Review Reports

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Page 2 — Line 57
“China has also integrated green space system optimization into its national strategy through policy directives.”
Please specify the exact national policy targets. In addition, explain how your proposed approach contributes to achieving these national objectives.

Page 2 — Lines 65–66
You state that peripheral parks have reduced service radii due to weak transport networks, while core parks lack age-friendly facilities and children’s play spaces. What are the underlying reasons for the unavailability of these facilities in core parks?

Page 2 — Lines 79–80
You note that current evaluations heavily rely on “quantitative indicators,” yet your method also utilizes numerous quantitative indicators. How does your approach differ substantively from previous studies that rely on similar metrics? Please clarify the methodological innovation.

Page 3 — Line 106
The acronym “POI” should be defined at first use.

Page 5 — Lines 166–169
The study area description is overly detailed. Please keep only the geo-climatic characteristics relevant to the research questions. Additionally, describe the seasonal patterns of the study area for the time period mentioned in line 189.

Page 6 — Line 193
“68% of mobile users in the area” — How was this factor incorporated into the analysis?

Page 6 — Line 193
Since POI appears earlier, it should be defined upon its first occurrence, not here.

Page 6 — Lines 197–198
You selected eight POI categories relevant to park use. What criteria or references guided the selection of these categories? Please cite any supporting literature.

Page 7 — Line 225
The acronym “ESAR” is not defined. All acronyms must be explained at first usage or included in an acronym list.

Line 237
“68% of mobile users” — Please clarify how this proportion was accounted for in the study design or data interpretation.

Lines 243–244
You acknowledge the inability to capture the activity patterns of minors without mobile phones, potentially underestimating children’s usage. How do the authors plan to address this in future research? Please provide concrete suggestions or proposed methodologies.

Line 258
“Object” appears to mean “objective.” Please revise.

Lines 327, 329, 341, 347
The term H is inconsistently referred to as “spatial heat values,” “daily spatial vitality heat value,” “activity intensity,” and “vitality heat value.” Please standardize terminology throughout the manuscript to avoid confusion.

Line 331
You state that “TSI > 3.0 indicates high stability with minimal vitality fluctuations.” Please provide the reference source for this threshold.

Lines 339–340
“STS ≥ 0.482” is described as excellent spatiotemporal synergy. What is the reference or empirical basis for this classification?

Line 353
The explanation of the Effective Walking Coverage Rate appears conceptually incorrect. Service population is defined as individuals visiting the park based on mobile data. Non-visitation does not imply that these individuals are outside the 15-minute walking buffer; it may simply reflect a lack of interest or preference for other parks. Please revise the definition to accurately reflect reachability versus behavioral choice.

Line 360
The phrase “spatial coordination between activity intensity and population density” is not clearly defined. Please provide a precise definition of what is meant by spatial coupling/coordination in this context.

Line 511
The term “Vitality Stability Index (TSI)” is used here, while Table lines 312–313 refer to the “Temporal Stability Index.” These appear to describe the same indicator. Please ensure consistent terminology.

Lines 561–562
The mean SCI for children is reported, yet earlier you note that mobile data for children are unavailable. How was this metric calculated? Please clarify the estimation method or assumptions.

Lines 590–592
The statement that low ESAR values indicate “resource waste issues” overlooks the broader ecological and environmental functions of green spaces. Limited service area does not equate to wasted resources, as greenways may provide essential ecosystem services (e.g., air quality improvement, habitat value). Please revise this interpretation.

Lines 781–782
The conclusion emphasizes human recreational use while neglecting the multiple ecosystem services provided by urban green spaces (e.g., noise reduction, biodiversity support, microclimate regulation). Recommendations to planners should avoid a purely anthropocentric perspective and acknowledge the full suite of green space benefits.

Author Response

Comment 1: Page 2 — Line 57

“China has also integrated green space system optimization into its national strategy through policy directives.”

Please specify the exact national policy targets. In addition, explain how your proposed approach contributes to achieving these national objectives.

 

Response 1: Thank you for your comment. We have supplemented specific national policy targets in Line 65–70 as follows: The National Land Greening Planning Outline (2022—2030) specifies core targets including a 43% green coverage rate in urban built-up areas and a per capita park green space area of 14.8 square meters. Meanwhile, the 14th Five-Year Plan for Urban and Rural Greening and Beautification Construction stipulates establishing "15-minute community living circles" to enable residents to access high-quality green spaces nearby. Furthermore, we have added the contribution of our proposed approach in Line 74–79 as follows: Our study can identify the spatiotemporal mismatch between park supply and demand, providing a quantitative basis for optimizing green space layout within the "15-minute community living circle". Among which, indicators such as the spatial coupling index for vulnerable groups can guide aging-friendly and child-friendly park renovation, contributing to the policy objective of "universally shared and accessible green spaces".

 

Comment 2: Page 2 — Lines 65–66

You state that peripheral parks have reduced service radii due to weak transport networks, while core parks lack age-friendly facilities and children’s play spaces. What are the underlying reasons for the unavailability of these facilities in core parks?

 

Response 2: Thank you for your comment. Based on urban planning practice and regional context, we have supplemented the following fundamental causes in in Line 82–84 as follows: This mainly stems from scarce core land that limits renovation and outdated planning concepts with little consideration of age/child-friendly needs during construction.

 

Comment 3: Page 2 — Lines 79–80

You note that current evaluations heavily rely on “quantitative indicators,” yet your method also utilizes numerous quantitative indicators. How does your approach differ substantively from previous studies that rely on similar metrics? Please clarify the methodological innovation.

 

Response 3: Thank you for your insightful comment, which helped clarify the distinction between our quantitative approach and prior studies. To address potential ambiguity and refine logical flow, we revised the manuscript as follows: Line 107–111 In contrast, our quantitative framework integrates dynamic, multi-dimensional merics (e.g., Temporal Activity Difference, Vitality Stability Index) that represent a deliberate departure from the static, single-dimensional quantification in prior studies, aiming to address these gaps. This revision eliminates potential ambiguity, emphasizing that our study does not reject quantitative indicators but advances them to address the limitations of prior work (e.g., failure to capture dynamic vitality fluctuations and service quality).

 

Comment 4: Page 3 — Line 106

The acronym “POI” should be defined at first use.

 

Response 4: Thank you for pointing this out. We apologize for the oversight in defining the acronym “POI” at first use. In the revised manuscript, we have added the full name when POI is first mentioned in Introduction: “Point of Interest (POI)”. This revision ensures compliance with academic writing standards and improves readability.

 

Comment 5: Page 5 — Lines 166–169

The study area description is overly detailed. Please keep only the geo-climatic characteristics relevant to the research questions. Additionally, describe the seasonal patterns of the study area for the time period mentioned in line 189.

 

Response 5: Thank you for your constructive comment. We have revised the Section 2.1, with key modifications as follows:

(1) To enhance relevance and conciseness, we removed overly detailed information (e.g., administrative divisions, economic data, national honors) while preserving geoclimatic features directly related to park service performance evaluation. The revised content (Lines 222–234) is: Geographically, it is backed by Banshan Mountain to the northeast, traversed north-south by the Beijing-Hangzhou Grand Canal, and exhibits a gently sloping topography that rises from west to east with hilly terrain in the east, plains in the west, and an average elevation of approximately 45 meters. Climatically, Gongshu is categorized as a subtropical monsoon climate with distinct seasonal characteristics, exhibiting mild and rainy springs, hot and humid summers, cool and dry autumns, and moderate winters with minimal precipitation.

