Identifying and Prioritising Public Space Demands in Historic Districts: Perspectives from Tourists and Local Residents in Yangzhou

Abstract

With the ongoing advancement of urban renewal and cultural tourism, public spaces within historic cultural districts face dual challenges of structural complexity and diverse user demands. There is an urgent need to establish a scientific, user-oriented evaluation system to enhance spatial quality and user satisfaction. This study takes the Nanhesha Historic and Cultural Quarter in Yangzhou as a case study, focusing on two primary user groups: tourists and local residents. Employing semi-structured interviews and grounded theory, it distils a demand evaluation framework comprising four dimensions—spatial structure, environmental perception, socio-cultural aspects, and facility systems—with a total of 21 indicators. Subsequently, employing the Delphi method, experts were invited to refine the indicators through two rounds of deliberation. The Kano model was then applied to classify the demand attributes of different groups, identifying five common demands and sixteen differentiated demands. These were categorised into three sensitivity levels. Further integrating the Satisfaction Increment Index (SII), Dissatisfaction Decrement Index (DDI), and sensitivity values, a two-dimensional prioritisation model was constructed. This yielded a unified three-tier priority system alongside independent ranking frameworks for each user group. Findings reveal that visitors prioritise immediate experiential attributes such as spatial accessibility, appropriate scale, and environmental cleanliness, whereas residents favour long-term usage-oriented aspects including cultural expression, convenient facilities, and climate adaptability. This research not only enriches the theoretical framework for studying public space perception in historic cultural districts but also provides actionable evaluation criteria and practical pathways for multi-stakeholder spatial optimisation design. It offers guidance for the high-quality, refined development of public spaces within historic quarters.

1. Introduction

In the post-pandemic era, the tourism market has gradually recovered [1], with visitor numbers to various cities steadily returning to pre-pandemic levels. Footfall at urban attractions and scenic areas has also seen a marked increase. During the global spread of COVID-19, international exchanges and cooperation faced severe disruptions. To minimise impacts on vital domestic industries, the Chinese government introduced the dual circulation strategy–domestic and international–in its November 2020 document ‘Proposal of the Central Committee of the Communist Party of China on Formulating the 14th Five-Year Plan for National Economic and Social Development and the Long-Range Objectives Through the Year 2035′. This approach centres on expanding domestic demand as a strategic foundation [2]. As a vital pillar industry in China, the cultural and tourism sector was among the hardest hit by the pandemic. Consequently, the ‘domestic tourism economic circulation’ policy emerged within the broader domestic circulation framework. This initiative aims to channel the substantial existing demand for tourism consumption within the country during this exceptional period, thereby enabling the entire tourism industry chain to maintain normal operations [3].

With the issuance of Announcement No. 7 of 2022 by China’s National Health Commission at the end of 2022, the nation formally entered the post-pandemic era [4]. However, the policy directive of ‘domestic tourism-driven economic circulation’ persists to this day. Whilst gradually restoring international tourism markets, the government remains committed to optimising the domestic tourism sector [5]. Through various measures—including subsidised travel, online and offline promotional campaigns, and support for regions with underdeveloped tourism infrastructure—it fosters the healthy development of the domestic tourism market [6,7,8]. Sustainable domestic tourism circulation has thus become the future trajectory for China’s tourism industry.

Globally, historic cultural districts stand as vital components of urban cultural heritage, with the preservation and revitalisation of their public spaces presenting a shared challenge for governments and urban governance systems worldwide. Diverse policy responses have been adopted across nations. Florence, Italy, through its Comprehensive Conservation Plan for Historic Districts, safeguards the urban fabric while explicitly guiding the multifunctional development of public spaces and enhancing citizen-friendliness; Kyoto, Japan, has established a meticulous ‘community co-governance’ mechanism, utilising resident self-governance organisations as platforms to achieve collaborative management of tourist flows and protection of indigenous residents’ rights; Edinburgh, UK, through its ‘World Heritage City Action Plan’, emphasises cross-departmental coordination mechanisms and data-driven decision support systems for public space management. These policy practices reflect a common trend: seeking equilibrium between cultural heritage conservation and urban functional renewal, while fostering harmonious coexistence among diverse groups within shared spaces.

Historical and cultural districts are an integral part of China’s historical and cultural cities. After undergoing the dual transformations of historical accumulation and rapid urban development following China’s reform and opening-up, these districts have accumulated significant resources in terms of physical space, social culture, and historical heritage [9]. They have gradually become the optimal choice for cities to showcase their historical trajectories, urban culture, and spiritual core, thereby emerging as popular destinations and the first stop on tourists’ itineraries. Meanwhile, historical and cultural districts still house a significant number of local Residents, who continue to provide living spaces and environments for local communities [10]. The presence of local Residents imbues these districts with a rich humanistic atmosphere and a unique lifestyle, which is precisely what visitors most cherish as the ‘vibrant, everyday life’ of the area. To a certain extent, historical and cultural districts and their local Residents form a symbiotic relationship. The districts provide familiar and cherished living spaces for the residents [11]; the residents, like tangible and intangible cultural heritage, serve as important carriers of the district’s historical and cultural spirit. This study, grounded in the context of China’s rapid urban transformation, centres on constructing a user-oriented assessment framework for public space requirements. It explores how to identify and reconcile the diverse spatial preferences of visitors and local residents. While addressing the practical demands of neighbourhood governance in China, it also aims to provide transferable and adaptable theoretical models and practical pathways for other nations facing similar circumstances.

Yangzhou is one of the first batch of national historical and cultural cities designated under the ‘Law of the People’s Republic of China on the Protection of Cultural Relics’ [12], and the Nanhexia Historical and Cultural District is one of the first batch of national historical and cultural districts designated by the National Administration of Cultural Heritage [13]. With the influx of a large number of tourists in the post-pandemic era, the Nanhesha Historical and Cultural District has seen a coexistence of two main groups of people: tourists and residents. The pressure on the district has inevitably increased, particularly as the capacity of its public spaces has neared its limits [14]. To alleviate this phenomenon, the Yangzhou government has conducted multiple renovations and upgrades of the public spaces in this historical district. While the improved quality of public spaces has attracted more tourists, the issue of public spaces struggling to simultaneously meet the needs of both tourists and local residents remains unresolved at its core. The continuous increase in tourist numbers has caused the pressure on the neighbourhood’s public spaces to continue to rise, threatening the existing living spaces of the local residents [15]. The dissatisfaction of the local residents towards this phenomenon has grown increasingly intense, and opposing voices have begun to emerge. The differing needs of tourists and local residents regarding public spaces may lead to conflicts between the two groups [16]. The current state of public spaces in historical and cultural districts fails to provide tourists with a satisfactory travel experience and has also caused local residents to express concerns and dissatisfaction about their future living spaces.

The current state and issues of public spaces in the Nanhexia Historical and Cultural District of Yangzhou are not unique; many historical and cultural districts that have undergone renovation, renewal, and integration into cultural tourism development are facing similar challenges. Therefore, national-level policies issued in recent years targeting historical and cultural districts have all included the improvement and optimisation of district public spaces as key tasks: In February 2024, the Office of the Ministry of Housing and Urban-Rural Development and the Office of the National Development and Reform Commission jointly issued the ‘Guidelines for the Construction of Protection and Enhancement Projects for Historical and Cultural Cities and Districts,’ emphasising the improvement of public spaces in historical and cultural districts, optimising the construction of green landscapes, and enhancing the landscape and environmental quality of public spaces [17]; In 2025, the Central Committee of the Communist Party of China and the State Council jointly issued the ‘Opinions on Continuously Promoting Urban Renewal Actions,’ which emphasised advancing the renewal and renovation of old neighbourhoods, commercial pedestrian streets, and old commercial districts, improving supporting facilities, optimising traffic organisation, enhancing the quality of public spaces, and improving living conditions and the living environment [18]. Therefore, integrating practical realities with societal needs, this study combines theoretical approaches with empirical pathways to explore how to construct a hybrid methodology that integrates “case-theory-practice-Empirical-Analytical” integrated hybrid approach. This systematically identifies and quantifies the spatial needs of diverse groups within the public spaces of historic districts, addressing the limitations of existing research that often remains confined to qualitative descriptions. By encompassing multiple key dimensions—spatial, perceptual, cultural, and facility-related—the findings demonstrate strong transferability and adaptability. The research subjects focus on identifying and synergistically optimising the differentiated needs of tourists and indigenous residents within ‘residential-type historic cultural quarters,’ extending the research paradigm traditionally centred on commercial quarters.

2. Literature Review

2.1. Research on Public Spaces in Historic Cultural Districts

Urban public spaces are an integral part of cities, providing shared venues for various groups of people to gather, rest, and hold various activities [19]. Common types of urban public spaces include parks, squares, and streets, which facilitate more convenient and efficient communication and interaction among people [20]. They also serve certain urban commuting functions and provide temporary gathering and dispersal spaces for large-scale human flows, making them an essential foundation for the normal operation of a city. Historical and cultural districts are integral components of cities, and the public spaces within these districts constitute a unique component of the city’s overall public space system. Compared to the traditional perception of urban public spaces represented by large-scale parks, streets, and wide avenues, due to various historical constraints and the strict enforcement of historical and cultural heritage protection in recent years [21], public spaces within historical and cultural districts generally exhibit small scales, scattered nodes, and inadequate supporting facilities. The phenomena and issues arising from this have attracted academic research.

Historical and cultural districts’ public spaces have attracted attention from multiple disciplines, including architecture, urban and rural planning, sociology, human geography, and management. The physical state of public spaces forms the foundation for their structure and normal use, so research into the physical characteristics of public spaces has become the mainstream and focus of this field of study, with significant contributions from architecture and urban and rural planning. Numerous studies have analysed data on the distribution, quantity, scale, and functions of public spaces, focusing on their fundamental attributes such as accessibility [22], diversity [23], connectivity [24], inclusivity [25,26], and publicness [22,27,28] and safety [29]. Based on this, there have also been some achievements in the overall environmental quality assessment [30] and long-term development strategies [31] of public spaces. In addition to systematic research from a macro perspective, some studies have also sorted out and researched the subcategories of public spaces. Sun Dong assessed the impact of green space ratio in historic and cultural districts on street space and quality [32]. Yang Zihan assessed and studied the impact of blue spaces in historical and cultural districts on the mental health of the elderly [33]. Zhang Wenhai compared and summarised the phenomena of resident-initiated gardening spaces and informal gardens in green public spaces [34]. Wen Zehua conducted a vitality assessment of corner public spaces in historical and cultural districts and argued for the importance of corner spaces to the entire historical and cultural district [35]. Historical and cultural districts feature numerous small-scale alleys, most of which are inaccessible to motor vehicles, functioning as a mix of pedestrian-only spaces and non-motorised vehicle lanes. Therefore, some studies have examined the environmental characteristics of these small-scale alleys while assessing pedestrian satisfaction and visual comfort in their walking spaces. Ziyao Yu assessed the current pedestrian environment from the perspective of post-occupancy evaluation (POE), combining machine learning technology with importance-performance analysis (IPA) to construct a pedestrian environment satisfaction index and identified 19 key factors influencing pedestrian satisfaction [36]. Gao Xiaoming collected Baidu Maps panoramic static images from 17 typical historical and cultural neighbourhoods and developed a comprehensive method framework based on semantic segmentation (Semseg) algorithms to assess the visual comfort of pedestrian spaces in public areas of historical and cultural neighbourhoods [37]. Additionally, the reliability assessment of fire safety spaces and facilities in public spaces of historical and cultural districts has also emerged in this field [38], with research on the physical state and characteristics of public spaces in historical and cultural districts showing a trend toward systematisation, refinement, and concretisation.

As research deepens, researchers have begun to explore the underlying driving factors behind the physical state and characteristics of public spaces in historic cultural districts, with some non-explicit yet critical influencing factors gradually being identified. Cultural elements constitute an important intangible asset of historic cultural districts and are key factors in attracting visitors. Integrating regional cultural elements into public space design through systematic organisation and summarisation can effectively enhance a sense of historical belonging and showcase urban identity [39]. Emel Birer confirmed that the historically designed public space Yalıboyu Riverside Promenade, which features meticulous design in terms of visual, auditory, and object recognition aspects, possesses excellent interactivity and immersion, effectively enhancing visitors’ perception of the historical environment and supporting historical and cultural preservation efforts [40]. Spatial vitality is a key factor influencing the quality and sustainable development of public spaces [35,41,42]. Zhang Fang proposed a vitality evaluation model based on multi-source data, considering both spatial and cultural characteristics, and identified the key factors influencing the vitality of historical and cultural districts. Additionally, the prediction of public space tranquillity [43] and the assessment of public space crowding under the context of external visitor visits [44] are also within the scope of research. The exploration and study of these non-explicit influencing factors have made research on public spaces in historical and cultural districts more diverse, layered, and interdisciplinary.

With the development of the times and technological progress, emerging research methods and technologies are gradually being applied to studies on public spaces in historical and cultural districts. Spatial syntax is an important method for studying public spaces in historical and cultural districts, using topology to explore the relationship between public spaces and society [45], and is widely applied in studies on the spatial characteristics of historical and cultural districts. Researchers combine spatial syntax with geographic information software to study and analyse the morphological characteristics [46,47,48], service functions [49], vitality [9], connectivity, and accessibility [24] of public spaces in historical and cultural districts. Yuyan Lyu used the Yushan Historical District as a case study, summarising and organising spatial morphological characteristics, and then employing a spatial syntax model to analyse the district’s connectivity values, control values, depth values, and integral values. This enabled the identification of potential renovation areas, the assessment of spatial attributes, and the proposal of innovative planning strategies [50]. Some scholars have integrated POI data, online map heatmaps, street view data, and building outline data [51,52] based on spatial syntax and geographic information technology, enhancing the scientific rigour and comprehensiveness of research and evaluation.

Historical and cultural neighbourhood public spaces are complex systems, and conducting scientific assessments and research requires the integration of diverse data and multi-faceted analyses. Therefore, some researchers have utilised professional platforms and instruments to incorporate technologies such as Wi-Fi probes [53], eye-tracking technology [54], user text analysis [55], audiovisual indicator analysis [43], street image analysis [56,57], spatial thermal environment measurement [58,59], and other technologies into the study of public spaces in historic and cultural districts. These new research technologies and methods introduce new influencing factors and expand the scope of research.

