I have analysed tree core images through the raster package in an attempt to perform image analysis. In the image:
http://dx.doi.org/10.6084/m9.figshare.1555854
You can see the measured "vessels" (black and numbered) and also annual lines (red) which have been drawn using the locator function and represent each year of growth of the tree core.
By generating a list of the maximum y coordinates of each annual line I have been able to sort the vessels into years for this image. Which is what I am looking for. However, it has occurred to me that in reality things can get a little more difficult as seen in the next image:
http://figshare.com/articles/Complicated/1555855
The approach above will not work on this image as vessels from each year overrun so using the maximum y coordinates will not return the correct result.
So can anyone suggest another approach which may overcome this limitation? I have thought about using spatialpolygons but not sure this will achieve what I am looking for.
If you are creating the lines by clicking on the plot, you can use raster function drawLine or, for polygons, drawPoly. You could rasterize the polygons and mask that with the original image to get the vessels grouped by polygon (year).
Related
I have gone through few tutorials and answers here in stackoverflow such as:
Overlap image plot on a Google Map background in R or
Plotting contours on an irregular grid or Geographical heat map of a custom property in R with ggmap or How to overlay global map on filled contour in R language or https://blog.dominodatalab.com/geographic-visualization-with-rs-ggmaps/
They either don't serve my purpose or consider the density of the data to create the image.
I am looking for a way to plot contour on a map of a certain data, and would expect the image to look something like this:
or something like this taken from https://dsparks.wordpress.com/2012/07/18/mapping-public-opinion-a-tutorial/:
I have a data here that gives a contour plot like this in plot_ly but i want this over the map given by latitudes and longitudes.
Please guide me on how this can be done. Any links to potential answers or codes would be helpful.
Ok I did some digging and figured that to plot the data -which in this case are point values randomly distributed across the Latitude and Longitude, one has to make it continuous instead of the discreetly distributed one. To do this I interpolated the data to fill in the gaps, this method is given in Plotting contours on an irregular grid and then take it from there. Now the interpolation here is done using a linear regression, one can use other methods such as IDW, Kriging, Nearest Neighbourhood etc for which R-packages are easily available. These methods are widely used in climatology and topographic analysis. To read more about interpolation methods see this paper.
I am trying to render some geographic data onto the map in Tableau. However, some data points located at the same point, so the shape images of the data points overlaps together. By clicking on a shape, you could only get the top one.
How can we distinguish the overlapped data points in Tableau? I know that we can manually exclude the top data to see another, but is there any other way, for example, make a drop down list in the right click menu to select the overlapped data points?
Thank you!
There are a couple of ways to deal with this issue.
Some choices you can try are:
Add some transparency to the marks by editing the color shelf properties. That way at least you get a visual indication when there are multiple marks stacked on top of each other. This approach can be considered a poor man's heat map if you have many points in different areas as the denser/darker sections will have more marks. (But that just affects the appearance and doesn't help you select and view details for marks that are covered by others)
Add some small pseudo-random jitter to each coordinate using calculated fields. This will be easier when Tableau supports a rand() function, but in the meantime you can get creative enough using other fields and the math function to add a little jitter. The goal here is to slightly shift locations enough that they don't stack exactly, but not enough to matter in precision. Depends on the scale.
Make a grid style heat map where the color indicates the number of data points in each grid. To do this, you'll need to create calculated fields to bin together nearby latitudes or longitudes. Say to round each latitude to a certain number of decimal places, or use the hex bin functions in Tableau. Those calculated fields will need to have a geographic role and be treated as continuous dimensions.
