (89.9)62 (91.3)63 (91.3)63 CSR −1.65~1.65 NS (92.7)69 (89.9)69 (91.3)69 (89.9)69 (88.4)69 (89.9)69 69(91.3) 69(91.3) Total (%) 69(100.0) (100.0)69 (100.0)69 (100.0)69 (100.0)69 (100.0)69 (100.0)69 (100.0)69 Total (%) Note. No cold spot was identified.(100.0) SC: Spatial(100.0) clustering;(100.0) HC:(100.0) High clustering;(100.0) HV:(100.0) High (100.0)value; CI:(100.0) Note.Confidence No cold spotinterval; was identified.CSR: Complete SC: Spatial spatial clustering; randomness; HC: High NS: clustering;Not significant; HV: High WD: value; Weekdays; CI: Confidence WE: interval;Weekends CSR:. Complete spatial randomness; NS: Not significant; WD: Weekdays; WE: Weekends.

TableTable 11 11.. SeasonalSeasonal activity activity hot hot spot during weekdaysweekdays and and weekends. weekends.

Spring Summer Fall Winter Cell Major Attraction Spring Summer Fall Winter Cell Major Attraction WD WE WD WE WD WE WD WE WD WE WD WE WD WE WD WE 88 OknyeoOknyeo Waterfall/Sasigol Waterfall/Sasigol Valley Valley ● ● ● ● ● ● 1616 OsaekOsaek Spring/Jujeongol Spring/Jujeongol Valley Valley ● ● ● ● 2323 SoseungSoseung Waterfall/Hangyeryeong Waterfall/Hangyeryeong Hill Hill ● ● ● ● ● ● 2424 SeorakSeorak Waterfall/Dokjugol Waterfall/Dokjugol Valley Valley ● ● ● ● ● ● ● ● 3131 GeoncheongolGeoncheongol Valley/Baekun Valley/Baekun Waterfall Waterfall ● ● 42 Yangpok Waterfall 42 Yangpok Waterfall ● ● ● ● ● ● ● ● 52 Shinheungsa/Gwongeumseong Peak 6152 HeundeulbawiShinheungsa/ Rock/UlsanbawiGwongeumseong Rock Peak ● ● ● ● ● ● ● ● 6361 Heundeulbawi Jubongsan Rock/Ulsanbawi Peak Rock ● ● ● ● ● ● ● ● Note.63 WD: Weekdays;Jubongsan WE: Weekends; Peak Cells with no seasonal hot spots during● weekdays and weekends are 1–7, 9–15,Note. 17–22, WD: 25–30, Weekdays; 32–41, 43–51, WE 53–60,: Weekends; 62, and 64–69.Cells with no seasonal hot spots during weekdays and weekends are 1–7, 9–15, 17–22, 25–30, 32–41, 43–51, 53–60, 62, and 64–69. 3.3. Summary of Results 3.3. Summary of Results Table 12 summarizes the analysis techniques and findings related to the seasonal spatial patterns of visitors’Table activities 12 summarize in SNP.s The the results analysis indicated techniques robust and seasonal findings effects related of visitation, to the seasonal and considerable spatial seasonalpatterns variations of visitors’ in activities activity distribution in SNP. The and results relevant indicate hotd spots. robust These seasonal findings effects were of visitation identified, and from twoconsiderable temporal perspectives: seasonal variations (1) seasonal in activity and (2)distribution mixed with and season relevant and hot day spots (weekdays. These findings and weekends). were identified from two temporal perspectives: (1) seasonal and (2) mixed with season and day (weekdays and weekends). Sustainability 2018, 10, 2263 14 of 21

Table 12. Summary of seasonal spatial pattern analyses.

