Identification and Causes of Neighborhood Commercial Areas: Focusing on the Development of Daily Life Circles in Urban Built Environments

Response to Reviewer 1 Comments

1. Summary

Thank you very much for taking the time to review this manuscript. Please find the detailed responses below and the corresponding revisions in the re-submitted files.

2. Questions for General Evaluation

Reviewer’s Evaluation

Response and Revisions

Does the introduction provide sufficient background and include all relevant references?

Yes

We have added explanations of key concepts to optimize Introduction section.

Is the research design appropriate?

Yes

We added more details of the data.

Are the methods adequately described?

Yes

-

Are the results clearly presented?

Yes

We have added a brief explanation of Results section to facilitate understanding.

Are the conclusions supported by the results?

Yes

We have proposed directions for future research based on the limitations of this study.

3. Point-by-point response to Comments and Suggestions for Authors

Comments 1: [The paper presents daily life cycle planning using a multisource level of user-generated data and relevant methods. Contents and used methods are appropriate for the IJGI's scope. I have found the study presented in the paper interesting and timely. The paper has a high potential for publication.

I have some suggestions and comments to improve the paper's quality.

First of all, I congratulate the authors for the research design and presentation because each section completes the others and the flows are well-developed.

The abstract is well-written and informative, I do not have any suggestions.

Introduction：Could you give other examples for developing a liveable and sustainable urban environment besides the 15-minute concept, so readers can understand why you exclusively focus on this concept (line 28)? ]

Response 1:

Thank you very much for your positive feedback on our manuscript and your valuable suggestions for improvement.

We agree with your comments. In the revised manuscript, we have added several examples to clarify the characteristics of the 15-minute city concept in the Introduction section. See lines 33-39, page 1:

[

Among various strategies, including Transit-Oriented Development (TOD), resilient cities, and eco-cities, the "15-minute city/life circle" concept has emerged as one of the most popular approaches to achieving these goals at the current stage, attracting global attention. This concept aims to enhance urban sustainability by clustering essential services within walking distance, thereby significantly reducing transportation demand and carbon emissions, ultimately improving community livability and service efficiency[1-3].

]

Comments 2: [Introduction: Could you provide a brief explanation for Table 1 and Table 2? Although both of them are self-explanatory, it would be nice to have a complete overview. For example, how did you benefit from these used methods, and what are the benefits of these algorithms, etc.?]

Response 2: 

Thank you very much for your valuable suggestions. In response, we have added a note to Table 1 and Table 2 to clarify our benefits from the methods used in previous studies (Table 1) and research deficiencies identified in previous studies (Table 2). See lines 83-86 in Introduction section, page 2:

[

Notably, the kernel density estimation method can reveal the continuous spatial density variation of facilities, enabling the identification of commercial clusters relative to the surrounding context rather than relying on POI density peaks (Table 1).

]

, and lines 104-106, page 3:

[

Furthermore, many models employed in previous studies assume linear relationships among variables, neglecting potential non-linear and threshold effects (Table 2).

]

Comments 3: [Materials and methods: Why did you collect the data in different periods, for instance, BHI (line 130) and POI (line 136) were collected at different times.]

Response 3: 

Thank you for your attention to the consistency of the data collection time. We sincerely apologize for our oversight in this regard. Your suggestion prompted us to review the data. we compared the POI data in Qinhuai District, Nanjing, collected in April, June, and July 2023.

The results show that the data is consistent, which can be attributed to the relative stability of urban service facilities over a period of time. We have clarified this in the paper. See lines 154-155 in Section 2.2, page 7:

[

Comparative analysis shows alignment between these data and the data from April and July 2023, attributable to the relative stability of urban service facilities over a period.

]

We appreciate your valuable suggestions and will address this issue with particular rigor in future studies.

Population density data is derived from the Seventh National Population Census conducted in 2020. Although the timing of this census does not align with that of other data used in this study, we consider it highly valuable due to its accuracy and authority. As no more recent census data is available, we prioritized accuracy in selecting this dataset.

Your suggestions have made us aware that this may be a limitation of this study, which we will address in future research.

Comments 4: [Materials and methods: If possible, could you indicate the location or the border of XJK on the map? I am not able to see the map in high quality if you already show it is OK. It would be good to see which place is a city-level business district (line 148).]

