University of California, Los Angeles Department of Statistics. More on digitizing Random sample from a raster map Data interpolation

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1 University of California, Los Angeles Department of Statistics Statistics C173/C273 Instructor: Nicolas Christou More on digitizing Random sample from a raster map Data interpolation As mentioned in a previous handout, digitizing an area requires more work. The figure below shows the area of UCLA that was digitized as follows: We click on Digitize new boundary and set mode to No category. Then define the boundary with the mouse and come back and click on Digitize new centroid and set mode to Manual entry. Finally click anywhere inside the boundary. However, it is very difficult to be able to close the boundary completely as shown below (zoomed north west part of UCLA). GRASS assumes that this a boundary and not a closed area. This is because the starting and ending point (nodes) did not match. In order to fix this problem we use the module v.clean and select snap and break as shown in the figures below. 1

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4 As a result we can see the digitized area of UCLA in the figure below: We can also display the area using the GIS Manager, where we can change the color of the area as shown below: 4

5 Random sample from a raster map We will use the raster map elevation from the PERMANENT mapset of the North Carolina LOCATION nc spm 07. From this map, suppose we want a random sample of 100 points. Here is the command: v.random out=sample_100 n=100 A vector map is created (sample 100) which consists of 100 points with their coordinates. Here is the vector map: We want now to assign the elevation to each one of these points. Remember: the previous command only gives you the coordinates of the points. In order to assign the elevation to each point, we have to create an attribute table with one column (let s name it el). Here is the command: v.db.addtable map=sample_100 columns="el double" This command links the vector map sample 100 to an attribute table that has one column (el). If needed, more columns can be added. The last step is to transfer the elevations from the raster map elevation to the random points of the vector map sample 100 through the attribute table. Here is the command: v.what.rast rast=elevation vect=sample_100 col=el To verify we can type the following command: v.db.select random This command prints the attributes of a vector map. Below we see the first few lines of the attribute table (there are 100!). 5

6 GRASS 6.3.cvs (nc_spm_07):~ > v.db.select sample_100 cat el You can also use the d.what.vect command and with the mouse you can get information about the elevation of individual points. You can also use v.digit -n map=sample 100 which will bring you to the digitization mode where you can get information about individual points, see the attribute table with the column el, etc. Here a screenshot: 6

7 Importing data points from an ascii file A useful way to import data as a vector map is described below. Suppose we want to import the data shown below: A B C D E 3.1 The points are identified by their coordinates (easting and northing), and there are two columns of data. We want to import these data into GRASS as a vector map through the MAPSET a1 of LOCATION westwood2. At the GRASS terminal type: v.in.ascii and you will see the window below: Choose the file that contains the data (here the filename is west coord5.txt. We give a name to the output vector map (points 5). The field separator is. Then click on columns located at the top of this window. You will see the next window: 7

8 In this window, we want to define the columns which are (in our example): x and y coordinates (columns 1 and 2 - double precision), name (column 3 - character), and value (column 4 - double precision). The result is shown below: Notes: We can also import data points from a spreadsheet program by saving the spreadsheet as a comma separated (.csv) text file, and then using the field separator fs=,. GRASS automatically creates an additional column named cat that contains the ID number for each point (1, 2, 3...) when we set cat=0. 8

9 Data interpolation - Inverse distance weighted (IDW) Suppose we have the following 35 data points from the westwood.green done map, (regular grid here), with the corresponding values as shown below: A F K P U Z AE B G L Q V AA AF C H M R W AB AG D I N S X AC AH E J O T Y AD AI Location A B C D E F G H I J Count Location K L M N O P Q R S T Count Location U V W X Y Z AA AB AC AD Count Location AE AF AG AH AI Count We first digitize these 35 points to create a new vector map (west100). During digitization we have created an attribute table that has two columns. First column: name of the location (here A, B,, AI). Second column: count (the value of the variable of interest). Please see previous handouts on digitization. As a reminder at the command line we type: v.digit -n map=west100 bgcmd="d.rast westwood.green_done" Once we are done with digitization we can display the 35 points using the GIS Manager and the Map Display. 9

10 The screenshot is shown below: Or if we just want to display the points: 10

11 Using these 35 points, their locations, and their values, we can interpolate and find values of other locations. The method used is the Inverse Distance Weighted (IDW). The idea is that the point searched for, will have a value that is determined by some of the 35 points surrounding it. The result is a weighted average, where the weights are 1 d i, or 1, or 1, d 2 i d 3 i where d i is the distance from known point i to the point s being interpolated. z s = ni=1 z i d i ni=1 1 d i Here is the command: GRASS 6.3.cvs (westwood2):~ > v.surf.idw And here is the GRASS module: 11

12 The result is a raster map (west200), that can be displayed through a monitor or using the GIS Manager and the Map Display as shown below: 12