(2) We have supplemented seasonal patterns in Line 253–258 as follows: The study period, spanning late March to early April, corresponds to spring in Hangzhou and is characterized by favorable climatic conditions with an average temperature of 12–20°C and low precipitation, recording a monthly rainfall of 80–100 mm [32]. These mild and dry conditions foster a peak period for outdoor activities among residents, resulting in significantly higher park usage frequency compared to winter and summer.

 

Comment 6: Page 6 — Line 193

“68% of mobile users in the area” — How was this factor incorporated into the analysis?

 

Response 6: Thank you for your valuable comments regarding the "68% of mobile users" parameter. We apologize for any ambiguity in the original manuscript and clarify its meaning and integration into the study below.

The 68% represents China Mobile’s market share in the study area—China Mobile is a leading mobile network operator in China—and not a sample proportion we calculated independently. Our data partner provided this figure. The value aligns with publicly reported mobile operator market share data for Hangzhou. As a specialist in telecom signaling data services, the provider verified the market share with regional user registration statistics to ensure accuracy.

This proportion primarily validates the signaling data’s representativeness. With China Mobile holding 68% market penetration in the study area, the dataset covers a large and demographically diverse user base. This ensures park use patterns derived from the data do not favor specific user groups. No additional mathematical adjustment was needed during analysis because the signaling data inherently reflects the actual spatiotemporal behavior of the operator’s subscribers. The data is not a sample but a complete record of these users, and the 68% market share confirms its representativeness.

To eliminate ambiguity, we added a clarifying statement in the manuscript at Line 283-285: …, covering over 68% of mobile users in the area which represents the market share of China Mobile in the study area as verified by the data provider through routine quality control processes and complies with the representativeness standard GB/T 35790-2023.

 

Comment 7: Page 6 — Line 193

Since POI appears earlier, it should be defined upon its first occurrence, not here.

 

Response 7: Thank you for pointing this out. We apologize for the incorrect placement of the POI definition. In the revised manuscript, we have moved the definition to the first occurrence of POI in the Introduction (Line 136): Point of Interest (POI) data can quantify the density of surrounding service amenities. The redundant definition in Section 2.2 has been deleted to ensure consistency and compliance with academic writing standards.

 

Comment 8: Page 6 — Lines 197–198

You selected eight POI categories relevant to park use. What criteria or references guided the selection of these categories? Please cite any supporting literature.

 

Response 8: Thank you for your comment regarding the selection criteria for POI categories. We appreciate your attention to this methodological detail and have clarified the basis for category selection in the revised manuscript. The classification framework builds on our previous research on urban park vitality, which systematically validated the effectiveness of these eight categories in capturing the interaction between park use and urban context. The prior study confirmed that these categories effectively reflect resident demands and park service complementarity, providing robust methodological continuity for the current research. We added in Line 296–300 as follows: The selection of these categories followed two core criteria: functional relevance to park visitation and alignment with residents’ daily activity demands. This classification framework builds on our previous research on urban park vitality [33], which verified that these eight categories effectively reflect the synergistic relationship be-tween park use and surrounding urban services in similar study contexts.

 

Comment 9: Page 7 — Line 225

The acronym “ESAR” is not defined. All acronyms must be explained at first usage or included in an acronym list.

 

Response 9: Thank you for pointing this out. We apologize for the oversight in the original manuscript and have revised it to comply with academic writing standards. In the revised manuscript, we have added the full name at the first occurrence of ESAR in Section 2.2: Effective Service Area Ratio (ESAR). For clarity and logical consistency, detailed explanations of the ESAR indicator are provided in the subsequent indicator quantification section (Section 2.3.1), where we systematically elaborate on all core metrics of the study.

 

Comment 10: Line 237

“68% of mobile users” — Please clarify how this proportion was accounted for in the study design or data interpretation.

 

Response 10: Thank you for your comment regarding the “68% of mobile users” parameter. This query has been fully addressed in our response to Comment 6 (see Response 6), where we clarified that the 68% represents China Mobile’s market share in the study area—used to validate the signaling data’s representativeness without requiring additional mathematical adjustment in analysis. The manuscript has also been revised to add a clarifying statement (Line 283-285) confirming the figure’s source and verification by the data provider. We refer you to the detailed explanation in Response 6 for further context.

 

Comment 11: Lines 243–244

You acknowledge the inability to capture the activity patterns of minors without mobile phones, potentially underestimating children’s usage. How do the authors plan to address this in future research? Please provide concrete suggestions or proposed methodologies.

 

Response 11: Thank you for prompting us to elaborate on future directions for studying vulnerable groups. We have refined the corresponding part in the Limitations section of the Discussion (Line 1019-1022: … such as partnering with schools or communities to deploy smart wearables for children, conducting targeted time-activity surveys among caregivers, or employing computer vision techniques in designated play areas to directly observe and quantify child-specific park usage patterns.)

 

Comment 12: Line 258

“Object” appears to mean “objective.” Please revise.

 

Response 12: Thank you for pointing this out. We have revised the “Object” to “objective”.

 

Comment 13: Lines 327, 329, 341, 347

The term H is inconsistently referred to as “spatial heat values,” “daily spatial vitality heat value,” “activity intensity,” and “vitality heat value.” Please standardize terminology throughout the manuscript to avoid confusion.

 

Response 13: Thank you for pointing out the inconsistency in terminology. We have standardized the terms throughout the manuscript to eliminate confusion. The variable H and its subscripted variants are now uniformly referred to as “vitality heat value” in the accompanying text descriptions (see revised Sections 2.3.1 and 3.1).

 

Comment 14: Line 331

You state that “TSI > 3.0 indicates high stability with minimal vitality fluctuations.” Please provide the reference source for this threshold.

 

Response 14: Thank you for your comment regarding the classification threshold for the Temporal Stability Index (TSI). We have clarified this point in Section 2.3.1. The threshold of TSI > 3.0 for identifying parks with “high stability” was established using the quantile division method—a consistent, data-driven classification approach applied throughout our study (as detailed in Section 2.3.3, “Performance Calculation”). This method ensures that the threshold objectively corresponds to a high percentile within the TSI value distribution, thereby enabling a transparent and reproducible distinction between parks with differing levels of temporal stability. We appreciate your attention to methodological clarity, and we believe this addition strengthens the robustness of our evaluation framework.

 

Comment 15: Lines 339–340

“STS ≥ 0.482” is described as excellent spatiotemporal synergy. What is the reference or empirical basis for this classification?

 

Response 15: Thank you for your comment regarding the empirical basis for the STS classification threshold. We have added an explanation in Section 2.3.1 (Indicator Quantification). The threshold of STS ≥ 0.482 for “excellent spatiotemporal synergy” was objectively determined using the quantile division method—the same consistent classification methodology applied throughout our performance evaluation (as specified in Section 2.3.3).

