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Article

Tourism-Induced Urbanization in Phuket Island, Thailand (1987–2024): A Spatiotemporal Analysis

by
Sitthisak Moukomla
1,* and
Wijitbusaba Marome
2
1
Research Unit in Geospatial Research and Analytics for Climate and Environment and Faculty of Liberal Arts, Thammasat University, 99, Khlong Nueng, Pathum Thani 12120, Thailand
2
Research Unit in Urban Futures and Policy and Faculty of Architecture and Planning, Thammasat University, 99, Khlong Nueng, Pathum Thani 12120, Thailand
*
Author to whom correspondence should be addressed.
Urban Sci. 2025, 9(3), 55; https://doi.org/10.3390/urbansci9030055
Submission received: 1 January 2025 / Revised: 9 February 2025 / Accepted: 19 February 2025 / Published: 20 February 2025
Browse Figures
Graphical abstract
"> Figure 1
<p>Thailand’s tourism industry’s resilience despite recurring disruptions from political, economic, and natural crises.</p> "> Figure 2
<p>Study area of Phuket, Thailand, showing the detailed coastal geography, tourist attractions, and its regional context.</p> "> Figure 3
<p>Number of scenes of Landsat images used in this study.</p> "> Figure 4
<p>General framework of the study illustrating the key methodological steps.</p> "> Figure 5
<p>Phuket LULC changes illustrated in spatial-temporal (<b>a</b>) and the changes in categories in Phuket over a span of years (<b>b</b>) from 1990 to 2024.</p> "> Figure 6
<p>Temporal variation in Phuket’s urban expansion.</p> "> Figure 7
<p>The urban landscape transformation underscores the critical need for strategic planning to address these challenges and ensure sustainable development for Phuket’s future.</p> "> Figure 8
<p>Correlation heatmap, revealing strong links between urban expansion, housing density, and economic indicators. (<b>a</b>) The relationship between urban growth, visitor numbers, and economic activity, highlighting long-term urbanization trends. (<b>b</b>) A strong alignment between actual and predicted urban expansion, confirming the model’s accuracy (<b>c</b>).</p> ">
Review Reports Versions Notes

Abstract

:
Historically known for its tin mining industry, Phuket Island has undergone significant transformation into a global tourism hub. This study aims at analyzing the evolutionary dynamics of Phuket Island from the years 1987 to 2024. We integrate Landsat satellite images and sophisticated analytical methods to assess the effects of tourism and economic policies on changes in land use and land cover using Google Earth Engine (GEE) for cloud-based data processing and Random Forest (RF) models for classification, and the Urban Expansion Intensity Index (UEII) and Shannon Entropy metrics for measuring the intensity of urban expansion and diversity, respectively. The results show that there has been a dynamic change in the patterns of land use which was brought about by the economic and environmental forces. Some of the major events that have had a great effect on Phuket’s landscape include the 1997 Asian Financial Crisis, the 2004 Indian Ocean Tsunami, and the COVID-19 pandemic; this highlights how the island is fragile and can be affected easily by events happening around the world. This work reveals a dramatic reduction in forest and mangrove cover, which calls for increased conservation measures to prevent the loss of biodiversity and to preserve the natural balance.

Graphical Abstract">
Graphical Abstract

1. Introduction

Urbanization, characterized by the transformation of the landscape and the shift from rural to urban living, profoundly shapes land use and land cover (LULC) patterns over time [1,2]. Regions undergoing urbanization are among the most dramatically altered in terms of land use and land cover (LULC) [3,4,5]. This long-term process contributes to socioeconomic development and improved quality of life. However, it also introduces significant challenges, such as biodiversity loss, water pollution, urban heat islands, and other environmental issues [6,7,8]. Addressing these concerns has made urbanization studies a key focus across various disciplines [9,10,11,12,13,14]. Also, urbanization patterns serve as critical indicators of a region’s economic development and spatial growth characteristics [2,15,16,17]. Understanding the evolution of urban spatial patterns and their associated social and economic changes is essential for sustainable development. These insights are invaluable for urban planners and managers in assessing the dynamics of human activity and economic expansion. General urban spatial structures, such as the concentric ring structure, multiple nuclei structure, and urban realm structure, have been well-documented in urban studies [18,19,20,21]. However, no single structure universally applies to all cities due to the dynamic and diverse nature of urbanization [15]. LULC change is one of the most direct expressions of urbanization. Monitoring this process requires long-term datasets. Remote sensing (RS) technology has enabled dynamic monitoring of urban LULC since the 1970s. The Landsat program has provided extensive imagery through Landsat 1–8, facilitating detailed analysis of LULC changes over extended periods [22,23,24].
Phuket Island, Thailand, offers a compelling case study for urbanization. Historically reliant on tin mining, the island transitioned to a tourism-driven economy in the mid-1980s. This shift, driven by declining tin prices and infrastructure developments connecting Phuket to the mainland, spurred rapid urban growth. The tourism boom profoundly reshaped Phuket’s LULC, as plantations and tin mines were converted into hotels, resorts, and other tourist-oriented facilities. Urban expansion frequently outpaced population growth, leading to unplanned settlements and raising significant environmental and cultural concerns.
The challenges faced by Phuket highlight broader issues associated with mass tourism, including cultural commercialization and environmental degradation. Visitor numbers and revenue generally show long-term growth but experience sharp declines during events like the 1997 Asian Economic Crisis, the 2004 tsunami, the 2020 COVID-19 pandemic, and the 2021 countrywide lockdown (Figure 1). While the industry recovers after crises, the correlation between revenue and visitors shows the country’s economic dependence on tourism. To safeguard against future disruptions, Thailand could benefit from diversifying its economy and promoting domestic tourism as a buffer during global downturns.
Nonetheless, the island’s rapid development underscores the importance of urban planning and tourism management strategies that balance growth with preservation. Urban planners must address the pressures posed by urban expansion while safeguarding the natural and cultural assets that make destinations like Phuket attractive. Despite its rapid growth, the urbanization patterns of Phuket remain understudied compared to other regions [25,26,27,28,29].
Current urbanization research predominantly emphasizes economic zones, transportation systems, and commercial centers, neglecting the influence of seasonal tourism fluctuations, policy-induced economic shifts, and topographical limitations on urban growth in tourism-reliant islands.
This study addresses key research gaps, including the following:
  • In what ways does tourism-driven urbanization contrast with traditional urbanization models?
  • How can economic policies and external crises (such as the 1997 Asian Financial Crisis, the 2004 Tsunami, and COVID-19) influence urban growth patterns?
  • In what ways do land alterations driven by tourism affect long-term sustainability and resilience in Phuket?
This work seeks to address the research gap by examining spatiotemporal urbanization patterns in Phuket from 1987 to 2024, utilizing remote sensing methodologies, machine learning categorization (RF models), and urban growth indices.
We employ Google Earth Engine (GEE) for cloud-based processing of Landsat images, facilitating long-term observation of land use alterations. Utilize the Urban Expansion Intensity Index (UEII) and Shannon Entropy to measure urbanization intensity and spatial complexity. Subsequently, we analyze the impact of economic policies, tourism surges, and external disturbances on land cover changes in Phuket.
This research establishes a fresh analytical paradigm that connects urbanization studies with tourism-driven spatial growth, relevant to other coastal economies reliant on tourism. The results will be essential for urban planners, legislators, and sustainability experts aiming to reconcile economic growth with environmental preservation in swiftly changing coastal areas.