Response 4: 

Thank you very much for your valuable suggestions, this will improve the clarity and quality of our presentation. We have marked the location of XJK business district in red in Figure 4. See lines 177-178 in Section 2.2, page 8:

[

]

Comments 5: [Do have any date for the community attribute based on the Lianjia (line 157), so readers can follow the up-to-dateness?]

Response 5: 

Thank you very much for your valuable suggestions. We provided clarification on the housing price data collection time. See lines 179-181 in Section 2.2, page 8:

[

This study extracted data on construction year, housing price, and number of floors of residential complexes from the platform. The housing price represents the average of values from April and July 2023.

]

Comments 6: [Results: Could you provide a small introduction, it makes easy-to-follow subsections (line 275).]

Response 6: 

Thank you very much for your valuable suggestions. We agree that adding a brief introductory paragraph would enhance the readability of the subsections. We have added a concise introduction at the beginning of Result section to provide context and guide readers through the following content. See lines 303-306 in Results section, page 12:

[

This study employed the proposed method to identify NCAs and delineated DLCs. The reliability of the NCA identification method was evaluated based on the results. Additionally, the causes, types and its influencing factors of NCA formation were quantitatively analyzed.

]

Comments 7: [Discussion：I have the same comment, please provide a small introduction (line 351).]

Response 7: 

Thank you for your suggestions. We agree with your comments and have added a concise introduction at the beginning of Discussion section. See lines 429-436 in Discussion section, page 18:

[

This study proposes a method for identifying NCAs and illustrates its application with Qinhuai District in Nanjing as a case. The characteristics of NCAs support their suitability as central areas of DLCs, indicating that the method establish a foundation for transcending administrative boundaries, facilitating precise, bottom-up allocation of community resources. Furthermore, the discussion on differences between factors influencing NCA formation and those affecting commercial facility distribution or commercial area vitality, along with analyzing the formation causes of various NCA types, provides reliable indicators for NCA planning.

]

Comments 8: [Discussion：What would be the reason for this differentiation compared to previous research (line 401)?]

Response 8: 

Thank you for your insightful question. We recognize the importance of addressing the reasons for the observed differentiation from previous research. In the revised manuscript, we have expanded the Discussion section to explore potential explanations. See lines 449-452 in Section 4.1.1, page 18:

[

This is because NCAs are situated within residents' daily living ranges, while urban commercial central districts are situated within residents' driving ranges. The distances between these two types of commercial agglomerations correspond to the acceptable walking and driving distances for residents.

]

Comments 9: [Conclusion: Could you provide one more paragraph to complete this section that contains what can be done for further studies to correspond to these limitations (line 515)? ]

Response 9:

Thank you very much for your valuable suggestions for improvement. We have added   additional future research prospects corresponding to research limitations to enhance the conclusion. See lines 577-588 in Conclusions section, page 20-21:

[

Future research could integrate BHI with travel survey data focused on specific populations, providing a more comprehensive view of activity patterns, particularly for children and the elderly. These data could also support analyses on how demo-graphic factors, such as age and gender, influence residents' utilization of and attitudes toward urban spatial functions. Second, this study focuses on socioeconomic and built environment factors when analyzing NCA formation, neglecting potentially influential from cultural and historical aspects. Future research should incorporate relevant variables to achieve a more comprehensive understanding of the formation mechanisms of the central areas of DLCs. The analysis model of NCA formation developed in this study also requires further validation across diverse regions. Expanding the research scope to include other regions will contribute to the robustness and applicability of the model.

]

4. Response to Comments on the Quality of English Language

Point 1: No comments regarding English language quality were provided by Reviewer 1.

5. Additional clarifications

We have updated the affiliation of the corresponding author and the funding sources for our work.

See lines 7-8, page 1:

[

² School of Architecture, Southeast University, Nanjing, China. Ageing-Responsive Civilization Think Tank Academic Committee, Naning, China; zhouying@seu.edu.cn

]

See lines 597-599 in Funding section, page 21:

[

This work was supported by the National Key R&D Program of China [grant number2022YFF0607003|; and Ageing-Responsive Civilization Think Tank grant number24LLWM15].

]

Response to Reviewer 2 Comments

1. Summary

Thank you very much for taking the time to review this manuscript. Please find the detailed responses below and the corresponding revisions in the re-submitted files.

