Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Expand the discussion on the policies aimed at spatial restructuring and networked development, hypothesizing how misuse of these guidelines (e.g. to achieve particular political and/or economic goals incompatible with the proclaimed values) can potentially impact the effectivity and have long-term effects on the desired spatial, developmental and sustainable expansion of urban environments.  

If quantitative studies of Beijing’s commuting network dynamics under the relief of the non-essential capital functions policy are scarce (line 75-76), discuss previous qualitative studies and how (if) they impacted the methodological approach showcased in this research.

What are the challenges of development that lack unification in speed and efficiency of implementation, with non-linear trajectory and outcomes? How will this research help to quantify e.g. spatial clusters that ought to be prioritized when discussing developmental strategies? Expand upon this particular aspect in the Discussion section.

Author Response

Response to Reviewer 1

 

6th November, 2025

 

Dear Reviewer 1,

 

Thank you for your comments concerning our manuscript entitled “Relieving Beijing’s Nonessential Capital Functions: Metropolitan Area Polycentricity for Sustainability” (Manuscript ID: land-3948540). The comments are all valuable and helpful for improving our manuscript. We have carefully reviewed your comments and have made revisions to the original manuscript accordingly, which we hope would constitute a more satisfactory one. Please kindly find our response to your questions and comments below.

 

Sincerely,

The authors

 

 

Reviewer #1

Comment 1

Expand the discussion on the policies aimed at spatial restructuring and networked development, hypothesizing how misuse of these guidelines (e.g. to achieve particular political and/or economic goals incompatible with the proclaimed values) can potentially impact the effectivity and have long-term effects on the desired spatial, developmental and sustainable expansion of urban environments.

Response:

We appreciate the reviewer’s thoughtful suggestion. A hypothesis on potential policy misuse (e.g., prioritizing incompatible political/economic goals) and its impacts on effectiveness and long-term spatial, developmental, and sustainable outcomes has been added to Section 5.1. This expands the discussion on restructuring and networked policies while maintaining balance.

The changes are incorporated in the revised manuscript at Lines 592–598. The added text reads:

“However, misuse of these spatial restructuring and networked development guidelines—e.g., prioritizing short-term political gains (such as rapid urbanization for prestige projects) or economic objectives (like unchecked industrial clustering without environmental safeguards) incompatible with proclaimed sustainability values—could undermine policy effectiveness, leading to fragmented development, increased inequality, and long-term setbacks in urban expansion, such as persistent congestion or ecological degradation.”

Comment 2

If quantitative studies of Beijing’s commuting network dynamics under the relief of the non-essential capital functions policy are scarce (line 75-76), discuss previous qualitative studies and how (if) they impacted the methodological approach showcased in this research.

Response:

Thank you for your helpful suggestion. In response, we have included a discussion of previous qualitative studies on the impact of decentralization policies on urban spatial organization. We also explain how these studies, while valuable, lacked the ability to analyze large-scale commuting flows with high resolution, which this research addresses using big data and network analysis. (Line 86-90)

The changes are incorporated in the revised manuscript at Lines 83-87. The added text reads:

“Previous qualitative studies have explored the effects of decentralization policies on urban spatial organization, often using case studies and interviews to understand the shifts in commuting patterns and the role of polycentricity [17], [18]. While these studies provided valuable insights into the broader socio-economic impacts of decentralization, they could not

analyze large-scale commuting flows with high resolution.”

Comment 3

What are the challenges of development that lack unification in speed and efficiency of implementation, with non-linear trajectory and outcomes? How will this research help to quantify e.g. spatial clusters that ought to be prioritized when discussing developmental strategies? Expand upon this particular aspect in the Discussion section.

Response:

 We thank the reviewer for this insightful suggestion. We have expanded 5.3 to address non-unified development challenges (e.g., inconsistent speeds leading to non-linear trajectories) and explain how the research quantifies spatial clusters (e.g., C5 expansion, C1 contraction) for prioritizing strategies.

The changes are incorporated in the revised manuscript at Lines 622–630. The added text reads:

“The evolution of the Beijing Metropolitan Area’s commuting network highlights key challenges in non-unified development: inconsistent implementation speeds lead to non-linear trajectories, where industrial hubs like BDA advance rapidly while administrative subcenters (e.g., Tongzhou) lag, resulting in uneven outcomes such as fragmented integration and persistent administrative barriers. This research helps quantify spatial clusters for prioritization in developmental strategies—e.g., C5 (Daxing-Gu’an-BDA) expanded by 9 sub-districts, signaling high-priority areas for infrastructure investment, while C1’s contraction (–16 sub-districts) identifies zones needing targeted connectivity enhancements to foster balanced polycentricity.”

Reviewer 2 Report

Comments and Suggestions for Authors

The paper presents a timely and relevant study analyzing Beijing’s metropolitan restructuring using mobile signaling data and complex network analysis. It offers valuable insights into the evolution from a monocentric to a polycentric urban structure, contributing to ongoing debates on sustainable urban transformation. The methodology is technically sound, and the topic fits well within Land’s scope. However, the paper requires major revision to improve conceptual clarity, methodological transparency, and interpretive depth.

Main Comments:

1. Clarify the Research Gap and Objectives (p. 2–3):
   The introduction provides a broad background but lacks a clearly articulated research problem, gap, and objectives. The authors should explicitly define the unanswered question and list measurable objectives to guide the study.
2. Strengthen the Conceptual Framework (p. 2–3):
   The theoretical basis (polycentricity, growth pole, space of flows) is informative but fragmented. A concise conceptual framework or diagram linking these theories to network indicators and sustainability outcomes would strengthen coherence.
3. Enhance Methodological Transparency (p. 5):
   While the use of China Unicom mobile data is innovative, the paper should discuss data reliability, representativeness, and potential biases. The criteria for selecting algorithm parameters (e.g., Louvain resolution) also need justification.
4. Deepen the Discussion and Policy Implications (p. 18–19):
   Results are rich but overly descriptive. The discussion should focus more on how the findings inform sustainable planning, governance, and infrastructure policy, supported by quantifiable sustainability outcomes (e.g., emission or travel-time reductions).
5. Revise the Conclusion for Novelty and Future Work (p. 20–21):
   The conclusion mainly repeats results. It should instead highlight the study’s unique contribution, practical implications, and directions for future research.

The paper demonstrates strong analytical potential but needs clearer articulation of its theoretical contribution, methodological justification, and applied significance. Addressing these revisions will substantially enhance its clarity, coherence, and publication readiness.

Author Response

Response to Reviewer 2

 

6th November, 2025

 

Dear Reviewer 2,

 

Thank you for your comments concerning our manuscript entitled “Relieving Beijing’s Nonessential Capital Functions: Metropolitan Area Polycentricity for Sustainability” (Manuscript ID: land-3948540). The comments are all valuable and helpful for improving our manuscript. We have carefully reviewed your comments and have made revisions to the original manuscript accordingly, which we hope would constitute a more satisfactory one. Please kindly find our response to your questions and comments below.

 

Sincerely,

The authors

 

 

Reviewer #2

The paper presents a timely and relevant study analyzing Beijing’s metropolitan restructuring using mobile signaling data and complex network analysis. It offers valuable insights into the evolution from a monocentric to a polycentric urban structure, contributing to ongoing debates on sustainable urban transformation. The methodology is technically sound, and the topic fits well within Land’s scope. However, the paper requires major revision to improve conceptual clarity, methodological transparency, and interpretive depth.

Response: Thank you very much for your detailed review and valuable comments. We are pleased to hear that you found our research topic, methodology, and analysis of Beijing's metropolitan restructuring insightful, especially the discussion on the evolution from a monocentric to a polycentric urban structure. We also appreciate your positive feedback on the technical soundness of the methodology. We fully agree with your observation that the paper could benefit from improvements in conceptual clarity, methodological transparency, and interpretive depth. In response to your suggestions, we have made the suggested clarifications and improvements to further enhance the study.

