Path: utzoo!utgpu!jarvis.csri.toronto.edu!mailrus!uwm.edu!zaphod.mps.ohio-state.edu!brutus.cs.uiuc.edu!psuvax1!psuvm!auvm!WATDCS!U5GE91MA From: u5ge91ma@WATDCS.UWATERLOO.CA (Graham Dudley) Newsgroups: bit.listserv.gis-l Subject: RE: Cluster analysis and GIS Message-ID: Date: 8 Feb 90 18:48:36 GMT Sender: Geographic Information Systems Discussion List Reply-To: Geographic Information Systems Discussion List Lines: 43 Approved: NETNEWS@AUVM Gateway In response to David Wong's question, the idea of using an agglomerative clustering approach is that if one can start with "non-modifiable" units (or as close as is practically possible), one can allow the data to "aggregate" themselves rather than imposing an a priori aggregation upon the data. Picking an appropriate level(s) of aggregation and mapping the clusters, one can see what, if any, spatial pattern appears at a particular scale/level of resolution. The objective is to overcome the aggregation effects and make explicit use of the scale effects (i.e., at different levels of aggregation, differing spatial arrangements may reflect processes operating at different scales). Although my own research in to this (or, more specifically, techniques which can overcome the aggregation effects and make use of the scale effects) is still at a very preliminary stage, the idea of using cluster analysis came to me as a result of looking at the retail mix of Toronto's underground pedestrian mall system (something many of you will get a chance to experience first hand in April :-) ). Using the proportion of particular types of retail establishments in each of the eight malls studied as the variables, a cluster analysis was performed (no spatial information was included in this part of the analysis). Three distinct clusters were evident as a result of the analysis and, when these clusters were mapped, a very definite spatial pattern emerged (a northern, central and southern cluster were apparent). The clusters appeared to reflect the particular market each of the individual malls served (for example, the central cluster involves some of the large of office towers in the CBD and, as such, has a higher proportion of coffee shops, restaurants, and other food establishments). Obviously this is a rather simplistic example but I found it interesting that in the absence of any explicit spatial information being included in the analysis, a very definite spatial pattern emerged. It is this sort of idea that I would like to pursue a little further. ..graham PS Thanks for the Arbia reference. It looks interesting (I hope to be able to examine it a bit more detail in the next few days). -------- Graham Dudley BITNET: U5GE91MA@WATDCS.UWaterloo.ca Department of Geography CONNECT: DUDLEYG University of Waterloo Waterloo, Ontario, Canada N2L 3G1