Exploring the spatial dynamics of cultural facilities based on multi-source data: A case study of Nanjing’s art institutions

Zhifeng Jiang

doi:10.1515/geo-2022-0532

Article Open Access

Exploring the spatial dynamics of cultural facilities based on multi-source data: A case study of Nanjing’s art institutions

Zhifeng Jiang

Published/Copyright: September 28, 2023

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From the journal Open Geosciences Volume 15 Issue 1

Abstract

Examining the spatial characteristics and accessibility of cultural facilities, such as art institutions, is crucial for understanding the lifestyles of citizens. With the increasing utilization of multi-source data in geographical studies, new opportunities have arisen for investigating the development of cultural facilities in urban areas. Consequently, this article investigates the spatial dynamics of art institutions in urban development, using Nanjing, a historic city, as a case study. The study obtained a total of 260,392 points of interest data for Nanjing in 2018 and 2022 from the GaoDe map API console. By employing network analysis and the two-step floating catchment area method, the geostatistical findings reveal that the distribution of art institutions in Nanjing has become more balanced over time. For instance, the research indicates that the proportion of residents able to reach art exchange institutions within a 30-min travel time increased from 75% in 2018 to 90% in 2022. However, the distribution of art training institutions remains more concentrated compared to art exchange institutions. Furthermore, the spatial arrangement of art institutions does not entirely align with the distribution of demand points, indicating the potential for further optimization. In conclusion, this study has significant implications for understanding the accessibility, social justice, and urban planning in Nanjing. The government should pay attention to the cultural demands of marginalized communities, promote social justice and cultural inclusiveness by increasing the accessibility of cultural facilities.

Keywords: ArcGIS; cultural facilities; accessibility; POI; multi-source data; Nanjing

1 Introduction

With the advancement of geospatial big data, open platforms such as Google Maps and Gaode Maps have increasingly provided spatial information for various life services, opening up new avenues for digital research on society and humanities. Point of interest (POI) data obtained from these open mapping platforms such as Gaode were classified into the following categories: (1) culture facilities, (2) food and dining services, (3) shopping services, (4) daily life services, (5) sports and leisure, (6) healthcare, (7) business and residential areas, and (8) public facilities. Among these categories, research on cultural facilities and services based on POI data has emerged in recent years. For instance, Zhao et al. [1] quantitatively analyzed the evolving spatial patterns of cultural facilities in Zhengzhou City between 2007 and 2017. They compared and analyzed the locational layout characteristics of five types of cultural facilities, namely museums, libraries, cultural centers, art galleries, and theaters, from both macro-level spatial forms and micro-level aggregation centers. Additionally, they employed geographical detectors and large-scale field survey data to explore the impact of resident behavior on the spatial forms of cultural facilities. Mou [2] suggested that cultural facilities can meet residents’ needs for art, culture, and self-realization. Using Yangpu District in Shanghai as a case study, she evaluated the construction of cultural facilities from perspectives such as per capita quantity, facility coverage rate, and a three-level service system. Through GIS analysis, they quantitatively analyzed the spatial distribution of various cultural facilities and compared the results with cultural facility indicators of globally renowned cities. The study revealed that Yangpu District in Shanghai had a diverse range of cultural facilities, but there were issues such as displacement of distribution centers and low coverage rates of city-level and district-level cultural facilities. Similarly, Li [3] examined the distribution characteristics and influencing factors of urban cultural and leisure spaces in Wuhan. They employed methods such as nearest distance index, kernel density estimation, and hotspot analysis to investigate the distribution characteristics of cultural and leisure spaces. The research findings indicated that cultural and leisure spaces in Wuhan primarily clustered around the city’s central core, showing a diffusion pattern that decreased gradually from the city center toward the outer regions. These high-density clusters exhibited clear spatial coupling characteristics with various levels of city centers. Different types of cultural and leisure spaces displayed distinct distribution patterns and diffusion modes, with significant spatial differentiation observed in the city’s central core. The overall distribution of cultural and leisure spaces was primarily influenced by residential density and transportation factors, with a relatively minor impact from economic levels. Yang et al. [4] utilized ArcGIS-based analysis methods and geographical detectors to quantitatively analyze the spatiotemporal distribution patterns of cultural facilities in Changsha City, as well as their influencing factors. Based on POI data from cultural facilities in 2010, 2015, and 2020, the study demonstrated that the spatial distribution of cultural facilities in Changsha City was influenced by multiple factors, with the strongest impact stemming from the level of economic development. These studies have emerged in recent years, primarily focusing on the overall analysis of cultural facilities in case study cities. However, among the various sub-categories of cultural facilities, limited investigations have been conducted on the distinct category of art institutions.

