Presented by the ORNL DAAC https://daac.ornl.gov
Functions featured in this section:
rasterOptions {raster}
set global options used by the raster package
In addition to the built-in functionality of R, we will use three packages throughout this exercise. Packages are a collection of documentation, functions, and other items that someone has created and compiled for others to use in R. Install the raster, rgdal, and tigris packages, as well as their dependencies, using the function install.packages().
install.packages("raster", dependencies = TRUE)
install.package("rgdal", dependencies = TRUE)
install.packages("tigris", dependencies = TRUE)Most functions we will use are from the raster package or are included upon installation of R. Notice that we can set options for the raster package with rasterOptions(). These will help you see how long your code will take to run and help manage large objects.
library(raster)
rasterOptions(progress = "text") # show the progress of running commands
rasterOptions(maxmemory = 1e+09) # increase memory allowance
rasterOptions(tmpdir = "temp_files") # folder for temporary storage of large objects
library(rgdal)
library(tigris) # provides states() function For package details try help() (e.g., help("raster")), and to view the necessary arguments of a function try args() (e.g., args(cover)).
Functions featured in this section:
raster {raster}
creates a RasterLayer object
states {tigris}
downloads a shapefile of the United States that will be loaded as a SpatialPolygonsDataFrame object
Two GeoTiff files are needed to complete this tutorial, both from the dataset titled "CMS: Forest Carbon Stocks, Emissions, and Net Flux for the Conterminous US: 2005-2010" and freely available through the ORNL DAAC integrated web platform. The dataset provides maps of estimated carbon emissions in forests of the conterminous United States for the years 2006-2010. We will use the maps of carbon emissions caused by fire (GrossEmissions_v101_USA_Fire.tif) and insect damage (GrossEmissions_v101_USA_Insect.tif). These maps are provided at 100 meter spatial resolution in GeoTIFF format using Albers North America projection. Refer to the accompanying "README.md" for instructions on how to download the data.
To begin, be sure to set your working directory using setwd() and the filepath to where you saved the data (we use the folder "./data/").
With the raster() function, load "GrossEmissions_v101_USA_Fire.tif" and name it fire then load "GrossEmissions_v101_USA_Insect.tif" and name it insect. The contents of these two files are stored as RasterLayer objects. fire and insect are the primary recipients of our manipulations throughout this exercise.
The function states() downloads a shapefile of the United States from the United States Census Bureau. Name the shapefile myStates, and it will be stored as a SpatialPolygonsDataFrame object.
fire <- raster("./data/GrossEmissions_v101_USA_Fire.tif")
insect <- raster("./data/GrossEmissions_v101_USA_Insect.tif")
myStates <- states(cb = TRUE) # will download a generalized (1:500k) fileFunctions featured in this section:
crs {raster}
gets the coordinate reference system of a RasterLayer object
Use print() to view details about the internal data structure of the RasterLayer object we named fire.
print(fire)## class : RasterLayer
## dimensions : 32818, 59444, 1950833192 (nrow, ncol, ncell)
## resolution : 100, 100 (x, y)
## extent : -2972184, 2972216, 36233.75, 3318034 (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23 +lon_0=-96 +x_0=0 +y_0=0 +datum=NAD83 +units=m +no_defs +ellps=GRS80 +towgs84=0,0,0
## data source : C:/Users/qnw/Documents/R/edwebinar_mar19-master/data/GrossEmissions_v101_USA_Fire.tif
## names : GrossEmissions_v101_USA_Fire
## values : 2, 373 (min, max)
The output lists important attributes of fire, like its dimensions, resolution, spatial extent, coordinate reference system, and the minimum and maximum values of the cells (i.e., carbon emissions).
fire@crs## CRS arguments:
## +proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23 +lon_0=-96 +x_0=0
## +y_0=0 +datum=NAD83 +units=m +no_defs +ellps=GRS80 +towgs84=0,0,0
The above command retrieves only the coordinate reference system (CRS) of fire in PROJ.4 format. The first argument of the CRS is "+proj=" and defines the projection. "aea" refers to the NAD83 / Albers NorthAm projection, and "+units=m" tells us that the resolution of the RasterLayer object is in meters. Refer to the attributes of fire provided by print(). The resoluton of the RasterLayer is "100, 100 (x, y)" meaning that each cell is 100 meters by 100 meters.
