[R-sig-Geo] Point pattern analysis

[Virgilio Gomez Rubio]

Hi,


> Thanks :) Actually, I'm busy with developing a Location-Based Service
> (a restaurant finder to be precise) utilising SDA. The goal of my
> research is to integrate SDA in an LBS. For this purpose, I've
> gathered about 13,000 unique restaurants in the Netherlands and would
> like to use 3 SDA techniques that enhance the restaurant finder either
> visually and/or analytically. The motivation behind my research is t
> start a discussion on how SDA can be used inside LBSs to enhance the
> services. In this case, to enable users to make better decisions about
> nearby restaurants. One thing that popped in my mind was to use kernel
> density estimation and overlay it on the google/microsoft map to allow
> users to easily grasp the proximity of restaurants.

Perhaps it would be better if you aggregated your data and considered
municipalities in The Netherlands. I guess that area level maps are
easier to understand. What I mean is that your users will find more
meaningful that there are, say, 20 Indian restaurants in Nijmegen than
saying that the intensity for the Indian restaurants have a peak in the
centre of Nijmegen. Regional maps will be helpful if you have a whole
map of the country, but if you allow them to zoom in then you probably
want to show the individual locations 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 quite interesting. Would this allow me to estimate a surface
> for let's say Italian restaurants vs Greek restaurants? I have ratings

Yes, you can compare the spatial distributions of different types of
restaurants.

>  for each restaurant. So a user might want to ask "Where can I find
> good Italian restaurants in the South?" Where good is any rating above
> a 7.0 for example.

This can be more complex because then you may want to produce a map
based on the rating, and then the rating becomes the response variable
in your model...

> 
>         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.
> 
> Would this mean that a kmulti analysis should be applied for each
> restaurant type and thus each subset I wish to test?

You will need to consider each pair of restaurants at a time.

> 
>         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?
> 
> This is something I'm looking for as well. Considering the fact that
> I'm in the process of developing such an LBS, it would be something
> along the line of: A user takes out his mobile phone. He starts the
> application and the applications looks acquires a position fix. When
> this is done, a user might want to know: "What type of restaurant is
> typical for my current location or current neighbourhood. So,
> analysing whether a certain type of restaurant tends to appear around
> the CURRENT area of the city. Is this possible?

Yes, I guess that you can make a buffer of, say, 300 m around the user's
location and then display your results based on the restaurants included
in that buffer.

> Overall, thanks very much for your reply. I'm really excited about
> using these SDA techniques and am very grateful for your quick reply.
> I'll look up a copy of the papers you mentioned and will read through
> them as soon as I can. When I've successfully analysed the dataset
> with some SDA techniques I can begin the process of constructing the
> appropriate architecture for the LBS. I'll definitely keep you guys
> posted if you're interested.

That would be good. And if you get free vouchers let us now as well!! :)

Best,

Virgilio