 

Comment 16: Line 353

The explanation of the Effective Walking Coverage Rate appears conceptually incorrect. Service population is defined as individuals visiting the park based on mobile data. Non-visitation does not imply that these individuals are outside the 15-minute walking buffer; it may simply reflect a lack of interest or preference for other parks. Please revise the definition to accurately reflect reachability versus behavioral choice.

 

Response 16: Thank you for your comment. We appreciate this important correction regarding the conceptual interpretation of EWCR. We have revised its definition in Section 2.3.1 to clarify that it measures “realized accessibility” — the actual usage by the theoretically accessible population — rather than implying that non-visitors are outside the service area. The revision is in Line 463-467 as follows: The Effective Walking Coverage Rate quantifies the proportion of the resident population within the 15-minute walking buffer who actually visited the park during the study period. It thus measures the realized accessibility or uptake of park services by the immediately surrounding population, acknowledging that non-visitation may stem from preference, alternative options, or barriers not captured by network distance alone.

 

Comment 17: Line 360

The phrase “spatial coordination between activity intensity and population density” is not clearly defined. Please provide a precise definition of what is meant by spatial coupling/coordination in this context.

 

Response 17: Thank you for your comment. We have added sentences in Section 2.3.1 (Line 473-477) as follows: The Vitality-Population Matching Index (VPMI) measures the spatial coupling between park visitation intensity and the residential population base. Specifically, it evaluates whether areas with higher population density generate proportionally higher park activity, indicating an efficient match between local demand and park attractiveness/supply.

 

Comment 18: Line 511

The term “Vitality Stability Index (TSI)” is used here, while Table lines 312–313 refer to the “Temporal Stability Index.” These appear to describe the same indicator. Please ensure consistent terminology.

 

Response 18: Thank you for pointing this out. We apologize for this clerical error. We have revised in Section 3.1.

 

Comment 19: Lines 561–562

The mean SCI for children is reported, yet earlier you note that mobile data for children are unavailable. How was this metric calculated? Please clarify the estimation method or assumptions.

 

Response 19: Thank you for your comment regarding the SCI for children. We apologize for the ambiguity in the original manuscript that led to this misunderstanding and clarify the data source and calculation method as follows:

Our previous statement referred specifically to the unavailability of mobile data for children without mobile devices, not all children. For children who use mobile devices, their spatiotemporal behavior data are fully included in the signalling dataset. We identified this group by filtering user profiles in the signalling data for individuals under 18 years old—consistent with the age definition of "children" in our study.

The SCI for children was calculated using Formula 8 applied to other vulnerable groups. We first extracted spatiotemporal activity patterns of users under 18 from the signalling data, then substituted these behavioral indicators into Formula 8 to derive the SCI for children. This approach ensures the metric is computed consistently with other groups while the child-specific SCI is based on actual behavioral data of mobile-using children.

To eliminate ambiguity, we have revised the relevant sentence in the manuscript (Line 347-348: Notably, mobile-using children (under 18) are included in the signaling dataset for SCI calculation) to clarify the scope of unavailable data, ensuring consistency with the calculation logic of SCI for children.

 

Comment 20: Lines 590–592

The statement that low ESAR values indicate “resource waste issues” overlooks the broader ecological and environmental functions of green spaces. Limited service area does not equate to wasted resources, as greenways may provide essential ecosystem services (e.g., air quality improvement, habitat value). Please revise this interpretation.

 

Response 20: Thank you for this important correction regarding the interpretation of low ESAR values. We have revised the relevant sentence to clarify that a low ESAR indicates underutilization of the park’s social function specifically, and added a clause stating that “this assessment, focused on service performance, does not negate the park's potential value in providing other critical ecosystem services.” The revision is in Line 777-780 as follows: … its actual service area was less than 4% of its potential in terms of recreational service reach, underscoring a stark underutilization of its social function. This assessment, focused on service performance, does not negate the park's potential value in providing other critical ecosystem services.

 

Comment 21: Lines 781–782

The conclusion emphasizes human recreational use while neglecting the multiple ecosystem services provided by urban green spaces (e.g., noise reduction, biodiversity support, microclimate regulation). Recommendations to planners should avoid a purely anthropocentric perspective and acknowledge the full suite of green space benefits.

 

Response 21: Thank you for raising this important point regarding the need to acknowledge the full range of ecosystem services provided by urban green spaces. We fully agree that a balanced perspective is crucial. We have already revised the relevant section in the Discussion. Line 967-976: Although these areas added green spaces through ecological restoration, the failure to simultaneously improve transportation connectivity and facility optimization led to the dilemma of “having green space but lacking recreational vitality,” which limits their social utility and service performance in the context of this study. This serves as a warning for future park planning in industrial transformation zones: to fully realize the multifunctional potential of parks (both ecological and social), green space construction needs to be carried out simultaneously with population influx and transportation improvements to avoid inefficient use of recreational resources, while still acknowledging the inherent ecological value of these green spaces.

The revised text now explicitly states that the goal is “to fully realize the multifunctional potential of parks (both ecological and social)” and concludes by “acknowledging the inherent ecological value of these green spaces.” This directly addresses your concern by moving beyond a purely anthropocentric view and recognizing parks as integrated assets that deliver both social and ecological benefits. We appreciate your comment, which reinforces the importance of this balanced framing in our manuscript.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This manuscript proposes a dynamic, multi-dimensional framework to evaluate the vitality performance of urban parks by integrating mobile phone signaling data, POIs and socio-demographic information, and applies it to 59 parks in Gongshu District, Hangzhou. The topic is timely and relevant to IJGI, the case study is interesting, and the attempt to link vitality, demand and supply into a unified evaluation system is conceptually appealing. However, in its current form, the paper suffers from several important weaknesses: the Introduction and study area description are overly long and sometimes read more like policy or publicity text than a focused scientific narrative; key methodological elements (in particular the definition and cleaning of the mobile signaling–based vitality indicators, the construction and interpretation of some indices, and the choice of thresholds and weights) are not sufficiently transparent; and the Results and Discussion are often overly descriptive, with limited critical interpretation and only partially developed reflection on limitations. Overall, I believe the study has potential, but it would require substantial revision and clarification before it can be considered for publication.

 

Introduction

1. Introduction. Clarify the conceptual framework of “vitality–demand–supply”. The Introduction repeatedly uses the terms service performance, vitality, demandand supply, and highlights “Vitality–Demand–Supply” as a key perspective. However, these concepts are not clearly defined in the Introduction. It would help readers if the authors explicitly specify, already in this section, how each dimension is conceptualized and operationalized in this study (e.g., vitality as temporal patterns of park use derived from mobile phone signaling; demand as population and user-group characteristics; supply as park area and facility provision). A short paragraph that turns “Vitality–Demand–Supply” from a slogan into a concrete analytical framework would substantially strengthen the motivation.

 

2. Lines 128–147 could be merged into a single paragraph that provides an explicit contribution statement.

 

3. The title emphasizes “59 parks in Gongshu District, Hangzhou”, but the Introduction does not explain why Gongshu is chosen. It would be helpful to add 1–2 sentences in the last paragraph of the Introduction to motivate the case: for instance, that Gongshu is a high-density urban district where park resources are relatively abundant but supply–demand mismatches and temporal crowding remain salient, and that it is a key area for implementing the “Park City” and “15-minute living circle” strategies. This will help readers understand the broader relevance of the case.

