Locational Analysis

School of Locational Analysis

• Spatial analysis, which is also called area analysis and locational analysis, is the study of human trends in a specific place.
• It is an approach in human geography that focuses on the spatial arrangement of phenomena. It tries to build accurate generalization, models, and theories with productive power.
  • It emerged as a significant part of the Quantitative Revolution in the 1950s and 1960s.
• Locational analysis is based on the philosophy of positivism and empiricism. Its usual methodology is that of spatial science.
• Key geographers: Peter Haggett, William Bunge, Torsten Hägerstrand, David Harvey, Walter Christaller, and others.

History and development of Locational School

• Pre-Quantitative Geography: Dominance of Regional Approach
  • Before the 1950s, geography was dominated by regional geography and the idiographic approach.
  • This approach emphasized the uniqueness of regions, relying heavily on description and classification rather than theory or laws.
  • Geography was largely qualitative, rooted in narratives of cultural and environmental distinctiveness.
  • Notable thinkers like Paul Vidal de la Blache (with his concept of “genre de vie”) promoted human-environment relationships, but largely avoided formal modeling.
• Critique of Regional Geography: Call for Scientific Reform
  • By the 1940s and 1950s, many geographers—especially in the USA and UK—began to critique regional geography as non-scientific, descriptive, and outdated.
  • Scholars like Fred K. Schaefer (1953, Exceptionalism in Geography) initiated the critique by arguing for nomothetic approaches—those that seek to formulate general laws, like in other sciences.
  • The debate between Schaefer and Hartshorne became the foundation for a paradigm shift in geography.
  • This created an intellectual environment favoring quantification, generalization, and model-building.
• Emergence of the Quantitative Revolution (1950s–60s)
  • The Quantitative Revolution marked a turning point where geography adopted mathematical and statistical methods.
  • Inspired by developments in economics, mathematics, and physics, geographers began using scientific modeling and spatial theories to explain locational decisions.
  • The goal was to transform geography into a spatial science—objective, law-seeking, and theory-driven.
  • Key Publications Influencing This Shift:
    • Walter Christaller (1933) – Central Place Theory
    • Alfred Weber (1909) – Least Cost Theory of Industrial Location
    • Zipf (1949) – Principle of Least Effort
    • Bunge (1962) – Theoretical Geography
    • David Harvey (1969) – Explanation in Geography
• Influence of Economics and Planning Science
  • Economists had long been interested in the location of industries, cities, and markets—concerns that directly overlapped with geographical inquiry.
  • Location theory in economics, especially the work of Weber and Losch, provided geographers with analytical tools to study space as a variable.
  • Geographers borrowed heavily from spatial economics, game theory, and regional planning to develop geographic models.
• Establishment of Locational Analysis as a Subfield
  • By the 1960s, locational analysis emerged as a distinct methodological approach within human geography.
  • Its core aim was to study how and why human and economic activities are located in particular places, using scientific models and quantitative tools.
  • William Bunge advocated for formal theoretical geography, while David Harvey emphasized explanatory models and methodology in Explanation in Geography (1969).
  • F.J. Lukermann (1964) added philosophical depth, asserting that geography must focus on spatial organization as its core concern, thus aligning geography with nomothetic scientific disciplines.
  • M. McCarty (1962) contributed by integrating computational methods and quantitative tools, bridging theory and practice and allowing geographers to test and apply locational models effectively.
• Institutional Support and Spread
  • The movement gained momentum through institutional support:
    • Establishment of Regional Science Association (1954) promoted scientific research in spatial patterns.
    • Academic centers like the Department of Geography at Lund University, Sweden, under Torsten Hägerstrand, played a central role in diffusion studies and spatial modeling.
    • Geography journals like Annals of the AAG, Geographical Analysis, and Environment and Planning began publishing research rooted in locational analysis.
• Key Figures in Locational Analysis
  • Peter Haggett: Developed models for spatial diffusion and spatial structure.
  • William Bunge: Argued for formal theory in geography (Theoretical Geography, 1962).
  • David Harvey: Bridged theory and practice with a deep critique of spatial science; later evolved into Marxist geography.
  • Torsten Hägerstrand: Introduced time-geography and diffusion modeling.
  • Brian Berry: Advanced urban spatial structures and central place systems with empirical rigor.

