System Model for Spatial Data Collection in Post-War Transport Infrastructure Planning

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The study proposes a system model for collecting and analyzing spatial data of transportation infrastructure development project environments in the post-war context to support sustainable management and restoration planning, which has great practical value. I greatly appreciate this article.
1.The logic of literature review is not very clear, and it is recommended that the author improve it.
2.The article selected 23 settlements for research, but did not explain the reasons and basis for the selection.
3. The analysis of linear regression equation is too simple, and the correlation cannot prove the influence of the two, especially corresponding tests should be added.
4.The article believes that the model also has practical value in other regions, but the situation varies in different response areas. Its replicability and feasibility need to be discussed in detail by the author.
5.Figure 1 is very simple, perhaps the author should draw the internal logical framework of the model itself.

Author Response

The author team thanks the reviewers for their constructive work and provides detailed responses to all comments.

 

Responses to reviewer #1's comments – edits highlighted in yellow

Remarks 1: Analysis of Literature Data and Problem Statement.

The logic of literature review is not very clear, and it is recommended that the author improve it.

Reply 1: Correction. Corrections made to the text of the article.

Remarks2: The article selected 23 settlements for research, but did not explain the reasons and basis for the selection.

Reply 2: Correction. Corrections made to the text of the article.

Remarks 3: The analysis of linear regression equation is too simple, and the correlation cannot prove the influence of the two, especially corresponding tests should be added.

Reply 3: Correction. Corrections made to the text of the article.

Remarks 4: The article believes that the model also has practical value in other regions, but the situation varies in different response areas. Its replicability and feasibility need to be discussed in detail by the author.

Reply 4: Correction. Corrections made to the text of the article.

Remarks 5: Figure 1 is very simple, perhaps the author should draw the internal logical framework of the model itself.

Reply 5: Figure 1 has been corrected in the text of the article.

Reviewer 2 Report

Comments and Suggestions for Authors

The manuscript presents a system model for collecting and analyzing spatial data on the project environment of transport infrastructure development in post-war contexts, with a focus on sustainable management and recovery planning. This is a timely and valuable study given the critical need for data-driven tools in post-conflict reconstruction. However, there are some better steps to be made to improve the quality of the manuscript.

Major issues

• The introduction section needs stronger emphasis on the research gap and novelty. While the importance of post-war transport infrastructure recovery is highlighted, the manuscript should more explicitly articulate how the proposed model addresses the specific limitations of existing approaches.The research motivation would benefit from clearer alignment between the model’s design and the urgent needs of war-affected regions.
• The literature review mentions SMART, ABCD, and SCAT models, but does not deeply analyze their shortcomings in post-war scenarios. For example, it overlooks that SCAT cannot integrate open-source real-time data, and ABCD lacks spatial dimension, making it hard to justify the proposed model’s innovation.
• Figures 4-6 present the road type distribution and centrality analysis of Bakhmut, but they do not explain why the “40% proportion of residential roads” affects reconstruction priorities or how centrality indicators can be used to optimize resource allocation.
• The discussion of results lacks depth in practical implications. The linear regression models are presented, but their limitations are not sufficiently explored. How might this affect recovery prioritization?

 

 

Minor issues

• In the phrase “russia’s military aggression”, the country name “Russia” should be capitalized to follow academic writing conventions for proper nouns.

Author Response

The author team thanks the reviewers for their constructive work and provides detailed responses to all comments.

 

 Responses to reviewer comments #2 – edits highlighted in light green

Remarks 1: The introduction section needs stronger emphasis on the research gap and novelty. While the importance of post-war transport infrastructure recovery is highlighted, the manuscript should more explicitly articulate how the proposed model addresses the specific limitations of existing approaches.The research motivation would benefit from clearer alignment between the model’s design and the urgent needs of war-affected regions.

Reply 1: Correction. Corrections made to the text of the article.

Remarks 2: The literature review mentions SMART, ABCD, and SCAT models, but does not deeply analyze their shortcomings in post-war scenarios. For example, it overlooks that SCAT cannot integrate open-source real-time data, and ABCD lacks spatial dimension, making it hard to justify the proposed model’s innovation.

