Dataset on Citizens’ Perceptions of Urban Resilience: Survey Results from Veracruz—Boca Del Río Metropolitan Area, Mexico

Abstract

This paper presents a dataset developed to characterize the citizens’ perceptions of urban resilience applied to the Veracruz—Boca del Río Metropolitan Area (VBMA) in Mexico. The data were obtained by conducting online surveys, which were administered to a total of 147 subjects, including 89 from the municipality of Veracruz, 35 from Boca del Río, 15 from Medellín de Bravo, and 8 from Alvarado, with ages ranging from 16 years to over 61 years. The survey was designed to estimate the population’s perception of the Urban Resilience Index (URI) and the Urban Resilience Profile (URP). It was developed derived from a methodology based on IMPLAN and enriched with questionnaires from Villada and SEDATU, resulting in a final questionnaire comprising 10 axes, 33 indicators, and 156 variables. A novel contribution was implemented as a significant study case, which uses the dataset to estimate the URI and URP to the VBMA applying the Entropy Method, considering three criteria: age, gender, and municipality. Here, citizens’ perceptions about urban resilience have been estimated in an URI equal to 0.4571, resulting in a moderate level of resilience. Moreover, this perception could be improved by conducting a full-scale survey with substantial financial investment.

DataSet: https://doi.org/10.5281/zenodo.17436266 (accessed on 8 January 2026)

DataSet License: CC-BY 4.0

1. Introduction

Urban resilience is a matter of critical importance, since a community’s ability to overcome adverse events not only ensures its survival in the face of disasters, but also promotes sustainable development and enhances its capacity to thrive in dynamic and challenging environments. According to Galceran [1], the concept that cities must be able to withstand the impacts of natural hazards has become increasingly notable in recent years. Once a city is exposed to an adverse event, it becomes essential to implement mechanisms that enable rapid recovery. This will minimize negative impacts and collateral damage to infrastructure, the economy, and society [2,3]. The objective of this research is to contribute to urban resilience through the development of a dataset for the Veracruz-Boca del Río Metropolitan Area (VBMA). The results of this research could be extrapolated to an international context by applying frameworks, such as the Sendai Framework for Disaster Risk Reduction and ISO 37123 [4,5,6]. Therefore, the dataset can be adapted to different geographic contexts, hazard profiles, governance systems, and Decision-Making Methods.

A dataset on citizens’ perceptions of urban resilience has been developed based on the application of a specific study conducted in the VBMA. The information is openly available in the Zenodo Data repository (https://doi.org/10.5281/zenodo.17436266 under the Creative Commons Attribution 4.0 International license) “URL (accessed on 8 January 2026)”. The objective of the survey was to obtain direct information from citizens regarding their assessment of the urban environment’s capacity to prepare for, respond to, and recover from disruptive events, such as natural disasters or human-caused events.

The background for this survey consists of three studies related to resilience: one conducted in León, Guanajuato (Mexico), by the Metropolitan Planning Institute (IMPLAN, 2016) [7]; another in Medellín, Colombia, by Villada [8]; and a third in Tijuana, Baja California Norte, Mexico, by the Secretariat of Agrarian, Territorial, and Urban Development (SEDATU) [9]. According to Villada’s study [8], 13 experts were consulted. However, the number of people interviewed or surveyed in the IMPLAN [7] and SEDATU [9] studies is unclear. These three studies formed the main basis for the selection and formulation of key social indicators, ensuring the conceptual validity and local relevance of the instrument.

The base instrument used is the one applied in the city of León (IMPLAN, 2016) [7], which consists of 10 axes, 33 indicators, and 131 variables, with 131 questions. The questionnaire was enriched by incorporating variables obtained from the assessment instruments used by Villada (2020) [8] and SEDATU (2017) [9]. This procedure culminated in the development of a final questionnaire structured around 10 axes, 33 indicators, and 156 variables, with a total of 156 questions. Data collection was carried out between July and August 2024, through the application of a questionnaire administered to a total of 147 subjects via the Google Forms platform. The information generated by the 147 study participants, regarding citizens’ perceptions of urban resilience, was processed using the Entropy Method, normalizing the data to obtain the Urban Resilience Index (URI) and the VBMA’s URP; as a case study, see Section 3. The analysis considered three criteria: age, gender, and municipality.