Use the plot() function to make a simple image of fire and visualize the carbon emissions from fire damage across the forests of the conterminous United States between 2006 and 2010. According to the documentation for the dataset, gross carbon emissions were measured in megagrams of carbon per year per cell.
plot(fire,
main = "Gross Carbon Emissions from Fire Damage\n across CONUS Forests (2006-2010)",
xlab = "horizontal extent (m)",
ylab = "vertical extent (m)",
legend.args = list(text = "Mg C/yr\n", side = 3),
colNA = "black",
box = FALSE)
The spatial extent of the RasterLayer object is displayed on the x- and y-axes. All NA cells (i.e., cells that have no values) are colored black for better visualization of fire damage. The legend offers the range of cell values and represents them using a default color theme.
Let's examine the RasterLayer object we named insect. crs() retrieves the CRS arguments for insect as a Vector object. We use identical() to determine if fire and insect have the same CRS.
identical(crs(fire), crs(insect))## [1] TRUE
The CRS for the two RasterLayer objects are identical.
Plot insect but change the content for the argument "main = ", which defines the main title of the plot.
plot(insect,
main = "Gross Carbon Emissions from Insect Damage\n across CONUS Forests (2006-2010)",
xlab = "horizontal extent (m)",
ylab = "vertical extent (m)",
legend.args = list(text = "Mg C/yr\n", side = 3),
colNA = "black",
box = FALSE)
You can likely imagine an outline of the United States given the spatial data distribution of the two RasterLayer objects.
Functions featured in this section:
CRS {rgdal}
creates a CRS object using PROJ.4 arguments
spTransform {rgdal}
provides re-projection using PROJ.4 projection arguments
crop {raster}
returns a geographic subset of an object as specified by an Extent object
mask {raster}
creates a new RasterLayer object with the same values as the input object, except for the cells that are NA in the second object
Next, we reduce the size of fire and insect by choosing a smaller extent of the RasterLayer objects. Use print() to view details about the internal data structure of the SpatialPolygonsDataFrame we named myStates.
print(myStates)## class : SpatialPolygonsDataFrame
## features : 56
## extent : -179.1489, 179.7785, -14.5487, 71.36516 (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +datum=NAD83 +no_defs +ellps=GRS80 +towgs84=0,0,0
## variables : 9
## names : STATEFP, STATENS, AFFGEOID, GEOID, STUSPS, NAME, LSAD, ALAND, AWATER
## min values : 01, 00068085, 0400000US01, 01, AK, Alabama, 00, 102257320053, 10264595056
## max values : 78, 01802710, 0400000US78, 78, WY, Wyoming, 00, 92790545247, 934334983
myStates has 56 rows (features, i.e., polygons) and nine columns (variables). Notice that "NAME" shows state names as the min and max values.
For this exercise, we will focus on carbon emissions for the states Idaho, Montana, and Wyoming. We can use column referencing and indexing to select all column information contained in myStates, but for only three rows (polygons). Name the resultant SpatialPolygonsDataFrame threeStates.
threeStates <- myStates[myStates$NAME == "Idaho" |
myStates$NAME == "Montana" |
myStates$NAME == "Wyoming", ]
print(threeStates)## class : SpatialPolygonsDataFrame
## features : 3
## extent : -117.243, -104.0391, 40.99475, 49.00139 (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +datum=NAD83 +no_defs +ellps=GRS80 +towgs84=0,0,0
## variables : 9
## names : STATEFP, STATENS, AFFGEOID, GEOID, STUSPS, NAME, LSAD, ALAND, AWATER
## min values : 16, 00767982, 0400000US16, 16, ID, Idaho, 00, 214042908012, 1861273298
## max values : 56, 01779807, 0400000US56, 56, WY, Wyoming, 00, 376964956503, 3866986696
threeStates has only three rows, but the same number of columns as myStates.