2. Questions for General Evaluation

Reviewer’s Evaluation

Response and Revisions

Does the introduction provide sufficient background and include all relevant references?

Can be improved

The Introduction section has been strengthened, and relevant literatures have been supplemented.

Is the research design appropriate?

Yes

We have added an explanation for the choice of model

Are the methods adequately described?

Yes

We added more details of the methods.

Are the results clearly presented?

Yes

We have added explanations of the terminology to simplify the understanding of Results section.

Are the conclusions supported by the results?

Can be improved

We have added practical implications and policy recommendations to improve Discussion section.

3. Point-by-point response to Comments and Suggestions for Authors

Comments 1: [Introduction: Background and Context (Lines 35-65) 

Observation: The introduction briefly outlines the study's relevance but lacks depth in connecting it to existing research on similar methodologies or frameworks.

Suggestion: To strengthen the introduction, include specific references to foundational studies or recent advancements that align with your research objectives. This would help situate your study within the broader field and highlight its contribution. Consider adding references such as Smith et al. (2020) on similar spatial analyses or Jones & Martin (2022) for recent trends in this area. This would provide readers with a clearer understanding of your study's position in the current research landscape.]

Response 1:

Thank you for your valuable suggestions. We agree that providing more context would strengthen the introduction. In the revised manuscript, we have incorporated the recommended literature and considered this study’s response to the issues and concepts it addresses. This addition clarifies our study’s positioning and contribution to the existing research landscape.

See lines 52-53 in Introduction section, page 2:

[

This contributes to bridging the inequalities in service access among different socioeconomic groups [13].

]

See lines 74-77 in Introduction section, page 2:

[

This approach aligns with emerging perspectives in regional geography, conceptualizing life circles as formations driven by residents' actual activities and network connections rather than aligning with administrative boundaries. It emphasizes the distinctiveness between different life circles [25].

]

Comments 2: [Research Design: Explanation of Model Selection (Lines 110-145)

Observation: While the study presents an innovative approach by combining multiple models, it does not fully explain why these models were chosen over other alternatives.

Suggestion: Provide a brief justification for the selection of these specific models. For example, you could discuss the unique strengths of each model in addressing different research objectives. Including a comparison with other models, even briefly, would also add depth to the study and clarify the rationale behind your methodological choices. ]

Response 2: 

Thank you for your suggestions. We agree that a more detailed explanation of our model selection would enhance the clarity of the research design. In the revised manuscript, we emphasized the distinct advantages of the selected model in achieving our research goals through comparisons with other models, thereby demonstrating its reliability.

See lines 201-204 in Section 2.3, page 8:

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Compared to density analysis methods for recognizing discrete dense clusters, KDE is more effective in identifying NCA within China's old cities, as daily life service facilities are uniformly distributed in these areas.

]

See lines 213-219 in Section 2.3, page 9:

[

Compared to subjective weighting methods, EWM objectively assigns weights to each indicator by calculating the information entropy, which reflects the uncertainty of the indicators [58]. Higher information entropy indicates lower uncertainty, meaning it provides less information and reduces its weight. EWM has been widely applied to evaluate the vitality of various spaces, including shopping centers [45], neighborhoods [59], and the metro [60], as it reflects the relative importance of multidimensional indicators in measuring spatial vitality.

]

See lines 226-228 in Section 2.3, page 9:

[

This method can identify precise hotspot areas and their extents based on the vitality distribution within local contexts. Therefore, it enables the determination of DLC central areas within the continuously distributed NCAs.

]

Comments 3: [Methods: Detailed Data Collection and Processing (Lines 160-210)

Observation: The data collection and processing steps are described, but the criteria for data inclusion and any preprocessing methods applied are not clearly stated.

Suggestion: Add a step-by-step outline of the data processing methods, specifying any criteria for data inclusion, cleaning techniques, or transformations applied. If certain preprocessing steps were necessary (e.g., normalizing variables or filtering outliers), detailing these will improve the transparency of your approach and make it easier for other researchers to replicate your study.]

Response 3:

We appreciate your valuable feedback, which will enhance the rigor of the Materials and Methods section. In the revised manuscript, we have included detailed data processing steps, including criteria for data inclusion and cleaning techniques, to improve the transparency and reproducibility of our study. See lines 164-170 in Section 2.2, page 7:

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The data processing steps include: (1) Excluding certain categories of services, such as daycare services, which are not required by all residents, long-distance travel services with low daily utilization, and digital cultural services lacking offline service. (2) Removing services located in the XJK city-level business district, where facilities primarily do not meet daily life needs. (3) Performing manual deduplication, correction, and completion of missing information to clean the filtered data.

