Mapping Vulnerability to Potential Crisis Events in Surabaya City: A GIS-Based Approach

Background This study aims to develop a vulnerability map for Surabaya using GIS-based Multi-Criteria Decision Analysis (MCDA) to assess the city’s vulnerability to COVID-19. Methods Six key factors influencing vulnerability were identified and their relative importance determined through the Analytic Hierarchy Process (AHP) pairwise comparison matrix. GIS was utilized to classify Surabaya’s vulnerability into five levels: very low, low, medium, high, and very high. Results The resulting vulnerability map provides essential insights for decision-makers, healthcare professionals, and disaster management teams. It enables strategic resource allocation, targeted interventions, and formulation of comprehensive response strategies tailored to specific needs of vulnerable districts. Conclusions Through these measures, Surabaya can enhance its resilience and preparedness, ensuring the well-being of its residents in the face of potential emergency outbreaks.


Results
The resulting vulnerability map provides essential insights for decision-makers, healthcare professionals, and disaster management teams.It enables strategic resource allocation, targeted interventions, and formulation of comprehensive response strategies tailored to specific needs of vulnerable districts.Any reports and responses or comments on the article can be found at the end of the article.

Introduction
In early 2020, countries worldwide faced susceptibility to the SARS-CoV-2 and COVID-19 coronaviruses. 1 The COVID-19 virus spread uncontrollably across the globe, evolving into a pandemic with severe health implications. 2he COVID-19 outbreak had far-reaching effects on various aspects of daily life in numerous countries across the world. 3,4e initial COVID-19 case in China, specifically in Wuhan, was identified. 5Nonetheless, the virus propagated swiftly, and within a few months, confirmed cases had emerged in most countries worldwide. 1pulation may be at the greatest danger in the event of any crisis (such as the COVID-19 outbreak). 6,7The city of Surabaya is the capital city of East Java Province, Indonesia, as well as the largest metropolitan city in the province.Surabaya is the second largest city in Indonesia after Jakarta. 8e distribution of societal susceptibility to the impacts of a disaster is often spatial. 9,10However, it's essential to recognize that social vulnerability is a dynamic process significantly shaped by government initiatives and mitigation strategies. 11Consequently, communities already facing vulnerability may experience an exacerbation of their situation due to an inadequate or delayed government response. 11e term "vulnerability" describes a situation in which there is a potential for increased exposure to a community's hazards. 12Vulnerability mapping is a commonly utilized approach that suggests utilizing multiple determining factors to classify a particular community into various health vulnerability groups. 13Population vulnerability significantly impacts crisis situations like the COVID-19 outbreak by influencing how communities are affected and how they can respond and recover. 14Limited healthcare infrastructure, including access to medical facilities and resources, exacerbates these vulnerabilities. 15Vulnerable populations are less able to prepare for, respond to, and recover from disasters.Understanding and addressing these vulnerabilities is crucial for effective crisis management and recovery strategies.
The concept of epidemic prediction mapping using multiple criteria analysis has been explored in several studies. 16hese studies often employ the multi-criteria decision analysis (MCDA) approach, considering numerous criteria in the vulnerability mapping of COVID-19. 179][20] One of the most commonly employed MCDA strategies in these studies is the Analytic Hierarchy Process (AHP). 21The AHP offers a systematic approach to assigning equitable weights to various influential criteria.
Recent research has employed Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA) to map and assess vulnerability to various crises, including pandemics.For instance, Shadeed and Alawna (2021) 3 utilized GIS-based vulnerability mapping in the West Bank, Palestine, to identify areas at higher risk of COVID-19 transmission.Similarly, Acharya and Porwal (2020) 15 developed a vulnerability index for managing and responding to COVID-19 in India, highlighting demographic, epidemiological, and ecological factors.These studies underscore the value of integrating spatial analysis with multi-criteria assessment to inform targeted public health interventions.This study addresses a GIS-based Multi-Criteria Decision Analysis (MCDA) framework to create a COVID-19 Vulnerability Index (CVI) specifically for Surabaya, Indonesia.This incorporates six critical factors: population size, population density, number of hospital beds, number of doctors, number of nurses, and availability of Intensive Care Units (ICUs) to produce a vulnerability map for Surabaya, enabling policymakers to identify high-risk areas and allocate resources more effectively.

