How does the TOD pattern affect urban tourism vitality? Insights from Nanjing based on land use and urban form

Abstract

A transit-oriented development (TOD) pattern important for alleviating urban traffic congestion and enhancing the intensive use of land. It is also a key factor for promoting the sustainable development of urban tourism and public transportation. Exploring the relationship between the TOD pattern and urban tourism is conducive to fostering positive interactions between public transportation infrastructure and the growth of urban tourism. Accordingly, an analytical human-scale-based TOD urban tourism vitality (TOD-UTV) framework is proposed to examine the impact of the TOD pattern on urban tourism in Nanjing, China, from the perspectives of land use and urban morphology. The results indicate that merely pursuing a high land development intensity in TOD areas does not effectively enhance tourism vitality. Instead, optimizing land function allocation according to the local tourism industry and adopting moderately differentiated functional distribution principles are effective strategies for promoting urban tourism vitality. Additionally, optimizing TOD spaces through appropriate design techniques, controlling their density, and developing commercial spaces around tourism resources are essential for enhancing the overall attractiveness of TOD areas to tourists. This study provides a novel framework for evaluating how the TOD pattern can promote urban tourism development, offers theoretical support for exploring the relationship between urban tourism and public transportation at the human scale, and provides insights into fostering positive interactions between TOD planning and urban tourism development.

Graphical abstract

Keywords

Tourism vitality / TOD / Land use / Urban morphology / Nanjing

1 Introduction

The development of tourism is crucial for enhancing a city’s attractiveness and competitiveness (Łapko, 2014). With increases in population, tourism demand, and the diversification of travel modes, many cities inevitably face problems such as traffic congestion and environmental pollution (Romão and Bi, 2021). Therefore, developing public transportation systems has been an effective method for addressing congestion and pollution issues in many cities (Fajri and Sumabrata, 2019; Shoval, 2018). Previous studies have shown that public transportation construction is a key element for promoting urban tourism development. It not only alleviates traffic congestion in tourist hotspots but also increases the convenience of travel for tourists (Ji et al., 2020), making it an essential factor for sustainable urban tourism development (Gronau and Kagermeier, 2007; Masson and Petiot, 2009). Currently, the transit-oriented development (TOD) pattern has gradually become a crucial factor considered when resolving urban congestion and intensive spatial development issues (Man et al., 2020). TOD is an effective model for improving land use efficiency and transportation efficiency (Cervero and Kockelman, 1997; Waddell, 2011), influencing daily urban activities, such as tourism behavior, by impacting land use and urban morphology within the development scope (Ratner and Goetz, 2013; Tang et al., 2022).

Numerous studies have extensively examined the relationship between urban public transportation and tourism development (Bursa et al., 2022; Becker and George, 2011; Zhao and Wang, 2007). These studies have focused mainly on the macro- or mesolevel, providing suggestions for urban public transportation construction from the perspective of tourism development. Additionally, some studies have investigated the relationship between public transportation and urban morphology considering the TOD pattern at the microscale (Bai et al., 2023; Yu et al., 2022). However, few studies have explored the relationship between TOD patterns and urban tourism vitality. Notably, how does the TOD pattern affect urban tourism vitality, and what are its mechanisms of influence?

On this basis, a novel analytical framework encompassing urban vitality theory (Jacobs, 1961) is proposed to evaluate the influence of the TOD pattern on urban tourism vitality through two important characteristics of the TOD pattern, namely, land use and urban morphology, with the aim of improving the adaptability practices of positive interactions between urban tourism and public transportation. The remainder of the study is organized as follows. In Section 2, the theoretical progress in areas such as public transportation and tourism, TOD, and urban vitality is described. In Section 3, we introduce the research case and study areas, as well as the data collection and analysis methods. In Sections 4 and 5, we use point-of-interest (POI) data and urban network analysis (UNA) techniques to examine the specific impacts of land use and urban morphology characteristics within TOD areas on tourism vitality, and the discussion and conclusions are presented in Sections 6 and 7, respectively.

2 Literature review

2.1 Public transportation and urban tourism

Research on tourism development and urban public transportation systems has made some progress. For example, Khadaroo and Seetanah (2008) used a gravity model method to assess the impact of public transportation on enhancing urban tourism attractiveness and reported that transportation infrastructure is a crucial determinant of urban tourism development. Schiefelbusch et al. (2007) introduced the concept of the “tourism chain,” positioning public transportation construction as the main driving force for promoting sustainable urban tourism development. Zheng et al. (2016) noted that transportation is a key factor in tourism development. In cities with abundant tourism resources, developed economies, and convenient transportation, the correlation between the tourism economy and public transportation is more significant than in other cities. Some scholars have applied coupled coordination models to analyze the relationship between tourism and transportation in Shaanxi Province (Zhang and Wen, 2023) and Xi’an City (Wang and Ma, 2011) in China, finding a close relationship between the two, with an increasing trend of integration and coordination between tourism transportation and tourism development. Moreover, some studies have discussed aspects such as satisfaction with tourism transportation (Le-Klähn et al., 2014; Lv et al., 2019; Wang, 2014) and the energy consumption of tourism transportation (Filimonau et al., 2014; Yang et al., 2023).

