Large-scale cultural events and festivals in cities are recognized as significant tools for building the city image and attracting visitors [1
]. Such events and festivals bring economic, social and cultural advantages to cities which then can be used to (re)generate places for better living, working and visiting conditions. Cities and event organizers have the responsibility to provide positive experiences for visitors in order to improve visitor satisfaction and stimulate repeated visits [2
]. Large-scale events usually result in short-term fluctuations in the city’s population because of the influx of visitors [3
]. Fluctuations in the city’s population might have an impact on the infrastructure and logistics, and cause negative experiences for visitors and residents (i.e., visitor congestion or overcrowding due to the promotion of already prominent public spaces and environmental deterioration due to litter and noise). Moreover, if visitors are not well navigated during an event, only the prominent locations of the city are frequented by the visitors and therefore not all exhibition locations may be visited. This might result in less economic, social and cultural advantages for these places.
During large-scale cultural events that are distributed to different areas in a city, it is difficult to obtain comprehensive feedback on visitors’ flows. Having such feedback can help understanding visitors’ spatial and temporal behavior, intra-event destinations and their relations and intra-event destination choices during the event. Consequently, diverse suggestions can be provided to visitors and event providers for better organized events and even distribution of visitors. Therefore, in order to evaluate the organization of the event and to develop sustainable policies for event tourism, it is vital to investigate visitors’ flows during large-scale events [4
In the last two decades, due to the increase in the availability of information and communications technology (ICT) tools (i.e., smartphone apps) and location positioning systems (i.e., global positioning system (GPS)), data-driven research on tourism and tourist flows has increased [5
]. These technologies allow for capturing the mobility patterns of individuals, episodes and location of activities, the points of interests and the topology of activities in relation to the urban environment. According to Shoval and Ahas (2016) [6
], the advantages of collecting data using GPS loggers and/or GPS-enabled smartphone apps are that they provide high-resolution data in time and space; they do not depend on respondents’ memory, thus providing more accurate data; and they reduce the burden on participants as the data collection is less dependent on the respondents. However, such data lack respondents’ background information such as socio-demographics, or sentiments about certain subjects. Therefore, GPS tracking is usually accompanied by online and/or paper surveys.
Current data-driven tourism studies focus on experiences and flows of tourists in a variety of settings such as touristic visitors in focal points within cities (i.e., ports, heritage districts, city centers) [7
], visitors of recreational parks and zoos [12
], and visitors of events [2
]. By using the newly available datasets derived from ICT tools, these studies investigate visitors’ perception of space and, also their behavioral patterns in time and space and the factors influencing these patterns such as socio-demographics characteristics of visitors (i.e., age, gender), spatial characteristics of visit location (i.e., origin of visit, location and centrality of attractions, accessibility of attractions) and time characteristics of the visit (i.e., starting time of visit, time at attractions). For instance, De Cantis et al. (2016) [9
] state that visitors’ age, gender, travel company and visitation frequency (being first- or second-time visitor) influence their visit patterns in port areas and therefore these characteristics can be used to segment visitors. A recent study from Gong et al. (2020) [19
] focuses on the behavior of visitors of the Sail event and King’s day in Amsterdam by using location-based social media data. This study shows that visitors differed in terms of their age and gender for their social media uploads per attraction. Moreover, Birenboim et al. (2013) [12
] state that visitors’ temporal patterns in theme parks are influenced by their time budget and also the park’s controlled environment (i.e., opening hours and show schedules). Furthermore, Shoval et al. (2011) [7
], Aranburu et al. (2016) [10
], and Sugimoto et al. (2019) [11
] show that due to distance decay, visitors’ movements are largely limited by the starting location of their visit and the spatial configuration of attractions play a role in the choice of visits to intra-destinations.
These studies have implemented various techniques in order to detect and visualize movement patterns from location-based datasets such as grid-based aggregation [7
], density-based clustering [19
] and network analysis [3
]. Grid-based aggregation and density estimation are usually used to identify the points of interest (POI)/area of interest (AOI) and network analysis are used to identify the relations between POIs/AOIs. The results of these studies contribute to policy formulations for destination planning and management, event impact management and transportation planning in touristic places.
