title: Point pattern similarity and comparison
body: <p>I recently started to work with a huge dataset, provided by medical emergency
  service. I have cca 25.000 spatial points of incidents.</p>
  
  <p>I am searching books and internet for quite some time and am getting more and more confused about what to do and how to do it.</p>
  
  <p>The points are, of course, very clustered. I calculated K, L and G function
  for it and they confirm serious clustering.</p>
  
  <p>I also have population point dataset  - one point for every citizen, that is similarly clustered as incidents dataset (incidents happen to people, so there is a strong link between these two datasets).</p>
  
  <p>I want to compare these two datasets to figure out, if they are similarly
  distributed. I want to know, if there are places, where there are more
  incidents, compared to population. In other words, I want to use population dataset to explain intensity and then figure out if the incident dataset corresponds to that intensity. The assumption is, that incidents should appear randomly regarding to population.</p>
  
  <p>I want to get a plot of the region with information where there are more or less incidents than expected if the incidents were randomly happening to people.</p>
  
  <p>How would you do it with R?</p>
  
  <p>Should I use Kest or Kinhom to calculate K function?
  I read the description, but still don't understand what is a basic difference
  between them.</p>
  
  <p>I tried using Kcross, but as I figured out, one of two datasets used
  should be CSR - completely spatial random.
  I also found Kcross.inhom, should I use that one for my data?</p>
  
  <p>How can I get a plot (image) of incident deviations regarding population?</p>
  
  <p>I hope I asked clearly.</p>
  
  <p>Thank you for your time to read my question and 
  even more thanks if you can answer any of my questions.</p>
  
  <p>Best regards!</p>
  
  <p>Jernej</p>