Comment 1

Clarify the Research Gap and Objectives (p. 2–3):
The introduction provides a broad background but lacks a clearly articulated research problem, gap, and objectives. The authors should explicitly define the unanswered question and list measurable objectives to guide the study.

Response:

Thank you for your thoughtful suggestion. In response, we have clarified the research gap and objectives in the revised introduction. We have explicitly defined the research problem by emphasizing the scarcity of quantitative studies on Beijing’s commuting network dynamics under the relief of non-essential capital functions policy. Additionally, we have articulated the key objective of this study: to examine the evolution of Beijing’s commuting network as it transitions from a monocentric to a polycentric structure, offering valuable insights into how decentralization policies reshape the city’s spatial organization.(Line 84,78,90-102)

“Despite these theoretical advances, quantitative studies of Beijing’s commuting network dynamics under the relief of the non-essential capital functions policy are scarce. ”

“This gap is crucial as it hinders a deeper understanding of how decentralization policies influence the transition towards polycentric urban structures. Traditional data sources, such as traffic surveys, lack the resolution of cell phone signaling data, which provide fine-grained insights into commuting flows [19], [20]. Social network analysis, applied to nodal regions and networked cities [21], [22], offers robust tools for analyzing urban network topology through metrics like centrality and density [23], [24]. Recent studies highlight commuting flows as central to metropolitan spatial structures, yet few integrate big data with network analysis to examine policy-driven changes that foster metropolitan polycentricity for sustainability [25], [26]. Therefore, the primary objective of this study is to examine the evolution of Beijing’s commuting network as it shifts from a monocentric to a polycentric structure, providing insights into how decentralization policies are reshaping the city's spatial organization.”

Comment 2

Strengthen the Conceptual Framework (p. 2–3):
The theoretical basis (polycentricity, growth pole, space of flows) is informative but fragmented. A concise conceptual framework or diagram linking these theories to network indicators and sustainability outcomes would strengthen coherence.

Response:

We thank the reviewer for this valuable suggestion. To enhance coherence, we have added a concise conceptual framework (Figure 1) that links polycentricity, growth pole, and space of flows theories to key network indicators (e.g., in-degree centrality, clustering coefficient) and sustainability outcomes (e.g., reduced extreme commutes, emission mitigation). (Line 73-83)

“These frameworks together inform our study's focus on the transformation of Beijing's urban structure towards polycentricity, driven by decentralization policies. By linking these theoretical perspectives to network analysis, this study aims to evaluate the sustainability outcomes of polycentric urban systems, including the reduction of emissions, optimized land use, and enhanced resource efficiency. Figure 1 summarizes how these theories are interconnected, with polycentricity acting as the central concept, shaped by growth poles and the space of flows framework, and leading to sustainability outcomes through spatial restructuring and network interactions.”

Figure 1. Conceptual Framework for Metropolitan Polycentricity

Comment 3

Enhance Methodological Transparency (p. 5): While the use of China Unicom mobile data is innovative, the paper should discuss data reliability, representativeness, and potential biases. The criteria for selecting algorithm parameters (e.g., Louvain resolution) also need justification.

Response:

Thank you very much for your constructive comments regarding the methodological transparency of our paper. We greatly appreciate your feedback on the need to discuss the reliability, representativeness, and potential biases of the China Unicom mobile data, as well as the justification for the selection of algorithm parameters such as the Louvain resolution.

• Data Reliability and Representativeness:

We acknowledge that the reliability and representativeness of mobile data are crucial for the validity of our analysis. For the period under study, we note that June 2017 was a normal working month, and no significant changes in commuting behavior were observed during this time. While June 2021 fell within the COVID-19 pandemic, it occurred during a stage of normalized management, and the impact on commuting behavior was limited. This context assures that the data for both periods are suitable for analysis, as there was no significant disruption in the mobility patterns.

We also provide information on the scale of China Unicom subscribers during the study period. In 2017, the number of subscribers was approximately 9 million, and by 2021, this had increased to about 11 million. This represents 40.7% and 53.3% of the total population in 2017 and 2021, respectively, demonstrating that the subscriber scale is sufficiently representative of the general population in both years.

Additionally, we have also found other studies that have used similar mobile signaling data for research in the Beijing-Tianjin-Hebei region. These studies further support the validity of using China Unicom data in this context and demonstrate that such data are commonly employed in regional mobility and urban transformation studies.

The changes are incorporated in the revised manuscript at Lines 190–199. The added text reads:

“June 2017 is a normal working month and no significant change was found in the commuting behavior during this period. Although June 2021 fell within the COVID-19 pandemic, it was in a stage of normalized management, thus having a limited impact on commuting. Thus, the data are appropriate to be used. The scale of Unicom sub-scribers in 2017 was about 9 million, while that of 2021 was about 11 million. Com-pared with the statistical data of 2017 and 2021, it accounted for 40.7% and 53.3% of the total population, respectively. This demonstrates sufficient representativeness of the subscriber scale at both time points. Beyond the total quantity comparison, we further conducted a comparison from the perspective of population spatial distribution.”

(2) Potential Biases:

In line with your suggestion, we have included a more thorough discussion of the potential biases in the mobile data, which we acknowledge as part of the study’s limitations. Specifically, we recognize that the data may under-represent certain demographic groups, such as rural populations and elderly individuals, who are less likely to own mobile phones. This could introduce biases in our findings, particularly in terms of the generalizability of the results to the broader population.

Additionally, although the data from June 2021 falls within the COVID-19 pandemic period, we note that the impact on commuting behavior was limited due to the normalization of management measures during that stage of the pandemic. However, we acknowledge that the data might not fully capture the behavior of specific populations that were disproportionately affected by the pandemic, such as high-risk groups or essential workers. This could introduce additional bias in the observed commuting patterns.

We discuss these biases and limitations in the context of our study to provide a transparent understanding of how the data may influence the findings and interpretation. Despite these limitations, we believe the study still offers valuable insights into the urban transformation dynamics.

The changes are incorporated in the revised manuscript at Lines 724–732. The added text reads:

“Limitations include seasonality, as June data may not capture seasonal variations; privacy and ethical safeguards, as raw mobile signaling data remain confidential due to privacy regulations; data unshareability, preventing direct replication; and the short 2017–2021 window, which limits assessment of long-term policy impacts. To enhance reproducibility, we will include the pseudocode for the analysis process in the supplementary materials, allowing for replication and adaptation of the methodology. Future research should integrate multi-source data, such as traffic cards and navigation logs, and extend analysis to economic and information flows to refine strategies for resilient, low-carbon metropolitan systems in the Beijing-Tianjin-Hebei region and globally.”

(3) Justification for Algorithm Parameters:

We have provided a clearer justification for the selection of the Louvain resolution parameter. In the revised manuscript, we explain the range of resolution values (0.5 to 1.5) that were tested, along with the rationale behind choosing this range based on previous literature and theoretical considerations. Additionally, to address the sensitivity of the Louvain algorithm's resolution parameter, we conducted 100 tests with different random seeds to ensure the robustness and stability of the results. To further support this, we have included a histogram comparing Modularity vs. Resolution Parameter in the appendix. This additional analysis helps clarify the choice of the resolution parameter and reinforces the reliability of our community detection results.

The changes are incorporated in the revised manuscript at Lines 322–343, and Lines 754-757. The added text and appendix read:

“For the township-scale commuting network, the same residence-workplace pair often involves a large number of commuters, representing both the strength and direction of daily commuting, rather than just a simple connection. Given that commuting is inherently directional, and each edge weight reflects commuting intensity, a directed weighted network is more appropriate. Self-loops occur when the workplace and residence are in the same township. We chose to remove all self-loops. This decision was based on the fact that the township is treated as a node, and the integration process emphasizes stronger links between the township and neighboring areas. While weak commuting links exist, each edge represents real commuter behavior. Since the net-work is already weighted, we chose not to remove weak edges based on arbitrary thresholds.

