Methods of Regionalization in Regional Planning

Region & Regionalization

• Regionalization is the process of delineating regions. In other words, we can define regionalization as the locating of boundaries of a region. A region is an area on the Earth’s surface marked by a degree of formal, functional, or perceptual homogeneity of some phenomenon.
• Regionalisation is not merely a technical exercise — it is a purposeful intellectual activity. Every time a geographer or planner delineates a region, they are doing so for a specific goal.
  • If the intention is to develop an arid region, the ‘region’ will be differently defined, including only arid areas.
  • If the intention is to remove congestion, the most congested and polluted areas form the ‘congested region‘.
  • If the intention is to substantially reduce poverty and unemployment, then a ‘depressed region‘ is to be delineated. 
• The homogeneity of a region will differ with the purpose for which delineation is being made.

“Regionalization deals with the differentiation of political measures in space.”— Groenman

“There is no universally acceptable method of regionalisation.”— S. Rengasamy, Madurai Institute of Social Sciences

• Key principle: All regions, in one way or another, reflect the level of development of the area. Regionalisation is the spatial expression of development thinking — it changes as development priorities change.

Regionalization vs. Region

• A region is the product or outcome — the bounded spatial unit with defined characteristics.
• Regionalization is the process — the systematic exercise of identifying, delimiting, and mapping those units.
• The same space may yield entirely different regions depending on the regionalization criteria chosen. This is why George Kimble sarcastically described his colleagues engaged in regionalization as “trying to put boundaries that do not exist around areas that do not matter” — a critique of arbitrary delimitation that ignores the purpose-driven nature of the exercise.

Concept	Region	Regionalization
Nature	Product / Outcome	Process / Method
What it is	A bounded spatial unit with defined characteristics	The exercise of delineating those boundaries
Key question	“What is this area?”	“How do we draw this boundary?”
Purpose	Unit of analysis or planning	Creating units for analysis or planning
Result	The Thar Desert, the NCR, the BIMARU region	The methodology used to delimit those areas

Forms of Regionalization & Determining Factors

• Regionalization may take several forms depending on two primary considerations:
  • The Purpose of Delineation
    • What problem is being solved? Developing arid land, reducing poverty, managing water, planning urban growth — each purpose produces a different regional boundary for the same space.
  • The Criterion / Criteria Used
    • Land size, employment rates, activity rates, migration trends, rainfall, language, per capita income — the selection of indicators determines which areas are grouped together and which are separated.
• If the physical region having homogeneity is also an administrative region, then all tasks of regional and national planning can be facilitated. However, this alignment is rare — physical and administrative boundaries almost never perfectly coincide, which is why regionalization always involves a compromise.

• Planning as a foundation: Planning means looking ahead and chalking out future courses of action to be followed. It is a preparatory step and systematic activity that determines when, how, and who is going to perform a specific job. Regionalization serves planning by defining the spatial units within which planning decisions will be made. A planning region should be internally cohesive, geographically contiguous, and resourcefully endowed such that a satisfactory level of production, consumption, and exchange is feasible.

What Makes a Good Regionalization?

• Awareness of regional problems and opportunities — the delineator must understand the spatial reality they are delimiting
• Anticipated capacity to act — the region must be a viable unit for planning and administrative intervention
• Internally cohesive and geographically contiguous — the region should hang together physically and functionally
• Resource endowment sufficient for viability — the region must support a satisfactory level of production, exchange, and consumption
• Presence of at least one nodal point — for functional regions, a central place or growth point around which the region is organized
• Data availability — regionalization cannot proceed without reliable spatial data on the chosen criteria

Evolution of Regionalization Methods

• Methods of regionalisation have evolved in step with the major philosophical revolutions in geography — from the classical descriptive tradition, through the quantitative revolution, to the critical-humanistic turn. Each era produced new tools and new critiques of earlier approaches.

Era / Approach	Method Type	Key Tool	Used For	Limitation
Classical (Pre-WWII)	Qualitative / Empirical	Field observation, cartographic tradition	Natural regions, cultural regions	Subjective; no precise boundary
Quantitative Revolution (1950s–70s)	Statistical / Mathematical	Gravity model, index methods, Thiessen polygon	Formal regions, functional regions	Static distance concept; ignores human behaviour
Critical Revolution (1970s–present)	Empirico-Statistical (Mixed)	Statistical + observation combined	Planning regions, naïve/cultural regions	Complex; data-intensive

Three Broad Approaches to Regionalization

Important: Methods of regionalisation can also be classified by the type of region being delimited — methods for formal (homogeneous) regions differ substantially from methods for functional (nodal) regions, which in turn differ from methods for naïve/cultural regions. The choice of method follows from the type of region. This is the organising principle of the sections that follow.