Hitherto, most of the current studies on understanding large-scale events with newly available datasets [2
] focus on subjective experiences of visitors and do not consider visitor flows during the event. Only few studies [3
] highlight the visitor’s spatial and temporal behavior in the large-scale cultural event setting by using Bluetooth, mobile network data and location-based social media data, respectively. This shows that there is a need for more studies on the large-scale cultural events in terms of understanding the visitors’ flows (visitor’s spatial and temporal behavior, intra-event destinations and their relations, and the factors influencing the intra-event destination choices) [18
]. Moreover, the data collection of these studies [3
] did not aim specifically at collecting data from event visitors, and therefore their data collection methods suggest more opportunistic approaches [22
] such as scraping data from social media, rather than interacting with the visitors. This current study contributes to the existing studies on visitor’s flows during large-scale events in terms of its GPS data collection methodology and analysis.
In this study, the aims are two-fold: (i) to understand the spatial and temporal behavior of visitors during the event, including the relations between intra-event destinations, and (ii) to understand the determinants of visitors’ intra-event destination choices. For the first aim, we used the GPS data of 281 visitors in order to identify visitors’ intra-event destinations, which refer to the areas of interest (AOIs). These AOI locations are determined based on places where visitors spend a certain amount of time (i.e., longer than 3 min) within a 100 m radius and how much the location is frequented by visitors. Then, we looked at the distribution of these AOIs in the city and how much time visitors have spent on average at these AOIs. Next, we applied network analysis in order to understand the relations between the visitations of each AOI. For the second aim, we combined the GPS data of visitors with their socio-demographics and applied bivariate analysis and cluster analysis.
This study is conducted within the framework of the European Union Horizon 2020 ROCK (regeneration and optimization of cultural heritage in creative and knowledge cities) project which aims to develop an innovative, collaborative and circular systemic approach for the regeneration and adaptive reuse of historic city centers. In this project, large-scale events are exploited as one of the enablers of sustainable urban transformation.
This paper is organized as follows: First, the case study area, data collection procedure and sample characteristics are explained. Then, the methodologies that are used to analyze the data are introduced. After that, the findings of the study are explicated. The paper concludes with the discussion of findings and the effectiveness of the methodologies for understanding visitor flows at large-scale cultural events, and with suggestions for event organizers and policy makers.
This section will firstly introduce the results of spatial and temporal behavior of visitors, including the network analysis findings. Then, the results for the determinants of intra-event destination (AOI) choices will be given.
3.1. Results of Spatial and Temporal Behavior of Visitors
In order to visualize the distribution of GPS logs, the case area was divided into 100 m hexagons. Figure 2
shows the distribution of GPS logs in space. According to the outputs of the Trace Annotator algorithm, the most frequented areas of interest (AOIs) were calculated. Table 2
represents the most visited AOIs by the visitors of the sample of this study, indicating the percentage of visitors and the mean duration of visits at each AOI. Moreover, Figure 3
represents the same information spatially. All the AOIs correspond to one or a cluster of exhibition locations.
It is important to note that the participants were recruited at the Central Station (C1) and most of the participants also brought the GPS loggers back to this location. This explains the high number of visits and visitors for the “Central Station” AOI. Moreover, it is seen that all AOIs were located only in Central and Strijp-S areas, meaning that there was no AOI in the East area.
In Figure 3
, it is seen that the most frequented AOIs were Central Station (C1) and Design Academy (C2), followed by Apparaten Fabriek (S1), Machinekamer (S2), Beukenlaan (S3) and Market Square (C3). Moreover, the average duration of the visit was the longest for the Design Academy (C2) and this is followed by Piet Hein Eik (S5), Beukenlaan (S3), Machinekamer (S2) and Temporary Art Center (C7), while 18 Septemberplein (C4), Central Station (C1) and Philips Museum (C11) were the least time spent locations. For the ease of reading, only the code of the AOI names will be mentioned from here on.