]

Comments 4: [Methods: Parameter Justification in Analysis (Lines 220-260)

Observation: The manuscript lacks sufficient information on how key parameters were set or calibrated within the models used.

Suggestion: Specify the parameters for each model and discuss how they were selected or calibrated. For instance, if parameter optimization techniques were employed, briefly describe the process and criteria. If specific values were chosen based on prior research or pilot tests, cite these sources or provide reasoning to enhance the methodological rigor. ]

Response 4: 

Thank you for your valuable suggestions. In the revised manuscript, we have cited the literature referenced during model development and evaluation and provided explanations for key parameter optimization techniques, along with the resulting optimized values. See lines 355-369 in Section 3.3.1, page 13-14:

[

3.3.1. Establishment and Evaluation of the CatBoost Model

To ensure the model's generalizability and prevent overfitting, we optimized the key parameters of the CatBoost model based on previous studies [16,46,67]. This study employed a grid search method combined with five-fold cross-validation to identify the optimal hyperparameters. Grid search is a parameter optimization technique that systematically explores combinations within specified parameter ranges to find the best configuration [71]. In each iteration, the training set was randomly divided into five subsets, with four subsets (70% of the data) utilized for model training and the remaining subset (30% of the data) reserved for testing. The parameter exploration range was defined as follows: the learning rate (controls the step size, affecting learning speed and overfitting) varied from 0.01 to 0.1, the maximum tree depth (determines model complexity) ranged from 2 to 8, the regularization lambda (prevents overfitting by penalizing large coefficients) from 1 to 9, and the number of iterations (the number of times the model is trained) was set at 200. The optimal model parameters identified were a learning rate of 0.1, a regularization lambda of 7 and a maximum tree depth of 8.

]

Comments 5: [Results: Interpretation of Findings (Lines 280-340)

 Observation: The results section presents data clearly, but some terms and concepts may be challenging for readers who are not familiar with the technical aspects of the methodology.

Suggestion: Consider including brief explanations or definitions for key terms (such as “CCD model outputs” and “spatial heterogeneity”). Additionally, a more detailed interpretation of how the results relate to the research objectives would strengthen this section. For example, you might discuss how variations in specific data points illustrate the effectiveness of your approach and what implications these have for future applications.]

Response 5: 

Thank you for the suggestions, and I apologize for the oversight. It seems "CCD model outputs" and "spatial heterogeneity" do not appear in the text. Could you please point out the specific lines where these terms were mentioned? We appreciate your attention to detail and will ensure such issues are addressed in future revisions.

Additionally, following your suggestion, we reviewed this section and found that the term 'SHAP main effect plot' lacked a brief explanation. To address this, we have added an explanation as a caption to Figure 14. See lines 397-398 in Section 3.3.2, page 15-16:

[

Figure 14. Non-linear relationships between the formation of NCAs and the research variables: (a) The local effect of population density is consistently positive; (b) The overall negative impact of housing price on NCA formation; (c) The overall positive impact of floor-area ratio on NCA formation; (d) The overall positive impact of commercial diversity on NCA formation; (e) The local positive effect of road density exhibits a threshold; (f) The overall positive impact of intersection on NCA formation; (g) The overall positive impact of Betweenness on NCA formation; (h) The overall positive impact of Closeness on NCA formation; (i) The local positive effect of public transport stop exhibits a threshold; (j) The local positive effect of parking exhibits a threshold.

(Note: x-axis represents the values of the variable indicated in the figure title, Y- axes represents SHAP values. A fitted curve (Locally Weighted Scatterplot Smoothing, LOWESS) is included to smooth scatter plots, with a steeper(flatter) curve indicating a higher(lower) marginal effect of the variable. Critical points where local effect change are marked)

]

Additionally, we have provided explanations of the key model parameters for better understanding. See lines 356-369 in Section 3.3.1, page 13-14:

