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Much like jbsoq. 1) Convert the x,y data to a shapefile, here is the tutorial. 2) "Sample" or "Extract Multi Values to Points" to get the raster into the point shapefile. I prefer multi value. 3) Run Exploratory Regression on the data and his will give you most of what you want. 4) You now have the original values and the raster values in the attribute ...


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To add to msi_g's answer above: since you have multiple rasters you can use Extract Multi Values to Points then add a field to the attribute table of the point data and calculate whatever you want for all points individually, but you will have to type out the equation yourself. Also be cautious with multiple rasters that projections are the same and rasters ...


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This tool might be of benefit to you if you want to convert your rasters to vector. There is a OWA function and a WLC(weighted linear combination) there appears to be support for ArcGIS 10.1 and above. http://mcda4arcmap.codeplex.com/ and a paper http://www.ryerson.ca/~crinner/pubs/Pages12-13_from_Cartouche86_Winter-Spring2013.pdf


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In my education in GIS, I met some statistical methods, however I never encountered better, than AHP for weighting objectively. Unfortunately, there was only one software which provided built-in AHP support (surprisingly an entire wizard) for this purpose: IDRISI. There are some user scripts for ArcGIS, like AHP 1.1 or AHP-OWA 2.0, which has Ordered weighted ...


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Not entirely sure what you are after experimentally but you may want to investigate a Weights of Evidence approach.


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Thank you both for your very quick answers, which both solve my problem from a process point of view. Using OpenJump is much easier, but requires PC-power. ArcGIS requires licence money and a few more steps before getting there, but less hardware resources. PS. I dug even further into my long time favorite, GME, and found out that it actually has the ...


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using ArcGIS basic you can perform "intersect_analysis" (assuming your grid is made of polygons, otherwise you first need to convert to polygons), then compute the area of the new polygon, then use "summarize table" based on the grid index field (and the land use field as well, optionnally) that will be stored in your new feature class. the similar ...


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OpenJUMP does have a solution. You need the Plus version which includes the Aggregation plugin (Plugins - Analysis - Aggregation). EDIT I took execution time from a test where I used similarly sized datasets. I created a polygon layer with 1.2 million polygons and a polygon grid to present 26000 map sheet rectangles. Computing the parcel area per mapsheet ...


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It would be very helpful for you to read up on some R basics, particularity pertaining to sp class objects. A very good starting point would be Bivand's ASDAR book and the sp vignette. Here are some other related R spatial analysis introductory material. As to your problem at hand. For one, you can easily create a variety of spatial weights matrices in ...


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you are using two distance fields which can be very similar. for instance if a condo is downtown and is only .2 km from desirable features, at the same time this condo can be .2 miles from the city center, that's where your multicollineraity comes into play, use other features for hedonic modelling, ie sq ft, lot size, years on market, etc.


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As you are not always exactly adjacent, I would take a small buffer along each polygon, then you can use "zonal statistics as a table" with those buffers and join the table to your polygons. Use the "DATA" option to ignore your nodata values. Edit : Exact solution assuming that the white in the illustration is NoData and the polygons are adjacent to the the ...



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