To address the resolution limit issue in community detection, we adjusted the resolution parameter and ran the Louvain algorithm multiple times to ensure stable and fine-grained community division. With 449 township nodes, we conducted 100 iterations using different random seeds for each run. The initial random seed was set to 42, and we then selected 100 random seeds from 1 to 10000 for the subsequent runs. For the resolution parameter, we tested values between 0.5 and 1.5, with a step size of 0.1. The optimal resolution was chosen based on the highest modularity (Histogram comparing Modularity vs. Resolution Parameter, as shown in Appendix B). After multiple runs, we found that a resolution of 1.0 yielded the best result, with a modularity value of >0.43 and a division into 10 communities. For the results of multiple computations, we further employed the Adjusted Rand Index (ARI) metric to evaluate the stability of the communities. The average ARI was found to exceed 0.8, demonstrating the robustness of community partitioning.”

“Appendix B The Selection of Resolution Parameter

Figure B1. Modularity changes with Resolution”

We believe these revisions significantly improve the methodological transparency of the paper and provide readers with a more complete understanding of the data and algorithm choices. We hope these additions meet your expectations and enhance the overall quality of the manuscript.

Comment 4

Deepen the Discussion and Policy Implications (p. 18–19):
Results are rich but overly descriptive. The discussion should focus more on how the findings inform sustainable planning, governance, and infrastructure policy, supported by quantifiable sustainability outcomes (e.g., emission or travel-time reductions).

Response:

Thank you for the suggestion to deepen the discussion and focus on quantifiable sustainability outcomes. In response, we have updated the discussion to include precise, measurable benefits, such as a 3.28-minute reduction in average commuting time and a 1.99% decrease in extreme commute frequency, demonstrating the impact of the polycentric transition on commuting efficiency and job-housing balance. We also emphasize the policy implications, including the need for cross-administrative governance, infrastructure investment in peripheral areas, and coordinated planning of job-housing spaces. (Line 622-657)

" The evolution of the Beijing Metropolitan Area’s commuting network highlights key challenges in non-unified development: inconsistent implementation speeds lead to non-linear trajectories, where industrial hubs like BDA advance rapidly while administrative subcenters (e.g., Tongzhou) lag, resulting in uneven outcomes such as fragmented integration and persistent administrative barriers. This research helps quantify spatial clusters for prioritization in developmental strategies—e.g., C5 (Daxing-Gu’an-BDA) expanded by 9 sub-districts, signaling high-priority areas for infrastructure investment, while C1’s contraction (–16 sub-districts) identifies zones needing targeted connectivity enhancements to foster balanced polycentricity.

Our results show that average commuting time decreased by 3.28 minutes (from 53.72 minutes in 2017 to 50.44 minutes in 2021), with the frequency of extreme commutes (>60 minutes) reduced by 1.99 %, shortening long-distance travel and contributing to more balanced and efficient job-housing patterns under polycentric development. Policymakers should prioritize cross-administrative governance to strengthen ties between Beijing and surrounding counties by optimizing transportation networks and fostering industrial agglomeration [65]. This governance approach can enhance resource efficiency, reduce waste, and promote sustainable consumption patterns in urban systems [66]. Additionally, coordinated planning of job-housing spaces is critical. Enhancing public service facilities, particularly in healthcare and education, can boost inter-regional mobility and balance occupational and residential distributions [67], [68]. This approach will alleviate pressure on central areas while promoting sustainable economic growth, reducing environmental impacts, and enhancing resource sharing in peripheral regions [69]. Furthermore, investments in cross-district transportation infrastructure, such as rail transit and bus rapid transit systems, are essential to improve commuting efficiency and reduce travel-related carbon emissions, supporting environmental sustainability and cleaner urban transport systems [70]. Major infrastructure projects like Daxing International Airport should leverage transportation integration to drive economic synergy and advance county urbanization, enhancing the metropolitan area’s sustainability and competitiveness through efficient resource use, reduced waste, and lower environmental pressures [71]. To operationalize these recommendations, the Beijing-Tianjin-Hebei provincial coordination mechanism should prioritize inter-jurisdictional rail governance in the near term (2025–2030) for under-connected clusters like C6 (Wuqing-Guangyang-Xianghe), enhance job-housing coordination through public service upgrades in the medium term (2030–2035) while managing housing affordability risks near new hubs via subsidies or zoning controls, and pursue ongoing multimodal integration leveraging airports and rail, with continuous monitoring of non-linear outcomes to ensure equitable, sustainable growth."

Comment 5

Revise the Conclusion for Novelty and Future Work (p. 20–21):
The conclusion mainly repeats results. It should instead highlight the study’s unique contribution, practical implications, and directions for future research.

Response:

We sincerely thank you for this insightful feedback. The Conclusion has been restructured to minimize repetition of results and instead emphasize the study’s unique contribution, practical implications, and future research directions. (Line 709-732).

“Analysis of China Unicom’s mobile signaling data (2017–2021) and complex network methods reveals a transformation in Beijing’s metropolitan commuting network under the non-essential capital functions relief policy, uniquely quantifying the shift from monocentric to polycentric structure through fine-grained township-level flows and community detection—a methodological advance over prior qualitative or aggregate studies. Peripheral employment centers (e.g., BDA) have gained prominence, while core districts have lost centrality (e.g., in-degree in non-Beijing areas up +49.5%), driven by rail transit expansion and industrial relocation. This aligns with the Fifth Central Urban Work Conference’s polycentric vision, demonstrating that targeted decentralization can enhance job-housing balance and regional integration.

Practically, these findings inform sustainable urban governance: prioritizing cross-jurisdictional rail and industrial clustering in under-connected peripheral counties (e.g., Wuqing, Zhuozhou) could further reduce long-distance commuting and emissions. Cluster analysis highlights emergent networked urban groups, offering a replicable framework for diagnosing integration barriers in other megacity regions.

Limitations include seasonality, as June data may not capture seasonal variations; privacy and ethical safeguards, as raw mobile signaling data remain confidential due to privacy regulations; data unshareability, preventing direct replication; and the short 2017–2021 window, which limits assessment of long-term policy impacts. Besides, the data may under-represent certain demographic groups, which could introduce bias in our findings, particularly in terms of generalizing to the broader population. Future research should integrate multi-source data, such as traffic cards and navigation logs, and extend analysis to economic and information flows to refine strategies for resilient, low-carbon metropolitan systems in the Beijing-Tianjin-Hebei region and globally.”

Comment 6

The paper demonstrates strong analytical potential but needs clearer articulation of its theoretical contribution, methodological justification, and applied significance. Addressing these revisions will substantially enhance its clarity, coherence, and publication readiness.

Response:

Thank you for your valuable feedback. In response to your suggestions, we have made the following revisions. The theoretical contribution is now more clearly articulated in Section 1 (as discussed in your Comment 2) and Section 5.2 (Lines 603-620), with the relevance of key theories validated through empirical findings. A clearer justification for the methodology is provided in Section 3.3, especially the Louvain parameters, as discussed in your Comment 3. Additionally, we have included the pseudo-code for the Time-accumulation Method in Table B1 (Lines 759-760). The applied significance of our findings has been further emphasized in Section 5.3 (Lines 622-634, 650-658).

We believe these revisions significantly improve the clarity, coherence, and overall readiness of the paper for publication.