What does threeStates look like plotted?
plot(threeStates)
We can get the fire and insect data that occurs "within" threeStates. First, we must confirm that the three objects share a CRS before we can "match" them on a coordinate plane.
identical(crs(fire), crs(threeStates))## [1] FALSE
threeStates does not have the same CRS as fire, so we will make a new SpatialPolygonsDataFrame object with the projection of fire using spTransform(). We also use CRS() to properly format the projection arguments of fire.
transStates <- spTransform(threeStates, CRS(fire@crs@projargs))
plot(transStates)
Plotting the new object transStates shows that the projection has changed. Notice how the orientation of the polygons has shifted to match the NAD83 / Albers NorthAm projection.
Now that our objects share a CRS, we will compare the extent of fire and transStates.
cat("fire extent\n"); fire@extent; cat("transStates extent\n"); transStates@bbox## fire extent
## class : Extent
## xmin : -2972184
## xmax : 2972216
## ymin : 36233.75
## ymax : 3318034
## transStates extent
## min max
## x -1715671 -595594.4
## y 2027604 3059862.0
fire has a much larger extent than transStates.
We will use the crop() function to reduce the extent of the two RasterLayer objects. Cropping will create a geographic subset of fire and insect as specified by the extent of transStates. We will name the new RasterLayer objects to reflect this manipulation.
# this will take a minute to run
cropFire <- crop(fire, transStates) # crop(raster object, extent object)
cropInsect <- crop(insect, transStates)Now when we plot cropFire and cropInsect, we will also plot transStates "on top" to envision how carbon emissions are distributed across the three states.
plot(cropFire,
main = "Gross Carbon Emissions from Fire Damage\n across ID, MT, WY Forests (2006-2010)",
xlab = "horizontal extent (m)",
ylab = "vertical extent (m)",
legend.args = list(text = "Mg C/yr\n", side = 3),
colNA = "black",
box = FALSE)
plot(transStates,
border = "white",
add = TRUE)
plot(cropInsect,
main = "Gross Carbon Emissions from Insect Damage\n across ID, MT, WY Forests (2005-2010)",
xlab = "horizontal extent (m)",
ylab = "vertical extent (m)",
legend.args = list(text = "Mg C/yr\n", side = 3),
colNA = "black",
box = FALSE)
plot(transStates,
border = "white",
add = TRUE)
If you look closely at the cells "outside" the boundary of the transStates polygons, you can still see cells values. That's because crop() changed the extent of the two RasterLayer objects to match that of the SpatialPolygonsDataFrame object, but the boundary of the transStates polygons are rotated to fit the NAD83 / Albers NorthAm projection and does not extend to the entire rectangular extent of the RasterLayer objects.
To remove those extraneous cell values, use the mask() function to create two new rasters, one for fire damage and one for insect damage. Note: You can use mask() or crop() in either order.
# this will take a couple of minutes to run
maskFire <- mask(cropFire, transStates) # mask(raster object, mask object)
maskInsect <- mask(cropInsect, transStates)Plot maskFire and maskInsect.
plot(maskFire,
main = "Gross Carbon Emissions from Fire Damage\n across ID, MT, WY Forests (2006-2010)",
xlab = "horizontal extent (m)",
ylab = "vertical extent (m)",
legend.args = list(text = "Mg C/yr\n", side = 3),
colNA = "black",
box = FALSE)
plot(transStates,
border = "white",
add = TRUE)
plot(maskInsect,
main = "Gross Carbon Emissions from Insect Damage\n across ID, MT, WY Forests (2005-2010)",
xlab = "horizontal extent (m)",
ylab = "vertical extent (m)",
legend.args = list(text = "Mg C/yr\n", side = 3),
colNA = "black",
box = FALSE)
plot(transStates,
border = "white",
add = TRUE)
These plots demonstrate that the extraneous cells has been removed from outside the boundary of the transStates polygons.
Functions featured in this fection:
extract {raster}
extracts values from a RasterLayer object at the locations of other spatial data
In this section, we will compare the three states by their carbon emissions from fire damage only.