2. Data Description

2.1. Study Area

The VBMA is located on the Gulf of Mexico coastline. The region’s geographical location renders it susceptible to a high occurrence of natural phenomena, including hydrometeorological events such as storms, floods, and moderate seismic activity. As illustrated in Figure 1, the VBMA configuration is characterized by significant physiographic features, with two of the most prominent municipal districts situated within this region [10,11,12]. The municipality of Veracruz is located between geographical coordinates 19°12′00.8″ N 96°08′18.7″ W, while the municipality of Boca del Río is between 19°06′01.8″ N 96°06′20.7″ W. The municipality of Veracruz is located within the Southern Gulf Coastal Plain province, in the subprovince known as the Veracruz Coastal Plain. The topography of the region is characterized by a coastal alluvial plain. In contrast, the municipality of Boca del Río comprises 43.89% of coastal plains, complemented by rolling hills, which constitute 56.11% of the municipality’s terrain [13,14].

The VBMA was initially delineated in 2004, encompassing the municipalities of Veracruz, Boca del Río, and Alvarado [15]. In 2024, the SEDATU [16], the National Population Council (CONAPO) [16], and the National Institute of Statistics, Geography, and Informatics (INEGI) [17] formalized the national criteria for defining metropolitan areas in Mexico. These guidelines provide a standardized framework for territorial classification, which is essential for assessing urban resilience, spatial planning, and the allocation of public resources. The VBMA includes the municipalities of Veracruz and Boca del Río as central hubs, while Jamapa, Medellín de Bravo, and Manlio Fabio Altamirano are considered peripheral municipalities. The rationale behind the exclusion of Alvarado Municipality remains unclear. However, the Veracruz-Boca del Río Metropolitan Area Land Use Plan, published by the Veracruz State Government in 2021 [18], defines the VBMA as comprising the municipalities of Alvarado, Boca del Río, Jamapa, Manlio Fabio Altamirano, Medellín de Bravo, and Veracruz. The municipalities in question are characterized by their strong geographic proximity and high levels of demographic dynamism and mobility. According to INEGI (2020) [17], the population of Veracruz Municipality is 607,209, while Boca del Río Municipality has a population of 144,550 inhabitants. Collectively, the VBMA encompasses 939,046 residents.

Table 1. Population distribution within the VBMA by municipality.

Municipality	Population 2020	%
Alvarado	57,035	6.07
Boca del Río	144,550	15.39
Jamapa	11,132	1.18
Manlio Fabio Altamirano	23,918	2.54
Medellín de Bravo	95,202	10.13
Veracruz	607,209	64.66
VBMA	939,046	100.00
Veracruz State	8,062,579	VBMA: 11.65

presents the population distribution by municipality within the VBMA.

The municipalities of Boca del Río, Medellín, and Alvarado are part of the VBMA and are linked to the Veracruz municipality. There is a strong correlation between these four municipalities due to their proximity, forming an economically stable and constantly developing metropolitan area. The Veracruz municipality has a historical importance to national level due to its economy, history, tourism, and prestige. Veracruz has significant infrastructure, such as its international port and airport, and prominent companies, such as “TENARIS-TAMSA” generally referred to by its acronym TAMSA, which is one of the world’s leading producers of seamless steel pipes and the only one in Mexico. These factors have a considerable impact on the potential development of neighboring municipalities [10]. For instance, the Boca del Río municipality has undergone substantial tourism development and is home to hotel infrastructure, major shopping malls, and an increase in the construction of high-rise buildings for residential use. Thus, most of its land has already been developed, leaving limited options for further expansion. Consequently, this has resulted in increased population density and the erection of high-rise buildings [11]. The robust development that has taken place in Boca del Río had a significant impact on the Alvarado municipality, with substantial residential expansion towards its coastal area, known as “La Riviera Veracruzana”. This area has seen the construction of several high-value housing developments, along with major commercial areas [19]. Medellín de Bravo Municipality has experienced a slower development trajectory compared to its neighboring municipalities. Nonetheless, a substantial portion of its population commutes daily to Veracruz and Boca del Río for employment, positioning Medellín de Bravo as a functional commuter town within the VBMA [20].

2.2. Dataset

The study sample consisted of 147 residents within the VBMA, surveyed as follows: 89 from Veracruz, 35 from Boca del Río, 15 from Medellín de Bravo, and 8 from Alvarado. The survey was conducted using a structured questionnaire, and the responses were recorded with precision in the Zenodo Data repository. The repository contains three files with the extension .xlsx, which forms the database that stores the responses collected from the questionnaires administered to the entire sample of individuals, one file with the total number of respondents, and two files with only the respondents from the municipalities of Veracruz and Boca del Río. In these files, columns A to D present the general data, columns E to N show the values assigned to each axis, and columns P to AV show the values assigned to each division of the axes (indicators). Finally, the columns from AX to GW show the values assigned to each question (variable).