Define your visualization to display one mark for each unique location, and then use color or size to indicate the number of data points at that location, as opposed to a mark for each individual data point
I have asked this question in the GIS part of stack exchange https://gis.stackexchange.com/questions/95265/r-how-to-create-a-pre-determined-number-of-identical-square-polygons-to-use-fo - I am asking it here as well as it has also topics of wider interest (e.g. calculation of density) - I hope not to be penalised for this! :)
I am trying to plot crime data density (again!) over a city map, say of NY. As a well known problem there are plenty of examples on this (http://www.obscureanalytics.com/2012/12/07/visualizing-baltimore-with-r-and-ggplot2-crime-data/). These methods plot the crime density through isoclines, while I need to represent it through identical density squares of a pre-determined area (and the area / side length may change from one iteration to the other). This is actually done in commercially available COTS packages like PredPol (see http://www.predpol.com). The reason for representing crime density through squares is that the square are the hotspot areas to be patrolled. The size will influence the overall amount of police people required.
This is what I am trying to achieve:
I would like to be able to create identical square polygons with pre-determined size to overimpose to the map (is it a raster? apologies but I've just started to learn to spell GIS!)
I would like to use the above squares as items to colour as in a choropleth map (i.e. different colouring in relation to frequency of crime in the area), probably using ggplot2 or similar.
This should allow me to see how the density of crimes per square kilometre varies changing the size (i.e. the area) of the square, proposing different patrolling areas.
I do not have a clue if it is possible to use R to create regularly shaped squares polygons of a pre-defined size to use for this (as the code snipped below attests). Any help or links to examples are welcome.
I would be glad to get some indication on alternative ways to calculate the density. I have used the stat_density2 (part of ggplot2) but maybe there are better / faster ways?
(
In hindsight, do I need a density function at all? I just need to count the crimes in a cell and colour-plot it accordingly...)
This is where I got to:
library(rgdal)
library(raster)
library(sp)
#NY boroughs shapefile downloaded from NY website
shp <- readOGR(dsn = "nybb_14a_av", layer = "nybb")
r <- raster(extent(shp))
res(r)=0.05
# using BoroCode as an experiment...
r <- rasterize(shp, field="BoroCode", r)
plot(r)
plot(shp,lwd=10,add=TRUE)
#don't know the result of the above: the laptop basically hangs processing
#plot(r) :)
Here is what I need: I have an image and want to plot on specific rectangle-shaped parts of it (e.g., imagine having a picture of a chessboard and wanting to fill every square with a different color). I would like to be able to easily specify the coordinates for these parts and take these coordinates into R for plotting.
I don't have any experience with making such plots. I've thought of simply inserting an image into a plot with rasterImage (), then plotting with polygon (), but the task of setting up the coordinates for the polygon function seemed too time consuming - hence the question above.
If you have any better ideas than using a set of coordinates for the polygon function, please share. Any leads or packages suggestions would also be helpful.
thank you. Marko.
I have a scanned map from which i would like to extract the data into form of Long Lat and the corresponding value. Can anyone please tell me about how i can extract the data from the map. Is there any packages in R that would enable me to extract data from the scanned map. Unfortunately, i cannot find the person who made this map.
Thanks you very much for your time and help.
Take a look at OCR. I doubt you'll find anything for R, since R is primarily a statistical programming language.
You're better off with something like opencv
Once you find the appropriate OCR package, you will need to identify the x and y positions of your characters which you can then use to classify them as being on the x or y axis of your map.
This is not trivial, but good luck
Try this:
Read in the image file using the raster package
Use the locator() function to click on all the lat-long intersection points.
Use the locator data plus the lat-long data to create a table of lat-long to raster x-y coordinates
Fit a radial (x,y)->(r,theta) transformation to the data. You'll be assuming the projected latitude lines are circular which they seem to be very close to but not exact from some overlaying I tried earlier.
To sample from your image at a lat-long point, invert the transformation.
The next hard problem is trying to get from an image sample to the value of the thing being mapped. Maybe take a 5x5 grid of pixels and average, leaving out any gray pixels. Its even harder than that because some of the colours look like they are made from combining pixels of two different colours to make a new shade. Is this the best image you have?
I'm wondering what top-secret information has been blanked out from the top left corner. If it did say what the projection was that would help enormously.
Note you may be able to do a lot of the process online with mapwarper:
http://mapwarper.net
but I'm not sure if it can handle your map's projection.