The growing complexity of spatial challenges in geography demands robust and adaptable decision-making frameworks [8, 9]. The Analytic Hierarchy Process (AHP), developed by Thomas L. Saaty (1977, 1980, 1988, 1995), stands out as one of the most widely recognized multi-criteria decision-making (MCDM) techniques. AHP provides a structured approach to decompose complex problems into hierarchical levels, enabling systematic evaluation and ranking of alternatives based on both qualitative judgments and quantitative data [2, 7]. By assigning weights to criteria through pairwise comparisons, AHP accommodates incomplete or inconsistent inputs while generating overall scores and consistency measures [3, 11].

Geographical decision-making is inherently multifaceted, as it involves the interplay of diverse natural, social, and economic factors [1, 6]. Geographers frequently face tasks such as evaluating land-use alternatives, assessing environmental risks, prioritizing resource allocation, or planning urban expansion—all requiring consideration of multiple, sometimes conflicting, criteria [8, 9]. For instance, identifying suitable land for urban development entails balancing topography, soil quality, transportation access, population density, and ecological sensitivity [3, 11]. Similarly, environmental management requires harmonizing conservation objectives with hazard mitigation and socio-economic development goals  [2, 7].

Traditional single-criterion models are insufficient for addressing such multidimensional complexity, as they often prioritize one factor at the expense of others [8, 10]. Multi-criteria decision analysis (MCDA) approaches, particularly AHP, have thus gained prominence in geography and related disciplines. AHP enables decision-makers to structure problems hierarchically, decomposing them into a goal, criteria, sub-criteria, and alternatives. Through pairwise comparisons, priority weights are derived that integrate expert knowledge and quantitative data [9]. The inclusion of a consistency ratio (CR) ensures logical coherence in judgments, enhancing reliability [11]. This structured yet flexible methodology aligns well with geospatial reasoning, where qualitative assessments (e.g., cultural values, community priorities) and quantitative measures (e.g., slope gradients, rainfall intensity, economic costs) must be combined for informed decision-making [1, 7].

Over the past four decades, AHP has become central to geographical analysis, underpinning research in urban and regional planning, environmental management, tourism development, and natural resource evaluation [2, 8]. Its integration with Geographic Information Systems (GIS) further strengthens its applicability by providing spatial data handling and visualization capabilities [6, 7]. Recent methodological innovations—including fuzzy AHP, Analytic Network Process (ANP), and AI-assisted frameworks—address limitations of subjectivity, uncertainty, and interdependent criteria, enhancing the method’s analytical power [3, 10].

This review synthesizes the conceptual foundations and methodological strengths of AHP, highlights its applications in geographical research, examines its limitations, and explores emerging trends—particularly the integration with GeoAI—that expand its potential for dynamic, data-driven spatial decision-making [2, 9]. By situating AHP within the evolving landscape of spatial analysis, this paper underscores its enduring relevance as a cornerstone methodology in geographical problem-solving.

The methodological framework for integrating the Analytic Hierarchy Process (AHP) with GeoAI involves a systematic multi-step approach, combining the strengths of multi-criteria decision-making (MCDM) with spatial data analysis and artificial intelligence techniques. This hybrid approach is designed to enhance accuracy, objectivity, and scalability in geographical decision-making and spatial planning.

The first step involves clearly defining the spatial problem and identifying objectives, criteria, sub-criteria, and alternative solutions. In AHP, the problem is decomposed hierarchically, where the goal occupies the top level, followed by criteria and sub-criteria at intermediate levels, and alternatives at the lowest level [8, 10]. For example, in urban land-use planning, criteria may include environmental sensitivity, accessibility, socio-economic potential, and topographical constraints. Sub-criteria under environmental sensitivity may include soil type, slope, and flood susceptibility.

Once the hierarchy is established, pairwise comparisons are conducted to assess the relative importance of criteria and sub-criteria. Expert judgment, stakeholder input, and quantitative data are combined to generate priority weights. The consistency of judgments is evaluated using the Consistency Ratio (CR), ensuring logical reliability in the decision-making process [3, 11]. This step allows the decision-maker to capture both subjective preferences and objective measures, a crucial requirement for spatial decision-making where factors are often interdependent.

Geographic Information Systems (GIS) serve as a critical platform for spatial data management, visualization, and analysis. Criteria and sub-criteria are represented as spatial layers, which are standardized, weighted, and combined to generate suitability or priority maps [6, 7]. For instance, slope maps, land-use/land-cover data, transportation networks, and population density maps can be integrated to evaluate the suitability of land for urban expansion.

