From AZucchi@asl.bergamo.it Mon Feb 19 01:11:49 2001 Received: from mxu2.u.washington.edu (mxu2.u.washington.edu [140.142.32.9]) by lists.u.washington.edu (8.9.3+UW00.05/8.9.3+UW00.12) with ESMTP id BAA143334 for ; Mon, 19 Feb 2001 01:11:46 -0800 From: AZucchi@asl.bergamo.it Received: from srvbggwer001.asl.bergamo.it ([194.179.159.11]) by mxu2.u.washington.edu (8.9.3+UW00.02/8.9.3+UW99.09) with ESMTP id BAA07093 for ; Mon, 19 Feb 2001 01:11:40 -0800 Subject: Rif: Re: testing spatial association To: waphgis@u.washington.edu X-Mailer: Lotus Notes Versione 5.01 (Intl) 11 agosto 1999 Message-ID: Date: Mon, 19 Feb 2001 10:11:28 +0100 X-MIMETrack: Serialize by Router on AZIMUTH3/ASLBERGAMO(Versione 5.0.4 |Giugno 30, 2000) at 02/19/2001 10:11:01 AM MIME-Version: 1.0 Content-type: text/plain; charset=us-ascii Dear colleagues, first of all thanks to all who kindly replied to my question. I I become aware of having been somewhat naive in putting the question and showing the cases map, it was just to exemplify; I obviously have all single administrative in-boundaries prevalence rates, and the map looks substantially the same. The boundaries are representing the administrative city or village boundaries; from north to south the highest distance is 40 kilometers. I was not interested in searching clusters (as with SatScan) tout court, but, as Dick Hoskins correctly defined, to look for clusters/aggregation/association along the roads (not radially, but along a linear -or, almost linear- feature). Professor Jim Case suggests to simply divide the case population into two classes: near a road- not near a road (after a sound definiton of the two, of the distance, and so on), and then to test the differences. Very correct is the suggestion to adopt empirical Bayes estimates to adjust the small numbers. I will do it. Could you suggest some similar published study to use as an example to follow? Thank you Alberto Zucchi .