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Settlement geography

Geography (BA) · Human geography · free study notes for exams, placements and interviews

In short

Settlement geography studies where and why people cluster their dwellings — the site, situation, form, pattern, size, spacing, function and hierarchy of villages, towns and cities, and the processes (urbanisation, migration, sprawl, decline) that reshape them. It links physical geography (water, relief, soil) with human geography (economy, transport, administration, planning). In Indian BA/MA syllabuses under UGC CBCS it is a core unit of Human Geography; it is also examined in UPSC and State PSC Geography papers, UGC-NET Geography (Unit on Settlement Geography), and GATE Architecture & Planning / urban planning entrance tests. Employers who use it: town and country planning departments, urban local bodies, the Census of India, NITI Aayog and Smart City SPVs, GIS/remote-sensing firms, real-estate and infrastructure consultancies, and development NGOs.

Explained

Basic concepts: site, situation, form and pattern

Rural settlements: types, patterns and problems

Urban settlements: definition, classification and functions

Hierarchy, spacing and central place theory

Urbanisation in India: trends, measures and problems

Measuring and mapping settlements

Key points

Formulas and examples

Rank-size rule: Pn = P1 / n — Pn = population of the city of rank n, P1 = population of the largest city, n = rank (dimensionless). Generalised: Pn = P1 · n−q, q = slope; q = 1 is the ideal rule.
Primacy index (two-city): PI = P1 / P2 — P1, P2 = populations of the first- and second-ranking cities. Four-city index: P1 / (P2 + P3 + P4); > 1 indicates primacy.
Degree of urbanisation: U (%) = (Urban population / Total population) × 100.
Tempo (rate) of urbanisation: per annum change = (U2 − U1) / t, in percentage points per year, where U1, U2 are urban shares at two censuses t years apart.
Density of population: D = P / A — persons per km².
Reilly's law of retail gravitation: T_a / T_b = (P_a / P_b) × (D_b / D_a)² — T = trade drawn, P = population of centres a and b, D = distance of the intermediate place from each (km).
Break-point distance from city B: d_b = D_ab / [1 + √(P_a / P_b)] — D_ab = distance between the two cities (km), giving the limit of B's market area along the route.
Christaller's hierarchy (k = 3): number of places at the m-th order below the top = 2 × 3m−1; market area at each lower order = (1/3) of the one above.
Nearest-neighbour index: Rn = 2·d̄·√(N/A) — d̄ = mean nearest-neighbour distance (km), N = number of settlements, A = area (km²). Rn ≈ 0 clustered, ≈ 1.0 random, ≈ 2.15 perfectly uniform/regular.
Shannon's entropy (sprawl): H = −Σ (p_i · ln p_i), p_i = proportion of built-up land in zone i of n zones; relative entropy H/ln n near 1 indicates dispersed sprawl, near 0 compact growth.

Example 1 — Rank-size rule. A state's largest city has 48,00,000 people. Predict the 2nd, 3rd, 4th and 8th cities.

Example 2 — Degree and tempo of urbanisation. A district had 6,40,000 urban people in a total of 32,00,000 in 2001, and 10,80,000 urban in 36,00,000 in 2011.

Example 3 — Reilly's law and break-point. City A has 9,00,000 people, City B 1,00,000; they are 60 km apart. A town T lies 20 km from A and 40 km from B.

Example 4 — Census town test. A village has 7,200 persons on 12 km², with 1,900 male main workers of whom 1,480 are in non-farm work.

Example 5 — Nearest-neighbour analysis. 50 villages lie in a 2,000 km² block with a mean nearest-neighbour distance of 1.8 km.

FeatureRural settlementUrban settlement
Dominant occupationPrimary — farming, fishing, forestrySecondary and tertiary
Size and densitySmall, low densityLarge, high density
Land useMostly agricultural, loose planIntensive, zoned, vertical
Social structureHomogeneous, kinship-basedHeterogeneous, impersonal
ServicesLow-order, fewLow to high-order, hierarchical
Indian administrationGram Panchayat (73rd Amendment)Municipality/Corporation (74th Amendment)
ModelAuthor/yearCore ideaMain weakness
Central place (k = 3, 4, 7)Christaller, 1933Nested hexagonal service hierarchyIsotropic-plain assumptions
Economic landscapeLösch, 1940Variable k, city-rich/poor sectorsComplex, hard to test
Concentric zonesBurgess, 1925Rings around a single CBDSingle-core, pre-automobile
SectorsHoyt, 1939Wedges along transport routesUnderplays multiple nuclei
Multiple nucleiHarris & Ullman, 1945Several specialised centresLittle predictive order
Rank-sizeZipf, 1949Pn = P1/nDescriptive, not explanatory

Where it is used

Interview questions

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Related topics

Population geography · Economic geography · Urban planning and land-use zoning · Transport geography and accessibility · GIS and remote sensing applications · Regional planning and development

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