2.2 Relationships among TOD, land use, and urban morphology

The TOD model effectively integrates transportation infrastructure with the surrounding urban fabric, typically describing areas within an 800-m radius of transit stations with a certain urban density, mixed-use land functions, and high-quality accessibility via walking (Adeel et al., 2021; Stojanovski, 2020; Yu et al., 2022). This compact development model effectively enhances the attractiveness of transit stations and surrounding areas to pedestrians. Studies have shown that the TOD pattern significantly impacts urban morphology and land use (Ratner and Goetz, 2013) and that the greater the connectivity of TOD areas in the urban transportation network is, the greater the attractiveness of the area (Xia et al., 2019).

Urban morphology refers to the spatial pattern of buildings and streets in a city (Munshi, 2016). A compact urban morphology is reflected in high-density urban spaces and high-intensity land use (Huang et al., 2015; Wu et al., 2012); this structure encourages walking and thus increases the frequency of public transportation use (Krizek, 2003; Lang et al., 2018; Sarkar and Mallikarjuna, 2013). Ma et al. (2018) proposed a multiobjective TOD planning pattern that integrates transportation and land use and obtained the optimal solutions for different land use schemes within the TOD scope via an improved immune genetic algorithm. Vale (2015) combined transportation, land use, and urban design to develop a planning tool for TOD areas, attempting to develop a balanced TOD strategy.

2.3 Impact of TOD on urban vitality

The concept of urban vitality was first proposed by Jacobs (1961). Many scholars subsequently enriched the definition of urban vitality from different perspectives (Bromley and Thomas, 2002; Fuentes et al., 2020; Lynch, 1984; Maas, 1984), agreeing that human gatherings and activities are direct manifestations of urban vitality (Montgomery, 1998; Shen et al., 2022; Wu et al., 2018). An active city requires an efficient transportation system (Tang et al., 2020). Ye et al. (2016) reported that areas with high levels of TOD-based construction are more capable of stimulating urban vitality than are other areas. Other scholars have examined the nonlinear relationship between vitality and transportation accessibility in Guangzhou (Wang et al., 2023) and Wuhan (Wang et al., 2023), pointing out that the TOD concept is effective for enhancing urban vitality. On a microlevel, Lang et al. (2020) used human-scale measurement methods to investigate the spatial correlation between urban morphology and social activities within TOD areas, noting that urban morphology within the TOD scope significantly influences social activities.

In summary, although previous research has made some progress in the fields of public transportation and tourism development, most studies have explored the relationship between the two from a macroperspective. The lack of a microperspective has led to challenges in implementing macropolicies within specific urban planning practices. Therefore, this study integrates social media data, land function data, and urban morphology data to explore the relationships and influence mechanisms between the TOD pattern and urban tourism vitality from a microperspective. An analytical framework for TOD urban tourism vitality (TOD-UTV) is constructed based on human-scale considerations (Fig. 1), thereby providing scientifically grounded and sustainable development strategies for enhancing positive interactions between urban tourism growth and the TOD pattern.

3 Methodology

3.1 Study area

Nanjing, the capital city of Jiangsu Province in eastern China, is located in the lower reaches of the Yangtze River. As one of the first national historical and cultural cities, Nanjing is rich in tourism resources and is a popular tourist destination in China. According to data provided by the Nanjing Municipal Government, as of the end of 2023, Nanjing had 31 tourist attractions rated AAAA and above. The Nanjing Bureau of Statistics reported that the city was visited by a total of 157 million domestic and international tourists throughout 2023. As a core city in the Yangtze River Delta region, Nanjing has high levels of economic and social development and a well-developed public transportation infrastructure. According to the Nanjing Metro, the city currently operates 14 subway lines with a total length of 459.4 km, and the metro passenger volume in 2023 was 1.01 billion trips, with an average daily passenger volume of 2.739 million trips. With the large number of tourists and well-developed public transportation facilities, considerable data are available for this study. Notably, all 156 subway stations in Nanjing (n = 156) were selected, and the buffer tool in ArcGIS 10 software was used to establish TOD areas with a radius of 800 m (approximately a 10-min walk) around each subway station as the research scope (Fig. 2).