Art institutions in the cultural facility category encompass various establishments aimed at enhancing artistic literacy, such as galleries, libraries, museums, and dance schools. Within the realm of art institutions, there are two primary categories: art training institutions and art exchange institutions. Art training institutions offer educational programs focused on developing artistic skills, such as photography, drawing, and dancing. On the other hand, art exchange institutions provide a place for individuals to appreciate art and share works and ideas, with galleries being a notable example. Both types of institutions contribute to elevating the overall artistic proficiency of residents, fostering societal innovation and aesthetic capabilities [5]. Sharabi [6] emphasizes the strong correlation between the creative output of individuals and the economic status of a society. Enhancing residents’ artistic abilities is advantageous for promoting social stability [7]. Given the crucial role played by art institutions in society, it is vital to strategically plan their distribution to effectively cater to residents’ cultural needs while avoiding inefficient resource allocation. This is essential for societal advancement.

In China, the establishment of art institutions can be traced back primarily to the Children’s Palace. In 1957, the Ministry of Education issued the Several Regulations on the Work of Children’s Palaces and Children’s Homes, which marked the first legal framework and security policies for children’s palaces in the country. During the initial stage of art development in China, access to art institutions was predominantly limited to the elite, and it was often perceived as a domain for the upper class. However, in the late 1980s, there was a rapid increase in the number of art institutions. This growth can be attributed to the country’s swift economic development, which led Chinese parents to increasingly prioritize their children’s cultural education. Consequently, the demand for art training surged [8]. In response to market demands, art institutions broadened their focus from catering exclusively to the elite to becoming public facilities that catered to families from all backgrounds. Concurrently, private art institutions emerged in China. Some art teachers recognized the demand from parents and began offering training in their own residences. Over time, these personal studios evolved into private art institutions as capital accumulated. Presently, the art institution market in China has gradually connected with the international market, and the advent of the internet has expanded teaching methods beyond traditional offline instruction [9].

The early distribution of art institutions was characterized by disorder, primarily due to historical factors [10]. Generally, public facilities tend to have an evenly distributed spatial pattern, while private facilities often follow the distribution of demand points [11]. Given the hybrid nature and societal significance of art institutions, their spatial distribution requires a comprehensive examination. Understanding the spatial distribution can serve as a valuable reference for urban facility planning. Traditional spatial research primarily relied on qualitative studies, which involved on-site surveys and questionnaires. However, such studies are often time-consuming and can introduce subjectivity due to sample bias. For instance, Mao et al. [12] employed questionnaires to investigate the rationality of urban green space planning, finding a positive correlation between environmental awareness and the desire for green space. Therefore, when planning urban green spaces, it is crucial to consider citizens’ environmental awareness. The advancement of information technology has facilitated a shift toward quantitative studies. Three widely accepted methods for spatial distribution research include the following: (1) Models that focus on supply and demand, such as the two-step floating catchment area (2SFCA) method and potential models. Xu et al. [13] optimized the 2SFCA model to analyze the accessibility of clinics, while Zhang et al. [14] employed the optimized 2SFCA method to evaluate the coverage and accessibility of urban parks in China at three levels. (2) Utilizing GIS spatial analysis methods, such as kernel density and standard deviation ellipse, to analyze the spatial arrangement of urban facilities. He et al. [15] utilized ArcGIS and POI data analysis to compare the distribution of cultural facilities and environmental resources in Beijing. (3) Applying topology to analyze existing facilities and optimize urban facility distribution from a mathematical modeling perspective. Griffiths and Vaughan [16] employed space syntax to analyze spatial cultures and draw conclusions regarding the urban history of nineteenth-century cities. Previous studies on the spatial distribution of cultural facilities often treated all facilities as a single category, neglecting the distinctions between its sub-categories. Therefore, this study focuses on the differences between various types of art institutions and examines the changing distribution patterns of art institutions in Nanjing over time.

