<div dir="ltr"><div><div><div>The three obvious categories are restaurants (inc. cafes), takeaways & pubs. Supermarkets is a small set. Schools we've already done. Retailers (other) covers convenience stores and other small food shops. The other categories are less useful (although care homes in conjunction with CQC Open Data is a relatively small group which is probably undermapped).<br><br></div>I'd actually avoid trying to filter on groups because in general it's much easier to maintain data for rows of shops which is complete. One runs into issues with nodes being slightly misplaced, name changes etc which in the end make it harder to track. It was Paul William's mapping of Mansfield Road in Nottingham back in 2011 which made me realise this. I'd done a bit of shop mapping before a London pub meeting and found my productivity was really low because bits & pieces were already there but not quite in the right place.<br><br></div>The selection criterion I used when I did this back in 2013 for Nottingham was numbers of FHRS entries per postcode. I used QGIS to show these as <a href="https://www.flickr.com/photos/sk53_osm/8559309309">bubbles </a>and was able to create a GPX file from QGIS for my Garmin. The advantage was that I could knock off a known number quickly: mainly I just visited a place on Sunday morning & took photos, a few house numbers from the surrounding area for additional context for addressing & that was it. Of course these leaves you with isolated ones in funny places, but most of these I linked to when I was doing something in the car so that it was easier to visit the sites quickly.<br><br></div>Jerry<br></div><div class="gmail_extra"><br><div class="gmail_quote">On 26 September 2016 at 12:40, Dave F <span dir="ltr"><<a href="mailto:davefoxfac63@btinternet.com" target="_blank">davefoxfac63@btinternet.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
<div bgcolor="#FFFFFF" text="#000000">
A. <br>
<br>
However the FHRS database is quite big, so maybe just do a subset,
say, restaurants?<br>
<br>
Dave F. <br>
<br>
<div>On 26/09/2016 12:20, Jez Nicholson
wrote:<br>
</div>
<blockquote type="cite">
<div dir="ltr">
<div><font color="#212121" face="helvetica neue, helvetica,
arial, sans-serif">5 days to go until the next UK Quarterly
Project</font></div>
<div><span><br>
</span></div>
<div><font color="#212121" face="helvetica neue, helvetica,
arial, sans-serif">Without spinning off into a discussion of
the merits of each suggestion....what do we need to do to
agree the subject?</font></div>
<div><font color="#212121" face="helvetica neue, helvetica,
arial, sans-serif"><br>
</font></div>
<span>I
believe that suggestions were:</span>
<div><span><br>
</span></div>
<div><span>A.
improving address data for town centres using the food
hygiene dataset</span></div>
<div><span>B. town centre
blitzes</span></div>
<div><span>C. civil parishes</span></div>
<div><span>D. trig points</span></div>
<div><span>E. speed limits</span></div>
<div><span>F. a </span><span>collaboration
with the German Community</span><br>
</div>
<div><span><br>
</span></div>
<div><span>please
repeat and expand the list if I missed one.</span></div>
<div><span><br>
</span></div>
<div><span>Regards,</span></div>
<div><span>
Jez</span></div>
</div>
<br>
<fieldset></fieldset>
<br>
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</blockquote>
<br>
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