Navigating the Pandemic with GIS: An Exploratory Factor Analysis of Israel’s Municipal Response

Abstract

This study examined the role of Geographic Information Systems (GIS) in municipal responses to the COVID-19 pandemic in Israel. A structured survey of officials from 130 municipalities was conducted, with a focus on the 87 municipalities that utilized GIS. An Exploratory Factor Analysis (EFA) was performed on the survey data from these GIS-user municipalities to identify the underlying dimensions of GIS application during the crisis. The analysis revealed that municipal GIS engagement is not a monolithic activity but is composed of three distinct, reliable, and interpretable latent factors: (1) Strategic and Operational Integration, reflecting the deep embedding of GIS into core governance and decision-making; (2) Temporal Engagement, capturing the sustained use of the system over the timeline of the pandemic’s fourth wave; and (3) Logistical Site Coordination, representing the specialized use of GIS for managing testing and vaccination sites. These findings move beyond documenting individual GIS tasks to provide an empirical, data-driven framework of how geospatial technology was operationalized. This study underscores the multidimensional nature of GIS in a public health emergency and offers a structured understanding that can inform future crisis preparedness, training, and technology implementation strategies for municipal governments.

1. Introduction

COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. The novel coronavirus was first identified in Wuhan, China, in late 2019 as the causative agent of severe pneumonia outbreaks. The virus rapidly spread across China and subsequently to many other countries. In March 2020, the World Health Organization declared COVID-19 a global pandemic. The first confirmed cases in Israel were reported at the end of February 2020, followed by a continuous rise in infections. The period from August to November 2021 is retrospectively referred to as Israel’s fourth wave of COVID-19.

Governments worldwide implemented strict measures to manage the pandemic, including nationwide lockdowns aimed at slowing viral transmission. While lockdowns proved effective in reducing infection rates, they also had severe economic consequences, disrupting businesses and employment. To manage the uncertainty and rapidly evolving situation, strategic planning was required, including predictive models for new COVID-19 cases and geographic spread.

Various models have been developed to predict and control COVID-19 transmission using artificial intelligence. Integrating these models with Geographic Information Systems (GIS) has demonstrated significant potential in pandemic management [1,2] GIS is not only utilized by governmental agencies but also benefits individuals. For example, mobile applications leveraging GIS technology can provide real-time alerts on localized disease outbreaks, helping individuals make informed decisions about potential exposure risks. GIS-based applications have also been developed to help individuals determine the necessity of self-isolation based on their movement history, synchronized with official health databases.

In Israel, GIS played a pivotal role in the municipal-level pandemic response, with local authorities tasked with managing COVID-19 containment efforts. Several municipalities implemented GIS for infection tracking, quarantine enforcement, vaccination site planning, and public communication. However, the extent of GIS adoption varied across different municipalities, raising questions about its overall impact on crisis management effectiveness.

This study evaluated the role of GIS in municipal pandemic response strategies in Israel. While many studies have documented specific applications of GIS, a gap remains in understanding the underlying structure of how municipalities operationalized this technology during the crisis [3,4]. This research moves beyond a simple inventory of tasks to ask a more fundamental question: What were the core, latent dimensions of GIS engagement as reported by municipal officials on the front lines of the pandemic? To answer this, this study employed a multivariate statistical framework, Exploratory Factor Analysis (EFA), on survey data collected from GIS-using municipalities. The aim is to provide a data-driven typology of GIS use, identifying the distinct factors that constitute a municipal GIS response and offering a more nuanced understanding of how this technology is integrated into crisis governance.

2. Literature Review

This literature review is structured in three sections to build a comprehensive foundation for the current study. The first Section 2.1 establishes the fundamental role of Geographic Information Systems (GIS) as a critical tool in public health and crisis management, drawing on both historical precedents and contemporary applications. The review then narrows its focus in Section 2.2 to the specific and indispensable role that GIS played in the global response to the COVID-19 pandemic. Finally, Section 2.3 shifts from the topical background to the methodological framework of this study, providing a justification for the use of survey-based data collection and the selection of Exploratory Factor Analysis (EFA) as the analytical technique. This structure is designed to logically frame the research problem and validate the study’s approach.