Reply 2: Correction. Corrections made to the text of the article.

Remarks 3: Figures 4-6 present the road type distribution and centrality analysis of Bakhmut, but they do not explain why the “40% proportion of residential roads” affects reconstruction priorities or how centrality indicators can be used to optimize resource allocation.

Reply 3: Correction. Corrections made to the text of the article.

Remarks 4: The discussion of results lacks depth in practical implications. The linear regression models are presented, but their limitations are not sufficiently explored. How might this affect recovery prioritization?

Reply 4: Correction. Corrections made to the text of the article.

 

Remarks 5: Minor issues.

In the phrase “russia’s military aggression”, the country name “Russia” should be capitalized to follow academic writing conventions for proper nouns.

Reply 5: Correction. Corrections made to the text of the article.

 

Reviewer 3 Report

Comments and Suggestions for Authors

The authors present an interesting research on enhancing project planning and operations using up-to-date data tools, with a particular focus on the Ukraine case study. Overall, the manuscript is well-structured and written in good English. The approach is well-described and sufficiently clear. The outcomes are justified through the case study, and the impact is well stated. I recommend publication of this study after addressing the following revisions:

1. Some figures need improved preparation. In particular, Figure 4 is low resolution and lacks clear labeling to indicate the different types of streets. Figure 3 might be better presented as a table or as a clearly indicated full view, rather than as a partial snapshot from the database.

2.  The analysis after Figure 7 is superficial, showing only simple relationships. A deeper exploration of the results and their implications is recommended.

3. The discussion section does not clearly explain how the proposed tool contributes to sustainability, which is a key aspect of this research and should be addressed.

4. The title is rather long and could benefit from revision to make it more concise and focused.

5. It would strengthen the manuscript to discuss how the tool can be incorporated into the decision-making and project planning process, and to highlight potential outcomes, such as improvements in efficiency.

Author Response

The author team thanks the reviewers for their constructive work and provides detailed responses to all comments.

 

 Responses to reviewer comment #3 – edits highlighted in gray

Remarks 1: Some figures need improved preparation. In particular, Figure 4 is low resolution and lacks clear labeling to indicate the different types of streets. Figure 3 might be better presented as a table or as a clearly indicated full view, rather than as a partial snapshot from the database.

Reply 1: We are grateful to the reviewer for their comment regarding the clarity and resolution of the figures. Figure 3 is intentionally presented as a fragment of a formed DataFrame containing 17 attributes (columns) and several thousand rows. This dataset is automatically generated in Jupyter Notebook by executing a special Python module developed for the study. The purpose of showing this partial view is to illustrate the structure and composition of the collected spatial data without overloading the article with a full export of the dataset, which would be impractical in print. As for Figure 4, it represents the direct result of the Python-based processing module and includes a legend indicating road types, as well as a visualization of the city's street network. The contours of the settlement are displayed in full, and the road types are listed to the right of the figure, providing a clear representation of both the spatial context and the classification of attributes. Although the resolution will be increased in the final version for publication quality, the format of the figure itself reflects the intended integration of geospatial data and road classification results into a single visual result.

Remarks 2: The analysis after Figure 7 is superficial, showing only simple relationships. A deeper exploration of the results and their implications is recommended.

Reply 2: Correction. Corrections made to the text of the article.

Remarks 3: The discussion section does not clearly explain how the proposed tool contributes to sustainability, which is a key aspect of this research and should be addressed.

 

Reply 3: Correction. Corrections made to the text of the article.

 

Remarks 4: The title is rather long and could benefit from revision to make it more concise and focused.

Reply 4: Correction. Corrections made to the text of the article.

Remarks 5: It would strengthen the manuscript to discuss how the tool can be incorporated into the decision-making and project planning process, and to highlight potential outcomes, such as improvements in efficiency.

Reply 5: Correction. Corrections made to the text of the article.