The document entitled “Global Questionnaire” (.docx) included a statement that explained the purpose of the study, as well as the intended use of the data collected. Likewise, participants were asked for their informed consent to participate in the research, which was understood to have been given explicitly by completing and submitting the form. The questionnaire consists of 156 items organized into 10 axes: the first axis, entitled “Organization to cope with disasters”, is subdivided into 4 indicators and comprises a total of 16 questions. The second axis, “Identification, understanding and use of risk scenarios”, is subdivided into 3 indicators and comprises 13 questions. The third axis, “Financial capacity of the municipality, its population and institutions”, is subdivided into 3 indicators and contains 20 questions. The fourth axis, “Urban design and development”, is subdivided into 4 indicators and comprises 13 questions. The fifth axis, “Environmental capacity”, is subdivided into 2 indicators and contains a total of 14 questions. The sixth axis, “Institutional capacity”, is subdivided into 4 indicators and comprises 22 questions. The seventh axis, “Social capacity”, is subdivided into 3 indicators and includes 16 questions. The eighth axis, “Infrastructure”, is subdivided into 3 indicators and comprises 18 questions. The ninth axis, “Adequate and effective response”, is subdivided into 3 indicators and includes 11 questions. Finally, the tenth axis, “Recovery and reconstruction”, is subdivided into 4 indicators and comprises 13 questions. To provide full details, the questionnaire contained 156 questions, 131 of which were taken from the questionnaire used in the city of León, Guanajuato, by IMPLAN [7]. Of these, 31 were adapted with the purpose of guaranteeing optimal comprehension by the respondents. Three questions (2.2.2.2.a, 3.1.2.a and 3.1.2.b) were adapted from the information considered by Villada [8] in the city of Medellín, Colombia. Furthermore, four questions were taken and adapted from the instrument used by SEDATU [9] (2.1.6.b, 3.2.5.a, 3.2.5.b and 10.1.1.1.a) to calculate the Urban Resilience Profile in the city of Tijuana, Baja California Norte. Eighteen additional questions have been developed by the authors with the purpose of enriching the questionnaire and obtaining additional information about the population under study. These questions are listed in the following sections: 1.1.3.a, 1.2.3.a, 2.1.6.a, 3.3.7.a, 3.3.7.b, 5.1.6.a, 5.1.6.b, 6.2.5.a, 8.2.2.a, 8.2.2.b, 8.2.2.c, 8.2.2.d, 8.2.2.e, 9.1.1.a, 10.1.2.a, 10.3.1.a, 10.3.1.b and 10.3.2.a.

The analysis of the data contained in the three documents entitled “Tables and graphs” (.docx) demonstrates the outcomes of the analyses conducted employing the Entropy Method [21] and incorporating segmentations by age, gender, and municipality of origin of the respondents. Two documents, also titled “Tables and graphs” (.docx), are presented with the names of the municipalities of Veracruz and Boca del Río, displaying the results of the analyses performed only with the responses of respondents living in these municipalities. For the five documents, the results obtained are presented by axis and indicator. The “W_j” columns show the weight values of each variable and indicator. The “X_ijprom” column shows the normalized values (using the Entropy Formula) of the variable [22], considering the totality of the responses obtained when applying the questionnaire. As previously stated, the weights of each variable and indicator were determined using the Entropy Method [23]. For example, the weights are given in Figure 2, which shows one of the graphs from the .docx file. The first position of the number indicates the axis, the second, the indicator, and the third, the variable. In Figure 2, the graph on the left (a) shows how the variables behave, while the graph on the right (b) shows the result of the indicators’ behavior.

3. Study Case: Preliminary Assessment of the Urban Resilience Perception by Using the Entropy Method

3.1. Methodology

The value of the data lies in determining the surveyed population’s perception of the urban resilience of their environment, as measured by a quantitative index (URI) generated by the proposed survey mechanism. The results are presented in the form of an Urban Resilience Profile (URP), which illustrates the strengths and weaknesses of the evaluated areas. The research was carried out in the VBMA. The study focused on assessing public perception of urban resilience. In a context of increasingly frequent and intense natural disasters, it is essential that communities are prepared to adapt and overcome these challenges. It is proposed that the URI be estimated using a methodology based on the Entropy Method [22].

