Geospatial science reshapes public health and citizen security decision-making

The authors find that contemporary GIS research increasingly integrates spatial statistics, modeling, and simulation to move beyond description. Quantitative analytical frameworks now dominate the literature, allowing researchers to test associations between environmental factors, social conditions, and health or safety outcomes. This transition reflects both advances in computational capacity and rising expectations from policymakers who demand evidence that can inform intervention design rather than merely illustrate problems.

Geographic Information Systems (GIS) are playing a critical role in how governments assess public health risks and urban safety, but a new global review finds that the field is becoming increasingly concentrated around a narrow set of methods and tools, raising questions about its ability to support evidence-based policy at scale.

The study, titled From Cartography to Computation: A Systematic Mapping and Review of GIS-Based Research in Public Health and Citizen Security, published in the ISPRS International Journal of Geo-Information, assesses how GIS methods, tools, and study designs are shaping real-world decision-making in health and urban safety.

From maps to models in health and safety research

The review documents a clear departure from the historical role of GIS as a primarily cartographic technology. In earlier decades, spatial analysis in public health and security focused largely on visualizing geographic patterns, such as disease clusters or crime hotspots. While these approaches remain valuable for situational awareness, they offer limited insight into underlying causes or future risk.

The authors find that contemporary GIS research increasingly integrates spatial statistics, modeling, and simulation to move beyond description. Quantitative analytical frameworks now dominate the literature, allowing researchers to test associations between environmental factors, social conditions, and health or safety outcomes. This transition reflects both advances in computational capacity and rising expectations from policymakers who demand evidence that can inform intervention design rather than merely illustrate problems.

Public health applications account for the majority of reviewed studies, with research commonly addressing infectious disease spread, chronic illness distribution, healthcare accessibility, and environmental exposure. These studies often operate at national or regional scales, enabling comparisons across large populations and supporting system-level policy planning. In contrast, citizen security research remains more localized, focusing on urban neighborhoods, infrastructure vulnerabilities, and site-specific risk factors. While this granularity supports targeted interventions, it limits the transferability of findings across cities or regions.

The review highlights a strong methodological bias toward observational and cross-sectional study designs. These approaches are effective for identifying spatial correlations but offer limited capacity for causal inference. Experimental and longitudinal designs remain comparatively rare, reflecting both ethical constraints and data availability challenges in real-world health and security contexts. As a result, many GIS-based studies can identify where problems occur but struggle to explain why they emerge or how interventions change outcomes over time.

Methodological concentration and the dominance of core tools

The review reveals a striking concentration in the analytical tools and platforms used across GIS-based research. A small group of software environments accounts for the majority of applications, forming a stable methodological core that defines current practice. Proprietary GIS platforms dominate institutional workflows, particularly in government and large research organizations, where standardized interfaces and licensing agreements support consistent use.

At the same time, open and semi-open computational tools play a growing role in advanced analysis. Programming languages and statistical environments are increasingly used to conduct spatial inference, integrate machine learning methods, and ensure reproducibility. This hybrid ecosystem reflects a broader convergence between GIS and data science, where spatial analysis is no longer isolated from mainstream statistical computing.

The authors argue that this concentration has both benefits and risks. On one hand, reliance on a shared set of tools improves comparability across studies and lowers barriers for institutional adoption. On the other, it creates technological dependency and may discourage methodological experimentation. Emerging tools, alternative analytical frameworks, and non-traditional data sources struggle to gain traction in a field where established platforms dominate training, funding, and publication norms.

Reproducibility emerges as a key concern throughout the review. While many studies report quantitative results, fewer provide sufficient detail to allow independent replication. Explicit reporting of software versions, coordinate systems, and data sources remains inconsistent. The authors emphasize that without stronger transparency standards, the growing computational complexity of GIS research risks undermining its credibility and long-term policy value.

Global gaps, policy limits, and future directions

Studies from North America, Europe, and parts of Asia dominate the literature, while Africa and South America remain severely underrepresented. This imbalance has direct implications for global health and security policy, as models developed in well-resourced contexts may not translate to regions with different social structures, data infrastructures, or environmental conditions.

The authors stress that this is not merely a matter of academic equity. Underrepresentation limits the ability to test whether commonly used GIS methods perform reliably across diverse settings. It also constrains efforts to address algorithmic bias, since spatial models trained on incomplete geographic coverage may systematically misrepresent vulnerable populations.

Policy relevance is another recurring theme. While GIS research is frequently positioned as decision-support, the review finds that many studies stop short of translating findings into actionable policy guidance. The dominance of descriptive and correlational analysis limits the ability to evaluate intervention effectiveness or compare policy options. The authors argue that closer collaboration between researchers, policymakers, and public institutions is needed to align analytical outputs with real decision-making needs.

Looking ahead, the study outlines several priorities for strengthening GIS-based health and security research. Greater methodological diversity is essential, including increased use of longitudinal data, simulation, and experimental designs where feasible. Broader geographic inclusion must be supported through data-sharing initiatives, capacity building, and international collaboration. Finally, reproducibility should be treated as a foundational requirement rather than an optional best practice.