2 Methods

2.1 Study area

The Yangtze River Delta city cluster is a flourishing and highly developed coastal economic region in China. Within this cluster, Nanjing city (Figure 1) stands out as one of the most prominent and well-developed cities. Nanjing holds the distinction of being the capital city of Jiangsu Province, which has consistently served as a political and economic center throughout China’s rich history. Encompassing an area of 6,598 km², Nanjing is composed of 11 districts and is home to a population of approximately 9.4234 million residents.

Figure 1

Research area: Nanjing.

2.2 Data collection

2.2.1 Nanjing art institution

A total of 260,392 POI data that contain cultural facilities in Nanjing in 2018 and 2022 were obtained through the GaoDe API platform. Screening out errors and irrelevant data, the remaining art institution data were further classified into art training institutions and art exchanging institutions. Art training institutions mainly engage in art training related to painting, music, chess, dance, etc. Art exchange institutions mainly include libraries, galleries, cultural palaces, museums, etc. According to the statistics, there were 5,353 and 8,713 training institutions in 2018 and 2022, and 5,473 and 5,130 exchange institutions in 2018 and 2022, respectively. Overall, the growth of training institutions is rapid, but exchange institutions have slightly decreased.

2.2.2 Residential area

Through Python programming, residential area data were obtained through data crawling from the website “HomeLink.” The data include latitude and longitude, the name of the cell, and the number of residents. A total of 50,848 pieces of data were collected.

2.2.3 Road and traffic

The traffic data came from the basic geographic information of Nanjing. Roads were divided into ten categories according to the speed limit and function. The transportation network of Nanjing (Figure 2) was constructed using the aforementioned collected data. The average pass time and distance were stored for further cost value calculation.

Figure 2

Transportation network of Nanjing.

2.3 Geographic analysis method

Kernel density map can be considered as a 3D, smooth histogram [17]. It performs the distribution and agglomeration of art institutions in Nanjing visually, by classifying travel modes and setting thresholds. The resident within the threshold is quantified through network analysis. The residents’ travel range within the threshold was classified into 5, 15, and 30 min according to travel time. Based on that, the corresponding isochronal circle was drawn. Accessibility was calculated by Ga2SFCA, a combination of traditional 2SFCA and Gaussian decay functions.

2.3.1 Kernel density

Kernel density analysis is a spatial analysis technique that transforms the discrete distribution of points into a continuous 3D surface. It assigns a height value to each location based on the frequency or intensity of the point distribution [18]. This analysis allows us to visualize the spatial patterns and concentrations of points across a study area. To calculate the kernel density, a kernel function is applied to each point in the dataset. The kernel function determines the shape and size of the influence area around each point. Commonly used kernel functions include the Gaussian, uniform, and exponential functions. The selected kernel function affects the smoothness and level of detail in the resulting density surface.

f ( x ) = ∑ i = 1 n 1 t 2 W x − c i t ,

where f ( x ) refers to the kernel density function at the spatial position x , t refers to the distance attenuation threshold, n is the number of elements whose distance from position x is less than or equal to t , and W function represents the spatial weight function.

2.3.2 Network analysis

Network analysis is a valuable technique that utilizes traffic network data to uncover optimal simulated routes and analyze service areas based on predefined travel thresholds. In the context of art institutions, network analysis can be employed to determine their service areas by considering the impedance coefficient of the road network. This approach allows us to calculate the accessible ratio at different impedance levels, providing valuable insights into the spatial reach and coverage of art institutions. The accessible ratio at each impedance level can be calculated using the following formula:

A i = a i ∑ i = 1 n a i ,

where A i is the service area within residents’ travel scope, ∑ i = 1 n a i calculates the total area of Nanjing, and i is the number of communities.