GeoAI methods, including machine learning, neural networks, and spatial predictive modeling, enhance the analytical power of traditional AHP by enabling dynamic, large-scale, and real-time spatial analysis. GeoAI can be applied to refine weights, model interdependencies, detect patterns in high-dimensional spatial datasets, and reduce subjectivity in expert judgments [1, 5]. Fuzzy logic can also be incorporated to handle uncertainty and vagueness in criteria evaluation, resulting in a more robust decision-making framework [3].

The final stage involves synthesizing weighted criteria using AHP-GeoAI integration to generate composite spatial suitability maps, prioritize alternatives, and support decision-making. Sensitivity analysis is performed to test the stability of results under varying weight scenarios [8, 9]. These outputs provide planners and policymakers with evidence-based, visually intuitive tools for spatial planning, resource allocation, and environmental management.

Validation of results is conducted by comparing model outputs with observed spatial patterns, historical data, or stakeholder assessments. Iterative refinement ensures that the integrated AHP-GeoAI framework remains adaptive and responsive to evolving spatial dynamics [2, 5].

This methodology demonstrates how AHP and GeoAI can be combined to create a powerful hybrid framework for complex geographical decision-making, enabling planners to evaluate multiple criteria simultaneously, account for uncertainty, and leverage large-scale spatial datasets efficiently.

The Analytic Hierarchy Process (AHP) has been widely adopted in geographical research due to its capacity to integrate multiple criteria, combine qualitative and quantitative data, and support structured decision-making. Its applications span urban planning, environmental management, tourism development, natural resource allocation, and hazard assessment.

AHP is extensively used for urban and regional planning, particularly in land-use suitability analysis, site selection, and infrastructure development. By evaluating multiple spatial factors such as topography, accessibility, land use, and population density, AHP helps planners rank alternative locations for urban expansion, industrial zones, or transportation networks [2, 7]. Integration with GIS allows spatial visualization of suitability maps, supporting evidence-based policy decisions [6].

Environmental planning often requires balancing ecological protection with socio-economic development. AHP assists in assessing the suitability of areas for conservation, identifying environmentally sensitive zones, and prioritizing sites for natural resource management. For example, AHP has been applied in watershed management, forest conservation, and habitat suitability analysis by weighing criteria such as slope, soil type, vegetation cover, and human impact [3, 8].

AHP facilitates the evaluation of hazard-prone areas by integrating multiple risk factors. In flood, landslide, and seismic vulnerability mapping, AHP is used to rank regions based on hazard intensity, exposure, and socio-economic vulnerability. Coupled with GIS, this approach enables the creation of hazard maps that inform disaster management and mitigation strategies [2, 9].

In tourism geography, AHP helps identify optimal sites for tourism development by considering accessibility, aesthetic appeal, cultural significance, infrastructure, and environmental sensitivity. This method allows planners to prioritize areas for investment while minimizing ecological and social impacts [8, 9].

 AHP is applied to prioritize locations for resource extraction, agriculture, and renewable energy development. For instance, criteria such as soil fertility, water availability, slope, and climatic conditions can be weighed to identify suitable areas for crop cultivation, solar or wind energy projects, and mining operations [3, 5].

Recent advancements in GeoAI, including machine learning and spatial predictive modeling, have expanded the potential of AHP in geography. By integrating AHP with GeoAI, planners can handle large spatial datasets, model interdependencies among criteria, and support dynamic, real-time decision-making. This hybrid approach is particularly effective in urban growth simulation, smart city planning, and environmental monitoring, where traditional methods alone may fall short [1, 5].

Overall, the versatility of AHP in geography stems from its structured, transparent, and adaptable methodology, which allows decision-makers to incorporate expert judgment, stakeholder preferences, and quantitative spatial data simultaneously.

Geospatial Artificial Intelligence (GeoAI) represents a transformative approach in spatial analysis, integrating artificial intelligence (AI), machine learning, and advanced computational techniques with geographic information systems (GIS) to analyze complex spatial phenomena [1, 5]. Unlike traditional GIS methods that primarily rely on static data and rule-based models, GeoAI can process large-scale, heterogeneous spatial datasets, identify patterns, and generate predictive insights for decision-making in real time.

GeoAI leverages machine learning algorithms, including deep learning and neural networks, to detect spatial patterns and relationships that may not be apparent through conventional analysis. For instance, it can identify urban growth trends, land-use changes, or environmental degradation by analyzing satellite imagery, sensor networks, and crowdsourced geographic data [5, 12]. This capability allows planners and researchers to uncover complex spatial interactions and correlations efficiently.

