I have a spatial objects with multiple points (buildings) on a map. What I wanted to do is divide an area based on the nearest point. The calculated Voronoi looks quite promising, but has some strange aspects if you know the "real world". For example a small part of a district is also at the other side of a river because of the closeness (surprise).
What I want to do is combine this with a multi linestring which contains rivers, railroads. What I want to do either end the district at this line OR add a penalty for 'crossing' it. Is anyone able to shed light on the problem, or possible suggest an alternative voronoi method that works?
Related
I'm neither a geometry student or a native speaker, so apologies if my question isn't clear enough.
As part of my master's thesis, I have to plot bounded regions of the night sky onto a 2D plane. My current solution consists of a rectangular mapping where (ra, dec) values are plotted to (x,y) coordinates. While this approach works well enough for small regions in relatively low ascension values, the resulting plots get progressively distorted for higher ||dec|| values, as expected.
At some point I'll have to change this to a more versatile approach. Thing is, I'm not exactly clear on what to search for. I guess I have to be able to map angular coordinates to a square (or hexagon) subgrid, but most search results I get are concerned with full-surface mapping.
I know I won't be able to achieve a perfect, distortion-free plotting, but I don't require perfect solutions; only a more general projection that will work well near the poles. Something like this, where I put my Photoshop skills to work and try to simulate a 20ยบ region under my current approach and the one I'm looking for:
What I want:
What I have:
TL;DR: how do I convert between coordinates on a sphere (ra/dec) to cartesian coordinates on a locally-defined grid?
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
What I'm looking forward is something like this:
I want the geographical(29 states points in the case) presented on the maps, with a mass amount users out side it.
All the examples I've found with leaflet package, igraph and ggmaps package is merely edges on maps or networks beside map. The only idea I have is to give the non geographical vertices a set of restricted lat/lon coordinates, for examples throw them to the Antarctic Pole.
I think there may be some method better to solve the problems.
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 situation where I'm only concerned with a few vector layers and two-dimensional line-of-sight. I know that line-of-sight is usually performed on raster data because the typical use is topography. Because that wording is vague and close to useless here's my situation:
I have a polygon shoreline vector shapefile, a "source" point placed in the water somewhere, and a "buffer" polygon layer that represents a large radius circle around the "source" point. I'm only interested in the parts of the buffer polygon that are "within sight" of the source point. In the image below the red dot is the source, the orange polygon is the buffer clipped with the shoreline, and the yellow polygon is what I'm interested in. Even this isn't as fine as I'd like.
Image: http://i.stack.imgur.com/IKBLv.png
I want to automate the process I use now (fairly time-consuming) and would prefer to use python/numpy/scipy/OGR/GRASS instead of ESRI's stuff.
Any idea how to trace along the line and check for "visibility"? I could rasterize everything and use a traditional radial line-of-sight script within GRASS but that seems like way dealing with too much data held in memory and running checks for pixels we know wouldn't produce a collision for the intersection of a few vectors. I want to be as light as possible while maintaining the highest accuracy possible.
How about considering (iteratively) the line between your point and each point in the shoreline shapefile? If it intersects the "land" polygon (crosses over land), then that point on shore is not visible. Take all the points that are visible, and use them to form a new polygon of the visible area.