2.3.3 Gaussian 2SFCA

The 2SFCA method is a widely utilized approach for assessing accessibility [19]. Initially introduced by Mu in 2000, this method has garnered significant recognition and has been applied in various studies [20–22]. The 2SFCA method aims to capture the interaction between supply and demand by measuring the ratio of service providers to population within a specified catchment area. In order to enhance the accuracy of the 2SFCA method, a Gaussian function is commonly employed to simulate the attenuation effect. The Gaussian 2SFCA calculation involves the following steps:

Step 1: Calculate the supply-demand ratio of studied unit:

R j m = S j m ∑ ( k ∈ d , kj ≤ d 0 m ) D k G ( d kj , d 0 m ) ,

G ( d kj , d 0 m ) = e − 1 / 2 × ( d kj / d 0 * ) 2 − e − 1 / 2 , d kj ≤ d 0 m 0 1 − e − 1 / 2 , d kj > d 0 m ,

where m indicates the class of facility, R j m refers to the demand-supply ratio of j th m class facility in the research threshold, S j m indicates the supply capacity of facility j , D k refers to the population of demand in point k , d kj refers to traveling time from k to j , d 0 m refers to the limit resident traveling time to facility m , and G ( d kj , d 0 m ) is the Gaussian function taking time impedance as parameter.

Step 2: Calculate accessibility index A i m of demand point i , where

A i m = ∑ j ∈ ( d ij ⩽ d 0 m ) R j m G ( d kj , d 0 m ) .

From the perspective of residents, according to the service capacity facilities in different levels and the traveling cost of residents, given the corresponding weights to different levels of facilities, the accessibility of art institution at all levels A i is obtained:

A i = C 3 ∑ m = 3 A i m + C 2 ∑ m = 2 A i m + C 1 ∑ m = 1 A i m ,

where C m is the weight for facilities class m , A i is the overall accessibility of demand point i , which is the summation of accessibility of different classes of art institution. Larger value of A i indicates better accessibility.

3 Results

3.1 Overall distributions

The kernel density analysis is a mathematical method used to visualize frequency distributions. In this study, the overall distribution of art institutions in Nanjing was analyzed using the kernel density map (Figure 3). The results reveal that both art exchange institutions and art training institutions are predominantly concentrated in the central areas of Nanjing. Additionally, small clusters of art institutions can be observed in the center of each district. However, there is a significant disparity in the distribution of art institutions between the central areas of Nanjing and other regions. Over 90% of the areas have kernel density values less than 2.05, indicating relatively low density of art institutions.

Figure 3

Kernel density map of Nanjing art institution. (a) Art training institutions and (b) art exchange institutions.

By comparing the kernel density maps of art exchange institutions and art training institutions, it is evident that both types of institutions exhibit similar distribution patterns, being primarily concentrated in the central areas of Nanjing. However, there are some notable differences. Art exchange institutions tend to have a more diffuse distribution, with substantial clusters present in the center of each district. This pattern could be attributed to historical reasons and the evolution of art institutions in Nanjing. In the early days, art institutions were predominantly converted from children’s palaces, which were government-funded public facilities. As Nanjing developed and the public’s emphasis on education increased, there was a growing demand for art training institutions, leading to the emergence of numerous private art training institutions. On the other hand, art exchange institutions, which have received less attention and support from the government, have undergone a slower process of privatization in Nanjing. Therefore, following market dynamics, art training institutions tend to cluster in densely populated areas, while art exchange institutions exhibit a relatively more evenly distributed pattern. This spatial variation in the distribution of art institutions reflects the interplay among historical factors, population concentration, market demands, and government support.

