Point pattern analysis
Hi Michael, A couple of thoughts. Many of the statistical methods are geared toward describing the pattern over a region. So methods like the k-functions, and such will describe the global covariance function over a range of spatial scales. Here you are looking at very local phenomena, I am standing in Rotterdam central with my crackberry, and I want to know what is the best Italian restaurant within a kilometer. Well one issue is euclidean distance a good proxy for walking distance? So nearest neighbor searching might better be done on Manhattan distance, or even a quick all shortest path calculation on the street network in the one kilometer circle. A nice way to think about these problems might be in terms of graphs (as in graph theory). Rather than point process ideas, you could look at some of the social network models. Also inherent in this is ranking the restaurants, what order should they be listed? If distance, quality, price, ... all play a part in my search criteria, than the ordering should reflect those weightings. So a neat application might be that I want to know (if I were younger) which cheap Italian restaurants are in walking distance of night clubs with live music. And get some ranking on those pairs weighted by distance and price. So the top ten pairs would be displayed in the view, with lines connecting them, and the lines weighted by the pair rankings. So what you want seems to be more on the lines of heuristics for picking the best in a limited query. "statistics" might help a little if you exploit the local nature of the queries, ie build your rankings normalized by all Italian restaurants in Rotterdam, and highlight the ones that are spatial outliers (really good) LISA's (local indicators of spatial association) might be a good approach for this. Again, these are just my musings, but in this context point pattern analysis may not have that much to offer. Nicholas ------------------------------------------------------------------------------- Dear Virgilio / Adrian, I do have data regarding districts, cities and provinces. I could easily ask for all the restaurants in Rotterdam or for the province. But for Location-Based Services, the users tend to be interested in local / nearby points of interests. I'm looking for techniques to allow the users to make better decisions about the restaurants. By aggregating the data and doing area data analysis it might be useful for users that have no real idea what place they're looking for. For example tourists that want to know which city has the most / best restaurants. but if you allow them to zoom in then you probably
want to show the individual locations of the restaurants.
Yeah, on a more detailed level, I want to analyse the nearby restaurants. Let's say someone's looking for nearby Italian restaurants, for example within a 1 kilometer radius. The user is presented with 10 restaurants. To move beyond pinpoints on a google map, I want to analyse these restaurants. I'm interested in showing more information about these 10 restaurants then just their location (which becomes obvious from the pinpoints). Let's say I have another point pattern dataset which contains the location for ATMs. I could use the *nncross* function in spatstat to return the nearest ATM for these 10 restaurants that match my criteria. Using arrows to pinpoint the ATMs would clearly help users to determine if restaurant 1 is better than restaurant 10. Any ideas / tips regarding analysing / comparing two point patterns? So far, I've played with the nndist and nncross functions provided by spatstat. 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...
Isn't this what smooth.ppp is trying to accomplish? Yes, you can compare the spatial distributions of different types of
restaurants.
Could you give me an example of such a comparison? Do you mean estimating a surface for Italian restaurants and for Greek restaurants. And show them next to each other, as used in split.ppp? Or different outputs such as tabular comparisons? Basically, I'm restricted to the screen size of either the iPhone or Nokia N95 8GB, since my research involves developing for either one of those two phones since they utilise A-GPS. My dataset has ratings on food / interior / service and a general rating. What type of analysis would best suit my dataset in this case? 4 kernel density estimations and comparing them? Please have a look at Part V of the e-book 'Analysing Spatial Point Patterns
in R' (version 3) available at < http://www.csiro.au/resources/Spatial-Point-Patterns-in-R.html> which contains a detailed description of how to analyse such data in the package 'spatstat' using both exploratory tools and formal statistical models.
I've read through Part V and other sections of the e-book. I want to utilise Visualisation techniques and Exploratory techniques. Modelling and thereby forming statistical models goes beyond the scope of my research. Given this limitations, are there any other papers/techniques/r packages I should consider? My dataset is clearly a point pattern dataset. I might be able to get some other point pattern datasets as well. I've looked through the ones mentioned under the section *point pattern analysis* at http://cran.r-project.org/web/views/Spatial.html. Thanks very much for the great input so far! Kind regards, Michel 009/2/17 Virgilio Gomez Rubio <Virgilio.Gomez at uclm.es>
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