Methods for Delimiting Formal (Homogeneous) Regions

• Delineation of formal regions involves the grouping together of local units which have similar characteristics according to certain clearly defined criteria and which differ significantly from the units outside the region on the basis of those criteria. Formal regions have precise boundary limitations — e.g., the 18°C isotherm defines a climatic region boundary; administrative boundaries define political regions.
• The criteria can be: unemployment rates, activity rate, migration trends, per capita income, rainfall, soil type, language, etc. The characteristics should differ significantly from units outside the region.
• When multiple criteria are used, problems arise — composite criteria require assigning weights to each variable. This gives rise to three distinct methods: the Fixed Index Method, the Variable Index Method, and the Cluster Method.

Criteria for Formal Region Delimitation

• Geographic Criteria
  • Soil type, rainfall (isohyets), temperature (isotherms), climate (Koppen’s classification), vegetation zones, topographic units.
• Economic Criteria
  • Per capita income, number of industries, unemployment rate, rate of industrialization, crop type, population density. More relevant for development planning.
• Socio-Cultural Criteria
  • Language, political affiliation, religion, ethnicity, literacy rate, customs. Produce fuzzy boundaries since cultural traits are always transitional.

The Core Mathematical Principle (Formal Region)

• Let us suppose (i) and (j) are two segments of space, and Yi and Yj are the per capita income of each. The equation Yi − Yj gives the value of difference between the per capita incomes of the two segments.
  • A geographer can fix a threshold limit — a critical value — beyond which the heterogeneity between (i) and (j) is so high that they can be differentiated and classified as separate regions
  • If the value Yi − Yj is less than the threshold, (i) and (j) are homogeneous and cannot be differentiated into two regions
  • If the value Yi − Yj exceeds the threshold, (i) and (j) belong to different regions

• This principle is the most accepted method for delimiting formal regions, particularly naive/backward regions. It is applied using per capita income, literacy rate, HDI, or any other relevant indicator.

Fixed Index Method

• Under the Fixed Index Method, a common characteristic feature is chosen for all regions (e.g., per capita income, literacy rate, unemployment rate) and used as a single, uniform criterion across the entire study area. A single weighted mean is obtained for each sub-unit, and contiguous sub-units with similar indices are grouped together to minimise variance within groups.

Application Example: Backward Region Delimitation

• To identify backward districts in India for the Backward Regions Grant Fund (BRGF), parameters such as per capita income, literacy rate, infant mortality rate, and road length per 100 sq. km are selected. Each is given a weight (reflecting its importance). Districts with low composite scores are grouped as backward regions — contiguous backward districts form a backward planning region.

The Fixed Index Method is described as a Weightage Index Method outlined by Boudeville. The aim is to isolate the main problem region — the area of economic malaise. Weights are assigned to each criterion and when taken together and weighted, one region can be isolated. This is the standard method used for identifying employment and income level regions: the study area is divided into several localities varying according to unemployment rates and per capita income levels.

Aspect	Fixed Index Method
Type	Quantitative — index-based
Criteria	Common across all sub-units (fixed — same weight for all areas)
Best used for	Economic backwardness regions, development planning zones, HDI-based regions
Advantage	Simple, comparable, transparent weights
Limitation	Arbitrary weight assignment; same weights applied everywhere even if relative importance varies by region
Indian example	Raghuram Rajan Committee (2013) composite index to identify 272 backward districts for BRGF; NITI Aayog’s 112 Aspirational Districts selection

Variable Index Method

• Under the Variable Index Method, variable weights are assigned to highlight different levels of activity in different regions.
• The weight given to each activity in each region is different, in accordance with the value or volume of that activity regionally produced.
• Unlike the Fixed Index Method, weights are not uniform — they vary from region to region based on regional specialization.

