Research on Social Service Effectiveness Evaluation for Urban Blue Spaces—A Case Study of the Huangpu River Core Section in Shanghai

Abstract

Urban blue spaces (UBSs) hold significant value in terms of public health, tourism economy, and residents’ well-being. The Huangpu River in Shanghai, renowned as a global urban blue space, currently faces challenges such as unequal service capacity across sections and varying levels of spatial vitality. This study incorporates the concept of “service effectiveness” into public space evaluation. Drawing inspiration from the 4E (economics, efficiency, equity, and effectiveness) principles of effectiveness evaluation, a social service effectiveness evaluation system is constructed to measure service efficiency and effect. Through the literature research, 6 primary indicators and 12 secondary indicators are set to investigate the utilization rate and realization effect of the UBS. The evaluation system utilizes field surveys, text analysis, and remote-sensing techniques to collect relevant data. Through standardized calculations, different aspects of the indicators are integrated into a single evaluation criterion. The “overall effectiveness index” and the “efficiency–effect balance index” is introduced to quantitatively analyze the overall effectiveness characteristics, including spatial characteristics and time-varying characteristics, as well as efficiency–effect balance and imbalances. The evaluation located three low-effectiveness sections and three imbalanced sections at the north and south ends of the core section of the Huangpu River. The influence factors of effectiveness are analyzed through correlation test and literature studies, mainly including the urban hinterland, service facilities, environmental quality, and management publicity factors. This study aims to provide research ideas and methods for waterfront area planning and city-refined management.

1. Introduction

In recent years, there has been a growing interest in urban blue spaces (UBSs) and their role in enhancing residents’ well-being and improving the quality of life. In the research related to urban renewal, UBS is a highly regarded urban living space. The Huangpu River waterfront public space in Shanghai, a globally renowned waterfront area, has become a shared public space for both citizens and tourists since the completion of its 45 km shoreline connection in early 2018 [1]. However, due to factors such as development timelines, the type of urban hinterland, and popularity of different districts, there are spatial disparities in the social service capacity of various sections of this public space. Moreover, the concept of the “people-oriented city” has introduced new demands for the renewal of the Huangpu River waterfront public space, emphasizing the need for the service capacity to match the usage demands and to provide satisfactory social services for both citizens and tourists.

Within the framework of the “people-oriented” approach, evaluating the usage status of spaces using the concept of “effectiveness” has emerged as a new direction in urban renewal research [2,3]. The concept of “effectiveness” originated from management research and represents the service capacity, efficiency, and effect of social organizations. It encompasses goal orientation, comprehensiveness, and capability [4,5]. Goal orientation refers to efficiency being the ultimate goal of organizational management, providing reference value throughout the entire process of organizational tasks. Comprehensiveness refers to efficiency, final effectiveness, and benefits, which are the results of comprehensive measurement. Capability refers to efficiency as a reflection of management capability, representing the comprehensive ability of the management system. By the early 1980s, evaluation research on service effectiveness had developed an evaluation method with the main principles of economics, efficiency, equity, and effectiveness (referred to as the 4E principles), which included evaluation indicators such as service quality, customer satisfaction, service efficiency, and cost-effectiveness. Subsequently, with the emergence of new modes of network governance, additional value demands such as sharing and co-governance [6,7,8] were integrated. Current research on “service effectiveness” emphasizes the public welfare and service nature of urban management, encompassing the reasonable and appropriate service objectives set by service providers, the process of achieving those objectives, and the resulting service effect.

In the study of the social service value of urban blue spaces, some scholars argue that this value is primarily reflected in community development [9,10,11], tourism economy [12,13,14,15], cultural and socioeconomic influence [16,17,18,19], environmental sustainability [20,21], and public health [22,23,24,25]. From a user perspective, we can summarize various social service values and propose that the social service of urban blue spaces aims to provide residents with fair and harmonious enjoyment of the spaces in terms of the material, spiritual, and cultural features [26]. The process of providing services to visitors within these spaces manifests as service efficiency, while the recreational experiences and perceptions of visitors represent the service effect. Thus, the social service of urban blue spaces has performance effectiveness.

As for the research on the social service capacity of the Huangpu River waterfront public space, it can be categorized into three areas. First, there are studies that have utilized multi-source data [27,28] to assess the spatial vitality of the waterfront public space based on crowd density and usage status [29,30]. Second, there are spatial quality evaluations that have considered aspects such as the transportation [31], sites [32], and facilities [33] of the waterfront public space. Third, there are assessments of tourist satisfaction and recreational experiences [34,35]. Although the current research perspectives on the Huangpu River waterfront public space are diverse and comprehensive, they lack systematic evaluation. These studies often evaluate the service capacity or service quality of public spaces from a single dimension, making it difficult to comprehensively evaluate the relationship between efficiency in realizing the social value and service effectiveness.

Evaluating the social service effectiveness of urban blue spaces helps to explore the comprehensive capacity and overall efficiency of these spaces in providing social services, as well as to examine the relationship between the space utilization rate and usage effectiveness, which are crucial for ensuring residents’ well-being and satisfaction. Existing studies mainly focus on residents’ preference [36,37,38], decision-making models [39], technological innovation [40,41], and policy frameworks [42] to improve the efficiency of waterfront areas. Among them, the enhancement in land value [43] and the role of tourism economy in driving district development [12,13,14,15] by urban blue spaces are currently hot topics in research. Such studies focus on the development models and limits of urban blue spaces in relation to tourism economy. They also address conflicts between the ecological functions of urban blue spaces and economic development, discussing effective and high-quality development approaches.

In response to this issue, this paper argues that quantitatively assessing the social service effectiveness of urban blue spaces is a necessary process to examine their current social service status and can provide a theoretical basis for future development strategies, management models, and urban planning decisions. This paper also aims to address the development challenges currently faced by the Huangpu River waterfront public spaces and accurately identify segments with inadequate efficiency, proposing targeted recommendations for space renewal.

In this paper, we introduce effectiveness theory from management studies into the evaluation of waterfront spaces. We construct an evaluation system for the social service effectiveness of UBSs and explore the numerical characteristics of social value in Huangpu Riverside. Drawing on the concept of “balance between efficiency and effect”, we analyze the balancing mechanism between the supply of public space services and the realization of service effectiveness, aiming to provide new perspectives for the overall improvement and precision management research of large-scale public space social value. The research framework is shown in Figure 1.