Network Analysis: Intra-Event Destinations and Their Relations
The centrality indicators resulting from the network analysis can be seen in Table 3
. Regarding degree centrality, C1 has the highest degree, as expected. Since it was the origin of all visitors and final destination of the majority of visitors. This is followed by C2, C3, C5, S2 and S1. These AOIs can be considered as the high attraction points for the visitors. Looking at the betweenness centrality amongst all AOIs, again C1 has the highest value and this is followed by C3 and C2 with high scores (>8.8). This means these AOIs were significant intermediary locations between pairs of other AOIs, because many visitors might have stopped at C1, C2 and C3 between their visitations of other AOIs. Considering closeness centrality, all AOIs have a high closeness centrality value (>0.6). This means that the majority of AOIs were reachable and in close distance from other AOIs. C1, C2, C4, C5, C3 and S3 have the highest scores, respectively, which signify that they have the shortest distances to all other nodes. The closeness indices above 0.6 might also indicate that the connections between AOIs in terms of mobility options provided were convenient. Finally, looking at the eigenvector centrality, again all AOIs perform above 0.5. The high scores for AOIs C1, C3, C2, S1, S2, C5 and S4 indicate that they were significant AOIs and also were well connected with other significant AOIs. An interesting finding is that, although S4 has relatively low scores for degree, betweenness and closeness centrality measures compared to the majority of other nodes in the network, its eigenvector centrality score is high. This might mean that S4 was not an interesting location for visitors but it received direct links (visitors) from important AOIs such as C1, C2 and S1. An explanation for this could be the nearby train station of Strijp-S and the DDW bus stop.
A directed network of visitor flows (in-strength), constructed based on the origin-destination matrix, is illustrated in Figure 4
. The size of the AOIs is proportional to the degree of centrality measurement. The color of AOIs represents the betweenness centrality value (the darker the color, the higher betweenness centrality) and the color of the links represents the weight of the link (the darker the color, the higher the weight of a link). The directed network shows that, in the central area, C1, C2 and C3 were well connected AOIs. After being at C1, the majority of visitors went to C2, and the rest visited mainly either C3, C4 or other AOIs in the city center. Moreover, the majority of visitors entered the Strijp-S area at S1, coming mainly directly from C1 and then visited the other AOIs in the Strijp-S area. After their visits to AOIs in the Strijp-S area, most of the visitors returned back to C1.
According to the results of this section, during DDW, C1 was an important node as expected since it was the starting point (origin) for the visitors in our sample. Moreover, it is seen that C2 was an important AOI due to its high centrality indicators. Moreover, the high average duration spent at this node indicates that C2 was the most attractive event location for the visitors. In addition to this, looking at the centrality indicators and average duration spent at the AOIs, C3 was also an important and attractive event location. Moreover, C5 has high centrality indicators; however, the average duration for that AOI was not as high as C2 and C3.
Since all these AOIs are located in the central area, it is also useful to look into the AOIs in the Strijp-S area separately. Amongst the AOIs within the Strijp-S area, S1, S2 and S3 have the highest centrality indicators, suggesting that these AOIs were significant and connected ones within the area. In addition, the average time spent at these AOIs was similar, suggesting that these AOIs were the most attractive ones within the Strijp-S area during the DDW. Finally, it can be said that although S4 was not an attractive AOI, it received direct visits from important and interesting nodes (high eigenvector centrality); therefore, it can be considered as an important AOI in the Strijp-S area.