3.2 Accessibility based on Gaussian 2SFCA

To assess the accessibility of art exchange institutions and art training institutions, a Gaussian 2SFCA method was employed. This method takes into account the threshold of art institutions, uses time as the metric, and integrates information from residential areas to calculate the coverage area based on different travel time thresholds. The population residing in each demand point is assigned a Gaussian attenuation coefficient corresponding to the art institution units. By creating an Origin-Destination cost matrix, the distances between residential points and art institutions are calculated. The demand-supply ratio is then adjusted by the Gaussian attenuation function, and the sum of these ratios yields the overall accessibility of art institutions. This approach allows for a comprehensive analysis of accessibility, taking into consideration both the spatial distribution of residential areas and the proximity of art institutions. By incorporating the Gaussian attenuation function, the accessibility results are more accurate and reflect the varying influence of distance on accessibility at different locations. This method provides valuable insights into the level of accessibility and the potential coverage of art institutions in relation to the surrounding population.

3.2.1 Service coverage evaluation of training institutions

The coverage areas of the art institutions within 0–5, 5–15, and 15–30 min of vehicle travel time were calculated, and the corresponding travel costs were assigned different color blocks. By mapping the travel time on the Nanjing map, the isochronal areas of Nanjing’s art institutions for the years 2018 and 2022 were obtained (Figure 4).

Figure 4

Service coverage evaluation of training institutions. (a) 5-, 15-, 30-min in 2018; (b) 2018 accessibility based on population-demand; (c) 5-, 15-, 30-min in 2022; (d) 2022 accessibility based on population-demand.

From Figure 4(a) and (c), it can be observed that in 2018, the 30 min isochronal area of art training institutions covered over 70% of Nanjing’s total area, with the 10 min isochronal area mainly concentrated in the city center. Considering the population distribution map of Nanjing, it can be concluded that the majority of residents in the city had access to art training institutions within a 30 min travel time, and more than half of the residents could reach art institutions within 10 min. In 2022, the 30 min isochronal area expanded to cover over 80% of Nanjing, while the 10 min isochronal area showed a slight increase. Overall, accessibility has improved over the years. The coverage of art training institutions in Nanjing can meet the demands of most residents. However, there is a need for more construction of art training institutions in the southern and northern parts of Nanjing as a significant portion of the population cannot reach art institutions within a 30 min travel time.

Figure 4(b) and (d) displays the accessibility calculated using Gaussian 2SFCA, represented by varying shades of color for different coordinates. Darker colors indicate higher accessibility. It can be observed that areas with high accessibility to art training institutions in Nanjing are mainly aligned with the city’s major highways and national roads. However, there is no significant correlation between the accessibility of art training institutions and provincial roads or other minor roads. In the central area of Nanjing, the influence of first-class roads on accessibility has spread. In other regions, no noticeable relationship was identified.

3.2.2 Service coverage evaluation of exchange institutions

The accessibility of art exchange institutions in Nanjing was assessed using the Gaussian 2SFCA method. The isochronal maps for the years 2018 and 2022 were generated by calculating the coverage area with the same vehicle travel time to art exchange institutions and representing it with color blocks. These maps provide valuable insights into the accessibility levels for residents in different areas of Nanjing (Figure 5).

Figure 5

Service coverage evaluation of exchange institutions. (a) 5-, 15-, 30-min in 2018; (b) 2018 accessibility based on population-demand; (c) 5-, 15-, 30-minutes in 2022; (d) 2022 accessibility based on population-demand.

In 2018, the isochronal map revealed that the overall accessibility of art exchange institutions in Nanjing was commendable. More than 75% of the residents could reach these institutions within a 30 min drive. The 10 min isochronal area was primarily concentrated in the city center, extending outward along the directions of major highways. By 2022, there had been significant progress in the development of art exchange institutions. The coverage area within a 30 min travel time expanded, enabling around 90% of the residents to access these institutions within the specified time frame. Additionally, the 10 min isochronal area exhibited noticeable growth, particularly in the northern direction, while some expansion was observed in other directions as well, although to a lesser extent.