2.1. GIS as a Tool in Public Health and Crisis Management

A Geographic Information System (GIS) is a spatial information system that collects, analyzes, visualizes, and shares geospatial data. It integrates thematic mapping layers with alphanumeric databases, where each layer represents a distinct feature (e.g., buildings, roads) mapped onto a uniform coordinate system. The system enables logical identification and spatial analysis of features through vector-based data structures, facilitating precise geographic representation [5].

The effectiveness of GIS in epidemic management has deep historical roots, tracing back to John Snow’s 1854 cholera mapping in London, and contemporary GIS applications build upon this legacy by integrating geospatial analysis with real-time epidemiological data [6,7,8,9]. In modern public health, GIS is pivotal for applications such as disease surveillance and analyzing environmental health risks [10,11]. For example, research in Fayoum, Egypt, employed GIS to predict malaria risk areas based on mosquito population density, enabling targeted intervention strategies [12]. Similarly, GIS applications in healthcare help identify disease hotspots, as demonstrated in studies on cutaneous leishmaniasis in Pakistan, where spatial data was used to map high-risk zones and inform public health strategies [13,14,15]. The use of spatial methods also enables the analysis of other health issues, such as the spatial effect of water pollution [16].

Broadening the scope beyond specific public health issues, GIS is a critical tool for wider crisis and disaster management. Its applications enhance disaster preparedness by enabling detailed risk assessment. For example, one study in Siliguri, India, mapped urban flood risks to demonstrate how GIS can identify high-risk zones [17]. The utility of GIS was also highlighted during the COVID-19 pandemic, where determining optimal locations for temporary healthcare centers involved weighing multiple criteria, leading to the use of a multi-criteria decision-making analysis approach [18]. These examples establish a clear precedent for the value of GIS in managing a wide range of emergency situations.

2.2. The Role of GIS in the COVID-19 Pandemic Response

During the COVID-19 pandemic, GIS became an indispensable tool for global response efforts, used for tracking the spread and severity of the virus and informing critical policy decisions [19,20]. Web-based GIS, in particular, was used to demonstrate to the public the rate of viral spread, and scientific communities leveraged GIS alongside statistical and socio-economic modeling in order to manage the public health emergency [21,22,23]. GIS-based analytics provided key insights for flattening the curve, as spatial models helped predict disease spread and enabled governments to implement targeted containment measures [22,24,25]. For instance, epidemiological models like the COVID-19 Hospital Impact Model for Epidemics (CHIME) integrated GIS to estimate infection trajectories, which guided policymakers in resource allocation [26,27]. Beyond governmental use, GIS-based applications also empowered individuals to manage their own risk. Mobile applications with GIS technology provided real-time alerts on localized outbreaks, while other tools helped individuals determine the need for self-isolation based on their movement history synchronized with official health databases [28,29]. This role has been described as transformative, with GIS helping public health professionals better manage both current and future health crises [30]. Echoing this, a comprehensive review of the techniques used during the crisis concluded that the pandemic underscored the critical importance of spatial-based methods in assessing the spread and impact of COVID-19 [22,31].

2.3. Methodological Frameworks: Integrating Surveys and Advanced Statistical Analysis

2.3.1. The Use of Surveys in Municipal-Level Research

The use of structured surveys is a foundational and widely accepted methodology in public administration research [32]. Recent literature highlights their prevalence, noting that surveys of public officials and citizens are a dominant form of data collection in the field [33]. This approach is particularly effective for providing valuable insights into perceptions of government and attitudes toward the adoption of digital services [34,35]. Furthermore, surveys are a key tool for evaluating digital government performance, assessing the adoption of new technologies, and measuring participation [36,37]. Therefore, building on this well-established practice, the present study employs a structured questionnaire to systematically assess and compare municipal-level responses during the pandemic. This method provides a robust framework for quantifying governance strategies, operational capacities, and the adoption of a specific technology—GIS—in a way that would be difficult to capture through qualitative methods alone.

