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One possibility underlying the poor kriging performance could be the field plots (location and sampling intensity) failing to capture the spatial autocorrelation (or spatial dependence) in the data. It would be necessary to observe how well the theoretical semivariogram fitted to the data (experimental semivariogram) (Figure 1). If it is the case, one ...


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Check geosphere package distance function or fossil deg.dist function. You have data in degrees and need to translate it into meters or feet before doing clustering.


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I think that using point density would be fine (not Kernel). Use the buildings as input and set search radius to about 263.5 feet, which is the radius of a 5 acre circle (assuming you are using a circle-typed neighbourhood). Than if your units of choice are feet as well, than any value higher than 4.591e-06 will represnt an area (or a raster cell) in which ...


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You could convert your points into a raster using the option count and perform a focal analysis on this raster. See ArcGis Help here and here. Choose a circular or square type corresponding to the size of 5 acres and use sum as statistic type. The result of this focal analysis will be a raster with the density of points per 5 acres.


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If you are using ERDAS 2013, what you can do is go to raster, choose zonal change in the change detection panel, then choose image difference: Make sure you specify correctly the before map and the after map images, then you can specify directories for both outputs for the change in image and the highlighted one as shown. After specifying your threshold ...


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If you want to make sure that your polygons are evenly distributed, you can create a fishnet then select the polygons that include your points. Note that in this case, the probability to select a polygon is proportional to its size: this might be good or bad depending on our needs. My recommendation is to compute a random value (random.random() with Python) ...


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There are multiple tools that can do this. One option though is to create a new field in the layer and generate a random numbers in it, here are the steps: Create new field type Double Right click on field and select Calculate Field option For the field calculation type in rnd, and press Ok button to calculate After calculation, right click field and ...


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there are a variety of ways that your data can be visualized in CartoDB. First, of you are trying to create a heat map that shows concentrations of crimes in space, and you are working with point data, the 'heat map' option in the editor is going to be a more accurate choice for this. If you want to make a choropleth, it is important that your data is ...


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A few nice tutorials, including some with a focus on interactivity (often listed as a weakness of doing GIS with R) Spatial data in R: Using R as a GIS: A tutorial to perform basic operations with spatial data in R, such as importing and exporting data (both vectorial and raster), plotting, analysing and making maps. by Francisco Rodriguez-Sanchez Using ...



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