Spatial–Temporal Correlation between the Tourist Hotel Industry and Town Spatial Morphology: The Case of Phoenix Ancient Town, China

Abstract

Despite the recognition of the correlation between the tourism industry and a town’s spatial morphology, there is a dearth of rigorous empirical specifications to examine it. This study uses geographic information system (GIS) tools and space syntax analysis to explore whether the tourist hotel industry and a town’s spatial morphology are consistent or if they have different spatial distributions. From a sample of Phoenix Ancient Town, China, our analysis shows the following results: First, there is a spatial correlation between the tourist hotel industry and urban spatial morphology, which is significantly related to tourist attraction distribution and traffic accessibility. Second, the spatial evolution of the tourist hotel industry and a town’s spatial morphology shows different characteristics in different periods. Third, the impact of the tourist hotel industry on a town’s spatial morphology is gradually decreasing as new business buildings arise. The analysis is theoretically important as it enriches the methodologies for analyzing the correlation between the tourist hotel industry and a town’s spatial morphology. It is important for government planners as it provides useful information for formulating territorial spatial planning.

1. Introduction

Ever since the 1990s, China has witnessed an increasing number of tourists in its ancient towns [1]. Nowadays, tourist hotels occupy substantial amounts of space within a town’s spatial morphology [2]. With the rapid growth of urbanization and the tourism industry, towns have become effective communication channels for the tourist hotel industry, reflecting the changes in human social spatial structure and revealing spatial patterns of the behavior of a tourism economy [3]. The location of tourist hotels is similarly susceptible to the evolution of urban spatial morphology. They constitute the basis for the research of sustainable development of the human–environment system [4]. Due to the increasing number of tourists and the need for economic development, the necessity to coordinate the relationship between tourism development and the preservation of cultural resources is becoming increasingly urgent. Consequently, understanding the existing correlation between the tourist hotel industry and a town’s spatial morphology has important implications for a town’s tourism development.

A town’s spatial morphology is utilized in a wide range of geographical applications. It can be explained by factors such as housing costs, traffic networks, and temperature variance [5]. The degree of town agglomeration and a town’s spatial structure have been important research topics in this field [6]. Sadewo discovered that population and employment density are regarded as the most significant characteristics of a town’s expansion from the core to the periphery [7]. Several studies indicate that racial and cultural differences may influence the pattern of urban morphology and the hotel industry [8,9]. Mcneill found that a town’s space illuminates the characteristics of urbanism in the twentieth century [10]. These studies have established a connection between the tourist hotel industry and a town’s spatial morphology. However, scholars have failed to devote adequate attention to the spatial evolution trend. In addition, it has not been determined whether the hotel industry, at the town level, such as in historic tourist towns, can affect the sustainable development of a town. Hence, as the pace of the tourist hotel industry accelerates, the rigorous and comprehensive analysis of town spatial morphology warrants more attention from different perspectives.

Currently, the analysis of the relationship between the tourist hotel industry’s growth and a town’s spatial morphology is still unclear. There is an urgent need to make progress, in terms of resource preservation and economic growth, to work on the tourism supply side. The most important innovations of this study are its utilization within spatial–temporal perspectives and its application to geographical information system tools and space syntax analysis to study the dynamic patterns of the tourist hotel industry and a town’s spatial morphology. This study seeks to contribute to this body of work by summarizing the rule of spatial correlation between the tourist hotel industry and a town’s spatial morphology and to provide practical implications for coordinated development.

2. Literature Review

A town’s spatial morphology can be affected by factors such as traffic topography and policy planning [11,12]. Moreover, favorable local rivers can also promote the construction and development of a town [13,14]. The spatial integration of the old and new areas of a town is essential for preserving the historical place [15,16]. Wu found that different factors have an impact on the development dynamics of heritage tourism sites and cities due to the variances in their spatial patterns [17]. With regard to the research methods, cellular automata, town class model, fan analysis, and expansion intensity index have been widely used to delineate town spatial morphologies [18]. Lityński validly utilized nighttime light data to evaluate the growth trend of towns [19]. In previous studies, space syntax was generally used to identify the spatial morphological characteristics of towns and their spatial integration. Li contrasted space syntactic analysis findings with proposed historic site designs to offer recommendations for evaluating plans [20]. They found that tourist preferences on the site of Gulangyu Island demonstrated a high correlation in relation to street network integration. Moreover, the expansion of urban morphology often occurs to support tourism development, causing the drastic destruction of ecosystem services [21]. Furthermore, it would make the development of tourism industry unsustainable [22].

