Raster objects have minimum and maximum (as well as mean and standardDeviation) properties that can be accessed in the raster calculator.
("raster" - "raster".minimum) / ("raster".maximum - "raster".minimum) * 100
This will work as long as you have already calculated statistics for the raster, otherwise it will fail as "raster".minimum will return None.
The best explanation I came across is this:
the ratio of the difference of the red and infrared radiances over their sum as a means to adjust for or “normalize” the effects of the solar zenith angle. Originally, they called this ratio the “Vegetation Index” (and another variant, the square-root transformation of the difference-sum ratio, the “Transformed ...
The builtin python min() and max() functions operate only on python iterables (i.e lists, tuples, etc.) and return the smallest/largest item in the iterable. They do not return the minimum/maximum value of a Raster object which is why a TypeError was raised.
You need to use the Raster.minimum and Raster.maximum properties.
An option to normalize* LiDAR point clouds (and keep it as a point cloud) is Fusion. One will need the command line ClipData together with the switches: dtm:file, which is the bare-earth model (DTM), and height.
ClipData description says:
...When used in conjunction with a bare-earth surface model, this logic allows for sampling a range of heights above ...
You could do a first loop for the min and max values using getRasterproperties. Example for the max value below (min can be done in the same loop).
for raster in rasters:
max_val_tmp = int(arcpy.GetRasterProperties_management(raster, "MAX").getOutput(0))
if max_val_tmp > max_value:
max_value = max_val_tmp
This is exactly what a white top-hat transform does. This is a simple mathematical morphology operator that involves differencing the original point elevations from the elevations derived from an erosion operation (minimum filter) on the point cloud, followed by a dilation operation (maximum filter). The following is an example of a point cloud for a ...
Since the question asks about USA....
In Sequoyah County, Oklahoma, roads in rural areas have numbers proportional to their distance from the county boundary. At intersections, a road may have the number on some street signs and the name on others. People and businesses may use either name or number. I have a mapping app which shows the road's number on ...
There are numerous Vegetation Indices that have been created in order to understand different biological properties of plants from remotely sensed data. A major indicator of plant health is the amount of chlorophyll contained within its leaves, since chloroplasts (which contain a majority of a leaves chlorophyll) is the primary site of photosynthesis. Very ...
If I have interpreted your question correctly, I believe that what you are trying to do is to remove the underlying ground surface from your digital surface model (DSM). I would recommend using a white top-hat transform for this operation, rather than subtracting a median filtered DEM. You can perform this operation in QGIS using the Whitebox for Processing ...
The data, drink driving incidents per postcode, comprises information of very different types. Some of which correlate, and some considerations arise:
You are dealing with absolute values (number of incidents). You may prefer relative ones: Like a percentage of cars with drunken drivers with respect to the total number of cars on the road. I think the goal ...
Two options to start with:
Use proportional / graduate symbol map of raw values.
Find a denominator for your numerator. That could include population count (startup per capita?), number of working adult population (labor force), number of all companies existing in given spatial unit or number of startups of certain category among all startups to name the ...
No, GWR is a frequentist, linear method but there is flexibility in how you define the distributional form of the model (ie., Poisson, Binomial, Gaussian). The GWR is a local regression that emphases 2nd order variation whereas OLS is a first order model. The general motivation in running both is to draw inference about first (global) and second (local) ...
The Rescale by function tool lets you rescale raster files, including performing basic normalization/standardization using a linear function.
The Calculate statistics lets you find the minimum and maximum inside the tool, and automatically populate the values.
There are no min or max functions that you can call directly in the raster calculator. You have to provide the raster statistics (min and max). You can get the global raster statistics by right clicking on the raster in the TOC and selecting properties. In the resulting window select the "source" tab and scroll down in properties. Use these minimum and ...
Landcover classified image values (discrete classes) could be scaled from either 0-1 or 0-255 (continuous scale) by Ranking them in accordance to their relative importance for particular purpose. In may case landslide susceptibility increases where there are barren land so Ranking order should be descending (Barren Class at the top i.e., 1). After Ranking, ...
Are you sure you wish to use a choropleth map? It is probably the most overused and inappropriately used thematic mapping technique. It is ideally used to show ratio statistics where the size of the regions (states) are sized in proportion to the denominator of the ratio statistic. If the statistic is total something per unit area then go ahead and let each ...
filters.height has been replaced by filters.hag so if you want to find the Height Above Ground (HAG) in point cloud data use filters.hag:
"dimensions":"HeightAboveGround = Z",
Yes, addresses can have numbers in the street name. e.g.
Grand Central Terminal, 89 E 42nd ST, New York, NY 10017
Carnegie Hall, 881 7th AVE, New York, NY 10019
Washington Monument, 2 15th ST NW, Washington, DC 20024
In your case atxgis is likely right in that you have a suite number. Your given example lends itself to the impression that there's a ...
The following procedure seems to fit the problem:
1. Transform all trips in a way that origin and destination are "on the equator" of the globe.
This is done to avoid the later distortion when applying Mercator transformation. It is important to do this in a way that origin is always on the same side of destination, e.g. left.
This seems to be the hardest ...
You could try using azimuthal equidistant, where the projection center is the origin. Distances are maintained from the center point so you'll have some distortion in the intermediate steps.
There's not an easy way to set the target point to 1,0 though. Possibly try using two-point equidistant, or rectified skew orthomorphic (RSO). Depending on the ...
You might want to consider using COUNTY PARCEL DATA if it is available. It should contain land, house and total market values by individual parcel. That way you can create a chloropleth map and use that market value data and categorize that anyway you want.
I'll be up front and say that I haven't applied much thought to this, so there's probably more elegant, and efficiant methods than this. Also, you haven't specifed using scripts/models for this, so I'll assume you're doing a manual job.
Start by performing an Intersect between your polygon (district) and point (complaints) data, with the output type to be "...
Besides adding the ground reference for normalizing the point cloud, there were other issues I had to address. They were:
bad scale factors x y z: 0.000000335839844, 0.000000499992188, 0.000000037939995 and bad offsets x y z: 31515000, 5686500, 42.900001525878906 of points, where scale factors had sub centimeter resolution.
So, before normalizing, I had to ...
Note the correct notation is -replace_z with an underline "_" before z (not -replace z).
Certify what you are using as the reference for normalization; i.e.; you need something to subtract from the point's elevation: usually a DTM (raster), but lasheight will accept the own point cloud if it is already classified regarding ground points (class = 2), or a ...
If you're working in python, you should operate on a numpy array:
# load data, convert to array
orig_raster = arcpy.Raster('path/to/project/dem.tif'
array = arcpy.RasterToNumPyArray(orig_raster)
# do your math
new_array = (array - array.min()) / (array.max() - array.min()) * 100
# back to a raster
new_raster = arcpy....
As pointed out by @mdsumner, You can read a SpatialGridDataFrame directly using readGDAL in the rgdal package. You can also easily coerce raster objects to a SpatialGridDataFrame or matrix. If your imagery is in separate files I would recommend reading them in as a stack and then coercing to a SpatialGridDataFrame.
# Using ...
The steps for normalizing point clouds in LiDAR360 are as follows:
(1) Remove outlier points to improve the quality (Data Management > Point Cloud Tools > Outlier Removal);
(2) Classify ground points (Classify > Classify Ground Points);
(3) Generate DEM (Terrasin > DEM);
(4) Normalize the point cloud data based on DEM file (Data Management > Point Cloud ...