2. Materials and Methods

2.1. Study Area

This study focused on the core section of the Huangpu River, which refers to the segment that flows through the central urban area of Shanghai. This area provides recreational and leisure services to citizens and tourists, serves as an important aesthetic feature and activity space, and showcases regional culture. For this study, we have selected the waterfront public space in the core section of the Huangpu River as the study area.

According to the “Development and Construction Plan for the Huangpu River Coastline (2018–2035)” (referred to as the PLAN) [1], the core section of the Huangpu River is defined as the segment between the Yangpu Bridge and the Xupu Bridge. This section contains areas with significant urban functions, such as finance, commerce, culture, and recreation, and provides globally influential public activity spaces. The study area in this paper specifically refers to the outdoor waterfront public space in the core section of the Huangpu River, which includes the nearshore waters, the waterfront green belts, and the first municipal road along the waterfront. The space beyond this road is considered the urban hinterland.

Based on the spatial characteristics, resource features, and activity characteristics of the waterfront space, the “Guidelines for Planning and Design of Huangpu River Waterfront Public Spaces” [44] classified the Huangpu River waterfront space into three categories: natural ecological, cultural vitality, and historical style.

Based on this classification and considering the specific spatial characteristics, the waterfront public space in the core section of the Huangpu River is further divided into three functional types:

Urban living room: This category encompasses waterfront segments that serve as urban showcases, primarily reflecting urban riverside styles. These areas function as the city’s living room, attracting and receiving visitors.

Urban green space: This category emphasizes ecological functions and landscapes, serving as the main recreational areas for urban residents in their daily leisure activities.

Industrial heritage: This category includes segments with industrial relics, incorporating landscapes that showcase industrial and historical cultures. These areas preserve and exhibit the industrial heritage of the region.

To conduct a comprehensive analysis, preliminary investigations of the spatial vitality status were performed using Baidu Heat Maps and tourists’ behavior surveys [45]. Additionally, 13 representative sample units were selected for in-depth investigations based on functional types and administrative divisions (

Table 1. Sample units in the core section of the Huangpu River.

Functional Segment	Keywords	Spatial Characteristics	Sample Section	Riverside	Administrative District
Industrial heritage	Business development	Plaza gathering space	Qiantan Friendship Park	East Bank	Pudong New Area
	Tourist gathering	Riverside viewing platform	Lujiazui Riverside	East Bank	Pudong New Area
	Cultural promotion	Complete service facilities	North Bund Riverside	West Bank	Hongkou District
			Bund Riverside	West Bank	Huangpu District
Urban green space	Natural ecology	Rich plant landscape complete recreational facilities Diverse site types Ecological landscape	Yaohua Riverside	East Bank	Pudong New Area
	Leisure and scenic views		Houtan Riverside	East Bank	Pudong New Area
	Sports and entertainment		Expo Park	East Bank	Pudong New Area
			Laobaidu Riverside	East Bank	Pudong New Area
			Nanyuan Riverside	West Bank	Huangpu District
			Manlizui Riverside	East Bank	Pudong New Area
Urban living room	Cultural exhibition	Cultural exhibition venues Thematic landscape elements Distinctive landscape features	Shipyard Riverside	East Bank	Pudong New Area
	Innovative art		Yangpu Riverside	West Bank	Yangpu District
			Xuhui Riverside	West Bank	Xuhui District

, Figure 2).

2.2. Data Resource

2.2.1. Spatial Environmental Data

The boundaries of the waterfront public space in the core section of the Huangpu River were obtained from the PLAN and the “Map of Public Space Connectivity on Both Sides of the Huangpu River”. To create an accurate representation, we used satellite images captured by Landset-8 OLI_TIRS (August 2020) and high-resolution maps from AMAP. These base resources were further refined through field surveys to include the urban transportation network, and accessibility calculations were conducted.

2.2.2. Field Survey Data

We employed the PSPL Survey (public space and public life) to gather data on activities at the 13 sample points. Surveys were conducted during weekends and weekdays in December 2020, and in April, July, and October 2021, specifically between 8:00 and 21:00. Data such as activity type, number of participants, age group, and location of stay were recorded.

In addition, a questionnaire and interview survey method were used to collect data from users at the 13 sample points. The survey covered aspects such as frequency of use, transportation modes, perception of crowding, satisfaction, and other relevant information. A total of 647 questionnaire responses were collected. The field survey data were utilized for calculating the efficiency indicators and evaluating the spatial quality.

2.2.3. Dianping Data

We employed Python 3.0 to scrape data from Dianping, a popular Chinese online platform for reviews and recommendations. The data included ratings and review text from entries corresponding to the names of the 13 sample units. The data collection period spanned from 1 January 2018 to 30 August 2022, resulting in a dataset of network text data. These data were used for calculating the influence indicators.

2.3. Methods

2.3.1. Construction of the Evaluation System

Dimension Construction

The evaluation of the social service effectiveness in UBSs is based on the mechanisms and definitions of the social service effectiveness, incorporating the 4E principles. The evaluation dimensions are categorized into service efficiency (L) and service effect (G) (Figure 3).

Service efficiency (L): This dimension assesses the implementation process of social service efficiency in waterfront spaces from the perspective of spatial utilization. It evaluates the efficiency characteristics of providing public services to users in waterfront spaces, including the service supply capacity and space utilization.

Service effect (G): This dimension measures the service outcomes of public spaces from the user’s perspective. It evaluates the value and effect of social services based on user experience, including the even distribution of resources among the population and the overall benefits of the distribution results.

Indicator Identification

Based on existing research and references related to spatial efficiency, vitality, service effectiveness, satisfaction, and fairness, six commonly used evaluation indicators for expressing efficiency and six indicators for evaluating the effectiveness dimension in waterfront space efficiency evaluation were selected and categorized as follows (

Table 2. Evaluation indicators and interpretation.

Evaluation Dimension	First-Level Indicators	Second-Level Indicators	Interpretation
Service efficiency	L1. Service capacity	L11. Spatial capacity	The maximum number of people that can be accommodated in a public space.
		L12. Population dispersal	Describes the dispersion and aggregation of people in a space.
	L2. Functional efficiency	L21. Cumulative density	The sum of population densities from activities in the space over a week.
		L22. Stability index	The level of stability in population density changes over a week.
	L3. Connectivity efficiency	L31. Service range	The area within 15 min travel time by various modes of transportation to reach the public space.
		L32. Transportation connectivity	The connectivity between the public space and urban transportation.
Service effect	G1. Fairness	G11. Population diversity	The age diversity of the active population in the public space.
		G12. Satisfaction disparity	The degree of difference between the average satisfaction levels of different user groups.
	G2. Satisfaction	G21. Average satisfaction	The overall satisfaction level of users regarding the social services provided in the public space.
		G22. Visitation frequency	The willingness of users to repeatedly visit the public space.
	G3. Influence	G31. Comment rating	The level of discussion about the public space by users.
		G32. Access rating	The level of attention given to the public space by users.