Analysis Technique Temporality Type of Analysis Findings (Figure and Table) Seasonal Distribution of Frequency The largest number of activity points occurred Activity Points (Figure4, Table1) during the fall, the smallest in the winter. Seasonal Central Tendency Spatial Centrographic Analysis, The largest area of standard deviational ellipse of Visitors’ Standard Deviational Ellipse occurred in the fall, followed by summer, Activities Analysis spring, and winter. (Figure5, Table2) Seasonal Point Patterns of Nearest Neighbor Analysis The distribution of all seasonal activity points Visitors’ Activities (Table3) was significantly clustered. Seasonal Clustering of Spatial Autocorrelation Analysis There existed a tendency towards the spatial Activity Areas (Table4) clustering of seasonal activity areas such as hot spots and cold spots. Seasonal Activity Hot Spots Spatial Hot Spot Analysis 7 out of 69 cells belonged to the activity hot (Figure6, Table5) spots in the spring and summer, 8 in fall and 6 in the winter. Seasonal + Daily Distribution of Frequency Visitors’ activities were concentrated largely Activity Points (Table6) during weekends for all four seasons. Seasonal + Daily Central Tendency Spatial Centrographic Analysis, The largest area of standard deviational ellipse of Visitors’ Standard Deviational Ellipse occurred during weekdays in the fall, while the Activities Analysis smallest during weekdays in the winter. (Figure7, Table7) Seasonal + Daily Point Patterns of Nearest Neighbor Analysis The distribution of all seasonal activities Visitors’ Activities (Table8) during weekdays and weekends were significantly clustered. Seasonal + Daily Clustering of Spatial Autocorrelation Analysis There existed a tendency toward the spatial Activity Areas (Table9) clustering of seasonal activity areas during weekdays and weekends. Seasonal + Daily Activity Hot Spots Spatial Hot Spot Analysis The number of significant activity hot spots (Figure8, Table 10, Table 11) during weekends was greater than hot spots for the weekdays.

4. Discussion and Implications

4.1. Discussion Using a GPS-based mobile exercise application dataset, this study examined the seasonal spatial patterns of visitors’ activities in SNP, South Korea. To achieve this purpose, several GIS-based spatial analytical techniques were used to explore the seasonal spatial patterns of activity points and areas during weekdays and weekends. As illustrated in Table1, there were seasonal effects of visitors’ activity and visitation, with the fall season (September–November) as the peak season. This finding could be explained by the ‘fall foliage’ effect during the peak season. SNP has one of the best fall foliage sites in the country which begins in late September. Also, this park is the first fall foliage mountain site, and initially attracts most visitors [27,28]. Rich colors, in combination with plenty of natural (e.g., rocks, forests, wildlife, hot springs) and cultural (e.g., ancient Shilla-era temples) sites, make SNP as a main attraction during the fall season. According to a Korean national tourism survey (2016), September (about 12.9%) was the most popular month for domestic travel, and nature/landscape appreciation (about 26.7%) was one of the main activities during domestic travel. Such results also support the importance of seasonal effects on visitors’ activity and visitation. Furthermore, this seasonal variation of visitation is consistent with previous national park studies which have identified the influence of variation in climate and visitation [10,11]. For example, Jones and Scott [10] identified that climate not only influences tourism and recreation activities, but also the length of stay and quality of experience. Hence, findings of this study provide further evidence of a seasonality effect as illustrated in the context of SNP. This study finds that there were considerable seasonal variations in activity distribution and relevant hot spots. As summarized in Table 12, the largest spatial activity boundary and number of activity hot spots occurred in the fall, while the smallest occurred during the winter season (also see Tables2 and5). These findings could be explained by seasonal climate variability, which can Sustainability 2018, 10, 2263 15 of 21 influence visitors’ resource use and spatial behavior [10]. Furthermore, seasonal variation in weather conditions, including temperature and wind, can affect visitors’ spatial movement and behavior in national parks [14]. As shown in Table 11, seasonal variations in activity hot spots occurred in cell 16 (Osaek Spring/Jujeongol Valley), cell 31 (Geoncheongol Valley/Baekun Waterfall), and cell 63 (Jubongsan Peak). For instance, since cell 16 is very notable for hot spring and fall foliage; cell 16 was found to be frequently visited during spring, fall, and winter. The reason why cell 16 was not popular during the summer can be explained by the seasonal climate and safety issues. Specifically, the park management’s official website identifies Jujeongol valley in cell 16 as a dangerous area, where “rock falls” may occur due to heavy rains during the summer season. These empirical findings support Ahas et al.’s [12] argument that seasonality can produce different visitor space consumption patterns in parks and tourist destinations. Another finding is that there were considerable variations in activity distribution and relevant hot spots between weekdays and weekends. This result could be explained by the weekend effects of outdoor activities. As individuals have elevated feelings of freedom and closeness during the weekends periods [47], they are more likely to engage in their preferred activities and spend time with friends and/or family members. Thus, different spatial activity boundaries and behaviors during weekdays and weekends might be associated with temporal contexts, as individuals tend to visit distant and larger parks. This study recommends that further studies could collect individual level data via a visitor survey to further understand how and why spatial variability of park visitors differs during weekdays and weekends.