As stated in Márquez-Dominguez et al. [24], the Entropy Method was selected between a range of Multi-Criteria Decision-Making (MCDM) methods due to its transparency. It is a data-driven weighting scheme that minimizes subjectivity by assigning weights based on richness of the dataset [25,26]. Therefore, the Entropy Method mitigates the impact of external judgments. It enables the evaluation of multiple dimensions, which is essential for analyzing complex urban systems in hazard scenarios. However, classical decision-making approaches, such as Wald, Hurwicz, Savage, and Laplace [27,28,29,30], present limitations. These approaches consider costs and benefits separately and overlook qualitative aspects [31,32]. The weighted sum method and the simple ranking method facilitate more integrated assessments, but remain highly dependent on subjective judgments [33,34]. Alternative weighting schemes, such as Principal Component Analysis (PCA) [35,36], Benefit-of-the-Doubt (BoD) [37], Equal Weights [38], and CRITIC [39], are approaches which rely on robust statistical assumptions, avoids oversimplification, and emphasizes correlations that may undervalue dimensions critical to urban resilience. For these reasons, the Entropy Method was selected as the most appropriate approach because it provides a large-scale, data-driven framework that minimizes subjectivity by assigning weights according to the variability and richness of the dataset itself, which is the core of this paper. This approach is particularly suitable for resilience studies, where indicators often combine diverse quantitative and qualitative dimensions and also directly reflect the dataset’s content, ensuring rigor, reproducibility, and coherence with the objectives of resilience assessment [27,28,29,30,31,32,33,34] and can provide a reproducible and adaptable framework for future comparative resilience studies. However, improvements are needed in the Entropy Method for resilience assessment. Key bottlenecks or challenges remain, such as bridging the gap between entropy and resilience theory, establishing consensus on normalization protocols, and preserving resilience signals while mitigating distortions from outliers. This requires strengthening its theoretical foundations by explicitly linking entropy to resilience properties, such as diversity, adaptability, and uncertainty [40]. Standardized normalization procedures are necessary to ensure comparability across heterogeneous datasets. Sensitivity analyses confirm the impact of different schemes on entropy weights [38]. Finally, robust treatments of extreme values must distinguish meaningful hydrometeorological events from statistical anomalies [41,42].

As outlined previously, the conceptual framework for assessing the urban resilience of the VBMA was based on 10 core categories or axis. The categories addressed are considered key to the definition of a resilient city, and are presented below:

• Organization to cope with disasters.
• Identification, understanding and use of risk scenarios.
• Financial capacity of the municipality, its population, and institutions.
• Urban design and development.
• Environmental capacity.
• Institutional capacity.
• Social capacity.
• Infrastructure.
• Adequate and effective response.
• Recovery and reconstruction.

As illustrated in

Table 2. Number of subjects surveyed, broken down by age.

Age Range	Number of Subjects Surveyed
16–20	63
21–25	56
26–30	7
31–35	1
41–45	6
46–50	8
51–55	3
+61	3

, the age range of subjects ranged from 16 to over 61 years old. This finding was derived from the electronic questionnaire disseminated to 147 individuals.

The methodology used by IMPLAN [7] in León, Guanajuato, in 2016 was employed to develop this study. Subsequently, it was implemented in [43] as a pilot test with twelve respondents to estimate the URI and URP of the VBMA. This methodology was enriched with the questionnaires used by Villada [8] in the city of Medellín, Colombia, and by SEDATU [9] to calculate the URP in the city of Tijuana, Baja California Norte. It is noteworthy that the initial instrument for the city of León comprised 10 axes, 33 indicators, and 131 variables, yielding 131 questions.

For this project, the original questionnaire was expanded by incorporating variables from the questionnaires used by Villada [8] in Medellín, Colombia, and by SEDATU [9] to calculate the Urban Resilience Profile in Tijuana, Baja California Norte. This resulted in a comprehensive questionnaire comprising 10 axes, 33 indicators, and 156 variables, which yielded 156 questions. The information integration model is based on a hierarchical analysis structured into three levels (see Figure 3) [7,44]. The first level comprises the thematic axes E_n (10 in total). The second level contains the indicators I_n (33 in total). The third level contains the variables V_n (156 in total).