2. Materials and Methods

2.1. Study Area

Phuket, an island in Thailand’s Andaman Sea, presents a compelling case study for examining urban growth patterns in coastal tourism-driven regions (Figure 2). The island’s distinctive topography, characterized by mountainous terrain and flat coastal plains, has significantly influenced its developmental trajectory. The economic landscape of Phuket has undergone a profound transformation since the 1970s and 1980s, shifting from a reliance on tin mining, rubber plantations, and fishing to a tourism-centric model. This transition has catalyzed substantial changes in the island’s urban fabric and socioeconomic composition. Notable developments include the expansion and modernization of Phuket International Airport, a proliferation of accommodation facilities, the growth of commercial and entertainment districts, and enhancements to transportation infrastructure [30]. Recent initiatives focusing on smart city concepts and sustainability efforts, such as the promotion of ecotourism and economic diversification, offer promising avenues for research in urban planning, environmental sustainability practices, and economic resilience strategies [31]. Phuket serves as an exemplary case study, providing valuable insights into the intricate interplay between tourism and urban growth dynamics within the context of environmental sustainability [32]. The island’s detailed evolutionary trajectory offers a rich dataset for longitudinal research and comparative analyses with similar tourism-oriented coastal regions. Scholars can investigate land use transformations, socioeconomic impacts, environmental challenges, and urban development considerations within the broader context of expanding coastal tourist destinations.

2.2. Data Collection

Here, we use Google Earth Engine (GEE), a cloud-based platform that is a key component for data acquisition, preprocessing, and initial analysis [33]. We directly accessed life-long-spanning satellite Landsat imagery from the Landsat 4–5 TM, 7 ETM+, and 8–9 OLI sensors from GEE’s extensive public repository. However, due to the high cloud cover during the monsoon season, to guarantee low levels of cloud cover and stable observing conditions, target dates for each time point were chosen to coincide with the dry seasons (between January and July of each year). Hence, the process began with the creation of image collections for each five-year interval, incorporating a ±6-month window around the target date to account for seasonal variations. It should be noted that due to the data availability, we have made a modest adjustment to the analysis timeframe for the periods 1987–1990 and 2021–2024. The analysis for 1987–1990 was initiated from this year due to the insufficient satellite imagery available prior to 1987. In the same vein, the most recent data available for the period 2021–2024 only encompassed 2024. These changes were implemented to guarantee the inclusion of the most comprehensive data feasible for these timeframes while maintaining consistency. Next, automated cloud masking algorithms, tailored to each sensor, were applied to filter out cloud-covered pixels and atmospheric noise. Then, median composites were made to obtain clear, representative images for each time point, and GEE’s radiometric calibration tools made sure that all the sensors worked together [34]. Besides these automated steps, we also export the processed Landsat images from GEE for LULC classification and more calculations, like the UEII and Shannon Entropy, which are important for the study’s objective. This comprehensive data preparation methodology, supported by the computational framework of GEE, resulted in a high-quality time series of cloud-free Landsat imagery spanning nearly four decades. Figure 3 exhibits the number of scenes of Landsat images used in this study. This integration of automated tools with manual oversight ensured a robust and reproducible analysis framework. These datasets form the foundation for analyzing urban growth and land cover changes in Phuket, providing insights into the impacts of economic policies and tourism-driven development on the region’s landscape.

2.3. LULC Classification

To categorize land use and land cover, we utilized machine learning techniques, the Random Forest (RF) model within GEE. We chose these models for their effectiveness in handling data patterns and preventing overfitting issues [35]. The RF algorithm, recognized for its methodology, was particularly appropriate for our research because of its capacity to handle extensive datasets and its resilience to disruptions such as noise and outliers typically present in remote sensing data [5,36]. We created labeled training data for each classification period by manually categorizing land cover classes using high-resolution imagery from Google Earth pro version 7.3.6.10201 and supplementary datasets, including Open Street view, historical maps, field survey data, and expert validation. Our dataset was then divided into two parts: 80% for training purposes and 20% for validation, ensuring a distribution of classes in both sets. This split allowed us to train the model effectively while keeping data aside for independent validation. We fine-tuned the hyperparameters to improve model accuracy focusing on factors such as the number of trees in the forest tree depth limits and the number of features used for splitting at each node. This optimization step was essential in finding a balance between model complexity and generalization capability [36]. We assessed the model’s performance using metrics like overall accuracy (OA) and the kappa coefficient (KC) to obtain a thorough evaluation of classification accuracy.
The overall accuracy measure provided a way to gauge the correctness of classifications across all categories, while the kappa statistic offered a more robust assessment by considering the potential for chance agreement [37]. Validation involved using a confusion matrix to delve into classification errors and confusion between classes. The study zone was divided into six LULC groups: urban areas, water bodies, agricultural zones, forested areas, barren lands, and mangrove forests. These specific categories were chosen for their significance in understanding landscape changes and urban development in Phuket. This thorough LULC classification method enabled an examination of how the landscape has transformed over time and the patterns of urban growth within our research area [38,39,40].

2.4. Urban Expansion Intensity and Diversity Analysis

The methodology for analyzing Urban Expansion Intensity and Diversity in Phuket from 1987 to 2024 provided valuable insights into urban growth patterns [41]. Here, we are calculating UEII [42,43] and Shannon Entropy [44,45] and analyzing their changes and trends, offering a comprehensive understanding of urban expansion dynamics, crucial for effective urban planning and sustainable development.
We adapted UEII to calculate the urban expansion, the average period rate relative to the total area; we used the following formula:
U E I I i t h = [ U L A i , b − U L A i , a t ] T L A i × 100 ,
where
UEIIith is the mean Urban Expansion Intensity Index of the (ith) zone in period (t). ULAi,a and ULAi,b are the amount of urban area during time spans a and b in the (ith) spatial zone, respectively. TLAi is the total area of Phuket (~543 square kilometers). The UEII values range from <0 to >1.92 and are divided into five categories (Table 1)
Shannon Entropy, which measures the diversity and distribution of urban areas, was calculated using the following formula:
H n = ∑ i n P i log ⁡ ( 1 P i )
where
Pi is the probability of occurrence of the i period. Probabilities were determined by dividing each year’s urban area by the total urban area over a 5-year period; the interpretation of Shannon Entropy is presented in Table 1.
Next, period-by-period UEII values were analyzed to identify significant fluctuations and trends in urban growth. High UEII values indicated periods of intense urban expansion, and high Shannon Entropy values reflected diverse and spread-out growth. Insights from UEII and Shannon Entropy trends informed strategic resource allocation for infrastructure and services [46]. The analysis helped plan for balanced and sustainable urban growth, ensuring that development did not exceed the region’s carrying capacity. Policymakers could adjust urban development policies based on identified trends to manage growth effectively [47].
Figure 4 presents the general framework of the study, outlining the key methodological steps undertaken. This framework provides a structured overview of the processes, including data collection, preprocessing, classification, and analysis, which collectively form the foundation for understanding land use dynamics and urban expansion in Phuket over the study period.