Owing to the large number of subway stations, to intuitively reflect the relationship between the TOD pattern and urban tourism vitality in subsequent analyses, 12 subway stations from among the 156 stations were selected on the basis of the distribution of famous scenic spots for further analysis. These stations were Fuzimiao station (Confucius Temple (Fuzimiao)), Daxinggong station (Nanjing Presidential Palace), Xinjiekou station (famous commercial district), Sanshanjie station (Qinhuai River), Yunnanlu station (Yihe Road scenic area), Zhujianglu station (famous commercial district), Hongshan Forest Zoo station (Hongshan Forest Zoo), Jiuhuashan station (Xuanwu Lake), Muxuyuan station (Xiaoling Mausoleum of the Ming Dynasty), Xiaolingwei station (Dr. Sun Yat-sen’s Mausoleum), Xiamafang station (Meiling Palace (Soong May-ling Villa)), and Tianlongsi station (Tianlong Temple). According to previous studies (Xiao et al., 2021; Zhou and Yang, 2021), land function density and building coverage ratio were used as the criteria to determine the TOD degree of the selected subway stations. Among these 12 stations, the first 6 stations have a high degree of TOD-based construction, whereas the latter 6 stations have a low degree of TOD-based construction. The comparison can intuitively reflect the differences in the impacts of varying degrees of TOD on urban tourism vitality.

3.2 Research data

The data used in this study include three main types: geo-spatial vector data, land function data, and social media data (Table 1). We extracted the 2023 road network and building vector data for Nanjing city from OpenStreetMap (OSM), an open online map resource website. The administrative boundary vector map of Nanjing was obtained from the, National Earth System Science Data Center. All the above data were processed via ArcGIS 10 software for coordinate projection transformation and topological correction of the road network to construct a network dataset.

Fine-scale POI modeling methods can accurately reflect urban land use conditions (Wu et al., 2018) and indicate the intensity of urban vitality (Zhou and Yang, 2021). Owing to their precision and comprehensiveness, these methods are gradually being used in tourism geography analyses (Zhang et al., 2021). The study used web crawler technology to collect 322,542 POI data points within Nanjing in May 2023 through the API provided by the Amap open platform. After irrelevant data outside the study area were removed, 165,167 valid data points were retained. Since POI data reflect various land function types and are vary in terms of size, there may be overlaps among some POI areas. To facilitate analysis, the collected POI data were reclassified on the basis of the “Urban Land Classification and Planning Construction Land Standards” issued by the Ministry of Housing and Urban‒Rural Development of China, and the number of POI records and proportion of each type of POI were calculated, as shown in Table 2.

The number of tourists is the most direct indicator of urban tourism vitality (Li et al., 2023). However, such traditional demographic data have certain limitations, as they do not provide detailed information about tourists’ behaviors, such as specific locations, times, activities, or number of participants in tourism activities (Sung and Lee, 2015). In the era of big data, social media data can record detailed information about tourists’ activities (Tu et al., 2020). The social media data used in this study include those for various tourism activities recorded on the Weibo platform, such as tourism check-in records. Additionally, Python tools were used to randomly obtain 46,280 records of tourism activities related to Nanjing from the Weibo open platform within one week. After removing duplicate and irrelevant data from outside the study area, 24,513 valid records were retained. The detailed geographic coordinates of these tourism activities were then obtained via the Amap API. Although social media data may not fully reflect the overall scale of tourist numbers due to differences in user groups and usage habits, they can accurately depict the specific locations, times, activities, and numbers of participants in activities. When combined with POI data, this approach allows for the exploration of urban tourism development from a human-scale perspective (Sui and Goodchild, 2011).

3.3 Data analysis methods

3.3.1 Land function data

3.3.1.1 Principal component analysis

Owing to the high correlation among the reclassified land function data (POI data), a linear combination method was used to preprocess the data. The original 19 major categories of POI data were subjected to principal component analysis (PCA) via SPSS 22.0 software. PCA is an efficient method for data feature extraction and dimensionality reduction, effectively reducing the correlated features in the dataset and transforming multiple indicators into several independent composite indicators (Farahabadi et al., 2021). This method is widely used in the dimensionality reduction of high-dimensional data (Li et al., 2023).

3.3.1.2 Univariate linear regression

Previous studies of land use considering the TOD pattern have focused primarily on land function density (Abdullah and Mazlan, 2016; Zhang et al., 2022) and functional mixed use (Niu et al., 2019; Jun et al., 2015), with few studies addressing the uniformity of land function distributions. While functional density and mixed use reflect land development intensity, can high-intensity land development strategies promote urban tourism vitality? In this study, univariate linear regression analysis is used to examine the relationships between land use characteristics and tourism vitality within the study area. The standard deviation of the number of POIs in each category within the study area is used as an indicator of the uniformity of the land function distribution―the larger the standard deviation is, the less uniform the distribution of land functions. Thus, univariate linear regression can be used to test the linear relationships between land function density, mixed use, and uniformity and tourism vitality, identifying which land use indicators significantly impact tourism vitality. The calculation methods for each indicator are shown in Table 3.