In previous empirical studies, tourist hotels are likely to be situated close to their potential markets [23,24]. Interest in the tourist hotel industry has grown since the 1980s, and some studies have focused on the spatial evolution of tourist hotels, mainly discussing the distribution pattern, economic effect, and formation mechanism of the hotel industry [25]. Different disciplines have introduced other theories to explain the location selection of hotels from different perspectives, such as geographical theories, economic theories, and marketing theories [26,27,28]. Moreover, the morphology of tourism agglomerations positively influences the local tourism industry, but excessive agglomeration might result in oversupply [29,30]. Consequently, it would be more probable that the regional space that tends to become saturated would be relocated [31]. The hotel location may be dramatically altered by the frequency of social events [32,33,34], and Li found that tourist attractions play a vital role in determining hotel location [35].

In the literature, town spatial morphology is described as the study of the physical structure of a town’s landscapes, as well as information on the adaptability of its structural characteristics, its dominant functions, and how these elements alternate over time [18]. The development of town spatial morphology has been impacted in a number of ways by the industrial expansion process. Wu identified commercial redevelopment as being a major change affecting Chinese urban morphology [36]. Evolutionary economics is a general theory that demonstrates the changes of economic activities. It emphasizes the historical basis of path dependence while focusing on the economics of industry creation [37]. The rapid growth of the tourist hotel industry has had a considerable impact on encouraging urban economic development, driving urban industrial revival, and altering internal city organization [35].

Only a few, if any, attempts have been made to research the correlation between the tourist hotel industry and a town’s spatial morphology [38]. They are likely to be spatially associated in various ways. On the one hand, the expansion of a town’s construction land area, ecological damage, and rising costs caused by the spatial evolution of hotels aggravate the deterioration of the spatial environment of such a town. On the other hand, the rationality and sustainability of spatial planning are affected by the acceleration of the spatial environment’s decline. The size of a town’s development depends on the growth of the tourist hotel industry [39].

3. Materials and Methods

3.1. Study Area

Phoenix Ancient Town, a typical river valley town, is located in central China. It is a part of China’s Wuling Mountains, a continuous region of extreme poverty, and has a surface area of approximately 10 square kilometers (see Figure 1). It is located between latitudes 27°56′56″ N to 27°57′49″ N and longitudes 109°35′28″ E to 109°36′22″ E. The altitude ranges from 300.866 m to 100 m. With 48 cultural relics at the county level, including eight provincial-level cultural relic protection units, Phoenix Ancient Town has a rich history that has been maintained up to this point. Its total revenue generated by tourism in 2019 was CNY 20.001 billion. A total of 20,019,300 tourist visitors generated CNY 240 million in revenue for the tourist hotel industry, an increase of 6.2% from the previous year. In the 1980s, hotels were developed to accommodate government reception requirements. As cultural tourism grew, the number of hotels gradually increased to accommodate local tourists. The town’s population has increased from 31,000 in 2001 to 146,000 in 2019, and its urbanization rate has grown from 10.01 to 41.79%.

3.2. Methodology

This paper utilized the nearest neighbor index (NNI) and Getis–Ord ${G_i}^{*}$ statistic to examine the distributional characteristics of the tourist hotel industry. In particular, the NNI analysis was used to compare the characteristics of an object’s clustering and random distribution [40]. The Getis–Ord ${G_i}^{*}$ statistic, commonly known as a hot-spot analysis, is a technique for assessing the place-associated tendency in the attributes of a geographic point or area data [41]. Based on the findings, we divided the spatial distribution of hotels into four categories: cold areas, sub-cold spots, sub-hot spots, and hot spots.

