I am working with a huge area, 7 states of forest and nonforest using the NLCD data. Within some of the forested areas is a plot (this is my master's thesis I am working on). I have stumped everyone I have asked with this large dataset but we are certain there is a resolution out there. The forest/nonforest area is a signed and discrete raster. I was able to make the forested area into polygons by subsetting out the forested area. I am not able to make the nonforest area into polygons (too large). So I was trying to get point distance (the point is within the polygon) to the edge of the forested polygon. Do you have suggestions for getting the distance of a point to the forest edge?
Well, this really does depend on a couple of things; specifically, which edge do you want? Do you want to find the nearest edge, or do you have some other criteria that you want to select an edge by (for example, cardinal direction)?
If you want to find the nearest edge, you basically want to iterate across all of the line segments the polygon defines, doing a line-segment-to-point distance calculation; this will find your distance. There's a good implementation of the algorithm in Python on this question, and there's some good description of the algorithms there.
Here is some code that output the distance from a point to an edge, wether the polygon is convex or not, CCW or not. You'll have to test for all your polygons' edges. It might be a little slow for a large set of edges.
- (double) distanceFromPoint:(yourPoint)testPoint
{
double pointX = edgePointB.x - edgePointA.x;
double pointY = edgePointB.y - edgePointA.y;
double k = pointX * pointX + pointY * pointY;
double u = ((testPoint.x - edgePointA.x) * pointX + (testPoint.y - edgePointA.y) * pointY) / k;
if (u > 1)
u = 1;
else if (u < 0)
u = 0;
double x = edgePointA.x + (u * pointX);
double y = edgePointA.y + (u * pointY);
double dx = x - testPoint.x;
double dy = y - testPoint.y;
return sqrt((dx * dx) + (dy * dy));
}
if you aren't sure that the point is within the outer polygon, test that first. Then, to test for the distance to closest forest edge, you could try something like this:
http://www.bdcc.co.uk/Gmaps/BdccGeo.js
Google has a wealth of results for 'distance from point to polygon edge'
Related
I am stuck on a problem that seems easy to solve but I can't seem to pinpoint the right formula.
I have a list of hexagon groups in a cube coordinate system. I know the cube coordinates of the groups but I need to calculate the "global" coordinate of a small hexagon in a given group.
For example, in the image below, I know the coordinates for GroupA (x=0, y=0, z=0) and GroupB (x=-1, y=1, z=0). How can I calculate the coordinates of the center tile of GroupB given that each group has the same radius (in this case the radius is 1) and they don't overlap each other (let's see it as a tiling of groups starting from 0,0,0 that creates a hex grid)?
In this simple example, I know as a human being that the center tile of GroupB is (x=-1, y=3, z=-2) but I need to code that logic in a way that a computer can calculate it for any given group on the map. I don't particularly need help on the code itself but the overall logic.
In this article, the author does the opposite (going from small hexagon and trying to find its group):
https://observablehq.com/#sanderevers/hexagon-tiling-of-an-hexagonal-grid
Any help would be greatly appreciated!
Thanks!
It looks like I have found something that seems to work.
Please feel free to correct me if I'm mistaken.
Based on the article I linked in my original question, I came up with an algorithm that calculates the small hexagon central coordinates based on its higher group coordinates (in this case, I've used a group with a radius of 10). I took the original algorithm and removed the area division the author did. The code is in javascript. The i, j and k variables are the cube coordinates of the group. The function returns the cube coordinates of the central small hex :
getGroupCentralTileCoordinates(i, j, k)
{
let r = 10;
let shift = 3 * r + 2;
let xh = shift * i + j;
let yh = shift * j + k;
let zh = shift * k + i;
return {
'x': (1 + xh - yh) / 3,
'y': (1 + yh - zh) / 3,
'z': (1 + zh - xh) / 3
};
}
If I have X amount of things (lets just randomly say 300)
Is there an algorithm that will arrange these things somewhat evenly around a central point? Like a 100 sided dice or a 3d mesh of a sphere?
Id rather have the things somewhat evenly spaced like this..
Rather than this polar way..
ps. For those interested, wondering why do I want to do this?
Well I'm doing these for fun, and after completing #7 I decided I'd like to represent the array of wires in 3d in Unity and watch them operate in a slowed down manner.
Here is a simple transformation that maps a uniform sample in the rectangle [0, 2 pi] x [-1, 1] onto a uniform sample on the sphere of radius r:
T(phi, z) = (r cos(phi) sqrt(1 - z^2), r sin(phi) sqrt(1 - zˆ2), r z)
The reason why this transformation produces uniform samples on the sphere is that the area of any region T(U) obtained by transforming the region U from the rectangle does not depend on U but on the area of U.
To prove this mathematically it is enough to verify that the norm of the vectorial product
| ∂T/∂phi x ∂T/∂z |
is constant (the area on the sphere is the integral of this vectorial product w.r.t. phi and z).
Summarizing
To produce a random sample uniformly distributed in the Sphere of radius r do the following:
Produce a random sample (phi_1, ..., phi_n) uniformly distributed in [0, 2 pi].
Produce a random sample (z_1, ..., z_n) uniformly distributed in [-1, 1].
For every pair (phi_j, z_k) calculate T(phi_j, z_k) using the formula above.
Here's a three-step approach. 1a) Make more points than you need. 1b) Remove some. 2) Adjust the rest.