3.3.1.3 Variance analysis and multiple linear regression analysis

In this study, it is hypothesized that differences in the land function distribution significantly impact tourism vitality. Single-factor variance analysis is used to test the differences in urban tourism vitality under various land function distributions within the study area. Multiple linear regression models are then employed to comprehensively analyze the predictive ability of various land functions for urban tourism vitality within the study area and clarify the effects of different land functions within TOD areas on tourism vitality.

3.3.2 Urban morphology data

3.3.2.1 Urban network analysis

Computer-aided spatial analysis models and methods enable the quantitative analysis of urban morphology by depicting real urban spaces (Batty and Longley, 1994). This study uses the urban network analysis (UNA) tool, which was developed within the GIS platform, to assess urban morphology in TOD areas. The UNA tool’s analysis framework centers on “nodes þ network,” where nodes represent buildings or service facilities and the network consists of pedestrian-accessible street routes. This analysis focuses on pedestrians’ travel paths along the street network, aiding in understanding the spatial relationships among buildings and the distributions of urban facilities and attractions. Compared with other analysis methods, UNA establishes spatial connections on the basis of transportation networks to effectively predict pedestrian activity (Sevtsuk, 2018) and reveal how urban design influences public space vitality (Yang et al., 2022). UNA describes urban spatial morphology via five indicators (the calculation methods are shown in Table 4):

Reach: Measures the number of reachable endpoints within a given radius from each starting point in the network. High values indicate a dens distribution of buildings and street networks.

Gravity: Gravity reflects the travel cost from the starting point to the endpoint on the basis of the reach index. This cost is generally represented by the distance between the starting point and the endpoint, with gravity decreasing as distance increases. Proposed by Hansen (1959), the gravity index measures spatial accessibility constrained by spatial impedance within a given search radius, assuming that the accessibility of starting point i is proportional to the gravity of endpoint j and inversely proportional to the distance between i and j.

Betweenness: Betweenness measures the likelihood of a node being traversed in the network. Defined as the ratio at which a point lies on the shortest path between all pairs of starting and ending points within a given search radius (Freeman, 1977), high values indicate high spatial usability.

Closeness: This parameter represents the inverse of the average distance between the starting and ending points within a given search radius (Sabidussi, 1966); it reflects the proximity of a building to surrounding buildings, with high values indicating short distances between buildings.

Straightness: The ratio of the straight-line distance to the shortest-path distance between starting and ending points within a given search radius. High values indicate high spatial convenience (Vragović et al., 2004).

3.3.2.2 Curve fitting analysis

The Pearson correlation coefficient is used to test the linear relationship between the five urban morphology indicators (UNAs) and tourism vitality. The results indicate a weak linear relationship (r:–0.08‒0.42). Therefore, curve fitting analysis (CFA) is performed to explore the extent of the impact of urban morphology on tourism vitality within TOD areas. Curve fitting analysis is a model that investigates nonlinear relationships among data, where the curve regression equation generally expresses the dependent variable as a polynomial of the independent variable. Using SPSS 22.0 software, curve fitting is conducted between the tourism vitality data and each of the five urban morphology indicators (UNAs), and the optimal curve regression equations are determined and used to predict the impact of urban morphology on tourism vitality considering the TOD pattern.

4 Results

4.1 Relationship between land use characteristics and tourism vitality in TOD areas

4.1.1 Results of principal component analysis

Due to the complexity and overlap of the original land function (POI) data, PCA was used to reduce the dimensionality of the original 19 indicators (a total of 122 specific land function categories). Notably, 8 principal components were extracted, each with an eigenvalue greater than 1 (Table 5). These 8 principal components, ranked by their contribution rates, essentially reflect the characteristics of land functions in the study area. Table 6 shows the loadings of the 8 principal components among the original 19 land function indicators. The first principal component contains extensive indicator information, with an eigenvalue of 2.342 and the highest variance contribution of 12.326%, primarily reflecting the land function information related to living and financial services. The second principal component displays high loading values for automobile service and consumption indicators, reflecting land function information related to these areas. The third principal component exhibits high loading values for retail and entertainment indicators, representing entertainment and leisure land types. The fourth principal component displays high loading values for cultural and medical indicators, primarily reflecting cultural and medical land functions. The fifth principal component reflects business-related land function information. The sixth principal component mainly reflects tourism and accommodation land functions. The seventh and eighth principal components reflect sports and fitness and transportation land functions, respectively.

4.1.2 Impact of land function distribution uniformity on tourism vitality

Figure 3 shows the distribution characteristics as well as the number, density, and mixed use of 8 types of land functions, indicating significant dispersion (6.78–680.51(M)±8.61–887.09 (STD)), suggesting an uneven distribution of land functions in the study area, whereas the distribution of land function mixed use (1.17 (M) ± 0.21 (STD)) is relatively uniform. Previous studies often used land function density and mixed use to measure the intensity of land development in areas of TOD (Abdullah et al., 2022). However, in this study, whether such high-intensity land use strategies effectively promote urban tourism vitality is explored. Therefore, land function uniformity is added as a land use indicator, and univariate linear regression is applied to assess the impacts of these three land use characteristics on urban tourism vitality.