2.3.2. Justification for Exploratory Factor Analysis (EFA)

When a researcher is faced with a large number of interrelated variables, such as the many survey items in this study, and has no strong pre-existing hypothesis about how these variables group together, Exploratory Factor Analysis (EFA) is the appropriate statistical technique [38]. EFA is a data reduction method designed to uncover the underlying, or “latent,” structure within a set of observed variables [39]. Its primary purpose is to simplify complexity by regrouping the original variables into a smaller, more manageable, and interpretable set of factors based on their shared patterns of correlation [39]. In the context of this study, the numerous individual GIS tasks reported by municipalities are the observed variables. The goal of EFA is to explore these variables to determine if they can be explained by a few fundamental, unobserved constructs—for example, a “strategic surveillance” function or an “operational logistics” function. This approach allows for the discovery of an empirical, data-driven typology of GIS use, rather than imposing a preconceived theoretical structure on the data.

3. Methodology

3.1. Data Collection and Survey Instrument

The data for this study was collected through a structured online questionnaire designed to assess the extent of GIS adoption and its impact on municipal pandemic management. The survey targeted municipal health officers and COVID-19 coordinators across Israel, yielding 130 responses from a diverse range of local authorities, including 87 GIS users and 43 non-GIS users. Between January and February 2022, the questionnaire was distributed to all 240 registered municipalities in Israel through direct contact with municipal officials. The 130 completed surveys represent a voluntary response sample, yielding a 54.2% response rate. The full survey instrument is provided in Appendix A.

The questionnaire begins with two initial screening questions that direct respondents down one of two distinct paths.

• Initial Questions: The first question is a binary (Yes/No) confirmation of the respondent’s role as a COVID-19 coordinator (Q1). The second is a binary (Yes/No) question, “Did you use a GIS?” (Q2), which routes them to either Section A or Section B of the survey.
• Section A: For GIS Users (N = 87)
• This path of the questionnaire contains questions numbered 3 through 23.
• General System Usage (Questions 3–4): This part begins with two multiple-choice questions. Question 3 assesses the number of officials with system access, while Question 4 gauges the frequency of system use.
• GIS in Decision-Making (Questions 5–12): This core block consists of eight questions on a five-point Likert scale (1 = “very little extent” to 5 = “very great extent”). It assesses the depth of GIS data utilization for situational assessments (Q5), welfare for isolated individuals (Q6) and patients (Q7), enforcement (Q8), public information (Q9), policy making (Q10), and planning for testing sites (Q11) and vaccination sites (Q12).
• Technical Aspects (Questions 13–14): This part includes two multiple-choice questions about system support, inquiring about the use of technical support (Q13) and the number of training sessions attended (Q14).
• Temporal Use and Collaboration (Questions 15–21): This block of seven questions returns to the five-point Likert scale to measure collaboration with the Ministry of Health (Q15), the intensity of GIS use during August (Q16), September (Q17), October (Q18), and November 2021 (Q19), the success of using the system to filter non-resident cases (Q20), and the extent to which system alerts were received (Q21).
• Response and Impact (Question 22): This question gauges reaction capabilities. Question 22 is a categorical question about the response time to contain a hotspot (with options: ‘Up to an hour’, ‘From two to three hours’, ‘A day or more’).
• Identification (Question 23): An open-ended question asks the respondent to name their local authority.

Although the survey collected data from both GIS users and non-GIS users, the detailed Exploratory Factor Analysis was performed exclusively on the GIS-user group (N = 87). This decision was driven by the central aim of the research, which is to deeply understand the nature and structure of an integrated GIS response, making this group the primary focus of the inquiry. The non-GIS group serves as a crucial baseline for comparison in the descriptive statistics, while the EFA provides a deep and focused analysis of the primary group of interest.