[

To ensure the model's generalizability and prevent overfitting, we optimized the key parameters of the CatBoost model based on previous studies [16,46,67]. This study employed a grid search method combined with five-fold cross-validation to identify the optimal hyperparameters. Grid search is a parameter optimization technique that systematically explores combinations within specified parameter ranges to find the best configuration [71]. In each iteration, the training set was randomly divided into five subsets, with four subsets (70% of the data) utilized for model training and the remaining subset (30% of the data) reserved for testing. The parameter exploration range was defined as follows: the learning rate (controls the step size, affecting learning speed and overfitting) varied from 0.01 to 0.1, the maximum tree depth (determines model complexity) ranged from 2 to 8, the regularization lambda (prevents overfitting by penalizing large coefficients) from 1 to 9, and the number of iterations (the number of times the model is trained) was set at 200. The optimal model parameters identified were a learning rate of 0.1, a regularization lambda of 7 and a maximum tree depth of 8.

]

Regarding your second suggestion, we have expanded the Discussion section to include an analysis of the effectiveness of the CatBoost model in providing clear thresholds for NCA planning. See lines 500-521 in Section 4.3.1, page 19:

[

Some findings of this study differ from earlier research. Research employing linear analytical models have shown that transportation facility and road density promote commercial aggregation [37,39] and increase pedestrian traffic in aggregation area [41,42]. However, the results from non-linear analytical models indicated that the positive impact of these two variables on NCA formation exists within a certain threshold range. Excessive density of transportation facilities and road network also has a negative effect. This finding appears plausible, as transportation infrastructure and roads reduce the available space for commercial facilities, and the resulting traffic flow may pose safety risks, thereby restricting residents' daily activities. This result provides more reliable indicators for NCA planning.

This study presents findings that are inconsistent with earlier research. Previous studies have suggested that areas with higher housing prices tend to have more amenities [75]. However, this study employs a similar modeling approach but finds a general negative impact of housing prices on NCA formation. This may be attributed to the fact that NCAs result from the combined selection of commercial facilities and resident activities. Housing prices generally reflect the economic status of residents. Higher-income groups tend to reside in low-density communities [76] and prefer diverse locations for daily activities [26,77]. This preference restricts the aggregation of daily life service facilities, which depend on a stable local customer base for operation. To address this issue, it is recommended that a portion of property fees in high housing price areas be allocated to reduce the rental of commercial facilities providing essential daily life services. This enhances the convenience of daily life for nearby residents.

]

Furthermore, based on research results, we have emphasized the necessity of developing an identification model for NCAs in revised manuscript. See line 483-485 in Section 4.2, page 19:

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Moreover, the distinction between NCAs and urban commercial central districts demonstrates the necessity of developing specialized identification methods for NCAs.

]

Comments 6: [Figures and Visual Data Representation (Lines 350-400)

Observation: The figures are informative but could benefit from more descriptive captions and additional labels to clarify data points or trends.

Suggestion: For each figure, consider including a brief caption that summarizes the key findings illustrated. Labels or arrows pointing out significant data points or trends would also be helpful. In Figure 3, for instance, highlighting regions with the highest and lowest values visually could make the results more accessible and impactful.]

Response 6: 

Thank you for your constructive suggestions. We agree that descriptive captions and additional labels will enhance the clarity of the figures. In the revised manuscript, we have included a concise caption to convey the figure’s meaning, added trend lines, and labeled significant data points. See lines 397-398 in Section 3.3.2, page 15-16:

Comments 7: [Discussion: Comparative Analysis (Lines 410-480)

Observation: The discussion of the findings is clear but could benefit from comparisons with similar studies or alternative methods.

Suggestion: Discuss how your findings align or contrast with those from other studies, citing recent examples where applicable. This would contextualize your results within the field and underscore your study’s contributions. For example, if other studies using different methodologies reached similar or divergent conclusions, mention these to illustrate the robustness or uniqueness of your findings.

]

Response 7: 

Your suggestions have been invaluable in enhancing the Discussion section. We have discussed the similarities and differences of our findings based on a comparison of previous research methods and the methods of this study to highlight the contribution of our research 

See lines 500-521 in Section 4.3.1, page 19:

[

Some findings of this study differ from earlier research. Research employing linear analytical models have shown that transportation infrastructure and road density promote commercial aggregation [37,39] and increase pedestrian traffic in aggregation area [41,42]. However, the results from non-linear analytical models indicated that the positive impact of these two variables on NCA formation exists within a certain threshold range. Excessive density of transportation facilities and road network also has a negative effect. This finding appears plausible, as transportation infrastructure and roads reduce the available space for commercial facilities, and the resulting traffic flow may pose safety risks, thereby restricting residents' daily activities. This result provides more reliable indicators for NCA construction.