One of the key strengths of GeoAI is its predictive capability. By integrating spatial and temporal datasets, GeoAI models can forecast land-use transformations, urban expansion, or environmental hazards, providing decision-makers with scenario-based insights for proactive planning [1, 4]. These predictive models support strategic interventions in urban planning, resource allocation, and disaster management, enabling adaptive and evidence-based policies.

GeoAI enhances traditional multi-criteria decision-making frameworks, such as AHP, by automating weight estimation, reducing subjectivity, and capturing interdependencies among criteria [3, 5]. For example, in land suitability analysis, GeoAI can refine the relative importance of factors such as slope, accessibility, population density, and ecological sensitivity based on spatial patterns extracted from data, thereby improving the accuracy and reliability of decision outcomes.

The integration of GeoAI with real-time data streams from IoT devices, remote sensing platforms, and social media feeds allows for dynamic spatial analysis [1, 5]. This capability is particularly valuable for disaster monitoring, smart city management, and environmental surveillance, where timely information is critical for effective interventions.

GeoAI facilitates advanced visualization of spatial patterns and decision scenarios through interactive maps, 3D modeling, and geospatial dashboards. By combining AI-driven analytics with GIS visualization, planners and policymakers can interpret complex spatial relationships intuitively, communicate insights effectively, and make informed decisions in multi-stakeholder environments [4, 12].

In summary, GeoAI serves as a powerful tool in spatial analysis by enhancing pattern recognition, predictive modeling, real-time monitoring, and decision support. When integrated with AHP, GeoAI not only strengthens multi-criteria evaluation but also enables dynamic, data-driven, and scalable solutions for complex geographical problems.

The integration of the Analytic Hierarchy Process (AHP) with Geospatial Artificial Intelligence (GeoAI) represents a significant advancement in geographical decision-making, combining the structured multi-criteria evaluation of AHP with the predictive, dynamic, and computational capabilities of GeoAI [1, 5]. This hybrid approach enhances the ability to address complex spatial problems that involve large datasets, multiple interdependent criteria, and uncertain or dynamic conditions.

The integration begins with problem structuring using AHP, where the decision objective, criteria, sub-criteria, and alternatives are hierarchically organized [8, 10]. GeoAI is then applied to enhance each stage of AHP:

 Integrating AHP with GeoAI provides several key benefits:

 The AHP–GeoAI hybrid framework has been successfully applied in:

The integration of AHP and GeoAI is expected to evolve further with advances in deep learning, cloud computing, and real-time geospatial data acquisition. These developments will enhance decision-making in complex, dynamic environments and support evidence-based planning at multiple scales [1, 5].

The integration of the Analytic Hierarchy Process (AHP) with Geospatial Artificial Intelligence (GeoAI) offers significant advantages in addressing complex geographical problems, yet it also presents certain limitations and challenges. A critical evaluation is essential to understand both the strengths and constraints of this hybrid framework.

While the integration of the Analytic Hierarchy Process (AHP) with Geospatial Artificial Intelligence (GeoAI) has significantly advanced geographical decision-making, several research gaps and opportunities for future exploration remain. Addressing these gaps can enhance the effectiveness, scalability, and applicability of the hybrid AHP–GeoAI framework.

The integration of the Analytic Hierarchy Process (AHP) with Geospatial Artificial Intelligence (GeoAI) represents a transformative advancement in geographical decision-making and spatial planning. AHP provides a structured, transparent, and systematic framework for multi-criteria evaluation, allowing decision-makers to incorporate both qualitative judgments and quantitative data. When combined with GeoAI, the framework benefits from enhanced data processing capabilities, predictive modeling, dynamic analysis, and the ability to handle large-scale and complex spatial datasets [1, 5].

This hybrid AHP–GeoAI approach addresses many limitations of traditional decision-making methods, including subjectivity, limited scalability, and difficulty in managing interdependent criteria. It has been successfully applied across urban planning, environmental management, disaster risk assessment, tourism development, and natural resource allocation. Moreover, by leveraging machine learning, deep learning, and real-time spatial data, AHP–GeoAI enables adaptive, evidence-based, and transparent decision-making [3, 4].

Despite its strengths, challenges remain, including data dependency, computational complexity, interpretability of AI models, and the need for stakeholder-friendly interfaces. Future research focusing on real-time integration, uncertainty modeling, participatory decision-support tools, and broader applications in climate adaptation, energy planning, and sustainable resource management will further enhance the utility of this hybrid framework.

In conclusion, AHP–GeoAI integration offers a robust, scalable, and versatile methodology for addressing complex geographical problems. It bridges structured multi-criteria analysis with advanced AI-driven spatial intelligence, providing a comprehensive toolkit for planners, policymakers, and researchers to make informed, transparent, and adaptive decisions in increasingly complex and dynamic spatial environments.