 

4. The Introduction is overly lengthy, with too much detail devoted to policy background. Please streamline this part (“slim it down”) and use the saved space to sharpen the discussion of the research gap and the need for a dynamic, multi-dimensional evaluation framework.

 

Method

5. Section 2.1 is overly long and contains a lot of descriptive information that is not directly relevant to the research questions or methods. Many sentences focus on general socio-economic achievements and honorary titles of Gongshu District (e.g., balanced compulsory education, “China’s Happiest District”, detailed GDP and retail figures), which read more like government publicity than a concise scientific description of the study area.

 

6. Figure 1. In the lower-left panel of Figure 1, which shows the location of Gongshu within Hangzhou, the internal patches are currently drawn at the sub-district / community level. For a location map at the city scale, it would be more appropriate and intuitive to show the administrative districts of Hangzhou instead of sub-districts or villages.

 

7. The description of the preprocessing of mobile phone signaling data (Section 2.2, around Lines 209–214). The manuscript does not explain whether home–work patterns, night-time stays, or land-use/POI information are used to identify and exclude residents, nor how signals close to park boundaries but outside the actual park space are treated. This ambiguity makes it difficult to interpret the vitality indicators as a clean measure of park use and should be clarified or explicitly acknowledged as a limitation.

 

8. In addition, the current “ping-pong handover” filter (≥3 switches between adjacent base stations within one hour) may fail to remove many transient passers-by. For example, a student walking from school to home across a park may only connect to one or two additional base stations while passing through the area, and would therefore not be flagged as ping-pong noise but still be counted as a park user if their trajectory falls within the park polygon for up to 30 minutes. This suggests that a substantial proportion of non-park-related movements may remain in the dataset, and the authors should either refine the filtering strategy or explicitly discuss this limitation when interpreting the vitality indicators.

 

9. The processing of the POI data is also not fully transparent. The manuscript explains that POIs from Amap are filtered by functional type and that “buffer density” is calculated with reference to the Standard for Urban Green Space Planning(GB/T 51346-2019), but it does not specify (i) the exact buffer radius or whether it is consistent with the 15-minute walking buffer used for population, (ii) whether POIs inside the park and those in the surrounding area are distinguished, (iii) whether different POI categories are weighted equally or differentiated, and (iv) how POIs that serve city-wide functions (e.g., large commercial complexes or transport hubs) versus strictly local services are treated. Without these clarifications, it is difficult to interpret the resulting POI “density” indicators as a robust measure of park-related service supply, and the authors should either provide more methodological detail or explicitly acknowledge this as a limitation.

 

Results

10. The Results section is very detailed and frequently lists individual parks, specific numerical values, and case-by-case descriptions (e.g., for TAD, TSI, NAR/STS, EWCR, VPMI, FAI, ESAR). While these examples are informative, the narrative becomes somewhat fragmented and difficult to follow.

 

11. In Section 3.2, classification thresholds for “high/medium/low performance” need justification and clearer presentation

Author Response

Comment 1: Introduction. Clarify the conceptual framework of “vitality–demand–supply”. The Introduction repeatedly uses the terms service performance, vitality, demandand supply, and highlights “Vitality–Demand–Supply” as a key perspective. However, these concepts are not clearly defined in the Introduction. It would help readers if the authors explicitly specify, already in this section, how each dimension is conceptualized and operationalized in this study (e.g., vitality as temporal patterns of park use derived from mobile phone signaling; demand as population and user-group characteristics; supply as park area and facility provision). A short paragraph that turns “Vitality–Demand–Supply” from a slogan into a concrete analytical framework would substantially strengthen the motivation.

 

Response 1: Thank you for your comment. We agree with your suggestion to clarify the “Vitality-Demand-Supply” conceptual framework. We have added explicit definitions and logical relationships for each dimension in Line 161–169:

To address this gap, this study defines the three core dimensions of the evaluation framework and their interactive relationship: Vitality Level refers to the intensity and stability of park use across spatiotemporal scales, reflecting the attractiveness and utilization efficiency of parks. Demand Matching represents the differentiated needs of residents for park services (e.g., age-specific, time-specific demands), with a focus on equity and inclusiveness. Service Supply denotes the resource allocation level of parks and their surroundings (e.g., facilities, accessibility, supporting services), emphasizing efficiency and adaptability. The three dimensions form a dynamic closed loop: Demand drives supply, supply supports vitality, and vitality reflects demand satisfaction.

 

Comment 2: Lines 128–147 could be merged into a single paragraph that provides an explicit contribution statement.

 

Response 2: We appreciate your suggestion to consolidate the contribution statement. Lines 179–195 have been merged into a single paragraph (Line 151–163) that explicitly articulates the study’s core contributions:

… this study refines the dynamic application of landscape performance theory in urban park research, addressing the long-overlooked gap of integrating “spatiotemporal heterogeneity and multi-dimensional performance” in traditional static assessment paradigms. Methodologically, we develop a three-dimensional dynamic evaluation framework (“Vitality Level-Demand Matching-Service Supply”) by integrating multi-source big data (mobile phone signaling, POIs, demographic statis-tics) and adopting a semi-dynamic hybrid weighting approach (entropy weight + policy adjustment), overcoming the limitations of traditional methods such as single-dimensional focus and lack of dynamic feedback. Practically, this framework provides a scientific tool for the refined planning and management of parks in high-density urban areas. Gongshu District, selected as the case study, is one of the core high-density urban districts in Hangzhou with abundant park resources, yet it faces prominent supply-demand mismatches and temporal crowding issues, while serving as a key pilot area for implementing the “Park City” and “15-minute living circle” strategies. Findings from this case are therefore expected to offer replicable insights for similar urban contexts, facilitate the transition of urban parks from “quantity compliance” to “quality adaptation.”

 

Comment 3: The title emphasizes “59 parks in Gongshu District, Hangzhou”, but the Introduction does not explain why Gongshu is chosen. It would be helpful to add 1–2 sentences in the last paragraph of the Introduction to motivate the case: for instance, that Gongshu is a high-density urban district where park resources are relatively abundant but supply–demand mismatches and temporal crowding remain salient, and that it is a key area for implementing the “Park City” and “15-minute living circle” strategies. This will help readers understand the broader relevance of the case.

 

Response 3: Thank you for your comment regarding the case study motivation. We fully agree that clarifying the rationale for selecting Gongshu District enhances the study’s relevance and readability. Following your suggestion, we have added a targeted sentence in the last paragraph of the Introduction to elaborate on the case selection (Line 189–194):

Gongshu District, selected as the case study, is one of the core high-density urban districts in Hangzhou with abundant park resources, yet it faces prominent supply-demand mismatches and temporal crowding issues, while serving as a key pilot area for implementing the “Park City” and “15-minute living circle” strategies. Findings from this case are therefore expected to offer replicable insights for similar urban contexts, …

 

Comment 4: The Introduction is overly lengthy, with too much detail devoted to policy background. Please streamline this part (“slim it down”) and use the saved space to sharpen the discussion of the research gap and the need for a dynamic, multi-dimensional evaluation framework.

