Spatio-Temporal Clustering of Sarawak Malaysia Total Protected Area Visitors

Abstract

Based on data of visitors to national parks, nature reserves and wildlife sanctuaries in Sarawak, this study’s objective is to use the spatial and temporal analysis to describe the underlying trend and temporal pattern of local and foreign visitors and ultimately infer the temporal distribution of visitors to 18 different TPAs. The second aim of the study is to cluster the visitors according to the location of TPAs using Wards hierarchical clustering method. By comparing average monthly visitors’ count, we observed that the average number of monthly visitors significantly reflects the distribution concentration of visitors based on the spatial map. Findings indicate that the monthly distributions of local and foreign visitors differ according to different TPAs. The spatial and temporal analysis found that local visitors’ arrival is high at the end of the year while foreign visitors showed significant arrival during the months of July, August and September. The Wards minimum variance method was able to cluster TPAs local and foreign visitors into very high, high, medium and low visitor area. This study provides additional information that could contribute to identifying the periods of highest visitor pressure, design measures to manage the concentration of visitors and improve the overall visitors’ experience. The findings of the study are also important to respective local authorities in providing information for planning and monitoring tourism in TPAs. Consecutively, this will ensure sustainability of TPAs resources while protecting their biodiversity.

Using Machine Learning to Predict Visitors to Totally Protected Areas in Sarawak, Malaysia

“…Information is imperative to predict tourist demand, to allow a better decision-making process, and to manage knowledge flows and interaction with customers [48]. Organizations that manage TPAs engaging in tourism need to understand and capitalize information to offer visitors with authentic, unique, and inspirational visiting experience [49]. Information on visitors' demographic profiles, behavioral, and motivational aspects will enable SFC to predict and restrategize products' offerings, presentations, delivery systems, infrastruc-ture, amenities, and human capital development.…”

“…Additionally, the study focused on classifying the visitor's arrivals, and the categorical variable was generated from the monthly total visitors' arrival. In the absence of a guideline threshold for the monthly number of visitors, the categories of visitors' arrival were determined as three groups of low, medium, and high visitor's arrival by reference to the previous studies [49]. Specifically, for the local visitors, the low visitors refer to a total number of fewer than 1000 tourists, medium visitors between the range of 1001 to 3000 while more than 3001 are classified as high visitors.…”

Spatiotemporal behavior pattern differentiation and preference identification of tourists from the perspective of ecotourism destination based on the tourism digital footprint data

“…Taking Sarawak Malaysia Total Protected Area as an example, Abdurahman AZ et al conducted spatial and temporal analyses of the monthly average trend of local and foreign tourists. They identified the months and places witnessing the maximum visits [32]. Huang Q et al quantized the spatiotemporal distribution characteristics of recreational behaviors of backpackers with different travel modes in Beijing [33].…”