As shown in Table 7, among the three land use characteristics, function density (ß = 0.557, p < 0.001) and function uniformity (ß = 0.538, p < 0.001) significantly impact urban tourism vitality, whereas land function mixed use (p > 0.05) does not. As illustrated in Fig. 4, the land function density and uniformity within the 156 TOD areas in Nanjing were divided into 9 levels via the natural breaks method (Jenks). Fiure 4(a) shows that high values indicate high function density, whereas Fig. 4(b) shows that high values indicate low function uniformity. The results indicate that high-value areas are concentrated in the core urban area, where the TOD land function density is high but the level of uniformity is low, with significant differences in the number of functional categories. This value decreases from the core to the periphery, indicating that land function density decreases with spatial outward movement, whereas function uniformity increases. A comparison of these results with those in Fig. 4(c) reveals a positive correlation between land function density and urban tourism vitality, whereas land function uniformity is negatively correlated with urban tourism vitality; notably, areas with high tourism vitality display low land function uniformity. Thus, the previously validated notion that high land development intensity effectively promotes vitality (Yu et al., 2022) does not apply to tourism areas with TOD patterns. Emphasizing land development intensity without specifically distinguishing the impact of different land function types on tourism vitality does not effectively enhance urban tourism vitality.

4.1.3 Variations in the influences of different land functions on tourism vitality

In this study, land function data were divided into 11 groups according to the administrative divisions in Nanjing, and single-factor variance analysis was used to test the differences in tourism vitality under various land function distributions. The results (Table 8) show that except the auto services function (F(10, 144) = 1.183, p > 0.05), the other land functions display significant differences in terms of effect on vitality. Among them, tourism and accommodation functions, F(10, 144) = 14.533, p < 0.001, η² = 0.587; traffic facility functions, F(10, 144) = 14.519, p < 0.001, η² = 0.827; and cultural and medical functions, F(10, 144) = 10.747, p < 0.001, η² = 0.58, display the most significant differences, indicating the greatest variation in tourism vitality under different distributions of these three land functions. Multiple linear regression analysis was subsequently conducted to predict the comprehensive impact of various land functions on urban tourism vitality within the study area. As shown in Table 9, only the tourism and accommodation (ß = 0.360, p < 0.01), fitness (ß = 0.353, p < 0.01), and cultural and medical (ß = 0.350, p < 0.001) functions significantly influence tourism vitality, and the impacts of the other functions are not significant. Therefore, whether from the perspective of differential distribution or overall distribution, tourism and accommodation and cultural and medical land functions are crucial for determining whether land use characteristics in TOD areas significantly impact urban tourism vitality. The more distinct the distribution differences are, the more significant the impact on tourism vitality.

To intuitively illustrate the impact of land function distribution differences on tourism vitality within the study area, 12 TOD areas were selected from the 156 samples, as shown in Fig. 5. The results indicate that in TOD areas such as Fuzimiao station (0.93(M)±3.06(STD)), Daxinggong station (1.10(M)±3.04(STD)), Xinjiekou station (0.95(M)±3.29(STD)), Sanshanjie station (1.03(M)±2.98(STD)), Yunnanlu station (1.32(M)±2.76(STD)), and Zhujianglu station (1.15(M)±3.00(STD)), the land function distribution differences are relatively large and heterogeneous, yet the level of tourism vitality is high. Conversely, in TOD areas such as Hongshan Forest Zoo station (1.36(M)±2.32(STD)), Jiuhuashan station (1.42(M)±2.05(STD)), Muxuyuan station (1.22(M)±2.06(STD)), Xiaolingwei station (1.14(M)±2.22 (STD)), Xiamafang station (1.26(M)±2.24(STD)), and Tianlongsi station (1.02(M)±2.31(STD)), the land function distribution differences are relatively small and uniform, but the level of tourism vitality is low.

Figure 6 further illustrates the differences in land function distribution among the selected 12 TOD areas. Among them, stations a‒f exhibit a higher degree of dispersion in land function distribution, indicating that these TOD areas have higher heterogeneity in land use patterns. In contrast, the data distribution for stations g‒l is more concentrated, reflecting a relatively uniform land function distribution and low spatial variability. It can be seen that stations a‒f show more prominent unevenness in land function distribution, which further confirms the characteristic of greater land use differentiation and stronger spatial heterogeneity in high degree of TOD areas.

