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Both forms rely on Toblers first law of geography: things that are close are more related than things that are further apart.
IDW is the simpler of the two techniques. It involves using known z values and weights determined as a function of distances between the unknown and known points. As such in IDW points that are far away have far less influence than ...
30
When you use "default values" you aren't really kriging, you're just applying the kriging algorithm--which as you have found, is poor when used with these data.
(I will step up on a soapbox for a brief rant: in my opinion, the fastest way to get bad results with a computer program is to accept its default parameters. ArcGIS is one of the richest, most ...
24
Aerial photos are continuous data. Each pixel represents the response of a region of a sensor to light directed at it and as that light varies, the response varies continuously. The result is usually discretized (often into 255 or 256) categories, but that doesn't change the nature of the data. Therefore you want to interpolate rather than using ...
18
I'd take a look at NumPy and Scipy - there's a good example of interpolating point data in the SciPy Cookbook using the scipy.interpolate.griddata function. Obviously this requires that you have the data in a numpy array;
Using the GDAL python bindings you can read your data into Python
using gdal.Dataset.ReadAsArray() for a raster.
With OGR you would loop ...
13
In order to interpolate prices with kriging you first need to convert your geographic coordinates to projected coordinates. Assuming you have them, below there is a reproducible example, showing a way to accomplish such task.
library(sp)
#Spatial data containing variables which can be interpolated.
#We will use the zinc column; as an equivalent for 'price'....
12
Have a look at the GDAL gridding API. I don't know if that is exposed in the Python bindings, but if not, you call call the gdal_grid utility via the subprocess module.
GDAL grid API only uses Inverse Distance Weighting, Moving Average and Nearest Neighbour, it doesn't implement splines. Another option is to use Scipy.
12
hallo
If your original table is called gps_p, your timestamp field is called ts and the points is called th_geom:
SELECT (geom).geom, ts1 + (((geom).path[1]-1) ||' seconds')::interval FROM
(SELECT ts1, ST_DumpPoints(ST_Segmentize(geom, ST_Length(geom)/5)) as geom FROM
(SELECT ts1, ST_LineFromMultipoint(ST_Union(geom1, geom2)) as geom FROM
...
12
This error is commonly returned because you you have duplicate locations. You can check this using the sp::zerodist function.
To remove duplicate locations you call zerodist within a bracket index.
WeatherData <- WeatherData[-zerodist(WeatherData)[,1],]
11
You may take the elevation-temperature relationship into account, especially in mountainous areas. Co-kriging or splines interpolation (e.g., 3D splines as supported by GRASS GIS) can be used for this.
For larger areas further variables may play a role: distance from the sea, latitude, etc.
Update: a reasonable method may also be multiple regression, for ...
11
IDW works by finding the data points located nearest each point of interpolation, weighting the data values according to a given power p of the distances to those points, and forming the weighted average. (Often p = -2.)
Suppose there is some amount of distance distortion around an interpolation point that is the same in all directions. This will multiply ...
10
Assuming you have Spatial Analyst license:
Interpolation: Spatial Analyst Tools -> Interpolation -> IDW
('Z value field' parameter will be your 'P' field).
Reclassification of interpolated surface into 3 zones: Spatial Analyst Tools -> Reclass -> Reclassify
Specify interpolated surface and ranges of desired classes: 0 - 5,...
Now you have a raster which ...
10
When you run geoprocessing operations in ArcMap (e.g. tools from the ArcToolbox pane) they conform to two sets of parameters. First are the parameters in the window itself, e.g. input file, output file path, etc. Second are the parameters in the Environment Settings window (see below).
These Environment Settings let you fine-tune your geoprocessing ...
10
This is not a pure GIS problem. When interpolating soil depths, you need to apply the GIS techniques on the basis of a geomorphological hypothesis. In other words, what is the geological history of the area you are mapping? What are the soil formation process in operation? Are there landscape features that limit the processes (eg. barriers such as rivers, ...
10
It appears this question is related to an earlier one that asks about disguising such data using an irregular grid. If we accept that a regular grid will be used, then it seems that
Most cells should be large enough to cover five or more buildings and
When cells do not cover five buildings, their values should be changed in unpredictable (but controlled) ...
10
One alternative is spline interpolation as suggested in the related post:
Interpolation of multibeam bathymetry.
From QGIS, use the GRASS tool v.surf.rst:
Performs surface interpolation from vector points map by splines.