3.2. Analytical Framework: Exploratory Factor Analysis (EFA)

To identify the underlying structure of GIS usage patterns among the 87 GIS-user municipalities, an Exploratory Factor Analysis (EFA) was performed. EFA was employed to explore the dimensionality of the survey items and identify the latent constructs that constitute municipal GIS engagement. The analysis was conducted on the 20 survey items related to the various functions of GIS use.

The EFA was performed in several stages. First, before running the main analysis, a series of preliminary tests were conducted to ensure the survey data was suitable for factor analysis. This is a crucial step to confirm that the results would be reliable and meaningful [40]. Bartlett’s test of sphericity was used to confirm that the survey items were sufficiently correlated, essentially checking if there were any relationships in the data worth exploring [41] Following this, the Kaiser–Meyer–Olkin (KMO) measure assessed if the data was “groupable” and had enough in common to form coherent underlying factors [42]. Finally, the determinant of the correlation matrix was examined to ensure that no variables were so highly correlated that they were redundant, a situation that can complicate the analysis.

Once the data was confirmed to be suitable, the optimal number of factors to extract was determined using Parallel Analysis, a reliable statistical method that distinguishes between real patterns and random noise [43]. After establishing this number, the main Exploratory Factor Analysis (EFA) was conducted using the minimum residual method with a Varimax rotation applied. This is a standard technique that simplifies the interpretation by making the groups of related questions as distinct as possible [38]. To decide which survey questions were significant to each new dimension, a common threshold was used where any variable with a factor loading of 0.40 or higher was considered an important contributor. Finally, the internal consistency of the resulting factors was assessed using Cronbach’s Alpha, a test that ensures the questions grouped together are all measuring the same cohesive concept [44]. All analyses were performed using the ‘psych’ package in R (version 2.5.3) [45].

4. Results

4.1. Descriptive Statistics

4.1.1. Demographic Characteristics of Responding Municipalities

The study sample includes responses from 130 local authorities across Israel. A detailed list of all participating municipalities and their individual demographic characteristics is provided in Appendix B.

The sample represents a diverse cross-section of Israeli municipalities. Geographically, the authorities are distributed across all regions of the country, with the largest portions of respondents coming from the Northern District (19.2%) and the Central District (17.7%), ensuring a wide national spread. The sample includes a robust mix of administrative forms; one-third are classified as cities (33.1%), and nearly another third are local councils (29.2%), with the remainder composed of smaller community settlements, villages, and other municipal types.

In terms of population, the sample is similarly varied and is not skewed toward only large urban centers. A majority of the municipalities (62.3%) are small, with fewer than 20,000 residents. Medium-sized municipalities (20,000–99,999 residents) make up 24.6% of the sample, while large cities with over 100,000 residents constitute the remaining 13.1%. This demographic diversity provides a strong foundation for examining the study’s research questions across different municipal contexts in Israel.

4.1.2. Survey Responses

Of the 130 municipal officials who responded to the survey, 87 (66.9%) reported using a GIS, while 43 (33.1%) reported they did not. The following sections summarize their responses, first detailing patterns unique to each group, and then providing a direct comparison of their data usage in key municipal functions.

Usage Patterns of GIS-User Municipalities (N = 87)

Among the municipalities that used a GIS, adoption was intensive. A significant portion used the system on a daily basis (48.5%), with another 14.6% using it every three days. Access to the system was also widespread within these municipalities, with 48.5% reporting that between two and four personnel had access, indicating that the GIS was an integrated team tool.

Data Sources for Non-GIS-User Municipalities (N = 43)

The 43 municipalities that did not use a GIS relied on a variety of alternative methods to manage the pandemic. The most common methods reported were telephone reports (13.8%), dedicated municipal applications (8.5%), and direct data from the Ministry of Health (6.2%).