4.2 Relationship between urban morphology characteristics and tourism vitality in TOD areas

Figure 7 reveals the nonlinear relationships between urban morphology indices and tourism vitality in 156 TOD areas. Among these indices, closeness has a significant negative impact on tourism vitality, whereas the other indices have significant positive impacts. Closeness reflects the density of buildings distributed in an area, indicating that overly dense buildings and narrow street spacing do not contribute to the enhancement of urban tourism vitality. The figure shows that when closeness is between 0 and 0.2, tourism vitality drops sharply; between 0.2 and 0.6, it decreases slowly; and after reaching 0.6, the downward trend tends to level off. This indicates that tourism activities in TOD areas are concentrated in relatively open spaces. Reach and gravity represent an area’s spatial accessibility and spatial attractiveness, respectively. The figure shows that tourism vitality increases gradually with increasing reach and gravity, with significant increases when these indices reach 2.4 and 2.0, respectively. This suggests that high spatial accessibility and attractiveness can promote tourism vitality. Betweenness reflects the likelihood of a node being traversed in the network, indicating spatial usability. The figure shows that tourism vitality gradually increases as the betweenness coefficient reaches 3.0, indicating that high spatial usability within TOD areas is correlated with high tourism vitality. Straightness measures spatial convenience, with the figure showing that tourism vitality significantly increases when the straightness value of between 0.1 and 0.3 but slowly decreases when it reaches 0.4. This suggests that overly convenient spatial structures may inhibit tourism vitality.

Figures 8–12 provide detailed insights into the relationships between urban morphology attributes and tourism vitality in 12 key TOD areas. Figures 8 and 9 show the results for reach and gravity, respectively. In these figures, TOD areas a to f have high values (reach M = 2.63, gravity M = 2.22), indicating high spatial accessibility and attractiveness. Dense tourism activities occur in these high-value areas. Conversely, TOD areas g to l have lower values (reach M = 2.34, gravity M = 1.95), indicating insufficient spatial accessibility and attractiveness due to a low street network density, resulting in fewer tourists and lower tourism vitality in these areas.

Figures 10 and 11 present the results for betweenness and straightness, respectively. Similarly, TOD areas a to f have high values (betweenness M = 3.97, straightness M = 0.31), indicating strong spatial usability and convenience. Dense tourism activities coincide with these high-value areas. Conversely, TOD areas g to l have lower values (betweenness M = 3.64, straightness M = 0.16), indicating lower spatial usability and convenience; notably, it is difficult for tourists to move freely in these less-open spaces, resulting in lower tourism vitality.

Figure 12 shows the results for the closeness in the 12 TOD areas. Unlike the results for the other four indices, dense tourism activities occur in areas with low closeness values. The low-value areas are centered on subway stations, whereas the high-value areas are distributed around the periphery of the TOD areas, indicating a denser building distribution toward the periphery. The lack of sufficiently spacious areas and the division of areas by dense building clusters make attracting tourists challenging, with tourism activities concentrated in relatively open spaces. This characteristic is most evident in TOD areas g to l. Therefore, overly dense building distributions can inhibit tourism vitality.

As shown in Table 10, we further conducted Mann-Whitney U tests to examine the differences in urban morphology indicators among the selected TOD areas. The results indicate that there are significant differences in several morphological characteristics across different subway stations. Specifically, the Mean Rank values for reach and gravity in stations a‒f are both 9.5, significantly higher than the values for stations g‒l 3.5 (p < 0.01), betweenness (8.83 vs. 4.17, p < 0.05) and straightness (9.5 vs. 3.5, p < 0.01) also show higher levels in the a‒f stations. In contrast, the Mean Rank of closeness is higher in the g‒l stations (3.83 vs. 9.17, p < 0.01). Overall, these test results statistically confirm the significant differences in urban morphological features among the selected TOD areas and further clarify the significant advantages of high degree of TOD areas in terms of spatial accessibility and attractiveness.

5 The influence of TOD pattern on tourism vitality

5.1 The distribution of TOD land functions significantly affects tourism vitality

From a global perspective, there is a symbiotic relationship between tourism activities and land use, where land use is driven by tourism activities and tourism activities are highly sensitive to the nature of land use (Williams and Shaw, 2009). Previous studies have shown that a high intensity of TOD enhances urban vitality (Li et al., 2022). However, this study revealed that high-intensity TOD strategies are not effective for promoting urban tourism vitality. Instead, adopting a moderately differentiated land function distribution principle is a more effective. The results of the variance analysis show that the greater the differences in the distributions of tourism and accommodations and the cultural and medical functions within TOD areas are, the greater their impact on tourism vitality. The regression analysis results also indicate that tourism, accommodation, and cultural land functions have the strongest positive impacts on tourism vitality. These functions are directly related to tourism and are indispensable to urban tourism development (Gao et al., 2021; Liang et al., 2021). In the 156 TOD areas in Nanjing, the distribution of these land functions displays the most sensitive relationship with tourism vitality. Therefore, under the TOD pattern, optimizing land function allocation based on the local tourism industry is crucial for promoting a positive cycle between public transportation and urban tourism development.