The spatial morphology of the settlement was then described using space syntax. The spatial syntax model depicts the spatial structure of the town by determining the morphological analysis value, including the connection value, depth value, integration degree, and intelligence degree. The integration degree, a significant indicator in spatial syntax, represents the degree of clustering or dispersion of other axial areas in a town’s street axis space [42]. In order to reflect the centrality of the morphology, the indicator of integration degree was applied [43].

We used a standard deviational ellipse (SDE) and buffer zones to examine the connection between the tourist hotel industry and the town’s spatial morphology. Fundamentally, a buffer analysis is based on particular map components such as points, lines, and areas. The vector element can be expanded in two-dimensional space with it as the center, together with the formation of a certain number and width of buffer zones surrounding it, and the superposition analysis of target elements to reveal the mechanism of action between various geographical elements [44]. Figure 2 shows the methodological framework in detail. The above-mentioned formulas are shown in

Table 1. The formulas of the indexes regarding the spatial structure characteristics of the tourist hotel industry and the town’s morphology.

Method	Equation	Equation Explanation	Significance
Nearest neighbor index	$NNI=\frac{D_n}{D_R}$	NNI is the nearest neighbor distance; $D_R$ is the expected average distance of the nearest neighbor; $D_n$ is the observed average distance; and n is the quantity of the samples.	The nearest neighbor index was used to study the distribution and evolution of the hotel industry. The study sample is concentrated when NNI < 1; when NNI > 1, the study sample is discretely distributed; and when NNI = 1, the study sample is randomly distributed. The likelihood of the study sample being randomly distributed increases as NNI approaches 1 [40].
Getis–Ord ${G_i}^{*}$ statistic	$Z\left({G_i}^{*}\right)=\frac{{\sum }_{j=1}^n{W}_{ij}{X}_j-\overline{X}{\sum }_{j=1}^n{W}_{ij}}{\sqrt{\frac{{\sum }_{j=1}^n{X}_j^2}{n}}\left({\overline{X}}^2\right)\sqrt{\frac{n{\sum }_{j=1}^n{W}_{ij}^2-\left({\sum }_{j=1}^n{W}_{ij}^2\right)}{n-1}}}$,	$Z\left({G_i}^{*}\right)$ is the agglomeration; $X_j$ is the coordinates of the hotel point j; n is the total number of hotels; and $W_{ij}$ is the spatial weight matrix.	The greater the value, the more aggregated it becomes. Based on the values, the spatial distribution of hotels is classified into four types using the natural breakpoint method: cold spots, sub-cold spots, sub-hot spots, and hot spots [45].
Standard Deviational Ellipse (SDE)	$SD{E}_x=\sqrt{\sum _{i=1}^n\raisebox{1ex}{${\left(x_i-\overline{x}\right)}^2$}\!\left/ \!\raisebox{-1ex}{$n$}\right.},$ $SD{E}_y=\sqrt{\sum _{i=1}^n\raisebox{1ex}{${\left(y_i-\overline{\overline{y}}\right)}^2$}\!\left/ \!\raisebox{-1ex}{$n$}\right.}$	$SD{E}_x$ is the short axis; $SD{E}_y$ is the long axis; $x_i$ and $y_i$ represent the coordinates of the element $i$; $(x_i$$,y_i$) is its center; and n is the number of the element i.	The smaller the $SD{E}_x$, the more pronounced the concentrated distribution of the matter. The greater the difference between $SD{E}_y$ and $SD{E}_x$, the more eminent the directionality of the distribution of the matters [46].

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3.3. Data Source and Processing

In this study, the spatial data of the hotels are comprised of inns, bed and breakfasts, hostels, budget hotels, and various star hotels, which were derived from the Bureau of Phoenix County Industrial and Commercial. The National Earth System Science Data Center (http://www.geodata.cn, accessed on 2 August 2020) was consulted for information on the case site’s boundaries, roads, and water. Additionally, we used a 50 m × 50 m grid as the analysis unit to discern the spatial agglomeration process and scale intensity of the hotels.