 

Response 4: Thank you for pointing this out. We have significantly condensed the policy background details and tightened the narrative. The saved space has been reallocated to sharpen the critique of traditional evaluation methods and to more clearly and forcefully articulate the research gap: namely, the lack of a dynamic, integrated framework that simultaneously addresses Vitality Level, Demand Matching, and Service Supply. The revised Introduction now presents a more focused and compelling argument for the necessity and contribution of our proposed three-dimensional evaluation model.

 

Comment 5: Section 2.1 is overly long and contains a lot of descriptive information that is not directly relevant to the research questions or methods. Many sentences focus on general socio-economic achievements and honorary titles of Gongshu District (e.g., balanced compulsory education, “China’s Happiest District”, detailed GDP and retail figures), which read more like government publicity than a concise scientific description of the study area.

 

Response 5: Thank you for your constructive comment. We have revised the Section 2.1. To enhance relevance and conciseness, we removed overly detailed information (e.g., administrative divisions, economic data, national honors) while preserving geoclimatic features directly related to park service performance evaluation. The revised content (Lines 222–234) is: Geographically, it is backed by Banshan Mountain to the northeast, traversed north-south by the Beijing-Hangzhou Grand Canal, and exhibits a gently sloping topography that rises from west to east with hilly terrain in the east, plains in the west, and an average elevation of approximately 45 meters. Climatically, Gongshu is categorized as a subtropical monsoon climate with distinct seasonal characteristics, exhibiting mild and rainy springs, hot and humid summers, cool and dry autumns, and moderate winters with minimal precipitation.

 

Comment 6: Figure 1. In the lower-left panel of Figure 1, which shows the location of Gongshu within Hangzhou, the internal patches are currently drawn at the sub-district / community level. For a location map at the city scale, it would be more appropriate and intuitive to show the administrative districts of Hangzhou instead of sub-districts or villages.

 

Response 6: Thank you for your comment regarding Figure 1. We acknowledge the original lower-left panel presented sub-district-level divisions, leading to the misunderstanding. We have revised Figure 1. We have updated the lower-left panel to display the administrative districts of Hangzhou (replacing the previous sub-district-level division), clearly illustrating the positional relationship between Gongshu District and other administrative regions of the city.

 

Comment 7: The description of the preprocessing of mobile phone signaling data (Section 2.2, around Lines 209–214). The manuscript does not explain whether home–work patterns, night-time stays, or land-use/POI information are used to identify and exclude residents, nor how signals close to park boundaries but outside the actual park space are treated. This ambiguity makes it difficult to interpret the vitality indicators as a clean measure of park use and should be clarified or explicitly acknowledged as a limitation.

 

Response 7: Thank you for raising this important point regarding data cleaning. We have clarified the preprocessing steps in the revised Section 2.2. Line 286-290: To distinguish recreational visitors from passers-by, we relied on continuous stay (≥30 minutes) with minimal displacement (≤50 meters) as indicators of purposeful park use, rather than applying home-work pattern filters. Signals within a 50-meter buffer outside park boundaries were excluded to avoid capturing adjacent road or building activity, thereby focusing vitality measurement on actual park space. And we acknowledge some edge-case ambiguity may remain: Line 1023-1029: Furthermore, the mobile phone signaling data preprocessing, while employing filters for transient noise and spatial buffering, may still include some edge cases. For instance, non-recreational traversers (e.g., pedestrians using a park as a shortcut) with limited base station switches could meet the minimum stay threshold, and signals very close to park boundaries might not be perfectly distinguished from adjacent street activity. These could lead to a modest overestimation of recreational vitality, particularly in linear parks that also serve as transportation corridors.

 

Comment 8: In addition, the current “ping-pong handover” filter (≥3 switches between adjacent base stations within one hour) may fail to remove many transient passers-by. For example, a student walking from school to home across a park may only connect to one or two additional base stations while passing through the area, and would therefore not be flagged as ping-pong noise but still be counted as a park user if their trajectory falls within the park polygon for up to 30 minutes. This suggests that a substantial proportion of non-park-related movements may remain in the dataset, and the authors should either refine the filtering strategy or explicitly discuss this limitation when interpreting the vitality indicators.

 

Response 8: Thank you for this nuanced observation. We have added a clarification in the Discussion/Limitations section acknowledging that the ping-pong filter may not capture all passers-by, especially in parks used as pedestrian corridors, which could modestly inflate vitality estimates. We consider this a reasonable trade-off for retaining genuine visitors with intermittent movement, and future studies could incorporate trajectory semantic inference to further improve filtering. The revision is in Line 1023-1029 as above (Response 7).

 

Comment 9: The processing of the POI data is also not fully transparent. The manuscript explains that POIs from Amap are filtered by functional type and that “buffer density” is calculated with reference to the Standard for Urban Green Space Planning(GB/T 51346-2019), but it does not specify (i) the exact buffer radius or whether it is consistent with the 15-minute walking buffer used for population, (ii) whether POIs inside the park and those in the surrounding area are distinguished, (iii) whether different POI categories are weighted equally or differentiated, and (iv) how POIs that serve city-wide functions (e.g., large commercial complexes or transport hubs) versus strictly local services are treated. Without these clarifications, it is difficult to interpret the resulting POI “density” indicators as a robust measure of park-related service supply, and the authors should either provide more methodological detail or explicitly acknowledge this as a limitation.

 

Response 9: Thank you for these detailed questions. We have added clarifications in Section 2.2 stating that: (1) the POI buffer radius is consistent with the 15‑minute walking service area; (2) POIs inside and outside park boundaries are not distinguished; (3) all categories are equally weighted; and (4) large, city‑scale POIs are included as part of the real‑world service environment. We acknowledge that alternative weighting or classification schemes could be explored in future work. The revision is in Line 319-323 as follows: The POI buffer radius matched the 15-minute walking service area of each park. All POIs within this buffer (inside and outside the park) were included and equally weighted. Large, city‑scale POIs within the buffer were retained as they reflect the actual service context influencing park visitation.

 

Comment 10: The Results section is very detailed and frequently lists individual parks, specific numerical values, and case-by-case descriptions (e.g., for TAD, TSI, NAR/STS, EWCR, VPMI, FAI, ESAR). While these examples are informative, the narrative becomes somewhat fragmented and difficult to follow.

 

Response 10: We thank the reviewer for the valuable feedback regarding the presentation of results. To address the concern about narrative flow, we have refined the writing in Section 3.1 while preserving its clear three-dimensional structure. Specifically, within each dimensional subsection, we have: (1) Strengthened the thematic opening and concluding statements to frame the analysis. (2) Consolidated the presentation of results by grouping parks with similar characteristics and highlighting contrasting patterns rather than itemizing cases sequentially. (3) Focussed the text on interpreting the spatial and temporal patterns evident in the data and their linkages to urban context, with the accompanying figures providing the detailed spatial distribution. We believe these revisions have significantly improved the readability and analytical flow of the Results section without sacrificing empirical detail. The revision of Section 3.1 is as follows:

The performance of the 59 urban parks in Gongshu District across the three dimensions revealed pronounced spatial patterns, closely tied to their functional type, location, and surrounding urban context. In terms of Vitality Level, a fundamental di-vide existed between parks with balanced, all-day use and those experiencing significant “weekend influx vs. weekday underutilization,” largely dictated by their integration with daily urban functions and transportation networks. Regarding Demand Matching, service equity exhibited a core-periphery gradient, with central parks achieving better alignment with the needs of local and vulnerable populations. For Service Supply, efficiency varied dramatically, with high-value areas concentrated along major amenities corridors, while parks in transformation zones often suffered from accessibility barriers or functional monotony. The following sections detail these dimensional characteristics, beginning with the spatiotemporal dynamics of park vitality.