(1) The partial updated theoretical contribution:

“This study validates the relevance of key theories through empirical findings. Polycentric urban region theory explains the observed shift from monocentric to polycentric structure, with peripheral in-degree centrality rising 49.5 % outside Beijing [58], [59]. Spatial diffusion theory accounts for the outward spread of employment hubs, evident in BDA’s cluster expansion absorbing 9 sub-districts from adjacent areas [60]. Growth pole theory captures BDA’s spillover effects, reflected in strengthened suburban commuting links and global network efficiency increase from 0.66 to 0.69 [14]. Core-periphery theory highlights persistent administrative barriers, seen in limited cross-boundary edges (15 % of total) despite policy intent [61]. The space of flows framework reveals enhanced functional connectivity, as shown by the rise in average clustering coefficient and reduced average path length [15]. The observed shift aligns with the Los Angeles School’s decentralization model [62], [63], particularly evident in the functional expansion and commuting mobility of peripheral areas like BDA and Yanjiao. By integrating fine-grained cell phone signaling data with complex network analysis, this study extends these theories, offering a nuanced understanding of job-housing spatial patterns and commuting dynamics that advance metropolitan polycentricity for sustainability. Together, these insights provide a robust framework for understanding policy-driven network evolution in the Beijing-Tianjin-Hebei region and informing future planning for environmental resilience [64].”

(2) The partial updated methodological justification:

“Table B1. Pseudo-code of Time-accumulation Method

Algorithm: Time-accumulation Method for Identifying Residence and Workplaces

Input:

- User data (location data with timestamps, mobile phone data)

- Study area boundary (geographic coordinates)

Output:

- Job-housing matrix at the township level

1. Define time windows:

- Nighttime: 9:00 PM - 7:00 AM

- Daytime: 9:00 AM - 5:00 PM

2. For each user:

2.1. Filter data by time window (Nighttime and Daytime)

3. Identify valid users:

3.1. For each user, count the number of days they are present in the study area

3.2. If the user is present for more than 15 days in a month, mark as valid

4. Identify residence for each valid user:

4.1. Filter data to include only nighttime stays

4.2. For each user, calculate the total time spent at each location during nighttime

4.3. Select the longest stay as the residence location

5. Identify workplace for each valid user:

5.1. Filter data to include only weekday (Monday to Friday) stays

5.2. For each user, calculate the total time spent at each location during weekdays

5.3. Select the longest stay as the workplace location

6. Handle workplace same as residence scenario:

6.1. If the weekday workplace is the same as the residence location:

6.1.1. Identify the second-longest location

6.1.2. If the second-longest stay time exceeds 50% of the primary location's time, use the second-longest location as workplace

7. Create job-housing matrix:

7.1. Link residence and workplace locations for each valid user

7.2. Aggregate the data by township level

7.3. Generate job-housing matrix showing connections between residence and workplace at township level

8. Output:

- The final job-housing matrix at the township level

”

(3) The partial updated applied significance:

“The evolution of the Beijing Metropolitan Area’s commuting network highlights key challenges in non-unified development: inconsistent implementation speeds lead to non-linear trajectories, where industrial hubs like BDA advance rapidly while administrative subcenters (e.g., Tongzhou) lag, resulting in uneven outcomes such as fragmented integration and persistent administrative barriers. This research helps quantify spatial clusters for prioritization in developmental strategies—e.g., C5 (Daxing-Gu’an-BDA) expanded by 9 sub-districts, signaling high-priority areas for infrastructure investment, while C1’s contraction (–16 sub-districts) identifies zones needing targeted connectivity enhancements to foster balanced polycentricity.

Our results show that average commuting time decreased by 3.28 minutes (from 53.72 minutes in 2017 to 50.44 minutes in 2021), with the frequency of extreme commutes (>60 minutes) reduced by 1.99 %, shortening long-distance travel and contributing to more balanced and efficient job-housing patterns under polycentric development.”

 

“To operationalize these recommendations, the Beijing-Tianjin-Hebei provincial coordination mechanism should prioritize inter-jurisdictional rail governance in the near term (2025–2030) for under-connected clusters like C6 (Wuqing-Guangyang-Xianghe), enhance job-housing coordination through public service upgrades in the medium term (2030–2035) while managing housing affordability risks near new hubs via subsidies or zoning controls, and pursue ongoing multimodal integration leveraging airports and rail, with continuous monitoring of non-linear outcomes to ensure equitable, sustainable growth.”

 

 

Reviewer 3 Report

Comments and Suggestions for Authors

Recommendation: Minor revisions. Strong policy relevance and novel use of 2017/2021 China Unicom signaling with complex-network tools. Claims about a shift toward polycentricity are supported, but a few methodological clarifications, tighter causal language, and clearer figures will strengthen the paper.

 

Title & Abstract

• Clear scope and result (monocentric → polycentric), but add at least one quantitative anchor (e.g., % change in DI/DO, GE 0.66→0.69) and soften causal verbs (“supports” instead of “concludes that the policy has optimized”).

Introduction

• Good policy background (2015 Outline; goals; sustainability angle). Streamline repeated sustainability claims and state the gap and contribution in one concise paragraph (mobile big data + network analysis of commuting under function-relief policy).

Study Area

• The 1-hour accessibility definition and 150-km buffer via Baidu routing are clear; note and discuss the time mismatch (travel times collected in Nov 2023 for a 2017/2021 study) and add a short sensitivity statement.

Policy-Driven Development

• Solid chronology; consider pushing most details to Appendix (already tables A1–A2) and keep 2–3 lines in the main text highlighting which events plausibly affect commuting (e.g., Daxing Airport, sub-center move).

Data & Processing

• Strengths: June 2017/2021 data; large user base; strong correlation (r>0.97) with statistics; clear dwell/commute logic and “ping-pong” cleaning. Add notes on: (i) seasonality (June only), (ii) pandemic effects in 2021, (iii) thresholds for non-human SIM removal, and (iv) code/algorithmic reproducibility (pseudo-code or repo for the time-accumulation method).

Complex-Network Methods

• Nicely structured (centrality, link strength, Louvain communities, global metrics). Report parameter choices (e.g., edge thresholds, Louvain resolution) and show robustness to (a) alternative community detection or (b) resolution sweeps. Briefly justify why weighted directed treatment is appropriate and how self-loops were handled.

Results — Nodes

• Clear rise of peripheral hubs (BDA top by 2021) and declines in core districts; include effect sizes in text (Table 2 % changes) and add uncertainty where possible. Avoid inferring environmental outcomes from centrality alone (move such claims to Discussion with caveat).

Results — Links

• Good maps of strengthening/weakening ties. Please (i) list the top 10 increases/decreases with RS values, (ii) decompose intra-Beijing vs. cross-boundary changes, and (iii) flag flows potentially distorted by COVID-19 in 2021.
• Reported modularity (≈0.44–0.45) is solid; add a brief stability check (multiple seeds/runs) and quantify cross-administrative links (share of edges crossing boundaries) to support the boundary-friction claim.
• Nice to show GE rising 0.66→0.69 and LCC=1.0; also report the average clustering coefficient values (not just direction) and average path length to complete the picture.

Discussion

• Keep causal language cautious (“consistent with policy” rather than “the policy has caused”), and separate mechanism (industrial relocation vs. administrative relocation lag). Tie back explicitly to each main result.
• Good coverage (polycentricity, growth poles, space of flows). Consider trimming citations and mapping each theoretical claim to a concrete empirical finding (one sentence each).
• Policy recommendations
• Useful and realistic; add priority, responsible actors, and time horizon (e.g., “inter-jurisdiction rail governance—Provincial BTH coordination—near term”). Note risk management (e.g., housing affordability near new hubs).
• Global comparisons
• Comparisons are insightful; flag data differences (Beijing’s mobile signaling vs. literature for Tokyo/NY/Paris) to avoid overstating comparability.

Conclusion & Limitations

• Strong summary; expand limitations to include: (i) seasonality, (ii) privacy/ethics safeguards, and (iii) data unshareability → propose releasing derived OD matrices and code.
• Figures & Tables (general)
• Make maps color-blind safe; enlarge labels for township names; add legends/units; annotate thresholds for displayed links; ensure tables are self-contained with notes on DI/DO definitions and sample sizes.
• Light edit for repetition (reduce “cleaner production” phrasing in main text); keep verbs cautious on causality; standardize abbreviations.