4.2. Implications This study provides several important implications to visitor experience and resource protection framework (VERP) that is essential for sustainable park management. Our findings reinforce the VERP framework, which was formulated by the U.S. National Park Service to protect park resources, as well as to optimize visitors’ experiences [48]. An essential purpose of protected areas, including a national park, is not only to protect and conserve biodiversity, but also to provide visitors an opportunity to experience and interact with natural and cultural resources [49]. Understanding the spatio-temporal patterns of visitation can contribute to visitor monitoring, as well as management of environmental impacts. Our framework is designed to explore the seasonal spatial patterns of visitors’ activities in SNP (Figure3), and offers comprehensive measurements of the spatio-temporal park visitations which can be incorporated into the VERP framework. In addition, this research also extends the literature on park management and outdoor recreation. Prior park management studies have been limited to identify “how to maintain the quality of park resources and visitor experiences” based on contemporary park management or planning frameworks. However, our methodological framework has widened this research focus to encompass the spatio-temporal element. This study also suggests that a GPS-based mobile exercise application can help park managers to monitor spatio-temporal information more accurately, such as specific locations of visitors and density at a specific site. Such in-depth monitoring and predictive analytics are only possible via the use of ordinary visitors’ real-time activity data. For instance, accumulated visitors’ movement data can provide an important ‘early warning’ indicator of crowding and other issues experienced during a certain time and place [19]. Figure9 illustrates the potential congestion or crowding of parts of the study area (A: Heundeulbawi Rock to Ulsanbawi Rock; B1: Min Park to Shinheung Temple; B2: Mini Park to Gwongeumseong; C: Huiungak Shelter Area; D: Huiungak Shelter to Daecheongbong; E: Osaek Spring Area). Sustainability 2018, 10, 2263 16 of 21

Sustainability 2018, 10, x FOR PEER REVIEW 16 of 22

Figure 9. Locations of potential congestion and/or crowding in SNP. Figure 9. Locations of potential congestion and/or crowding in SNP.