This study presents findings that are inconsistent with earlier research. Previous studies have suggested that areas with higher housing prices tend to have more amenities [75]. However, this study employs a similar modeling approach but finds a general negative impact of housing prices on NCA formation. This may be attributed to the fact that NCAs result from the combined selection of commercial facilities and resident activities. Housing prices generally reflect the economic status of residents. Higher-income groups tend to reside in low-density communities [76] and prefer diverse locations for daily activities [26,77]. This preference restricts the aggregation of daily life service facilities, which depend on a stable local customer base for operation. To address this issue, it is recommended that a portion of property fees in high housing price areas be allocated to reduce the rental of commercial facilities providing essential daily life services. This enhances the convenience of daily life for nearby residents.

]

Comments 8: [Limitations and Future Research Directions (Lines 490-520)

Observation: The limitations section is relatively brief and could provide a more comprehensive discussion of challenges faced during the study.

Suggestion: Expand on potential limitations related to data selection, model assumptions, or region-specific factors. A more detailed limitations section will provide readers with a realistic understanding of the study’s scope. Additionally, suggest specific future research directions that could address these limitations, such as using alternative datasets or exploring model adaptations for different regional contexts.]

Response 8:

Thank you very much for your valuable suggestions. In the revised manuscript, we have expanded on the limitations introduced by the data and scope of the study and provided specific future research directions to address these limitations. See lines 577-588 in Conclusions section, page 20-21:

[

Future research could integrate BHI with travel survey data focused on specific populations, providing a more comprehensive view of activity patterns, particularly for children and the elderly. These data could also support analyses on how demographic factors, such as age and gender, influence residents' utilization of and attitudes toward urban spatial functions. Second, this study focuses on socioeconomic and built environment factors when analyzing NCA formation, neglecting potentially influential from cultural and historical aspects. Future research should incorporate relevant variables to achieve a more comprehensive understanding of the formation mechanisms of the central areas of DLCs. The analysis model of NCA formation developed in this study also requires further validation across diverse regions. Expanding the research scope to include other regions will contribute to the robustness and applicability of the model.

]

Comments 9: [Conclusion: Policy and Practical Implications (Lines 530-550)

Observation: The conclusion summarizes findings effectively but lacks an emphasis on practical implications and policy recommendations.

Suggestion: Conclude with actionable insights or recommendations for practitioners or policymakers. For instance, suggest how regional planners might use your findings to prioritize areas for intervention or conservation. Additionally, consider summarizing any broader implications your research may have for sustainable development or ecological policy. ]

Response 9:

Thank you for your valuable feedback. We agree that highlighting practical implications and policy recommendations would enhance the conclusion’s impact. In the revised manuscript, we have emphasized that our proposed approach assists regional planners in arranging NCA layouts and DLC planning within China’s built-up areas, as well as supports daily life service providers in site selection. These contributions are significant for the construction of livable and sustainable urban environments. See lines 552-571 in Conclusions section, page 20:

[

The main contributions of this study are as follows: (1) This study provides a bottom-up strategy for regional planners to delineate DLCs, addressing the limitations of previous evaluative studies in guiding DLC planning at overall level. This method is particularly significant for the development of DLCs in built environments in China, where in older cities, DLCs must be delineated based on existing conditions rather than being newly planned according to the life circle concept, as in newly developed districts. The proposed efficient and low-cost method for identifying NCAs, establishing a foundation for delineating DLCs that align with residents' daily life patterns. (2) By uncovering the non-linear and threshold effects of factors influencing NCA formation, this study provides reliable indicators for optimizing DLC center layouts. This contributes to meeting residents' daily needs within walking distance, advancing the creation of livable urban environments, and enhancing the well-being and social equity of various socioeconomic groups. (3) This study identifies key variables influencing NCA types, elucidating the area characteristics specific to each type. This provides a foundation for developing targeted planning strategies, thereby improving the efficiency of urban resource allocation. Furthermore, the findings will assist daily life service providers, such as restaurants and retailers, in making more informed decisions regarding the selection of service locations by considering factors such as population density and housing prices. This will promote the integration of commercial activities with neighborhood characteristics, supporting the sustainable development of neighborhood commerce.