We will use the extract() function to collect the cell values of maskFire where the transStates SpatialPolygonsDataFrame object overlaps the RasterLayer object on their shared coordinate reference system. The argument "df = TRUE" tells R that we want the results returned as a DataFrame object. We will use summary() to examine the distribution of cell values that we collect.
# this will take about an hour to run
val_fireStates <- extract(maskFire, transStates, df = TRUE) # extract(raster object, extent object)
summary(val_fireStates)## ID GrossEmissions_v101_USA_Fire
## Min. :1.000 Min. : 2
## 1st Qu.:1.000 1st Qu.: 27
## Median :2.000 Median : 47
## Mean :2.043 Mean : 56
## 3rd Qu.:3.000 3rd Qu.: 76
## Max. :3.000 Max. :333
## NA's :84454950
There are two columns for val_fireStates. One is ID, which corresponds with the three states; 1 = Idaho, 2 = Montana, and 3 = Wyoming. The second column is a summary of all cell values across those three states. On average, 56 megagrams of carbon per year are a result of forest destruction by fire damage for all states combined.
To look at the summary for cell values by state, we will use subset() to split the DataFrame into three. In the code below, we subest val_fireStates so that only the rows with a "1" for the ID number will be returned. We name the new object with the prefix "temp".
temp_val_id <- subset(val_fireStates, subset = ID %in% 1)
summary(temp_val_id)## ID GrossEmissions_v101_USA_Fire
## Min. :1 Min. : 2
## 1st Qu.:1 1st Qu.: 27
## Median :1 Median : 47
## Mean :1 Mean : 55
## 3rd Qu.:1 3rd Qu.: 75
## Max. :1 Max. :333
## NA's :21264586
The summary demonstrates that there is now only a single value is included in the ID column, and that the distribution of cell values has changed. This resultant DataFrame object is quite large and has more information than we need. We need only the second column and we don't care for the large number of NA's.
We will use the functions which() and is.na() to make a new object from the temporary one. We tell R that we want only the second column and the rows of temp_val_id that are not NA.
val_id <- temp_val_id[which(!is.na(temp_val_id$GrossEmissions_v101_USA_Fire)), 2]
summary(val_id)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.0 27.0 47.0 55.2 75.0 333.0
The resultant object, val_id, is a Vector object (a single column of numbers) with no NA's.
We will do the same with val_fire for the states Montana and Wyoming.
temp_val_mt <- subset(val_fireStates, subset = ID %in% 2)
val_mt <- temp_val_mt[which(!is.na(temp_val_mt$GrossEmissions_v101_USA_Fire)), 2]
temp_val_wy <- subset(val_fireStates, subset = ID %in% 3)
val_wy <- temp_val_wy[which(!is.na(temp_val_wy$GrossEmissions_v101_USA_Fire)), 2]What's the average and range of values for carbon emissions from fire damage within each state for the period 2006 to 2010?
cat("Idaho\n"); summary(val_id); cat("Montana\n"); summary(val_mt); cat("Wyoming\n"); summary(val_wy)## Idaho
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.0 27.0 47.0 55.2 75.0 333.0
## Montana
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.00 27.00 49.00 58.06 79.00 254.00
## Wyoming
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 4.00 24.00 43.00 53.46 70.00 230.00
On average, Montana has the highest carbon emissions, but the maximum gross carbon emissions from a single cell occured in Idaho.
In addition to using summary(), we can create graphs to visualize carbon emissions from fire damage within each of the three states. The function hist() plots the frequency of cell values. We will set some arguments of the plot so that we can compare carbon emissions across all three states.
par(mfrow=c(2,2))
hist(val_id,
main = "Idaho",
ylab = "number of cells",
xlab = "megagrams of carbon per year (Mg C/yr)",
ylim = c(0, 120000), # same y-axis limit for all three states
xlim = c(0, 350)) # same x-axis limit for all three states
hist(val_mt,
main = "Montana",
ylab = "number of cells",
xlab = "megagrams of carbon per year (Mg C/yr)",
ylim = c(0, 120000),
xlim = c(0, 350))
hist(val_wy,
main = "Wyoming",
ylab = "number of cells",
xlab = "megagrams of carbon per year (Mg C/yr)",
ylim = c(0, 120000),
xlim = c(0, 350))
The histogram shows the number of times (on the y-axis) each unique cell value (on the x-axis) occurs in each state. In other words, it illustrates the variation in carbon emissions from fire damage within the three different states.
