I am using R and Leaflet for R to plot 1000s of points. The raw data is imported to a data frame from SQL Server as British National Grid (BNG) coordinates (Transverse Mercator) which are then converted to lat/long using rgdal before outputting to a stand-alone html via from a Leaflet widget.
The BNG coordinates have been produced by geocoding postcodes and so, when >1 person has the same postcode the coordinate is the same.
I would like to add some random noise to the last 2-digits of each easting/northing coordinate so that all points are likely to be visible in Leaflet. What would be the simplest way to achieve this?
thanks
mike
Two solutions to overplotting are
"jittering" the points by adding some random noise to their coordinates and
adding transparency to the point color so you can see point density.
x=jitter(x), y=jitter(y) will accomplish #1.
col=scales::alpha("blue", 0.5) will accomplish #2.
Related
Hopefully the following makes sense and apologies if not!
I have a dataset of GPS locations (of various species footprints), and am measuring the distance from where each point was found to the boundary of a national park in R. Im doing this with a series of environmental factors, (roads/villages/lakes etc), and for all other enviro variables (and kml files) Ive had no problems, however when I run my park boundary data, (using a kml file read in as a spatial polygon of the national park), I keep getting negative values for all the GPS points that occur within the park boundary? (So anything within the polygon essentially). The results output I am getting shows the correct measurements from GPS points to the boundary that occur on the OUTSIDE of the boundary, (or polygon), but anything inside the park/polygon outputs as a zero value. Ive tried to reproject the polygon as just an outline and tried removing the 'fill' etc, along with a few other tricks Ive found, but no luck so far?
Am I correct in assuming the data is just not there and that I need to recreate the boarder of the park some other way? Or is it more an issue with how Im asking r to calculate the distance measurements?
Below is an example of the code I am using
KSNP_Poly <- readOGR("KSNPboundaryexport.kml")
Points <- read.csv("AllPoints.csv")
sptsPoints = SpatialPoints(Points)
plot(KSNP_Poly)
plot(sptsPoints)
apply(gDistance(sptsPoints,KSNP_Poly,byid=TRUE),2,min)
KSNPResults <- apply(gDistance(sptsPoints,KSNP_Poly,byid=TRUE),2,min)
Hope this made sense and any advice greatly appreciated!
Thanks!
Kass
In R, I am trying to create a choropleth map. I have built a database of businesses, some are part of chains (e.g. McDonalds) and others are independent. I want to calculate how many businesses are within 30km of each point on the map, but treat the different locations of chains as a single business.
For example, if you a point is:
5km from a McDonalds,
10km from Taco Bell
15km from Chick-Fil-A
20km from KFC
25km from McDonalds
35km from Five Guys
The colour will show that there are 4 fast food outlets within 30km.
I am happy to use any R package but I am mostly familiar with tmaps and ggplot2 maps.
At this stage the best approach I can think of is to create polygons for each chain and stack them as transparent layers of the same colour. I don't think this would be very efficient and wouldn't create a very nice looking choropleth.
The other answers I could find were either counting points (e.g https://gis.stackexchange.com/questions/229066/counting-how-many-times-a-point-is-inside-a-set-of-intersecting-polygons-in-r) or for GIS software.
EDIT:
I have managed to create a 30km radius from every location of every chain (using rgeos gIntersection). I now have a series of polygons.
To solve my question the additional thing I need to do is create polygons for where:
Only one polygon covers the area,
Two polygons covers the area,
etc.
To try to visual is this I used the answer from https://gis.stackexchange.com/questions/229066/counting-how-many-times-a-point-is-inside-a-set-of-intersecting-polygons-in-r
In the linked question they are trying to count how many polygons cover the numbered points (the image on the right). What I am trying to do is to create the image on the left, where there are polygons of no overlap (1), two overlapping polygons (2) and so on.
I think what you are trying to accomplish would be best approached using a raster approach rather than a chloropleth. To make a chorlopleth, you define a set of (generally irregular) polygons, summarize something within each polygon, then color the polygons based on the attributes. This would be a good approach if you wanted to say how many fast food resteraunts are within each state or county, or how many fast food joints per capita by state.