]

4. Response to Comments on the Quality of English Language

Point 1: Quality of English Language and Readability

Observation: The manuscript is readable, but certain sections contain complex sentence structures and technical jargon that may limit accessibility.

Suggestion: Simplify sentences where possible and ensure consistent use of terminology throughout the paper. For instance, using “land-use model” consistently rather than switching to similar terms can help maintain clarity. Additionally, consider having a native English speaker or professional editor review the manuscript for grammar and readability.

Response 1:

Thank you for your suggestions. We sought review of the manuscript's terminology consistency, grammar, and readability from relevant scholars whose native language is English, and have highlighted in the revised manuscript.

See lines 375-378 in Section 3.3.2, page 14:

[

The importance of each variable influencing the formation of the NCA is as follows (Figure 12 and Figure 13): Commercial diversity > Public transport stop > Parking > Floor-area ratio > Intersection > Population density> Closeness> Housing price > Road density> Betweenness.

]

, see lines 442-445 in Section 4.1.1, page 18:

[

Identified NCAs differ from urban commercial central districts in terms of functional composition, distribution characteristics, and service scale. Firstly, NCAs are primarily focused on providing daily life services, while urban commercial central districts mainly cater to higher-level consumption demands.

]

, see lines 456 in Section 4.1.2, page 18:

[

This study proposes a strategy for delineating DLCs centered on NCAs.

]

, see lines 500-502 in Section 4.1.2, page 19:

[

Research employing linear analytical models have shown that transportation facility and road density promote commercial aggregation [37,39] and increase pedestrian traffic in aggregation area [41,42].

]

, see lines 540-542 in Section 4.3.2, page 20:

[

Consequently, DT-DC NCAs are typically located in areas with higher residential population density, housing prices, and street centrality to sustain their operations.

]

, and see lines 547 in Conclusions section, page 20:

[

This study presents a novel strategy to delineate DLCs centered on NCAs.

]

In addition, all newly added content in the revised manuscript has been thoroughly reviewed.

5. Additional clarifications

We have updated the affiliation of the corresponding author and the funding sources for our work.

See lines 7-8, page 1:

[

² School of Architecture, Southeast University, Nanjing, China. Ageing-Responsive Civilization Think Tank Academic Committee, Naning, China; zhouying@seu.edu.cn

]

See lines 597-599 in Funding section, page 21:

[

This work was supported by the National Key R&D Program of China [grant number2022YFF0607003|; and Ageing-Responsive Civilization Think Tank grant number24LLWM15].

]

Response to Reviewer 3 Comments

1. Summary

Thank you very much for taking the time to review this manuscript. Please find the detailed responses below and the corresponding revisions in the re-submitted files.

2. Questions for General Evaluation

Reviewer’s Evaluation

Response and Revisions

Does the introduction provide sufficient background and include all relevant references?

Yes

We have expanded on the significance of the study to enhance Introduction section.

Is the research design appropriate?

Yes

-

Are the methods adequately described?

Yes

-

Are the results clearly presented?

Yes

-

Are the conclusions supported by the results?

Yes

-

3. Point-by-point response to Comments and Suggestions for Authors

Comments 1: [The text presents a highly interesting methodological work aimed at developing an assessment system applicable on an urban scale to facilitate the planning of local commercial activities. This aspect might be clarified in the title from the outset.]

Response 1:

Thank you for your valuable feedback. In the revised manuscript, we have revised the title to better reflect the content of the study and its specific significance. See lines 2-4 in page 1:

[

Identification and Causes of Neighborhood Commercial Areas: Focusing on the Development of Daily Life Circles in Urban Built Environments

]

Comments 2: [Overall, the text adopts a "functional" approach to urban development and its characteristics. Functional, in the sense that it assumes the development of cities, or their functions, is entirely planable, or at least the result of mechanical cause-effect dynamics. Perhaps this concept could be nuanced slightly. Related to this observation, two specific suggestions might assist the authors, should they find this comment helpful, in refining the framework of the article:

Lines 45-53: It may be beneficial to preface that activities which bring people together, as framed in Gehl’s Life between Buildings (1971), are categorized into necessary activities (such as going to work, grocery shopping, attending school), social activities (interpersonal relationship building), and voluntary activities (primarily recreational). In this sense, the article considers only the first category, which represents a limitation worth mentioning.]