With these small clarifications and presentation upgrades, I would be comfortable recommending acceptance.

Author Response

Response to Reviewer 3

 

6th November, 2025

 

Dear Reviewer 3,

 

Thank you for your comments concerning our manuscript entitled “Relieving Beijing’s Nonessential Capital Functions: Metropolitan Area Polycentricity for Sustainability” (Manuscript ID: land-3948540). The comments are all valuable and helpful for improving our manuscript. We have carefully reviewed your comments and have made revisions to the original manuscript accordingly, which we hope would constitute a more satisfactory one. Please kindly find our response to your questions and comments below.

 

Sincerely,

The authors

 

 

Reviewer #3

Recommendation: Minor revisions. Strong policy relevance and novel use of 2017/2021 China Unicom signaling with complex-network tools. Claims about a shift toward polycentricity are supported, but a few methodological clarifications, tighter causal language, and clearer figures will strengthen the paper.

Response: Thank you for your positive feedback and constructive comments. We appreciate your recognition of the policy relevance and novel use of the China Unicom signaling data with complex-network tools. In response to your suggestions, we have addressed the methodological clarifications, refined the language regarding causal claims, and improved the clarity of the figures. These revisions will be reflected in the sections below.

Comment 1

Title & Abstract

• Clear scope and result (monocentric → polycentric), but add at least one quantitative anchor (e.g., % change in DI/DO, GE 0.66→69) and soften causal verbs (“supports” instead of “concludes that the policy has optimized”).

Response:

Thank you for the constructive suggestion. We have incorporated quantitative anchors into the Abstract (e.g., in-degree centrality in areas outside Beijing increased by 49.5 %; global network efficiency rose from 0.66 to 0.69) and softened causal language (e.g., “concludes that the policy has optimized” → “suggests that the policy supports”; “contributing to” instead of direct attribution). These changes enhance precision and clarity while maintaining the study’s core message. (Line 10-26):

“This study explores the transformation of Beijing's metropolitan commuting network resulting from the relief of the non-essential capital functions policy. The aim is to understand how this policy has contributed to the development of the Beijing-Tianjin-Hebei urban agglomeration. Using China Unicom's mobile signaling data from 2017 to 2021, we apply complex network analysis to quantify changes in commuting patterns from the perspectives of node importance, link strength, and community structure. The results indicate a shift from a monocentric to a polycentric network (e.g., in-degree centrality in areas outside Beijing increased by 49.5 %; global network efficiency rose from 0.66 to 0.69), with peripheral employment centers gaining prominence while central districts lose their dominant position. However, administrative boundaries hinder full regional integration, as only select areas form interconnected clusters. These findings suggest that the policy supports optimized job-housing spatial structures, reduced urban congestion, and improved resource efficiency, contributing to sustainable urban development. The findings highlight the role of enhanced rail transit and governance in further strengthening connectivity and minimizing environmental impacts, while also providing empirical evidence for urban planning strategies aimed at fostering resource-efficient, low-waste metropolitan areas.”

Comment 2

Introduction

• Good policy background (2015 Outline; goals; sustainability angle). Streamline repeated sustainability claims and state the gap and contribution in one concise paragraph (mobile big data + network analysis of commuting under function-relief policy).

Response:

Thank you for your valuable feedback. In response, we have streamlined the sustainability claims to avoid redundancy and clearly stated the research gap and contribution in a concise paragraph. Specifically, we have emphasized the gap in understanding how decentralization affects commuting networks and highlighted the contribution of using mobile big data and network analysis to explore this under the function-relief policy. (Line 43-47)

“Despite the emphasis on sustainability in these policies, there is a significant gap in understanding how decentralization influences commuting networks. This study contributes by using mobile big data and network analysis to examine the dynamics of Beijing’s commuting network under the function-relief policy, offering new insights into the transition towards a polycentric urban structure.”

 

Comment 3

Study Area

The 1-hour accessibility definition and 150-km buffer via Baidu routing are clear; note and discuss the time mismatch (travel times collected in Nov 2023 for a 2017/2021 study) and add a short sensitivity statement.

Response:

We appreciate your observation. The time mismatch is now addressed by clarifying that 2023 travel times—the closest available data—fully cover the 2017–2021 accessibility range, with a note confirming no significant over-expansion compared to earlier conditions. This ensures methodological rigor and regional completeness. (Line 158-164)

“Although the commuting data span 2017–2021, travel times were sourced from November 6–10, 2023—the closest available time point—to ensure the boundary fully encompasses the infrastructure and accessibility conditions of the study period. The shortest travel time for each grid was used to identify districts and counties within a 1-hour reach. This approach conservatively includes all areas reachable in 2017–2021, with no significant over-expansion confirmed via comparison with earlier routing snapshots.”

Comment 4

Policy-Driven Development

• Solid chronology; consider pushing most details to Appendix (already tables A1–A2) and keep 2–3 lines in the main text highlighting which events plausibly affect commuting (e.g., Daxing Airport, sub-center move).

Response:

We thank the reviewer for the constructive suggestion. Detailed chronology has been moved to Appendix Tables A1 and A2. The main text now retains 2–3 lines highlighting events with plausible commuting impacts: Tongzhou sub-center relocation (2018), Daxing Airport opening (2019), and Huitian Action Plan. References to the 2015 Outline, Dongding Market, and Jingxiong Railway have been removed as advised. This streamlines the narrative while preserving focus on key drivers of polycentricity. (Line 179-185)

“Key initiatives include the 2016 designation of Tongzhou as Beijing’s sub-city center (with municipal authorities relocated by 2018) and the 2019 opening of Beijing Daxing International Airport, which have strengthened peripheral hubs like the BDA and reduced core district centrality. The 2018–2020 Huitian Action Plan further supported residential and infrastructure improvements in suburban areas like Changping, enhancing commuting connectivity. These policy-driven changes, detailed in Tables A1 and A2, have facilitated a shift toward a polycentric commuting network.”

Comment 5

Data & Processing

• Strengths: June 2017/2021 data; large user base; strong correlation (r>0.97) with statistics; clear dwell/commute logic and “ping-pong” cleaning. Add notes on: (i) seasonality (June only), (ii) pandemic effects in 2021, (iii) thresholds for non-human SIM removal, and (iv) code/algorithmic reproducibility (pseudo-code or repo for the time-accumulation method).

Response:

Thank you for your constructive feedback. In response to your suggestions, we have addressed the following points in section 3.1: (i) we have added a note on the seasonality issue (June data only) and (ii) the potential effects of the pandemic on the 2021 data. (iii) The issue of non-human SIM cards has been addressed primarily through number segments filtering. (iv) We have included the pseudo-code for the time-accumulation method in the Table B1 to ensure algorithmic reproducibility. (Line 190-194; 210-212; 758)

“June 2017 is a normal working month and no significant change was found in the commuting behavior during this period. Although June 2021 fell within the COVID-19 pandemic, it was in a stage of normalized management, thus having a limited impact on commuting.”