From your description, however, you are looking for how many fast food joints within a set radius for all points. This is more of a raster question, since you can represent your data on a regular grid.
The raster package is a good start for working with raster data and works well with the sf package.
You need to determine what density you need to accomplish your goal, then use this to determine the resolution of your raster. Once you've got that you can use raster::rasterize() to summarize your (I'm assuming) point data.
I'm assuming you have an object that has the locations of each restaurant, I'll call this object "points".
library(raster)
library(sf)
# create raster template with 30km resolution (I'm assuming your projection is in meters)
raster_template = raster((extent(points),
resolution = 30000,
crs = st_crs(points)
)
# rasterize your point data
r = rasterize(points, raster_template, fun = "count")
This should create a grid where each cell has the number of points within each 30km cell. You should then be able to plot the raster, but may want to either clip or mask it to just show parts that are within New Zealand
I have a csv file with longitude and latitude coordinates at various times and then another column that assigns a value from 1-10 for each of the location points. I want to create a contour map of each location and its value as a visualization. I was thinking of using mathematica but the very little programming experience I have is with python only.
You can try my implementation for geographic maps in PHP at https://contourplot.codeplex.com. It uses a delaunay triangulation and a linear transformation along the edges of the triangles. It also uses 2 colors to show the difference between local and statewide z-values of any triangles and also the triangles and isolines maps nicely with the border (from the shapefile). Some shapes with extreme concavities, holes and islands can be a problem. Another algorithm is conrec from Paul Bourke. There is also the algorithm from indiemaps blogs but only for openlayers.
Here's my problem. I want to compare the area within multiple polygons in different parts of the world. I have the longitude and latitudes for each point of each polygon. My problem is that I don't know what projection to use to get x-y coordinates from the long-lat coordinates. I know OpenStreetMap has the projectMercator() function, but areas are known to inflate quite badly with latitude. (http://en.wikipedia.org/wiki/List_of_map_projections)
--> Do you guys know of an R function like projectMercator, that doesn't have such a distortion? I've been going over different types of projections in Wikipedia, but it's very unclear to me which is best for area comparisons, and then if those projections exist in R as functions (if they don't I'm fine hand coding them, though!)
Thanks!!!
Hillary
I have a set of points like this (that I have clustered using R):
180.06576696, 192.64378568
180.11529253999998, 192.62311824
180.12106092, 191.78020965999997
180.15299478, 192.56909828000002
180.2260287, 192.55455869999997
These points are dispersed around a center point or centroid.
The problem is that the points are very close together and are, thus, difficult to see.
So, how do I move the points apart so that I can distinguish each point more clearly?
Thanks,
s
Maybe I'm overlooking some intricacy here, but...multiply by 10?
EDIT
Assuming the data you listed above are Cartesian (x,y) coordinate pairs, you can visualize them as a scatter plot using Google Charts. I've rounded your data to 3 decimal places, because Google Charts doesn't appear to handle higher precision than that.
I don't know the coordinates for your central point. In the above chart, I'm assuming it is somewhere nearby and not at (0,0). If it is at (0,0), then I imagine it will be difficult to visualize all of the data at once without some kind of "zoom-in" feature, scaling the data, or a very large screen.
slotishtype, without going into code, I think you first need to add in the following tweaking parameters to be used by the visualization code.
Given an x by y display box, fill the entire box, with input parameters [0.0 to 1.0]...
overlap: the allowance for points to be placed on top of each other
completeness: how important is it to display all of your data points
centroid_display: how important is it to see the centroid in the same output
These produce the dependent parameter
scale: the ratio between display distances to numerical distances
You will need code to
calculate the distance(s) to the centroid like you said,
and also the distances between data points, affecting the output based on the chosen input parameters.
I take inspiration from the fundamentals in the GraphViz dot manual. Look at the "Drawing Orientation, Size and Spacing" on p12.