3. Results

3.1. The Analysis of Phuket Land Use and Land Cover Changes

Figure 5 shows that there were changes in land use and land cover between 1987 and 2024 across different categories. The study proves its strength with consistently high accuracy levels with OA ranging from 0.9426 to 0.9606 and KC values between 0.9156 and 0.9400.
Table 2 illustrates the changes over time in land cover categories within the total area of land.
Urban areas have grown significantly over the years, expanding from 78.9 km (14.5%) in the late 1980s to 102.4 square kilometers (18.9%) in the early 2020s, reaching a peak of 106.0 square kilometers (19.5%) around 2012. This growth trend mirrors urbanization patterns, although there was a slight dip in size during the mid-2010s that calls for further examination. These fluctuations hint at phases of urban development interspersed with possible shifts in policies or economic influences that temporarily slowed down city expansion. Meanwhile, water bodies experienced changes showing a slight overall increase from 11.3 square kilometers (2.1%) to 14.6 square kilometers (2.7%). This stability suggests that significant hydrological alterations or interventions in water management were likely limited during this period.
The LULC exhibited significant variations over time, with an initial decrease from 267.4 square kilometers (49.2%) in the late 1980s to 223.0 square kilometers (41.1%) by the mid-2000s, followed by a substantial increase to 288.6 square kilometers (53.2 %) by the late 2010s before slightly dropping to 278.9 square kilometers (51.4 %) in the period. These changes likely reflect shifts in policies, economic circumstances, or land usage priorities throughout different eras. The forest area has been consistently decreasing, dropping from 138.2 square kilometers (25.5%) to 109.9 square kilometers (20.2%) with a significant decline between the periods of 2006–2010 and 2011–2015, reaching a low of 102.5 square kilometers (18.9%). This downward trend raises questions about what is driving deforestation and the potential impacts on biodiversity and ecosystem services. The barren land exhibited fluctuations increasing from 13.5 square kilometers (2.5%) in 1987–1990 to a peak of 51.9 square kilometers (9.6%) in the period of 2011–2015 before returning to its original level of 13.5 square kilometers (2.5%) in the timeframe of 2021–2024. This fluctuation pattern likely indicates cycles of land degradation followed by efforts for reclamation or reforestation [48].
Overall, mangrove ecosystems experienced a decline, reducing from an area of 33.7 km (6.2%) to 23.6 square kilometers (4.3%) with an increase noted during the years between 1996 and 2000. This declining trend is worrisome considering the crucial ecological role that mangroves play in coastal protection. Figure 2 clearly shows that there are changes and trends in land cover over time. For example, the proportion of areas has been consistently increasing, indicating urban growth. On the other hand, both agricultural and forest areas display varying levels of decrease and fluctuation. The percentages for Water, Barren land and Mangrove categories remain relatively stable with changes. This visual representation highlights the nature of land use in Phuket, reflecting significant shifts influenced by factors like urban development, environmental alterations, and land management practices [49].

3.2. The Analysis of Phuket’s Urban Expansion Evolution

The Phuket cityscape has undergone changes over the past 37 years due to the development of tourism and infrastructure. Starting from 1987, when the urban area covered around 51.2 km, there was a steady increase to 109.5 square kilometers by 1990, mainly driven by early tourism initiatives. As Phuket gained popularity as a tourist spot the urban area expanded further to 150.8 square kilometers in 1996–2000. The early 2000s witnessed growth with the urban area reaching 223.5 square kilometers during the 2006–2010 period. This expansion trend. Throughout 2016–2020, the Phuket urban area had grown to encompass 312.8 square kilometers, reflecting ongoing development pressures and urban sprawl. By the period of 2021–2024, this growth trend continued as the cityscape extended to cover an area of 345.6 square kilometers.
The expansion of Phuket’s landscape has shown an average annual growth rate of about 6.65% with notable fluctuations indicated by a standard deviation of approximately 29.34%. Various factors such as conditions, policies, and environmental influences contribute to this variability, leading to extreme annual growth rates ranging from a high of around 88.08% to a low of 52.75%. Despite these fluctuations, there is a positive trend with a median annual growth rate of approximately 11.85%, suggesting that expansion outweighs contraction. This dynamic pattern of growth underscores the factors shaping Phuket’s urban development and emphasizes the importance of sustainable planning and management practices. Understanding these patterns is essential for creating strategies that strike a balance between progress, environmental conservation, and social equality to tackle the issues linked with swift urban growth [50].
The evolution of Phuket’s landscape follows a fluid and changeable trajectory, sensitively reacting to economic and environmental occurrences. The city has undergone fluctuations characterized by pivotal moments of transformation [42]. In the period of 1996–2000, a consistent urban expansion fueled by tourism came to an abrupt halt due to the impact of the 1997 Asian Financial Crisis, leading to a sharp decline in the city size from 115 sq km to 65 sq km. This shrinkage reflected the repercussions of the crisis on Phuket’s economy and tourism industry, resulting in reduced foreign investments and slowed progress [16].
In the 2001–2005 period, there was a resurgence in growth marked by significant changes following the devastating impact of the 2004 Indian Ocean Tsunami. The city area surged from 85 sq km to around 160 sq km during the 2006–2010 period due to intensive reconstruction endeavors, enhanced planning strategies and expanded infrastructure. After the tsunami event urban development remained unpredictable. The onset of the COVID-19 pandemic initially led to a decrease in Phuket’s expanse from about 115 sq km down to roughly 90 sq km owing to business shutdowns and stalled projects. In 2021–2024, the city area expanded to 130 km, showing positive progress in recovery initiatives and upgrades to infrastructure.

