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26

"Geographically weighted PCA" is very descriptive: in R, the program practically writes itself. (It needs more comment lines than actual lines of code.) Lets begin with the weights, because this is where geographically weighted PCA parts company from PCA itself. The term "geographical" means the weights depend on distances between a base point and the ...


22

Depending on which Kriging type you want to apply, there are different packages to choose from: Ordinary Kriging The most common version is implemented for example in: GRASS - v.krige QGIS Kriging as part of the SDA4PP plugin SAGA - Module: Ordinary Kriging HPGL - ordinary-kriging() (PDF page 12) gstat - krige Simple Kriging Simple Kriging uses the ...


21

As always, it depends on your objectives and the nature of the data. For completely mapped data, a powerful tool is Ripley's L function, a close relative of Ripley's K function. Lots of software can compute this. ArcGIS might do it by now; I haven't checked. CrimeStat does it. So do GeoDa and R. An example of its use, with associated maps, appears in ...


14

It looks like there are a few options with GRASS GIS. Check out the GRASS Kriging Wiki page: http://grass.osgeo.org/wiki/Kriging A Google Summer of Code project in 2009 produced V.krige: http://grass.osgeo.org/wiki/V.krige_GSoC_2009 The GPL gstat package should work by itself or interfaced with GRASS GIS. http://www.gstat.org/ Dylan Beaudette has a ...


14

In a nutshell: Start with QGIS. There are several Free and Open Source tools for geospatial statistics. QGIS (Quantum GIS) There are several spatial statistics plugins in Quantum GIS, such as fTools: Tools for vector data analysis and management Zonal Statistics: Extended zonal statistics and report generation manageR: Interface to the R statistical ...


14

There's quite a few resources for learning 'spatial' R online, some of the better ones are: spatial-analyst.net A really solid blog post by Frank Davenport, with notes on some basic spatial data manipulation in R R Spatial Tips Barry Rowlingson's site containing some great examples and a cheatsheet


13

ArcGIS v10 will do this. First run "Add XY coordinates". Then run Dissolve, select Point_X and Point_Y as the dissolve fields, add a statistics field, Sum. I just tested it on overlapping Points. The output has a single Point at each overlap location while the numeric field is summed, for that location.


13

There's no strict algorithmic relationship between latitude and longitude and zip code - they're all custom areas generated by the postal service. You need access to a dataset that codes polygons / polygon centroids by zip code. 1) Complex Traditionally, this task (coupled with address lookup) is termed 'Geocoding'. The most convenient method for full ...


12

If you are happy to read your raster into a numpy array (gdal can do this), then you could use the High Performance Geostatistics Library implementation from Python or C/C++. HPGL implements the following algorithms: Simple Kriging (SK) Ordinary Kriging (OK) Indicator Kriging (IK) Local Varying Mean Kriging (LVM Kriging) Simple CoKriging ...


12

GeoDa is free, cross-platform software designed for dynamic visualization, exploratory spatial data analysis, and spatial statistics. It has been around for almost 15 years (starting as an ArcView 3.x extension, it was recoded to be independent of ArcView after ESRI abandoned the old AV architecture). It is associated with an illustrious group of GIS ...


11

Regarding R in general I'd really recommend having a look at Applied Spatial Data Analysis with R book. It offers very good introduction to spatial concepts within R framework. And webpage provides lots of code snippets and data to practice what you read about. Regarding coupling R and ArcGIS specifically, Python is one of the options here. Have a look at ...


11

The R-project has substantial number of spatial statistics software packages, but R has rather steep learning curve.


11

Update: There is now a specialized R package available on CRAN - GWmodel that includes geographically weighted PCA among other tools. From author's website: Our new R package for Geographically Weighted Modelling, GWmodel, was recently uploaded to CRAN. GWmodel provides range of Geographically Weighted data analysis approaches within a single ...


11

A couple I've found useful: I'd strongly recommend Analysing spatial point patterns in 'R' by Prof. Adrian Baddeley at the CSIRO in Australia. It covers the spatstat module in depth and I think it's a great resource for cluster analysis. Applied Spatial Data Analysis in R (Bivand, Roger S., Pebesma, Edzer J., Gómez-Rubio, Virgilio) and Spatial Statistics ...


