Identification of centrality of West Kalimantan tourist attractions based on network analysis

ABSTRACT


INTRODUCTION
Tourism is an incredibly important industry worldwide, with a significant economic impact.In Indonesia, it is one of the biggest contributors to foreign exchange earnings, accounting for 10-12 million USD in 2013-2015 (Kemenparkraf/ Baparekraf RI, 2020), ranking 4th after palm oil, new coals, and oil & gas.This result makes it a more reliable sector than oil and gas exports, which have a limited lifespan.However, the number of foreign tourists visiting Indonesia fluctuates.In the 2019 Travel & Tourism Competitiveness Index report (World Ecomic Forum, 2019), Indonesia ranked 40th out of 140 countries, and 12th out of 22 countries in the Asia Pacific region.This is still behind Thailand, Malaysia, and Singapore.Despite this, Indonesia has made good progress in recent years, improving its ranking in the index.This suggests that the tourism sector has the potential to grow further and become even more important to the Indonesian economy.
Tourism destinations are a vital element and the main driving force for tourists in deciding where to travel and visit.For this reason, good planning is essential to provide services that meet the motivations of tourists and are aligned with the country's strategic tourism plan.The national tourism development plan considers several factors when developing tourism destinations, including: the relationship between tourist destinations and regional and/or national tourism gateways, the relationship between components of attractiveness and movements in the tourist destination, the development of transportation networks and the integration of infrastructure networks between gateways and existing destinations (National Tourism Development Master Plan, 2011) In the dynamic landscape of the tourism industry, the interplay between tourism attraction connectedness and transportation networks has emerged as a critical area of study (Canson & Caelian, 2022) Tourism is a multifaceted phenomenon that involves the movement of people from their origin to various destinations, where they engage in activities and consume services.(Hernández & González-Martel, 2017) This complex web of interactions between tourism stakeholders, including tourists, destination hosts, and transportation providers, highlights the need for a comprehensive understanding of the underlying dynamics.(Hartman & Heslinga, 2022) Tourism scientists are increasingly discussing the concept of "network systems" in tourism.Tourism can be broadly defined as a complex network of activities, including tourist movements, travel between tourist attractions and routes, relationships between tourism and management businesses, and more (Gajdošík, 2015).The movement of tourists forms a network that connects their origin to their destination, also known as tourism flow (Chung et al., 2020).Tourism flow is the central focus of tourism geography, and it consists of at least three components: direction, level of visit, and the relationship between locations (Peng et al., 2016).
In a tourist destination, the destination itself can be seen as a "node" and the route between attractions as a "link".Network analysis can be used to classify the relationships between attractions and other stakeholders, and to explain the characteristics of these connections.Network analysis is a useful tool for tourism research because it can visualize the movement of tourists, calculate the relationships between nodes, and explain the structural patterns that connect them (Zeng, 2018) A tourism destination network is a geographic system that connects destinations and the routes between them.It consists of three main components: destinations (as nodes), routes (as links), and the relationships between nodes.Destinations can be connected directly or indirectly, and the weight or form of these relationships creates a structure of further linkages (Hua & Wondirad, 2021).In a tourist destination, the tourism industry, including hotels and tourist attractions, tends to cluster together.This suggests that the tourism industry in a destination is geared towards serving tourists who want to visit multiple attractions (Chhetri et al., 2013).Tourism development is a complex process that involves many interrelated resources.For this reason, it is possible to use dynamic processes to form clusters and networks between different elements in tourism studies (Brás et al., 2010).
In an urban spatial configuration, the road network is a key element that can shape access, distance, distribution, placement, "closeness", and "degrees".As Ni et al. (2016) noted, the urban road network plays a very important role in shaping urban activities and the spatial distribution of urban service facilities.Centrality/centralization is one of the measuring instruments used to evaluate urban road networks.The field of urban study has long been a subject of fascination for researchers, urban planners, and policymakers alike, as they seek to understand the complex dynamics that shape the growth and development of cities (Haghani et al., 2023).One particularly insightful approach to this field is the application of network analysis, which provides a framework for examining the intricate relationships and interconnections that underlie urban systems (Yap et al., 2023).From this perspective, the urban landscape can be conceptualized as a complex network, where various elements such as roads, transportation systems, and social interactions serve as the nodes and links that define the overall structure (Ward et al., 2011), and it can be divided into several substances, such as transactions, direction/destination, structural, density, connection, and optimization.One of these substances is the measurement of centrality/centrality (Pavlovich, 2003;Scott et al., 2008).Network analysis can be used to study tourist destinations.A tourist destination can be considered a location that interacts and collaborates with other parties to support a tourism product.In general, network analysis in tourism can explain the relationship between the parties or resources (actors) that are quantitatively related in terms of relationships.Each actor can access the network as a whole or as a specific actor (Gajdošík, 2015).
West Kalimantan province is comprised of 14 cities and regencies, encompassing a total area of 146,807 square kilometers (Kalbarprov.go.id).As indicated in the news publication delivered by RRI (Harmanta, 2023) West Kalimantan Province has considerable tourism potential, comprising 744 objects of tourist attraction.The aforementioned tourism potential is divided into several categories.A total of 499 tourist attractions are classified as "nature," comprising the largest portion of the province's tourism industry.Another 92 attractions are designated as "cultural," while 53 are categorized as "artificial," 33 as "religious," and 28 as "marine."Additionally, there are 16 historical, 10 culinary, and 10 agricultural tourist attractions, as well as two shopping tours and one educational tour.It is evident that further planning is required in order to integrate these diverse tourist attractions, while simultaneously ensuring that each retains its unique identity and appeal, as well as aligning them effectively with target markets.As stated by Hua and Wondirad (2021), destinations can be connected or not connected.In addition, a tourist destination can be defined as a location that interacts and cooperates with other parties to support a tourism product, and thus it is also necessary to pay attention to each cluster in order to create equity in accordance with the potential and the market.As outlined by Purwanto et. al. (2021), in order to support tourism in West Kalimantan, which boasts a considerable number of attractions, it is essential to map tourism development with the support of a diverse range of stakeholders, namely the provincial, regency/city, and village governments, in addition to the private sector and investors.
From several description above, the purpose of this study is to identify the relationships between tourist attractions in West Kalimantan to see how they are structured as a network, including in terms of centers, connections, and clusters (Figure 1).This information can then be used to optimize the network or clustering in accordance with the nature of the relationships between the different tourist attractions