REVISED Amendments from Version 1
This version of the article includes several significant updates and improvements based on reviewer feedback.We have clarified the rationale for selecting Surabaya for the study, highlighting its unique challenges and the expected outcomes of the research.The methodology section now provides a detailed explanation of the GIS techniques and the Analytic Hierarchy Process (AHP), including the steps taken to ensure consistency in the pairwise comparison matrix.We have also incorporated recent studies and literature to enhance the background and context of the research.Additionally, we have validated our findings using statistical methods to ensure robustness and reliability.The results section has been expanded to include specific data points and notable trends, along with practical implications for disaster management and public health preparedness.
This study aims to assess community vulnerability in emergency situations based on COVID-19 data.By establishing a COVID-19 vulnerability map for Surabaya city.Which is extremely valuable, Surabaya, a critical area due to its high population density and diverse socio-economic conditions, was chosen for this study to map vulnerabilities.These factors, along with its status as a major financial hub, necessitate understanding and mitigating potential crises.
In addition, it helping decision-makers identify potential COVID-19 outbreaks and, in turn, implement appropriate mitigating strategies to protect public health, particularly in the governorates that are most at risk.

Study area
Surabaya is a city located in Indonesia.It is also the capital of the Jawa Timur province.The city is one of the most significant financial hubs in the country.As of the 2015 Census, the population of the city is 2.880.000.It is the second most populous city in Indonesia.The city proper contains a total surface area of 350.5 km 2 (135.3 sq mi).
The metropolitan area however sprawls out to 5,925 km 2 (2,288 sq mi).The population density reaches upward of 9,900/km 2 (26,000/sq mi) in the city proper, and drops toward 2,200 per square kilometer (5,700 per square mile) as one moves toward the edge of the metropolitan area.The area of Surabaya City is divided into 5 regions (East, North, South, West, and Center) divided into 31 sub-districts and 163 villages see Figure 1 (Surabaya City Statistics Center, 2022).

COVID-19 Vulnerability Index (CVI)
The COVID-19 vulnerability map in this study was constructed using the compiled CVI map.The design of the CVI map considered six crucial factors, as outlined in Table 1.These criteria were selected due to their capacity to increase COVID-19 vulnerability (P, PD, HB, D, N, and ICU) see Table 1.

Data collection
The demographic data was obtained from the Surabaya City Statistics Center, while the healthcare infrastructure data was sourced from the Ministry of Health and the Regional Health Office.
The overall methodological approach for developing the COVID-19 vulnerability map is detailed below.This includes the use of weighted overlay summation and the natural breaks (Jenks) method, as illustrated in Figure 2.

Result
The AHP pairwise comparison matrix approach, presented in Table 2, was employed to allocate weights for the various CVI criteria. 28Afterward, the consistency of these assigned weights was assessed by calculating a consistency ratio (CR) as follows. 28 ¼ CI RI CI ¼ λ À n n À 1 CI: consistency index, RI: random consistency index that depends on the number of criteria, λ: maximum eigenvector of the matrix, and n: the number of criteria.To ensure the robustness of the statistical analysis, validating the weights assigned using the Analytic Hierarchy Process (AHP) is essential.The Consistency Ratio (CR) plays a crucial role: a CR ≤ 0.1 signifies acceptable consistency, whereas a higher CR suggests the need for review.In this study, a CR value of 0.06 was achieved, indicating that the COVID-19 Vulnerability Index (CVI) criteria matrix is consistent and reliable. 29 the CVI map, every criterion was categorized into nine value classes, and each class was assigned a score ranging from 1 less important to 9 highly important. 28The chosen criteria were then converted into raster format and reclassified using various GIS tools (see Figure 3).
GIS was utilized to calculate the CVI by employing the weighted overlay summation process. 29This involved aggregating the weighted cell values of various selected criteria.Each criterion's input layer was multiplied by its respective weight, and the outcomes were combined through summation.In the end, the comprehensive CVI was computed using the natural breaks (Jenks) method in GIS.This CVI value was then employed to create the COVID-19 vulnerability map covering the entirety of the Surabaya city.
The COVID-19 vulnerability map for the Surabaya was designed (see Figure 4).This map classified the Surabaya districts into five distinct COVID-19 vulnerability categories, ranging from very low to very high.Additionally, Table 3 provides the population counts for each COVID-19 vulnerability class in the Surabaya city.