Locational Analysis

• Locational Analysis is an approach to human geography that focuses on the spatial arrangement of the phenomenon.
  • e.g. Weber’s Industrial Location Model for profit maximization
• Its usual methodology is that of spatial science.
• The main objective of Locational Analysis was expressed as building accurate generalization, models, and theories with productive power.
• Accurate Generalization refers to assumptions made in different models which are then used for the generalization of outcomes.
• According to Bunge, Geography is the ‘Science of Locations‘ as described in his book Theoretical Geography (1966).

Characteristics

• Locational Analysis is based on the philosophy of Positivism.
• It concentrates on identifying theories of spatial arrangements and is hence closely linked to Quantitative Revolution.
• Locational Analysis is based on empiricism. Empiricism accords special privilege to empirical observations over theoretical statements.
• In the empirical enquiry, it is assumed that its facts ‘speak for themselves‘.
• Haggett in his book, Locational Analysis in Human Geography (1965), appealed to adopt the geometrical tradition to explain the order, location order, and patterns in Human Geography.
  • e.g. Locational Triangle Model uses a Geometrical approach
• Such a focus is needed –
  • To adopt a systems approach which concentrates on the patterns and linkages within an assemblage
  • To employ models as to understand the man and environment relationships, and
  • To use quantitative techniques to make precise statements (generalizations) about locational order
• For the Spatial Analysis, it was suggested to adopt a ‘linear model‘, spatial autocorrelation, and regression.
• Other Geographers – Morril, Col, Chorley, Cox, Harvey, Johnston, etc.
• Morril in his book, The Spatial Organization of Society, argued that people seek to maximize spatial interaction at minimum cost and so bring related activities into proximity, and the result is that human society is surprisingly alike from one place to another.

Core Objectives of Locational Analysis

• Explanation of Spatial Patterns
  • The primary goal of locational analysis is to understand, explain, and model the spatial distribution of human and physical phenomena on the Earth’s surface.
  • It focuses on questions like: Why is a city located here? Why is this area industrialized? What factors influence the location of a market or service?
• Development of Spatial Models and Theories
  • It aims to formulate abstract models to simulate real-world spatial relationships.
  • These models include Von Thünen’s Agricultural Location Theory, Weber’s Industrial Location Theory, Christaller’s Central Place Theory, and others.
  • The purpose is to generalize locational decisions through mathematical or logical frameworks.
• Quantification and Measurement of Spatial Phenomena
  • A key objective is the use of statistical, mathematical, and graphical techniques to analyze geographical data.
  • It transforms geography from a descriptive to a scientific and analytical discipline by emphasizing measurement, accuracy, and empirical validation.
• Emphasis on Decision-Making and Optimization
  • Locational analysis studies how people and institutions make locational choices, often with the goal of optimizing costs, maximizing profits, or improving accessibility.
  • For instance, determining the best location for a factory, warehouse, or hospital.
• Identification of Spatial Structure and Interaction
  • It seeks to reveal underlying spatial structures, such as networks of transportation, flow of goods and people, and settlement hierarchies.
  • Spatial interaction is analyzed to understand the movement between places and the forces of attraction or repulsion (e.g., gravity models).
• Scientific Objectivity and Generalization
  • Locational analysis strives for objectivity, avoiding subjective interpretations.
  • It aims to discover general laws and universal principles that can apply across different regions and scales.
• Application to Planning and Policy-Making
  • Findings from locational analysis are applied in urban planning, regional development, economic zoning, infrastructure location, and transportation networks.
  • It provides a rational basis for spatial planning and efficient resource allocation.
• Integration with Technological Tools
  • Modern locational analysis incorporates GIS (Geographic Information Systems), remote sensing, and computational modeling.
  • These tools enhance the precision and visualization of spatial patterns and enable real-time decision-making.

