In my data mining term project I need to conduct a work on geospatial data related to a big city to show possible urbanization fields in next years. I've used GRASS lately on other projects so I'm a bit familiar with GRASS.
After some research I've seen that R can be used for spatial statistics issues and I've read about forecast, raster, sp, spatstat, geospt etc libraries of R and it seems it can be helpful for my project. But on the other hand it has a very steep learning curve. Though I'm not sure that GRASS is whether sufficient or not at that point, learning process of R is seems scaring to me.
I'm coding for 4 years with C, Java, C#, Python etc but R's syntax, working mechanism is like an alien to me. What do you suggest for this problem. Do you think GRASS can be used solely or do i need to invest on R?
Thanks in advance.
EDIT: I have etm+ images produced at 3 different years(2010, 2011, 2012) for same location. I will use first 2 for training and last one for test. I will define parameters such as city centers, roads, urbanized places etc. to predict areas which are probable to be urbanized. There are some researches using Bayesian Belief Networks or Artificial Neural Networks to achieve this. I will use 2 or 3 parameters(roads, rivers) to estimate urbanization model of a city. Using vector data and raster data I think I can predict urbanization model more or less. So I need to evaluate raster cells against vector data to calculate their urbanization probability.