The widespread adoption of 5G and machine learning technologies has introduced more precise and efficient research methods for historical and cultural districts. Camatti Nicola demonstrated the potential of combining Fuzzy Expert Systems (FES) and Self-Organising Maps (SOM) as a methodological approach within the field of cultural heritage research in historical and cultural regions [60]. Reif Julian combined mobile phone signalling data with passive mobile data to track visitor paths in the public spaces of historical and cultural districts, thereby determining visitors’ preferences for public spaces [61]. Yu Ziyao and Wang Jia used machine learning technology to assess the walking satisfaction of alleyways in historic and cultural districts [36] and the overall impact of public spaces on historic and cultural districts [62]. Ye Ziyun combined dynamics and ant algorithms to simulate physical environments, using simulated and calculated crowd paths to enhance objectivity and aesthetics in the design process of community public spaces [63].

2.2. Research on Public Spaces in Historic Cultural Districts and User Needs

The uniqueness of public spaces in historic and cultural districts extends beyond the physical characteristics mentioned earlier; the demographic of their users is also a distinctive feature. Historic and cultural districts typically boast a rich history and cultural heritage, having once served as the heart of the city, and are home to a stable indigenous population that constitutes the primary users of these public spaces. With the development of historical and cultural district conservation and tourism, these areas have attracted a large number of tourists. As the tourism service industry has matured, merchants and tourism service staff have also become users of the public spaces in historical and cultural districts. The users of these public spaces primarily consist of three groups: local Residents, tourists, and merchants, with local Residents and tourists being the main users.

Some researchers have conducted studies based on user classification, further dividing users into categories such as the elderly, children, and women, to explore the relationship between public spaces and the needs of specific user groups. Huang Jiaxin utilised the Fuzzy Delphi Method (FDM) and the Analytic Hierarchy Process (AHP) to construct an evaluation system for the restoration potential of public spaces, exploring the promotional role of public space restoration potential for local elderly care models [64]. Yang Zihan examined the factors influencing the mental health of the elderly in blue spaces within historical and cultural districts [33]. Zhang Zhanhao used questionnaires and binary logit and linear regression models to analyse the impact of public space environments in historical and cultural districts on children’s activities and health, and provided improvement suggestions [65]. Li Qin combined the Kano model with IPA to assess the quality of public spaces from a women-friendly perspective [66]. Another group of researchers studied the relationship between public spaces in historical and cultural districts and the overall perceived needs of users from the perspectives of psychological perception [54], audiovisual perception [43,67], environmental thermal comfort [58], spatial inclusivity [26], and fairness [22,28].

From the perspectives of supply and demand, public spaces in historical and cultural districts are the supply side, while users are the demand side, forming a supply-demand relationship. Local Residents and tourists are the primary users of public spaces in historical and cultural districts, with significant demand for public spaces in daily life and during sightseeing. Therefore, the satisfaction levels of local Residents and tourists regarding public spaces are important indicators for measuring public space quality. Chen Nuo summarised the dominant factors influencing residents’ satisfaction with the public space environment in old residential areas and identified priority improvement items by applying the influence asymmetry analysis (IAA) and gradient boosting decision tree (GBDT) methods [68]. Lee Sumin analysed online reviews using Selenium and Beautiful Soup when studying pedestrian space and employed the pandas and KoNLPy’s Okt library for sentiment analysis, summarising 16 factors influencing tourist and community resident satisfaction [69]. Song Yimin established a post-evaluation indicator system for the use of urban historical alley spaces using factor analysis, and employed an improved Important Performance Analysis (IPA) method to conduct a quadrant analysis of tourist satisfaction evaluation indicators. Based on tourist satisfaction results and resident interview information, he formulated more inclusive and sustainable strategies for the protection and reuse of historical alley spaces [70].

Research on the relationship between local Residents and tourists in destinations under tourism pressure is no longer novel [71], and the phenomenon of erosion caused by overtourism and commercialisation in destinations has gradually become the norm [72]. Most current studies still focus on the social relationships and communication methods between local Residents and tourists, with greater concern for the attitudes of local Residents towards the influx of large numbers of tourists into their living areas and the perceptions between the two groups [73,74]. These studies tend to favour long-term policy implementation and social guidance models, which require significant time and effort to implement, and have not addressed the anxiety felt by local Residents regarding the need to share living spaces with tourists or the difficulty in improving tourists’ satisfaction with public spaces. Although current research on historical and cultural public spaces and their users employs diverse methods and techniques, with research outcomes exhibiting pluralism, studies on the supply-demand relationship among historical and cultural district public spaces, local Residents, and tourists remain scarce.

Public spaces are an important component of historical and cultural districts, serving as the living spaces relied upon by local residents and a key factor in enhancing tourist satisfaction. This study focuses on the two main user groups of public spaces in historical and cultural districts: local Residents and tourists. By positioning public spaces on the supply side and user groups on the demand side, the study seeks to identify public spaces that can simultaneously meet the needs of both primary user groups through planning and design. This approach aims to alleviate current pressures and conflicts from a physical perspective, thereby contributing to the sustainable development of historical and cultural districts and fostering harmonious coexistence among diverse populations.

Therefore, the analysis and discussion in this study mainly focus on three aspects:

(1)

What are the needs of tourists and local Residents, as two types of space users, for public spaces in historic and cultural districts?

(2)

What are the factors that influence local Residents and tourists in their use of public spaces, respectively? What are the commonalities in the needs of local Residents and tourists for public spaces?

(3)

How should the priority of these factors be arranged to enhance the quality of public spaces in historic cultural districts and improve user satisfaction?

3. Study Area

Yangzhou City is in the central part of Jiangsu Province and has a history of 2500 years. It is one of the 24 national historical and cultural cities designated by the Law of the People’s Republic of China on the Protection of Cultural Relics [12]. It is also a well-known tourist city in China and was named the ‘Capital of East Asian Culture’ in 2020. Its tourism industry also has a certain appeal internationally. The Nanhexia Historical and Cultural District is one of the first batch of national historical and cultural districts recognised by the National Administration of Cultural Heritage of China [13] and is one of Yangzhou’s four major historical and cultural districts. The district originated during the Sui and Tang dynasties and flourished during the Qing dynasty due to the prosperity of grain and salt transportation. It currently houses multiple cultural heritage sites, including national key cultural relics protection units, and is an important tourist destination within Yangzhou (Figure 1).

The district exhibits distinct spatial characteristics, featuring diverse alleyway configurations, multifaceted spatial compositions, and an abundance of varied public spaces. These encompass multiple types including alleyway junctions, rest areas, pocket spaces, and cultural exhibition venues, providing rich activity domains for diverse user groups. Nanhexia Historic and Cultural District retains a significant number of original residents, making it the most densely populated by indigenous inhabitants among Yangzhou’s four historic cultural districts. Based on interviews and information collated from the He Garden Community Residents’ Committee and the Dongguan Subdistrict Xuningmen Street Residents’ Committee, the current Nanhexia Historic and Cultural Quarter covers an area of 0.7 km². It is home to 3742 resident households and a permanent population of 9892, resulting in an indigenous population density of 14,131 persons km². This makes it the most densely populated area within Yangzhou’s ancient city centre. Approximately 64% of the population is aged 50 or above, 21% are aged 20 to 49, and 15% are under 19, making it a relatively typical residential historical and cultural district with medium-to-high population density (Figure 2). Concurrently, the combination of nationally protected cultural sites such as He Garden and Xiao Pangu, alongside emerging cultural tourism attractions like Pishi Street and the Guangling Salt Art District, draws substantial visitor numbers. Interviews with the Nanhexia Historic Cultural Quarter Management Committee and Pishi Street Tourism Service Centre, alongside compiled data, indicate an average daily visitor reception of approximately 24,000. rising to 108,000 during peak holiday periods. Visitor numbers show steady growth, exhibiting characteristics of all-age coverage and diverse visitation purposes. Finally, this study consulted the Guangling District Culture and Tourism Bureau and the Guangling District Natural Resources and Planning Bureau, responsible for the cultural tourism development and long-term planning of the Nanhexia Historical and Cultural Quarter. Information compilation indicates the quarter currently falls within the category of residential historical and cultural quarters. Owing to the substantial presence of indigenous residents and residential land, cultural tourism development intensity remains low beyond existing national-level cultural heritage sites. While plans exist for large-scale cultural tourism development, implementation has yet to commence. Concurrently, the district possesses considerable cultural tourism potential. Its authentic traditional living environment attracts substantial visitor numbers. In recent years, the emergence of non-government-led, spontaneous cultural tourism clusters, exemplified by Pishi Street, has further enriched its cultural tourism elements. Consequently, Nanhexia has become the second most popular tourist destination within Yangzhou’s ancient city centre.

In summary, Nanhexia Historical and Cultural Quarter exemplifies a typical residential-type historical and cultural quarter in terms of functional positioning, density and structure of indigenous residents, cultural and tourism activity levels, and visitor numbers. Its characteristics of historical continuity, spatial complexity, user diversity, and renewal dynamism align with this study’s core theme of ‘diverse and collaborative demands for public space usage.’ Among comparable residential-type historical and cultural quarters, it possesses high representativeness for research and exemplary value for practice.

4. Materials, Methods, and Results

This study aims to explore how public spaces in historic cultural districts can simultaneously meet the needs of two main user groups: local Residents and tourists. Therefore, it is necessary to conduct scientific and systematic communication with both groups to identify their genuine needs and determine the direction of adaptive renovations for public spaces. Consequently, this paper adopts a comprehensive research method combining qualitative and quantitative approaches, comprising grounded theory, the Delphi expert consultation method, and the KANO model (Figure 3).

4.1. Grounded Theory

Grounded Theory (GT) was proposed by scholars Glaser and Strauss in 1967, based on the theoretical generalisation of a large amount of raw data to establish a framework closely related to phenomena [55]. It can utilise data sources such as online texts and interview data to analyse multiple perspectives and enhance the comprehensiveness of the theoretical framework. GT has been widely applied in studies of various urban public spaces and their users [75,76,77,78] and has also emerged as a research method in studies of historic and cultural districts [79].

The public spaces in this study differ from the macro-level evaluation of historical and cultural districts as a whole, as well as from the concrete and fixed spaces within districts such as commercial spaces, dining spaces, and green spaces. They exhibit characteristics such as non-standardisation, strong interconnectivity, and ambiguous typology, making them difficult to define and quantify using structurally clear official data or online data. Additionally, the actual usage of public spaces often transcends functional boundaries, rooted in daily experiences and cultural practices. Their perceived value and behavioural preferences require construction and reconstruction through user narratives. Therefore, this study employs semi-structured interviews as the core data source, aiming to collect users’ experiences of public space usage, spatial evaluations, and expressions of potential needs, thereby delving into the underlying cognitive structures and evaluation dimensions. This study conducted interviews with 60 volunteers in the Nanhexia Historical and Cultural District of Yangzhou City from 20 June to 15 July 2025. All interviewees had normal mobility and communication abilities, aged between 21 and 74, including 25 local residents, 22 tourists, and 13 district managers (

Table A1. Semi-structured interview respondent basic information form.

Number	Category	Gender	Age	Identity	Number	Category	Gender	Age	Identity
P1	Resident	Male	21	Student	P27	Resident	Male	51	Company manager
P2	Resident	Male	68	Retired	P28	Tourist	Female	22	Student
P3	Resident	Female	66	Retired	P29	Resident	Male	33	Staff
P4	Tourist	Male	26	Student	P30	Tourist	Female	27	Bank clerk
P5	Resident	Female	34	Teacher	P31	Tourist	Male	64	Retired
P6	Tourist	Male	41	Merchant	P32	Resident	Male	29	Driver
P7	Tourist	Female	39	Freelancer	P33	Resident	Female	26	Delivery person
P8	Resident	Male	55	Civil servant	P34	Resident	Female	22	Student
P9	Tourist	Female	63	Retired	P35	Manager	Male	36	-
P10	Tourist	Female	63	Teacher	P36	Tourist	Female	27	Freelancer
P11	Resident	Female	71	Retired	P37	Manager	Male	39	-
P12	Resident	Female	45	Staff	P38	Resident	Male	74	Retired
P13	Tourist	Male	52	State-owned enterprise	P39	Tourist	Male	31	Police Officer
P14	Manager	Male	47	-	P40	Tourist	Female	33	Police Officer
P15	Tourist	Female	53	Staff	P41	Manager	Female	28	-
P16	Resident	Male	63	Retired	P42	Manager	Female	35	-
P17	Manager	Male	27	-	P43	Resident	Male	65	Retired
P18	Manager	Male	31	-	P44	Tourist	Female	42	Painter
P19	Resident	Female	58	Training personnel	P45	Manager	Male	37	-
P20	Tourist	Male	36	Staff	P46	Manager	Female	46	-
P21	Tourist	Male	38	Teacher	P47	Tourist	Male	22	Online blogger
P22	Resident	Female	56	Cleaner	P48	Tourist	Female	23	Student
P23	Resident	Male	51	Online blogger	P49	Resident	Male	71	Retired
P24	Manager	Male	43	-	P50	Tourist	Male	63	Retired
P25	Tourist	Female	23	Student	P51	Manager	Male	42	-
P26	Tourist	Male	48	Unemployed	P52	Tourist	Female	67	Retired

). Based on the research context and on-site conditions, we prepared separate interview guidelines for the three categories of volunteers (

Table 1. Interview outline.