):

• Service efficiency (L):

Service capacity (L1): This represents the maximum extent of social service supply. It is assessed through a land type-based spatial capacity calculation (L11) to evaluate the capacity of public spaces to accommodate visitors [46,47,48]. Population dispersal (L12) is included as a limiting indicator to address the overcrowding and safety concerns caused by gathering behaviors [49].

Functional efficiency (L2): This evaluates the state of public spaces in terms of accommodating crowd activities under normal conditions. It reflects the average efficiency and stability of the efficiency implementation process. Two indicators, cumulative density (L21) and stability index (L22), were selected to assess the functional efficiency based on previous research [50,51,52,53]. Cumulative population density represents the comprehensive use of public spaces within a week, while the stability index indicates the extent of population density fluctuations within a day.

Connectivity efficiency (L3): This measures the connecting factors in the efficiency implementation process, specifically the ease with which citizens and tourists can access and use public spaces. Service range (L31) and public transportation connectivity (L32) were chosen as the evaluation indicators based on accessibility research [54] and the concept of the 15 min community living circle.

2.

Service effect (G):

Fairness (G1): This refers to equal access to public spaces for visitors of different ages, regions, and income levels, as defined by social fairness [55]. Population diversity (G11) was selected as an indicator to measure the fairness of opportunities for different population groups. Satisfaction disparity (G12) was included to assess the extent to which public spaces meet the needs of different groups, considering their varying functional demands.

Satisfaction (G2): This measures the overall implementation of public spaces in meeting visitor demands. Average satisfaction (G21) was employed as an indicator to evaluate visitors’ satisfaction [56]. Visitation frequency (G22) was included to measure visitors’ willingness to revisit the waterfront public spaces, inspired by the evaluation method for social organizational service efficiency.

Influence (G3): This represents the online discussion and level of attention generated by the public spaces. The ratings of comments (comment rating, G31) related to waterfront public spaces on platforms such as Dianping and page view ranking (access rating, G32) were selected as indicators to measure their social influence.

Establishment of Comprehensive Effectiveness Index

This paper proposes the “overall effectiveness index (T)” to represent the overall level of the social service effectiveness in the waterfront public spaces and reflects the efficiency level using numerical values. Significance testing was employed, and confidence intervals were set to express a state of moderate efficiency. When the overall efficiency index of a certain space exceeded the upper limit of the interval, the service efficiency and effectiveness of that space were considered good. Conversely, if the index fell below the lower limit, the efficiency was considered poor.

This paper focuses on spaces with moderate overall efficiency, characterized by phenomena such as “high popularity but low satisfaction” or “high satisfaction but low utilization”, indicating an imbalance between service efficiency and effect. Therefore, the “efficiency–effect balance index (B)” is proposed to represent the degree of balance between the service efficiency and service effect in the public spaces, serving as a basis for efficiency regulation research.

After the above three steps, we established the social service effectiveness evaluation system for UBSs, as shown in Figure 4.

2.3.2. Calculation of Indicator

The various indicators of service efficiency were derived from on-site survey data and spatial environmental data. Calculation formulas and fuzzy evaluation methods were used to determine the values of these indicators. Similarly, the various indicators of the service effect were based on questionnaire survey data and activity survey data. The calculation formulas for each indicator are shown in

Table 3. Index calculation formulas.

Second-Level Indicators	Formula	Interpretation
L11. Spatial capacity	$D_C=D_{C1}\times 2+D_{C2}\times 5$	DC1: Cumulative crowd density throughout the day on weekends. DC2: Cumulative crowd density throughout the day on weekdays.
L12. Population dispersal	$V=\sqrt{\frac{n-1}{\sum _{i=1}^n{(D_i-D)}^2}}$	Di: Population density in the space during the i-th hour. D: Hourly average of spatial population density.
L21. Cumulative density	$U=\frac{1}{S}{\sum }_{i=1}^n\frac{S_i}{S_n}\times \frac{T}{t}$	S: Total area of space. Si: Area of the i-th category of space in public areas. Sn: Per capita reasonable space capacity index. T: Daily opening hours of the space. t: Average visitor’s touring time.
L22. Stability index	$C=\frac{1}{{\sum }_{j=0}^n(P_i/S_{ij}\times F_{ij}\times {\lambda }_{ij})}$	C: Crowd aggregation level at the i-th sampling point. Pi: Number of participants in the j-th activity at the i-th node. Sij: Activity range of the j-th activity at the i-th node. Fij: Influence range of the j-th activity at the i-th node. λij: Duration of the j-th activity at the i-th node.
L31. Service range	(network analysis) $S={\displaystyle \bigcup _{i=1}^3}\left(S_i\right)$	S1, S2, S3: Within a 15 min range from a certain waterfront public space, reachable by rail transportation (60 km/h), roadway (40 km/h), and pedestrian pathway (5 km/h), respectively.
L32. Transportation connectivity	(fuzzy evaluation) $T=T_1+T_2$	T1: Number of subway stations within a 15 min walking range. T2: Number of bus stops within a 15 min walking range.
G11. Population diversity	$H_a={\sum }_{k=1}^n\frac{P_{ak}}{P_a}\times {\log }_2\frac{P_{ak}}{P_a}$	K: Identification of different age groups, categorized in this study as children, young adults, middle-aged adults, and elderly individuals. Pak: Cumulative number of active individuals in the k-th age group within the sampled space. Pa: Total number of active individuals in the sampled space.
G12. Satisfaction disparity	$H_d=\sqrt{\frac{n-1}{\sum _{i=1}^n{(H_i-H)}^2}}$	Hi: Total number of active individuals in the sampled space. H: Average satisfaction score for the section. n: Number of activity population types in the section.
G21. Average satisfaction	(fuzzy evaluation) $M_p=\frac{1}{n}{\sum }_{i=1}^n{M}_i$	n: Number of questionnaires in the sampled points. Mi: Satisfaction score in the i-th questionnaire.
G22. Visitation frequency	(fuzzy evaluation) $F=\frac{1}{n}{\sum }_{i=1}^n{F}_i$	n: Number of questionnaires in the sampled points. Fi: Frequency level of visits in the i-th questionnaire.
G31. Comment rating	Obtain the number of comments from January 2018 to August 2022	–
G32. Access rating	Obtain the number of visits from January 2018 to August 2022	–

.