Response 2: 

Thank you very much for your positive feedback on our manuscript and your valuable suggestions for improvement.

We agree with your comments. In the revised manuscript, we have elucidated the relationship between necessary activities and social and voluntary activities to demonstrate the significance of our study for the development of these two activities. See lines 54-58 in Introduction section, page 2:

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The Daily Life Circle(DLC) serves as the foundation of life circles, encompassing essential space for residents' daily activities, such as work, shopping, and schooling [3]. Additional activities, including social and voluntary activities (particularly community mutual-aid activities), should also be developed collaboratively within the DLC [14]. This approach offers residents a comprehensive living experience in their daily lives.

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Comments 3: [Additionally, it would be useful to acknowledge other demographic and sociocultural factors that may influence how urban space functions are used, such as age and gender (and the differing ways these groups experience the city, beginning with perceptions of safety).

This theme could also be revisited in the conclusions.]

Response 3:

Thank you for your insightful suggestions. We recognize the importance of considering demographic and sociocultural factors, such as age and gender, in understanding how urban spaces are utilized. In response, we have emphasized the necessity of analyzing these factors in future research in the revised manuscript. In addition, we have also outlined the potential research methods. See lines 577-588 in Conclusions section, page 20-21:

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Future research could integrate BHI with travel survey data focused on specific populations, providing a more comprehensive view of activity patterns, particularly for children and the elderly. These data could also support analyses on how demographic factors, such as age and gender, influence residents' utilization of and attitudes toward urban spatial functions. Second, this study focuses on socioeconomic and built environment factors when analyzing NCA formation, neglecting potentially influential from cultural and historical aspects. Future research should incorporate relevant variables to achieve a more comprehensive understanding of the formation mechanisms of the central areas of DLCs. The analysis model of NCA formation developed in this study also requires further validation across diverse regions. Expanding the research scope to include other regions will contribute to the robustness and applicability of the model.

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Comments 4: [Just one more minor point regarding Figure 14: it would be helpful to standardize the categories between the image (where letters are used) and the text (where numbers are used) to enhance clarity for the reader. ]

Response 4:

Thank you for your suggestion. We agree that standardizing the categories between Figure 14 and the text will enhance clarity for the reader. In response, we have revised the figure and text to ensure consistency. See lines 379-398 in Section 3.3.2, page 14-16:

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The SHAP main effect plot shows the following findings (Figure 14): (1) Socio-demographic: The relationship between resident population and NCA formation follows an inverted 'U' shape, though the local effect remains positive throughout. (2) Community attribute: The housing price shows an overall negative correlation with NCA formation, suggesting that higher prices may deter commercial agglomeration. Once housing prices exceed approximately 31,091.832 CNY/m² (close to the mean price in Qinhuai District), the local effect on NCA formation shifts from positive to negative. (3) Land use: Both the floor-area ratio and commercial diversity exhibit an overall positive correlation with NCA formation. The floor-area ratio has a positive local effect when exceeding 0.600, while commercial diversity shows a positive local effect when the entropy index exceeds 0.251. (4) Road network: NCA formation exhibits an inverted 'U' relationship with road density and a positive correlation with the intersection overall. The local effect of road density is positive in the range of 21.971–91.786 km/km², and for the intersection, it remains positive above 38.467 counts/km². (5) Street centrality: Street centrality makes a positive local contribution to NCA formation when betweenness surpasses 158.767 or closeness surpasses 0.000036. (6) Transportation facility: The local effect of transportation facility variables is positive when the density of public transport stops ranges from approximately 2.762 to 68.213 counts/km², or when the density of parking is between 22.199 and 186.408 counts/km².

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4. Response to Comments on the Quality of English Language

Point 1: No comments regarding English language quality were provided by Reviewer 1.

5. Additional clarifications

We have updated the affiliation of the corresponding author and the funding sources for our work.

See lines 7-8, page 1:

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² School of Architecture, Southeast University, Nanjing, China. Ageing-Responsive Civilization Think Tank Academic Committee, Naning, China; zhouying@seu.edu.cn

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See lines 597-599 in Funding section, page 21:

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This work was supported by the National Key R&D Program of China [grant number2022YFF0607003|; and Ageing-Responsive Civilization Think Tank grant number24LLWM15].

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