Reviewer 4 Report

Comments and Suggestions for Authors

I acknowledge the technical scope and methodological contributions of the manuscript; however, I must decline to provide a formal review. The submission contains explicit references to identifiable parties, territories, and politically sensitive events associated with an ongoing and highly delicate military conflict. These elements are not limited to neutral background context but are integrated throughout the conceptual framework, case study selection, and discussion of results, making the work inseparable from current politically contentious narratives.
The peer review process requires maintaining strict neutrality and impartiality. When a manuscript is set within the context of an active political conflict, any assessment—whether favorable or critical—may be perceived as implicitly aligning with one side. Such a situation conflicts with the core principle that evaluations should focus solely on scientific rigor and methodological soundness, regardless of political considerations.
Established academic ethics also advise against using an active political conflict as the main empirical focus unless is clearly non-partisan and supported by verifiable evidence, free from politically charged framing. In this case, the language and contextual orientation do not meet the necessary standard of political neutrality, making an objective and academically sound evaluation impossible.
For these reasons, I must formally recuse myself from reviewing this manuscript. This decision is solely based on the principles of objectivity and avoiding perceived political alignment and does not reflect any judgment on the technical quality or scholarly merit of the research.

Author Response

We express our gratitude for your position on our research topic. We wish peace in your country.

Reviewer 5 Report

Comments and Suggestions for Authors

The main question addressed by this research is:

How can spatial data extracted from open-source platforms (like OpenStreetMap) be effectively used to support sustainable transport infrastructure planning in post-war environments, particularly in terms of accessibility, accuracy, and informed decision-making?

The study specifically aims to propose a system model that allows for efficient collection, processing, and analysis of geospatial data to assist stakeholders in managing and planning the recovery of transport infrastructure in war-affected regions.

The topic is both original and relevant. It addresses a clear gap in the intersection of post-conflict reconstruction, spatial data analytics, and sustainable infrastructure management.

• Originality: While there is growing literature on spatial data usage and digital governance, the integration of open-source geospatial platforms like OSM with custom decision-support tools specifically for post-war infrastructure recovery is still underexplored. The Ukrainian case study adds unique contextual relevance.
• Relevance: With an increasing number of post-conflict regions worldwide (Ukraine being a prominent example), the need for tools that support rapid and sustainable reconstruction is critical. This model responds to that need by operationalizing spatial data into actionable insights.

This research contributes to the field in several ways:

• Tool-based Innovation: It operationalizes the Overpass API and Python into a functional, modular model tailored for real-time spatial analysis, moving beyond purely theoretical contributions.
• Application in Post-War Contexts: Unlike most spatial planning tools which are applied in stable or developing regions, this study focuses on post-conflict areas, addressing challenges like incomplete data, urgency of reconstruction, and limited planning resources.
• Attribute-Based Evaluation: The use of 17 transport-related attributes and typological classification across settlements adds granularity and offers a replicable methodology for other contexts.
• Support for Decision-Makers: The focus on transparency, data reproducibility, and ease of use aligns with modern digital governance goals.

While the methodology is solid, the following improvements are recommended:

• Validation of Accuracy: Include a validation step that compares the extracted OSM data with ground-truth data (e.g., government records, satellite imagery) for selected settlements to assess accuracy.
• Scalability Testing: Extend the methodology to a larger set of settlements or different countries to demonstrate its generalizability and scalability.
• Weighting or Prioritization Framework: The study mentions 17 attributes but doesn’t explain how these are weighted or ranked in decision-making. A more explicit multicriteria evaluation or scoring system (e.g., AHP, SAW) could enhance the practical value of the model.
• Stakeholder Input: Incorporating participatory elements (input from local planners or affected communities) could improve both relevance and impact.
• Automation Level: While custom Python modules are used, the degree of automation (e.g., update scheduling, error handling in Overpass queries) could be further elaborated to clarify usability.

The conclusions are generally consistent with the evidence and do align with the research objectives. The case study results support the claim that the proposed system can aid in informed, sustainable decision-making in post-war reconstruction.