The street space, which was shaped by the function of transportation, serves as the entry point for this study’s examination of the morphology of Phoenix Ancient Town. First, the road axis models of the corresponding years were drawn in CAD. The dangling and isolated line segments were then removed before being input into Depthmap software to determine the parameter values. Finally, the information was imported into the ArcGIS 10.2 program to link it to the map. The colors from red to green indicate a high to a low value, respectively, thus providing a comparative analysis of the evolution of the town’s internal morphology.

4. Findings

4.1. The Characteristics of the Tourist Hotel Industry Agglomeration

During the period 2001 to 2019, the number of hotels increased from 31 in 2001 to 1037 in 2019, exhibiting strong spatial phase features. In 2001, the average nearest neighbor index was 7.090, indicating a random spatial distribution (see

Table 2. The nearest neighbor indicator of hotels in Phoenix Ancient Town from 2001 to 2019.

Year	Numbers	Average Nearest Neighbor Index (km)	Theoretical Average Nearest Neighbor Distance (km)	Average Nearest Neighbor Index	Z Scores	Distribution Types
2001	31	34.408	4.852	7.09	30.827	Random state
2010	535	13.199	53.674	0.245	−33.491	Agglomeration state
2019	1037	9.125	47.338	0.192	−49.705	Agglomeration state

). In accordance with the strategy for the large-scale development of western China, the government gradually transferred operating rights to Huanglongge Street. In addition, the county’s tourism resources were classified based on traditional minority characteristics, historical tourism, and cultural resources. In 2010, as a result of the expansion of hotels, the average nearest neighbor index increased to 0.245, indicating a weak agglomeration. Through the use of cutting-edge folkloric tourist performance items like Smoky Phoenix and Border Town, the development of tourism was fiercely supported. In 2019, the nearest neighbor index fell to below 1 (0.192). To hasten the optimization of high-quality hotel reception capacity, the PPP Projects for Phoenix Tourism Infrastructure and Transformation and Upgrading and Territorial Tourism Infrastructure Construction were sequentially launched. These projects assist the supply-side structural transformation that reforms the distribution of the tourist hotel industry.

4.2. The Characteristics of the Hot Spots in the Tourist Hotel Industry

In order to analyze the spatial characteristics of the hotels from the perspective of spatial evolution, ArcGIS software was used to calculate the global Moran’s I of the hotels. The Moran’s I for 2001, 2010, and 2019 was 0.259, 0.766, and 0.810, respectively. All of these values passed the significance test, indicating that the hotel distribution exhibited a substantial positive spatial correlation. Figure 3 depicts how a hot spot analysis can be performed. Green represents the cold spot area, yellow indicates the sub-cold spot area, orange is the sub-hot spot area, and red is the hot spot area.

The study’s findings reveal that the hotel spatial arrangement is unstable, with an expanding number of hot spots. It demonstrates the trend of a more transparent, path-dependent mode from 2001 to 2019. Beibian Street, Laoyingshao Street, and Banying Street were the epicenters of the most vigorous growth. These three hot spots cover 1.75 km², 2.25 km², and 2 km², or 1.19%, 1.54%, and 1.3% of the research area, respectively. In total, they cover 31% of the hotels in the research area. These hotels are clustered in a single grid cell, which has statistical relevance for hot spots. The majority of the streets described above are located in the scenic region surrounding the Tuo River. In the early years, these areas accommodated a large number of community members while embracing a natural environment and the core functions of urban production, settlement, and transportation. In addition, there are well-preserved hanging foot structures on both sides of the river, showcasing the Chinese minority style in its entirety.