Interview Participants	Question Number	Interview Outline
Local residents of the neighbourhood	Q1	Which public Spaces in the blocks do you use on a daily basis? Why?
	Q2	What do you usually do when you use these Spaces? When is it used approximately during the day?
	Q3	Do you feel comfortable and safe in these Spaces? Are there any unsatisfactory aspects?
	Q4	Do you think the public Spaces in the neighborhood meet your daily needs? For example, rest, socializing, passage, etc.?
	Q5	Are there any Spaces that you seldom use now? What’s the reason?
	Q6	Do you feel that the activities of tourists have an impact on your daily use of the space? In what aspects?
	Q7	What types of public Spaces do you hope to add or improve in the neighborhood?
	Q8	Is there any space that makes you feel a sense of belonging or cultural identity? What are its features?
	Q9	If it could be planned or adjusted, what kind of spatial changes would you most hope to achieve?
	Q10	Are there any other opinions or suggestions regarding the public Spaces in the neighborhood?
Tourists and visitors	Q1	Which public Spaces in the neighborhood did you mainly stay at during your visit this time? Why?
	Q2	What is your main purpose when using these Spaces? Such as taking photos, resting, browsing, etc.?
	Q3	Did you encounter any inconvenience during the usage process? Such as guidance, seats, shade, noise, etc.?
	Q4	Do you think these Spaces have any distinctive features? Could you please talk about the part that impressed you the most?
	Q5	Have you ever had a space you wanted to use but couldn’t enter due to congestion, restrictions, etc.?
	Q6	In your opinion, what are the differences between the public Spaces here and those in other historical districts?
	Q7	If you come again in the future, in what aspects do you hope the public Spaces here will be improved?
	Q8	Do you feel the cultural atmosphere in the space? In what form?
	Q9	Do you think tourists’ use of these Spaces will have an impact on local residents?
	Q10	Are there any other usage experiences, needs or suggestions that you would like to add?
Neighbourhood manager	Q1	Which public Spaces in the neighborhood are you mainly responsible for or involved in managing?
	Q2	In your opinion, for which groups of people are these Spaces mainly served? How often is it used?
	Q3	Which Spaces frequently encounter complaints, conflicts or usage issues? In what aspects is it manifested?
	Q4	Have you noticed the different needs or conflicts between residents and tourists when using the space?
	Q5	Are there any difficulties in the current space management? For example, maintenance, crowd organization, facility renewal, etc.?
	Q6	Have you ever collected feedback from users? Which problems or demands do they most frequently mention?
	Q7	What management experiences in the block space have been relatively successful or effective?
	Q8	In your future planning, which aspects do you think should be strengthened the most?
	Q9	Do you think the current spatial structure is reasonable? Are there any blind spots or dead corners in use?
	Q10	If you have sufficient resources, what types of public Spaces would you like to add or adjust?

) and conducted all interviews with informed consent, including audio and document records.

4.1.1. Open Coding

Open coding is the first step in grounded theory coding, involving the classification of semantic concepts to systematise and conceptualise interview information. In this study, audio recordings of interviews with 52 volunteers were transcribed into text. Each text was then dissected, analysed, and conceptually transformed to ensure that the extracted concepts aligned closely with the interviewees’ expressed views. For example, original statements such as ‘museums that are difficult to find,’ ‘excessively long walking distances between attractions,’ ‘slippery brick paths in rainy weather,’ ‘lack of AED equipment,’ and other original statements were preliminarily converted into the concepts of ‘unclear signage,’ ‘unsafe walking environment,’ ‘poor node connectivity,’ and ‘lack of emergency equipment.’ These concepts were then categorised into the categories of ‘signage facility completeness,’ ‘spatial connectivity,’ ‘spatial safety,’ and ‘emergency facility completeness’ (

Table 2. Examples of conceptualization and categorization from open coding.

Category Coding	Concept Coding	Examples of Original Interview Materials	Interviewee
Number and distribution of public toilets	Public toilets	“There are many old houses that don’t have separate bathrooms. We have to go out to look for public toilets to use, but there are too many tourists and we have to queue up for toilets. We have no place to use the toilet……”	P8
Number and distribution of rest facilities	Public seating	“Nowadays, there are many tourists in the neighborhood every day. Originally, there weren’t many public seats set up. In the past, the elderly in my family liked to go to the street to find a seat to rest and chat after meals, but now there’s nowhere to go……”	P18
Pathway signage	Path guidance	“Sometimes the signs on the walls of the blocks and at the four-way intersections are not clear, and our group of visitors get lost in the blocks and can’t find the museum they want to go to……”	P6
Number and distribution of rubbish bins	Rubbish bins	“There are too few trash cans and no one cleans them regularly. With such a large flow of people now, the trash cans are full in less than an hour and the stench is unbearable……”	P14
Bus arrival times	Bus arrival times	“After getting off the bus, we tourists couldn’t find the entrance to the block. Even after finding the entrance, we still had to walk a long way to reach the center of the block, which was quite troublesome……”	P13
Road width	Narrow roads	“The roads in the block are too narrow. First of all, there are randomly parked electric bikes on both sides of the street, and there are also fast non-motorized vehicles passing through the middle of the street. We really felt unsafe when we came out to play with children and the elderly……”	P28
Complex road turns	Complicated routes between destinations	“Sometimes, the straight-line distance from one scenic spot to another is very short, but it takes a long time to detour, and there are no clear route signs because Baidu Maps is of no use in such small neighborhoods……”	P29
Tourist noise	Noisy tourists	“There are really too many tourists. Although our home is not on the main street, the noise of tourists and the cries of vendors through loudspeakers from morning till night are really annoying……”	P12
Shade	Avoidance of sunlight	“When the weather is very hot, we can’t find a shady place to stay. We can only go to stores to escape the heat. However, many stores require payment for a break, which is not worth it……”	P25
Non-motorised vehicle parking	Illegal parking of non-motorised vehicles	“My child studies at the primary school in this neighborhood. Every day, I come to pick him up from school. There’s no place to park. As soon as the school was over, tourists and local residents were so crowded that the entire street was packed to the brim……”	P3
Ground cleanliness	Litter on the ground	“There is too much garbage on the road. People’s qualities vary greatly. Many people throw garbage randomly, but there is no one to clean it up in time. The garbage is getting more and more……”	P2
First aid equipment	First aid equipment	“I think the neighborhood lacks equipment like AEDs. In case any tourists or residents feel unwell, such devices can play a significant role……”	P7

Note: Due to space limitations, only selected excerpts are presented as examples.

). After further comparison, reorganisation, and categorisation, and the removal of duplicate or highly similar entries, the final open coding analysis results were obtained, encompassing 77 concept codes and 47 preliminary category codes.

4.1.2. Axial Coding

Main axis coding involves reorganising and reclassifying all independent category codes to establish connections between different categories. At this stage, the interrelationships between independent category codes were not yet clear. Therefore, the 46 category codes were re-examined in the context of the original text derived from the audio recordings to identify potential interconnections. Based on this analysis, this study conducted iterative comparisons and cluster analyses, ultimately consolidating the 47 category codes into 24 primary category codes (

Table 3. Axial coding analysis results.

Main Category Coding	Category Coding
M1 Spatial Accessibility	1. Accessibility of primary and secondary entrances 2. Accessibility of external transportation systems
M2 Spatial Connectivity	3. Spatial connectivity 4. Road simplicity
M3 Spatial Diversity	5. Spatial diversity
M4 Appropriate Scale	6. Street width 7. Building height on both sides
M5 Functional Comprehensiveness	8. Non-motorised vehicle access space 9. Pedestrian space
M6 Pedestrian Safety	10. Open views 11. Independent pedestrian space 12. Independent non-motorised vehicle access space 13. Separation of pedestrians and vehicles
M7 Visual Comfort	14. Greenery coverage rate 15. Harmonious building aesthetics 16. Visual harmony
M8 Sound Environment Quality	17. Tourist noise 18. Activity noise
M9 Microclimate Adaptability	19. Heat adaptability 20. Street ventilation 21. Rain shelter facilities
M10 Spatial Privacy	22. Resident privacy 23. Individual activity privacy
M11 Spatial Cleanliness	24. Ground cleanliness 25. Clutter storage 26. Non-motorised vehicle parking
M12 Historical and Cultural Expression	27. Historical and Cultural Expression 28. Lifestyle Atmosphere
M13 Spatial Sense of Belonging	29. Sense of Spatial Belonging
M14 Spatial Immersion	30. Sense of Spatial Immersion
M15 Activity Supportiveness	31. Community Activities 32. Cultural and Tourism Activities
M16 Population Inclusiveness	33. Population Inclusion
M17 Rest Facility Completeness	34. Number and Distribution of Rest Facilities
M18 Sanitary Facility Completeness	35. Number and Distribution of Public Toilets 36. Number and Distribution of Trash Bins
M19 Wayfinding Facility Completeness	37. Pathway Signage 38. Neighborhood Maps 39. Attraction Signage 40. Historical and Cultural Landmark Information Boards
M20 Transportation Facility Completeness	41. Comprehensive Transportation Facilities
M21 Emergency Facility Completeness	42. Public Safety Facilities and Systems 43. Emergency Medical Facilities
M22 Convenience Facility Completeness	44. Convenience Facilities
M23 Interactive Facility Completeness	45. Interactive Information Devices 46. Temporary Stages
M24 Accessibility Facility Completeness	47. Accessibility Facilities

).

4.1.3. Selective Coding

Selective coding is achieved by extracting primary categories from the axial coding. Through multi-level coding and in-depth analysis, the primary categories are linked to other categories to form a theoretical model of the public space needs of users in historical and cultural districts. During the selective coding phase, this study first reviewed the categories or concepts that had been created and identified the most important and representative options. Based on the two coding phases of open coding and axial coding, the core categories of this study were extracted, and the 24 main categories from the axial coding were categorised into six core categories (

Table 4. Six core categories and their respective main categories.

Core Categories	Main Category Coding
C1 Spatial Structure Rationality	M1 Spatial accessibility; M2 Spatial connectivity; M4 Appropriate scale
C2 Spatial Environment Comfort	M7 Visual comfort; M8 Sound environment quality; M9 Microclimate adaptability; M10 Spatial privacy; M11 Spatial cleanliness;
C3 Spatial Functionality	M3 Spatial diversity; M5 Functional comprehensiveness; M15 Activity supportiveness; M16 Population inclusiveness
C4 Spatial Safety	M6 Pedestrian environment safety; M21 Emergency facility completeness; M24 Accessibility facility completeness
C5 Spatial Cultural Significance	M12 Historical and cultural expression; M13 Sense of belonging to a space; M14 Sense of immersion in a space
C6 Spatial Facility Completeness	M17 Rest facility completeness; M18 Sanitary facility completeness; M19 Signage facility completeness; M20 Transportation facility completeness; M22 Convenience facility completeness; M23 Interactive facility completeness

).

4.1.4. Theoretical Saturation Test

As part of the formalisation process of grounded theory, a theoretical saturation test was conducted on the key elements of users’ evaluations and expressions of needs regarding public spaces. First, this study invited two urban planning experts and three urban spatial design experts to independently review the raw data and the results of the three-level coding process, ensuring the scientific rigour, comprehensiveness, and accuracy of the coding process and its outcomes. Second, eight interview materials (

Table A2. Saturation Test Sample Information Table.

Number	Category	Gender	Age	Identity	Number	Category	Gender	Age	Identity
P1	Resident	Female	72	Retired	P5	Manager	Male	49	-
P2	Manager	Male	23	-	P6	Tourist	Female	26	Student
P3	Tourist	Male	55	Teacher	P7	Resident	Male	24	Freelancer
P4	Resident	Female	54	Staff	P8	Tourist	Male	66	Retired

) that had been reserved from the previous 60 interview samples were re-coded using the same three-stage procedure. The final results of the eight interview samples consistently reproduced the six core categories and 24 main categories, with no new concepts or categories emerging, thereby confirming that theoretical saturation had been achieved. The study can now proceed to the next phase.

4.2. Delphi Expert Consultation Method

After completing the preliminary extraction of indicators based on grounded theory, it is still necessary to further validate the scientific validity, representativeness, and operational feasibility of each indicator to enhance the rigour and practical guidance of the indicator system. To this end, this paper introduces the Delphi Method for expert consultation.

The Delphi Method is a qualitative survey technique [80]. It is a structured consensus-building technique that uses iterative feedback to gather and refine expert opinions [81]. This method relies on a structured expert panel that answers questionnaires over multiple rounds; through repeated feedback and revisions, expert opinions gradually converge to form a statistically significant collective judgement [64,82]. The Delphi Method has been widely applied in the construction of evaluation indicator systems across various fields [83,84,85].

The needs of users of public spaces in historic and cultural districts are complex and diverse. The three main user groups exhibit certain cognitive biases and discrepancies in their evaluations of spatial structure, environmental perception, socio-cultural aspects, and facility systems. Therefore, while an indicator system constructed solely based on interview data has an empirical foundation, it still requires rational judgement from an expert group to refine and standardise it, thereby enhancing the scientific validity, generalisability, and adaptability of the indicators. This ensures that the indicator system is grounded in field experience while also having methodological reliability support.

Therefore, this study employs the Delphi method to conduct a comprehensive assessment of the preliminary street furniture design element system. The selection of expert panel members is a critical aspect of the Delphi method. An ideal Delphi panel should consist of 10 to 50 experts who are familiar with the research topic and have at least five years of professional experience in the relevant field [86]. In this study, the expert panel comprises 24 members from three different categories (

Table A3. Basic information about experts tested using the Delphi expert consultation method.

Group	Number	Place of Employment	Title/Position	Age
Government administrators (Group A)	A1	Yangzhou Municipal Bureau of Natural Resources and Planning	-	53
	A2	Yangzhou Housing and Urban-Rural Development Bureau	-	45
	A3	Yangzhou Housing and Urban-Rural Development Bureau	-	42
	A4	Yangzhou Municipal Bureau of Culture, Radio, Television and Tourism	-	55
	A5	Guangling District Housing and Urban-Rural Development Bureau	-	47
	A6	Guangling District Bureau of Natural Resources and Planning	-	49
	A7	Guangling District Culture and Tourism Bureau	-	38
	A8	Yangzhou City Construction State-owned Assets Holding (Group) Co., Ltd.	-	52
Teachers and scholars (Group B)	B1	-	Associate professor	33
	B2	-	Professor	48
	B3	-	Professor	59
	B4	-	Professor	61
	B5	-	Associate professor	42
	B6	-	Professor	47
	B7	-	Associate professor	46
	B8	-	Researcher	58
	B9	-	Researcher	55
	B10	-	Associate professor	54
	B11	-	Professor	62
Designers (Group C)	C1	Yangzhou City Construction State-owned Assets Holding (Group) Co., Ltd.	Designer	36
	C2	Yangzhou City Construction State-owned Assets Holding (Group) Co., Ltd.	Project manager	44
	C3	Yangzhou Architectural Design Institute Co., Ltd.	Designer	25
	C4	Yangzhou City Planning and Design Institute	Designer	27
	C5	Yangzhou City Planning and Design Institute	Senior engineer	43
	C6	Yangzhou City Planning and Design Institute	Designer	32

Note: Some experts chose not to disclose their place of work or position.