Applications of Locational Analysis

• Urban Geography and Urban Planning
  • Locational analysis plays a central role in understanding urban settlement patterns, land-use structures, and urban hierarchies.
  • It helps in identifying the most efficient locations for public amenities like schools, hospitals, parks, fire stations, etc.
  • It is used to analyze commuting patterns, accessibility, and distribution of services using models like Central Place Theory and Gravity Model.
• Industrial and Economic Geography
  • Used to determine optimal locations for industries and warehouses by minimizing transport costs, labor costs, and other factors (e.g., Weber’s Least Cost Theory).
  • Helps in zoning for industrial corridors, industrial clustering, and regional economic development.
  • Supports economic planning by identifying resource-based vs. market-based locations.
• Transportation and Communication Networks
  • Locational analysis helps in planning and optimizing transportation networks (e.g., roads, railways, air routes).
  • Models such as the Gravity Model or Network Analysis help explain flow and interaction between places.
  • Used in traffic flow modeling, logistics, and route optimization.
• Regional Planning and Development
  • Helps in delineating functional regions, such as city regions, economic zones, or growth poles.
  • Applied in regional inequality assessment and balanced regional development strategies.
  • Assists in allocating funds and resources based on spatial needs.
• Retail and Service Location Analysis
  • Widely used in the private sector for market analysis.
  • Helps in determining the best sites for supermarkets, malls, petrol pumps, etc., based on customer density and accessibility.
  • Reilly’s Law of Retail Gravitation and Huff Model are practical models derived from this approach.
• Population and Settlement Studies
  • Assists in analyzing population distribution, migration flows, and settlement patterns.
  • Helps in identifying population-resource imbalances and planning urban expansion or rural service centers.
• Agricultural Geography
  • Used to analyze spatial patterns of land use based on models like Von Thünen’s Agricultural Land Use Model.
  • Helps in planning cropping patterns, identifying agricultural zones, and forecasting agricultural outputs.
• Disaster Management and Environmental Planning
  • Applied in determining risk zones for floods, earthquakes, droughts, etc.
  • Helps in site selection for shelters, resource allocation, and emergency response planning.
  • Models can simulate the impact of natural hazards and help develop mitigation strategies.
• Housing and Real Estate Development
  • Helps in analyzing land value gradients, housing affordability, and urban sprawl.
  • Used by developers and planners to choose profitable and accessible housing sites.
• Integration with GIS and Spatial Technologies
  • Locational analysis is fundamental to GIS-based applications, allowing real-time spatial decision-making.
  • Combined with remote sensing and spatial statistics, it enhances the precision of spatial data interpretation.

Criticisms Against Locational Analysis

• Overemphasis on Quantification and Positivism
  • Locational analysis is rooted in positivist philosophy, which emphasizes scientific objectivity, measurement, and statistical modeling.
  • Critics argue that this approach ignores normative questions—i.e., what ought to be—especially in the context of man-environment relationships.
  • The assumption that positive theory would automatically lead to normative insight has proven misguided.
• Neglect of Human and Cultural Values
  • Cultural, emotional, and ethical considerations are often central to spatial decision-making but are largely ignored in locational analysis.
  • For instance, the “ideal” economic location predicted by models may not align with societal or individual preferences.
  • The humanistic and behavioral geographers argue that humans are not mere dots or data points, but active, feeling, and thinking agents—a nuance often overlooked by spatial science.
• Oversimplification of Complex Realities
  • Models in locational analysis often oversimplify or conceal the complexities of the real world.
  • Real-world decision-making involves multiple interdependent factors—social, cultural, historical, and political—which cannot be fully captured through mathematical models.
• Assumption of Homogeneity and Rationality
  • Most locational theories assume a homogeneous, isotropic surface and uniform decision-making behavior, which do not reflect real-world diversity.
  • Human actions are not always rational, and economic agents do not always act to maximize profits or minimize costs, especially in rural or traditional societies.
• Failure to Reflect Actual Decision-Making Processes
  • The predictive power of location models has been questioned because they do not represent how people actually make decisions.
  • Individual biases, local knowledge, socio-political influences, and historical inertia often shape location choices more than abstract logic.
• Static and Acontextual Nature
  • Many locational models are static in nature, i.e., they fail to account for change over time.
  • They also lack contextual sensitivity, overlooking regional specificities and temporal dynamics like urban evolution, migration, or technological change.
• Incompatibility with Global Interdependence
  • In today’s globalized economy, spatial interdependence across nations and regions is far more important than isolated local factors.
  • The locational analysis, being overly localized or spatially bounded, struggles to integrate global processes, such as supply chains or international capital flows.
• Reinforcement of Capitalist Ideology
  • Critical geographers argue that locational analysis promotes capitalist logic, facilitating profit optimization for elites at the cost of social equity.
  • It is said to have accelerated over-industrialization, social inequality, and environmental degradation, and has given unchecked freedom to capitalists under the guise of spatial efficiency.
• Criticized by Behavioural and Humanistic Schools
  • Behavioural geography emerged as a reaction, emphasizing actual human behavior rather than assumed rationality.
  • Humanistic geography further criticized locational analysis for its dehumanizing tendency, treating people as statistical units devoid of consciousness, culture, and lived experience.
  • Both schools sought to bring humans back into geography, focusing on perception, values, and experience.
• Limited Domain of Applicability
  • Critics argue that statistical methods and models may work well for certain sectors like transport or retail geography, but cannot be generalized to all fields—especially where qualitative and subjective variables dominate.
• Ends vs Means Confusion
  • Many scholars, like Spate (1960), argue that quantification should be a means to an end, not an end in itself.
  • Counting what can be counted should not replace understanding what needs to be understood—classification should not be confused with comprehension.
• Philosophical Challenges and Paradigm Shifts
  • By the 1970s, critical geographers began questioning the validity of positivism itself, pushing geography toward post-positivist, interpretive, and critical paradigms.
  • Scholars like Goodall (1952) emphasized that statistical tools are only adjuncts to explanation, not substitutes.