How It Differs from Fixed Index Method

Feature	Fixed Index Method	Variable Index Method
Weights	Same for all regions	Different for each region based on specialization
Principle	Common criterion across all areas	Region-specific weighting reflecting local dominance
Best suited	When all areas are broadly comparable	When regions specialize in different economic activities
Example	Backward district index: same weights for all districts	Wheat region: high wheat weight; coal region: high coal weight

Classic Illustration

• If Region A is a wheat-growing region and Region B is a coal-producing region:
  • In Region A — the weight of the wheat index will be the largest, since wheat dominates agricultural output and land use here
  • In Region B — the weight of the coal index will be the largest, since coal defines the economic character of this region
  • This variable weighting better captures the economic identity of each region

Economists and geographers such as Ashok Mitra, Schwartzberg, M.J. Hagood, and M.N. Pal popularized different methods of variable weighting to delineate regions. The clusters mapped by variable weighting are used in conjunction with mapping techniques; inter-related variables are mapped with the help of superimposed map techniques. The composite ranking of areas is used when variables are too many and have weak correlations with each other.

Cluster Method (Superimposition Method)

• The Cluster Method means grouping together areas that share the most in common across multiple incompatible variables. It is employed when neither fixed nor variable index methods work — particularly when the variables being compared cannot be reduced to a single meaningful index (e.g., combining literacy with steel production). It uses the superimposition of thematic maps or the development of a composite index of development.
  • Cluster means grouping together. This concept is used in planning as a strategy to strengthen lateral links and to dissipate growing vertical links in the settlement system. Such a cluster, while providing greater viability and threshold for development efforts, will also create for itself a greater bargaining power in bringing about reciprocity in exchange of goods and services.

Two Ways to Execute the Cluster Method

Method 1: Superimposition of Maps

• In this approach, each variable/parameter is mapped separately as a thematic map, and then all the maps are overlaid (superimposed) on each other. The common region that emerges after this exercise — the area where all or most criteria align — forms the identified region.
  • Map Each Variable Separately
    • Draw individual thematic maps: rainfall map, soil map, crop distribution map, groundwater availability map — each as a separate layer.
  • Superimpose All Layers
    • Stack all maps one over another. Using the India example: plot the rainfall map, superimpose the soil map above it, then the water availability map — the area with high land use + high rainfall + good soil + water availability becomes identifiable.
  • Identify the Common Region
    • The area common to all or most layers is the identified region. The clusters are mapped with the help of mapping techniques; inter-related variables are mapped with superimposed techniques.
  • Determine the Boundary
    • The outermost boundary where the clustering of favourable conditions persists forms the regional boundary. Drawback: There is no clear mathematical demarcation — the boundary depends on subjective judgement about how many criteria must coincide.

Method 2: Composite Index of Development

• A composite development index is constructed by combining multiple indicators (even those that cannot be directly compared) using standardised scores or ranks. Areas with similar composite ranks are grouped together into development clusters.

• The cluster method is used to implement the Integrated Rural Development Programme (IRDP) in India. Clustering of villages in a block creates viable development units — a cluster of villages large enough to share infrastructure (a secondary school, a primary health centre, an agro-service centre). Both at macro level (state/region) and micro level (block/village), clustering can be done by: (1) superimposing of maps and (2) developing a composite index of development. Modern GIS software (ArcGIS, QGIS) has made superimposition far more precise than manual overlay.

Method	Best When	Tool	Output	Limitation
Fixed Index	Same variable type across all regions	Weighted mean scoring	Ranked development zones	Arbitrary weights; ignores regional variation
Variable Index	Regions have different economic specializations	Region-specific weighting	Specialization-based economic regions	Breaks down with incompatible variables
Cluster / Superimposition	Multiple incompatible variables; need spatial co-occurrence	Map overlay, composite ranking	Clustered development zones	No precise boundary; subjective cut-off

Methods for Delimiting Functional (Nodal) Regions

• Functional regions are organised around a node (central city, hub, or focal point) through flows of people, goods, money, and information. Their delimitation requires measuring these flows and finding the boundary where the intensity of interaction falls to a defined minimum — i.e., where people begin to orient toward a different node. Two methods are used: Flow Analysis and Gravitational (Gravity Model) Analysis.
  • Each flow shows decreasing intensity as it becomes more distant from the main centre (distance decay), and increasing intensity as it approaches another centre. The boundary of the sphere of influence of the dominant centre will be where the flow intensity is at a minimum — this cut-off defines the tentative regional boundary.