). The first group (Group A) includes eight government officials specialising in urban planning and the preservation and utilisation of historical heritage. The second group (Group B) includes 11 university teachers and scholars with expertise in the preservation and renewal of urban public spaces and historical and cultural districts. The third group (Group C) comprises seven designers working in design institutes specialising in the design and implementation of historical and cultural district renewal. According to existing research, expert consensus using the Delphi method can typically be reached within two rounds of consultation [87]. Therefore, this study conducted two rounds of expert consultation from 21 July to 29 July 2025. Interventionary studies involving animals or humans, and other studies that require ethical approval, must list the authority that provided approval and the corresponding ethical approval code.

4.2.1. Survey Design and Expert

In the two rounds of expert consultations, each expert will receive a document folder or email containing three separate documents: an overview of the research objectives, indicators derived from the grounded theory section along with their corresponding explanations, and a questionnaire. The questionnaire consists of two parts. The first part includes the personal basic information of the interviewed experts, such as age, organisation, position, or title. The interviewed experts may choose to complete all or part of this section. The second part involves the evaluation of current indicators, where experts rate each indicator using a five-point Likert scale (5 = very important, 4 = important, 3 = neutral, 2 = not important, 1 = very not important). Additionally, an open-ended question is included to collect expert suggestions for indicators to be removed or added. Indicators with poor consistency will be re-evaluated in the second round. In the first round, 24 questionnaires were distributed, and all 24 were returned on time, resulting in a response rate of 100%. In the second round, 24 questionnaires were distributed, and all 24 were returned, also achieving a response rate of 100%. Additionally, nine participating experts provided additional comments in the first round, while three participants offered further suggestions in the second round.

4.2.2. Indicator Revision

This study calculated the arithmetic mean (hereinafter referred to as ‘M’), standard deviation (hereinafter referred to as “SD”), and coefficient of variation (hereinafter referred to as ‘CV’) to evaluate expert scores. These statistical indicators are widely used to assess and quantify the consistency of expert judgements [88,89]. The mean is used to assess central tendency; when M > 3.75, it indicates that a certain category or indicator is sufficiently important. The standard deviation is used to assess convergence; SD < 1 indicates that the indicator has high reliability. The coefficient of variation is used to measure dispersion; CV < 25% indicates that experts have a high degree of consensus on a specific item [88]. This study defines M > 3.75, SD < 1, and CV < 0.25 as the conditions for reaching consensus. The overall consistency of expert opinions was assessed using the Kendall harmony coefficient, which ranges from 0 to 1, with higher values indicating stronger consistency.

Table 5 presents the results of the first and second rounds of expert consultations. Based on the analysis and summary of feedback data, this study made three major revisions to the indicator system after the first round of consultations.

• Analysis and modification of feedback on core categories. The core categories ‘spatial safety’ and ‘spatial culturality’ did not meet the consensus criteria (M < 3.75, SD > 1, CV > 0.25). Additionally, among the 10 experts who provided additional suggestions in the first round, 4 suggested merging the core categories ‘spatial structural rationality’ and ‘spatial functional comprehensiveness.’ After evaluation, this study merged these two core categories into ‘spatial physical attributes’; Three experts believed that the core categories ‘spatial environmental comfort’ and ‘spatial safety’ are in a hierarchical relationship and suggested merging them into ‘environmental perception dimension’; the concept of ‘spatial culturality’ is relatively vague, with significant differences in expert interpretations. After assessment, this study renamed it as ‘social cultural dimension’; ‘spatial facility completeness’ was retained and renamed as ‘facility system dimension.’
• Seven indicators that did not meet the consensus criteria (M < 3.75, SD > 1, CV > 0.25) were re-evaluated and modified based on expert recommendations. Four experts unanimously agreed that, from a logical association perspective, the three categories of ‘spatial diversity,’ ‘activity supportiveness,’ and ‘population inclusiveness’ should be grouped under ‘functional comprehensiveness.’ Therefore, this study included these three indicators under ‘functional comprehensiveness’; Two experts believed that ‘spatial privacy’ was already covered within the core categories of ‘spatial structural rationality’ and ‘spatial environmental comfort,’ so this study removed it; One expert believed that the importance of interactive facilities in public spaces within historical and cultural districts was low, so this study removed ‘interactive facility completeness.’ The two categories of ‘spatial sense of belonging’ and ‘spatial immersion’ were retained but renamed ‘spatial locality’ and ‘spatial narrative,’ respectively, due to conceptual ambiguity and interpretative differences, and were forwarded to the next round of expert consultation.
• Revision of individual category indicators. Although the category ‘visual comfort’ met the criteria, two experts suggested merging it into ‘scale appropriateness’ due to their causal relationship. Additionally, the two concepts ‘green coverage rate’ and ‘harmony of historical building aesthetics’ from ‘visual comfort’ were proposed to be coded separately and added to the indicator system as independent categories due to their importance. Therefore, the ‘visual comfort’ category was removed, and the concepts of ‘green coverage rate,’ ‘historical building style coordination,’ and ‘store sign uniformity’ were incorporated into the core category ‘spatial perception dimension.’

This study conducted a second round of expert consultation on the revised indicator system. After the first round of revisions, all data in the indicator system met the consensus standards (M < 3.75, SD > 1, CV > 0.25), and we also received additional comments from three experts. Two experts noted that the conceptual definition of the category indicator ‘spatial locality’ was not sufficiently standardised. One expert suggested renaming this indicator to ‘spatial sense of place’ to enhance understanding among different types of researchers in future studies. Another expert recommended renaming the indicator ‘green coverage rate’ to ‘spatial green visibility rate’ to enhance its scientific rigour and specificity. Based on these expert suggestions, we conducted a second round of textual revisions.

Finally, we used SPSS 31 software to calculate the Kendall’s coefficient of concordance to assess the degree of expert consensus on the importance of each indicator. The results showed that the W value increased from 0.309 in the first round to 0.521 in the second round, with both rounds yielding statistically significant results (p < 0.001). This indicates a significant improvement in expert consensus [90], thus eliminating the need for further consultation rounds.

Table 5. Results of expert consultation in Round 1 and Round 2.

Number	Element Name	First Round (Number of Experts: 24)				Number	Element Name	Second Round (Number of Experts: 24)
		M	SD	CV	Result			M	SD	CV	Result
M1	Spatial accessibility	4.75	0.53	0.11	Retained	M1	Spatial accessibility	4.04	0.20	0.05	Retained
M2	Spatial connectivity	4.50	0.93	0.20	Retained	M2	Spatial connectivity	4.95	0.20	0.04	Retained
M3	Scale appropriateness	4.20	0.65	0.15	Retained	M3	Scale appropriateness	4.91	0.28	0.05	Retained
M4	Visual comfort	3.95	0.90	0.22	Integrated	M4	Functional comprehensiveness	4.95	0.20	0.04	Retained
M5	Acoustic environment quality	4.25	0.89	0.21	Retained	M5	Green coverage rate’	4.87	0.44	0.09	Renamed
M6	Microclimate adaptability	4.04	0.69	0.17	Retained	M6	Architectural style coordination	4.91	0.40	0.08	Retained
M7	Spatial privacy	3.66	1.34	0.36	Removed	M7	Storefront signage uniformity	4.95	0.20	0.04	Retained
M8	Spatial cleanliness	4.29	0.46	0.10	Retained	M8	Pedestrian environment safety	4.04	0.20	0.05	Retained
M9	Spatial diversity	3.29	1.19	0.36	Integrated	M9	Spatial cleanliness	3.95	0.20	0.05	Retained
M10	Functional comprehensiveness	4.16	0.76	0.18	Retained	M10	Microclimate adaptability	4.91	0.28	0.05	Retained
M11	Activity supportiveness	3.58	0.88	0.24	Integrated	M11	Acoustic environment quality	4.04	0.20	0.05	Retained
M12	Population inclusiveness	4.04	1.12	0.27	Integrated	M12	Historical and cultural expression	40.40	0.20	0.05	Retained
M13	Pedestrian environment safety	4.45	0.65	0.14	Retained	M13	Sense of place	4.28	0.28	0.06	Renamed
M14	Emergency facility completeness	4.54	0.88	0.19	Retained	M14	Narrative quality	4.75	0.20	0.12	Retained
M15	Accessibility facility completeness	4.20	0.65	0.15	Retained	M15	Completeness of recreational facilities	3.91	0.28	0.07	Retained
M16	Historical and cultural expression	4.29	0.95	0.22	Retained	M16	Completeness of sanitary facilities	4.62	0.71	0.05	Retained
M17	Sense of belonging	3.66	0.96	0.26	Reassessed	M17	Completeness of wayfinding facilities	3.91	0.28	0.07	Retained
M18	Immersive experience	3.62	0.92	0.25	Reassessed	M18	Completeness of transportation facilities	4.04	0.35	0.08	Retained
M19	Sufficiency of recreational facilities	4.25	0.67	0.15	Retained	M19	Completeness of emergency facilities	4.12	0.61	0.14	Retained
M20	Sufficiency of sanitary facilities	4.75	0.44	0.09	Retained	M20	Completeness of convenience facilities	4.29	0.69	0.16	Retained
M21	Sufficiency of wayfinding facilities	4.12	0.85	0.20	Retained	M21	Completeness of accessibility facilities	4.50	0.65	0.14	Retained
M22	Sufficiency of transportation facilities	4.25	0.73	0.17	Retained	C1	Physical attributes dimension	4.54	0.50	0.11	Retained
M23	Sufficiency of convenience facilities	4.16	0.96	0.23	Retained	C2	Environmental perception dimension	4.45	0.58	0.13	Retained
M24	Sufficiency of interactive facilities	3.00	1.31	0.40	Removed	C3	Social and cultural dimension	4.37	0.71	0.16	Retained
C1	Rationality of spatial structure	4.83	0.38	0.07	Retained	C4	Facility system dimension	4.58	0.58	0.12	Retained
C2	Comfort of spatial environment	4.95	0.20	0.04	Retained
C3	Comprehensive functionality of space	4.58	0.50	0.10	Integrated
C4	Safety of space	3.62	0.57	0.18	Integrated
C5	Cultural character of space	3.58	0.97	0.27	Reassessed
C6	Sufficiency of spatial facilities	4.75	0.44	0.09	Retained
M25	Green coverage rate	-	-	-	Added
M26	Harmony of architectural style	-	-	-	Added
M27	Uniformity of store signage	-	-	-	Added

Table 5. Results of expert consultation in Round 1 and Round 2.

Number	Element Name	First Round (Number of Experts: 24)				Number	Element Name	Second Round (Number of Experts: 24)
		M	SD	CV	Result			M	SD	CV	Result
M1	Spatial accessibility	4.75	0.53	0.11	Retained	M1	Spatial accessibility	4.04	0.20	0.05	Retained
M2	Spatial connectivity	4.50	0.93	0.20	Retained	M2	Spatial connectivity	4.95	0.20	0.04	Retained
M3	Scale appropriateness	4.20	0.65	0.15	Retained	M3	Scale appropriateness	4.91	0.28	0.05	Retained
M4	Visual comfort	3.95	0.90	0.22	Integrated	M4	Functional comprehensiveness	4.95	0.20	0.04	Retained
M5	Acoustic environment quality	4.25	0.89	0.21	Retained	M5	Green coverage rate’	4.87	0.44	0.09	Renamed
M6	Microclimate adaptability	4.04	0.69	0.17	Retained	M6	Architectural style coordination	4.91	0.40	0.08	Retained
M7	Spatial privacy	3.66	1.34	0.36	Removed	M7	Storefront signage uniformity	4.95	0.20	0.04	Retained
M8	Spatial cleanliness	4.29	0.46	0.10	Retained	M8	Pedestrian environment safety	4.04	0.20	0.05	Retained
M9	Spatial diversity	3.29	1.19	0.36	Integrated	M9	Spatial cleanliness	3.95	0.20	0.05	Retained
M10	Functional comprehensiveness	4.16	0.76	0.18	Retained	M10	Microclimate adaptability	4.91	0.28	0.05	Retained
M11	Activity supportiveness	3.58	0.88	0.24	Integrated	M11	Acoustic environment quality	4.04	0.20	0.05	Retained
M12	Population inclusiveness	4.04	1.12	0.27	Integrated	M12	Historical and cultural expression	40.40	0.20	0.05	Retained
M13	Pedestrian environment safety	4.45	0.65	0.14	Retained	M13	Sense of place	4.28	0.28	0.06	Renamed
M14	Emergency facility completeness	4.54	0.88	0.19	Retained	M14	Narrative quality	4.75	0.20	0.12	Retained
M15	Accessibility facility completeness	4.20	0.65	0.15	Retained	M15	Completeness of recreational facilities	3.91	0.28	0.07	Retained
M16	Historical and cultural expression	4.29	0.95	0.22	Retained	M16	Completeness of sanitary facilities	4.62	0.71	0.05	Retained
M17	Sense of belonging	3.66	0.96	0.26	Reassessed	M17	Completeness of wayfinding facilities	3.91	0.28	0.07	Retained
M18	Immersive experience	3.62	0.92	0.25	Reassessed	M18	Completeness of transportation facilities	4.04	0.35	0.08	Retained
M19	Sufficiency of recreational facilities	4.25	0.67	0.15	Retained	M19	Completeness of emergency facilities	4.12	0.61	0.14	Retained
M20	Sufficiency of sanitary facilities	4.75	0.44	0.09	Retained	M20	Completeness of convenience facilities	4.29	0.69	0.16	Retained
M21	Sufficiency of wayfinding facilities	4.12	0.85	0.20	Retained	M21	Completeness of accessibility facilities	4.50	0.65	0.14	Retained
M22	Sufficiency of transportation facilities	4.25	0.73	0.17	Retained	C1	Physical attributes dimension	4.54	0.50	0.11	Retained
M23	Sufficiency of convenience facilities	4.16	0.96	0.23	Retained	C2	Environmental perception dimension	4.45	0.58	0.13	Retained
M24	Sufficiency of interactive facilities	3.00	1.31	0.40	Removed	C3	Social and cultural dimension	4.37	0.71	0.16	Retained
C1	Rationality of spatial structure	4.83	0.38	0.07	Retained	C4	Facility system dimension	4.58	0.58	0.12	Retained
C2	Comfort of spatial environment	4.95	0.20	0.04	Retained
C3	Comprehensive functionality of space	4.58	0.50	0.10	Integrated
C4	Safety of space	3.62	0.57	0.18	Integrated
C5	Cultural character of space	3.58	0.97	0.27	Reassessed
C6	Sufficiency of spatial facilities	4.75	0.44	0.09	Retained
M25	Green coverage rate	-	-	-	Added
M26	Harmony of architectural style	-	-	-	Added
M27	Uniformity of store signage	-	-	-	Added

4.2.3. Expert Feedback and Indicator Revision

After two rounds of Delphi consultations, based on expert assessment scores and recommendations, this study refined the evaluation index system for public space needs in historic and cultural districts. The final evaluation index system was refined from six core categories to four core categories, namely physical attributes, spatial perception, social culture, and facility systems. The category indicators were refined from 24 to 21 (

Table 6. Public Space Needs Assessment System for the Nanhexia Historical and Cultural District in Yangzhou.