2.3.3. Data Standardization

To ensure a unified standard for the discussion of various indicators, each indicator was standardized. The traffic connectivity indicator (L32) was evaluated using fuzzy assessment and natural breakpoints. The visitor evaluation (G21) and visit frequency (G22) indicators were standardized on a scale of 0–10 during the standardization process.

For indicators such as cumulative density (L21) and service range (L31) that exhibit significant differences across segments, logarithmic transformation was applied for standardization using the following formula:

$$X^{\prime }=10\times {\log }_{10}\left(X\right)/{\log }_{10}(X_{max})$$

For other indicators, the min–max method was used for standardization with the following formula:

$$X^{\prime }=10\times (X-X_{min})/(X_{max}-X_{min})$$

where $X^{\prime }$ is the standardized value, $X$ denotes the original value, $X_{max}$ stands for the maximum value of the indicator, and $X_{min}$ represents the minimum value of the indicator.

2.3.4. Weight Setting and Calculation of Comprehensive Effectiveness Index

Considering the differences in the functional positioning and target audience of public spaces, a weight analysis was conducted for each performance evaluation factor based on the functional classification. The analytic hierarchy process and expert scoring method were employed, consulting relevant management personnel from the Shanghai Greenery Bureau and the Pudong Riverfront Office, and involving 4 designers specializing in Huangpu River waterfront spaces, 4 landscape architecture practitioners, 2 university teachers, and 11 postgraduate and doctoral landscape architecture students. After clarifying the functional positioning of three types of public spaces, the importance of each indicator was ranked to determine the weights (

Table 4. Weighting table for social service efficiency factors for different segment types.

Functional Segment	Target Layer Weights	Criterion Layer Weights	Factor Layer Weights
Urban living room	A 0.5143	L1 0.0600	L11 0.0351	L12 0.0250
		L2 0.1713	L21 0.1301	L22 0.0412
		L3 0.2830	L31 0.1119	L32 0.1712
	B 0.4857	G1 0.0935	G11 0.0533	G12 0.0402
		G2 0.2124	G21 0.1939	G22 0.0185
		G3 0.1797	G31 0.0851	G32 0.0956
Urban green space	A 0.4934	L1 0.3016	L11 0.1765	L12 0.1251
		L2 0.0617	L21 0.0360	L22 0.0257
		L3 0.1302	L31 0.0808	L32 0.0493
	B 0.5066	G1 0.0860	G11 0.0566	G12 0.0293
		G2 0.3103	G21 0.1441	G22 0.1662
		G3 0.1103	G31 0.0766	G32 0.0337
Industrial heritage	A 0.4611	L1 0.1186	L11 0.0803	L12 0.0383
		L2 0.0937	L21 0.0665	L22 0.0272
		L3 0.2488	L31 0.1448	L32 0.1040
	B 0.5389	G1 0.1716	G11 0.1170	G12 0.0546
		G2 0.2443	G21 0.1768	G22 0.0675
		G3 0.1231	G31 0.0427	G32 0.0803

).

Based on the weights obtained, the service efficiency score (L) and service effect score (G) were calculated as follows:

$$L={\sum }_{i=1}^n{l}_i\times L_i$$

where $l_i$ represents the weight of the i-th service efficiency factor, and $L_i$ denotes the standardized score of the i-th factor.

$$G={\sum }_{i=1}^n{g}_i\times G_i$$

where $g_i$ represents the weight of the i-th service effectiveness factor, and $G_i$ denotes the standardized score of the i-th factor.

Based on the standardized calculation results, the overall effectiveness index (T) represents the social service efficiency level of the waterfront spaces. The comprehensive service efficiency index (T) and the efficiency–effect balance index (B) were calculated using the following formulas:

$$T=l\times L+g\times G$$

$$B=L/G$$

where $l$ represents the weight of service efficiency (L) in relation to the overall effectiveness index (T), and $g$ denotes the weight of the service effect (G) in relation to the overall effectiveness index (T).

3. Results

3.1. Calculation Results and Threshold Intervals

Based on the calculation results of the effectiveness index (

Table 5. The conclusion results of the effectiveness index.