3.3. Urban Expansion Intensity and Diversity Analysis for Phuket

The UEII and Shannon Entropy analyses for Phuket reveal dynamic patterns of urban growth, with significant expansions and periods of stability (Table 3). High Shannon Entropy values reflect diverse and spread-out urban development, while stable periods indicate concentrated growth [51]. These insights are crucial for guiding future urban planning and ensuring sustainable development in the region [52].
Analyzing the growth of areas and related indicators across various time frames unveils shifting urban development patterns. From 1987 to 1990 the urban space encompassed 78.9 km showing a notable UEII of 19.43 and a Shannon Entropy of 1.94 indicating high and even expansion. In the period from 1991 to 1995, the urban area expanded to 89.6 square kilometers; however, the UEII dipped into negative territory at 3.40, indicating a decline in growth intensity. Shannon Entropy rose to 2.29, reflecting increased diversity in urban area distribution patterns [44]. This trend of growth persisted through 1996–2000 as the urban area extended to 92.6 square kilometers with another negative UEII (−4.73), while Shannon Entropy saw a slight dip to 2.27. A substantial surge occurred between 2001 and 2005 as the urban space expanded to reach 97.0 km with a UEII of 23.70 denoting rapid expansion [53]. Nonetheless, Shannon Entropy slightly decreased to 2.25, hinting at the uniform expansion despite the overall area increase.
From 2006 to 2010, the urban area kept expanding to 105.9 square kilometers with a moderate UEII of 9.15 and the highest Shannon Entropy of 2.31, showcasing an intricate urban configuration. From 2011 to 2015, there was growth in the urban area, which measured 106.0 square kilometers and had a lower UEII of 3.77. Despite this, the Shannon Entropy remained high at 2.31, indicating stable diversity in the spatial distribution of urban areas. Subsequently, there was a decrease in the urban area to 89.8 square kilometers between 2016 and 2020, with a UEII of 3.40 and a slightly reduced Shannon Entropy of 2.30, suggesting less complex urban growth during that period. In contrast, the urban area rebounded to 102.4 square kilometers from 2021 to 2024 with an UEII of 14.20, signaling renewed expansion intensity. However, there was a decrease in Shannon Entropy to 1.98, indicating a shift towards uniform urban growth patterns. These data showcase periods of both rapid. Slowed urban expansion with varying spatial complexities over time as discussed by Woldegebriel Tessema and Girma Abebe (2023) [54]. For our study, the interaction between growth intensity and spatial complexity highlights the evolving nature of development throughout these years (Figure 6).

3.4. Analysis of Urban Growth and Densification Patterns

Phuket has undergone a transformation in its urban development as shown through a series of maps illustrating the changes over time. Different stages of growth, each presenting characteristics and challenges, define this evolution. In the 1990s, urbanization was mainly concentrated around town centers and coastal areas. Over the years, there was a gradual expansion with new urban zones emerging and existing ones extending along major transportation routes. The period around the year 2000–2005 marked a phase of urban growth for Phuket. Between 2000–2005 and 2006–2010, there was development within existing urban areas leading to increased density in city cores. By 2010 the cityscape had become more compact and interconnected due to economic progress and initial signs of urban sprawl.
Moving into the decade from 2011–2015 to 2016–2020, there was a shift towards consolidating and densifying spaces. High-density projects became more prevalent in central city areas. Suburbs also experienced population densities supported by enhanced infrastructure and transportation systems. The recent period spanning from 2016–2020 to 2021–2024 demonstrates a more balanced approach to urban expansion, focusing on sustainable practices and smart growth concepts. This phase likely incorporates areas and efficient land use strategies to sustain quality living standards while handling continued development. In years past, the coastal and central areas of Phuket have seen a surge in urban development, mainly due to the booming tourism industry and the construction of related infrastructure. This rapid urbanization has resulted in shifts in land usage patterns as natural landscapes and farmlands are transformed into bustling urban zones. The swift expansion of areas brings forth various opportunities and challenges for Phuket. While it signifies economic progress, it also raises concerns regarding environmental consequences, infrastructure capacity, and sustainable resource management (Figure 7).

3.5. Analysis of Urban Expansion and Tourism

To understand the effect of tourism on urban expansion, we extend our analysis by extracting urban area data from Landsat imagery using GEE. This allows us to examine its relationship with visitor numbers over time. Additionally, we incorporated supplementary economic and demographic variables into our analysis. We gathered information on the Gross Provincial Product (GPP) of the service sector in Phuket and the population density, which encompasses both registered residents and migrant laborers who are likely to be employed in the tourism sector. Furthermore, we investigated the density of housing, which serves as a metric for the expansion of residential areas (see Appendix A).
Then, we apply the lagged regression analysis on urban expansion and tourism to investigate the correlation between accumulated urban area expansion and tourism growth (visitor numbers), while integrating economic and demographic variables, including Gross Provincial Product (GPP) in the service sector and housing density. The investigation employed a lagged regression model to forecast urban expansion based on historical data on tourist numbers, GPP, and housing density (Figure 8).
The regression model (Appendix A) indicates that visitor numbers are not statistically significant in forecasting urban expansion at 1-year, 2-year, or 3-year intervals. The one-year lag coefficient (17.14, p = 0.113) indicates a minor although statistically insignificant effect. This outcome suggests that urbanization is not primarily influenced by transient variations in tourism but instead by a confluence of enduring economic and demographic elements.
The lagged housing density variable (1-year lag) exerted the most significant impact on the urban expansion (coef = 6.52, p < 0.0001). This indicates that urbanization is mostly propelled by residential development rather than direct increases related to tourism. The 1-year lagged GPP variable was statistically significant (p = 0.021) and had a negative coefficient (−0.0017). This may suggest that heightened economic activity could impede urban expansion, potentially due to rising land prices, legislative changes, or congestion impacts. The regression model accounted for 99.3% of the variance in urban expansion (R2 = 0.993, Adjusted R2 = 0.991). The F-statistic (489.0, p < 0.0001) indicates that the model is very significant and appropriately fitted (Appendix A). The map comparing anticipated and actual accumulated urban areas demonstrates that the model accurately reflects real-world urbanization trends.
Urban expansion adheres to a protracted trajectory, predominantly shaped by housing development and economic conditions rather than transient tourism swings. Although tourism fosters economic growth, its direct influence on urban expansion is neither quick nor substantial. Policymakers must prioritize the equilibrium between tourism expansion and housing and infrastructure policy to facilitate sustainable urban development.

4. Discussion

4.1. Changes in the Land Use and Cover Patterns in Phuket

Over the three decades, there has been an expansion of urban and agricultural areas at the expense of forest and mangrove areas. This reflects human influence on the landscape affecting environmental sustainability, biodiversity, and efforts to mitigate climate change [55]. More exploration into these drivers and their long-term consequences would be beneficial for land use planning and management strategies. The expansion of areas is primarily fueled by population growth, economic development, and urban planning initiatives [56]. At times urban growth may slow down due to challenges, shifts in policies, or natural calamities. The rising cost of living near tourist attractions often pushes individuals with incomes towards suburban areas, contributing to urban sprawl. Water bodies in Phuket have remained stable thanks to effective water management practices. Many existing water bodies were once tin mines, and geographical constraints in Phuket hindered reservoir construction despite increasing water demands. The utilization of land has fluctuated due to changes in para rubber plantations’ rotation cycles, shifts in agricultural policies, economic conditions, and evolving land use priorities. Climate change and natural disasters can impact productivity, leading to changes in land use [57]. Economic shifts and market demand also play a role in affecting areas. The decrease in forest cover is a growing concern due to deforestation caused by expansion and urban development. Economic growth increases the demand for land and resources, contributing to forest loss. Barren lands often experience fluctuations due to land degradation and restoration efforts linked to tin mining activities. Various initiatives seek to rehabilitate these lands by reforesting or converting them for agricultural purposes. Mangrove ecosystems have suffered from development, pollution, and climate change despite sporadic conservation attempts. Preserving mangroves is vital for biodiversity conservation fisheries support and protection against erosion and storms. Understanding these factors is crucial for land use planning and sustainable development that balances human needs with environmental preservation.