The instrument used by IMPLAN [7] in León was originally applied to a group of municipal managers. In our case, as it was aimed at the VBMA population, a six-point Likert scale (from 0 to 5) was used, where 0 represents the lowest rating and 5 the highest. The number of categories used was an even number (resulting 6 outcomes), to prevent responses from being concentrated in the middle, encouraging respondents to lean their opinion toward one extreme or the other.

To calculate the weight (w_j) of each variable in the third level, it is necessary to normalize the data using the following formula [22], which applies a data normalization indicator (Y_ij), applying x_ij as the average of each segment of the data analysis, according to the selected criteria (age, gender, and municipality), as well as the sum of these averages:

$${\mathrm{Y}}_{\mathrm{ij}}={\displaystyle \frac{{\mathrm{x}}_{\mathrm{ij}}}{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{x}}_{\mathrm{ij}}}}$$

(1)

The entropy value (e_j) is then estimated.

$${\mathrm{e}}_{\mathrm{j}}=-{\displaystyle \frac{1}{\mathrm{Ln}\left(\mathrm{n}\right)}}{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{Y}}_{\mathrm{ij}} \mathrm{Ln}({\mathrm{Y}}_{\mathrm{ij}})$$

(2)

The next step is to calculate the diversity of entropy indices (g_j).

$${\mathrm{g}}_{\mathrm{j}}=1- {\mathrm{e}}_{\mathrm{j}}$$

(3)

Finally, the weight ($w_j$) of each variable is calculated.

$${\mathrm{w}}_{\mathrm{j}}={\displaystyle \frac{{\mathrm{g}}_{\mathrm{j}}}{{\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{g}}_{\mathrm{j}}}}$$

(4)

Respondents were selected using convenience sampling. Given that the authors of the study teach at a university, they drew on their personal and academic networks to test the questionnaire. As a result of this selection of participants, 81% of respondents were aged between 16 and 25. Despite this method of selecting respondents, the geographical distribution of participants, with respect to distribution by municipality, shows a slight bias of ±10% (

Table 3. Distribution by municipality.

Municipality	Population 2020	%	Number of Respondents	%	Difference %
Veracruz	607,209	67.17	89	60.54	6.63
Boca del Río	144,550	15.99	35	23.81	−7.82
Medellín de Bravo	95,202	10.53	15	10.20	0.33
Alvarado	57,035	6.31	8	5.44	0.87
Totally	903,996	100.00	147	100.00

), but this difference is not considered statistically significant.

3.2. Results

Three analyses were conducted: firstly, by age group, as illustrated Table 2; secondly, by gender, categorized as male, female, and the option ‘I prefer not to say’; and thirdly, by municipality considering Veracruz, Boca del Río, Medellín, and Alvarado. The results consider the entire sample of people surveyed, which are shown in

Table 4. URI in accordance with the established analysis criteria.

Criterion	Number of Ranges	URI
Age	8	0.4627
Gender	3	0.4277
Municipalities	4	0.4810

.

As illustrated in the analysis by age, 31 variables exhibited average responses of ‘one’ or ‘zero’. Additionally, five indicators and one axis displayed values of one and zero. In such cases, it was necessary to discard these values when applying the Entropy Formula.

Values with a natural logarithm of “zero” and “one” had to be eliminated, and the formula adjusted, without considering those values. Remember that the natural logarithm of zero is undefined, and the natural logarithm of one is zero.

Regarding the URP, the Figure 4 and Figure 5 can be found below.

Finally, to facilitate a comparison of how the resilience profile is presented, for example, according to the three criteria of analysis considered, please refer to

Table 5. Comparative Values of the Urban Resilience Index (URI) by Analytical Criteria and Axes.

Axes	Criterion			Average
	Age	Gender	Municipality
1	0.51	0.46	0.52	0.50
2	0.53	0.44	0.51	0.49
3	0.50	0.42	0.49	0.47
4	0.49	0.42	0.52	0.48
5	0.45	0.41	0.51	0.46
6	0.48	0.43	0.51	0.47
7	0.49	0.45	0.54	0.49
8	0.46	0.42	0.47	0.45
9	0.45	0.39	0.44	0.43
10	0.33	0.27	0.39	0.33

.

