Methods of Deriving Multi-Factor Uniform Regions
Bibliographic Data
| ID | 8964221 |
|---|---|
| Authors | Douglas Pocock (corresponding author), D C D Pocock, D Wishart |
| Year | 1969 |
| Issue | 47 |
| Pages | 73-73 |
| Publication date | 1969-09-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Transactions of the Institute of British Geographers (JOURNAL) |
| Journal identifiers | ISSN: 0020-2754 • E-ISSN: 1475-5661 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.2307/621736 |
| OpenAlex | W2333442462 |
| Language | EN |
| Citations received | 3 |
This paper introduces a new method of obtaining multifactor uniform regions. Those fusion techniques such as 'centroid' and 'group average', which are based on the imposition of minimum-variance constraints, may well generate artificial classifications, while the step-wise clustering procedure and its corresponding dendrogram facility is inefficient when dealing with large data sets. The new method, termed the dense-space method, searches initially for dense spheres which signal the presence of important uniform regions or dense space, and then derives distinct regions by linking any dense spheres which intersect. Three classification levels are suggested: nuclear, basic and complete. The nucleus of each distinct region is described by a set of intersecting dense spheres of radius Jr, each of which has the property that it does not intersect any dense sphere from any other distinct region. The subset of sample points inside the dense spheres are termed nuclei points and constitute a cluster. Those points which lie outside the dense spheres but at a distance not greater than r from the centre of a dense sphere are included with the classification for the sphere at the basic level. Points which are unclassifiable at the basic level are relatively remote and their classification at the complete level, into the regions which contain the points' nearest dense spheres, should only be used when a best fit is demanded for every sample. The dense-space method achieves more 'natural' classifications and demands less computation and memory storage. Its advantages over other methods are shown by a reworking of U.S.A. census data, first used by B. J. L. Berry, and by reference to an urban survey of Middlesbrough. THE PROBLEM of regional classification in geography is essentially the classification problem common to all the behavioural sciences. A set of samples (observation units) is divided into a small number of subsets or clusters so that each subset represents a grouping of samples which have a basic common similarity with respect to the survey variables. Classifications involving total uniformity in the cluster samples, that is, every variable having uniform values for each subset, are derived by imposing constraints on the cluster's overall variance. Two such techniques, centroid and group-average, are compared here before introducing a third method, that of dense space. The new method, it is claimed, achieves more 'natural' classifications and is suitable for rapid analysis of large surveys. Computation details are given for data used by B. J. L. Berry in a recent paper1 from nine census divisions of the U.S.A., and the speed facility of the dense-space method when used on large surveys is demonstrated with reference to a 23 I-sample urban survey. A formal presentation of the methods is given in a mathematical appendix
Factor (programming language · Programming language · Computer Science · Spatial and Panel Data Analysis
| Unique citing works | 3 |
|---|---|
| Citations per year | 0,05 |
| Citation span | 1970 - 1983 (14) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 3 |