In terms of the changes in morphology, the hot spots in 2001 were dispersed along Beibian Street and covered a total area of 1.25 km², occupying only 0.8% of the study area. The development area is divided into three areas: the upper, middle, and lower reaches. The intermediate areas are found in the center of Phoenix, where the most well-preserved ancient buildings of the Ming and Qing dynasties are concentrated along the streets and where the consumer population is relatively concentrated. In 2010, hot spots were gradually concentrated in Laoyingshao and Dongzheng Street, and Banying Street has emerged as a new center. The fundamental cause is the transformation of Laoyingshao Street into a bar street, providing additional consumer space and integrating itself into the tourist landscape. With a total investment of CNY 157.74 million, the night scenery lighting project was built in 2012. As a result of foreign capital investment and government initiatives, the tourist hotel industry increased rapidly. The coverage area of the central plan extends from Shawan Site beneath the Hongqiao Bridge to the middle portion of the Tuo River South China Bridge, where the Night Phoenix serves as the focal point of the evening scenic tourist mode. It drives the growth of the night economy, together with the catering, lodging, and sightseeing tourism industries. By integrating these prospering industries, the Tuo River Tourism Industry Belt has acquired its national features.

In 2019, Jinjiayuan Road, Qingxi Road, and Dragon Court Road were added as new growth points. As time passed, the number of secondary hot spots decreased as the spatial distribution of hotels extended upstream and downstream from the middle reaches of the Tuo River. The majority of the cold and sub-cold spots are located around the periphery of the town. With the construction of new sites and the relocation of residents, the upstream area, which was vacant land at the county’s border, became a new functional development zone in which the hammock buildings have been gradually preserved.

4.3. The Spatial–Temporal Evolution of the Town’s Spatial Morphology

In order to determine the pattern of spatial morphology within the town, the evolutionary path of the logic of the spatial structure of the town in the core region was investigated by comparing the axial modes of various times. The global integration nuclei of Phoenix Ancient Town are Laoyinshao Street, Beibian Street, Banying Street, Wenxing Street, and Dongzheng Street, as shown in Figure 4. The two sides of the Tuo River have been the principal locations for production, settlement, and transportation, while Dongzheng Street and Beibian Street, two of the town’s earliest commercial centers, laid the framework for the layout of the town. There is a close relationship between the distribution of hotels and the topography. The topography, which is elevated in the northwest and low in the southeast, consists primarily of three terrace patterns. Consequently, the core area is focused in the heart of the relatively low-elevation old town.

In particular, Beibian Street, which is closely followed by Dongzheng Street and densely scattered along the middle reaches of the Tuo River, has the highest integration level. It highlighted the single-core central pattern of the spatial structure of the town in 2001. In 2010, the global integration nucleus expanded significantly, and the number of roads in the central integration nucleus increased, becoming a large, single-core integration area. The global integration degree mainly ranges from 0.192 to 0.582, with a mean value of 0.375. Beibian Street has the highest valuation, followed by Hongqiao East Road. In 2019, the size of the global integration nucleus grew significantly, resulting in the establishment of a double-core cluster spatial pattern. With the large-scale construction of the new town, the integration axis along Hongqiao Middle Road expanded dramatically. In contrast, the global integration of the Tuo River’s upstream and downstream has become more evenly dispersed. It is mainly concentrated between 0.236 and 0.609, with a mean value of 0.408. The transformation of the new town into an integrated core, which reinforced the connection between the new and the old areas, had a direct effect on the dispersion of the global integration nucleus axis throughout this time period.

4.4. Spatial Association between the Tourist Hotel Industry and the Town’s Spatial Morphology

4.4.1. The Expansion Trends in the Growth of the Tourist Hotel Industry and the Town’s Spatial Morphology

Figure 4 depicts the entire distribution of hotels for tourists in Phoenix Ancient Town. The elliptical azimuth changed from 122.696° in 2001 to 125.796° in 2019, indicating that the distribution of tourism hotels has stayed consistent with the northwest transfer direction. The ellipse area of the hotels’ spatial distribution has increased from 0.00039 km² in 2001 to 0. 556 km² in 2019, but the ellipse area of the town’s spatial morphology expanded form 1.953 km² to 2.562 km². These modifications to the long half-axis demonstrate a rise in the corresponding value from 0.09 km in 2001 to 0.646 km in 2019. These changes have led to a diffusion trend in the north–south development of tourist hotels. The short semi-axis of the ellipse has grown from 0.001 km in 2001 to 0.425 km in 2019, indicating that there is an east–west diffusion tendency (see Figure 5).