Figure 10 further illustrates the spatial distribution of visitors’ activity density and risky points. Figure 9. Locations of potential congestion and/or crowding in SNP. FigureThe Korean 10 further National illustrates Park Service the spatial defines distribution risky points of as visitors’ being related activity to densityfalling-rocks, and riskyrisk of points. structure collapse, or lightning. As of 2016, 33 risky points existed in SNP. By overlapping the The KoreanFigure National 10 further Park illustrates Service definesthe spatial risky distribution points as beingof visitors’ related activ toity falling-rocks, density and riskrisky of points structure. density of visitors’ activities and risky points, park managers can identify the specific locations of collapse,The Korean or lightning. National As Park of 2016, Service 33 risky defines points risky existed points in asSNP. being By overlappingrelated to falling the density-rocks, ofrisk visitors’ of potential risky trails or warning areas (e.g., Osaek Spring Area in SNP). That is, based on the activitiesstructure and c riskyollapse points,, or lightning. park management As of 2016, can 33 identify risky points the specific existed locations in SNP. ofBy potential overlapping risky the trails spatio-temporal visitation information, SNP managers can establish a visitor management density of visitors’ activities and risky points, park managers can identify the specific locations of or warningframework areas and (e.g., control Osaek visitors’ Spring resource Area use in SNP).in SNP That to ensure is, based safety on. For the instance, spatio-temporal park visitations visitation potential risky trails or warning areas (e.g., Osaek Spring Area in SNP). That is, based on the information,can be control managersled in canthe establishidentified acongested visitor management or warning areas framework when forest and fires control occur visitors’ frequently resource spatio-temporal visitation information, SNP managers can establish a visitor management use in(spring SNP to and ensure fall), safety. heavy rainFors instance, (summer park), or heavyvisitations snow can falls be (winter). controlled Lastly, in the information identified on congested the framework and control visitors’ resource use in SNP to ensure safety. For instance, park visitations or warningseasonal areas hot spots when of forest park visitors fires occur during frequently weekdays (spring and weekends and fall), also heavy can provid rains (summer),e locations with or heavy can be controlled in the identified congested or warning areas when forest fires occur frequently snowpotential falls (winter). congestion Lastly, or crowding information issues on per the daily seasonal basis (e.g., hot weekdays spots of parkand weekends) visitors during. weekdays (spring and fall), heavy rains (summer), or heavy snow falls (winter). Lastly, information on the and weekends also can provide locations with potential congestion or crowding issues per daily basis seasonal hot spots of park visitors during weekdays and weekends also can provide locations with (e.g., weekdays and weekends). potential congestion or crowding issues per daily basis (e.g., weekdays and weekends).

Figure 10. Spatial distribution of visitors’ activity density and risky points in SNP.

Figure 10. Spatial distribution of visitors’ activity density and risky points in SNP. Figure 10. Spatial distribution of visitors’ activity density and risky points in SNP. Sustainability 2018, 10, 2263 17 of 21 Sustainability 2018, 10, x FOR PEER REVIEW 17 of 22

TheThe findingsfindings ofof thisthis studystudy alsoalso cancan bebe applied,applied, alongalong withwith thethe nationalnational parkpark visitorvisitor informationinformation systemsystem designed, toto enhanceenhance visitors’visitors’ experienceexperience withwith aa predictivepredictive geographicalgeographical insightinsight intointo whichwhich locationslocations areare crowdedcrowded duringduring aa specific specific season season andand day. day. The quality of visitors’ visitors’ experience experience is is compromisedcompromised due to crowdedness,crowdedness, and seasonality is aa keykey criticalcritical componentcomponent toto assessassess inin nationalnational parksparks [[5,8,10]5,8,10].. Essentially, accessaccess to information information can can encourage encourage enhanced enhanced action action [[5501].]. Therefore, informationinformation about about park park visitors’ visitors’ activity activity hot spots hot spots (i.e., locations (i.e., locations of potential of potential congestion congestion or crowding) or acrosscrowding) different across seasons different and seasons days allow and days visitors allow to decidevisitors whetherto decide to whether visit specific to visit sites specific at a specific sites at timea specific via GPS-based time via mobileGPS-based application mobile (see application Figure 11 ).(see The Figure use of GPS-based 11). The use mobile of GPS application-based mobile offers visitorsapplication a viable offer opportunitys visitors a viable to compile opportunity useful real-time to compile data useful information real-time as datait highlights information locations as it wherehighlights potential locations crowding where and potential congestion crowding may occur.and congestion may occur.

Figure 11. Real-time national park crowding information system using web GIS and smartphone. Figure 11. Real-time national park crowding information system using web GIS and smartphone.