Name	Qiantan	Lujiazui	The North Bund	The Bund	Yaohua	Houtan	Manlizui	Expo Park	Laobaidu	Nanyuan	The Shipyard	Yangpu	Xuhui
Bank	East	East	West	West	East	East	East	East	East	West	East	West	West
Administrative district	Pudong	Pudong	Hongkou	Huangpu	Pudong	Pudong	Pudong	Pudong	Pudong	Huangpu	Pudong	Yangpu	Xuhui
Functional type	Urban living room	Urban living room	Urban living room	Urban living room	Urban green space	Urban green space	Urban green space	Urban green space	Urban green space	Urban green space	Industrial heritage	Industrial heritage	Industrial heritage
Spatial capacity	0.59	1.96	2.65	10.0	2.55	0.59	0.00	6.57	1.27	3.92	7.94	3.33	6.67
Population dispersal	6.90	0.73	4.71	0.00	7.16	3.84	10.0	2.37	3.35	5.74	3.26	4.09	1.59
Service capacity	0.70	0.55	1.05	2.53	0.56	0.24	0.52	0.60	0.27	0.58	1.32	0.69	1.04
Cumulative density	4.37	8.32	6.76	10.0	4.19	3.25	2.69	6.92	7.64	6.82	7.90	7.24	8.36
Stability index	5.53	1.44	0.37	0.00	3.67	10.0	8.05	4.21	6.35	3.43	2.64	6.03	4.16
Functional efficiency	0.55	0.71	0.54	0.78	2.43	3.70	3.00	3.54	4.34	3.31	1.59	1.76	1.80
Service range	6.26	7.83	6.34	10.0	4.87	5.71	0.00	6.80	5.14	5.28	5.94	3.90	9.12
Transportation connectivity	4.00	7.00	9.00	6.00	5.00	4.00	2.00	6.00	6.00	8.00	8.00	4.00	5.00
Spatial efficiency	2.69	4.03	4.37	4.17	1.30	1.34	0.20	1.71	1.44	1.67	3.67	2.13	3.99
Service efficiency	3.95	5.29	5.96	7.48	4.29	5.27	3.72	5.86	6.05	5.56	6.58	4.58	6.84
Population diversity	5.00	9.17	6.74	7.13	0.00	5.33	3.00	5.17	7.17	5.05	2.83	10.0	5.07
Satisfaction disparity	0.98	3.62	6.07	2.24	2.75	1.82	0.67	10.0	8.70	0.00	3.98	4.85	3.09
Fairness	0.63	1.31	1.24	0.97	0.16	0.70	0.37	1.16	1.31	0.56	1.02	2.66	1.41
Average satisfaction	8.14	7.37	8.10	7.13	8.21	8.13	7.14	7.57	7.72	7.98	7.64	8.67	8.27
Visitation frequency	6.66	5.57	2.32	3.05	5.25	6.50	0.00	5.25	8.45	9.41	1.19	6.79	10.0
Satisfaction	3.50	3.16	3.32	3.03	4.06	4.45	2.03	3.88	4.97	5.36	2.66	3.69	3.96
Comment rating	7.57	5.03	6.27	10.0	0.74	5.52	0.00	3.40	0.84	3.01	1.28	6.78	9.56
Access rating	0.40	6.80	5.60	10.0	0.00	1.20	0.80	3.60	3.20	3.40	3.20	5.60	7.30
Influence	1.40	2.21	2.19	3.70	0.11	0.91	0.05	0.75	0.34	0.68	0.58	1.37	1.85
Service effect	5.54	6.67	6.75	7.70	4.33	6.06	2.46	5.79	6.61	6.61	4.25	7.73	7.22
Overall effectiveness index	4.72	5.96	6.35	7.59	4.31	5.70	3.04	5.82	6.35	6.12	5.40	6.18	7.03
Efficiency–effect balanced index	0.71	0.79	0.88	0.97	0.99	0.87	1.52	1.01	0.92	0.84	1.55	0.59	0.95

, Figure 5), a significance test was conducted using an ANOVA variance test to determine the confidence interval of the overall service efficiency index (T) and the efficiency–effect balanced index (G) to examine the segments with excessively large differences in values.

Assuming that the overall efficacy index of the 13 sampling points is in the moderate interval, the T index was tested using the variance analysis method. The test results indicate a 95% confidence interval of 5.02–6.43 (

Table 6. Significance test results of moderate efficacy and balanced efficacy intervals.

Independent Samples Test
	Test Value = 0
	t	df	Sig. (2-Tailed)	Mean Difference	95% Confidence Interval of the Difference
					Lower	Upper
Overall effectiveness index	17.112	12	0.000	5.76858	5.0341	6.5031
Efficiency–effect balance index	12.686	12	0.000	0.96871	0.8023	1.1351

). This range is identified as the moderate performance range.

Three sections fell into the category within the low-efficiency range (0–5.02): the Manlizui Waterfront (3.04), the Yaohua Waterfront (4.31), and the Qiantan Waterfront (4.72). Additionally, two sections were included within the high-efficiency range (6.43–10): the Bund Waterfront (7.59) and the Xuhui Waterfront (7.03).

The efficiency–effect balance index (B) was also evaluated using a significance test. The results indicate uniformity in the values, suggesting that most sections are in a state of efficiency–effect balance. The 95% confidence interval for the efficiency–effect balance was set from 0.802 to 1.135 (Table 6). Excluding the three sections within the low-efficiency range, the remaining ten sample points were analyzed.

Among these ten sample points, two sections were found to have a low efficiency–effect balance index: the Lujiazui Waterfront (0.794) and the Yangpu Waterfront (0.593). One section, the Shipyard Waterfront, had a higher efficiency–effect balance index of 1.548.

Overall, this analysis identified sections with different levels of efficiency and effect, highlighting the areas where there is an imbalance between the service efficiency and service effect in the waterfront spaces.

3.2. Spatial Characteristics of Overall Effectiveness Index (T)

Based on the analysis, the overall effectiveness index (T) for the 13 sample points was calculated to be 5.77, with a standard deviation of 1.13. This indicates significant differences in the overall effectiveness among different sections of the waterfront.

When considering functional types, the order of the overall service effectiveness index (T) is as follows: industrial heritage section (6.20) > urban living room section (6.15) > urban green space section (5.22). The urban green space sections exhibited a relatively lower overall effectiveness level (Figure 6).

In terms of spatial distribution, no significant differences were found in the overall effectiveness index (T) among the administrative districts (Figure 7). However, notable differences were observed when comparing the functional sections. On the east and west banks of the river, the overall effectiveness index of the west bank was found to be higher than that of the east bank, showing a “higher in the west, lower in the east” pattern. The west bank exhibited a “higher in the middle, lower at both ends” pattern, with effectiveness peaks occurring in the Bund section (7.59) and the Xuhui section (7.03), both located in the middle of the region. By contrast, the effectiveness peak on the east bank was observed in the Lujiazui–Lao Baidu section. The three low-effectiveness sections were located in the southern part of the Pudong region, forming a “higher in the north, lower in the south, with a single peak” pattern.

Combining the survey of visitor origins and analysis of the spatial environmental factors, it is evident that the overall effectiveness level of each section is significantly influenced by external environmental factors, such as land-use types, construction timelines, and the transportation network patterns in the urban core.

3.3. Time-Varying Characteristics of Overall Effectiveness Index (T)

The service effectiveness of waterfront public spaces exhibits three distinct characteristics in relation to time variations: daily changes reveal dual peaks in the morning and evening; weekly changes demonstrate three distinct patterns; and seasonal changes show higher effectiveness during spring and summer compared with autumn and winter.

Through the PSPL Survey, it was observed that vitality experienced a minor peak between 8:00 and 10:00, whereas the highest peak occurred between 17:00 and 20:00, displaying the dual morning and evening peak characteristics (Figure 8). Moreover, due to a significant increase in tourists during the evening, recreational and viewing functions exhibited a substantial single peak.

Regarding weekly changes, certain sections exhibited higher service efficiency on weekends compared with weekdays, resulting in more pronounced fluctuations over time, referred to as the weekend peak section. On the other hand, some sections demonstrated higher functional efficiency (L1) on weekdays than on weekends, termed the weekday peak type. Furthermore, there were sections where the functional efficiency remained consistent between weekdays and weekends, categorized as the balanced type. The analysis of these variations is associated with factors such as the commuting function, the traffic environment, and the landscape characteristics of the riverside space (

Table 7. Characteristics and distribution sections of public space in waterfront spaces on three peak dates.