In addition, the elliptical orientation shifted from 122.696° to 91.739°, demonstrating a general shift to the northeast, and the overall direction of the distribution of the town’s spatial morphology shifted from northwest to southeast. It demonstrated that the development in the northeast direction area is superior to that in the southwest direction. With the expansion of tourist hotels, the east–west direction continues to spread outward, as seen by the expanding trend that has been maintained by the long and short semi-axis modifications.

In summary, the overall direction of the elliptical distribution of the tourist hotels is consistent with the town’s spatial morphology. However, the expansion trend is incompatible. The tourist hotel industry tends to spread to the southwest, but the town’s spatial morphology tends to extend to the northeast. The current azimuth of the tourist hotel industry growth and spatial pattern of the town’s expansion stays in the northwest direction, that is, in the upper reaches of the Tuo River area. The Nanhua National Forest Park and the Duosailuo Scenic Area are situated in the southwest, which explains the significance of the transportation growth and the creation of new areas.

In 2012, after Phoenix Ancient Town exceeded its spatial carrying capacity, the government began constructing new communities along the upper reaches of the Tuo River. Some merchants also moved out of the old town, resulting in a significant expansion of the town’s morphology. The spatial functions of the town depend greatly on the expansion of tourist hotels. Consequently, a solid foundation has been formed for the future development of these hotels as a result of the town’s gradual expansion of its multiple spatial responsibilities. The evolution of the spatial morphology of cities is also hastened by the rise in tourist accommodations.

4.4.2. High Overlap of the Tourist Hotel Industry and the Town’s Spatial Morphology

To further investigate the relationship between the role of tourist attractions on the growth of tourist hotels and the morphology of the town, a 50-meter buffer zone was first established, along with the leading road network (see Figure 6). The fact that 85.6% of tourist hotels were located in this area shows that the town’s core is where most of the district’s land is distributed. As the new town of Phoenix is constructed and the government undertakes the relocation projects, new hotels are gradually relocating from the distribution pattern around the river valley landscape. In general, the hotel industry is increasingly dependent on tourism resources. A 50-meter buffer zone was formed around the tourist attractions, and all the hotels built in 2001 were included. In 2010, the proportion of hotels in the buffer zone declined to 79% of the total. In 2019, 88% of the hotels were situated within the buffer zone. The existing cultural resources can be split into two major blocks. One is the natural landscape along the Tuo River. The other is the humanistic landscape associated with the town’s spatial morphology, which demonstrates their synchronicity with that development.

To conclude, based on the combined analysis of the above factors, the hot spots of tourism hotels in different periods and the global integrated cores of the town’s morphology coincide spatially. They are all located in areas with abundant human tourist resources and picturesque landscapes. This concentration reflects the critical influence of the cultural heritage in these national, historic, and cultural areas on the spatial center of the town.

However, there is a difference in the evolutionary path, with the tourist hotels evolving from a single core to multi-core while the town’s morphology gradually changes from a single core to a double core. The diffusion of the regional tourist hotels is accelerating from the center to the periphery, and new tourist hotel agglomeration centers are being added as a result of the oversupply of tourist hotels in the central area, along with the protection provided by the government and the development of new tourism projects. The diffusion of the industry can accelerate the balance of the town’s spatial function distribution and reshape the morphology.

5. Conclusions and Discussion

5.1. Summary and Conclusions

This study uses GIS methods and tools to investigate the spatial relationship between the tourist hotel industry and a town’s spatial morphology, with Phoenix Ancient Town as an example. The town spatial morphology in tourist destinations such as Phoenix Ancient Town is significantly associated with the growth of the tourist hotel industry. In addition, we find that evolutionary trends vary over time. Traffic accessibility may have a significant effect on spatial patterns. Moreover, the distribution of heritage resorts is a crucial factor in coordinating the spatial correlation between the hotel industry and a town’s morphology.