3.2. Results for the Determinants of Intra-Event Destination (AOI) Choices
After determining the AOIs, the influence of visitors’ characteristics and the AOI-specific characteristics on the visitors’ choice of AOIs was investigated. C1 (central station) was removed from the analysis since it was the origin of the trip and not an intra-event destination. The data and results can be seen in Table A1
in Appendix A
According to the results, age of visitors shows a significant result at the 10% significance level (X2 (NDF = 15, n = 1120) = chi-square 23.350, p = 0.077), meaning that there were significant differences between the choice of AOIs for different age groups. The results show that C7, C9 and S4 were preferred more by visitors below 30 years old, while S5 was preferred by visitors older than 30 years old. This might be related to the content of exhibition within this AOI and also the atmosphere of the area related to the services around. Moreover, there is a significant association between the intended duration of the event visit and the choice of AOI, at 10% level (X2 (NDF = 15, n = 1120) = chi-square 22.606, p = 0.093). People who intended to visit the event more than 5 h preferred to include mostly C7 and C9 to their visits. In terms of distance, AOIs C7 and C9 are relatively further from the city center and the origin of the visit (C1). Thus, it can be said that visitors who had more time for DDW visitation preferred to include further locations to their itinerary.
In terms of AOI-specific characteristics, arrival time to each AOI shows significant association with the choice of AOI (X2 (NDF = 15, n = 1120) = chi-square 39.178, p = 0.001). The results indicate that most of the visitors preferred to visit the AOIs in the central area before 13:00. Moreover, the duration at AOI has a significant relationship with the choice of AOI (X2 (NDF = 15, n = 1120) = chi-square 184.092, p < 0.001). Especially at C2, C7, S3 and S5, most of the visitors tended to spend more than 45 min. These AOIs included several exhibitions which were represented in large spaces. This might have caused the longer time spent at these AOIs. Finally, it is found that occurrence of rain at arrival time to AOI shows a significant relationship with the choice of AOI, at 10% level (X2 (NDF = 15, n = 1120) = chi-square 22.604, p = 0.093). When there was rain, C2, S2, S3 and S4 were the most preferred AOIs. This might be because C2 included several exhibitions in a large and closed space. S2, S3 and S4 were similar in that sense and, in addition, these AOIs contain several eating and drinking services such as cafes and restaurants.
These results denote that intra-event destination (AOI) choices of DDW visitors depended on the visitors’ age, their intended duration for their visit and AOI-specific characteristics, namely, arrival time to AOI, duration at AOI and the occurrence of rain at the arrival time to AOI. It is found that the association was highly significant between AOI choice and AOI-specific characteristics, especially for temporal aspects. The reasoning behind these choices might be explained with the characteristics of the exhibitions in these AOIs, and also the spatial characteristics of these AOIs in terms of the services provided and the type and size of exhibition buildings.
Clustering of Intra-Event Destination (AOI) Choices
In order to understand whether there were clusters amongst DDW visitors based on their AOI choices, K-means cluster analysis was performed. The number of clusters was decided to be two after several trials. For two clusters, convergence was achieved at the 8th iteration. The two clusters of AOIs can be seen in Figure 5
and their spatial distribution can be seen in Figure 6
a,b. Cluster 1 contains 115 visitors, Cluster 2 contains 166 visitors. An analysis of variance (ANOVA) test was conducted in order to check the differences between the clusters (See Appendix A
, Table A2
). According to this test, visitors’ choices on visiting C4, C7, C8, C9, C11, C12 did not differ significantly between the clusters (at 10% significance level). As can be seen in Figure 5
, Cluster 1 contains visitors that visited mainly the AOIs in the Strijp-S area who also visited some of the AOIs in the Central area, largely C2. Cluster 2 consists of visitors that visited the AOIs mainly within the Central area and rarely combined their visit with the Strijp-s area.
shows the results of chi-square analysis between the determined clusters and the visitor characteristics. According to the Pearson chi-square test, there is a significant difference between the clusters in terms of visitors’ arrival time to DDW event. The results show that the majority of the visitors who are in Cluster 1 preferred starting the event activity early (before 11:00 am). It is possible that visitors considered time restrictions for their visit before the event started so that their visit could cover both areas. It is also possible that visitors who arrived early decided to visit both locations considering the spatial organization of the event. Visitors who are in cluster 2 (tended to mainly visit the Central area) did not show a strong preference for the arrival time to DDW.