Because park visitors’ behavior can be influenced by seasonal variations in climate and preferredBecause activities park visitors’ [10,11] behavior, information can be influenced of seasonal by activity seasonal hot variations spots induring climate weekdays and preferred and activitiesweekends [ 10can,11 ],also information contribute of seasonal to monitoring activity hot systems spots during designed weekdays to protect and weekends natural andcan alsohistoric/cultural contribute to resources. monitoring Such systems information designed may to be protect utilize naturald by management and historic/cultural agencies to resources. allocate Suchfinancial information budgets mayand behuman utilized resources by management more effectively agencies by to pinpointing allocate financial the locations budgets andof potential human resourcescongestion more or crowding effectively (see by Figure pinpointing 12). To the achieve locations a balance of congestion between and/or conservation crowded and sites use (see of Figureresources, 12). park To achieve managers a balance also need between to determine conservation why and visitors use of use resources, certain park sites management or resources also less needfrequently to determine during a whyparticular visitors season(s). use certain sites or resources less frequently during a particular season(s).As the preferences and activities of park visitors change over time, the findings of this study couldAs provide the preferences insights andfor current activities and of parkfuture visitors planning change/management over time, theto ensure findings sustainability of this study of could the providepark and insights its environmental for current and resources. future planning/managementWithin this context, the to determination ensure sustainability of existing of the visitors’ park andpreferred its environmental sites enables resources. managers Within to understand this context, thepotential determination preferences of existing of visitors, visitors’ as preferred well as sitesimplement enables dispersal managers initiatives to understand to minimize potential pressurepreferences on of specific visitors, locations as well as that implement are routinely dispersal or initiativesseasonally to crowded minimize [52] pressure. on specific locations that are routinely or seasonally crowded [51]. ThisThis study study also has has implications implications to for mitigate remedying potential potential visitor visitor conflicts conflicts in in national national parks. TheManaging management visitor ofconflict conflict is important is important,, as visitors’as visitors’ activities activities tend tend to spatiallyto spatially coincide coincide [53] [.52 In]. Inaddition, addition, if visitors if visitors’’ activities activities and andthe natural the natural environment environment are not are properly not properly managed, managed, conflict conflict might mightoccur among occur among/between visitor groups and visitor other groups stakeholders and other [54 stakeholders,55]. Thus, understanding [53,54]. Thus, understandingthe sites preferred the sitesby visitors preferred should by visitors be intensively should managed. be intensively managed. Sustainability 2018, 10, 2263 18 of 21