Core Dimensions	Secondary Indicator	Indicator Definitions
C1. Physical attributes dimension	B1. Spatial accessibility	The ease of access to public spaces from the main and secondary entrances of the neighbourhood or surrounding transportation hubs.
	B2. Spatial connectivity	The physical connectivity between public spaces, whether there are breaks, dead ends, or complex turns, affects the overall passability and continuity of the space.
	B3. Scale appropriateness	Do public spaces comply with human visual comfort and usage standards in terms of horizontal width, vertical viewing distance, and spatial boundary control?
	B4. Functional comprehensiveness	Does the public space simultaneously satisfy the basic functions of access, stay, socialising, and various activities for both indigenous people and tourists, and possess good versatility and flexibility?
C2. Environmental perception dimension	B5. Green coverage rate’	The proportion of green vegetation or natural landscape elements visible within the field of view in public spaces in neighbourhoods reflects the ecological perception quality of the space.
	B6. Architectural style coordination	Do the various buildings within the neighbourhood maintain harmony with the overall background in terms of height, scale, materials, colour and style?
	B7. Storefront signage uniformity	The standardisation and uniformity of commercial store signs within the neighbourhood in terms of font, colour, size, and installation location.
	B8. Pedestrian environment safety	Does the public space have good openness, visibility, and measures to prevent collisions between pedestrians and vehicles?
	B9. Spatial cleanliness	The cleanliness of public spaces during daily maintenance, including floor cleaning, facility integrity, and no disorderly accumulation of items.
	B10. Microclimate adaptability	The adaptability of public spaces to climatic conditions, including the regulatory effects of facilities such as shading, ventilation, and rain protection, as well as spatial layout, on the thermal environment.
	B11. Acoustic environment quality	Is there significant noise pollution in public spaces that affects daily life, conversation, and lingering?
C3. Social and cultural dimension	B12. Historical and cultural expression	Whether public spaces effectively showcase the historical and cultural elements of the neighbourhood, including traditional architecture, cultural symbols, intangible cultural heritage
	B13. Sense of place	Refers to whether a space has distinctive historical and cultural characteristics and regional imagery that enable users to feel an emotional connection and sense of belonging to a specific location.
	B14. Narrative quality	Whether cultural awareness and immersive experiences can be obtained in public spaces, and whether the spaces are readable and narrative.
C4. Facility system dimension	B15. Completeness of recreational facilities	Are there sufficient and well-designed seating, backrests, sunshades, and other rest facilities in public spaces to meet the needs of staying, resting, and communicating?
	B16. Completeness of sanitary facilities	The number, cleanliness, and reasonable distribution of facilities in public spaces, such as public toilets, hand washing facilities, and rubbish bins, and whether environmental hygiene can be quickly achieved and maintained.
	B17. Completeness of wayfinding facilities	Are public spaces equipped with clear and recognisable signage systems, including path guidance, neighbourhood maps, place name signs, and historical and cultural information boards?
	B18. Completeness of transportation facilities	The connectivity and convenience between public spaces and the external transportation system of the neighbourhood, as well as the internal slow-moving transportation system
	B19. Completeness of emergency facilities	Whether public spaces have basic emergency response capabilities, such as first aid kits, alarm devices, surveillance cameras, or emergency lighting, to ensure personal safety.
	B20. Completeness of convenience facilities	Are the various convenience facilities (such as drinking fountains, vending kiosks, charging equipment, and temporary rain shelters) set up in public spaces diverse, practical, and easy to use?
	B21. Completeness of accessibility facilities	The accessibility of public spaces for elderly people, children, people with disabilities, and other groups, including barrier-free access, wheelchair ramps, low-level signage, handrails, and other facilities.

).

4.3. Kano Model

The Kano model, proposed by Noriaki Kano in 1984, is a tool for categorising and prioritising user needs in detail. The model posits that user satisfaction is not a linear function of a single dimension but rather a complex system composed of multiple types of needs [66]. These needs collectively determine users’ perceptions of a particular feature or service and the resulting satisfaction. The model has been widely applied to explore the relationship between various spaces and their users’ needs [91,92,93], and has been effective in improving spatial quality [94,95] and user satisfaction [96,97]. The Kano model includes five attributes:

(1)

Must-be Quality (M): If a particular feature or service is inadequate, user dissatisfaction will rise sharply. However, merely meeting the basic requirements of a feature or service is insufficient to enhance users’ additional satisfaction;

(2)

One-dimensional Quality (O): It has a direct positive correlation with user satisfaction. When a particular feature or service is improved, user satisfaction also increases accordingly;

(3)

Attractive Quality (A): Improving a certain feature or service can significantly enhance user satisfaction. However, if a certain feature or service is missing, the decline in user satisfaction will not be too noticeable;

(4)

Indifferent Quality (I): This indicates that users are not very sensitive to whether a certain feature or service is improved or not, and these factors have little impact on user satisfaction;

(5)

Reverse Quality (R): This reflects certain features or services that users may not be aware of. Overemphasising the satisfaction of these aspects may actually reduce user satisfaction.

Figure 3 clearly illustrates the Kano model. It is important to recognise that the horizontal axis represents the presence or absence of quality, while the vertical axis measures the level of satisfaction. These two dimensions are determined based on the correlation between quality and satisfaction.

The analysis process of the KANO model involves several key steps. First, collect requirements and determine the research topic. Next, ask users positive and negative questions. Finally, perform a classification analysis by overlaying the results of the KANO questionnaire with the importance of the requirements.

Table 7. Evaluation Results Classification Comparison Table.

Function/Service		Negative Questions
		Like (5 Points)	Must be (4 Points)	Neutral (3 Points)	Live with (2 Points)	Dislike (1 Points)
Positive Questions	Like (5 points)	Q	A	A	A	O
	Must be (4 points)	R	I	I	I	M
	Neutral (3 points)	R	I	I	I	M
	Live with (2 points)	R	I	I	I	M
	Dislike (1 points)	R	R	R	R	Q

shows a comparison of the KANO model assessment results.

The Kano model uses the ‘maximisation’ method to identify attributes, which is essentially a qualitative analysis. This method may introduce some bias into the results. To improve accuracy, a Better-Worse coefficient analysis was introduced, which provides a quantitative perspective on the issue. The calculation is as follows:

Better = (O + A)/(M + O + A + I)

(1)

Worse = (−1) × (O + M)/(M + O + A + I)

(2)

In these formulas, the Better coefficient represents user satisfaction when specific needs are met, while the Worse coefficient represents dissatisfaction when needs are not met. A scatter plot will be created with the Worst coefficient on the x-axis and the Better coefficient on the y-axis. Based on the Better-Worst coefficients, the plot is divided into four quadrants, as shown in Figure 4:

The first quadrant is ‘One-dimensional Quality (O)’; the second quadrant is ‘Attractive Quality (A)’; the third quadrant is ‘Indifferent Quality (I)’; and the fourth quadrant is ‘Must-be Quality (M)’ (Figure 5). The priority for addressing requirements is as follows: Must-be Quality (M) > One-dimensional Quality (O) > Attractive Quality (A) > Indifferent Quality (I).

4.3.1. Kano Questionnaire Design and Survey

To capture the classification, priorities, and similarities and differences in the demand for public spaces in historic and cultural districts among local Residents and tourists, we designed a questionnaire based on the Kano model and the final indicator system derived from the previous section (

Table 8. Kano questionnaire example.

B1 Spatial Accessibility		Like (5 Points)	Must be (4 Points)	Neutral (3 Points)	Live with (2 Points)	Dislike (1 Points)
Positive Questions	If it is convenient to reach the public space you want to go from the main and secondary entrances of the block or traffic nodes, how would you evaluate it?
Negative questions	If it is inconvenient to reach the public space you want to go from the main and secondary entrances of the block or traffic nodes, how would you evaluate it?

). To ensure the timeliness, response rate, accuracy, and balance of the questionnaire, this study was conducted by two pairs of researchers in groups A and B from 2 August to 5 August 2025. All participants signed informed consent forms, and we simultaneously collected basic personal information. Group A distributed questionnaires to 121 tourists at major entrances/exits with high visitor traffic, cultural and tourism nodes, and popular shops within the neighbourhood. After excluding illogical data, 108 valid questionnaires were collected, yielding a response rate of 89.2%; Group B distributed 126 questionnaires to the original residents of the neighbourhood with the assistance of the neighbourhood management department and residents’ committee. After excluding illogical data, 112 valid questionnaires were recovered, with a recovery rate of 88.8%. A total of 247 questionnaires were distributed offline, with 220 valid data recovered, achieving an effective data recovery rate of 89%. The gender ratio of respondents was balanced, and the age distribution was comprehensive. Among them, tourists accounted for 49.09%, and original residents accounted for 50.91%. The valid data has reached an ideal balanced state (

Table 9. Summary of basic personal information of test subjects.

Project	Category	Quantity	Percentage
Overall overview	Actual distribution	247	-
	Effective recycling	220	89%
Population category	Tourist	108	49.09%
	Resident	112	50.91%
Gender	Male	117	53.18%
	Female	103	46.82%
Age	18–25	25	11.36%
	25–30	46	20.90%
	31–40	51	23.18%
	41–50	41	16.83%
	51–60	34	15.45%
	>60	23	10.45%

).

4.3.2. Kano Questionnaire Analysis

According to the analysis, 21 projects were categorised into different attributes based on feedback from two groups: indigenous people and tourists. This study conducted separate statistical analyses of the feedback from the two groups, as shown in

Table 10. Classification results of demand attributes.

Category	Tourist							Local Residents
	A%	O%	M%	I%	R%	Q%	Result	A%	O%	M%	I%	R%	Q%	Result
B1	25.93	25	33.33	13.89	0.93	0.93	M	24.11	33.93	23.21	16.07	1.79	0.89	O
B2	14.81	45.37	18.52	20.37	0.93	0	O	24.11	20.54	23.21	31.25	0.89	0	I
B3	19.63	40.19	21.5	18.69	0	0	O	23.21	17.86	35.71	21.43	0.89	0.89	M
B4	21.3	24.07	32.41	21.3	0.93	0	M	18.75	24.11	33.04	23.21	0.89	0	M
B5	20.37	41.67	18.52	19.44	0	0	O	31.25	26.79	18.75	23.21	0	0	A
B6	44.44	25	15.74	13.89	0.93	0	A	17.86	18.75	25.89	36.61	0.89	0	I
B7	41.12	16.82	23.36	18.69	0	0	A	21.43	30.36	24.11	22.32	0.89	0.89	O
B8	19.44	25.93	35.19	18.52	0.93	0	M	16.96	25.89	38.39	17.86	0	0.89	M
B9	17.59	43.52	16.67	21.3	0.93	0	O	25.89	22.32	31.25	18.75	0.89	0.89	M
B10	13.89	45.37	25.93	13.89	0.93	0	O	37.5	27.68	18.75	15.18	0.89	0	A
B11	40.74	15.74	21.3	21.3	0.93	0	A	20.54	21.43	32.14	24.11	1.79	0	M
B12	24.07	41.67	17.59	16.67	0	0	O	22.32	21.43	18.75	37.5	0	0	I
B13	13.89	27.78	18.52	39.81	0	0	I	33.93	22.32	24.11	19.64	0	0	A
B14	39.81	23.15	18.52	17.59	0	0.93	A	25.89	22.32	20.54	30.36	0	0.89	I
B15	19.44	25.93	29.63	24.07	0.93	0	M	19.64	21.43	35.71	22.32	0.89	0	M
B16	18.52	25	32.41	23.15	0.93	0	M	21.43	20.54	34.82	22.32	0.89	0	M
B17	21.3	20.37	38.89	18.52	0.93	0	M	19.64	24.11	22.32	33.04	0.89	0	I
B18	16.67	44.44	22.22	15.74	0.93	0	O	16.07	39.29	25	17.86	0.89	0.89	O
B19	25	37.04	20.37	17.59	0	0	O	35.71	28.57	17.86	16.96	0.89	0	A
B20	21.3	18.52	23.15	37.04	0	0	I	17.86	32.14	26.79	22.32	0	0.89	O
B21	21.3	40.74	15.74	21.3	0.93	0	O	33.04	23.21	17.86	25.89	0	0	A

. These tables provide a detailed breakdown of the percentage distribution of project classifications. For example, for Project B1, 33.33% of tourists considered it a must-have attribute, representing the highest percentage among all feedback categories. Therefore, in the tourist category, B1 was classified as ‘Must-be Quality (M).’ Meanwhile, in the indigenous people’s feedback, 33.93% of indigenous people considered B1 to be an expected attribute, with the highest percentage, so in the indigenous people’s category, B1 was classified as ‘One-dimensional Quality (O)’. Similarly, all projects will be classified in this manner.