Functions featured in this section:
reclassify {raster}
reclassifies groups of values of a RasterLayer object to other values
calc {raster}
calculates values for a new RasterLayer object from another RasterLayer object using a formula
Now we are going to change the values of our two RasterLayer objects using different methods.
Beginning with maskFire, we will use the calc() function to code all cells that have fire damage to be two. To use calc(), we must define a function that will detect certain cell values and change them to other values.
reclassFire <- calc(maskFire,
fun = function(x) {
x[x > 0] <- 2
return(x) })The function we defined changed all maskFire cell values that were greater than zero to be two.
Check that our reclassification of maskFire worked as expected using summary()
summary(reclassFire[])## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 2 2 2 2 2 2 115010681
Yes, all values are two or NA.
All cell values of reclassFire should be at the same locations as maskFire but with a single value.
plot(reclassFire,
main = "Locations of Forest Disturbance from Fire Damage\n across ID, MT, WY Forests (2006-2010)",
xlab = "horizontal extent (m)",
ylab = "vertical extent (m)",
legend = FALSE,
col = "red",
colNA = "black",
box = FALSE)
plot(transStates,
border = "white",
add = TRUE)
The plot of reclassFire now illustrates locations where there were carbon emissions owing to fire damaging the forest. Notice that we chose a single color to represent the presence of values using the argument "col = "red"".
Now we will reclassify all values of maskInsect that are greater than zero to be one, but instead of using calc(), we will use the reclassify() function. reclassify() uses a matrix to identify the target cell values and to what value those cells will change.
reclassInsect <- reclassify(maskInsect,
rcl = matrix(data = c(1, 285, 1),
# c(from value, to value, becomes)
nrow = 1, ncol = 3))The argument following "rcl =" tells R that values from two to 285 should be reclassified as one. Essentially, we are making the presence of insect damage equal one.
Check the reclassification of maskInsect using summary().
summary(reclassInsect[])## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1 1 1 1 1 1 113254553
All values are one or NA.
Plot reclassInsect. All the cell values should be at the same locations as maskInsect but will all be the value one.
plot(reclassInsect,
main = "Locations of Forest Disturbance from Insect Damage\n across ID, MT, WY Forests (2006-2010)",
xlab = "horizontal extent (m)",
ylab = "vertical extent (m)",
legend = FALSE,
col = "dark green",
colNA = "black",
box = FALSE)
plot(transStates,
border = "white",
add = TRUE)
The plot illustrates locations where there were carbon emissions owing to insect damaging the forest, so now the information coveyed by the maskInsect RasterLayer object is presence or absence of insect damage.
Functions featured in this section:
cover {raster}
replaces NA values in the first RasterLayer object with the values of the second
Next, we will join reclassFire and reclassInsect to form a single RasterLayer object. According to the documentation for this dataset, there are no overlapping, non-NA cells between the two RasterLayer objects. That is, if you were to combine the two RasterLayers object, a cell could take only the value provided by reclassFire (i.e., two) or reclassInsect (i.e., one), or be NA. This allows us to use the cover() function to combine objects. cover() is unique because it will replace NA values of reclassFire with non-NA values of reclassInsect.
# this will take a couple of minutes to run
fireInsect <- cover(reclassFire, reclassInsect)
summary(fireInsect[])## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1 1 1 1 1 2 112648512
The data distribution of the new RasterLayer object shows that the minimum value is now one (i.e., the insect damage value we specified during reclassification) and the maximum value is two (i.e., the fire damage value).
