Historical Perspective of Quantitative Revolution in Geography!
Statistical methods were first introduced into the discipline in the early 1950s.
Consisting mainly of descriptive statistics, there was also some attempt at hypotheses testing using, for example, chi-square. Bivariate Regression Analysis followed shortly, but it was not until the 1960s that the General Linear Model was fully explored. Since then, much attention has been paid to a set of very sophisticated dynamic linear (e.g., Space-Time Forecasting Model) and non-linear (e.g., spectral analysis) statistical techniques, including those that bear peculiarly upon geographical problems (e.g., spatial autocorrelation).
The inspiration for mathematical modelling came from at least two sources: first Social Physics, which focused initially on the ‘Gravity Model’ and later ‘Entropy Maximization’ and, second Neoclassical Economics which influenced geography principally through the regional science movement and ‘Location Theory’. Associated with each were often different questions, and hence a different branch of mathematics.
The typical pre-occupation of social physics is the spatial interaction among a set of discrete geographical points (frequently, but not always, leading to the use of matrix algebra), while for neoclassical economic it is with optimization over continuous space (usually resulting in the use of differential calculus).
Although geographers borrowed several models from economics and sociology, e.g., the ‘Crop Intensity Model’ of J.H. Von Thunen (1826), Alfred Weber’s model of ‘Industrial Location’ (1909), Christaller (1893-1969) was the first geographer who made a major contribution to location theory in his study of Central Places in Southern Germany. Subsequently, American urban geographers developed theoretical models of urban places. It was during the post-
Second World War period that A. Ackerman (1958) encouraged his pupils to concentrate on quantification cultural processes and systematic geography. Weaver, another American geographer, delineated crop combination regions in Middle West (1954) by applying the standard deviation technique which brought quantitative revolution in agricultural geography.
Hager-strand became interested in the possibilities of investigating the process of innovation with the help of mathematical and statistical methods. He was able to construct a general ‘Stochastic Model’ of the process of diffusion. Stochastic literally means at random. Stochastic or probability models are based on mathematical probability theory and build random variables into their structures.
Empirical studies indicated that the movement of persons between two urban centres was proportional to the product of their populations and inversely proportional to the square of the distance between them. Stewart pointed out the isomorphic (equal form or structure relationship between this empirical generalization and Newton’s law of gravitation. Thereafter, this concept became known as the ‘Gravity Model’. In other branches of the discipline, e.g., population, regional, economic, cultural and political geography a number of statistical techniques were gradually diffused.
Thus, the diffusion of quantitative techniques steadily took place in the 1960s. In Britain Richard Chorley and Peter Haggett, both from Cambridge University, applied quantitative techniques vigorously and inspired the new generation to adopt sophisticated statistical and mathematical tools and techniques to explain and interpret geographical patterns and spatial relations.