11

The ESRI Spatial Statistics tools do not calculate great circle distance if the data is in a geographic coordinate system (Lat/Long). As such, distance based spatial analysis is incorrect. The tools require that your projection units be in feet or meters. The "ZONE_OF_INDIFFERENCE" is a term made up by ESRI that basically means that within a local ...


10

Simple question, difficult solution. The best method I know uses simulated annealing (I have used this to select a few dozen points out of tens of thousands and it scales extremely well to selecting 200 points: the scaling is sublinear), but this requires careful coding and considerable experimentation, as well as a huge amount of computation. You should ...


10

Few more from my side: CSDE (University of Washington) course on GIS has some spatial R materials, focusing on ESDA, GWR, spatial regression Department of Geography (University of Colorado) has materials from the Introduction to Quantitative Methods course that among other topics explore spatial autocorrelation, spatial regression (part 2), GWR and point ...


9

Your task could be done easily by using Zonal Statistics tool of Spatial Analyst extension.


9

Calculation Subtract one raster from the other. (The direction of subtraction does not matter.) -1 0 -1 3 Square the result. 1 0 1 9 Average the values. (1 + 0 + 1 + 9)/(1 + 1 + 1 + 1) = 11/4. (I wrote this in a suggestive way to show how missing-data cells can be handled if your GIS does not have this capability: Create an indicator grid with 1's ...


9

"Statist" plugin calculates StDev. It has a "use only selected features option". You'll need to use "Select by location" first to select the features within your polygon-of-interest. Then run Statist.


9

In R you can do library(raster) library(rgdal) r <- raster('raster_filename') p <- readOGR('shp_path', 'shp_file') e <- extract(r, p, fun=mean) e is a vector with the mean of the raster cell values for each polygon.


9

The ArcGIS online help page has the answer here: http://webhelp.esri.com/arcgisdesktop/9.3/index.cfm?TopicName=How%20GWR%20Regression%20works In particular this quote seems particularly pertinent: Parameter estimates and predicted values for GWR are computed using the following spatial weighting function: exp(-d^2/b^2). There may be differences in this ...


8

I would stick to R. If speed is really a problem ( I doubt so 90.000 is not such a big number) you could try finding relationships between a subset of your data. Actually the first thing I would do is make a plot to look for obvious relationships. Even if arcgis contains tools to compare rasters, R will always give you a lot more statistical tools. Eg: ...


8

The function get.knnx in package FNN can compute the N-nearest neighbours in point patterns. x1 = cbind(runif(10),runif(10)) x2 = cbind(runif(10),runif(10)) nn = get.knnx(x1,x2,2) now nn$nn.index is a matrix such that nn$nn.index[i,j] is the row in x1 of the two nearest neighbours to row i in x2 - sorted so that the nearest is [i,1], and the next ...


8

A Caveat A standard error is a useful way to estimate an uncertainty from sampled data when there is no systematic error in the data. That assumption is of dubious validity in this context, because (a) the KDE maps will locally have definite errors that may persist systematically among the layers and (b) a potentially huge component of uncertainty due to ...


8

This does not seem to be documented in the clusthr manual page, but because the source code is available, we can try to figure it out. By typing > clusthr at the R prompt (shown as the initial >), you can see the code. It's opaque, but a quick look indicates (1) there's no plotting or color selection going on and (2) the clustering is performed ...


8

Evaluation of the options Contour lines represent continuous surfaces, so their comparison ultimately is a proxy for comparing those surfaces. Because both the surface values (elevations) and locations are potentially subject to error, there are two components to the comparison: in terms of value and in terms of position. The two cannot be separated, ...


8

Whenever you are doing an analysis that involves distance measurements you should project your data (at present the spatial statistics tools in ArcGIS do not calculate geodesic distances, unfortunately). This link will tell you more about projected coordinate systems: The ZONE OF INDIFFERENCE conceptualizations is not appropriate when your data is measured ...


7

Note: the following was edited following whuber's comment You might want to adopt a Monte Carlo approach. Here's a simple example. Assume you want to determine if the distribution of crime events A is statistically similar to that of B, you could compare the statistic between A and B events to an empirical distribution of such measure for randomly ...


7

The Geospatial Modelling Environment (the successor of Hawth's Tools for ArcGIS) links python scripting, R and ArcGIS in a useful way. I haven't investigated the links with R in detail, but it looks like it may be useful for what you're trying to do. If you're trying to do something yourself then StatCONN may be useful.



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