Comparative Analysis of Data Use in Municipal Functions

To provide a clear and organized comparison of how each group utilized their respective data sources,

Table 1. Comprehensive Comparison of Mean Data Utilization Scores.

Municipal Function/Activity	Relevant Question in Appendix A (GIS Users/Non-GIS Users)	GIS Users Mean Score (N = 87)	Non-GIS Users Mean Score (N = 43)
Use in Professional Meetings and Situation Assessments	A: Q5/B: Q4	4.23	3.74
Policy Making and Infection Control	A: Q10/B: Q9	4.1	3.33
Public Information and Communication	A: Q9/B: Q8	4.19	3.49
Welfare Support for Isolated Individuals	A: Q6/B: Q5	4.09	3.12
Welfare Support for COVID-19 Patients	A: Q7/B: Q6	4.21	3.28
Enforcement for Isolated Individuals	A: Q8/B: Q7	3.51	2.91
Coordination of COVID-19 Testing Sites	A: Q11/B: Q10	4.02	3.23
Coordination of Vaccination Sites	A: Q12/B: Q11	4	3.33
Use of Technical Support	A: Q13/B: N/A	2.98	N/A
Frequency of Training Attendance	A: Q14/B: N/A	2.1	N/A
Response Time to Infection Clusters	A: Q22/B: Q19	2.01	2.4

presents the mean scores for both groups across a comprehensive range of activities. These scores are based on a five-point Likert scale where 1 indicated “very little use” and 5 indicated “very much use.”

As demonstrated in the comprehensive table, municipalities that used a GIS reported a consistently and significantly higher mean level of data utilization across nearly all pandemic-related functions. The data shows a clear and robust trend: from strategic activities like policy-making to operational tasks like welfare support and site coordination; the integration of data was substantially higher in municipalities equipped with a GIS.

4.2. The Dimensions of GIS Engagement: Exploratory Factor Analysis Results

An Exploratory Factor Analysis (EFA) was performed on the survey responses from the 87 GIS-user municipalities to identify the underlying dimensions of GIS usage.

4.2.1. Preliminary Analysis: Assessing Factorability

Before conducting the EFA, three tests were performed to assess the suitability of the data. Bartlett’s test of sphericity was highly significant (χ²(190) = 1121.01, p < 0.001), indicating that the correlation matrix was suitable for factor analysis. The Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy was 0.78, which is considered ‘good’. Finally, the determinant of the correlation matrix was 0.000000628, indicating that multicollinearity was not an issue. These results confirmed the data was appropriate for EFA.

4.2.2. Determining the Number of Factors

The Parallel Analysis strongly suggested that a three-factor solution was optimal for explaining the data.

4.2.3. Factor Solution and Interpretation

The three-factor model, which accounted for 56.4% of the total variance, revealed that GIS usage during the pandemic was not a single activity but was composed of three distinct dimensions. After examining the variables that loaded onto each factor, these dimensions were named and described as follows:

• Strategic and Operational Integration (7 items): This factor is the broadest and reflects the deep integration of GIS into core municipal governance and crisis management. It is defined by seven survey items related to using GIS for high-level policy making (Q10), day-to-day professional meetings (Q5), and crucial operational functions like public information (Q9), enforcement (Q8), and welfare support (Q6, Q7), as well as use of technical support (Q13). This dimension represents the overall strategic use of GIS as a central management tool.
• Temporal Engagement (5 items): This factor captures the consistency and timeline of GIS use throughout the crisis. It is defined by five survey items that measure the intensity of system use during the four key months of the fourth wave—August (Q16), September (Q17), October (Q18), and November (Q19)—as well as collaboration with the Ministry of Health on filtering data (Q20). This dimension measures sustained, long-term engagement.
• Logistical Site Coordination (3 items): This third, highly specific factor relates to the use of GIS for the critical logistical tasks of the pandemic response. It is defined by just three survey items focused on the coordination of testing sites (Q11), vaccination sites (Q12), and collaboration with the Ministry of Health on these sites (Q15). This dimension represents a specialized, task-oriented application of GIS.