METHODS
This 2023 research will take West Kalimantan Province as a case study to investigate the distribution of tourist attractions, destinations, and interconnectivity.It will use the network analysis approach to study the relationships between tourist attractions/destinations (nodes/vertices) and their interconnections (links/edges).According to Borgatti et al. (2013), there are at least three basic approaches in network analysis: (1) centrality, (2) subgraphs or groups, and (3) equivalence.Each approach has several calculation methods, such as in/out degree, closeness, betweenness, and eigenvectors.Once the network structure is obtained, the results will be discussed to identify the characteristics of the existing attractions/destinations and the potential/problems that exist.The research activity entailed the completion of several stages, including the following: 1. Collection of data in the form of road and transportation networks (national, provincial, city, district).
2. The collection of data in the form of the distribution of tourist attractions.
3. The data collection of the distribution of tourist destinations in West Kalimantan 4. A comprehensive listing of all relevant relationships between the road networks and other geographical areas in West Kalimantan Province is required.Each relationship must be entered in a systematic manner, with all relevant data input in a node-by-node format.5.A list of relationships between attractions in relation to the road and regional network in West Kalimantan Province was compiled and entered into a database.This database contains node-by-node relationships 6.A summary of the interaction data (incoming/outgoing) of the road network/region and tourist attractions in West Kalimantan Province.This presented in the form of a network diagram, with each point (nodes/vertices) connected to its connectors (links/edges) 7. The network analysis will examine and elucidate the centrality of the road and region network in West Kalimantan Province, as well as the interconnected tourist attractions.The analysis will be conducted using the UCINET software program.Faust, 1994) Source: Summarized from several sources 8.A superimposition of network trend analysis with the distribution of tourist attractions will reveal the tendency of tourist attraction distribution towards the road network or ease of access, proximity, connecting points, as well as the most central/main location in the existing interconnectivity configuration.Finally, the discussion will address the results of the analysis and the centrality which is formed