These findings highlight the positive advancements in the accessibility of art exchange institutions in Nanjing over the years. The expansion of the coverage area indicates that more residents have convenient access to these institutions within a reasonable travel time. However, it is worth noting that further efforts are needed to improve accessibility in certain areas, particularly in the south and other regions where the coverage remains limited.

3.2.3 Overall evaluation of art institutions in Nanjing

The accessibility of both art exchange institutions and art training institutions in Nanjing generally met the primary demand of the majority of residents. The isochronal areas within a 30 and 15 min travel time covered a significant portion of the city, aligning with Nanjing’s aim to create a “15-min life circle.” High accessibility areas were predominantly concentrated in the city center, extending in the direction of expressways, which correlated with the population distribution in Nanjing. Overall, the accessibility of both types of art institutions exhibited an upward trend over time. Examining Figure 5(b) and (d), the spatial distribution pattern of accessibility for art exchange institutions in Nanjing was similar to that of art training institutions. Both were concentrated in the city center and extended along the routes of highways and national roads. These spatial distribution patterns remained relatively consistent over the 4-year period. Comparing the two, the overall accessibility of art exchange institutions in Nanjing was slightly better than that of art training institutions. This was evident in the larger coverage radius of the 30-min isochronal area and the broader expansion of the 5-min isochronal area. Despite these differences, the distribution pattern of accessibility remained similar, with high accessibility areas aligning with highways and national roads. Notably, the distribution of accessibility did not undergo significant changes during the 4-year period.

4 Discussion and conclusions

4.1 Findings

Art institutions play a crucial role in shaping residents’ lives, promoting social diversity, and ensuring cultural equity. This study specifically examined the distinctions between art exchange institutions and art training institutions, while exploring the spatial and temporal evolution of both types of institutions in Nanjing. The findings revealed the following:

Both art exchange institutions and art training institutions are primarily concentrated in the central area of Nanjing, extending toward the surrounding regions along the direction of the Highway. A smaller cluster of institutions can be observed in the southernmost part of Nanjing. However, there is a need for increased focus on constructing art institutions in the northern part of the city.
Over the 4-year period from 2018 to 2022, the accessibility of art training institutions has shown improvement. However, this improvement is primarily observed within the 30-min isochronal area, while the 5- and 15-min isochronal areas have not expanded significantly.
In the case of art exchange institutions, the most notable enhancement in accessibility between 2018 and 2022 is observed within the 15-min isochronal area. Overall, the accessibility of art exchange institutions has experienced significant improvement, with no apparent deficiencies in the overall construction.
Comparatively, art training institutions have a higher quantity in relation to art exchange institutions. However, the increase in accessibility for art training institutions is not as substantial as that for art exchange institutions. This discrepancy can be attributed to the predominantly private nature of art training institutions. Privatization often results in these facilities being established in proximity to residential areas, potentially leading to a lack of equity in accessibility.
The overall accessibility patterns of both art exchange institutions and art training institutions in Nanjing exhibit similarities. The areas of high accessibility coincide with the direction of the Highway and the national road. Notably, the general distribution pattern of accessibility has remained relatively stable over the course of the 4-year study period.

4.2 Implications

Based on the Gaussian 2SFCA method and isochrone analysis, this study evaluated the accessibility of art institutions in Nanjing and drew some important conclusions and insights. First, the research results indicate that art institutions in Nanjing are mainly concentrated in the city center, while the accessibility in the southern and northern regions of Nanjing is relatively low. This finding is consistent with previous urban studies, such as the research findings of Tanner [23], Simon [24], and El-Emam [25], which found that cultural facilities in large cities like London are generally concentrated in the city center. However, compared to previous research, this study reveals the accessibility characteristics of art institutions in Nanjing more comprehensively through a more specific analysis of travel time costs. Additionally, we also found that the accessibility of art institutions in Nanjing has improved over time, which may be related to urban development and infrastructure improvements.