Discussion
Ongoing and resurging diseases that have the potential to become pandemics remain a persistent challenge for nations and healthcare systems, resulting in significant human and economic tolls.This underscores the importance of prioritizing global health readiness in the face of emerging epidemics.Enhancing healthcare infrastructure stands as the most effective safeguard against disease outbreaks and other health-related risks, making it a vital component of health security for all countries. 30 terms of demographic factors, districts like Simokerto, Wonokromo, Gubeng, Sawahan, Tambaksari, Bubutan, Tegalsari, Semampir, and Kenjeran are identified as being in very high to high vulnerability zones.These districts share The vulnerability map of Surabaya, as depicted in Figure 4 plays a crucial role in assessing the city's preparedness for a potential emergency outbreak.By identifying districts within Surabaya that exhibit high vulnerability, especially those with high population density and other relevant criteria, this map serves as an essential tool for understanding where vulnerabilities are most pronounced.In the context of a potential emergency outbreak, such as a public health crisis or a natural disaster, areas with high vulnerability, as indicated on the map, may face greater challenges in responding to and managing the crisis effectively.These challenges could include a shortage of healthcare facilities, limited access to medical resources, overcrowding, and socioeconomic factors that hinder residents' ability to cope with emergencies.
This study proposes a COVID-19 Vulnerability Index (CVI) for Surabaya using GIS-based Multi-Criteria Decision Analysis (MCDA) to integrate demographic, and healthcare factors, identifying high-risk areas to aid resource allocation and targeted interventions.Compared to existing studies that focus on static disaster relief center allocation.Future studies should expand on this research by including dynamic aspects of crisis management and applying GIS and MCDA in various disaster and epidemic contexts, such as evaluating relief centers' efficiency and emergency response performance. 31In addition, this can guide policymakers in resource allocation, enabling strategic planning to target high-risk areas.The findings emphasize the need for improved healthcare infrastructure, better-trained healthcare professionals, and efficient emergency response coordination, ultimately aiding in disaster management and public health preparedness.

Conclusion
In this research, a vulnerability map was created for the Surabaya using CVI values derived through GIS-based MCDA.Six significant factors were chosen, Weightings for these factors were determined using the AHP pairwise comparison matrix.The GIS was employed to categorize Surabaya's CVI values into five COVID-19 vulnerability levels: very low, low, medium, high, and very high.
The information provided by this map empowers decision-makers, healthcare professionals, and disaster management teams to allocate resources strategically, implement targeted interventions, and develop comprehensive response strategies tailored to the specific needs of vulnerable districts.By doing so, Surabaya can enhance its resilience and preparedness, ultimately safeguarding the well-being of its residents in the face of potential emergency outbreaks.

Extended data
Fighshare: STROBE Checklist for "Mapping vulnerability to potential crisis events in Surabaya city: A GIS-based approach", https://doi.org/10.6084/m9.figshare.25598073.v1. 32xpertise to confirm that it is of an acceptable scientific standard.

Version 1
Reviewer Report 07 June 2024 https://doi.org/10.5256/f1000research.159093.r276913 © 2024 Roy S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Subham Roy
University of North Bengal, West Bengal, India Clearly state why Surabaya was chosen for this study.For instance, mention the specific challenges Surabaya faces that make it a critical area for vulnerability mapping.Highlight the expected outcomes of the study, such as identifying high-risk areas for targeted interventions.
○ Add overall methodological flowchart for better understanding to the readers.