4.2. Phuket Changing Urban Landscape

The transformations in land usage and coverage in Phuket throughout the years emphasize the importance of conducting studies to comprehend the factors behind these changes and their possible consequences. In the three decades, urban and agricultural zones have expanded, while forested and mangrove areas have dwindled, indicating significant human influence on the terrain. This has implications for sustainability, biodiversity, and efforts to address climate change [49]. Delving deeper into these driving forces and their long-term impacts could assist in making informed decisions regarding land use planning and management. The growth of regions can be attributed to population growth, economic advancements, and governmental urbanization strategies. Water bodies have remained relatively constant due to water resource management practices. Changes in agricultural land use are influenced by factors such as rubber plantations and shifts in policies and economic circumstances. The decrease in forest cover is a result of expansion and urban sprawl. Barren land exhibits fluctuations caused by periods of degradation and reclamation typically associated with tin mining activities [58]. Mangrove ecosystems have shrunk due to development projects and environmental deterioration. Comprehending these drivers is crucial for fostering sustainable progress that meets both human needs and environmental concerns [59].
Phuket’s urban evolution from 1987 to 2024 has been characterized by its nature of responding sensitively to economic shifts as well as environmental events. Phuket has transformed into a sanctuary for international visitors. Consequently, the demand for land is surging, resulting in land prices soaring, particularly in excellent tourist locations. In the western part of the island, where high-end hotels and resorts are situated, they have encountered a higher cost of living. The service sector, propelled by tourism, necessitates a considerable workforce; nevertheless, numerous local employees find residence in these expensive regions unaffordable. The residential displacement has compelled residents to congregate in the eastern region of the island, especially in Phuket Downtown and adjacent neighborhoods, where housing and living expenses are comparatively more economical. Phuket’s geographical limitation as an island, which restricts expansion into adjacent provinces, has resulted in land scarcity that necessitates the transformation of agricultural regions into urban areas to meet population growth and economic needs. This urban evolution underscores the island’s distinctive developmental challenges, as alterations in land use are influenced by the convergence of tourism dynamics, housing affordability, and geographical constraints.
Also, Phuket’s development has been shaped by many national and local policy measures aimed at promoting economic growth and tourism, alongside these worldwide problems. In the early 2000s, these laws catalyzed a significant increase in investment, especially in areas such as Patong Beach and Karon, where agricultural land was converted into commercial zones and resorts. A pivotal moment in the island’s urban growth occurred during the 2004 tsunami, which inflicted significant damage on Phuket’s coastline. International assistance and governmental initiatives were employed to facilitate reconstruction operations, specifically targeting the rehabilitation of tourism infrastructure. The implementation of new zoning restrictions aimed to limit growth in high-risk coastal regions; yet enforcement was irregular. In specific areas, like Kamala Beach, post-tsunami rehabilitation led to the establishment of high-rise hotels and rapid urban growth, but other regions were designated for conservation to mitigate future dangers. This paradox underscores the tension between environmental preservation and economic recovery that has defined much of Phuket’s development.
The island had a substantial rise in the development of resorts and condominiums due to initiatives like Amazing Thailand (1998) and Visit Thailand Year (2015). The campaigns provided financial incentives for tourism investment. Consequently, they boosted tourism and economic diversification, leading to increased investment in tourism infrastructure and real estate expansion. The expansion of luxury home projects in sought-after locations like Laguna and Kata Beach was greatly impacted by these policies. The alteration of Phuket’s land use patterns was expedited by the easing of foreign ownership laws for real estate, drawing international purchasers and developers [60,61]. The Smart City Initiative, however, launched in 2015, marked a decisive shift in Phuket’s developmental path by emphasizing sustainable urban planning and digital innovation. The island’s economy has diversified with investments in technological parks, transportation facilities, and upscale residential complexes, thus reducing its reliance on conventional tourism. These shifts have also impacted land use objectives, placing increased focus on urban densification and mixed-use developments in inland areas such as Kathu and Chalong. While the Phuket Sandbox initiative, allowing vaccinated international tourists to visit without quarantine, sparked a surge in tourism-driven businesses and real estate investment [62,63]. The government expedited infrastructure upgrades but faced challenges related to resource management, environmental conservation, and sustainable tourism. Both campaigns highlight the complex relationship between economic growth, urban planning, and environmental sustainability [64].
Numerous events, both global and local, impact tourism in Phuket. The 1997 Asian Financial Crisis, the 2004 Indian Ocean Tsunami, and the 2020 COVID-19 pandemic all led to substantial declines in tourist arrivals and corresponding alterations in land use dynamics (for detailed information, please refer to Figure 1). The 1997 financial crisis led to a significant decline in foreign investment and the deferral of various tourism-related infrastructure initiatives. The Thai government enacted economic rehabilitation measures, including tax incentives for international investors and subsidies for domestic tourism operators. In 2020, the COVID-19 pandemic posed unique issues, since the collapse of the worldwide tourism industry led to widespread economic upheavals and the underutilization of tourism infrastructure [65].
In 2021, the Thai government created the Phuket Sandbox program to mitigate these repercussions. This program allowed immunized international passengers to visit the island without quarantine requirements. The tourism sector had a temporary enhancement due to this policy, which also prompted a reevaluation of land use objectives. A multitude of properties, including hotels and resorts, were repurposed for domestic tourism or alternative uses, signifying a shift towards more flexible and resilient urban design techniques. Consequently, land use decisions have been markedly affected by environmental and zoning restrictions, notwithstanding their erratic implementation.
Insights gleaned from studying Phuket’s growth between 1987 and 2024 reveal a city characterized by dynamic yet occasionally turbulent expansion patterns. By employing metrics like the Urban Expansion Intensity Index (UEII) and Shannon Entropy analysis tools, phases of rapid growth juxtaposed with periods of relative stability can be discerned. Throughout this span of 37 years, Phuket’s UEII values exhibited fluctuations of varying rates of urban expansion. On average, these values portrayed an upward trend but not without inconsistencies. The surge in values observed from 1988 to 1990 was largely attributed to a surge in tourism activity driving rapid urbanization. Conversely, a negative UEII value recorded in 1996–2000 (−4.73) signaled a contraction in areas possibly linked to the fallout from the Asian Financial Crisis of 1997. The mid-1990s through the early 2000s saw subdued yet steady growth trends taking place within Phuket’s cityscape [42].
Since the year 2000, UEII values have been on the rise, indicating ongoing urban development and recovery from previous economic challenges. The late 1980s and early 1990s witnessed fluctuations while the following decades showed more consistent growth driven by various factors. Shannon Entropy is a measure of the diversity and extent of growth. The overall Shannon Entropy reveals diversity. From 1987 to the 1990s, rising entropy values signify diverse and widespread urban expansion propelled by tourism. In the mid-1990s, entropy values stabilized, suggesting uniform growth. Post-2000, entropy values fluctuated with an upward trajectory, indicating ongoing diversification in urban expansion. High-entropy periods indicate development that requires strategic infrastructure planning, while low-entropy periods signal concentrated growth in specific areas [44]. The analysis of UEII and Shannon Entropy patterns in growth in Phuket emphasizes the necessity for adaptable and resilient urban planning strategies. Decision makers can utilize these findings to allocate resources during periods of rapid growth to ensure sustainable infrastructure development. Understanding these trends aids in planning for urban expansion by effectively managing both dispersed and concentrated development [47,48]. By adjusting rules and regulations based on these assessments, we can encourage progress and lessen the effects of changes in the economy and environment. The growth of Phuket’s cityscape from 1987 to 2024 shows a mix of expansion and steadiness influenced by economic and environmental elements. Valuable insights from UEII and Shannon Entropy analyses play a role in guiding upcoming urban planning endeavors, ensuring sustainable progress, and harmonizing economic development with environmental conservation efforts [45].