“secondly, to carry out data cleaning, mainly for the identification of the "ping-pong effect" and the elimination of non-human number cards [38], which was distinguished by the non-individual mobile number segments”

“Table B1. Pseudo-code of Time-accumulation Method

Algorithm: Time-accumulation Method for Identifying Residence and Workplaces

Input:

- User data (location data with timestamps, mobile phone data)

- Study area boundary (geographic coordinates)

Output:

- Job-housing matrix at the township level

1. Define time windows:

- Nighttime: 9:00 PM - 7:00 AM

- Daytime: 9:00 AM - 5:00 PM

2. For each user:

2.1. Filter data by time window (Nighttime and Daytime)

3. Identify valid users:

3.1. For each user, count the number of days they are present in the study area

3.2. If the user is present for more than 15 days in a month, mark as valid

4. Identify residence for each valid user:

4.1. Filter data to include only nighttime stays

4.2. For each user, calculate the total time spent at each location during nighttime

4.3. Select the longest stay as the residence location

5. Identify workplace for each valid user:

5.1. Filter data to include only weekday (Monday to Friday) stays

5.2. For each user, calculate the total time spent at each location during weekdays

5.3. Select the longest stay as the workplace location

6. Handle workplace same as residence scenario:

6.1. If the weekday workplace is the same as the residence location:

6.1.1. Identify the second-longest location

6.1.2. If the second-longest stay time exceeds 50% of the primary location's time, use the second-longest location as workplace

7. Create job-housing matrix:

7.1. Link residence and workplace locations for each valid user

7.2. Aggregate the data by township level

7.3. Generate job-housing matrix showing connections between residence and workplace at township level

8. Output:

- The final job-housing matrix at the township level

”

Comment 6

Complex-Network Methods

• Nicely structured (centrality, link strength, Louvain communities, global metrics). Report parameter choices (e.g., edge thresholds, Louvain resolution) and show robustness to (a) alternative community detection or (b) resolution sweeps. Briefly justify why weighted directed treatment is appropriate and how self-loops were handled.

Response:

Thank you for your positive feedback on the paper's structure. In response to your suggestions, we have provided additional details on the parameter choices, including edge thresholds and Louvain resolution, especially we have added the histogram of Modularity changes with Resolution in Table B1. And we have conducted experiments with different random seeds to demonstrate the robustness of the community detection results. We have also clarified the network construction process and how self-loops were handled, in section 3.2.3.

The changes are incorporated in the revised manuscript at Lines 322–343;756-757. The added text reads:

“For the township-scale commuting network, the same residence-workplace pair often involves a large number of commuters, representing both the strength and direction of daily commuting, rather than just a simple connection. Given that commuting is inherently directional, and each edge weight reflects commuting intensity, a directed weighted network is more appropriate. Self-loops occur when the workplace and residence are in the same township. We chose to remove all self-loops. This decision was based on the fact that the township is treated as a node, and the integration process emphasizes stronger links between the township and neighboring areas. While weak commuting links exist, each edge represents real commuter behavior. Since the net-work is already weighted, we chose not to remove weak edges based on arbitrary thresholds.

To address the resolution limit issue in community detection, we adjusted the resolution parameter and ran the Louvain algorithm multiple times to ensure stable and fine-grained community division. With 449 township nodes, we conducted 100 iterations using different random seeds for each run. The initial random seed was set to 42, and we then selected 100 random seeds from 1 to 10000 for the subsequent runs. For the resolution parameter, we tested values between 0.5 and 1.5, with a step size of 0.1. The optimal resolution was chosen based on the highest modularity (Histogram comparing Modularity vs. Resolution Parameter, as shown in Appendix B). After multiple runs, we found that a resolution of 1.0 yielded the best result, with a modularity value of >0.43 and a division into 10 communities. For the results of multiple computations, we further employed the Adjusted Rand Index (ARI) metric to evaluate the stability of the communities. The average ARI was found to exceed 0.8, demonstrating the robustness of community partitioning.”

“

Figure B1. Modularity changes with Resolution

”

Comment 7

Results — Nodes

• Clear rise of peripheral hubs (BDA top by 2021) and declines in core districts; include effect sizes in text (Table 2 % changes) and add uncertainty where possible. Avoid inferring environmental outcomes from centrality alone (move such claims to Discussion with caveat).

Response:

Thank you for your valuable feedback. In response to your suggestions, we have updated Table 2 in section 4.1 to include absolute values and added percentage changes in the text. However, as the values are derived from direct calculations, estimating uncertainty is challenging. Regarding your point about inferring environmental outcomes from centrality alone, we appreciate your suggestion and have removed such claims from the paper, moving them to the Discussion section with the appropriate caveat. (Line 409-410)

“

Table 2. Changes in Centrality of Different Sectors

Beijing Metropolitan Area Classification	DI				DO
	2017	2021	Absolute Change	Percentage change(%)	2017	2021	Absolute Change	Percentage change(%)
Core Area for Capital Functions	68.97	61.8	-7.17	-10.40%	34.44	31.05	-3.39	-9.80%
The Four Central Districts of Beijing	252.77	240.39	-12.38	-4.90%	221.43	202.18	-19.25	-8.70%
Beijing Suburbs (10 Districts, Outer Districts of Beijing)	106.46	115.21	8.75	8.20%	166.87	177.45	10.58	6.30%
Areas outside Beijing	21.8	32.6	10.8	49.50%	27.26	39.32	12.06	44.20%

* Note: DI and DO denote in-degree and out-degree, respectively.

”

Comment 8

Results — Links

• Good maps of strengthening/weakening ties. Please (i) list the top 10 increases/decreases with RS values, (ii) decompose intra-Beijing vs. cross-boundary changes, and (iii) flag flows potentially distorted by COVID-19 in 2021.

Response:

Thank you for your constructive feedback. In response to your suggestions, we have updated the RS map in section 4.2 and provided a detailed breakdown of the changes into intra-Beijing and cross-boundary categories, with separate analyses for each. Regarding the potential impact of COVID-19, we have also included a discussion on the newly added checkpoints between 2019 and 2021 to account for any distortions in the data. (Line 451-463; 476-479; 439-442)

“

In contrast, cross-border commuting changes are primarily observed in the areas sur-rounding the new Daxing International Airport. These include increased commuting from neighboring towns such as Yufa Town and Lixian Town (Daxing) to Jiuzhou Town (Guangyang). On the other hand, reductions in commuting are mainly seen from areas like Yanjiao (Sanhe) to Jianwai and Hujialou Streets in central Beijing, as shown in Figure 8. Regarding the reduction in commuting linkages between Sanhe and Beijing, we consider the possibility that this may have been influenced by the COVID-19 pan-demic. To address this, we supplement the analysis with POI data from Amap (Gaode Map) to include the newly added checkpoint locations from 2019 to 2021 in Figure 8. From the distribution of the added checkpoints, we observe that there has indeed been a significant increase between Sanhe and Beijing, but there has also been an increase in other areas, including around Daxing International Airport. Therefore, we believe the above results are still valid.”

“

These changes of cross-boundary suggest shifts in commuter patterns driven by the development of the Daxing International Airport and its surrounding areas, while tra-ditional employment centers in central Beijing have experienced a decline in commut-ing connections, likely due to the decentralization of employment opportunities.

”

 

Figure 7. Network of commuting links and Changes of intra-Beijing: (a) increase, (b) decrease

 

 

Figure 8. Network of commuting links and Changes of cross-boundary: (a) increase, (b) decrease

”

• Reported modularity (≈0.44–0.45) is solid; add a brief stability check (multiple seeds/runs) and quantify cross-administrative links (share of edges crossing boundaries) to support the boundary-friction claim.

Response:

Thank you for your thoughtful suggestion. To further enhance the robustness of our findings, we have conducted stability checks by running the community detection algorithm 100 times with different random seeds. This ensures the reliability and consistency of the modularity results (≈0.44–0.45). Additionally, we have included the proportion of cross-administrative links (i.e., edges crossing boundaries) to quantify the extent of boundary friction, which further supports the claim regarding cross-boundary interactions. (Line 334-341; 485-489)

“With 449 township nodes, we conduct 100 iterations using different random seeds for each run. The initial random seed is set to 42, and we then select 100 random seeds from 1 to 10000 for the subsequent runs. For the resolution parameter, we test values between 0.5 and 1.5, with a step size of 0.1. The optimal resolution is chosen based on the highest modularity (Histogram comparing Modularity vs. Resolution Parameter, as shown in Appendix B). After multiple runs, we find that a resolution of 1.0 yields the best result, with a modularity value of >0.43 and a division into 10 communities.”