The analysis of vitality levels revealed a fundamental divide shaped by parks’ integration with urban daily life. This was first evident in temporal usage patterns (TAD). Parks in northern industrial transformation zones (e.g., Hongang River Greenway, Ducheng Ecological Park) exhibited high TAD values (mean up to 0.579), characterized by intense weekend use (1.58 times weekday intensity) and pronounced afternoon peaks—a classic “weekend influx vs. weekday underutilization” pattern. This was linked to low surrounding residential density (<40 persons/ha) and a lack of integrated daily functions. In stark contrast, central multi-functional parks (e.g., West Lake Culture Square, Chengbei Sports Park) showed balanced, all-day use (mean TAD = 0.289), with vitality fluctuations between weekday commute and weekend leisure periods under 20%. This stability was supported by dense commercial/office amenities (~45 POIs/ha) and full subway coverage, facilitating continuous use.

Regarding Temporal Stability Index (TSI), a similar spatial dichotomy was observed. Weekday vitality was overall more stable (mean TSI = 2.84) than on weekends (2.64). Core-area parks (e.g., Kangle Park) showed greater volatility (TSI < 2.5), often experiencing >40% vitality drops at noon due to weaker population mobility.

The Spatiotemporal Synergy coefficient (STS) across the district was generally low (mean = 0.398), highlighting a widespread challenge of nighttime vitality decay. Only 14 parks (23.7%) achieved good synergy (STS ≥ 0.482). Success was tied to supportive environments: historic-commercial blocks like Xiaohe Straight Street Historical Block (STS = 0.584) retained over 60% nighttime vitality through lighting and catering, whereas peripheral greenways like Dianchang River Greenway (STS = 0.176) suffered severe decay (~ 85%) due to inadequate lighting and safety.

Finally, Service Efficiency per Unit Area (SEUA) underscored the concentration of demand, with weekend efficiency (26.89 persons/hectare/hour) 12.7% higher than on weekends. The extremes were stark: major central hubs (e.g., West Lake Culture Square) reached peak efficiency (66.76 persons/hectare/hour), fueled by commercial and tourist flows, while inaccessible peripheral greenways (e.g., Dianchang River Greenway) saw minimal use (0.74 persons/hectare/hour). In summary, sustained park vitality is not inherent but engineered, requiring strategic location within diverse urban fabrics, coupled with functional completeness and robust accessibility to mitigate temporal im-balances and maximize spatial utility.

The Demand Matching dimension revealed a pronounced core-periphery gradient in service equity, underscoring how spatial and demographic contexts shape the alignment between parks and their communities.

Accessibility, measured by the Effective Walking Coverage Rate (EWCR), showed a stark divide. Parks in central areas (e.g., West Lake Culture Square) achieved higher EWCR (42.3%-67.6%), where over 60% of the theoretical population within a 15-minute walk were actual visitors, benefiting from dense, continuous pedestrian networks. Conversely, parks in northern suburban and industrial zones often had EWCR below 30%. Physical barriers were a key constraint; for instance, Ducheng Ecological Park, severed by railway and expressway, reached only 20.6% EWCR, serving less than a quarter of its potential population due to disrupted pedestrian connectivity.

The match between Vitality-Population Matching Index (VPMI) further high-lighted supply-demand imbalances. Only 6 parks (10.2%) reached a high matching level (VPMI ≥ 1.66). These, like the Canal Sports Park (VPMI=3.80), successfully attracted cross-regional visitors with diverse amenities despite high local density (120 persons/ha). Nearly half of the parks (44.1%) suffered from clear mismatch (VPMI < 0.78). Jinsong Park exemplified this: despite a substantial surrounding population (90 per-sons/ha), its activity intensity was one-third of high-performing parks, hampered by outdated and inadequate facilities (e.g., only 2 rest seats, no age-friendly features).

Results for the Spatial Coupling Index for Vulnerable Groups (SCI) displayed strong temporal and spatial dependencies. Usage by children and the elderly was significantly higher on weekends (mean child SCI = 1.46) than weekdays (0.83). Spatially, parks in sub-districts with high proportions of children (e.g., Xiangfu and Kangqiao) or the elderly (e.g., Zhaohui and Wenhui) recorded elevated SCIs (1.32–2.917), indicating better service alignment with local demographics. Furthermore, smaller greenways and gardens (e.g., Dongxin Sub-district Greenway, elderly SCI= 2.15) often outperformed large comprehensive parks in serving vulnerable groups, likely due to more targeted, accessible facilities like age-friendly rest platforms.

In essence, equitable park access and use are not automatic but are contingent on overcoming physical barriers, providing facility quality that matches local demographic needs, and ensuring service designs are sensitive to the temporal rhythms of different user groups.

The Service Supply dimension revealed substantial disparities in how effectively park resources are allocated and utilized, with efficiency closely tied to their integration within the broader urban service network.

Function Adaptation Index (FAI) displayed strong spatial clustering. High-value areas were concentrated along major amenities corridors such as the canal and around core business districts. Parks like the Canal Asian Games Park and Qiaoxi Straight Street (FAI 0.389-0.746) were embedded in rich service environments, with surrounding catering and sports/leisure POI densities exceeding 50 per hectare, which effectively extends park-based activities. In contrast, parks within predominantly residential neighborhoods (e.g., Mishixiang Sub-district Characteristic Cultural & Sports Square, Dongxinyuan Park) had FAI below 0.153, where over 70% of surrounding POIs were basic residential facilities, reflecting a functional monotony that limits their appeal and service performance.

The Effective Service Area Ratio (ESAR) showed an even more polarized pattern. While the district's mean ESAR was 1.34, over half of the parks had an ESAR below 0.78, indicating that their actual service area fell significantly short of the theoretical 15-minute walking range. Parks with unique regional attractions defied this trend; the Gongshu Canal Sports Park, functioning as a cultural and sports hub, achieved an exceptional ESAR of 10.91, attracting visitors from beyond 3 km and even cross-district. Conversely, parks impaired by accessibility barriers exemplified severe underperformance. The Dianchang River Greenway, isolated by transportation infrastructure, had an ESAR of only 0.0359—its actual service area was less than 4% of its prominent, underscoring a stark case of resource underutilization.

Therefore, efficient park service supply depends not merely on the internal provision of facilities, but critically on strategic placement within a diverse urban service ecosystem and the removal of external accessibility barriers, which together determine whether a park fulfills its local role or achieves wider regional influence.

 

Comment 11: In Section 3.2, classification thresholds for “high/medium/low performance” need justification and clearer presentation.