4.3.3. SII-DDI Two-Dimensional Quadrant Analysis

After completing the Kano questionnaire analysis and statistics, this study conducted a Better-Worse coefficient analysis and constructed a two-dimensional quadrant analysis diagram of the Satisfaction Increase Index (SII) and Dissatisfaction Reduction Index (DDI). The SII coefficient represents user satisfaction when specific needs are met, while the DDI coefficient represents dissatisfaction when needs are not met. This quadrant diagram allows for a more intuitive observation of the clustering, distribution, and priority of various indicators. A scatter plot was created based on the statistical results of the valid questionnaire data, with the DDI coefficient on the x-axis and the SII coefficient on the y-axis. Since the DDI coefficient on the x-axis is likely to be negative, all DDI values are taken as their absolute values. These values range from 0 to 1, with higher values indicating greater impact and lower values indicating lesser impact. The reference lines for the quadrants are selected based on the average values of each data series. The formula for calculating the SII-DDI coefficient is as follows:

SII = (A + O)/(M + O + A + I)

(3)

DDI = (O + M)/(M + O + A + I)

(4)

The Better and Worse calculation values for factors affecting residents and tourists are shown in

Table 11. Statistics on the Better-Worse Coefficient between Tourists and Indigenous People.

Category	Tourists		Local Residents
	Better	Worse	Better	Worse
B1	0.5189	−0.5943	0.5964	−0.5871
B2	0.6074	−0.6449	0.4505	−0.4414
B3	0.5981	−0.6168	0.4182	−0.5455
B4	0.4579	−0.5700	0.4324	−0.5766
B5	0.6204	−0.6019	0.5804	−0.4554
B6	0.7009	−0.4112	0.3694	−0.4504
B7	0.5795	−0.4018	0.5273	−0.5546
B8	0.4579	−0.6169	0.4324	−0.6486
B9	0.6168	−0.6075	0.4909	−0.5455
B10	0.5981	−0.7196	0.6577	−0.4685
B11	0.5700	−0.3738	0.4273	−0.5454
B12	0.6574	−0.5926	0.4375	−0.4018
B13	0.4167	−0.4630	0.5625	−0.4643
B14	0.6355	−0.4206	0.4864	−0.4324
B15	0.4580	−0.5608	0.4144	−0.5766
B16	0.4392	−0.5794	0.4235	−0.5586
B17	0.4206	−0.5981	0.4414	−0.4685
B18	0.6168	−0.6729	0.5636	−0.6546
B19	0.6204	−0.5741	0.6486	−0.4685
B20	0.3982	−0.4167	0.5045	−0.5946
B21	0.6262	−0.5700	0.5625	−0.4107

. The two-dimensional quadrant charts of SII-DDI for the two groups are shown in Figure 6 and Figure 7, respectively.

The results of the SII-DDI two-dimensional quadrant chart are consistent with the statistical results of the Kano questionnaire, proving the accuracy and rationality of the valid data feedback, and enabling further analysis to be conducted.

4.3.4. Priority Ranking

American scholars Berger et al. introduced the demand factor sensitivity S index based on the traditional Kano model. The higher the S value, the more important the demand item is, and it should be given priority in planning and practice. The sensitivity of the two user groups to the 21 demand indicators was calculated separately according to the formula to obtain the priority of the demand items. The calculation formula is as follows:

$$\mathrm{S} = \sqrt{{Better}^2+{\left|Worse\right|}^2}$$

(5)

Table 12. Priority ranking results.

Category	Tourists			Local Residents			Category
	Number	Sensitivity	Sorting	Sorting	Sensitivity	Number
M	B1	0.7890	1	1	0.7795	B8	M
	B8	0.7683	2	2	0.7339	B9
	B17	0.7312	3	3	0.7207	B4
	B4	0.7311	4	4	0.7101	B15
	B16	0.7270	5	5	0.7010	B16
	B15	0.7241	6	6	0.6929	B11
O	B10	0.9357	1	7	0.6874	B3
	B18	0.9128	2	1	0.8638	B18	O
	B2	0.8859	3	2	0.8369	B1
	B12	0.8851	4	3	0.7798	B20
	B9	0.8657	5	4	0.7653	B7
	B5	0.8644	6	1	0.8075	B10	A
	B3	0.8513	7	2	0.8001	B19
	B21	0.8468	8	3	0.7377	B5
	B19	0.8453	9	4	0.7294	B13
A	B6	0.8126	1	5	0.6965	B21
	B14	0.7621	2	1	0.6508	B14	I
	B7	0.6987	3	2	0.6437	B17
	B11	0.6816	4	3	0.6307	B2
I	B13	0.6229	1	4	0.5940	B2
	B20	0.5764	2	5	0.5825	B6

shows the calculation results and ranking.

5. Discussion

According to the analysis results of the Kano model, the 21 indicators produced five consistent demand attributes and 16 inconsistent demand attributes when assessed by two types of users of public spaces in historical and cultural districts: tourists and indigenous people. This study will analyse and summarise the consistent and inconsistent demands in turn, and determine the priority of demands in planning and practice based on the current situation in the study area.

5.1. Consistency of Demand

Based on the feedback from the results, five indicators showed consistent final attributes when evaluated by both indigenous people and tourists, presenting two combination patterns: ‘M + M’ and ‘O + O,’ accounting for 24% of all indicators. These are B4 (Comprehensive functionality), B8 (Safety of the walking environment), B15 (Completeness of recreational facilities), Q16 (The completeness of sanitation facilities), and B18 (The completeness of transportation facilities) (

Table 13. Consistency check.

Number	Number Name	Tourist	Resident
B4	Comprehensive functionality	M	M
B8	Safety of the walking environment	M	M
B15	Completeness of recreational facilities	M	M
B16	The completeness of sanitation facilities	M	M
B18	The completeness of transportation facilities	O	O

).

The five consistency requirement attributes are divided into two categories: Must-be Quality (M) and One-dimensional Quality (O). Among these, B4 (Comprehensive functionality), B8 (Safety of the walking environment), B15 (Completeness of recreational facilities), and B16 (Completeness of sanitation facilities) belong to the ‘Must-be Quality’ (M) category. These attributes are considered foundational and critical functions; even if optimised to a high degree, they have limited impact on user satisfaction, but their absence would significantly reduce satisfaction. The feedback results indicate that flexible and versatile functionalities to accommodate various activities such as movement, stay, and socialising (Figure A1) for both tourists and local residents are essential physical attributes in the public spaces of historical and cultural districts. Additionally, the safety of walking—the primary mode of transportation in such districts—must be ensured to prevent non-motorised vehicles from posing safety risks to pedestrians (Figure A2). If the functionality and safety of public spaces cannot be fully guaranteed, it will lead to an increase in user dissatisfaction. Rest facilities and sanitation facilities are infrastructure needs universally recognised by both tourists and local residents (Figure A3) within the facility system dimension. Sufficient quantities and reasonable distribution of public seating, public backrests, fixed multi-person sunshades, public toilets, public handwashing facilities, and trash bins can maintain user satisfaction with public spaces.

B18 (The completeness of transportation facilities) belongs to ‘One-dimensional Quality’ (O). The completeness of these attributes is closely related to user satisfaction. When implemented well, it can significantly improve satisfaction, while deficiencies may lead to a slight decline. Feedback indicates that the pedestrian paths and non-motorised vehicle parking (Figure A4) areas in historical and cultural districts, as representatives of the slow-moving system, need to have good connectivity with external transportation systems to facilitate communication and interaction between tourists, residents, and the outside world. Improving this aspect can enhance user satisfaction, and vice versa.

5.2. Differences in Demand

Based on the feedback results, a total of 16 indicators exhibited different levels of demand when evaluated by tourists and indigenous peoples, accounting for 76% of all indicators. These indicators formed six combinations: ‘M + O,’ ‘M + A,’ ‘M + I,’ ‘O + A,’ ‘O + I,’ and ‘A + I.’ Referring to the priority of needs in the Kano model: Must-be Quality (M) > One-dimensional Quality (O) > Attractive Quality (A) > Indifferent Quality (I), this study categorises the six demand combinations into three classes: the five combinations containing ‘Must-be Quality (M)’ are classified as Class A; the eight combinations containing ‘One-dimensional Quality (O)’ but not ‘Must-be Quality (M)’ are classified into Category B; and the three combinations containing ‘Attractive Quality (A)’ but not ‘Must-be Quality (M)’ or ‘One-dimensional Quality (O)’ are classified into Category C (

Table 14. Differential diagnosis classification.

Category	Number	Tourist	Resident
Class A	B1	M	O
	B3	O	M
	B9	O	M
	B11	A	M
	B17	M	I
Class B	B5	O	A
	B7	A	O
	B10	O	A
	B19	O	A
	B21	O	A
	B2	O	I
	B12	O	I
	B20	I	O
Class C	B6	A	I
	B13	I	A
	B14	A	I

).

5.2.1. Class A

According to the grouping (

Table 15. Class A.

Must-be Quality (M) + One-Dimensional Quality (O)
Number	Number Name	Tourist	Resident
B1	Spatial accessibility	M	O
B3	Suitability of spatial scale	O	M
B9	Spatial tidiness	O	M
Must-be Quality (M) + Attractive Quality (A)
Number	Number Name	Tourist	Resident
B11	Sound environment quality	A	M
Must-be Quality (M) + Indifferent Quality (I)
Number	Number Name	Tourist	Resident
B17	The completeness of wayfinding facilities	M	I

), there are three attribute combination patterns in Class A: ‘M + O’, ‘M + A’ and ‘M + I’. These include five demand attributes: B1 (Spatial accessibility), B3 (Suitability of spatial scale), B9 (Spatial tidiness), B11 (Sound environment quality) and B17 (The completeness of wayfinding facilities).

Among the three ‘M + O’ combinations, visitors consider B1 (Spatial accessibility) to be a ‘Must-be Quality (M)’, while B3 (Suitability of spatial scale) and B9 (Spatial tidiness) are ‘One-dimensional Quality (O)’; Indigenous people consider B3 (Suitability of spatial scale) and B9 (Spatial tidiness) to be ‘Must-be Quality (M)’, while B1 (Spatial accessibility) is ‘One-dimensional Quality (O)’. These results indicate that the three demand levels—B1, B3, and B9—have a significant impact on the satisfaction of both indigenous people and tourists. They are considered foundational and critical functions. When optimised to a high degree, they not only ensure the satisfaction of users with ‘Must-be Quality (M)’ evaluation attributes but also enhance the satisfaction of users with ‘One-dimensional Quality (O)’ evaluation attributes. Resources should be concentrated on maintaining and optimising these functions. Taking B9 (Spatial tidiness) as an example, ensuring the cleanliness of public space floors, the integrity of facilities, and the absence of clutter in historical and cultural districts can guarantee the satisfaction of local Residents. Further improving the overall spatial tidiness can enhance the satisfaction of tourist groups.

In one ‘M + A’ combination, tourists perceive B11 (Sound Environment Quality) as ‘Attractive Quality (A),’ while residents perceive it as ‘Must-be Quality (M).’ Noise interference is an unavoidable negative environmental perception factor in the cultural tourism development process of historical and cultural districts. Construction site noise, commercial activity noise, cultural tourism activity noise, and noise from tourists have become one of the primary factors contributing to negative emotions among local residents when using public spaces. Tourists generally aspire to a vibrant historical and cultural district to achieve a better atmosphere, so they can tolerate noise that significantly impacts local Residents. However, excessive noise inevitably leads to adverse effects (Figure A5). Implementing noise control and noise insulation measures can significantly enhance tourists’ satisfaction when using public spaces while preventing the emergence of dissatisfaction among local Residents.

In a ‘M + I’ combination, tourists consider B17 (The completeness of wayfinding facilities) to be a ‘Must-be Quality (M)’, while residents view it as an ‘Indifferent Quality (I)’. This is evident because residents, as the ‘owners’ of the historic cultural district, are familiar with the pathways within the district, so the wayfinding system has little impact on their satisfaction. Conversely, wayfinding systems are viewed by tourists as foundational and critical facilities within public spaces (Figure A6). A well-designed wayfinding system can assist tourists in easily and smoothly locating their destinations. If this aspect is lacking, it may lead to significant dissatisfaction among tourists.

In summary, any demand attribute within the Class A category is fundamental and critical, and should be maintained and continuously optimised to avoid dissatisfaction among users who consider the demand attribute to be a ‘Must-be Quality (M)’ in the combination model, while also effectively improving the satisfaction of another category of users, thereby holding a high priority across all levels.

5.2.2. Class B

According to the grouping (

Table 16. Class B.

One-Dimensional Quality (O) + Attractive Quality (A)
Number	Number Name	Tourist	Resident
B5	Spatial green view rate	O	A
B7	Uniformity of store signs	A	O
B10	Microclimate adaptability	O	A
B19	Completeness of emergency facilities	O	A
B21	Completeness of barrier-free facilities	O	A
One-dimensional Quality (O) + Indifferent Quality (I)
Number	Number Name	Tourist	Resident
B2	Spatial connectivity	O	I
B12	Historical and cultural expressiveness	O	I
B20	The completeness of convenience facilities	I	O

), the Class B group includes two combination modes: ‘O + A’ and ‘O + I’ two combination modes. These include B5 (Spatial green view rate), B7 (Uniformity of store signs), B10 (Microclimate adaptability), B19 (Completeness of emergency facilities), B21 (Completeness of barrier-free facilities), B2 (Spatial connectivity), B12 (Historical and cultural expressiveness), and B20 (The completeness of convenience facilities).

Among the five ‘O + A’ combination models, visitors consider B5 (Spatial green view rate), B10 (Microclimate adaptability), B19 (Completeness of emergency facilities), and B21 (Completeness of barrier-free facilities) to be ‘One-dimensional Quality (O)’, while B7 (Uniformity of store signs) is considered ‘Attractive Quality (A)’; Local Residents consider B7 (Uniformity of store signs) to be ‘One-dimensional Quality (O)’, while B5, B10, B19, and B21 are classified as ‘Attractive Quality (A)’. It can be seen that the presence of reasonable greenery arrangements (Figure A7), complete heat and rain protection measures (Figure A8), good neighbourhood ventilation, and adequate emergency and barrier-free facilities in the public spaces of historical and cultural districts is positively correlated with visitor satisfaction. Improving and continuously optimising these facilities can further enhance visitor satisfaction while significantly increasing the satisfaction of local Residents. Meanwhile, the increase in the number of shops has led to a variety of store signs. Although signs in public spaces that have not been uniformly optimised do not significantly affect visitor satisfaction, the diverse styles of signs can impact the visual experience of long-term residents (Figure A9), creating a sense of ‘intrusion’ and thereby reducing their satisfaction.