 

“The share of cross-Beijing boundary commutes increases modestly from 3.31% in 2017 to 3.65% in 2021 (a relative rise of 10.3%), yet stays well below the 5% empirical benchmark from the radiation model, underscoring ongoing boundary friction that impedes metropolitan commuting integration [48].”

 

• Nice to show GE rising 0.66→69 and LCC=1.0; also report the average clustering coefficient values (not just direction) and average path length to complete the picture.

Response:

Thank you for your insightful comment. In response, we have included the average clustering coefficient values and average path length in the revised manuscript. Our analysis reveals that the average clustering coefficient has increased, while the average path length has decreased, which further supports the conclusions of the study. (Line 552-556)

“We also examine the average clustering coefficient and average path length, revealing an increase in the former and a decrease in the latter. These shifts have enhanced commuting efficiency across the metropolitan area and the compact evolution of its polycentric configuration.

Table 5. Results of clustering coefficient and network efficiency indicators

Date	Largest Connected Component	Global Efficiency	Average Clustering Coefficient	Average Path Length
201706	1.0	0.66	0.69	1.70
202106	1.0	0.69	0.71	1.63

”

Comment 9

Discussion

• Keep causal language cautious (“consistent with policy” rather than “the policy has caused”), and separate mechanism (industrial relocation vs. administrative relocation lag). Tie back explicitly to each main result.

Response:

We thank the reviewer. Causal language has been softened throughout (“consistent with”), industrial vs. administrative relocation mechanisms are now explicitly separated, and each claim is tied directly to node/link/cluster results (e.g., +49.5 % peripheral in-degree, C5 expansion). (Line 561-582)

“The relief of the non-essential capital functions policy, a cornerstone of the Beijing-Tianjin-Hebei coordinated development strategy, has reshaped the Beijing metropolitan area's commuting network from 2017 to 2021, with patterns consistent with a transition from a monocentric to a polycentric structure [49], [50]. Node-level results show peripheral employment centers (e.g., BDA in-degree +49.5 %) gaining prominence while core districts (e.g., Capital Functional Core Area in-degree –10.4 %) declined; link-level strengthening in suburban flows and cluster expansion around BDA further support this shift. Emerging employment centers, such as the BDA and Xibeiwang Town, have driven peripheral growth, while traditional core districts (Dongcheng, Xicheng) and employment hubs (Jianwai Street, Financial Street) have experienced declining centrality. This shift reflects the policy’s success in decentralizing employment and residential functions through industrial diffusion to peripheral areas [27], [51], [52]. These decentralization patterns are consistent with potential reductions in environmental pressures, such as carbon emissions from long commutes, by promoting job-housing balance—though direct inference from centrality changes alone is limited and requires further validation through travel distance or emission modeling. Supported by infrastructure investments, such as the Jingxiong Intercity Railway, and policy incentives, BDA has emerged as a pivotal node, with Xibeiwang and Fengtai’s Xincun Sub-district forming new employment hubs, challenging the traditional monocentric model. However, the Tongzhou subcenter’s weak commuting linkages with distant suburbs indicate a lag in administrative relocation impact, as government function relocation requires longer to influence commuting patterns compared to industrial relocation [50], [53].”

• Good coverage (polycentricity, growth poles, space of flows). Consider trimming citations and mapping each theoretical claim to a concrete empirical finding (one sentence each).

Response:

We thank the reviewer for this constructive feedback. Citations have been trimmed where overlapping while retaining key references. Each theoretical claim is now explicitly mapped to one concrete empirical finding in a dedicated sentence (e.g., +49.5 % peripheral in-degree, BDA cluster expansion), enhancing clarity and linkage to results. (Line 603-620)

“This study validates the relevance of key theories through empirical findings. Polycentric urban region theory explains the observed shift from monocentric to polycentric structure, with peripheral in-degree centrality rising 49.5 % outside Beijing [54], [55]. Spatial diffusion theory accounts for the outward spread of employment hubs, evident in BDA’s cluster expansion absorbing 9 sub-districts from adjacent areas [56]. Growth pole theory captures BDA’s spillover effects, reflected in strengthened suburban commuting links and global network efficiency increase from 0.66 to 0.69 [14]. Core-periphery theory highlights persistent administrative barriers, seen in limited cross-boundary edges (15 % of total) despite policy intent [57]. The space of flows framework reveals enhanced functional connectivity, as shown by the rise in average clustering coefficient and reduced average path length [15]. The observed shift aligns with the Los Angeles School’s decentralization model [60], [61], particularly evident in the functional expansion and commuting mobility of peripheral areas like BDA and Yanjiao. By integrating fine-grained cell phone signaling data with complex network analysis, this study extends these theories, offering a nuanced understanding of job-housing spatial patterns and commuting dynamics that advance metropolitan polycentricity for sustainability. Together, these insights provide a robust framework for understanding policy-driven network evolution in the Beijing-Tianjin-Hebei region and informing future planning for environmental resilience [64].”

• Policy recommendations
• Useful and realistic; add priority, responsible actors, and time horizon (e.g., “inter-jurisdiction rail governance—Provincial BTH coordination—near term”). Note risk management (e.g., housing affordability near new hubs).

Response:

We thank the reviewer for the suggestion. The three-point list has been condensed into a single, fluid sentence for smoother integration into the paragraph while preserving all details on priority, actors, time horizon, and risk management. (Line 650-657)

“To operationalize these recommendations, the Beijing-Tianjin-Hebei provincial coordination mechanism should prioritize inter-jurisdictional rail governance in the near term (2025–2030) for under-connected clusters like C6 (Wuqing-Guangyang-Xianghe), enhance job-housing coordination through public service upgrades in the medium term (2030–2035) while managing housing affordability risks near new hubs via subsidies or zoning controls, and pursue ongoing multimodal integration leveraging airports and rail, with continuous monitoring of non-linear outcomes to ensure equitable, sustainable growth.”

• Global comparisons
• Comparisons are insightful; flag data differences (Beijing’s mobile signaling vs. literature for Tokyo/NY/Paris) to avoid overstating comparability.
• Response:

Thank you for your insightful comment. In response, we have clarified the data differences between Beijing’s mobile signaling data and the traditional data sources used for cities like Tokyo, New York, and Paris. This distinction helps to avoid overstating the comparability of commuting networks across these cities. (Line 666-669; 694-697)

“However, it is important to note that the data used for Beijing’s commuting network, derived from mobile signaling data, may differ from the traditional data sources used for Tokyo, New York, and Paris, such as census data or transport surveys.”

“Again, it is worth highlighting that Beijing’s mobile signaling data may not fully capture the complexities of transportation infrastructure in Tokyo, New York, or Paris, where different methodologies may provide more comprehensive data on commuting patterns.”

Comment 10

Conclusion & Limitations

• Strong summary; expand limitations to include: (i) seasonality, (ii) privacy/ethics safeguards, and (iii) data unshareability → propose releasing derived OD matrices and code. (Line 711-734)

Response:

• We thank you for this helpful suggestion. Limitations have been expanded to include seasonality, privacy/ethics safeguards, and data unshareability. To address the latter, we have added: “Besides, the data may under-represent certain demographic groups, which could introduce bias in our findings, particularly in terms of generalizing to the broader population.” This strengthens transparency while respecting data constraints. (Line 711-734)

“Analysis of China Unicom’s mobile signaling data (2017–2021) and complex network methods reveals a transformation in Beijing’s metropolitan commuting network under the non-essential capital functions relief policy, uniquely quantifying the shift from monocentric to polycentric structure through fine-grained township-level flows and community detection—a methodological advance over prior qualitative or aggregate studies. Peripheral employment centers (e.g., BDA) have gained prominence, while core districts have lost centrality (e.g., in-degree in non-Beijing areas up +49.5%), driven by rail transit expansion and industrial relocation. This aligns with the Fifth Central Urban Work Conference’s polycentric vision, demonstrating that targeted decentralization can enhance job-housing balance and regional integration.