4.3. Urban Growth and Densification Trends

Between the 1991–1995 and 2021–2024 periods, the urban expansion in Phuket transitioned from growth to more effective land use. In its stages, there were developments with low density on the outskirts driven by tourism demands and available land. As land became scarcer, the period between 2000–2005 and 2006–2010 witnessed infill developments leading to increased density, focusing on compact growth and reducing sprawl [48]. From 2011–2015 to 2016–2020 high-density constructions alongside development became prevalent with policies promoting mixed-use projects. This strategy catered to a growing population while lessening impacts. By 2021–2024 there was an emphasis on densification and smart growth, integrating residential, commercial, and recreational spaces to minimize travel needs, supporting a sustainable city lifestyle. The distinctive geography of Phuket, with its mountains encircling the sea, underscores the importance of land usage.
The evolving urban landscape from 1987 to 2024 underscores the importance of flexible planning that harmonizes economic progress with environmental conservation and social equality [51]. Prioritizing urban growth ensures Phuket flourishes as a sought-after tourist spot while safeguarding the well-being of its residents and preserving its natural and cultural legacy. It is crucial to implement strategies for holistic development, preservation of natural resources, and enhancement of societal welfare [49]. Encouraging development, improving transportation networks, and fostering mixed-use urban areas play a pivotal role in this regard [52]. By fortifying infrastructure to accommodate population densities and embracing comprehensive urban planning practices, cities can achieve enduring sustainable progress, creating livable environments that strike a balance between advancement and conservation. The expansion of areas and increase in population density influence Phuket’s economy by boosting efficiency, generating employment opportunities, and attracting investments. Nonetheless, rapid expansion can strain existing infrastructure, leading to escalated costs and gentrification. Strategic growth management boosts revenues, which further aid in infrastructure upgrades and community enrichment. Development fosters economic resilience by drawing environmentally conscious businesses and residents thereby uplifting the local economy.

4.4. Policy Recommendations for Urban Development in Phuket

Due to the rapid urban expansion, fueled by the tourism industry in Phuket, to promote sustainable urban development. In this section, we proposed the policy recommendations for adaptation to rapid urban expansion in Phuket. First, the adoption of zoning regulations should be geographically tailored. These regulations are designed to safeguard fragile coastal ecosystems and tourism sites while simultaneously directing compact, mixed-use development to urban centers such as Phuket Downtown. By instituting definitive urban growth limits, the island can mitigate unrestricted expansion into agricultural areas and vulnerable ecosystems, thus assuring compatibility with long-term ecological and infrastructural capacities. To optimize land utilization, new buildings must comply with rigorous sustainability criteria, including energy-efficient designs and the obligatory incorporation of renewable energy sources. These strategies not only mitigate environmental impacts but also safeguard development against future climatic difficulties.
Next, to mitigate the severe housing crisis intensified by tourism-induced gentrification, focused affordable housing programs should concentrate on service-sector workers displaced from expensive coastal regions. Public–private partnerships may motivate developers—via tax incentives or density bonuses—to incorporate subsidized units in buildings adjacent to employment centers. Concurrently, modernizing antiquated facilities (water, sewerage, and electricity) in eastern regions will provide dependable services for expanding populations. Equitable planning must incorporate marginalized groups in decision-making processes, guaranteeing access to healthcare, education, and public places to promote inclusive and cohesive communities.
Plus, Phuket’s transportation infrastructure necessitates immediate renovation to mitigate congestion and diminish dependence on private vehicles. Enhancing high-capacity transit systems, such as bus rapid transit (BRT) or light rail, to link essential locations like the airport, beaches, and Phuket Town will optimize mobility. In addition, walkable urban planning and designated bike lanes in highly populated regions can facilitate sustainable commuting and improve quality of life. Integrating transit planning with zoning regulations will facilitate effortless access to employment, housing, and facilities, hence diminishing urban sprawl and travel demand.
Environmental resilience is also important. These include the destruction of essential ecosystems for tourism-driven development, for instance, the destruction of coastal mangroves that are natural storm barriers. Increasing the effectiveness of the enforcement of conservation laws and incorporating the restoration of the mangroves into the climate adaptation strategies could restore these vital systems. One of the frequent natural disasters in Phuket is a flash flood. Here, green infrastructure such as permeable pavements, urban parks, and rooftop gardens might also be strengthened to help fight flooding while enhancing biodiversity and improving air quality. Further environmental impact assessment of all projects (from high-end hotels to small resorts) could also help to reduce the ecological effects. Economic diversification is therefore necessary to reduce overreliance on the tourism sector. Creating other sectors like technology, healthcare, and education through innovation hubs or tax incentives makes the economy stronger against global shocks. However, sustainable tourism models, for example, agrotourism partnerships and ecotourism activities, can be used to conserve agricultural land and earn money for conservation. Success also relies on democratic government. This ensures that citizens, businesses, and environmental NGOs are involved in the decision-making process and therefore, the process is transparent and participatory. Climate change threats such as sea level rise and resource competition must be addressed by policymakers, and resilience must be addressed in every decision made on development. Therefore, through the integration of smart zoning, equitable housing, green infrastructure, and economic innovation, Phuket can create a path for sustainable development to sustain its natural assets, improve the living standard of its citizens, and build long-term strength.

