Dear all,
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.
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?
Which methods do you recommend me using for tackling the above mentioned
questions? My dataset contains 10,000+ restaurant with associated user
ratings on interior, service and food. In addition, I have the latitude /
longitude coordinates and the associated kitchen type, which makes my
dataset a marked point pattern. I've read through the spatstat PDF and
several other book chapters including Ripley's Spatial Statistics book. So
far, kernel estimation and cartograms seem like good techniques to tell me
more about the dataset. What do you suggest?
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'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?
Kind regards,
Michel
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