RESULTS AND DISCUSSIONS Data and Distribution of Tourist Attractions in West Kalimantan
Coding of road networks and tourist attractions was performed at the analysis stage.This was done to facilitate the calculation of centrality in the algorithm or software used.

Pattern and Centralization of Areas Based on Access (Roads)
Road relationship data (access) were collected from Google Earth and https://www.openstreetmap.org/.This data revealed hundreds of relationships between road junctions and tourist attractions.The road network and the distribution of tourist attractions in West Kalimantan were illustrated using Adobe Illustrator, and a different code was assigned to each intersection point and tourist attraction.For the analysis, each intersection was given a code to facilitate the analysis.Tourist attractions were also given different codes to facilitate the calculation and superposition process with the road network (access).The analysis was performed using UCINET software and visualized using NetDraw.The results revealed the following trends in distribution patterns and centrality:

Degree
Degree is the number of lines (links) associated with one point, or the number of connections (lines) owned by one point (Borgatti, S et al., 2013;N Scott et al., 2008).Judging from the results of the analysis obtained (Figure 3), the relationship or degree (degree) of the road network (access) in West Kalimantan is generally evenly distributed, with many relationships between points that have the same degree.This can be seen from the NetDraw visualization, which shows dots/nodes of the same average size.However, there are a few points with a significantly larger score or size, namely in the Sambas area (Point No.

Closeness
Closeness is a measure of how close an actor or point is to all other points in a network.A point is considered central if it can interact quickly with other points (Wasserman & Faust, 1994).Judging from the results of the analysis (Figure 4

Betweenness
Betweenness is a measure of how central an actor or point is in a network by counting the number of shortest paths between other points that pass through the actor or point.A point with a high betweenness is considered a bridge point or flow controller in the network (Borgatti, S et al., 2013;S. Wasserman & Faust, 1994).
Judging from the results of the analysis (Figure 5), the road networks (access) in West Kalimantan generally tend to lead to several areas, especially Sanggau Regency (point No. 141: Intersection Jl.Raya Sosok II -Jl.Barage,.This can be seen based on the score or size visualized NetDraw shows the largest sizes.In addition, these largest sizes are access to meetings from several districts/cities including Pontianak City, Landak Regency, and Ketapang Regency.These results show that several points in the Sanggau Regency are the points that are the liaison between road networks (access) in West Kalimantan.

Eigenvector
Eigenvector is a measure of the centrality of an actor or point in a network, taking into account the centrality of its neighbors (Borgatti, S, 1995;Borgatti, S et al., 2013;Hanneman, R & Riddle, 2005).Judging from the results of the analysis (Figure 6  The following is a thematic map of the pattern and attachment to the area based on access (roads) of West Kalimantan based on the analysis that has been conducted.

Patterns Of Tourist Attraction Access to Roads
This section presents a superposition analysis of the road network (access) and tourist attraction data in West Kalimantan.Tourist attractions are connected directly to the intersections (nodes) that are directly related to existing tourist attractions.The relationship between the road network (access) and the distribution of tourist attractions is shown in Figure 8.