From a local perspective, in TOD areas such as Fuzimiao Station, Daxinggong Station, Xinjiekou Station, Sanshanjie Station, Yunnanlu Station, and Zhujianglu Station, the land function distribution differences are pronounced, resulting in high tourism vitality. Conversely, in TOD areas such as Hongshan Forest Zoo Station, Jiuhuashan Station, Muxuyuan Station, Xiaolingwei Station, Xiamafang Station, and Tianlongsi Station, the land function distribution is relatively uniform, resulting in low tourism vitality. Unlike previous studies (Yu et al., 2021), this study revealed that in addition to functional density and diversity, the degree of difference in the land function distribution is a significant factor affecting urban tourism vitality. The greater the differences in the land function distribution are, the more effectively urban tourism vitality is enhanced. Additionally, the study revealed that the lower tourism vitality levels at the latter six stations are not due to the lack of surrounding tourism resources but rather to low TOD levels and weak connections between the metro station space and surrounding areas. Without sufficient interactions with surrounding tourism resources, these areas lack enough space and functions for tourists to gather or stay, thus serving only as transportation hubs. Therefore, while optimizing land function allocation, developing commercial spaces in TOD areas on the basis of surrounding tourism resources and improving the transitional areas between transportation stations and nearby attractions can provide better spatial experiences for tourists.

5.2 The urban morphology of TOD areas significantly affects tourism vitality

According to the curve fitting analysis results, various urban morphology indices are significantly related to tourism vitality in the 156 TOD areas in Nanjing. Indices reflecting spatial accessibility, attractiveness, usability, and convenience all exhibit significant positive correlations with urban tourism vitality, whereas indices reflecting spatial density display significant negative correlations. Experience from other regions globally suggests that the TOD strategy emphasizes a compact urban morphology (Sun et al., 2017), leading to relatively dense road and building distributions. This not only enhances the utilization rate of rail transit and reduces the burden on station traffic (Sung and Eom, 2024) but also encourages walking through the creation of an intensive land use structure (Krizek, 2003). Therefore, the urban morphology associated with the TOD pattern is conducive to promoting tourism vitality.

Moreover, this study revealed that maximizing density of buildings or street distribution is not an effective way to increase tourism vitality; notably, it can inhibit tourism vitality. This trend is evident in the 12 TOD cases, as shown in Fig. 12, where tourism activities occur in relatively open areas with certain distances between buildings and streets, indicating that tourists prefer activities in spacious open areas. This finding contradicts the previous conclusion that a dense urban morphology promotes urban vitality (Lang et al., 2020). High-density areas often have high land plot ratios, indicating that the land functions mainly correspond to residential or office use (Shen et al., 2020; Zhang et al., 2010), aligning with prior planning requirements and lacking the attributes to attract tourists in terms of spatial functions. Additionally, a high building density further compresses the outdoor space (Yang et al., 2023), reducing the utilization rates of public open spaces (Wang et al., 2022) and making it difficult for tourists to find sufficient open spaces for gathering and activities. Therefore, in TOD areas, moderately controlling the building density and increasing the utilization of public open spaces may be effective means to attract tourists.

6 Discussion

6.1 Theoretical framework

As global competition among cities intensifies, developing the tourism industry has become an effective means of enhancing urban attractiveness and competitiveness (Łapko, 2014). The sustainable development of urban tourism has increasingly been linked to broader urban ecological, economic, social, and cultural subsystems (Bennett et al., 2016). Exploring the complexity, nonlinearity, and uncertainty between public transportation and tourism development is important in the context of considerable changes in the global population, transportation, ecology, and markets (Hernández-Delgado, 2015). The relationship between public transportation and urban tourism is intricately linked across multiple systems, spanning ecological, economic, social, and cultural domains (Becken et al., 2014), making it difficult to conduct a detailed analysis of the relationship from the perspective of a single discipline (Gao et al., 2024). Therefore, it is necessary to strengthen interdisciplinary analyses in areas such as ecology, geography, tourism, urban planning, economics, and social sciences.

However, while there is a connection between transit-oriented urban development and tourism growth, previous studies mostly explored the relationship from a macro socioeconomic perspective (Bursa et al., 2022; Becker and George, 2011; Zhao and Wang, 2007), with few micro-perspectives introduced. This has made it difficult to translate macropolicies into specific urban planning practices. In contrast, the urban vitality analysis model, as a classic theoretical model in geography, provides an analytical framework for exploring how TOD development can drive tourism growth. Based on the urban vitality analysis model, the interaction between humans and the environment can be systematically understood from a planning perspective.