Types of Flows Used in Functional Region Delimitation

Flow Type	What It Captures	Example Data Source
Commuting flows	Intra-regional commuting (journey-to-work trips)	Census data on place of work vs residence
Commodity flows	Trade and supply linkages between city and hinterland	Transport origin-destination surveys, mandi records
Migration flows	Labour catchment area of a city	Census migration data, state NSS surveys
Trade area flows	Extent of a city’s retail and wholesale hinterland	Wholesaler delivery records, retail catchment surveys
Newspaper circulation	Information sphere of influence of a city	Publisher ABC circulation data by district
Telephone/communication	Communication density — intensity of interaction	TRAI district-level call data, digital interaction records

Flow Analysis (Qualitative Method for Functional Region)

• Flow Analysis identifies the core node of a region and, based on primary data, estimates how far the flow of goods and services — or the cultural traits of a place — can be identified.
• The boundary of the functional region is where these flows cease to be oriented toward the studied node and begin orienting toward another.

R.L. Singh’s Study — Varanasi (Banaras) Region: R.L. Singh conducted a pioneering analysis of the sphere of influence (Umland) of Varanasi. He mapped flows of: vegetable supply routes to the city, newspaper circulation, milk supply shed, bus route catchment areas. These flow maps were superimposed to delimit the functional region of Varanasi. The R.L. Singh study was followed by U. Singh’s study of Allahabad (1961) and later the KAVAL towns — Kanpur, Allahabad, Varanasi, Agra, Lucknow (1962) — creating overlapping functional regions.

Application to Cultural Regions

• This method can be used for classifying even cultural/naïve regions. For the cultural region, the elements of culture are identified — language, religion, dressing sense, etc. — and based on their occurrence in surrounding areas, the regional boundaries can be vaguely drawn.
• However, due to the complexity and contradictory nature of elements constituting cultural regions, only vague and transitional boundaries can be delineated.
• E.g., it is difficult to draw linear boundaries for a cultural region due to the transitional nature of cultural zones (Buddhist cultural zones in India merge gradually into Hindu zones).

Aspect	Flow Analysis
Type	Qualitative / Empirical (primary data-based)
Output	Sphere of influence / functional region boundary
Best suited for	City regions, hinterland analysis, cultural regions
Pioneer	R.L. Singh (Varanasi Umland study)
Strength	Captures actual functional linkages; reflects ground reality
Limitation	Subjective; data-intensive; produces irregular, non-mathematical boundaries; city region is largely a mental construct, and delimitation by purely quantitative methods is difficult

Gravity Model and Reilly’s Law of Retail Gravitation

• The Gravity Model is concerned with the theoretical forces of attraction between two centres — rather than actual observed flows. It is analogous to Newton’s Law of Gravitation: the interaction between two places is directly proportional to the product of their masses (populations) and inversely proportional to the square of the distance between them.
• Due to the Quantitative Revolution, gravity models were used for the precise demarcation of regions. After the Second World War, geographers used scientific techniques to precisely demarcate a region from its neighbours. The sphere of influence of a region was demarcated by using the Law of Retail Trade, which states the area of influence of a city or region in providing goods and services to surrounding areas.

• We must have noticed that the flow of goods and services from the village is maximum to the nearest city and very small to a far-distant city — this everyday observation is the intuitive foundation of the Gravity Model. Through gravitational analysis, we can calculate the possibility of the flow of goods and services between two geographical areas.

Reilly’s Law of Retail Gravitation (1931)

• William J. Reilly (1931) applied the gravity model specifically to retail trade to find the breaking point (BP) — the boundary between the trade areas of two competing cities.

Converse’s Breaking Point Formula (1949)

P.D. Converse extended Reilly’s work to produce the Breaking Point (BP) formula — the exact location between two cities where the trade boundary lies:

Worked Example

• Settlement K (population 1,000) is located 12 km from City I and 6 km from City J (Population of I = 40,000; Population of J = 5,000).
• Applying Reilly’s Formula:
  • Mₖᵢ / Mₖⱼ = (40,000 / 5,000) × (6 / 12)² = 8 × 0.25 = 2:1
• Interpretation: For every 2 people from Settlement K who travel to City I for goods and services, 1 travels to City J. City I has a larger sphere of influence over K despite being further away — because it is much larger.
• This method provides a well-defined cut-off area of influence and is widely appreciated and used in developed countries.