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Provide specific details on the selection criteria for the six key factors influencing vulnerability.Mention the tools and techniques used in the GIS analysis and how the Analytic Hierarchy Process (AHP) was applied.

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Include specific data points or notable trends found in the results.For example, state the percentage of the city classified as high vulnerability and the key factors contributing to this classification.

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Expand the literature review to include a broader range of studies related to vulnerability mapping and GIS-based MCDA.Discuss different methodologies and their findings, and how they relate to your study.

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Clearly identify the gaps in the existing literature that your study aims to fill.Articulate the novelty and significance of your research objectives.For instance, if previous studies have not focused on the integration of certain criteria or regions, highlight this as a gap your study addresses.

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Include more studies that have used similar methodologies or have focused on similar objectives (Add the literature in table format).Discuss the strengths and limitations of these studies to provide a comprehensive background for your research.

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Critically analyze the existing literature by highlighting the methodologies, findings, and gaps.Explain how your study builds on or diverges from these studies.For example, compare the use of different GIS techniques or criteria in vulnerability mapping.

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Provide a detailed explanation of how the AHP process was applied, including the steps taken to ensure the consistency ratio was ≤0.1.Describe how the pairwise comparison matrix was constructed and used to assign weights.

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Clearly describe the sources of your data, including any databases, surveys, or other sources.Explain how the data was collected and any limitations or biases that may affect the results.

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Elaborate on the GIS techniques used in your study.Explain the weighted overlay summation process and the natural breaks (Jenks) method in more detail.Provide a step-bystep description of how these techniques were applied to create the vulnerability map.

Are the conclusions drawn adequately supported by the results? No
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Spatial analysis, GIS, Remote Sensing I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.
Author Response 28 Jun 2024

Ali Jarghon
Author response: Thank you so much for your comment, I have corrected/ added upon your request.
1. Clearly state why Surabaya was chosen for this study.For instance, mention the specific challenges Surabaya faces that make it a critical area for vulnerability mapping.Authors need to propose their study and compare your study with efficient crisis management by selection and analysis of relief centers in disaster integrating GIS and multicriteria decision methods, evaluating the efficiency of relief centers in disaster and epidemic conditions using multi-criteria decision-making methods and GIS: A case study, evaluating the performance of emergency centers during coronavirus epidemic using multicriteria Decision-making methods, data-driven modeling using system dynamics simulation to provide relief in earthquake based on different scenarios Author response: Thank you so much for your comment, I have corrected/ added upon your request from line 173-183

Competing Interests: No competing interests to disclose
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Figure 1 .
Figure 1.Location map of the City of Surabaya.

Table 2
presents the Analytic Hierarchy Process (AHP) pairwise comparison matrix used to allocate weights for the various criteria related to the COVID-19 Vulnerability Index (CVI).The table showcases the relative importance of each criterion in assessing vulnerability, aiding in the prioritization and decision-making process for strategic interventions and resource allocation.