Degree
Figure 9 shows that the degree (number of connections) between road networks and tourist attractions in West Kalimantan is generally evenly distributed across all districts/cities in the province.This is evident from the

Closeness
Figure 10 shows that the closeness (ability to be accessed quickly) between road networks and tourist attractions in West Kalimantan is generally evenly distributed across all districts/cities in the province.This is evident from the score or size of the points/nodes in NetDraw (larger points indicate higher closeness).Judging from the score or largest size visualized by NetDraw, the tourist attractions with the highest closeness scores/sizes are: N1 (Telok Atong Bahari, Sambas Regency), N2 (Dermaga Temajuk, Sambas Regency), N4 (Pantai Camar Bulan, Sambas Regency), M16 (Gunung Pasi, Singkawang City), M17 (Taman Agrowisata Bukit Bougenville, Singkawang City), M26 (Danau Sarantangan, Singkawang City).These attractions are closest to other points and can therefore be accessed quickly from within the region.These results indicate that some tourist attractions in Sambas and Singkawang City are the easiest areas to reach.

Betweenness
Figure 11 shows that the betweenness (importance as a bridge between other points) of road networks is generally much higher than that of tourist attractions in West Kalimantan.This is evident from the score or size of the points/nodes in NetDraw (larger points indicate higher betweenness).The points with the highest betweenness scores/sizes are 143 and 144, which are located on the Jalan Kalimantan Poros Tengah, a connecting road between Landak Regency and Sanggau Regency.These points serve as a "hub" in the road network, connecting many different areas.Tourist attractions have a lower impact on the overall betweenness of the network, but the highest betweenness scores/sizes among tourist attractions are found at: J12 (Gunung Tiong Kandang, Sanggau Regency), and G2 (Taman Nasional Gunung Palung, Ketapang Regency).These attractions are located on important roads that connect many other areas, which gives them a relatively high betweenness score/size.The following is a thematic map of the pattern and attraction of tourist attractions on access (roads) of West Kalimantan based on the analysis that has been conducted.

Patterns of Tourist Attractions In West Kalimantan
Based on the analysis results, the following patterns and attractions of tourist attractions in West Kalimantan can be summarized: degree, closeness, betweenness, and eigenvectors.Degree measures the number of connections between nodes.The average node as visualized by NetDraw in West Kalimantan has a similar number of connections, but there are a few nodes in certain areas that have a large number of connections to other nodes.These nodes are evenly distributed throughout the region.Closeness measures how easy it is to reach other nodes from a given node.On average, each node in West Kalimantan is easily accessible from other nodes.However, there are a few regions, such as Sambas Regency, Ketapang Regency, Sekdau Regency, Melawi Regency, Sintang Regency, and North Kayong Regency, that have a higher number of nodes that are easily accessible from other nodes.The following tourist attractions in West Kalimantan have the highest level of relationship or interaction with the road network and other tourist attractions: Mimiland Batu Payung (Bengkayang Regency), Gereka Katedral Santo Yosef (Pontianak City), Masjid Raya Mujahidin (Pontianak City), Taman Nasional Gunung Palung (North Kayong Regency), Air Paoh (North Kayong Regency), Taman Nasional Gunung Palung (Ketapang Regency), Rumah Adat Dayak Kabupaten Ketapang, Danau Sekawi (Lake Sekawi ( Kapuas Hulu Regency), and Danau Mupa Kencana (Kapuas Hulu Regency).These attractions were identified based on a measurement of the relationship between road networks and tourist attractions in West Kalimantan.
The closeness of road networks and tourist attractions in West Kalimantan is generally similar in each district/city region.However, there are a few tourist attractions that have a higher closeness than others as indicated by the colors, meaning that they are more easily accessible from other tourist attractions and road networks.These attractions include: Teluk Atong Bahari (Sambas Regency), Dermaga Temajuk (Sambas Regency), Pantai Camar Bulan (Sambas Regency), Gunung Pasi (Kota Singkawang), Taman Agrowisata Bukit

Figure 1 .
Figure 1.Distribution of Tourist Attractions in West Kalimantan Source: Google Earth, accessed in July, 2023