The results indicate that the population’s perception of the VBMA’s current capacity to deal with disasters is at medium levels, according to the scale established for the evaluation of variables and indicators. The URI is expressed on a standardized scale of values between 0 and 1. Values closer to 0 represent a low level of resilience, i.e., greater vulnerability or a lower capacity to respond to and recover from disturbances. Conversely, values closer to one indicate a high level of resilience, reflecting a governance structure that is more robust and adaptable and capable of maintaining its essential functions in the face of adverse events. This scale allows for the quantitative comparison of dimensions and variables, facilitating the identification of areas requiring improvement to enhance the urban system’s overall resilience. The highest-rated area was the municipality’s disaster response organization, categorized under Axis 1. However, as with other areas, this may not reflect the current reality, since only one stakeholder (the population) was consulted and others (government, private sector, public institutions involved in disaster prevention) did not participate. This most likely influenced the ratings shown in this study, which contribute to and affect the URI.

However, a closer analysis of the URP evaluated against the three criteria reveals that axis 10, entitled “Recovery and Reconstruction”, is the axis that received the lowest ratings from the population. In this context, the concept of “Recovery and Reconstruction” in this paper is addressed to estimate the urban resilience perception of the population associated with the damages caused by the nature of the events that have affected the VBMA primarily of hydrometeorological origin, and refers to the restoration of vital services and critical infrastructure, particularly the supply of potable water and electricity, which are frequently disrupted by the direct impact of these phenomena, or by their secondary hazards, e.g., intense rainfall, floodings and extreme winds [24]. Moreover, since Hurricane Karl (2010) struck the region, causing significant infrastructure damage, fatalities and disruptions to vital services, no large-scale “reconstruction” or “recovery” processes related to hydrometeorological or geological hazards have been documented [45,46]. Thus, the respondents’ risk perception may be influenced by this event, since the age range of those surveyed corresponds to the period in which it occurred, based on the dataset explained in Section 2 (core of this study).

Nevertheless, despite the recurrent exposure to these risk conditions, it is important to note that, in relation to this sector, there is limited public awareness of mechanisms, contingency plans, statistics, figures, programs, and risk areas. This information deficit is linked to a lack of coordination and response capacity on the part of the VBMA.

The URI is influenced by the analysis criteria. It is recommended that the index be calculated using the averages of three analysis criteria, to obtain a value that considers all the analysis options. Similarly, to calculate the profiles, it is recommended that the averages of each analysis criterion be integrated to estimate the profile per axis, considering three analysis criteria. The average Resilience Index in this exercise is 0.4571, considering the three analysis criteria: age, gender, and municipalities. The average is obtained by adding the three values and dividing the number of values. This can be seen in

Table 6. Average Urban Resilience Index (URI).

Criterion	URI
Age	0.4627
Gender	0.4277
Municipalities	0.4810
Average	0.4571

.

3.3. Conclusions

This dataset offers a novel, openly accessible resource for understanding citizens’ perceptions of urban resilience in the Veracruz-Boca del Río Metropolitan Area (VBMA), Mexico, a region highly exposed to hydrometeorological risks. In the case study, the Entropy Method was applied to a 156-variable survey (n = 147) to compute an Urban Resilience Index (URI = 0.4571) and Profile (URP). Likewise, the results revealed moderate resilience, with critical gaps in recovery planning. The dataset enables reproducible resilience assessments and supports strategic decision-making by providing detailed data that can be utilized for scenario modeling, vulnerability mapping, and institutional capacity-building. Its scientific relevance is anchored in three key contributions. The first is related to methodological innovation, specifically due to the dataset integrates, and adapts established resilience assessment frameworks [7,8,9] into a unified 156-variable questionnaire structured across 10 axes and 33 indicators. This methodology enables standardized measurement of perceived urban resilience through the Urban Resilience Index (URI) and Urban Resilience Profile (URP), computed via the Entropy Method. The resulting URI value of 0.4571 (moderate resilience) provides a quantitative baseline for comparative studies in similar metropolitan contexts. The second contribution focuses on reusability and adaptability. In this regard, the dataset supports reproducibility and cross-regional analysis. Thus, the methodology can be replicated to other urban contexts to assess resilience perceptions on a global scale and validate or refine the questionnaire instrument for a wide range of socio-environmental configurations. Finally, the third contribution addresses the policy and practical utility. From this perspective, the dataset captures perceptions stratified by demographic characteristics (e.g., age, gender, and municipality). It reveals critical gaps in public awareness of disaster preparedness (e.g., Axis 10: “Recovery and reconstruction” scored lowest at 0.33).

