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 _______________________________________________ R-sig-Geo mailing list R-sig-Geo@stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/r-sig-geo