Second, this study also found certain similarities in the spatial distribution characteristics of art exchange institutions and art training institutions in Nanjing. Both types of institutions are primarily concentrated in the city center and align with the directions of highways and national roads. This is consistent with previous research findings, such as the study by Rossetti et al. [26], which found a certain correlation between the spatial distribution of public institutions and the transportation network. However, compared to training institutions, the accessibility of exchange institutions is slightly better, with a broader coverage. This may be related to the government’s attention to spatial equity in art exchange institutions, where even though the quantity has not increased, the fairness of public resources has been enhanced. It should be noted that although the accessibility of art exchange institutions has improved, there are still issues of poor accessibility in some remote areas, requiring further policy interventions and resource allocation.

Furthermore, this study also emphasizes the relationship between accessibility and social justice. The accessibility of art institutions in Nanjing is higher in the city center but lower in other areas, indicating an uneven distribution of cultural resources in the city. Similar research findings have been validated in previous studies, such as the research by Bing et al. [27], which identified significant spatial imbalances in the distribution of cultural resources in Chinese cities. Therefore, the government and relevant departments should pay attention to the cultural demands of marginalized communities, promote social justice and cultural inclusiveness by increasing the accessibility of cultural facilities.

In conclusion, this study has significant implications for understanding the accessibility, social justice, and urban planning of art institutions in Nanjing. Compared to previous research, this study reveals the accessibility characteristics of art institutions in Nanjing through a more comprehensive analysis of travel time costs and specific spatial distribution characteristics. The research results provide important references and recommendations for policymakers, encouraging the construction of art institutions in the southern and northern regions of Nanjing to enhance residents’ cultural participation and enjoyment of rights and benefits. Additionally, this study provides data support and an analytical framework for digital humanities research, offering insights for further exploration of the distribution of cultural institutions, social participation, and patterns of cultural exchange. However, there are still some issues that require further research, such as the influencing factors of cultural facilities and the interactions of socio-cultural geography, which will provide new directions and challenges for future research.

4.3 Limitations

Several limitations should be noted in this study, which provide opportunities for future research and improvement. First, this study did not conduct a detailed selection of public and private art exchange and art training facilities. Further research can explore the specific characteristics and accessibility of different types of institutions, as their funding sources and operational models may differ, leading to different accessibility patterns. Second, the measurement of travel costs in this study relied only on the overall average speed of the road network. It did not take into account the variations in travel time during different periods, such as peak hours or periods of traffic congestion. Future research can incorporate real-time traffic data or consider specific time intervals to capture more accurate travel costs and better reflect the dynamics of the transportation network. Additionally, this study focused on the spatial distribution and accessibility of art institutions in Nanjing but did not investigate the factors influencing the location choices of these institutions, such as urban planning policies, cultural preferences, and socio-economic factors. Exploring these factors can provide valuable insights into understanding the underlying mechanisms shaping the distribution and accessibility of art institutions. Furthermore, this study primarily analyzed the accessibility of art institutions based on geographic proximity. However, accessibility is a multidimensional concept influenced by factors beyond physical distance, such as economic capabilities, cultural barriers, and individual preferences. Future research can consider incorporating these factors to provide a more comprehensive understanding of the accessibility of art institutions. In conclusion, while this study provides valuable insights into understanding the accessibility of art institutions in Nanjing, it is important to recognize these limitations. Future research should conduct more detailed analyses, consider additional factors, and incorporate more comprehensive data sources to further advance our understanding of the relationship between cultural facilities and accessibility in the context of digital humanities research [28].

Acknowledgments

The author thanks the anonymous reviewers for their valuable suggestions.

Author contributions: ZJ designed the experiments and carried them out, developed the spatial model and performed the analysis, and prepared the manuscript.
Conflict of interest: Author states no conflict of interest.
Data availability statement: Our data will be accessible upon reasonable requests.

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Received: 2023-05-10

Revised: 2023-06-09

Accepted: 2023-08-15

Published Online: 2023-09-28

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

https://doi.org/10.1515/geo-2022-0532

Keywords for this article

ArcGIS; cultural facilities; accessibility; POI; multi-source data; Nanjing

Creative Commons

BY 4.0