Sustainability 2018, 10, x FOR PEER REVIEW 18 of 22

Figure 12. Type of seasonal activity hot spots. Figure 12. Type of seasonal activity hot spots. 5. Limitations and Future Studies 5. Limitations and Future Studies Despite the methodological and practical implications of this study, several limitations can be Despiteidentified. the First, methodological the findings are and limited practical to a single implications national p ofark this (SNP study,) and severalcannot b limitationse generalized. can be identified.Park visitors’ First, theactivities findings can be are affected limited by to their a single level of national access to park preferred (SNP) settings and cannot [56]. Each be n generalized.ational Parkp visitors’ark has activitiesits own color can and be affected specific by settings their levelthat can of accesscreate todifferent preferred seasonal settings spatial [55 ]. patterns Each national of parkvis hasitors’ its own flow colors and andactivities. specific So, settings future studies that can should create be different conducted seasonal in other spatial types patternsof park areas of visitors’, such as marine parks or marine protected areas, to compare the seasonal spatial patterns of visitors’ flows and activities. So, future studies should be conducted in other types of park areas, such as marine activities by considering the heterogeneity of each type of national park. Second, this study was not protectedable to areas identify to compare activity hot the spots seasonal at different spatial times patterns of the of day. visitors’ Spatio activities-temporal withdynamics consideration of park of the heterogeneityvisitors’ flows ofcan each reveal type different of national space consumption park. Second, patterns this study at different was not times able of today identify (morning, activity hot spotsaftern atoon different and evenin timesg) during of the weekdays day. Spatio-temporal and weekends. dynamicsSo, future research of park should visitors’ focus flows to explore can reveal differentthe spatio space-temporal consumption patterns patterns of visitors’ at activities different at timesdifferent of times day (morning,of the day during afternoon weekdays and evening)and duringweekends. weekdays Third, and although weekends. this study So, futureidentified research the location should of potential focus to crowding explore theor congestion spatio-temporal in patternsSNP, of the visitors’ meaning activities of crowding at different or conges timestion was of thenot dayclearly during defined. weekdays Crowding and issues weekends. are major Third, althoughconcerns this study that management identified the should location consider. of sites In with addition, potential crowding crowding might or occur congestion with too issues many in SNP, the meaningvisitors simultaneous of crowdingly or in congestion one place wasor on not a clearly given day defined. and time Crowding. Thus, futureissues arestudies major should concerns quantitatively measure the level of crowding at different time scales. Fourth, this study was not able that management should investigate. In addition, crowding might occur with too many visitors to identify visitors’ preferences for their recreational activities that are essential to establish simultaneouslylong-term management in one place plans. or on Hence a given, a daysurvey and-based time. data Thus, collection future studiescould be should combined quantitatively with measureGPS- thebased level visitation of crowding data and at GISdifferent information time scales. for future Fourth, research. this studyLastly, was due not to limited able to data identify visitors’availability, preferences only for two their data recreational points of one activities activity— thatstarting are essential and ending to establish—were used long-term for the analysis management. plans.This Hence, result as survey-basedin an exclusion dataof all collectionthe locational could data be of combinedeach activity. with Therefore, GPS-based future visitation studies could data and GIS informationincorporate detailed for future locational research. points Lastly, during due a specific to limited activity data journey, availability, which only could two facilitate data points the of one activity—startingformulation of a complete and ending—were illustration of the used spatio for- thetemporal analysis. patterns This of results park visitations. in an exclusion of all the locational data of each activity. Therefore, future studies could incorporate detailed locational points during a specific activity journey, which could facilitate the formulation of a complete illustration of the spatio-temporal patterns of park visitations. Sustainability 2018, 10, 2263 19 of 21

6. Conclusions This exploratory study examined the seasonal spatial patterns of visitors’ activities in the SNP. Despite the importance to understand seasonal spatial patterns of visitors’ activities, to our knowledge, no empirical studies have been conducted in this line of inquiry. Based on the utility of a large dataset of GPS-based mobile exercise application users’ visitations, this study examined the seasonal spatial patterns of visitors’ activities via several geospatial analytical techniques (e.g., spatial centrographic analysis, NNA, spatial autocorrelation analysis, hot spot analysis and kernel density estimation), in combination with GIS-based mapping. Such a new methodological framework allows for more comprehensive measurements of visitors’ spatio-temporal behaviors in national parks. Results indicated that there were considerable seasonal and weekdays/weekends variations in activity distribution and areas. The findings can contribute to aspects of management, whose main goals are to protect park resources and enhance visitor experiences. The findings broaden the scope of research questions with additional spatio-temporal insights into visitors’ activities. The use of a GPS-based mobile application in combination with GIS-based spatial analytical techniques is an emerging area of research that has major significance for management in national parks and other protected areas. We hope that this study will stimulate park researchers and managers to levy additional emphasis to seasonal spatial activity patterns of visitors via GPS-based mobile application users.

Author Contributions: All authors conceived and designed the research. J.K. and B.T. wrote Sections1, 2.2, 2.3, 3.1, 3.2,5 and6. S.J. collected the dataset and wrote Section4. E.Y. analyzed the data and wrote Section 2.1. Funding: The authors received no specific funding for this work. Acknowledgments: The authors are thankful to Chi-Kuk Jang, CEO of Tranggle, for providing a sample dataset of GPS-based outdoor mobile exercise application. In addition, publication of this article was funded by the University of Florida Open Access Publishing Fund. Conflicts of Interest: The authors declare no conflict of interest.

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