Then, you can test different types of parameterization available within the tool. There is an option to apply a leave-one-out cross ...
9
Both of these solutions require extensions to the standard ArcGIS desktop, such as Spatial Analyst.
If you don't want to interpolate between points, the solution is easy. Simply use the Conversion Toolbox > To Raster > Points to Raster tool to create a raster of your points. You can use the Cell Size field to determine the size of the grid, and you can ...
9
I want to give you some hints about the differences in the methods.
More information can also be found on the esri help pages An overview of the Interpolation toolset
Because your variable (sunshine) depends on a second variable (level of pollution) Kriging may be a good method. You can use your second variable as an “external drift”. Kriging requires very ...
9
It is partially explained here http://www.gistutor.com/quantum-gis/20-intermediate-quantum-gis-tutorials/51-inverse-distance-weighting-idw-interpolation-using-qgis.html by first showing examples of using coefficient values of 1 and 3, and then
As you can see, a larger coefficient means it takes a larger distance for the values of the surface to become ...
8
You have two tasks: (a) find the grid data for the vertices of a grid cell in which a GPS location (x,y) lies and (b) interpolate those data.
An efficient way to accomplish (a) is to store the grid as an array with a single index. Often grids are stored by rows with data progressing left to right within each row. A grid is determined by the coordinates of ...
8
<begin shameless plug>
In 2008 I wrote "Tapping Into the Power of Python: Data mining, data processing, logging, and e-mails" for ArcUser. Here are the code listings - some of the code is outdated, but the concepts could be helpful.
<end shameless plug>
8
From the website of ESRI:
ArcGIS Geostatistical Analyst complements Spatial Analyst. Most of the
interpolation methods available in Spatial Analyst are represented in
ArcGIS Geostatistical Analyst as well, but in Geostatistical Analyst,
there are many more statistical models and tools, and all their
parameters can be manipulated to derive optimum ...
8
There are many algorithms of interpolation, each one with its characteristics. If you want to limit the results to the original values, you need to choose:
the TIN algorithm (with GRASS GIS: v.delaunay + tin.to.raster.py of Antonio Alliegro)
In other cases you need to use a raster MASK (see Restricting Tin interpolation in QGIS or Raster Masks in QGIS/...
8
In a nutshell, the problem lies in a mismatch between data behavior and some (strong) assumptions you are implicitly making.
Diagnosis
The strongest of these is that the data are one realization of a second-order stationary process. They clearly are not, as you can tell by comparing the region near (450000, 5075000) in the upper "neck" (which I will call "...
8
Heatmaps and interpolations are completely different things though they might look similar.
A heatmap visualizes "hotspots" in the distribution of features on the map i.e. dense areas will be highlighted in a heatmap, based on the parameters you use for processing it.
However the purpose of an interpolation is to estimate feature values at locations where ...
7
I would like to add Block Statistics as another method to alter the resolution of a raster. Depending upon your specific goals, Block Statistics allows fine control of how pixels are assigned based on:
A user defined neighborhood (e.g. rectangle, circle, wedge etc)
The type of statistics calculated within each block (e.g. mean,
majority, variety etc).
In ...
7
I would first design the schema of your desired feature class either using ArcGIS Diagrammer or through ArcCatalog. Save this in a file geodatabase which will be used solely as a template.
Next use Copy (Data Management) to copy the template file geodatabase to the desired location, renaming it if necessary.
Next, use Make Feature Layer (Data Management) and ...
7
I know you mention this in your question, but I'd be very wary of down-scaling 5km precipitation grids to 100m. Precipitation is notoriously difficult to interpolate, as so many factors (local topography, temperature etc.) affect its spatial distribution. The two methods described by Aaron and Martin will work, but I wouldn't have much faith in the output if ...
7
This is likely not entirely an issue with the interpolation model. Bathymetric data can exhibit considerable noise. Because of an equal weight associated with each TIN facet and outlier effect, A TIN base interpolation can extenuate this noise and is not recommended. I would apply a Topogrid (Topo to raster tool) Spline interpolation and then apply a ...
7
Yes, there are such algorithms (see for example here, there and here) but since you seem to have only one single road, would it not be easier to do it by hand? (!). Using for example QGIS, you could import your GPS traces, create a new layer, digitalise the centerline of the bundle, and then export it in whatever format.
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