Among the three ‘O + I’ combination models, visitors perceive B2 (Spatial Connectivity) and B12 (Historical and Cultural Expressiveness) as ‘One-dimensional Quality (O)’, while B20 (The Completeness of Convenience Facilities) is classified as ‘Indifferent Quality (I)’; Local Residents consider B20 (The completeness of convenience facilities) to be ‘One-dimensional Quality (O),’ while B2 and B12 are ‘Indifferent Quality (I).’ While the connectivity of public spaces in historical and cultural districts does not affect local Residents familiar with the area’s path distribution, it directly impacts tourist satisfaction (Figure A10). Unexpected barriers and dead-end alleys in public spaces can hinder tourists’ mobility, thereby reducing their satisfaction. Meanwhile, the proper presentation of distinctive traditional buildings, cultural symbols, and intangible cultural heritage content—which are taken for granted by locals and have become part of their daily lives—can enhance visitors’ satisfaction in the social and cultural dimension. The improvement and optimisation of convenience facilities such as drinking water points, sales kiosks, charging equipment, and temporary shelters have limited impact on visitors but can enhance the satisfaction of locals in this area and should be continuously optimised.

In summary, while the priority of demand attributes within Class B is lower than that of Class A, the absence or poor quality of such demands will inevitably impact the satisfaction of a certain group of people. Improving these demands can simultaneously enhance the satisfaction of both the ‘O + A’ user group and the ‘O + I’ user group, ensuring that the assessment results for the ‘O + I’ group remain ‘One-dimensional Quality (O)’. It is recommended to allocate resources to maintain and optimise these aspects at appropriate times.

5.2.3. Class C

According to the grouping (

Table 17. Class C.

Attractive Quality (A) + Indifferent Quality (I)
Number	Number Name	Tourist	Resident
B6	The degree of coordination of the style of historical buildings	A	I
B13	Spatial sense of place	I	A
B14	Spatial narrative	A	I

), there is one combination pattern, ‘A + I’ in Class C. This includes B6 (The degree of coordination of the style of historical buildings), B13 (Spatial sense of place), and B14 (Spatial narrative).

Among the three ‘A + I’ combination models, visitors perceive B6 (The degree of coordination of the style of historical buildings) and B14 (Spatial narrative) as ‘Attractive Quality (A)’, while B13 (Spatial sense of place) is classified as ‘Indifferent Quality (I)’; local Residents, however, view B13 as ‘Attractive Quality (A)’, while B6 and B14 are categorised as ‘Indifferent Quality (I)’.

From the tourists’ perspective, we can conclude that historical and cultural districts contain a large number of historical buildings, which serve as important carriers of the district’s deep historical and cultural heritage. Renovating and optimising these buildings to achieve overall coordination and showcase the historical and cultural elements they contain can significantly enhance tourist satisfaction. Public spaces can be enhanced with public art (Figure A11) installations to improve the readability of the overall space, document and display significant events that have occurred in the public space, and effectively enhance spatial immersion. Improving the above demand attributes can significantly enhance visitor satisfaction.

From the perspective of local Residents, we can observe that whether public spaces possess regional imagery can enable users to feel an emotional connection and sense of belonging to a specific location, thereby maintaining local Residents’ attachment to the place (Figure A12), increasing their sense of belonging and identity, and enhancing their satisfaction when using public spaces. However, the quality of this attribute does not directly impact visitor satisfaction.

In summary, all demand attributes in Class C have lower priority and can be addressed at appropriate times during the ongoing optimisation of historical and cultural districts to enhance the satisfaction of users classified as ‘Attractive Quality (A)’.

5.3. Priority of Improvement

Based on the analysis of the three major categories—Class A, Class B, and Class C—in the previous section on the SII-DDI two-dimensional quadrant diagram and the analysis of differences, we have identified the priority of public space demand attributes for historical and cultural districts among tourists and indigenous people.

5.3.1. Unified Priority Ranking for Balance

Based on the five requirement attributes determined by consistency assessment and the three major categories of Class A, Class B, and Class C, as well as the 16 requirement attributes of the six combination modes, this study reclassified all requirement attributes into three priority categories from the perspective of ease of management and collaborative balance, with Priority 1 > Priority 2 > Priority 3.

(1)

The four combinations of consistency judgements presented as ‘M + M’ have the highest priority among the five demand attributes of Class A. This study sets B4, B8, B15, B16, B1, B3, B9, B1, and B17 as Priority 1 in

Table 18. Priority 1.

Priority 1
Number	Number Name	Tourist	Resident
B4	Comprehensive functionality	M	M
B8	Safety of the walking environment	M	M
B15	Completeness of recreational facilities	M	M
B16	The completeness of sanitation facilities	M	M
B1	Spatial accessibility	M	O
B3	Suitability of spatial scale	O	M
B9	Spatial tidiness	O	M
B11	Sound environment quality	A	M
B17	The completeness of wayfinding facilities	M	I

.

Overall, the inadequacy of demand attributes within Priority 1 will lead to a significant increase in dissatisfaction among at least one category of users, resulting in a noticeable decline in overall satisfaction. Among these, the attribute combination patterns ‘M + O’ for B1, B3, B9, and B11 can be continuously optimised while maintaining their current levels, thereby improving the satisfaction of users who perceive them as ‘One-dimensional Quality (O)’. Therefore, resources should be allocated to maintain the current level of all demand attributes within the Priority 1 group and to conduct incremental improvements and optimisations in the future. The secondary priority ranking within Priority 1 is (B4 + B8 + B15 + B16) > (B1 + B3 + B9 + B1) > B17.

(2)

Consistency judgement: One combination presenting ‘O + O’ has a high priority with the eight requirement attributes of Class B. This study sets B18, B5, B7, B10, B19, B21, B2, B12, and B20 as Priority 2 in

Table 19. Priority 2.

Priority 2
Number	Number Name	Tourist	Resident
B18	The completeness of transportation facilities	O	O
B5	Spatial green view rate	O	A
B7	Uniformity of store signs	A	O
B10	Microclimate adaptability	O	A
B19	Completeness of emergency facilities	O	A
B21	Completeness of barrier-free facilities	O	A
B2	Spatial connectivity	O	I
B12	Historical and cultural expressiveness	O	I
B20	The completeness of convenience facilities	I	O

.

Overall, the degree of improvement in any of the requirement attributes in Priority 2 is closely related to user satisfaction, and the quality of implementation will directly affect the satisfaction of at least one category of users. The consistency assessment of B18, which has an attribute combination pattern of ‘O + O,’ directly impacts the satisfaction of both tourists and locals. It should be maintained as is and receive ongoing improvements with appropriate resource allocation; B5, B7, B10, B19, and B21, which have an attribute combination pattern of ‘O + A,’ can be enhanced at appropriate times while maintaining the current status to significantly improve the satisfaction of users who perceive them as ‘Attractive Quality (A).’ For attribute combination patterns of ‘O + I,’ B2, B12, and B20 should maintain their current quality and do not require significant resources for optimisation. Within Priority 2, the secondary priority ranking is B18 > (B5 + B7 + B10 + B19 + B21) > (B2 + B12 + B20).

(1)

The three demand attributes in Class C have a lower priority. In this study, B6, B13, and B14 are set as Priority 3 in

Table 20. Priority 3.

Priority 3
Number	Number Name	Tourist	Resident
B6	The degree of coordination of the style of historical buildings	A	I
B13	Spatial sense of place	I	A
B14	Spatial narrative	A	I

.

Overall, the demand attributes of Priority 3 do not have a significant impact on the satisfaction of tourists and indigenous people, and they have the lowest priority among all demand attributes. When dealing with these attributes, it is only necessary to invest limited resources at the appropriate time to significantly improve the satisfaction of users who consider them to be ‘Attractive Quality (A)’, without the need to invest excessive resources in systematic optimisation and improvement. No secondary priority ranking is conducted within Priority 3.

5.3.2. Targeted Prioritisation of Differences

Based on SII-DDI analysis and an analysis of the sensitivity of the two target user groups, we found that, in addition to five consistent judgements, the remaining 16 demand attributes had not only different attributes but also different priority rankings when evaluated by tourists and indigenous people. This study analysed the priority rankings of the two groups based on the Kano model: Must-be Quality (M) > One-dimensional Quality (O) > Attractive Quality (A) > Indifferent Quality (I). The study included ‘Must-be Quality (M)’, ‘One-dimensional Quality (O)’, and ‘Attractive Quality (A)’ in the analysis of differing priority rankings.

In the order of importance to tourists, among those classified as ‘Must-be Quality (M)’, B1 > B17; among those classified as ‘One-dimensional Quality (O)’, B10 > B2 > B12 > B9 > B5 > B2 > B3 > B21 > B19; among those classified as ‘Attractive Quality (A)’, B6 > B14 > B7 > B11.

In the indigenous population’s priority sequence, among those classified under ‘Must-be Quality (M),’ B9 > B11 > B3; among those classified under ‘One-dimensional Quality (O),’ B1 > B20 > B7; among those classified under ‘Attractive Quality (A),’ B10 > B19 > B5 > B13 > B21.

Based on the analysis, when dealing with historical and cultural districts facing high levels of cultural and tourism development and the irreversible phenomenon of severe indigenous population loss, B1 and B17 are the foundational needs that public spaces must address, with the highest priority. Resources should be allocated to improve and maintain these areas to prevent dissatisfaction. B10, B2, B12, B9, B5, B2, B3, B21, and B19 directly impact visitor satisfaction and have a high priority. They should be continuously optimised based on the actual conditions and priorities of the district to enhance visitor satisfaction; B6, B14, B7, and B11 should be allocated appropriate resources at the right time to achieve the most efficient improvement in visitor satisfaction.

When dealing with public spaces in historical and cultural districts where cultural tourism development is in its early stages, to ensure the quality of the living environment and daily activity spaces relied upon by local Residents, the development process should prioritise the improvement of B9, B11, and B13. This will prevent local Residents from feeling excluded by tourism development, which could lead to their departure, and promote harmonious relations between local Residents and future visitors. B1, B20, and B7 directly impact the satisfaction of the local community and should be prioritised in the development process to continuously optimise these aspects of public space, thereby preventing a decline in satisfaction levels. B10, B19, B5, B13, and B21 have the potential to significantly enhance indigenous satisfaction. Improving these areas at the appropriate time can significantly increase indigenous well-being and trust in the historical and cultural district as a living environment, thereby maintaining the continuity of the district’s humanistic spirit.

5.4. Comparison with Previous Studies

This study constructed a public space demand assessment system comprising four primary dimensions—spatial structure, environmental perception, socio-cultural factors, and facility systems—with a total of 21 secondary indicators. The Kano model was used to clarify the priority of uniformity and the priority of differentiated demand attributes under the assessments of the two target groups.

5.4.1. Comparison at the Consistency Judgement Level

This study identified multiple indicators that are jointly recognised by tourists and local Residents as ‘Must-be Quality (M)’ and ‘One-dimensional Quality (O)’, which validates the core assessments of the functionality and safety of public spaces in historic districts as outlined in previous research.

This study clearly identifies B4 (Comprehensive functionality), B8 (Safety of the walking environment), B15 (Completeness of recreational facilities), and B16 (The completeness of sanitation facilities) as ‘Must-be Quality (M)’ criteria unanimously recognised by both tourists and local residents. B4 aligns with the findings of Y. Wu (2022) [28] and J. Huang (2023) [23], all of which emphasise the importance of functional diversity in the public spaces of historic districts; B8 aligns with the findings of Z. Yu (2024) [36] and X. Gao (2025) [37], which conclude that ‘walking safety holds foundational weight in spatial quality perception.’ Additionally, this study proposes that B15 and B16 in the facility system dimension have the same priority. Although this has rarely been systematically explored in previous studies, it is highly valued by users in this study, indicating that the support functions for daily use in the facility system have potential high sensitivity and should be given adequate attention in future neighbourhood upgrades; The consistency judgement of ‘One-dimensional Quality (O)’ for B18 aligns with the research findings of Q. Wang (2019) [98], which proposed optimisation paths for enhancing the continuity of pedestrian flow, demonstrating consistency.

5.4.2. Comparison at the Difference Judgement Level

The findings in Class A, specifically B1 (Spatial accessibility), B3 (Suitability of spatial scale), and B11 (Sound environment quality), align with the research outcomes of Y. Yuan (2017) [22], Y. Gao (2023) [54], and W. Yan (2024) [43]. Additionally, we propose that the two demand attributes B9 (Spatial tidiness) and B17 (The completeness of wayfinding facilities) have the same level of importance. In Class B, B5 (Spatial green view rate), B10 (Microclimate adaptability), B2 (Spatial connectivity), and B12 (Historical and cultural expressiveness), respectively, validate the research findings of Sun Dong (2024) [32], Q. Ling (2024) [59], Y. Chen (2025) [24], and Y. Li (2022) [39]. Additionally, this study proposes that the demand attributes B7 (Uniformity of store signs), B19 (Completeness of emergency facilities), B21 (Completeness of barrier-free facilities), and B20 (Completeness of convenience facilities) are equally important. The uniformity of store signs, emergency facilities, convenience facilities, and barrier-free facilities are also direct factors influencing the satisfaction level of public space usage; The B13 (Spatial sense of place) attribute in Class C aligns with the findings of Xu Xianfeng (2022) [11], while B6 (The degree of coordination of the style of historical buildings) and B14 (Spatial narrative) have been mentioned in some forward-looking studies by Chinese scholars [99,100], demonstrating their potential as demand attributes to enhance the satisfaction of both tourists and local residents.

5.4.3. Comparison at the Priority Sorting Level

In addition to validation and breakthroughs in the dimensional composition and indicator content, this study further introduces the Kano model to categorise the preference types of local Residents and tourists, clarifying the psychological attribute structure and priority of demand satisfaction for various indicators in user perception. This provides a more structured supplement to existing research, which often remains at the superficial level of ‘indicator importance ranking’ or ‘satisfaction differences.’

