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8

An elegant principle provides a simple answer: All points on a smooth curved surface are flat at a sufficiently large scale. This means that after affine change of coordinates (usually involving just a rescaling of one of them), we can use formulas of Euclidean geometry, such as the Pythagorean Theorem for computing distances and the ...


7

It is a consequence of a theorem of Archimedes (c. 287-212 BCE) that for a spherical model of the earth, the area of a cell spanning longitudes l0 to l1 (l1 > l0) and latitudes f0 to f1 (f1 > f0) equals (sin(f1) - sin(f0)) * (l1 - l0) * R^2 where l0 and l1 are expressed in radians (not degrees or whatever). l1 - l0 is calculated modulo 2*pi (e.g., -179 ...


3

This is how you can do that with R/raster library(raster) r <- raster() # by default 1 by 1 degree, just what you want a <- area(r) Each cell of RasterLayer 'a' has a value representing its approximate area To illustrate the results for one column (it is the same for all columns), as area varies by latitude, not by longitude lat <- yFromRow(r, ...


3

I would convert the GRID to ASCII format first using Raster to ASCII (Conversion). If you have many GRIDs, use the batch conversion by right-clicking on the tool and selecting "batch". Then you can easily bring in the ASCII files into MATLAB using arcgridread. Here's an example from Mathworks: [Z,R] = arcgridread('MtWashington-ft.grd'); ...


3

Your projection is likely to be the World Mercator projection. (EPSG 3395) You can use the package proj4 in R to transform your coordinates. It is also supported by Matlab (as you can see here). projection = +proj=merc +lon_0=0 +k=1 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs inverse=true to go from cartographic coordinates to geographic ...


3

Some (slightly) theoretical pointers: Instead of focusing on attributes, one approach to the problem might focus on exploring characteristics of movement patterns. Those could be explored by calculating aggregated characteristics of movement or dividing your data into logical 'chunks' (for instance, daily trajectories of certain objects). At next stage you ...


2

I suggest you learn the differences between aerial and satellite images first. They are not the same! The image geometry is different and therefore you would have to have different processing. Stereo images are an advantage, yes, but quite expensive. Lidar is expensive too and you cannot cover a huge area. What are your requirements; height accuracy, ...


2

Assuming you're using ArcGIS 10, you can use the arcpy RasterToNumPyArray command to get a NumPy array, which if you read the NumPy Input and Output routines documentation you can see you can easily dump the raster data to disk as a text file with a format of your choice. For example: import arcpy import numpy as np arr = ...


2

If you have gdal command line setup you can try this: gdal_translate -outsize xsize[%] ysize[%] <src_dataset> <dest_dataset> Example: creating 25% of original image. gdal_translate -otusize 25% 25% input.tif output.tif ......


1

You may achieve the desired result by going to your raster layer properties, pressing the plus (+) sign to add the deired color, and then setting the transparency percent: You can see, in may example, how the raster target color transparency works: Before (no white color transparency / or the transparency percent for the white color = 100%) After ...


1

You can add the matrices as ASCII Grid file to QGIS, and create contours from the lat and lon values. This will create a degree grid like this: In this mailing list topic I described how to set up a suitable projection for it: http://osgeo-org.1560.x6.nabble.com/Have-LCC-center-terms-need-PROJ-4-terms-td5102799.html It is also possible to create a VRT ...


1

A similar question has actually been posed on stackoverflow before and is answered there (with matlab): http://stackoverflow.com/a/702174/545346 Edit: I was actually thinking about writing something about spatial indexing, but I then thought it was not necessary since you spoke about 12 edges. A logical approach would be a filter using the distance of both ...


1

You can find some MATLAB scripts to import openstreetmap files here: http://www.mathworks.com/matlabcentral/fileexchange/35819-openstreetmap-functions these include functions for plotting the map and network, as well as a routing example.


1

This can be accomplished in the same way we would lay out a grid of squares in the plane: Lay off a baseline in any direction. Mark it off in equal increments (of 20 km). Call these points p(0,0), p(0,1), ..., p(0,n), in order. It does not have to be geodesically straight, but it should be close to straight. Starting with the first marked point p(0,0), ...


1

Matlab has a function to read in shapefiles. Census data is in a geographic projection which is not terribly useful for distance calculations. You may want to use OGR to project it to a suitable coordinate system, first.


1

In documentation there is this page it can interesting you with this paragraph: You can draw Marker Using Custom Icon and Description Data: Define location. lat = 42.299827; lon = -71.350273; Specify text in description balloon and name. description = sprintf('%s<br>%s</br><br>%s</br>',... '3 Apple Hill ...


1

There looks to be a related question and answer over at StackOverflow that incorporates what looks like a solution to this question. It suggests that shapefile may be the easiest format to use.


1

Google T&C doesnt allow to read the satellite images using any external tools. It doesnt matter if you do it with or without google's knowledge. Its illegal. you can take a screenshot of the satellite maps from browser but then its an digital image not a Geospatial data. you wont have any spatial information from it. KML/KMZ gives you data in vector ...


1

Given you've tagged R in your question I'd take a look at the CRAN Spatial View and the gstat package in particular. Not sure if it has the exact PRISM interpolation method, but it would certainly be a good place to start. Alternately you could look into the SciPy interpolation and ndimage subpackages if you'd prefer to work in Python. Good luck!



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