Practically, these findings inform sustainable urban governance: prioritizing cross-jurisdictional rail and industrial clustering in under-connected peripheral counties (e.g., Wuqing, Zhuozhou) could further reduce long-distance commuting and emissions. Cluster analysis highlights emergent networked urban groups, offering a replicable framework for diagnosing integration barriers in other megacity regions.

Limitations include seasonality, as June data may not capture seasonal variations; privacy and ethical safeguards, as raw mobile signaling data remain confidential due to privacy regulations; data unshareability, preventing direct replication; and the short 2017–2021 window, which limits assessment of long-term policy impacts. Besides, the data may under-represent certain demographic groups, which could introduce bias in our findings, particularly in terms of generalizing to the broader population. Future research should integrate multi-source data, such as traffic cards and navigation logs, and extend analysis to economic and information flows to refine strategies for resilient, low-carbon metropolitan systems in the Beijing-Tianjin-Hebei region and globally.”

• Figures & Tables (general)
• Make maps color-blind safe; enlarge labels for township names; add legends/units; annotate thresholds for displayed links; ensure tables are self-contained with notes on DI/DO definitions and sample sizes.

Response:

Thank you for your valuable suggestions. In response, we have made the following revisions. We have updated the colors in the figures to use color-blind friendly options, from Figure 5 to Figure 10 and Figure C1 and C2. We have enlarged the labels for the township names to improve readability. The maps now include legends, and as the units are ratio-based, they are not displayed. However, the thresholds for the displayed links are clearly shown in the legends. We have ensured that the tables are self-contained, with added notes on the definitions of DI/DO and sample sizes in Table 1 and Table 2.

“

 

Figure 5. Centrality change from 2017-2021: (a) in-degree, (b) out-degree

 

 

Figure 6. Network of commuting links and Changes: (a) 2017, (b) 2021, (c) increase, (d) decrease

 

 

Figure 7. Network of commuting links and Changes of intra-Beijing: (a) increase, (b) decrease

 

 

Figure 8. Network of commuting links and Changes of cross-boundary: (a) increase, (b) decrease

 

 

Figure 9. Community detection:(a) 2017, (b) 2021, (c)changes from 2017 to 2021

 

Figure 10. Node efficiency calculation results

 

Figure C1. In degree measurement:(a) 2017, (b) 2021

 

 

Figure C2. Out degree measurement:(a) 2017, (b) 2021

 

Table 1. Changes in Centrality of the top 10 in 2017

Rank2017	DI Sub-district	DI		DO Sub-district	DO
		2017	2021		2017	2021
1	Jianwai	10.84	9.29	Huilongguan	13.24	9.44
2	BDA	10.63	14.44	Shahe	7.24	7.21
3	Haidian	9.56	8.60	Shibalidian	6.66	5.19
4	Shangdi	9.43	10.05	Beiqijia	6.27	6.30
5	Financial Street	8.17	7.66	Huangcun	5.99	5.57
6	Zhongguancun	8.09	6.67	Lugouqiao	5.77	4.68
7	Hujialou	6.41	5.58	Yongshun	5.40	5.64
8	Xincun	5.72	6.62	Sijiqing	4.91	3.66
9	Zhanlan Road	5.59	4.93	Laiguangying	4.85	4.97
10	Donghuamen	5.23	4.45	Liyuan	4.71	5.15

* Note: (1) DI and DO denote in-degree and out-degree, respectively; (2) The network comprises 449 townships as nodes.

 

Table 2. Changes in Centrality of Different Sectors

Beijing Metropolitan Area Classification	DI				DO
	2017	2021	Absolute Change	Percentage change(%)	2017	2021	Absolute Change	Percentage change(%)
Core Area for Capital Functions	68.97	61.8	-7.17	-10.40%	34.44	31.05	-3.39	-9.80%
The Four Central Districts of Beijing	252.77	240.39	-12.38	-4.90%	221.43	202.18	-19.25	-8.70%
Beijing Suburbs (10 Districts, Outer Districts of Beijing)	106.46	115.21	8.75	8.20%	166.87	177.45	10.58	6.30%
Areas outside Beijing	21.8	32.6	10.8	49.50%	27.26	39.32	12.06	44.20%

* Note: DI and DO denote in-degree and out-degree, respectively.

”

• Light edit for repetition (reduce “cleaner production” phrasing in main text); keep verbs cautious on causality; standardize abbreviations.

Response:

Thank you for your feedback. We have made light edits to reduce repetition of the phrase "cleaner production" in the main text, adjusted the verbs to ensure caution regarding causality, and standardized the abbreviations throughout the manuscript.

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

The article falls within a broader category of research exploring the usefulness of new data sources—in this case, mobile phones. Most studies that have addressed this source of information have usually limited themselves to testing hypotheses related to the pre-existence of spatial structures or the emergence of new ones. The article presented by the authors is important because it focuses more on spatial processes, namely the shaping of Beijing's polycentric system. This orientation is also required by the orientation of the results towards an expert analysis in the service of spatial planning policies, a mission that the authors have fulfilled.

The methodology used is robust, the graphics and tables are used appropriately, and the appendices are useful, allowing for a more fluid reading of the text.

As a general consideration, the readability of the maps (especially the legends) could be improved.

Comments:

Line 246: the parentheses should be removed: in-degree and out-degree (In-degree and out-degree):

The legend for Figure 6 should be placed next to the main figure for better readability.

Author Response

Response to Reviewer 4

 

6th November, 2025

 

Dear Reviewer 4,

 

Thank you for your comments concerning our manuscript entitled “Relieving Beijing’s Nonessential Capital Functions: Metropolitan Area Polycentricity for Sustainability” (Manuscript ID: land-3948540). The comments are all valuable and helpful for improving our manuscript. We have carefully reviewed your comments and have made revisions to the original manuscript accordingly, which we hope would constitute a more satisfactory one. Please kindly find our response to your questions and comments below.

 

Sincerely,

The authors

 

 

Reviewer #4

The article falls within a broader category of research exploring the usefulness of new data sources—in this case, mobile phones. Most studies that have addressed this source of information have usually limited themselves to testing hypotheses related to the pre-existence of spatial structures or the emergence of new ones. The article presented by the authors is important because it focuses more on spatial processes, namely the shaping of Beijing's polycentric system. This orientation is also required by the orientation of the results towards an expert analysis in the service of spatial planning policies, a mission that the authors have fulfilled.

The methodology used is robust, the graphics and tables are used appropriately, and the appendices are useful, allowing for a more fluid reading of the text.

As a general consideration, the readability of the maps (especially the legends) could be improved.

Response:

Thank you very much for your thoughtful and encouraging feedback. We greatly appreciate your recognition of the significance of our study in focusing on spatial processes, particularly in the context of Beijing's polycentric system, and its relevance for spatial planning policies. We are also glad that you found our methodology robust and the use of graphics and tables appropriate. Regarding your comment on the readability of the maps, particularly the legends, we have already made improvements to enhance their clarity in the revised manuscript.

Comment 1

Line 246: the parentheses should be removed: in-degree and out-degree (In-degree and out-degree)

Response:

Thank you for your helpful suggestion. It was a typographical error, and we have removed the parentheses as per your recommendation.

Comment 2

The legend for Figure 6 should be placed next to the main figure for better readability.

Response:

Thank you for your suggestion. We have moved the legend for Figure 6 next to the main figure, as recommended, to improve readability. Additionally, we have updated the colors in the figure to use color-blind friendly options.

“

Figure 9. Community detection:(a) 2017, (b) 2021, (c)changes from 2017 to 2021

”

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

Accept in present form