5. Conclusions

Phuket’s transformation from 1987 to 2024 showcases a tale of development fueled by tourism in a coastal setting. By utilizing Landsat satellite images and sophisticated measurements this examination reveals the evolution, obstacles and adjustments on the island. The urban area of Phuket experienced an expansion from 51.2 square kilometers in 1987 to 345.6 square kilometers in 2024 with an average annual growth rate of 6.65%. While this expansion reflects prosperity, it also presented notable environmental and social hurdles. The research underscores the importance of striking an equilibrium between progress and sustainability, particularly in fragile ecosystems like those found in Phuket.
Key events such as the 1997 Asian Financial Crisis and the devastating 2004 Indian Ocean Tsunami left a lasting impact on Phuket’s landscape, underscoring the fragility of economies reliant on tourism. These events, evident in the patterns of development, underscore the necessity for resilient urban planning and diversified economic approaches. The study revealed that urban expansion came at the expense of forested areas. The reduction in forest cover and mangrove habitats underscores a call for conservation efforts to safeguard biodiversity and preserve ecological harmony.
Measures such as the Urban Expansion Intensity Index (UEII) and Shannon Entropy provided insights into the extent and distribution of urban growth. These tools serve as resources for city planners and decision makers to grasp and regulate growth patterns. Looking into the future, Phuket is encountering sustainability challenges. The rapid growth of cities has put a strain on infrastructure and natural resources, calling for innovative urban management strategies. It will be essential to implement city projects and sustainable development practices to tackle these issues and find a balance between economic progress and environmental conservation.
This study provides actionable recommendations for achieving sustainable development in Phuket, including the following:
  • Strategic resource allocation to support growing populations while conserving natural ecosystems.
  • Adaptive urban planning to address economic shifts and environmental challenges.
  • Promotion of eco-friendly practices and green infrastructure to mitigate negative impacts of urbanization.
  • Continuous monitoring and analysis of urban growth patterns to inform long-term planning.
The transformation of Phuket in decades provides a valuable model of coastal urbanization driven by tourism. It highlights the significance of holistic, sustainable approaches to city planning. With areas around the world facing similar dilemmas, insights from the Phuket journey can inform strategies for harmonizing economic advancement with environmental protection and social fairness. This study enriches our comprehension of how cities grow in tourist destinations, laying a groundwork for future research initiatives and policy choices aimed at fostering more resilient and sustainable cityscapes.

Author Contributions

S.M. conducted the experiment, methodology, analyzed the data, and co-wrote the paper. W.M. conceived and supervised the experiment, contributed to the analysis of the results, and co-wrote the paper. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the the Integrated Urban Climate Action for Low-Carbon & Resilient Cities (Urban-Act) project (agreement number: 81300507; Project processing number: 20.9015.7-002.00) financed by GIZ commissioned by the Government of the Federal Republic of Germany.

Data Availability Statement

Dataset available on request from the authors.

Acknowledgments

This paper is supported by Thammasat University Research Unit in Urban Futures and Policy. The authors would like to thank the anonymous reviewers for their valuable feedback and suggestions. Also, we thank the Research Unit in Geospatial Research and Analytics for Climate and Environment (GRACE Lab), Thammasat University for their support and resources that made this study possible. During the preparation of this work the authors used ChatGPT-4o for the preparation of this manuscript. These tools were employed for refining language, improving grammatical accuracy, and summarizing content to enhance the clarity and readability of the text. The use of these tools was limited to language enhancement and did not involve generating original research content, formulating the study design, or interpreting the results. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

This appendix presents a summary of the dataset used in the study, focusing on Phuket’s urban tourism development over time. The dataset includes key indicators such as visitor numbers, economic impact, and urban expansion. The dataset consists of 37 records spanning multiple years, capturing various tourism-related metrics. Below is a description of the key variables:
  • Year: The year of data collection.
  • visitor (m): Number of tourists visiting Phuket (in millions).
  • GPP_Service (m baht): Gross Provincial Product from service sectors (in million baht).
  • Revenue (m baht): Revenue generated from tourism (in million baht).
  • House_Den: Housing density.
  • Pop_Den: Population density.
  • Acc_Urban: Cumulative urban area (in square kilometers).
  • urban_area: Urbanized land area (in square kilometers).
Table A1. Key statistics summarizing the dataset.
Table A1. Key statistics summarizing the dataset.
YearvisitorGPP_ServiceRevenueHouse_DenPop_DenAcc_UrbanUrban_Area
19880.8843263.0n/an/an/a66.566.5
19890.9724269.06705.9n/an/a154.988.4
19901.2544768.08574.3n/an/a264.4109.5
19911.2095005.010,700.0n/an/a367.7103.3
19921.6335412.433,891.2n/an/a482.4114.7
19932.0886347.641,037.0104.7357.6558.375.9
19942.1197813.328,989.7111.9367.5622.964.6
19952.3048592.031,000.2125.6382.6712.689.7
19962.2919445.728,442.4137.1395.381198.4
19972.40210,475.729,836.5145.5408.5892.481.4
19982.66014,805.942,692.5149.7425.8973.881.4
19993.08316,156.555,714.4156.8444.71039.265.4
20003.46019,033.462,248.7164.2461.51118.779.5
20013.79019,739.169,669.3174.7481.41183.264.5
20023.99120,635.872,599.4187.84981265.682.4
20034.05019,642.273,263.7202512.91359.593.9
20044.79324,485.385,670.6218.1526.41444.284.7
20052.51016,088.728,181.5235.9538.21533.689.4
20064.49925,065.977,595.9251.3553.8162389.4
20075.00630,868.994,239.5269.15811738.6115.6
20085.31330,121.7101,684.4289.8602.21832.393.7
20093.37625,968.694,006.9312.6618.61937.1104.8
20105.47131,064.6108,446.2332.7635.52063.1126
20119.46734,638.0188,822.5348.1651.72150.687.5
20128.829n/a199,820.2368.5664.72249.899.2
201311.960n/a260,442.1394.3680.52361.4111.6
201411.960n/a260,442.1415.3696.82490.3128.9
201511.959156,413.3259,290.5434.57122592.9102.6
201613.203182,029.5313,005.6447725.92692.799.8
201713.411200,176.8377,878.1455.7740.42767.174.4
201814.013222,984.8423,012.9469.5755.52837.670.5
201914.409233,607.7449,100.7490767.22928.590.9
202014.576123,788.3442,890.7502.6763.33041.9113.4
20214.02987,364.7113,173.2510.3771.23130.788.8
20221.236140,010.421,735.7517.9769.63218.287.5
20235.767n/a192,040.0526.6780.13220.2102
202411.694n/a437,808.0542.1791.13451.6131.4
remark n/a is data not available.
Table A2. Lagged regression coefficients.
Table A2. Lagged regression coefficients.
VariableCoefficientStd. Errort-Statisticp-Value95% CI Lower95% CI Upper
const77.717283752.02628291.493808120.15254882−31.585881187.020448
visitor_lag117.142442410.29130151.665721520.1130765−4.478779738.7636644
visitor_lag20.5250591218.27761770.028726890.97739852−37.87479138.9249091
visitor_lag3−9.743139614.1999892−0.68613710.50136996−39.5762120.0899307
GPP_lag1−0.00174770.00069159−2.52702870.02108735−0.0032007−0.0002947
House_Den_lag16.516145790.3919394916.6253872.28 × 10−125.692711467.33958011
Table A3. Lagged regression summary.
Table A3. Lagged regression summary.
MetricValue
Dependent VariableAcc_Urban
ModelOLS
MethodLeast Squares
R-squared0.993
Adj. R-squared0.991
F-statistic489
Prob (F-statistic)1.45 × 10−18
Log-Likelihood−134.77
No. Observations24
AIC281.5
BIC288.6
Df Model5
Df Residuals18
Covariance TypeNonrobust