The plotting arguments below now reflect the "breaks" in the values we would like to see illustrated on the plot. Insect damage is displayed as green cells and fire damage as red.
plot(fireInsect,
main = "Locations of Forest Disturbance\n across ID, MT, WY Forests (2006-2010)",
xlab = "horizontal extent (m)",
ylab = "vertical extent (m)",
legend.args = list(text = " Disturbance\n", side = 3),
breaks = c(0, 1, 2),
col = c("dark green", "red"),
axis.args = list(at = c(0.5, 1.5), labels = c("insect", "fire")),
colNA = "black",
box = FALSE)
plot(transStates,
border = "white",
add = TRUE)
Functions featured in this section:
projectRaster {raster}
projects the values of a RasterLayer object to a new one with a different projection
writeRaster {raster}
writes an entire RasterLayer object to a file
Reprojecting a raster in R is different than transforming the CRS as we did with the SpatialPolygonsDataFrame earlier in the exercise. To reproject a raster we use the projectRaster() function and the CRS() function to correctly format the projection information.
# this will take several minutes to run
prjFireInsect <- projectRaster(fireInsect,
crs = CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs"))
print(prjFireInsect)## class : RasterLayer
## dimensions : 12086, 12796, 154652456 (nrow, ncol, ncell)
## resolution : 0.00126, 0.000888 (x, y)
## extent : -119.2667, -103.1437, 39.60561, 50.33798 (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
## data source : C:/Users/qnw/Documents/R/edwebinar_mar19-master/data/prjFireInsect.tif
## names : prjFireInsect
## values : 1, 2 (min, max)
Now we have a new RasterLayer object named prjFireInsect that has the standard Geographic projection with latitude and longitude expressed in decimal degrees (DD) as its CRS.
We will plot prjFireInsect with slightly different arguments than fireInsect to "zoom in" to the center of the plot. Also, we will use threeStates instead of transStates because threeStates also uses the Geographic projection.
plot(prjFireInsect,
main = "Locations of Forest Disturbance\n across ID, MT, WY Forests (2006-2010)",
xlab = "longitude (DD)",
ylab = "latitude (DD)",
legend.args = list(text = " Disturbance\n", side = 3),
las = 1,
ext = prjFireInsect@extent/1.25,
breaks = c(0, 1, 2),
col = c("dark green", "red"),
axis.args = list(at = c(0.5, 1.5), labels = c("insect", "fire")),
box = FALSE)
plot(threeStates,
border = "black",
add = TRUE)
Let's use the writeRaster() function to save prjFireInsect to the data directory. We will save the file in *.tif format so that the geographic information of the RasterLayer object is retrievable outside of R.
writeRaster(prjFireInsect, filename = "./data/prjFireInsect.tif")
file.exists("./data/prjFireInsect.tif")## [1] TRUE
According to the function file.exists(), which tests for the existence of a given file, our attempt to write prjFireInsect to our working directory was successful. Now we are able to share the RasterLayer with others or open it in another program.
Functions featured in this section:
KML {raster}
exports RasterLayer object data to a KML file
To save the final plot, we use png(). This function will open a graphics device that will save the plot we run in *.png format. We will use the function dev.off() to tell R when we are finished plotting and want to close the graphics device.
png("prjFireInsect.png", width = 800, res = 80)
plot(prjFireInsect,
main = "Locations of Forest Disturbance\n across ID, MT, WY Forests (2006-2010)",
xlab = "longitude (DD)",
ylab = "latitude (DD)",
legend.args = list(text = " Disturbance\n", side = 3),
las = 1,
ext = prjFireInsect@extent/1.25,
breaks = c(0, 1, 2),
col = c("dark green", "red"),
axis.args = list(at = c(0.5, 1.5), labels = c("insect", "fire")),
box = FALSE)
plot(threeStates,
border = "black",
add = TRUE)
dev.off()Let's also save prjFireInsect in *.kml format. KML stands for Keyhole Markup Language, a notation developed for geographic visualization in Google Earth. We'll also check to be sure the file was written to our data directory.
KML(prjFireInsect, "./data/prjFireInsect.kml", col = c("dark green", "red"))
file.exists("./data/prjFireInsect.kml")## [1] FALSE
We successfully saved the RasterLayer object as a KML file.
This is the end to the tutorial. If you liked this tutorial, please tell us on Twitter or Facebook. If you would like to make a suggestion for a new tutorial, please email [email protected].
There is a supplemental document included on GitHub that offers two additional sections, Perform a Focal Analysis and Get Cell Coordinates.