Evolution and Contemporary Relevance

• 🧠 Integration of Behavioral Approaches
  • Post-1960s, the rigid assumptions of classical locational models (e.g., perfect rationality, complete information) were increasingly questioned.
  • Scholars like Torsten Hägerstrand, Allan Pred, and Herbert Simon introduced behavioral dimensions to explain real-world decision-making.
    • Allan Pred introduced the concept of a behavioral matrix, accounting for time, space, and individual decision constraints.
    • Herbert Simon proposed the idea of bounded rationality, acknowledging that people make “satisficing” choices, not optimal ones.
  • This gave rise to behavioral geography, which sought to humanize locational analysis, making it more reflective of ground realities.
• 🔴 Influence of Radical and Marxist Geography
  • Marxist geographers like David Harvey and Manuel Castells criticized locational analysis for supporting capitalist rationality.
  • However, they engaged with locational questions, particularly how:
    • Capital accumulation shapes spatial structure.
    • Labor exploitation and uneven development manifest spatially.
    • Locational conflicts arise between capital interests and social welfare (e.g., gentrification, urban displacements).
  • This reoriented locational analysis toward studying the political economy of space, not just cost minimization.
• 🖥️ Rise of GIS and Geocomputation
  • The development of Geographic Information Systems (GIS) revolutionized locational analysis.
    • GIS enables spatial data management, visualization, and overlay analysis, enhancing the precision and applicability of models.
  • Advanced techniques such as:
    • Spatial econometrics for location-based regression modeling,
    • Agent-based modeling for simulating individual location decisions,
    • Machine learning to predict optimal sites from complex, multidimensional data
    • have enhanced realism and analytical capacity of locational studies.
• 🧭 Continued Practical Applications
  • Despite critiques, locational analysis remains indispensable in several domains:
    • Urban planning (e.g., site suitability for housing, zoning),
    • Regional planning (e.g., industrial corridors, transport nodes),
    • Market research (e.g., retail store location),
    • Disaster risk management (e.g., positioning of relief centers),
    • Environmental modeling (e.g., optimal siting of renewable energy plants).
  • Businesses employ location analytics in logistics, supply chains, and real estate for strategic planning.
• 🌱 Focus on Sustainability and Equity
  • Recent applications of locational analysis increasingly:
    • Emphasize environmental sustainability (e.g., minimizing ecological impact in siting industries),
    • Promote social equity (e.g., ensuring equal access to public services like schools, hospitals, and water).
  • Tools now evaluate multi-criteria decisions involving cost, environment, access, and social justice.
• 📚 Refinement of Theories
  • Classical theories such as those by Weber, Christaller, and Lösch are still taught, but:
    • They are used as foundational frameworks.
    • Contemporary researchers build upon them, modifying assumptions to fit real-world variability.
    • Contextual, regional, and hybrid models are now preferred over overly abstract generalizations.
• 🇮🇳 Locational Analysis in India
  • Indian geographers have adapted locational theories to suit national contexts:
    • Agricultural location studies (e.g., crop pattern analysis in Punjab and Deccan),
    • Industrial location (e.g., Iron and Steel industry near resource bases),
    • Urban hierarchy and regional development planning.
  • Agencies like Planning Commission, NITI Aayog, and state planning boards use locational analysis to:
    • Plan infrastructure and utilities,
    • Delineate planning regions and growth centers.
  • The spread of GIS infrastructure, remote sensing, and National Spatial Data Infrastructure (NSDI) has greatly enhanced data-driven spatial planning in India.