To confirm the internal consistency of these three dimensions, Cronbach’s Alpha was calculated for each set of items. All three factors demonstrated good to excellent reliability: the Strategic and Operational Integration factor had an alpha of 0.84, the Temporal Engagement factor had an alpha of 0.82, and the Logistical Site Coordination factor had an alpha of 0.78. These strong reliability scores confirm that the items within each dimension are cohesively measuring the same underlying construct.

The full factor loadings for each variable are presented in Appendix C.

5. Discussion and Conclusions

This study provides compelling multivariate evidence that Geographic Information Systems (GIS) were a multidimensional tool in strengthening municipal decision-making during a public health crisis. The Exploratory Factor Analysis conducted on survey data from 87 GIS-using municipalities in Israel reveals that GIS application was not a monolithic activity. Instead, it was structured around three distinct and reliable dimensions: Strategic and Operational Integration, Temporal Engagement, and Logistical Site Coordination. These findings offer a novel, data-driven framework for understanding how municipalities operationalize geospatial technology in an emergency, moving beyond a simple inventory of tasks to reveal the core functions of a GIS in crisis governance.

The emergence of the Strategic and Operational Integration factor highlights that for many municipalities, GIS was far more than a simple mapping tool; it was deeply embedded in the core functions of governance. This dimension, which includes high-level activities like policy-making and situational assessments alongside operational tasks like public information and enforcement, aligns with literature emphasizing the role of GIS in data-driven decision-making [11]. It suggests a level of maturity in GIS adoption where the technology serves as a central nervous system for crisis management, integrating diverse functions into a coherent response.

The Logistical Site Coordination factor represents a more specialized, task-oriented application of GIS. This dimension, focused exclusively on the planning of testing and vaccination sites, reflects the critical role of spatial analysis in resource allocation—a theme well-documented in studies of GIS in public health emergencies [20]. Its emergence as a distinct factor underscores the unique importance of this logistical challenge during the pandemic and demonstrates that municipalities dedicated specific GIS efforts to this critical function.

Perhaps the most novel finding is the identification of Temporal Engagement as a standalone factor. This dimension, defined by the sustained intensity of GIS use throughout the four months of Israel’s fourth wave, suggests that consistent, long-term system engagement is a distinct characteristic of a municipality’s GIS response. It implies that the persistence of GIS use over the timeline of a crisis is as fundamental as the specific tasks for which it is used. This provides a new lens for evaluating technology adoption in crisis management, where the ability to maintain engagement over time is a key measure of its integration and utility.

Given these insights, policymakers and municipal managers should recognize the multidimensional nature of GIS when planning for future emergencies. Capacity-building initiatives should not only focus on technical skills for logistical tasks but also on fostering the strategic integration of GIS into the fabric of municipal decision-making. The findings strongly support the view that GIS adoption should be regarded not as a supplementary enhancement but as a fundamental requirement for modern municipal governance. The municipalities in this study demonstrated that GIS can be leveraged for strategic oversight, sustained engagement, and targeted logistics. Continued and expanded integration of GIS technologies into municipal operations will be crucial for optimizing public health interventions, enhancing governance performance, and fostering data-driven resilience in the face of future emergencies.

In conclusion, this study empirically establishes that municipal GIS use in a crisis is a structured, multidimensional phenomenon. The evidence from the Exploratory Factor Analysis confirms that GIS-equipped municipalities in Israel deployed the technology along three core dimensions: strategic integration, temporal engagement, and logistical coordination. As municipalities prepare for future public health and societal challenges, understanding and cultivating these distinct GIS capabilities will be vital in ensuring data-driven, effective, and resilient local governance.