Type	Peak Date	Section Features	Sample Sections
Weekday peak	Weekday	The hinterland is predominantly office and commercial, featuring convenient transportation and a strong connection between sports routes and urban roads.	Yaohua Waterfront, Houtan Waterfront, Laobaidu Waterfront, Shipyard Waterfront
Balanced	Both	The site is well known, offering high-quality landscapes and good traffic connectivity, along with commercial facilities.	Lujiazui Waterfront, Bund Waterfront, Expo Park, Xuhui Waterfront, Yangpu Waterfront
Weekend peak	Weekend	The site boasts excellent landscape quality and characteristics, but is poorly connected to urban traffic, offering distinctive recreational activities.	Qiantan Waterfront, North Bund Waterfront, Manlizui Waterfront, Nanyuan Waterfront

).

In terms of seasonal changes, the overall effectiveness index was higher in spring and summer compared with autumn and winter, influenced by temperature conditions and seasonal changes in plants. Notably, effectiveness was lower in summer than in spring. Before 16:00, when the sun was exposed, the number of activities remained small and stable. However, after 17:00, as the temperature dropped following sunset, the number of activities sharply increased (Figure 9).

3.4. Characteristics of Efficiency–Effect Balance Index (B)

Based on the results of the significance tests, among the ten sample points with moderate overall effectiveness, seven fell within the balance range of the efficiency–effect balance index (B). Among them, two points were classified as balanced sections, and five points fell into the category of basically balanced sections. Three points were categorized as imbalanced sections, with two points indicating low efficiency and one point representing low effect.

The balanced sections were typically situated in well-developed urban areas with convenient transportation, stable visitor sources, and high-quality landscapes, attracting both residents and tourists. The five sections with moderate overall effectiveness mainly consisted of residential and office areas that offer waterfront landscapes of high quality, along with excellent service facilities. The stable flow of visitors and visit frequency contribute to the balanced service efficiency and effect state in waterfront public spaces.

Moreover, the efficiency–effect balance in two highly effective sections relied on the optimization of internal site structures and spatial management models. Taking the Bund section as an example, it prominently features open spaces such as waterfront promenades and activity squares, thereby increasing pace capacity and providing abundant recreational opportunities. Being located in the city’s commercial center, the high population density in the surrounding area, along with its visibility and social influence, attracts a significant number of residents and tourists. By implementing crowd control measures to regulate gatherings and ensure a balance between efficiency and effectiveness, the Bund section maintains a high level of service efficiency.

3.5. Correlation Analysis of Influencing Factors

In terms of internal factors, in order to clarify the impact of space construction and space management factors on the social service effectiveness, so as to propose improvement measures, this study used the 5D-POS scale (5 dimensions scale of public open space, including activities supply, environmental quality, service facilities, management publicity, and space safety) [57] to measure the spatial quality of the sample section and to determine its activity supply (Q1), environmental quality (Q2), service facilities (Q3), management publicity (Q4), and space safety (Q5). Through the correlation analysis of space quality and various first-level indicators of overall efficiency (

Table 8. Correlation analysis between the spatial quality index and the social service effectiveness index.

		Functional Efficiency (L1)	Spatial Capacity (L2)	Connectivity Efficiency (L3)	Fairness (G1)	Satisfaction (G2)	Influence (G3)
Activity supply (Q1)	Pearson.	−0.244	−0.142	0.025	0.430	−0.116	−0.143
	Sig. (2-tailed)	0.422	0.645	0.936	0.143	0.707	0.642
	fn	13	13	13	13	13	13
Environmental quality (Q2)	Pearson.	0.525	−0.386	−0.271	−0.070	0.984 **	−0.231
	Sig. (2-tailed)	0.065	0.192	0.371	0.821	0.000	0.448
	fn	13	13	13	13	13	13
Service facilities (Q3)	Pearson.	0.144	−0.109	0.301	0.735 **	0.590 *	0.230
	Sig. (2-tailed)	0.639	0.722	0.318	0.004	0.034	0.449
	fn	13	13	13	13	13	13
Management publicity (Q4)	Pearson.	−0.629 *	0.757 **	0.643 *	0.157	−0.262	0.920 **
	Sig. (2-tailed)	0.021	0.003	0.018	0.608	0.386	0.000
	fn	13	13	13	13	13	13
Space safety (Q5)	Pearson.	−0.333	0.238	0.521	0.753 **	−0.170	0.416
	Sig. (2-tailed)	0.266	0.433	0.068	0.003	0.578	0.158
	fn	13	13	13	13	13	13

** Correlation is significant at the 0.01 level (two-tailed). * Correlation is significant at the 0.05 level (two-tailed).

), we found that environmental quality strongly affects satisfaction (G2), probably by providing regional natural and cultural landscapes. Service facilities (Q3) affect fairness (G1) and satisfaction (G2) by providing social services to different groups of people. The management publicity (Q4) factor has a certain correlation with the indicators of service efficiency (L). By affecting the popularity of the venue, it affects the number of daily active people in the venue. In addition, it also directly expresses influence (G3). The space safety (Q5) factor has a strong correlation with fairness (G1), indicating that different age groups may have different needs for space safety.

4. Discussion

Based on the evaluation results, the social service effectiveness in the core section of the Huangpu Riverside public space is relatively high, but there are still several issues that need to be addressed:

• “Mostly high, but low at the both ends, while north higher than south”—the overall effectiveness index (T) represents the general use status of riverside spaces.
• “Imbalance” sections on some certain segments—these sections exhibit a lack of coordination between the service efficiency and service effect.
• There are significant differences in effectiveness characteristics among different types of waterfront public spaces—the effectiveness index of urban green spaces is much lower than the others, and still needs improvement.

To clarify the causes of these issues, discussions and analyses have been conducted to identify the factors contributing to low efficiency and imbalances in service quality.

4.1. Causes of Low-Effectiveness Sections

In previous studies, spatial efficiency and satisfaction have been found to be influenced by various factors such as variety of attraction, hostel activities, availability and connection, people cooperation, aesthetic appeal, and hygiene [2,4,29,35,36,58,59,60]. Combining the literature research and empirical cases, it was found that the influencing factors of urban blue space social service effectiveness can be divided into two parts: internal and external factors.