Limitations of Gravity Model / Reilly’s Law

• In the Gravity Model, it is not easy to calculate M₁, M₂, and R — accurate mass and distance data are required
• The concept of distance is static — does not account for improvements in transport or mode changes over time
• Assumes flat geography — presumes no rivers, roads, or mountains alter consumer decisions
• Assumes consumers are indifferent between cities — does not account for brand preferences, cultural affinities, or personal histories
• Does not account for intervening opportunities — a smaller city between two larger ones may attract trade that the formula does not capture
• Price structures assumed equal in all cities — unrealistic in India where urban-rural price differentials are significant

Thiessen Polygon Method (Proximal / Geometric Method)

• The Thiessen Polygon Method (also called the Proximal Method or Voronoi Diagram) is a simple geometric technique to demarcate regions in an isotropic (uniform) plain.
• It was originally proposed by A.H. Thiessen to show the area of influence of meteorological stations in a given area. In regional geography, it is used to mark the boundaries of functional regions around urban nodes. There is a unique property: each polygon contains only one node.
• The Thiessen polygon method was used by Bogue (1949) to demarcate 67 metropolitan territories in the United States — one of the most significant early applications to urban functional regions.
• Construction of Thiessen Polygons — Step-by-Step
  1. Plot All Urban Nodes on the Map
     • Urban centres (cities, towns) of the same hierarchy are plotted over the map. Each node will be the centre of one polygon, representing its area of functional influence.
  2. Connect Adjacent Nodes with Straight Lines (Triangulation)
     • Connect all adjacent nodes with straight lines, forming a series of triangles that cover the study area. This is the Delaunay triangulation — each triangle is formed by connecting three mutually nearest nodes.
  3. Locate the Midpoints of All Triangle Sides
     • Find the midpoint of each connecting line (the midpoint of each side of every triangle formed in the previous step).
  4. Draw Perpendicular Bisectors from Each Midpoint
     • Draw a perpendicular bisector from each midpoint. These perpendicular bisectors form the edges of the Thiessen polygons; their intersections become the polygon vertices (corners).
  5. Erase Triangle Lines — Polygons Remain
     • Erase all the original triangle-connecting lines. The leftover perpendicular bisectors form irregular polygons, each containing exactly one node. The boundary of each polygon is equidistant from the node inside it and the nodes in adjacent polygons.

Key Properties of Thiessen Polygons

• Each polygon contains exactly one and only one node
• Any point within a polygon is closer to its own node than to any other node
• The boundary between two polygons is equidistant from the two respective nodes — it is the perpendicular bisector of the line connecting them
• The method produces a complete, non-overlapping partition of the study area — every point is assigned to exactly one region

Applications in Regional Geography

Application	How Thiessen Polygons Are Used	Example
Functional region delimitation	Urban centres as nodes; polygon = sphere of influence	Bogue’s 67 US metropolitan territories (1949)
Rainfall regionalization	Rain gauge stations as nodes; polygon = area assigned to that station’s reading	Average rainfall calculation in hydrological basins
Service area delimitation	Hospitals, schools, post offices as nodes; polygon = service catchment	District hospital service area planning
Administrative regionalization	Administrative centres as nodes; equidistance principle for fair jurisdiction	Police station jurisdiction, panchayat service planning

Limitations of Thiessen Polygon Method

• Assumes an isotropic plane — ignores terrain, rivers, roads, and mountains that affect actual travel behaviour and regional interaction
• Assumes all nodes are equal in attractiveness — does not account for the size or quality of the centre (a small town and a metro city are treated equally if equidistant from the boundary)
• Produces rigid geometric boundaries that rarely correspond to real administrative or functional boundaries
• Does not capture historical or cultural factors that shape real spheres of influence
• Distance is measured as straight-line (Euclidean) distance — not actual travel time or cost distance

Methods for Delimiting Naïve / Cultural / Perceptual Regions

• Naïve regions (backward regions, cultural regions, perceptual regions) have no clear, universally agreed boundary. Due to the complexity and contradictory nature of elements constituting such regions, only vague and transitional boundaries can be delineated. The methods applied are primarily empirico-statistical — combining the statistical approach with empirical observation.
• This is the most accepted method applied to delimit naïve regions. Although the boundaries cannot be precisely defined, the method provides the best available approximation.

Method A: Statistical Comparison (Yi − Yj Approach)

• As described in the formal region section: fix a threshold limit of heterogeneity. If the difference in a chosen indicator (per capita income, literacy, HDI composite) between two areas exceeds the threshold, they belong to different regions; if below, they are part of the same region. Applied iteratively across all administrative units to identify the backward region boundary.

Method B: Flow Analysis for Cultural Boundary

• Identify the core of a cultural region. Based on primary data, estimate how far the traits of culture — language frequency, religious practice, dress, food habits, festival patterns — can be identified. For example, for the Maithili cultural region (Mithila), the core is identified around Darbhanga-Madhubani in Bihar. The presence of Madhubani painting traditions, Maithili script usage, and specific marriage customs are mapped — as they fade out in the periphery, the transitional boundary of Mithila is drawn.

