Dear Nicholas,

But from your description, it seems like there might be other questions
> that should guide your analysis.
> Context should drive your exploration of the data.
>

The most important context is the user's current location in my case since
I'm researching the usefulness of SDA for Location-Based Services.

As Virgilo pointed out you won't get much milage plotting 10000 points.
> You need some way of aggregating.


Yeah, I think so too. 10,000+ points is the entire dataset. But in the
application it would be perhaps 100 points which are nearby restaurants. If
the entire Netherlands is shown, I may need to aggregate it otherwise the
clutter just won't say much to the user.

I am not a big
> fan of pie charts, but if you have only a few categories they my show a
> pattern.
>

I have 90 kitchen types / categories. The summary() function from spatstat
might enable me to make these pie charts and display the frequency.

Another route that might be interesting is if you have street maps, look
> at clustering of restaurants on different
> streets. It may show interesting patterns, ie fast food clustered near
> freeways and walmarts.


This would be beneficial to users of the LBS, since they might be positioned
on a certain street, and might want to know whether restaurants are close to
each other / clustered. With street maps, do you mean the actual shapefiles
of the streets?

You might want to look at flowingdata.com there are some nice map
> visualizations
> there.


Thanks very much for the link. There are truly nice map visualisations.
There might be something I could use.

Yes, if you need help rating restaurants, put me in your grant too :-)


Hehe, as soon as the LBS prototype is ready, I'll give you a sign :) I
originally planned to implement ratings as well, but have restricted myself
to just analysing the ratings I have so far. I'm not sure if
rating_interior, rating_food, rating_service would qualify the dataset as a
Spatial Continuous Dataset, or that they're still covariates.. Visualising
the restaurants based on the ratings by dimensions is something I'm
investigating as well. I didn't come across any SDA technique to support
this though.

Thanks very much for the effort of replying and supplying me with valueble
tips, Nicholas. Very much appreciated.

2009/2/16 Nicholas Lewin-Koh <nikko@hailmail.net>

> Hi,
> Yes, if you need help rating restaurants, put me in your grant too :-)
> Seriously, there are many ways to skin a cat. I don't think cartograms
> will help you much
> in this particular case. If you have data besides your point pattern, eg
> postal codes, census data,
> zoning, ... You could look for the obvious patterns, eg Italian
> restaurants clustered in little Italy,
> and Chinese in china town, and then look for the more interesting not so
> obvious patterns.
>
> But from your description, it seems like there might be other questions
> that should guide your analysis.
> Context should drive your exploration of the data.
>
> As Virgilo pointed out you won't get much milage plotting 10000 points.
> You need some way of aggregating.
> Glyphs might be one way if you have some polygonal unit that makes
> sense, such as census blocks. I am not a big
> fan of pie charts, but if you have only a few categories they my show a
> pattern. Kernel density estimation is limited,
> it will show you the spatial distribution of one particular type.
>
> Another route that might be interesting is if you have street maps, look
> at clustering of restaurants on different
> streets. It may show interesting patterns, ie fast food clustered near
> freeways and walmarts.
>
> The sky is the limit. Once you have done a lot of this more basic EDA,
> than think about what kind of analytical
> methods you want to use to address specific questions. You are more
> likely to get what you want. You might
> want to look at flowingdata.com there are some nice map visualizations
> there.
>
> Nicholas
>
>
>
>
>
>
>
> > ------------------------------
> >
> > Message: 10
> > Date: Sun, 15 Feb 2009 22:17:29 +0100
> > From: Virgilio Gomez Rubio <Virgilio.Gomez@uclm.es>
> > Subject: Re: [R-sig-Geo] Point pattern analysis
> > To: Michel Barbosa <cicaboo@gmail.com>
> > Cc: r-sig-geo@stat.math.ethz.ch
> > Message-ID: <1234732649.8833.84.camel@Virgilio-Gomez>
> > Content-Type: text/plain
> >
> > Dear Michel,
> >
> > > I'm new to Spatial Data Analysis and have just begun working through
> > > "Applied Spatial Data Analysis wit R" by Bivand et al. For my research
> I
> > > would like to use SDA to be able to tell more about my restaurant data
> set
> > > than just pinpointing them on a google map. So far, from reading the
> > > literature on SDA I've been able to construct the following questions.
> >
> > Interesting problem. Let me know if you need help collecting data. ;)
> >
> > >
> > > 1. How far / close are restaurants from each other? (answered by using
> > > kernel density estimation)
> > > 2. Which type of restaurants stand next to each other?
> > > 3. How are the restaurants positioned relatlivey from each other?
> > > 4. What's the difference between restaurant A and restaurant B?
> >
> >
> > Questions 2 and 3 are much alike, and I believe that question 4 is too
> > general and not necessarily about the spatial distribution of the
> > restaurants.
> >
> > Depending on the number of different types of restaurants, you may want
> > to estimate a different surface for each type. Basically, you may
> > consider a multivariate point pattern, so that you estimate a different
> > surface for each type and  you compare then to see if they are similar
> > or not. This will address the question of whether the spatial
> > distribution of different types of restaurants is the same or not. This
> > is discussed in Diggle et al. (2005, JRSS Series A). Some of the methods
> > described in the paper are implemented in package spatialkernel.
> >
> > You may also want to compute bivariate K-functions (see 'k12hat' in
> > splancs; 'Kmulti' in spatstat) to detect differences between the spatial
> > distributions of types of restaurants. This will give you a partial
> > answer to Question 2.
> >
> > If you have a set of covariates for each restaurant and you want to
> > estimate their effect and how they explain the spatial distribution of
> > the data you can check Diggle et al. (2006, Biometrics). There is also
> > an example of this in Bivand et al. (2008).
> >
> > I am not sure about the best way of tackling Question 3 (and why this is
> > important). Have you considered to test for whether a certain type of
> > restaurant tends to appear around a particular area of the city? For
> > example, are Chinese restaurants clustered around Chinatown?
> >
> > Finally, another option is to aggregate your data (counts per
> > neighbourhood, for example) and do a similar analysis as in disease
> > mapping.
> >
> > > I've exported a subset of my dataset to CSV in order to import it in R.
> > > Currently, my CSV file is of the form
> > >
> > > *restaurant name; latitude; longitude; type*
> > > Amigo;52.996058;6.564229;Italian
> > > Bella Italia;52.99281;6.560353;Italian
> > > Isola Bella;52.993764;6.560245;Italian
> >
> > I would not use long/lat but UTM to do your analysis. You can do this
> > very easily with R.
> >
> > >
> > > I've tried to import the CSV in R by doing:
> > >
> > > library(spatstat)
> > > info <- read.csv(file = "sample.csv", sep = ";", strip.white = TRUE)
> > > win <- owin(c(0,100),c(0,100))
> > > pattern <- ppp(info$lat, info$lng, window = win, marks=info$name)
> > >
> > > However, if I plot the pattern, the points are all cluttered. What
> advice
> > > could you give me on setting the window size?
> >
> > If you try to plot more than 10,000 points, then I am not surprised that
> > they are all cluttered. :) I would plot the estimated intensity of the
> > point patterns. Or you may aggregate your data and produce a map based
> > on the neighbourhoods in your area.
> >
> > Hope this helps.
> >
> > Virgilio
> >
> >
>
>

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