I'm working on a large project that involves creating a node-and-link network between cities and then gridding that data onto a preset grid. I've used the R raster library for this process.
Currently, the rasterized data do not quite match up with where I expect them to be (i.e. the grid cells that are supposed to cover cities don't match up with exactly where the cities actually are).
The dark red grid cells are supposed to be on top of the black dots, but they all seem to be shifted one grid cell to the west.
I suspect that there is something I'm missing in terms of the projection and the 'crs' option in the raster library, but I'm having trouble figuring out what I did wrong, and how to fix it.
Update: I don't think I can pick out a single chunk of code that gets at the problem, but my raster process looks like this:
rasterize(SpLin, fine.raster, field=*a number*, crs="proj=longlat", background=0)
And the image came from this (slightly trimmed):
pC <- ggplot(aes(x=long, y=lat, fill=value), data=dat_grid) + # dat_grid is the rasterized data
geom_tile() + scale_fill_gradient(low="white",high="red") +
geom_polygon(data=maine_state,aes(x=long,y=lat,group=group),colour="grey",fill="white",alpha=0) +
... (trimmed)
geom_point(data=points.df,aes(long,lat),size=2) # the black dots marking the cities
Related
I’ve been following the following tutorial https://axlehner.github.io/SpatialRDD/articles/spatialrdd_vignette.html#assign-treatment for SpatialRDD using my own dataset and maps. I’ve run into a problem when attempting to use border_segment. In the tutorial the author has a cut off line he uses which describes a spatial discontinuity. In my own project I am trying to create such a cut off line using historical boundaries and by taking the intersection. I’ve been using two neighboring polygons and taking the intersection of their boundaries using st_intersection to create this cut off. However, once I reach the point where I need to use border_segment it does not work as intended.
If for instance I want 2 border segments to be created using my region, points in that region, and my cut off line, it appears border_segment creates 2 segments for each line segment of my cut off line (the cut off line I created using the intersection of boundaries). Furthermore, comparing the cut off line from the tutorial (can be loaded into R using SpatialRDD::data("cut_off.sf")) to the one I create, the one in the tutorial has one big matrix describing the geometry, while the one I create is described by many 2x2 matrices. I think this is where the problem might be stemming from, but I have no idea how to fix it. I think by finding a solution it would help to understand how to create better geographical objects using just R.
I’ve created a reproducible example to illustrate the issue:
library(SpatialRDD)
library(sf)
library(tmap)
library(spData) #loading various maps
#getting worldmap
data("world")
#changing to a localised projection system
world <- st_transform(world, "EPSG:3035")
#getting just Germany
germany <- world[world$name_long == "Germany",]
#getting just Poland
poland <- world[world$name_long == "Poland",]
#taking the union of Germany and Poland to later distribute random points in them
union <- st_union(germany, poland)
#taking the intersection of their borders, which will act as a cut off line
cut_off <- st_intersection(germany, poland)
#creating random points for exercise to work (similar code to what is done in tutorial)
set.seed(1088) # set a seed
points_samp <- sf::st_sample(union, 1000)
points_samp <- sf::st_sf(points_samp) # make it an sf object
#using border_segment to sort points into 2 border regions
points_samp$segment2 <- border_segment(points_samp, cut_off, 2)
#mapping the points in their respective border segments
tm_shape(points_samp) + tm_dots("segment2", size = 0.1) + tm_shape(cut_off) + tm_lines()
As you will see when plotting the points after creating the border segments, that there will be many border segments even though I indicated for border_segment to create only two.
I’m fairly new to R and geospatial manipulation, so maybe there is a simple solution, but I’ve searched everywhere and haven’t been able to find one. This is the first time after using stack overflow for years that I’ve had to actually post. Please let me know if you need any other information or if I was not clear enough at some point!
I am creating a map of Brazil with its state boundaries, which is straight forward to achieve using ggplot2 and geom_sf.
However, this time around, instead of color filling each state with data, I want to fill each state's boundaries with an external image (png), similar to this example of the largest employer in each state.
I have tried a few settings of geom_image and even geom_flag to no avail (hence why I am not posting any code here).
Are there any suggestions on how to approach this problem?
You can try cartography::getPngLayer/pngLayer. These two functions takes a png as input, geotag and crop it to the shape of a given polygon and creates a rasterbrick object (as a tile) that you can manipulate and plot:
https://dieghernan.github.io/cartographyvignette#png-layer
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) :)
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.
I am using R to create a floorplan of a house with several layers like below, starting from the bottom layer:
basemap: a scanned version of the floorplan which I put it at the bottom layer to aid the reading
bed: the house have several dozens of beds, scattered in different rooms of the house, they have different colours based on the characteristics of the residents
piechart: each bed has a piechart of top of it, again the piecharts are created based on the residents' other set of characteristics, some beds have piecharts, some don't.
The bed and piechart were created based on the shp file created based on the basemap (i.e. I use Mapwindow the create a vector layer, import the basemap as raster layer and put it at the bottom, then draw the beds one by one. The bed shp file is then imported into R, the bed polygons' centroid are calculated and that centroid helps to position the piecharts)
I used read.jpeg to import the basemap to imagematrix object, then use the new annotation_raster function in ggplot2 0.9 to put the basemap at the bottom map layer, since the bed layer is created based on the basemap also, the bed layer superimpose on the basemap layer perfectly in ggplot2.
I can create the map without problem - if the basemap is small enough (3000 x 3000 pixels), now I have a basemap of 8000+ x 3000+ pixels (object.size 241823624 bytes), I did not aware of the R memory issue when I was creating the shp file, the ggplot object can be compiled if I have the annotation_raster disabled, but R keeps saying that I can allocate memory with xxxMB when I try to include the basemap into the ggplot object.
I think this is nothing to do with the compression of the jpg files, as the dimension is not changed even I further compress the jpg file. But I can't resize the jpg file as my bed layer is created based on the original jpg file's dimension.
Can anyone help to shrink the size of the basemap's imagematrix, without changing the jpeg's dimension, or some other tricks to deal the R's memory limitation? Thanks.
I fixed it.
I first created a new basemap image file with width and height halved, then in the annotation_raster I did the following:
chart <- chart + annotation_raster(db$temp.basemap,
xmin=0,
xmax=basemap.xlength*2, # I stretched the image in R
ymin=0,
ymax=basemap.ylength*2) # I stretched the image in R
Now the map can be compiled within R's memory limit, the drawback I can think of is the reduce in image quality, but that is bearable, as it was 8000 x 3000 originally.