Table 3 .
Continued high population density.The study specifically selected districts with scores ranging from 7 to 9, which revealed that approximately 47% of Surabaya's inhabitants are in a high vulnerability zone.On the other hand, related to nurse number to population indicates district of Sambikerep, Sawahan, Kenjeran, Rungkut, Jambangan, Bubutan, Gunung Anyar, Karang Pilang, Asemrowo, Bulak, and Krembangan are under high to very high vulnerable zone.More ever, related to number of doctors to population indicates districts of Jambangan, Sambikerep, Sukomanunggal, Sawahan, Bubutan, Rungkut, Kenjeran, Krembangan, Gunung Anyar, Karang Pilang, Asemrowo, and Bulak are under high to very high vulnerable zone.The criteria of nurses and doctors play a crucial role in responding to an emergency outbreak.However, these factors suffer from a shortage of doctors and nurses to effectively manage any outbreak.Meanwhile, districts of Dukuh Pakis, Tandes, Semampir, Lakarsantri, Sambikerep, Jambatan Bubutan, Karang Pilang, Gunugng Anyar, Rungkut, Sawahan, Asemrowo, Krembangan, Kenjeraan, and Bulak are under high to very high vulnerable zone because of bed hospital to population in districts.The study selected districts with scores ranging from 7 to 9 only.This revealed that 15 districts, accounting for 47.8% of the total population, have a shortage of hospital beds.This highlights a high vulnerability for the city of Surabaya in the event of a potential emergency outbreak.However districts of Pakal, Tegalsari, Tenggilis Mejoyo, Lakarsantri, Dukuh Pakis, Karang Pilang, Jambangan, Gunung Anyar, Rungkut, Bulak, Kenjeran, Semampir, Krembangan, Bubutan Asemrowo, Tandes, and Sambilerep are in a highly vulnerable zone due to the ICU capacity in relation to the district's population.The study selected districts with scores ranging from 7 to 9, revealing that 17 districts have an ICU shortage.This highlights a high vulnerability for the city of Surabaya in the event of a potential emergency outbreak. a

Is the work clearly and accurately presented and does it cite the current literature? No Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? No Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes
Include specific data points or notable trends found in the results.For example, state the percentage of the city classified as high vulnerability and the key factors contributing to this classification.Clearly identify the gaps in the existing literature that your study aims to fill.Articulate the novelty and significance of your research objectives.For instance, if previous studies have not focused on the integration of certain criteria or regions, highlight this as a gap your study addresses Author response: Thank you so much for your comment, I have corrected/ added upon your request from line 36-49 7. Include more studies that have used similar methodologies or have focused on similar objectives (Add the literature in table format).Discuss the strengths and limitations of these studies to provide a comprehensive background for your research.Author response: Thank you so much for your comment, I have corrected/ added upon your request from line 36-49 8. Critically analyze the existing literature by highlighting the methodologies, findings, and gaps.Explain how your study builds on or diverges from these studies.For example, compare the use of different GIS techniques or criteria in vulnerability mapping.Author response: Thank you so much for your comment, I have corrected/ added upon your request from line 36-49 9. Provide a detailed explanation of how the AHP process was applied, including the steps taken to ensure the consistency ratio was ≤0.1.Describe how the pairwise comparison matrix was constructed and used to assign weights.Thank you so much for your comment, I have corrected/ added upon your request Summarized in Figure2 12. Present the results with detailed explanations of the tables and figures.For example, explain what Table2and Figure2show and how they contribute to the overall findings.Provide context for the data presented in these tables and figures.Thank you so much for your comment, This study utilizes spatial analysis and GIS to predict areas at risk based on collected data.Our approach identifies weaknesses by geographical location, ranking areas from most to least vulnerable according to the weighted results of each parameter.Ultimately, the findings are presented in the form of a map.14.Ensure the statistical analysis is robust and clearly interpreted.Provide more details on the consistency ratio calculation and its significance.Explain how the weights were validated and the implications of the statistical findings.Author response: Thank you so much for your comment, I have corrected/ added upon your request from line 106 15.Discuss the practical implications of your findings for disaster management and public health preparedness.Provide specific recommendations for policymakers, healthcare professionals, and emergency responders based on your findings.Author response: Thank you so much for your comment, I have corrected/ added upon your request from line 173-183 16.Summarize the main findings of the study, highlighting the key contributions to the field.For example, state how the vulnerability map can be used to improve emergency preparedness in Surabaya.Author response: Thank you so much for your comment, I have corrected/ added upon your request from line 163-172 17.Emphasize the practical implications of the findings for policymakers and emergency preparedness.For example, discuss how the findings can inform resource allocation and strategic planning Author response: Thank you so much for your comment, I have corrected/ added upon your request from line173-183 knowledge gaps identified and link them to your paper goals.Please reason both the novelty and the relevance of your paper goals.Clearly discuss what the previous studies that you are referring to.What are the Research Gaps/Contributions?Please note that the paper may not be considered further without a clear research gap and novelty of the study.Literature Review has the chance to be further improved: it seems that the authors have made the retrospection.However, via the review, what issues should be addressed?What is the current specific knowledge gap?What implication can be referred to?The above questions should be answered.Authors need to propose their study and compare your study with efficient crisis management by selection and analysis of relief centers in disaster integrating GIS and multicriteria decision methods, evaluating the efficiency of relief centers in disaster and epidemic conditions using multi-criteria decision-making methods and GIS: A case study, evaluating the performance of emergency centers during coronavirus epidemic using multi-criteria Decisionmaking methods, data-driven modeling using system dynamics simulation to provide relief in earthquake based on different scenarios What role does population vulnerability play in crisis situations like the COVID-19 outbreak?How does social vulnerability contribute to the impact of disasters, and how can government responses exacerbate or alleviate these vulnerabilities?What methods are commonly used for vulnerability mapping, especially concerning health-related crises like COVID-19?How does Geographic Information Systems (GIS) aid in assessing vulnerability, particularly in the context of epidemic prediction mapping?What factors were considered in developing the COVID-19 Vulnerability Index (CVI) for Surabaya, and how were weights assigned to these factors?How does the vulnerability map of Surabaya help decision-makers identify and address potential COVID-19 outbreaks, especially in high-risk areas?
Highlight the expected outcomes of the study, such as identifying high-risk areas for targeted interventions.Author response: Thank you so much for your comment, I have corrected/ added upon your request from line 57-60 in red font 2. Add overall methodological flowchart for better understanding to the readers.Author response: Thank you so much for your comment, I have corrected/ added upon your request.Figure 2. 3. Provide specific details on the selection criteria for the six key factors influencing vulnerability.Mention the tools and techniques used in the GIS analysis and how the Analytic Hierarchy Process (AHP) was applied.Author response: Thank you so much for your comment, I have corrected/ added upon your request from line78-81 4. Author response: Thank you so much for your comment, I have corrected/ added upon your request from line 93-110 10.Clearly describe the sources of your data, including any databases, surveys, or other sources.Explain how the data was collected and any limitations or biases that may affect the results.Author response: Thank you so much for your comment, I have corrected/ added upon your request from line 82 11.Elaborate on the GIS techniques used in your study.Explain the weighted overlay summation process and the natural breaks (Jenks) method in more detail.Provide a step-bystep description of how these techniques were applied to create the vulnerability map.Author response: Competing Interests: No competing interests were disclosed.Reviewer Expertise: Disaster management I

confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.
Thank you so much for your comment, I have corrected/ added upon your request from line 6-8 What are the Research Gaps/Contributions?/ Clearly discuss what the previous studies that you are referring to.Author response: Thank you so much for your comment, I have corrected/ added upon your request from line 43-49 What role does population vulnerability play in crisis situations like the COVID-19 outbreak?Author response: Thank you so much for your comment, I have corrected/ added upon your request from line 21-27 How does social vulnerability contribute to the impact of disasters, and how can government responses exacerbate or alleviate these vulnerabilities?Author response: Thank you so much for your comment, I have corrected/ added upon your request from line 21-27 What methods are commonly used for vulnerability mapping, especially concerning healthrelated crises like COVID-19?Author response: Thank you so much for your comment, I have corrected/ added upon your request from line 33-37 How does Geographic Information Systems (GIS) aid in assessing vulnerability, particularly in the context of epidemic prediction mapping Author response: Thank you so much for your comment, I have corrected/ added upon your request from line 50-53 What factors were considered in developing the COVID-19 Vulnerability Index (CVI) for Surabaya, and how were weights assigned to these factors?Author response: Thank you so much for your comment, I have corrected/ added upon your request from line (78-81), (116-121) How does the vulnerability map of Surabaya help decision-makers identify and address potential COVID-19 outbreaks, especially in high-risk areas?Author response: Thank you so much for your comment, I have corrected/ added upon your request from line 163-171