4. Discussions and Conclusions
Research on visitors’ flow during large-scale events is still limited but highly important for event planning and management, transportation development and impact management. This study contributes to the few existing studies on large-scale events in terms of its data collection with GPS loggers and a survey, and the analysis of the data for understanding the visitor flows and visitors’ intra-event destination choices.
Although the city dedicated three areas for the DDW, only two of them (Central and Strijp-S) were visited enough to be considered to have AOIs. The results indicate that visitors of DDW preferred AOIs in the Central area and Strijp-S (West) area, rather than in the East area of the city. The East area is a new ‘alternative design’ district of the city. Since this area’s concept for design is new, it was less known to DDW visitors compared to the Center and Strijp-S areas. Moreover, the most attractive AOIs, which are C2 and C3, are in the highly central and busy district of the city, close to each other, to the central train station and to other activities and services such as shops, cafes and bars. The same applies to the most attractive AOIs of Strijp-S area which are S1, S2 and S3. Moreover, it is found that the Central area and Strijp-S area are connected to each other, especially by the flow of visitors between C1 to S1. These results show that the high attraction points of the city were again the most significant ones during the DDW event. Moreover, the distance decay factor might have influenced the flows to far locations within the areas. This might also be a reason for the limited attractiveness of the East area. In order to distribute the flows to other less represented locations, exhibition contents and their locations should be reconsidered in the future events. Additionally, the distance decay factor can be eliminated by strengthening the provision of a variety of mobility options (i.e., busses and bikes associated with the event) in many of the AOIs. The lack of spatial dispersal of visitors might also be due to the need for a clearer wayfinding system in terms of maps, roads and transport signs. Improvements in these aspects can support the visitors to find their way and explore different locations of the city during the DDW event. For that purpose, city managers should make an on-site assessment of the current navigation services.
The intra-event destination (AOI) choices were mainly influenced by visitors’ age, visitors’ intended DDW visit duration, arrival time to AOI, duration at AOI and rain occurrence at the arrival time to AOI. Visitors’ age might have created a taste variance based on the content of exhibition at AOIs and the location and atmosphere of AOIs. Additionally, AOI choices of visitors were associated with time constraints. Time-related restrictions (such as starting time of the event) were influential also on the choice of exhibition areas. In order to maximize the time span of visitors and navigate the visitors through different areas based on visitors’ tastes, themed trips of certain durations can be organized. These themed trips can include suggested itineraries for different themes and can be provided as maps and/or brochures at the ticket offices or as a smartphone app service.
This study proves that GPS data together with a survey is a valid approach for future studies on visitor flows at large-scale events. However, the data collection process is a limitation for recruiting larger numbers of respondents since people need to stop at the ticket office to answer the survey questions and be instructed regarding the GPS loggers. In addition, the distribution and collection of GPS loggers at a specific location is another limitation of this study since visitors who start the event visiting from another location might visit different AOIs. Therefore, this approach can be further advanced in future studies by developing a dedicated user-friendly smartphone application. This app can also accommodate questions regarding visitors’ choices for AOIs. Another limitation of the study was that we considered a place an AOI if it was frequented by more than 25 visitors. The reason for that was to conduct bivariate analysis with enough samples of visitors. However, this might have caused bias for the network representation because there might have been other places visited and included in the origin destination matrix. It was also surprising that visitor characteristics (except age and intended duration of visit) were not found to be significant. This might be because the surveyed characteristics were not differentiating or because our sample was homogenous. This should be further examined in a future study.
Overall, this study provided useful insights for event managers and city planners for better organized events. It shows that understanding visitors’ flows at large-scale events can reveal how much of the space is consumed by different visitor profiles, and therefore the level of attraction of different locations in the city. With the suggestions provided in this study, large-scale events such as DDW can increase the potential of exhibitions for more and repeating visitors. It will also contribute to further event planning for different target groups. In the future, this approach can be further extended for modeling scenarios to predict the results of different event interventions for better organized and sustainable events.