Additionally, the lowest-rated axes reveal critical structural and institutional weaknesses in infrastructure management, emergency response capacity, and post-disaster recovery governance within the municipality. Regarding Axis 8 (Infrastructure), the lack of public knowledge about different process such as audits, the public investment for maintenance, resource balance, and the use of disaster reconstruction funds reflect significant deficiencies in infrastructure oversight, and long-term maintenance planning. In Axis 9 (Adequate and Effective Response), weaknesses are associated with the absence or lack of awareness of Emergency Operations Centers, insufficient coverage and accessibility of warning systems, and low-risk awareness among the population, which constrain the municipality’s operational readiness and real-time response capacity.

Finally, Axis 10 (Recovery and Reconstruction) reveals critical weaknesses associated with limited formal recovery coordination mechanisms, and also limited access to recovery funds, and lack of post-disaster recovery plans aligned with the Municipal Development Plan which constrains adaptive capacity and long-term resilience.

Regarding the method applied, the Entropy Method is used in the construction of urban resilience index, due to its inherent objectivity, high reproducibility, and solid statistical robustness. Moreover, it is especially suitable for complex urban environments, since it enables an integrated assessment of resilience, sustainability, and adaptive capacity. Therefore, their methodological consistency makes an appropriate tool for urban resilience studies [47,48,49].

This empowers urban planners to design targeted interventions, such as community engagement programs or infrastructure upgrades, aligned with frameworks like the Sendai Framework and ISO 37123 [5,6].

3.4. Limitations

A limitation concerning the Entropy Method Adjustments involves the exclusion of variables with natural logarithms of “0” or “1” from URI/URP calculations. This is performed to prevent mathematical errors. This omission may underestimate certain resilience dimensions, particularly in axes with high rates of neutral responses. Finally, the subjectivity of the perceptions of the dataset captures self-reported perceptions rather than objective resilience metrics. There may be a discrepancy between public awareness and actual institutional capacities or infrastructure readiness.

4. Dataset Final Remarks

The dataset is designed to provide a replicable and scalable model for assessing societal perceptions of urban resilience. To maintain the integrity of its contributions to global discourse on evidence-based urban resilience, further work is needed to strengthen methodological consistency, stakeholder inclusivity, and data harmonization. Although the current instrument was tailored to the specific risk conditions of the Veracruz–Boca del Río Metropolitan Area (VBMA), its open-access structure and methodological transparency enable adaptation to diverse urban contexts and hazard profiles. To enhance its scientific and practical relevance, future efforts should prioritize the following: First, the contextual adaptation of the questionnaire to different cultural, institutional, and environmental settings, ensuring conceptual validity across regions. Second, the inclusion of additional stakeholders, including government agencies, infrastructure managers, and private sector representatives, is encouraged to complement citizens’ perceptions with institutional perspectives. Third, the improved representativeness through stratified and larger-scale sampling strategies, addressing current demographic imbalances, and expanding the respondent base. Fourth, the methodological refinement, particularly of the Entropy Method’s theoretical foundation, normalization procedures, and treatment of extreme values, to enhance analytical rigor and interpretability and Finally, the integration and interoperability with other open data repositories and international frameworks (e.g., Sendai Framework [5], ISO 37123 [6]) to foster comparative resilience metrics and collaborative research.

It is acknowledged that the definition of the sample size did not adhere to the requisite methodological exactitude. Given a finite population of 903,996 inhabitants in the VBMA and employing simple random sampling with a confidence level of 95%, the significant sample would be approximately 384 respondents. The initial trial of the data collection instrument involved a sample of 147 respondents. Even though the dataset provides significant information on perceived urban resilience, a high concentration of participants in the 16–25 age range was observed during the data collection process, accounting for over 81% of the responses. This distribution of age groups suggests the presence of a bias within the sample population. This bias is associated with the methodology of participant selection and the medium employed for questionnaire administration. The medium for administration of the questionnaire was an online form via Google Forms. Given the increased accessibility and prevalence of this tool among younger demographics, participation from other age groups was found to be minimal. It is important to note that, given the author’s academic background and affiliation with a university, the sample population is likely to be predominantly composed of university students and the author’s closest acquaintances. This may have influenced the representativeness of the results in terms of generational groups.

With respect to the geographical distribution of respondents, the data demonstrate a discrepancy of ±10% in the distribution of the data compared to the distribution of the population by municipality. The authors consider this difference to be of negligible significance, and they posit that it does not exert a significant influence on the distribution of the sample by municipality.