Method C: Empirico-Statistical (Mixed) Approach — The NCR Example

• The demarcation of the NCR (National Capital Region) of Delhi is the best Indian example of this approach:
  • Delhi NCR Delimitation — Empirico-Statistical Method: Two components were used simultaneously:
  • (1) Statistical approach: Which districts should be included based on quantitative criteria — commuter flow percentages, proportion of workforce employed in Delhi, per capita income thresholds?
  • (2) Empirical approach: Migration of people into NCR — observed settlement patterns, actual urban sprawl, ribbon development along national highways projecting outside Delhi’s boundaries.
  • It is due to the empirical approach that the demarcated NCR area is projected outward along all important roadways going out of Delhi — reflecting real functional integration along transport corridors rather than uniform circular expansion. This mixed approach captures the reality of Delhi’s functional region far better than either method alone.

Region Type	Primary Method	Boundary Character	Indian Example
Formal (Physical)	Isopleth mapping (isotherms, isohyets)	Fairly precise where data is clear	Thar Desert (rainfall isohyet), Tundra boundary (isotherm)
Formal (Economic)	Fixed/Variable Index, composite ranking	Based on threshold-exceeding units	Backward districts (BRGF), Drought-prone districts (DPAP)
Functional (Nodal)	Flow analysis + Gravity model	Gradient — fades with distance decay	Delhi NCR, Mumbai MMR, R.L. Singh’s Varanasi Umland
Naïve / Cultural	Empirico-statistical (mixed)	Transitional zone; never a sharp line	Mithila, Avadh, Bundelkhand
Planning Region	Administrative boundaries + composite criteria	Administrative units used pragmatically	KBK region, Tribal Sub-Plan areas, NCR

Regionalization of India

Major Attempts at Regionalization in India

Scholar / Body	Criteria Used	Number of Regions	Method Used
National Atlas Organisation	Physiographic homogeneity — landforms, geology, soils	5 macro + 12 meso regions	Superimposition of physical maps
V. Nath	Soil, climate, topography, land use	15 resource development regions	Multi-criteria composite
Planning Commission of India	Agro-climatic conditions + administrative units	15 National Agro-Climatic Zones	Fixed index method + agro-climatic criteria
National Sample Survey (NSS)	Economic and geographic homogeneity	58 meso regions	Composite criteria; cluster method
Raghuram Rajan Committee (2013)	Composite development index: poverty, education, health, infrastructure, financial inclusion	272 backward districts (BRGF)	Fixed index — composite ranking
NITI Aayog (2018)	49 indicators across 5 development themes	112 Aspirational Districts	Fixed index — delta-ranking composite

Physiographic Regions of India (Formal Region by Superimposition)

The standard physiographic regionalization of India uses the superimposition of geological maps (structural composition), topographic/relief maps (landform character), and soil/drainage maps to identify the following major formal regions:

1. The Himalayan Mountain System (fold mountains — Kashmir to Arunachal)
2. The Great Northern Plains (alluvial Indo-Gangetic-Brahmaputra plains)
3. The Peninsular Plateau (ancient crystalline block — Deccan, Chota Nagpur, Eastern/Western Ghats)
4. The Thar Desert (arid zone — Rajasthan, Kutch)
5. The Coastal Plains (eastern and western coastal strips)
6. The Island Territories (Lakshadweep — coral atolls; A&N — volcanic islands)

HDI-Based Regionalization of India (Composite Index)

Regionalizing India based on the Human Development Index uses a fixed composite index of: per capita income (adjusted), literacy/education index, and health index (life expectancy/IMR). This produces an economic spatial pattern:

• High HDI Region: Kerala, Delhi, Goa, Himachal Pradesh, Punjab
• Medium HDI Region: Maharashtra, Karnataka, Tamil Nadu, Andhra Pradesh, Gujarat
• Low HDI Region: Bihar, Uttar Pradesh, Madhya Pradesh, Chhattisgarh, Jharkhand, Odisha

India has been divided into 11 to 20 macro regions based on agro-climatic or resource criteria by different scholars. The Planning Commission of India uses just 5 zonal councils (Eastern, Northern, Central, Western, Southern) comprising certain states — but beyond this there is no formal macro-regionalization in India. These so-called macro regions have to cooperate on inter-state matters: utilization of river water, electricity grids, and other shared resources. NSS has identified 58 meso regions of India but they are not shown on official maps as formal planning regions.