THE EFFECTIVENESS OF SPLINE URBAN DENSITY-FUNCTIONS - AN EMPIRICAL-INVESTIGATION

Authors
Citation
G. Alperovich, THE EFFECTIVENESS OF SPLINE URBAN DENSITY-FUNCTIONS - AN EMPIRICAL-INVESTIGATION, Urban studies, 32(9), 1995, pp. 1537-1548
Citations number
18
Language
INGLESE
art.tipo
Article
Categorie Soggetti
Environmental Studies","Urban Studies
Journal title
ISSN journal
0042-0980
Volume
32
Issue
9
Year of publication
1995
Pages
1537 - 1548
Database
ISI
SICI code
0042-0980(1995)32:9<1537:TEOSUD>2.0.ZU;2-W
Abstract
Recent studies of population distribution in urban settings suggest th at cubic-spline functions may be preferable to the conventional expone ntial form. It is contemplated that this specification is more suitabl e for untangling, discovering and depicting the complex density patter ns of today's relatively dispersed urban areas. This paper examines th e usefulness as well as the amenability of the cubic-spline function f or describing and testing hypotheses on the processes underlying the d etermination of population densities in Tel Aviv-Yafo. The principal f indings of the analysis are threefold. First, from the theoretical and empirical points of view the cubic-spline function is unlikely to be useful for testing hypotheses. Multicollinearity among distance variab les renders the cubic-spline function without much practical merit for this purpose. Secondly, an exponential spline form which does not uti lise high-order terms of distance is better suited for this purpose an d should therefore be preferred to the cubic-spline. Thirdly, an alter native approach which employs an improved exponential form obtained by incorporating pertinent information on actual patterns of land-use de velopment into the theoretically derived exponential form was highly s upported by the data. Utilisation of the latter approach led to an inc rease in the explanatory power of the model from a mere 0.24 to a resp ectable 0.83. Indeed, the general lesson to be learned from the analys is is that utilisation of general functional forms cannot by itself co rrect for possible biases in sample selection, model specification or, for that matter, replace thorough understanding of the processes one is trying to model.