In terms of external factors, according to the spatial distribution characteristics of the overall effectiveness index (T) of the riverside public space in the core section, it is not difficult to see that the overall effectiveness index (T) of the space is greatly affected by the development sequence of the hinterland, the type of urban land use, and the density of the resident population. This finding aligns with previous research on spatial vitality factors [2,27,30]. The Huangpu River waterfront public space remains a primary recreational area for citizens, with the urban hinterland serving as the main source of visitors.

In addition, the influence of the urban traffic environment is relatively obvious. For example, the Manlizui Riverside (3.04, 1.51), as well as suburban areas such as Longhua Riverside, presented “low efficiency–low effect” characteristics. The hinterland part of the riverside space in this section is mostly in the early stages of development. The riverside space is less connected to public transportation, lacks landscape features, resulting in fewer tourists, limited types of activities, and a lower cross-section of people.

Regarding internal factors affecting the social service effectiveness, based on the correlation analysis of space quality factors, the correlation analysis suggests that in addition to common influencing factors such as environmental quality and service facilities [61,62], management and promotional factors also play a crucial role in service efficiency and effect. Online promotion through platforms like news releases and social media has gradually become an important source of recreational vitality for urban blue spaces. In practical usage, popular landmarks and attractions (such as camping sites in Yaohua Riverside and art galleries in Xuhui Riverside) have become significant attractions for attracting both local residents and tourists.

From the perspective of three functional types, the spatial vitality of the urban living room section and the industrial relic section is much higher than that of the urban green space section, primarily due to differences in functional positioning. These sections revealed a lower connectivity efficiency (L3) index, which can be attributed to their remote locations with inadequate transportation conditions and lower levels of urban development. Additionally, the influence index (G3) of urban green space sections was significantly lower than that of the other two types of sections, possibly due to their specific landscape characteristics and cultural promotion. These results also support the previous research conclusion on residents’ preference for the UBS landscape [63]. Urban residents with little experience with nature tend to choose riverside public spaces with concrete paving and urban views, rather than ecological sections. It implies that landscape elements such as paving and structures in the waterfront public space also have a certain impact on the vitality of the space and service efficiency.

4.2. Reasons for the Unbalanced Section

This study introduces the innovative concept of the “efficiency–effect balance”. This concept describes the phenomenon where a space exhibits “high quality but low space utilization” or “high efficiency but low satisfaction” with relatively high effectiveness. Some studies suggest that when there is an imbalance between efficiency and effect, it is usually necessary to consider the combined effects of factors such as residents’ needs, preferences, and spatial services [63,64]. To validate this viewpoint, this paper conducts a descriptive analysis of the unbalanced sample points.

4.2.1. Sections of Unbalanced Efficiency due to Low Efficiency

Low efficiency can be attributed to two factors: a mismatch between the service capacity and functional positioning, and poor transportation connectivity, resulting in low accessibility.

Concerning service capacity limitations, disparities in the quality of various activity spaces within the site can result in an excessive concentration of people in specific areas, thereby affecting the maximum service capacity of the space and inhibiting service efficiency. For example, in the Yangpu Riverside, there is an overcrowding issue with people congesting the riverside promenade (Figure 10). Additionally, factors such as low connectivity to public transportation and unfriendly urban interfaces affect the accessibility of the sites, leading to an imbalance between efficiency and effect. This phenomenon is evident in both the Lujiazui Riverside and the Yangpu Riverside (Figure 11), where low accessibility results in fewer visitors enjoying the high-quality landscapes available.

4.2.2. Sections of Unbalanced Efficiency due to Low Effect

The overall quality of the Huangpu River core section is commendable, and visitor satisfaction is generally high. Consequently, disparities in the service effect (G) among sections primarily arise from factors related to fairness. When examining specific examples, fairness is chiefly influenced by activity spaces, and facilities within the area, giving rise to discrepancies in recreational opportunities among various age groups. Consider the Shipyard Riverside as an illustration, situated in a highly developed urban area with good accessibility and a high influx of visitors, which contribute to its high service efficiency. However, due to its proximity to a ferry terminal and its impact on commuting functions, the transportation mode is restricted (Figure 12). The spatial layout within the section is straightforward, primarily comprising waterfront promenades and activity squares, with a limited variety of activities and facilities. Therefore, the lack of recreational opportunities for the elderly and children leads to a limited diversity of visitors (Figure 13), diminished fairness factors, and reduced service effect.

4.3. Suggestions for Improving the Social Service Effectiveness

Based on the factors analysis, the following suggestions are proposed for improving the performance in the core section of the Huangpu River.

4.3.1. Strategies to Improve Efficiency in Low-Effectiveness Sections

The sections with low social service effectiveness often suffer from deficiencies in spatial functionality or service facilities. In the process of daily management and improvement, it is crucial to prioritize the enhancement in spatial quality based on current usage. This can be achieved by improving the spatial functionality and strengthening the connectivity with urban transportation networks to effectively eliminate the barriers in delivering social services.

For the sections that already have complete structures and adequate facilities but lack attractiveness, enhancing the waterfront features and improving the fairness in recreational opportunities is recommended [65]. Providing service facilities that cater to the preferences of different user groups, such as children’s playgrounds and activity and fitness facilities, can effectively enhance the recreational satisfaction of minority groups within the site. Additionally, combined with factors analysis, we can also start from site management and promotion to improve efficiency. By online promotion and activities guidance, the characteristic landscape can attract more visitors.

4.3.2. Strategies to Achieve Balance in Sections with Balanced Efficiency and Effectiveness

For the sections with imbalanced service efficiency and service effect, the first step is to clarify the functional positioning and target users of the area. This means that during the initial spatial design and construction, designers should invest more effort in site investigation and planning analysis. The internal spatial structure of the waterfront public space should align with the composition of the residents in the urban hinterland. Given the gradual aging of society, greater attention should be paid to providing recreational opportunities and spatial preferences for the elderly and children within the site.

For already established waterfront public spaces experiencing an imbalance between efficiency and effect, in addition to space renewal, public activities and shared governance approaches can be organized to understand the needs of visitors and introduce new vitality to the space. Flexible utilization of existing sites, such as opening up camping activities on lawns, can effectively enhance the appeal of the space and attract more visitors. When it is necessary to control efficiency and reduce the number of participants, reservation mechanisms or spatial diversion methods can be implemented, linking the preceding and succeeding sections to dynamically regulate efficiency among multiple sections.