 

Response 11: Thank you for pointing out the need for clarity on the classification thresholds. We have added a clear justification in Section 3.2, immediately following the introduction of the performance classification. The added is in Line 792-796 as follows: Parks were classified into high-, medium-, and low-performance tiers using quantile division (terciles) based on their comprehensive performance scores. This data-driven approach ensures that each category contains a roughly equal number of parks and is commonly used in urban performance studies [43] for its objectivity and simplicity. This clarifies the methodological basis for the categorization used throughout the results and discussion.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

The authors address two main questions in the article. The first concerns the construction of an integrated park service performance evaluation model based on a three-dimensional framework – “Vitality Level Monitoring – Service Demand Diagnosis – Resource Allocation Assessment.” The second concerns testing this model using an example of densely populated urban areas, such as the Gongshu District in Hangzhou.

The topic of the article is relevant for urban planning because, as the authors correctly note in the introduction, despite the considerable number of studies concerning green infrastructure in urbanized areas, these studies still insufficiently account for the efficiency of green space use. Moreover, there is a clear lack of research that incorporates big data technologies to assess the effectiveness of green infrastructure.

The scientific novelty of the article lies in the development of an integrated model for evaluating park service efficiency that can be applied in various densely populated urban contexts. This methodology enables improvements in the planning and management of urban parks.

The methodology chosen by the authors – namely, the development of a model for assessing the dynamic efficiency of urban parks – is generally clear and appropriate for the presented research. The references cited in the article are relevant.

The figures and tables included in the article are also appropriate. However, in Section 2.1, the authors should have added information about the specific research objects, namely the parks themselves. In particular, it would have been useful to include a map showing the locations of individual parks in the Gongshu District of Hangzhou, as well as a table providing information on each park’s name, size, and type (based on functional designation, origin, or another criterion). Such information would significantly improve the comprehension of Section 3, which presents the research findings.

The conclusions are consistent with the evidence and arguments provided. However, since the authors posed two separate research questions at the beginning of the article (lines 135–140), it would be advisable to structure the conclusions somewhat differently to more clearly highlight the answers to these questions.

The reviewer has no substantial comments on the article but does offer one suggestion. It may be worthwhile for the authors, either in the discussion or in the introduction, to further address the issue of spatial justice and how their research contributes to the development of this concept.

Author Response

Comment 1: it would have been useful to include a map showing the locations of individual parks in the Gongshu District of Hangzhou, as well as a table providing information on each park’s name, size, and type (based on functional designation, origin, or another criterion). Such information would significantly improve the comprehension of Section 3, which presents the research findings.

 

Response 1: Thank you for your insightful comment. We fully agree that detailed spatial and attribute information of the research parks would enhance the comprehensibility of the study findings. Following your suggestion, we have made targeted revisions to supplement the relevant content as follows:

(1) We have expanded Section 2.1 to explicitly clarify the park selection criteria and provide brief foundational information about the 59 parks, ensuring consistency with the newly supplemented materials. Line 259-268: Considering the 50 m spatial granularity of mobile signaling data and the assumption of homogeneous user distribution within Voronoi polygons, precise alignment between the boundaries of small-scale parks (≤3 hectares) and the signaling dataset poses considerable challenges—especially within high-density urban contexts. Such limitations could introduce notable calculation deviations and impede the accurate quantification of actual visitor numbers. To balance data precision with research objectives, this study focused on urban parks exceeding 3 hectares, resulting in a total of 59 research sites. These include 6 comprehensive parks (154.96 ha), 10 special-category parks (635.16 ha), 9 community parks (83.28 ha), and 34 garden parks (204.57 ha), whose spatial distribution is presented in Figure 2.

(2) We have added a map illustrating the exact locations of all 59 parks in Gongshu District, Hangzhou (Figure 2), which presents their spatial distribution.

(3) We have included a supplementary table (Table S1) in the supporting information, which systematically lists each park’s serial number, full name, total area (in hectares), and functional type. This table provides comprehensive attribute details to facilitate readers’ reference when interpreting the research results in Section 3.

 

Comment 2: since the authors posed two separate research questions at the beginning of the article (lines 135–140), it would be advisable to structure the conclusions somewhat differently to more clearly highlight the answers to these questions.

 

Response 2: Thank you for your insightful comment on structuring the Conclusions section. We fully agree that organizing the conclusions to clearly highlight the answers to the study’s core objectives enhances readability and logical rigor. Following your suggestion, we have revised the Conclusions section (Line 1056-1096): A primary objective of this study was to develop an integrated park service performance evaluation model that balances spatiotemporal dynamics and multi-dimensional synergy. To achieve this, this study broke through the limitations of traditional static evaluations by designing a three-dimensional dynamic framework that integrates multi-source big data and adopts a semi-dynamic hybrid weighting method, combining the entropy weight method and policy adjustment, balanced the objectivity and policy orientation of indicator weights. Dynamic indicators such as Temporal Activity Difference (TAD) and Spatiotemporal Synergy coefficient (STS) successfully captured intra-day/weekly fluctuation characteristics of park usage, overcoming the single-dimensional and static constraints of traditional methods. Time-series cross-validation, external validity verification, and Monte Carlo simulations further confirm the model's strong resistance to data noise and weight disturbances, ensuring its stable applicability to performance evaluation of parks in high-density urban areas.

Another core objective was to validate the model's effectiveness in identifying performance shortcomings and providing precise support for planning optimization. The empirical application in Gongshu District yielded clear and actionable findings: Park service performance in Gongshu exhibited a significant core-periphery gradient, with high-performance parks concentrated in core areas (e.g., West Lake Culture Square and Xiaohe Straight Street Historical Block) due to excellent transportation accessibility, high functional diversity, and strong population demand, while low-performance parks scatter in northern industrial transformation zones (e.g. Kangqiao and Banshan) due to low population density, transportation barriers, and insufficient facilities. Temporally, parks with high TAD values (mean up to 0.579) face prominent time-mismatch issues, whereas those with low TAD values (mean of 0.289) achieve all-day efficient utilization through functional and transportation support. Across the three evaluation dimensions, the Vitality Level dimension highlights the “weekday stability, weekend fluctuation,” the Demand Matching shows a “high coupling in the core, low adaptation in the suburbs” gradient, and the Service Supply reflects the principle that “functional adaptability determines use efficiency.” —collectively pinpointing core shortcomings such as low vitality, supply-demand mismatch, and inefficient supply in suburban parks, as well as peak crowding in core parks, and leading to the core conclusion that “park performance is the result of the synergistic interaction of space, people, and facilities.” These results fully demonstrate the model's value in targeted performance diagnosis.

 

Comment 3: It may be worthwhile for the authors, either in the discussion or in the introduction, to further address the issue of spatial justice and how their research contributes to the development of this concept.

 

Response 3: Thank you for your suggestion regarding spatial justice. We fully agree that linking the research to this concept enhances its theoretical depth and social relevance. Following your guidance, we have added a concise section in the Discussion to address this aspect (Line 977-985): This study also enriches the discourse on spatial justice in urban green spaces—emphasizing equitable access to quality recreational resources for all residents [43]. The core-periphery performance gradient directly reflects spatial inequities: core areas enjoy high-quality park services, while suburban zones face supply-demand mismatches and inadequate access. By quantifying these disparities via the “Demand Matching-Service Supply” dimensions, this research provides a data-driven tool to mitigate injustice. The proposed strategies (e.g., improving suburban accessibility, enhancing facility inclusiveness) align with spatial justice tenets, aiming to bridge the service gap and promote inclusive public space development.