Thus, urban tourism vitality is combined with the TOD development pattern in this study, and an analysis model suitable for examining the relationship between TOD development and urban tourism and the corresponding mechanisms is developed (Fig. 13). This model conceptualizes TOD areas as a human-land interaction-based complex system, which includes four core components: the TOD pattern, land use and urban form, user behavior, and urban tourism. Among them, the TOD pattern serves as a spatial framework that guides the optimization of land use and urban form, forming the spatial foundation for tourism vitality. Land use and urban form function as “resource carriers,” influencing tourists’ travel choices and behavior through functional configuration and spatial form. Users, as the core agents of tourism activities, directly determine the level of tourism vitality in a region through their preferences and behaviors, while also generating feedback that influences the spatial environment, enabling dynamic adjustment. Urban tourism acts as the external manifestation of system operation and the feedback mechanism of spatial planning and policy regulation, thereby further promoting a virtuous system cycle. These four elements interact continuously to drive the sustained enhancement of tourism vitality. In this process, the model incorporates external factors―such as resource utilization, technological innovation, planning policies, and socioeconomic conditions―as part of the macro-level context. Based on empirical findings, the model also proposes micro-level optimization strategies, such as increasing the proportion of cultural and tourism-related land use, expanding open space, and improving commercial facilities around tourism resources, reflecting a human-centered perspective on spatial optimization.

This model is used to analyze the relationship between TOD development and urban tourism and gain microscale insights into the interactive mechanisms between urban tourism and public transportation, thus providing a comprehensive understanding of how TOD spaces influence tourist behavior. This model can also provide support for scientific and sustainable development and planning strategies and the development of adaptive practices that foster positive interactions between urban tourism and public transportation.

6.2 Policy implications

The research results indicate that land use characteristics and urban morphology characteristics within TOD areas can effectively influence urban tourism vitality. In terms of land use, relying on differentiated land function layouts can significantly enhance tourism vitality; in terms of urban morphology, overemphasizing compact layouts can inhibit tourism vitality. It is evident that urban tourism vitality is not only limited by tourism resources but also closely related to the TOD pattern. Conversely, enhancing tourism vitality signifies an improvement in the overall urban development level oriented toward public transportation, making tourism vitality a catalyst for revitalizing urban tourism and promoting sustainable public transportation development.

To promote a positive cycle of tourism development and urban public transportation construction, TOD should focus on the following factors. First, land function allocation should be optimized on the basis of local tourism resources, avoiding a uniform land function distribution due to emphasizing the development intensity. The supply of cultural, tourism, and accommodation land types within TOD areas should be increased to enhance tourism vitality. Second, the density of buildings and the street distribution within TOD areas should be moderately controlled, especially in high-traffic TOD areas; additionally, the areas of public open spaces such as commercial plazas, leisure green spaces, and street parks should be increased, and related service facilities should be improved, providing tourists with sufficient space for gathering and participating in activities, thus avoiding reduced attractiveness due to an overly dense TOD morphology. Finally, for station areas with abundant surrounding tourism resources, commercial spaces should be developed on the basis of these resources to avoid functional monotony in transportation station areas. The development of transitional areas between transportation stations and surrounding attractions could provide a better spatial experience for tourists, thereby enhancing the overall attractiveness of TOD areas to tourists.

7 Conclusion and limitations

In this study, the interactive mechanisms between the TOD pattern and urban tourism vitality are comprehensively explored, aiming to promote adaptive practices that foster positive interactions between urban tourism and public transportation. Combining urban tourism vitality with the TOD development pattern, we propose an analysis model (TOD-UTV) suitable for examining the relationship between TOD development and urban tourism and the corresponding mechanisms. According to the results of Nanjing case study, the density and uniformity of land functions within TOD areas are significantly related to tourism vitality, whereas the diversity of land functions is not. Specifically, the uniformity of land functions has a significant negative relation with tourism vitality, indicating that merely pursuing high-intensity TOD cannot effectively promote tourism vitality. Instead, optimizing land function allocation on the basis of the local tourism industry and adopting moderately differentiated functional distribution principles are effective strategies for promoting urban tourism vitality. Furthermore, the results reveal that the spatial accessibility, attractiveness, usability, and convenience of TOD spaces all have significant positive impacts on tourism vitality, whereas spatial density has a negative impact. Therefore, TOD spaces should be optimized through appropriate design techniques, and their density should be moderately controlled to increase their attractiveness to tourists.

These findings are meaningful because tourism vitality models based on social media data, POI data, and urban morphology data can provide policymakers and planners with detailed insights into how tourism users utilize TOD spaces, helping them accurately identify aspects that require improvement. Moreover, the results can be used to establish scientific land use strategies and make spatial planning recommendations to decision-makers, facilitating the implementation of more targeted planning measures and providing support strategies and improvement methods for urban tourism development and TOD.

Given that the study area is Nanjing, a city rich in tourism resources and with a high level of development, the applicability of the results to other Chinese cities remains to be assessed. Additionally, the data used in the study have certain limitations. First, POI data reflect urban land use characteristics only at a specific time, and longitudinal analyses and comparisons are lacking. Second, social media data include only check-in information from users of certain applications, and the data rely on users’ active operations, leading to potential data sampling issues. Finally, urban network analysis techniques are limited to planar space analysis and lack three-dimensional spatial measurements. Therefore, exploring multiple data sources is necessary in future research and requires focused attention.

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