• The use of practical examples (e.g., Bakhmut) and analytical outputs (graphs, typologies) reinforces the system’s functionality.
• The conclusions are forward-looking, suggesting future development of a full-scale decision-support system, which logically extends the current work.
• However, the conclusions could be slightly strengthened by quantitatively demonstrating the model’s effectiveness (e.g., time saved, improved planning accuracy, reduced data gaps).

This is a valuable and timely contribution that addresses an urgent and underexplored problem. The integration of open-source spatial data, programmatic data processing, and sustainable development principles makes this model highly applicable in both academic and policy contexts.

The study has the potential to be a strong reference for future work in post-conflict infrastructure planning.

Author Response

The author team thanks the reviewers for their constructive work and provides detailed responses to all comments.

 

Responses to reviewer's comments #4 – edits highlighted in blue

Remarks 1: Validation of Accuracy: Include a validation step that compares the extracted OSM data with ground-truth data (e.g., government records, satellite imagery) for selected settlements to assess accuracy.

Reply 1: Correction. Corrections made to the text of the article.

Remarks 2: Scalability Testing: Extend the methodology to a larger set of settlements or different countries to demonstrate its generalizability and scalability.

Reply 2: We agree that applying the methodology to a larger set of settlements and, where possible, to post-conflict contexts in other countries would provide stronger evidence of its generalizability and scalability. While the current study focused on 23 Ukrainian settlements to allow for detailed validation and methodological refinement, extending the analysis to a broader geographic scope is planned as part of future research. This next stage will enable us to assess how variations in mapping completeness, infrastructure typology, and local recovery priorities influence the model’s performance and adaptability.

Remarks 3: Weighting or Prioritization Framework: The study mentions 17 attributes but doesn’t explain how these are weighted or ranked in decision-making. A more explicit multicriteria evaluation or scoring system (e.g., AHP, SAW) could enhance the practical value of the model.

 

Reply 3: Correction. Corrections made to the text of the article.

 

Remarks 4: Weighting or Prioritization Framework: The study mentions 17 attributes but doesn’t explain how these are weighted or ranked in decision-making. A more explicit multicriteria evaluation or scoring system (e.g., AHP, SAW) could enhance the practical value of the model.

Reply 4: Correction. Corrections made to the text of the article.

Remarks 5: Stakeholder Input: Incorporating participatory elements (input from local planners or affected communities) could improve both relevance and impact.

Reply 5: Correction. Corrections made to the text of the article.

Remarks 6: Automation Level: While custom Python modules are used, the degree of automation (e.g., update scheduling, error handling in Overpass queries) could be further elaborated to clarify usability.

Reply 6: We appreciate the reviewer’s observation regarding the degree of automation in the current implementation. At this stage, the developed Python modules are capable of performing automated data extraction from OpenStreetMap via the Overpass API, preprocessing the results, and generating analytical outputs without manual intervention for each query. However, we acknowledge that certain aspects – such as automated scheduling of periodic updates, advanced error handling for Overpass query timeouts or incomplete responses, and automated logging of data quality issues – are not yet fully implemented. In future iterations, these modules will be enhanced with scheduling mechanisms (e.g., CRON jobs or task schedulers) to allow routine updates at predefined intervals, as well as more robust exception handling routines to manage API limitations and temporary connectivity issues. This will improve both the reliability and the usability of the system, particularly for local planners and stakeholders who require regularly updated datasets without technical overhead.

Remarks 7: However, the conclusions could be slightly strengthened by quantitatively demonstrating the model’s effectiveness (e.g., time saved, improved planning accuracy, reduced data gaps).

Reply 7: Correction. Corrections made to the text of the article.

 

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

No

Author Response

Thank you for your constructive work.
We have corrected the English language and comments.
Sincerely, the author team

Reviewer 3 Report

Comments and Suggestions for Authors

The quality of this manuscript has been greatly improved and the reviewer's comments has been enough addressed. However, there are still some minor issues like the title of Table 3 is not in English. Mostly the current manuscript meets the publication standard.

Author Response

Thank you for your constructive work.
We have corrected the English language and comments,
Sincerely, the author team