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Figure 1. Thailand’s tourism industry’s resilience despite recurring disruptions from political, economic, and natural crises.
Figure 1. Thailand’s tourism industry’s resilience despite recurring disruptions from political, economic, and natural crises.
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Figure 2. Study area of Phuket, Thailand, showing the detailed coastal geography, tourist attractions, and its regional context.
Figure 2. Study area of Phuket, Thailand, showing the detailed coastal geography, tourist attractions, and its regional context.
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Figure 3. Number of scenes of Landsat images used in this study.
Figure 3. Number of scenes of Landsat images used in this study.
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Figure 4. General framework of the study illustrating the key methodological steps.
Figure 4. General framework of the study illustrating the key methodological steps.
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Figure 5. Phuket LULC changes illustrated in spatial-temporal (a) and the changes in categories in Phuket over a span of years (b) from 1990 to 2024.
Figure 5. Phuket LULC changes illustrated in spatial-temporal (a) and the changes in categories in Phuket over a span of years (b) from 1990 to 2024.
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Figure 6. Temporal variation in Phuket’s urban expansion.
Figure 6. Temporal variation in Phuket’s urban expansion.
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Figure 7. The urban landscape transformation underscores the critical need for strategic planning to address these challenges and ensure sustainable development for Phuket’s future.
Figure 7. The urban landscape transformation underscores the critical need for strategic planning to address these challenges and ensure sustainable development for Phuket’s future.
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Figure 8. Correlation heatmap, revealing strong links between urban expansion, housing density, and economic indicators. (a) The relationship between urban growth, visitor numbers, and economic activity, highlighting long-term urbanization trends. (b) A strong alignment between actual and predicted urban expansion, confirming the model’s accuracy (c).
Figure 8. Correlation heatmap, revealing strong links between urban expansion, housing density, and economic indicators. (a) The relationship between urban growth, visitor numbers, and economic activity, highlighting long-term urbanization trends. (b) A strong alignment between actual and predicted urban expansion, confirming the model’s accuracy (c).
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Table 1. UEII and Shannon Entropy show the pace of urban expansion while explaining the diversity and concentration of urban development.
Table 1. UEII and Shannon Entropy show the pace of urban expansion while explaining the diversity and concentration of urban development.
UEIIInterpretation
Negative UEIIDecline in urban areas, potentially due to de-urbanization, land reclassification, or environmental impacts.
Low UEII (<0.5%)Slow growth or stagnation indicated mature urban areas with limited expansion.
Moderate UEII (0.5–2%)Steady and manageable urban growth. Indicates balanced development.
High UEII (>2%)Very high-speed development indicated significant urban expansion.
Shannon Entropy
High Entropy (4.5 to 5.25)High diversity and spread in urban development.
Moderate Entropy (2.5 to 4.5)Balanced but not extreme diversity.
Low Entropy (0 to 2.5)Low diversity and high concentration.
Table 2. Accuracy assessment and land use and land cover changes during the study periods.
Table 2. Accuracy assessment and land use and land cover changes during the study periods.
AccuracyLULC (km2)
YearOAKCUrban (%)Water (%)Agriculture (%)Forest (%)Barren (%)Mangrove (%)
1987–19900.96060.940078.9 (14.5)11.3 (2.1)267.4 (49.2)138.2 (25.5)13.5 (2.5)33.7 (6.2)
1991–19950.95690.934489.6 (16.5)12.8 (2.4)238.9 (44.0)143.6 (26.4)25.0 (4.6)33.0 (6.1)
1996–20000.95000.934892.6 (17.1)13.2 (2.4)229.4 (42.2)135.5 (25.0)36.4 (6.7)35.7 (6.6)
2001–20050.94870.938097.0 (17.9)13.9 (2.6)235.3(43.3)134.9 (24.8)35.1 (6.5)27.0 (5.0)
2006–20100.94520.9204105.9 (19.5)15.1 (2.8)223.0 (41.1)136.2 (25.1)31.0 (5.7)31.7 (5.8)
2011–20150.94270.9156106.0 (19.5)15.1 (2.8)238.5 (43.9)102.5 (18.9)51.9 (9.6)29.0 (5.3)
2016–20200.94510.918489.8 (16.5)12.8 (2.4)288.6 (53.1)105.9 (19.5)18.9 (3.5)27.0 (5.0)
2021–20240.94260.9270102.4 (18.9)14.6 (2.7)278.9 (51.4)109.9 (20.2)13.5 (2.5)23.6 (4.3)
Table 3. Phuket’s urban area, UEII, and Shannon Entropy.
Table 3. Phuket’s urban area, UEII, and Shannon Entropy.
Study PeriodUrban Area (Square Kilometers)UEIIShannon Entropy
1987–199078.919.431.94
1991–199589.6−3.402.29
1996–200092.6−4.732.27
2001–200597.023.702.25
2006–2010105.99.152.31
2011–2015106.03.772.31
2016–202089.83.402.30
2021–2024102.414.201.98
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Moukomla, S.; Marome, W. Tourism-Induced Urbanization in Phuket Island, Thailand (1987–2024): A Spatiotemporal Analysis. Urban Sci. 2025, 9, 55. https://doi.org/10.3390/urbansci9030055

AMA Style

Moukomla S, Marome W. Tourism-Induced Urbanization in Phuket Island, Thailand (1987–2024): A Spatiotemporal Analysis. Urban Science. 2025; 9(3):55. https://doi.org/10.3390/urbansci9030055

Chicago/Turabian Style

Moukomla, Sitthisak, and Wijitbusaba Marome. 2025. "Tourism-Induced Urbanization in Phuket Island, Thailand (1987–2024): A Spatiotemporal Analysis" Urban Science 9, no. 3: 55. https://doi.org/10.3390/urbansci9030055

APA Style

Moukomla, S., & Marome, W. (2025). Tourism-Induced Urbanization in Phuket Island, Thailand (1987–2024): A Spatiotemporal Analysis. Urban Science, 9(3), 55. https://doi.org/10.3390/urbansci9030055

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