The study’s findings also support McNeill’s argument that the hotel industry represents a sort of residential and commercial space that is essential to methods for revitalizing a town’s spatial morphology [10]. According to the findings, a town’s spatial morphology and the evolution pattern of the tourist hotel industry were comparable. Larrinaga suggests that it is possible to accomplish the planned growth of the hotel industry and a town’s spatial morphology by creating new tourist resources and boosting transit accessibility [33]. However, in contrast to earlier studies, we discovered that the effects of the expanding hotel industry on the direction of a town’s spatial morphology have gradually diminished. It illustrates the development of new composite spatial functions driven by tourism urbanization, which has a greater impact on the change of a town’s spatial morphology. The tourist hotel industry has been particularly hit by the COVID-19 pandemic, and it needs to actively extend the tourism industry chain to boost travel demand. Additionally, the beneficial development of the tourism industry also promotes the economic growth of tourist destinations and the preservation of traditional architecture.

The study’s findings further confirm tourism area life cycle theory [47]. The characteristics of the spatial–temporal evolution pattern of tourist hotels and a town’s spatial morphology can be detailed in the following three stages: (1) The emerging period, which is the conversion of historical and cultural resources into tourist landscapes, has become a precondition for the economic development of a town and the hotel industry. The development direction and distribution of the industry chain, including the hotel industry, are determined by resources endowment. The formation of the hotel agglomeration core strengthens the traffic construction of the central town. (2) The development period is represented by the fact that the agglomeration of the tourist hotel core increases the spatial load of the central area of a town. The hotel market is typically saturated. For the optimization of the layout and the realization of the hotel passenger flow diversion, the hotel distribution gradually expands to the core of the sub-conglomeration. Thus, a town’s spatial morphology is expanded gradually, and the urbanization process is accelerated. (3) During maturity, the spatial layout of a town is unable to adapt to the market expansion due to restrictions imposed by historical and cultural urban planning. Hence, the government gradually develops new areas to strengthen the tourism function of an old region, protect the historical buildings, and alleviate the burden of urban loads. Additionally, the spatial connection between the old and new regions is gradually strengthened, which promotes the development of tourist hotels in new regions.

5.2. Discussion and Implications

The spatial–temporal evolution of tourism hotels and a town’s spatial morphology exhibits path-dependent characteristics. As a result of the transformation of cultural resources into cultural capital, a scale-effect economy has emerged. The preservation projects of old town renovation, new town development, and ecological space landscaping will yield greater returns. These areas, which originally served as living and production spaces, have been turned into businesses to stimulate town expansion. The framework of this study can be applied to other historical tourist destinations outside Phoenix Ancient Town in order to investigate the tourism and hospitality sectors, as well as the town’s spatial morphology.

This research makes a theoretical contribution. It contributes to the following broad literature: Firstly, previous studies have mostly focused on the correlation between the tourism industry and a town’s spatial morphology. This study contributes to the body of knowledge on the effects of the tourist hotel industry on a town’s spatial morphology and addresses the need for further empirical research on logical space planning by proposing the systematic placement of hotels in historical towns. Second, this study provides a visualized and quantitative approach to tourist geographies by applying space syntax and geographic information system (GIS) tools to delineate the characteristics of a town’s spatial morphology and the tourist hotel industry. It also uses time series data from 2000 to 2019 to explore the changes in spatial patterns by examining temporal rules in detail. Thirdly, Phoenix Ancient Town can be regarded as an excellent example of historic towns seeking to balance sustainable tourism development and the conservation of historic towns.

This study also has practical implications. The results contribute to the conservation issues of Phoenix Ancient Town and a large number of historic towns around the world that are confronting similar issues. Furthermore, local governments should attach more importance to the distinctiveness of tourism hotels in relation to both urban spatial morphology and tourist sites. This may involve implementing scientific indices to protect traditional buildings, minimizing the loss of historic cultural heritage. Finally, local governments should follow the objective laws of urban spatial evolution, reasonably coordinate the planning of urban spatial patterns, and optimize the internal spatial structure of urban space.

However, some of the main barriers of this study need to be addressed. Due to the lack of data, this study primarily examines the spatial association between the tourist hotel growth and the town’s spatial morphology from the physical side, ignoring social attributes. The social effects of tourist hotels on the spatial morphologies of towns have rarely been studied. Future studies should investigate the variety of tourism industries that have an impact on town spatial morphologies in various places.