Building on previous studies that explored the preferences of users of public spaces in historic cultural districts, this research further introduces the Kano model to identify demand types from a psychological expectation dimension, constructing a classification framework including ‘Must-be Quality,’ ‘One-dimensional Quality,’ ‘Attractive Quality,’ and ‘Indifferent Quality.’ The SII-DDI two-dimensional quadrant is used to assist in identifying the value sensitivity of these attributes, ultimately summarising them into a hierarchical system of Priority 1, Priority 2, and Priority 3, achieving dual expansion at both the theoretical and practical levels.

The demand attributes within Priority 1 not only validate the high weighting of these indicators in existing research but also demonstrate, through user classification, that such foundational needs exhibit a dual effect characteristic of ‘satisfaction when met—dissatisfaction when unmet.’ This classification addresses the interpretative challenge in traditional satisfaction analysis where ‘high satisfaction ≠ high importance,’ enhancing the explanatory power of the indicators. While some indicators in previous studies were assigned ‘medium-high importance,’ they lacked further strategic direction; the demand attributes within Priority 2 are highly sensitive to changes in satisfaction, with satisfaction significantly increasing when met and rapidly decreasing when unmet. This attribute classification is more policy-guiding and spatially optimised in priority than the mean ranking method used in previous studies. Especially in public space renovation and upgrading, it can serve as a high ‘cost–benefit ratio’ leading direction; the demand attributes within Priority 3 reveal ‘potential-type’ demand attributes in future neighbourhood development. Although their quality currently does not affect overall satisfaction, they have the potential to become factors that significantly improve satisfaction in the future.

Unlike common approaches in existing research based on Likert mean scores or satisfaction analysis, this study emphasises the psychological perception structure of demand attributes. While previous studies identified high-frequency importance indicators such as spatial functionality and safety, they primarily focused on superficial average importance rankings, failing to distinguish heterogeneous attributes. After introducing the Kano model, this study identified indicators such as B4 and B8 as ‘Must-be’ attributes; while B15 and B16, which have received little attention in previous studies, are shown in this study to be as important as functionality as ‘Must-be Qualities,’ demonstrating a psychological structure identification capability that is more closely aligned with the actual needs of space users. Although B13 and B14 are mentioned in some literature, their demand attribute types have not been clearly defined. This paper identifies them as ‘Attractive Qualities’ through Kano classification, indicating their potential to enhance satisfaction. Furthermore, the Priority grouping results show that attributes such as B5 (green visibility rate) and B12 (historical and cultural expressiveness) in Priority 2 possess both psychological sensitivity and strategic value, offering a high return on investment. This has not been explicitly identified in most existing studies, highlighting the innovative and practical nature of this study’s strategy-oriented ranking approach.

Furthermore, this paper categorises and ranks both tourist and indigenous groups separately, then integrates them into a unified structure to accommodate the spatial usage characteristics of historic districts at different development stages. This integration strategy based on group-specific Kano classification is a first in existing research, providing a more flexible and precise tool for evaluating shared spaces involving multiple stakeholders.

In summary, this paper not only verifies and supplements the content of the indicators but also introduces a psychological attribute structure model in the methodological approach. Through Kano classification and Priority grouping, it achieves a methodological leap from ‘importance ranking’ to ‘psychological-strategic coupled ranking,’ providing decision-support with explanatory power and practicality for the optimisation of public spaces in historical and cultural districts.

6. Conclusions

This study focuses on residential historical and cultural districts as its research subject, aiming to explore the commonalities and differences between tourists and local residents in the use of public spaces, and to identify the key factors influencing perceptions of public space quality in these districts, as well as their priority rankings. The research revolves around the following three core questions: (1) What are the specific needs of tourists and local residents regarding public spaces in historical and cultural districts? (2) How do these needs differ between tourists and residents when evaluated? (3) How should these factors be prioritised to more effectively enhance the quality and satisfaction of public space use in the district?

6.1. From Qualitative Coding to Consensus Refinement: Constructing an Evaluation Indicator System Suitable for Two Groups of People

To address the first question, this paper first employed grounded theory to conduct open, axial, and selective coding of the spatial usage experiences of 60 interview participants, including tourists, local Residents, and neighbourhood managers. This process initially yielded a public space needs assessment framework comprising four primary dimensions (spatial structure, environmental perception, socio-cultural factors, and facility systems) and 21 secondary indicators. Subsequently, using the Delphi expert consultation method, 24 experts from government departments, universities, and design institutes were invited to participate in two rounds of indicator screening and revision to ensure the professional validity and academic supportability of the indicator system. Based on this, Kano questionnaires were distributed to both tourists and local residents to quantitatively identify the psychological attribute classifications of different groups’ spatial needs.

6.2. Commonality and Diversity: Reorganisation and Secondary Unification Under Shared Psychological Cognition

To address the second question, this paper identified five indicators with consistent judgments and 16 indicators with inconsistent judgments based on the Kano model’s dual-population classification consistency analysis. According to the Kano model’s demand attribute ranking, the 16 inconsistent judgement indicators were reorganised into three categories: Class A, Class B, and Class C, and high-sensitivity attributes for tourists and local Residents were identified.

Consistent judgement attributes include those belonging to ‘Must-be Quality (M)’—B4, B8, B15 and B16. These are foundational demand attributes; their absence significantly reduces satisfaction and are protective elements. B18 belongs to ‘One-dimensional Quality (O)’, is a performance-oriented element, and its improvement can significantly enhance satisfaction.

Among the differentiation-judgement attributes, Class A includes five demand attributes: B1, B3, B9, B11 and B17. Any one of these attributes within the group is foundational and critical; Class B includes B5, B7, B10, B19, B21, B2, B12 and B20. Resources should be allocated to maintain and continuously improve these attributes; Class C includes B6, B13 and B14. Resources can be allocated at appropriate times to improve these attributes, achieving a more noticeable cost–benefit ratio.

Additionally, high-sensitivity attributes for both tourists and local Residents were identified. High-sensitivity attributes for tourists include B1, B3, B6, B11, and B17; high-sensitivity attributes for local Residents include B2, B5, B9, B10, and B12.

6.3. Prioritisation and Optimisation Recommendations for Different Factors

By combining the two-dimensional indicators of SII (Satisfaction Improvement Potential) and DDI (Dissatisfaction Reduction Potential), the study conducted an integrated ranking of 21 attributes and ultimately proposed the following three categories of uniformity priorities and differentiation priorities: Priority 1: Includes B4, B8, B15, B16, B1, B3, B9, B1, and B17, all of which are rigidly guaranteed priority levels, are critical for meeting basic functional and safety requirements for space usage and should be prioritised. The secondary priority ranking within Priority 1 is (B4 + B8 + B15 + B16) > (B1 + B3 + B9 + B1) > B17; Priority 2: Includes B18, B5, B7, B10, B19, B21, B2, B12, and B20, all of which are key areas for improving usage efficiency and user experience. These should be prioritised as key directions for optimising spatial structure and facility systems. Within Priority 2, the secondary priority ranking is B18 > (B5 + B7 + B10 + B19 + B21) > (B2 + B12 + B20); Priority 3: Includes B6, B13, and B14, which only require limited resources to be invested at appropriate time points for improvement and optimisation. No secondary priority ranking is conducted within Priority 3.

Additionally, we conducted attribute requirement rankings for two distinct groups—tourists and local Residents—to serve as supplementary guidance for the unified priority ranking. Among tourists’ priorities, B1 > B17 within the ‘Must-be Quality (M)’ category; Among those classified as ‘One-dimensional Quality (O),’ B10 > B2 > B12 > B9 > B5 > B2 > B3 > B21 > B19; Among those classified as ‘Attractive Quality (A),’ B6 > B14 > B7 > B11.

In the indigenous population’s order of priority, among those classified as ‘Must-be Quality (M)’, B9 > B11 > B3; Among those classified as ‘One-dimensional Quality (O)’, B1 > B20 > B7; Among those classified as ‘Attractive Quality (A)’, B10 > B19 > B5 > B13 > B21.

In summary, the study proposes an optimised strategy framework that prioritises unified priority ranking as the main approach and group difference ranking as a supplementary approach, balancing the three values of basic safeguards, performance improvement, and cultural expression. This framework aims to provide structural support and application pathways for the efficient renewal of public spaces in historic cultural districts and the enhancement of user satisfaction.

6.4. Short-Term Flexible Operations and Long-Term Structural Strategy

To swiftly address the frequent, fundamental needs of both visitors and local residents within historic cultural districts, this study proposes a series of implementable short-term operational measures. These include enhancing wayfinding systems, increasing rest and sanitation facilities, and improving spatial cleanliness and microclimate comfort, primarily focusing on enhancing spatial convenience and environmental friendliness. During implementation, urban management departments, sub-district offices, and community committees should serve as primary responsible entities. They should collaborate with relevant functional departments such as cultural tourism and landscaping, establishing rapid interdepartmental coordination and resource allocation mechanisms to form an ‘interdepartmental collaboration–community embedding–rapid response’ operational framework. Such operational optimisations can significantly enhance spatial satisfaction in the short term while laying a solid foundation for long-term institutional reforms.

To address deep-seated structural issues in public spaces within historic cultural districts, this study proposes long-term institutional optimisation pathways. It recommends establishing a ‘resident-tourist co-governance’ spatial consultation mechanism, fostering regular dialogue platforms among indigenous residents, visitors, government administrators, and third-party design agencies to transform ‘diverse demands’ into ‘consensus-driven collaboration’. Concurrently, a participatory spatial optimisation platform may be developed to guide community residents and visitors in jointly engaging with design and evaluation processes. Introducing data-sensing and feedback mechanisms within urban operational systems would enhance the precision and intelligence of public space governance. These strategies should be spearheaded by urban governance departments, collaborating with universities, design teams, community organisations, and other stakeholders. Guided by the core principles of ‘clear accountability, stable mechanisms, and closed-loop feedback,’ this approach would propel public spaces from functional enhancement towards systemic reconstruction.

7. Contributions and Shortcomings

7.1. Theoretical Contributions

This study focuses on residential historic cultural quarters, employing grounded theory and the Delphi expert consultation method to systematically analyse the commonalities and differences in public space usage needs between visitors and local residents. By introducing the Kano model and SII-DDI analysis methodology, it constructs a prioritisation system based on psychological attributes classification. This multi-stage mixed-methods research process enriches the research paradigm for evaluating public spaces. This study’s evaluation framework for public spaces overcomes the limitations of existing research, which often relies on single methodologies or solely qualitative analysis. It achieves systematic identification, categorisation, and prioritisation of spatial needs across diverse user groups. Unlike previous studies confined to satisfaction or importance rankings, this paper proposes a ternary classification logic of ‘expected attributes—perceived attributes—attractive attributes’. By integrating satisfaction potential and dissatisfaction risk, it establishes a three-tier explanatory framework of ‘psychological attributes—priority ranking—demographic variation’. This expands the theoretical boundaries for collaborative research on diverse public space user groups, providing new analytical tools for understanding multi-user demands and spatial optimisation in historic districts. It further advances public space research from ‘average satisfaction’ towards ‘tiered governance priorities’.

7.2. Practical Significance

At the practical level, the research clearly identifies the specific indicators, sensitive attributes, and prioritisation differences between tourists and local residents in public spaces, providing precise guidance for neighbourhood spatial enhancement strategies. Results indicate that tourists place greater emphasis on tangible experiences such as “wayfinding systems”, “transport facilities”, and “visual tidiness”, while local residents prioritise daily conveniences and liveability features like “green visibility ratio”, “microclimate adaptability”, and “accessibility facilities”. Integrating these classification findings with prioritisation, the paper proposes three spatial optimisation strategies: prioritising improvements to essential elements, progressively enhancing performance-oriented features, and selectively investing in potential-driven components. This framework provides empirical evidence and practical pathways for differentiated interventions and resource allocation in historic district regeneration. This research not only establishes a scientific methodology for identifying and prioritising diverse public space needs within historic districts but also provides actionable decision-making guidance for local governments and planning teams in formulating differentiated optimisation strategies and enhancing spatial service quality. By constructing a unified yet differentiated prioritisation framework, this study offers insights into ‘differentiated synergy’ governance under constrained public space resources and provides a transferable evaluation framework for advancing people-centred urban renewal.

7.3. Innovative Features

This study demonstrates significant innovation in three aspects: first, in terms of evaluation subjects, it incorporates both tourists and local Residents into a unified evaluation system, emphasising the importance of both ‘collaborative consensus’ and ‘differentiated demands,’ thereby breaking through the limitations of traditional, fragmented analyses of distinct population groups; Second, in terms of research methodology, it innovatively integrates grounded theory, the Delphi method, and the Kano-SII-DDI composite model to construct an integrated ‘qualitative-expert-quantitative-ranking’ pathway. Third, in terms of indicator output, it proposes a three-tier priority sequence based on psychological attributes, dividing sensitivity into Classes A/B/C, providing a structured basis for the layered optimisation and precise intervention of public spaces.

7.4. Lack of Research

Although this study established a relatively systematic evaluation indicator system and ranking model through the integration of multiple methods, certain limitations remain. First, the Kano model and SII-DDI analysis are primarily based on user perception evaluations and have not been cross-validated with spatial behaviour data. In the future, spatial behaviour observation or location tracking technologies could be introduced to enhance the objectivity of the results. Second, the sample selection in this study is concentrated on a specific type of residential historical and cultural district, which may result in differences in applicability across different types of districts (e.g., commercial or tourism-driven districts). Further expansion of sample types and geographical coverage is needed. Finally, although this study grouped and integrated the preference needs of the two populations, future research should further explore the dynamic evolution of collaborative mechanisms and conflict resolution strategies among multiple groups.

This study endeavoured to ensure representativeness and structural soundness during the sample collection phase, yet certain limitations remain. During the Kano model questionnaire survey, only basic participant information (identity, gender, age) was statistically analysed. Crucial variables influencing perceptual differences—such as occupation, educational background, and spatial dwell time—were not comprehensively collected. Particularly among tourist groups, factors including high mobility, brief dwell times, and strong privacy awareness limited access to more detailed background data. This may introduce representational bias to some extent, potentially affecting the comprehensiveness and interpretability of analytical outcomes. Future research could integrate spatial behaviour observation or trajectory tracking techniques to enhance data objectivity and scientific rigour.