Figure 3 .
Figure 3. Road Network (Access) Degree Measurement Source: UCINET/NetDraw Analysis, 2023 ), the road networks (access) in West Kalimantan are generally closest to several areas, especially in Sambas Regency, Ketapang Regency, Sekadau Regency, Melawi Regency, Sintang Regency, and North Kayong Regency.This can be seen from the score or size of the points in NetDraw, which are the largest.The largest scores or sizes are found in Sambas Regency (point No. 1: T-junction Jl.Abdul Malik -Jl Takam Putih -Jl.Pasir Putih, Point No. 2: T-junction Jl.Pembangunan -Jl.Tawani, Point No. 25: Jl.Ahmad Yani, & Point No. 26: Jl.Ahmad Yani).These results show that several road access points in Sambas Regency have the fastest access from other points.

Figure 5 .
Figure 5. Road Network Betweenness Measurement (Access) Source: UCINET/NetDraw Analysis, 2023 ), the road networks (access) in West Kalimantan are generally most central to several areas, especially in Sintang Regency (point No. 189: Simpang Tempunak, Point No. 191: Entabuk, & Point No. 192: Tugu Karet Simpang Pinoh) and Sekadau Regency (point No. 181: Rawak Hilir -Rawak Hulu & 182: Intersection Jl.Pangeran Limboro).This can be seen from the score or size of the points in NetDraw, which are the largest.These results show that several points in West Kalimantan, namely in Sintang Regency and Sekadau Regency, are the most central points in terms of overall network structure.

Figure 7 .
Figure 7. Thematic Map of Regional Patterns and Centerization Based on Access (Road) in West Kalimantan Source: Author Analysis, 2023

Figure 9 .
Figure 9. Degree Measurement of Access and Distribution of Tourist Attractions in West Kalimantan Source: UCINET/NetDraw Analysis, 2023

Figure 10 .
Figure 10.Closeness Measurement of Access and Distribution of Tourist Attractions in West Kalimantan Source: UCINET/NetDraw Analysis, 2023

Figure 11 .
Figure 11.Betweenness Measurement of Access and Distribution of Tourism Attractions in West Kalimantan Source: UCINET/NetDraw Analysis, 2023EigenvectorFigure12shows the eigenvector centrality (importance relative to other nodes) of road networks and tourist attractions in West Kalimantan.The points/nodes with the largest scores/sizes are the most central points in the network.The most central point in the network is in Singkawang City, with the largest score/size marked by points number 27 and 28.The most central tourist attractions are: M2 (Taman Burung Singkawang), M3 (Masjid Raya Singkawang), M4 (Bangunan Cagar Budaya Marga Tjhia), M5 (Vihara Tri Dharma Bumi Raya).These results indicate that Singkawang City is a central point in the configuration of the distribution of tourist attractions in West Kalimantan.

Figure 12 .
Figure 12.Eigenvector Measurement of Access and Distribution of Tourist Attractions in West Kalimantan Source: UCINET/NetDraw Analysis, 2023

Figure 13 .
Figure 13.Thematic Map of Patterns and Centralization of Tourism Attractions on Access (Road) in West Kalimantan Source: Author Analysis, 2023 Betweenness measures the importance of a node in connecting other nodes.Sanggau Regency (point No. 141: Intersection Jl.Raya Sosok II, point No. 160: T-junction Jl.Raya Sosok II -Jl.Barage, & Point No. 161: Intersection Jl.Lintas Malindo -Jl.Balai Sebut) is the most important node in the road network of West Kalimantan, as it is a meeting point for several other districts and cities in West Kalimantan, namely Pontianak City, Landak Regency, and Ketapang Regency.Eigenvectors measure the centrality of a node in the network.The most central nodes in the road network of West Kalimantan are located in Sintang Regency and Sekadau Regency.

Table 1 .
Analysis Tools