4.3.3. Dynamic Adjustment Model for Social Service Efficiency

In conclusion, the level of social service effectiveness in the waterfront public spaces is significantly influenced by the development of the surrounding urban areas. At different stages of urban development, the functional positioning of the waterfront spaces may vary, and the goals of social service efficiency should reflect these differences. Improving social service efficiency is an ongoing process of adjustment and optimization.

In the early stages of development, when the urban areas surrounding the waterfront spaces are less developed and visitor numbers are limited, the main focus should be on enhancing the service effect. This can be achieved by providing adequate children’s play areas and fitness facilities to serve local residents. Additionally, emphasizing ecological conservation and experiential functions, based on the location and site conditions, can create high-quality services for visitors, while allowing for future development by reserving green spaces.

In the middle stages of development, as the pace of urban development accelerates and the population structure around the waterfront spaces changes, increased population density leads to a rise in recreational demand. At this stage, the social service effectiveness focus should gradually shift from effect to efficiency improvement. The waterfront public spaces should strive to serve a larger number of users by enriching the activity spaces and facilities, improving infrastructure development, and enhancing the recreational functions. Moreover, organizing theme-based activities and creating distinctive landscapes, coupled with effective online promotion, can enhance the attractiveness and visibility of the spaces, as well as improve the usage rates and fairness.

In the later stages of development, when the waterfront public spaces reach a mature state and the surrounding visitor population becomes more stable, these spaces possess significant allure. At this point, it is crucial to analyze the types and numbers of users of the public spaces. While addressing functional repairs, optimizing activity guidance through crowd control, flow management, and activity zoning helps maintain service efficiency within a reasonable range. Furthermore, optimizing collaborative mechanisms with adjacent sections can further enhance the service capabilities of the public spaces.

4.4. Innovation and Limitations

Based on previous studies on the vitality of the Huangpu River waterfront public spaces and visitor satisfaction [27,29,45], this study optimized data acquisition methods and integrated evaluation dimensions to reevaluate the social service value of the waterfront spaces. The research results are generally consistent with existing studies regarding the characteristics of service efficiency changes and influencing factors.

In this study, more accurate statistical methods were employed to provide a more detailed discussion on the spatiotemporal changes in service efficiency. The research results showed discrepancies with the study conducted by Yating and Song et al. [2,29], who used mobile signaling data as a data source to study spatial vitality. The reason for these differences can be attributed to the usage habits of certain groups, such as the elderly and children, regarding mobile phones.

Regarding the analysis of influencing factors, the research results align with previous research [2,27,28,60], indicating that the urban core and urban residents remain the primary sources of vitality for the Huangpu River waterfront spaces. In residential areas with high population density and activity levels, the service efficiency of the waterfront spaces is high. Additionally, through correlation analysis of the influencing factors, it was discovered that management and publicity factors are also important influences on the social service effectiveness. However, they are often overlooked in the current management and maintenance of these spaces. The research results of this article support Ashiboe’s study [63] on the recreational preferences of urban residents, suggesting that urban residents tend to prefer landscape styles with more hard elements.

This article integrates the evaluation dimensions of efficiency and effect, proposing the concept of “efficiency–effect balance” to discuss the utilization of spatial social services. By tracing the problems and conducting descriptive analysis of imbalanced sections, it was found that transportation accessibility is the main cause of the imbalance in the quality and efficiency.

The limitations of this study lie in the fact that the evaluation system is limited to similar sections within the same urban blue–green system, which share similar background conditions such as urban environment, cultural background, and socioeconomic conditions. The application of this evaluation system for cross-city comparisons of waterfront spaces is limited. Furthermore, while this article introduces the concept of efficiency–effect balance and provides descriptive analysis, there is a lack of quantitative analysis on the reasons for the occurrence of imbalances. The next step in the research direction would be to provide a more detailed definition of the efficiency–effect balance and to quantitatively analyze the causes.

5. Conclusions

In conclusion, this study has contributed to the regional planning and refined management of waterfront public spaces by constructing the evaluation system and the analysis of effectiveness factors. This study has made four key contributions:

First, the construction of an evaluation system for urban waterfront public spaces’ effectiveness. The evaluation system includes two comprehensive effectiveness indices: the overall service effectiveness (T), which indicates the social service value of urban blue spaces, and the efficiency–effect balance index (B), representing the balance between space utilization and the service effect as a higher developmental pursuit.

Second, the empirical study conducted on the Huangpu River waterfront public spaces analyzed the general patterns of the social service effectiveness in terms of seasonal and temporal variations, addressed in the low-effectiveness and imbalanced section.

Third, preliminary exploration of the correlation between spatial quality and effectiveness highlighted the influence of both spatial functionality and the urban environment on effectiveness. Building upon existing research, this study supplements the impact mechanism of management and promotional factors on the social service effectiveness. The analysis of existing unbalanced sample points suggests that accessibility is a significant factor causing an imbalance between efficiency and effect.

Last, based on the current effectiveness status of the Huangpu River, this study proposes corresponding planning and management recommendations with the goal of “enhancing effectiveness and achieving efficiency–effect balance”. Additionally, by analyzing the influencing factors, the social service effectiveness of waterfront spaces is linked to the development of the urban hinterland, proposing a “full life-cycle effectiveness construction” model.

Building upon the previous research, this study integrates multiple evaluation dimensions to quantify the social service value of urban blue spaces, allowing for horizontal comparisons among different sections within the same system. This has value in providing recommendations for planning and design, as well as management models, for the low-efficiency sections identified in the positioning of urban blue spaces. Furthermore, the innovative concept of the efficiency–effect balance index, introduced in this study, demonstrates its applicability and usefulness in describing the spatial utilization status of urban blue spaces in the case study.

The research framework developed in this study is also applicable to other urban blue spaces focused on recreational activities and urban tourism functions. It can be used to identify low-efficiency sections within certain waterfront systems and develop targeted measures to help improve effectiveness. Future research can expand with respect to three main areas. First, the efficiency indicator measurement methods, such as service capacity determination, can be further explored to increase accuracy and comprehensiveness. Second, the concept of efficiency encompasses both efficiency and maximum capacity, but this study has limited discussion on the service capacity of urban blue spaces. Future research can further explore the usage status and development degrees of spaces, providing planning recommendations for economic development in waterfront areas. Last, the applicability of the evaluation model can be expanded by conducting comparative studies using larger sample sizes, including suburban waterfront spaces with a focus on ecological functions and urban blue spaces of differing scales and sizes.