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

Dear authors, the manuscript has strong methodological and data sources basis. The results and foundings are scientificaly relevant and they are providing practical value for the urban planning.

These are some comments related mosltly on the providing sufficient definitions and references for the theories and concepts you are relly your research.

 

111 What is definition of urban park vitality? What kind of vitality, ecological, social, economical? Please provide definition and references accordingly.

129 Is this equal to vitality?

141-142 What this theory is about? Need to be elaborated in introduction as one of the theoretical backgrounds, with relevant references.

260-262 If this is theoretical background, it should be recognized and elaborated in introduction as a theoretical background, with relevant references.

265-266 Any other theorists than Jane Jacobs? Please provide few more contemporary authors.

278 Please name the references for this theory.

779-782 This is the question of the role of the park in the urban environment. There are other vital services it provides (eg. ecological stewardship) than fulfillment of the residents and other visitors recreational and leisure needs.

785-786 What are the park services that are relevant for vitality? Is there any research or other official data that provides the answer for what are the citizens needs and activities that they are expecting in parks? This question should be addressed in the introduction as well.

Comments for author File: Comments.pdf

Author Response

Comment 1: 111 What is definition of urban park vitality? What kind of vitality, ecological, social, economical? Please provide definition and references accordingly.

 

Response 1: Thank you for your comment. We have clarified the definition of “urban park vitality” in the revised manuscript (Line 157-160): In this study, vitality refers specifically to social vitality, characterized by the spatio-temporal intensity and stability of human activities within parks. This aligns with Jane Jacobs' conception of vibrant public spaces and contemporary studies using big data to quantify spatial vitality [34, 35]. Ecological and economic vitality, while important, are beyond the scope of this performance evaluation focused on recreational services.

 

Comment 2: 129 Is this equal to vitality?

 

Response 2: Thank you for raising this important point. We have clarified the relationship between “recreational service performance” and “vitality” in the revised manuscript (Line 161-169): To address this gap, this study defines the three core dimensions of the evaluation framework and their interactive relationship: Vitality Level refers to the intensity and stability of park use across spatiotemporal scales, reflecting the attractiveness and utilization efficiency of parks. Demand Matching represents the differentiated needs of residents for park services (e.g., age-specific, time-specific demands), with a focus on equity and inclusiveness. Service Supply denotes the resource allocation level of parks and their surroundings (e.g., facilities, accessibility, supporting services), emphasizing efficiency and adaptability. The three dimensions form a dynamic closed loop: “Demand drives supply, supply supports vitality, and vitality reflects demand satisfaction.”

 

Comment 3: 141-142 What this theory is about? Need to be elaborated in introduction as one of the theoretical backgrounds, with relevant references.

 

Response 3: Thank you for your suggestion. We added an explanation in Line 180-181: …, which is centered on the evidence-based evaluation of landscape outcomes to inform design and decision-making [35]…This clarification strengthens the theoretical foundation of our three-dimensional performance framework.

 

Comment 4: 260-262 If this is theoretical background, it should be recognized and elaborated in introduction as a theoretical background, with relevant references.

 

Response 4: Thank you for your suggestion. We agree that the theoretical underpinnings of the indicator system should be foregrounded. Accordingly, we have revised the theoretical background section in the Introduction to explicitly introduce the three core theories (in Line 170-178: The theoretical foundation of this study rests on a triad of complementary theories that inform our three-dimensional evaluation framework. First, Urban Spatial Vitality Theory, notably advanced by Jane Jacobs on the interplay of “people, activity, place” [33,34], underpins the Vitality Level dimension, focusing on the intensity and dynamics of space usage. Second, Landscape Performance Theory [35] provides the lens for the Service Supply dimension, emphasizing the evidence-based assessment of resource allocation and functional outcomes. Third, Spatiotemporal Behavior Theory [36] in-forms the Demand Matching dimension, addressing the temporal rhythms and spatial patterns of human needs and their alignment with services. Building upon this integrated theoretical base…)

 

Comment 5: 265-266 Any other theorists than Jane Jacobs? Please provide few more contemporary authors.

 

Response 5: Thank you for the suggestion to broaden the theoretical references. We have revised the sentence in Section 2.3 to include references to contemporary scholars who have significantly advanced the study of urban spatial vitality. Line 369-373: Theoretically, the vitality dimension draws upon foundational and contemporary urban design theories that emphasize active public life, spanning from Jane Jacobs' discourse on the interaction of “people, activity, place” shaping spatial vitality [34] to Jan Gehl's principles of “cities for people”[40] and William H. Whyte's studies of social life in small urban spaces[41]…

 

Comment 6: 278 Please name the references for this theory.

 

Response 6: Thank you for your suggestion. References for Public Service Supply Theory and Ecosystem Service Theory have been added in Section 2.3 where these theories are mentioned.

 

Comment 7: 779-782 This is the question of the role of the park in the urban environment. There are other vital services it provides (eg. ecological stewardship) than fulfillment of the residents and other visitors recreational and leisure needs.

 

Response 7: Thank you for raising this important point regarding the multifaceted roles of urban parks. We agree that parks provide vital ecological services beyond recreational functions. In the revised manuscript, we have clarified our discussion to specifically refer to “recreational vitality” rather than vitality in a broader sense, and have explicitly acknowledged the inherent ecological value of green spaces even when their recreational performance is low. We revised in Line 970-976 as follows: … “having green space but lacking recreational vitality,” which limits their social utility and service performance in the context of this study. This serves as a warning for future park planning in industrial transformation zones: to fully realize the multifunctional potential of parks (both ecological and social), green space construction needs to be carried out simultaneously with population influx and transportation improvements to avoid inefficient use of recreational resources, while still acknowledging the inherent ecological value of these green spaces.

 

Comment 8: 785-786 What are the park services that are relevant for vitality? Is there any research or other official data that provides the answer for what are the citizens needs and activities that they are expecting in parks? This question should be addressed in the introduction as well.

 

Response 8: Thank you for this suggestion. We have added a sentence in the Introduction citing established research which confirms that the quality dimensions mentioned—such as amenities, comfort, and inclusivity—are empirically linked to park visitation and satisfaction, thus directly addressing the question of which services are relevant for vitality and providing the requested research foundation. The revision is in Line 96-99: Empirical studies have established that these dimensions of quality—amenities, comfort, safety, and inclusivity—are key determinants of park visitation frequency, duration, and user satisfaction, thereby constituting the core of service vitality [11–14].

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

All my comments have been revised in this version.  One item should be clarified: Is the ID in Table S1 the same as the ID in Figure 2? This must be clarified in the caption of Figure 2.

Author Response

Comment: Is the ID in Table S1 the same as the ID in Figure 2? This must be clarified in the caption of Figure 2.

Response: Thank you for your feedback. As suggested, we have updated the caption of Figure 2 to explicitly state that the park IDs correspond to those in Supplementary Table S1. The revised caption now reads: Figure 2. Spatial distribution of urban parks with different areas. Park IDs correspond to those listed in Supplementary Table S1.

Author Response File: Author Response.docx