# Tag Info

## New answers tagged spatial-statistics

3

Reproducible example, just fix the script to make a dummy data.frame. voronoipolygons = function(layer) { require(deldir) crds = layer@coords z = deldir(crds[,1], crds[,2]) w = tile.list(z) polys = vector(mode='list', length=length(w)) require(sp) for (i in seq(along=polys)) { pcrds = cbind(w[[i]]\$x, w[[i]]\$y) pcrds = rbind(pcrds, pcrds[1,]) ...

0

Use a permutation test. You begin by constructing a quantitative way to measure proximity. It could be anything you think relevant that measures typical distances between type-A and type-B buildings. Abstractly, given any configuration consisting of a layer X of type-A buildings and another layer Y of type-B buildings, let this measure be called t(X,Y). ...

0

You will need to determine a distance that is your threshold for whether or not Building B is "close" to a Building A. Use that distance to create buffers around Building A. Each Building A needs its own buffer so do not dissolve the output. Once you have your buffers, you can either Select By Location as esset mentioned or use an intersect to create a ...

1

You can do this with the "Select By Location" Tools. In ArcMap go to the meny "Selection" click "Select By Location". In the "Select By Location" window in "Target layers" chose your layer Building B. Then as "Source layer" you choose Building A. Then as "Spatial Selection method for target layer features" you choose an appropiate selection method. Why I ...

0

There are some multiple regressions (including GWR) functions in SAGA GIS that offer many options of rasters as predictors, dependent variables and outputs. I hope it can help you.

2

You cannot use standard point pattern statistics because the assumed spatial process is being constrained to a linear process. The assumption of a Poisson distributed Complete Spatial Randomness (CSR) does not hold. The entire problem becomes one-dimensional and expectations based on area need to be redefined to distance thus, the single dimension of the ...

3

As @whuber states: "Unless the study region was determined a priori, this "area" input is arbitrary, making the tool practically worthless--and even deceiving". This is advice work heeding. The nearest neighbor index is the ratio of the observed and expected mean neighbor distances. The expected is a function of the area and the number of observations. ...

3

In ESRI parlance, systematic quadrats are referred to as a fishnet. You can perform the analysis you are describing by first using the "Arctoolbox > Data Management Tools > Feature Class > Create Fishnet" tool. You will have to define the parameters of the quadrat sizes as there is no sensible default. Once you have the fishnet polygons created you can ...

4

You should look at the output. In the toolbox window click on the results tab at the bottom (and if necessary, uncollapse the Average Nearest Neighbor entry). The NNI ratio, p value, expected and observed are all reported. You need to interpret the actual statistic and not rely in ESRI's GUI interpretation. A random or uniform distribution would be near ...

3

You cannot use the Moran's I on an unmarked process. The values, at each location, are what the statistic is based on and therefore cannot be absent or uniform. Your only real option, in ArcGIS, for evaluating the spatial process (dispersion/clustering) of an unmarked point process is the Nearest Neighbor Index (Average Nearest Neighbor Tool).

6

The Modifiable Aerial Unit Problem (MAUP) is a change of support issue associated with arbitrary aggregate units. Two classic examples are census tracks and wildlife game units. These have been found to be arbitrary political units and the underlying statistical response in demography acts independent of the unit. Because of this, the unit is not an accurate ...

2

Open your table in ArcMAP, click the button in the upper left corner of the table and select "create graph", plot k against distance as a point or line graph. This could also be done in a spreadsheet program.

2

You should read the Gong (2010) paper "Clarifying the Standard Deviational Ellipse" which describe the geometry behind this statistic. And no, the standard deviation ellipse is not necessarily an ellipse but, rather a curve function. Folks need to quit paying attention to ESRI and start reading the primary literature! Gong, J., (2010) Clarifying the ...

0

Try the Normalized Perimeter Index (http://clear.uconn.edu/tools/Shape_Metrics/). The normalized perimeter index uses the equal area circle to normalize the metric. Thus the formula is effectively (in Python, import math) normPeriIndex = (2*math.sqrt(math.pi*Area))/perimeter For your example: Polygon 1: Normalized Perimeter Index = 0.358 Polygon 2: ...

1

I understand you want mode and not mean, median or count ? The following query should works : SELECT polygon_id, val AS mode, MAX(cval) AS max_val_count, geometry FROM ( SELECT polygon_id, val, count(*) AS cval, geometry FROM ( SELECT polygon_layer.id AS polygon_id, point_layer.id AS point_id , ...

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