1a) To make more points that you need, take any quasiregular polyhedron with sides that tessellate (triangles, squares, diamonds). Tesselate the spherical faces by subdivision, generating more vertices. For example, if you use the regular icosahedron you get geodesic domes. (Subdivide by 2, you get the dual to the C60 buckyball.) Working out exact formulas isn't hard. The number of new vertices per face is quadratic in the subdivision.
1b) Randomly remove enough points to get you down to your target number.
2) Use a force-directed layout algorithm to redistribute the vertices over the sphere. The underlying force graph is just that provided by the nearest neighbors in your underlying tesselation.
There are other ways to do step 1), such as just generating random points in any distribution. There is an advantage of starting with a quasiregular figure, though. Force-directed algorithms have a reputation for poor convergence in some cases. By starting with something that's already mostly optimal, you'll bypass most all of any convergence problems you might have.
One elegant solution I came across recently is a spherical fibonacci lattice (http://extremelearning.com.au/how-to-evenly-distribute-points-on-a-sphere-more-effectively-than-the-canonical-fibonacci-lattice/)
The nice thing about it is that you can specify the exact number of points you want
// C# Code example
Vector3[] SphericalFibonacciLattice(int n) {
Vector3[] res = new Vector3[n];
float goldenRatio = (1.0f + MathF.Sqrt(5.0f)) * 0.5f;
for(int i = 0; i < n; i++)
{
float theta = 2.0f * MathF.PI * i / goldenRatio;
float phi = MathF.Acos(1.0f - 2.0f * (i + 0.5f) / n);
Vector3 p = new Vector3(MathF.Cos(theta) * MathF.Sin(phi),
MathF.Sin(theta) * MathF.Sin(phi),
MathF.Cos(phi));
res[i] = p;
}
return res;
}
The linked article extends on this to create an even more uniform distribution, but even this basic version creates very nice results.
Please see the image below for a visual clue to my problem:
I have the coordinates for points 1 and 2. They were derived by a formula that uses the other information available (see question: How to calculate a point on a circle knowing the radius and center point).
What I need to do now (separately from the track construction) is plot the points in green between point 1 and 2.
What is the best way of doing so? My Maths skills are not the best I have to admit and I'm sure there's a really simple formula I just can't work out (from my research) which to use or how to implement.
In the notation of my answer to your linked question (i.e. x,y is the current location, fx,fy is the current 'forward vector', and lx,ly is the current 'left vector')
for (i=0; i<=10; i++)
{
sub_angle=(i/10)*deg2rad(22.5);
xi=x+285.206*(sin(sub_angle)*fx + (1-cos(sub_angle))*(-lx))
yi=y+285.206*(sin(sub_angle)*fy + (1-cos(sub_angle))*(-ly))
// now plot green point at (xi, yi)
}
would generate eleven green points equally spaced along the arc.
The equation of a circle with center (h,k) and radius r is
(x - h)² + (y - k)² = r² if that helps
check out this link for points http://www.analyzemath.com/Calculators/CircleInterCalc.html
The parametric equation for a circle is
x = cx + r * cos(a)
y = cy + r * sin(a)
Where r is the radius, cx,cy the origin, and a the angle from 0..2PI radians or 0..360 degrees.
I have some point on a 2D grid (x, y) and I need to find all points that are n distance away from that point. The way I'm measuring distance is by using the distance formula between the two points. Anyone know how to do this?
Edit: Just for reference, what I'm trying to do is to write some AI path finding that will maintain some distance away from a target in a system that uses grid based locations. Currently I'm using A* path finding, but I'm not sure if that matters or makes a difference since I'm kind of new to this stuff.
Here's what I would do:
First filter out all points that are further than D on either x or y. These are certainly outside the circle of radius D. This is a much simpler computation, and it can quickly eliminate a lot of work. This is a outer bounding-box optimization.
You can also use an inner bounding-box optimization. If the points are closer than D * sqrt(2)/2 on either x or y, then they're certainly within the circle of radius D. This is also cheaper than calculating the distance formula.
Then you have a smaller number of candidate points that may be within the circle of radius D. For these, use the distance formula. Remember that if D = sqrt(Δx2+Δy2), then D2 = Δx2+Δy2.
So you can skip the cost of calculating square root.
So in pseudocode, you could do the following:
for each point
begin
if test 1 indicates the point is outside the outer bounding box,
then skip this point
if test 2 indicates the point is inside the inner bounding box,
then keep this point
if test 3 indicates the point is inside the radius of the circle,
then keep this point
end
This problem is known as range query. The brute force solution is just as you described: computed the distance of all points from the reference point and return those whose distance is less than the desired range value.
The brute force algorithm is O(N^2). There are, however, more efficient algorithms that employ spatial indexes to reduce algorithm complexity and the number of distance calculations. For example, you can use a R-Tree to index your points.
Its called nearest neighbor search. More at http://en.wikipedia.org/wiki/Nearest_neighbor_search
There are open libraries for that. I have used one written for C and recommend it: http://www.cs.umd.edu/~mount/ANN/. ANN stands for Approximate Nearest Neighbor, however, you can turn the approximation off and find the exact nearest neighbors.
This wouldn't use the distance formula, but if you're looking for points exactly n distance away, perhaps you could use sin/cos?
In pseudocode:
for degrees in range(360):
x = cos(degrees) * n
y = sin(degrees) * n
print x, y
That would print every point n away in 360 degree increments.
Java implementation:
public static Set<Point> findNearbyPoints(Set<Point> pts, Point centerPt, double radius) {
Set<Point> nearbyPtsSet = new HashSet<Point>();
double innerBound = radius * (Math.sqrt(2.0) / 2.0);
double radiusSq = radius * radius;
for (Point pt : pts) {
double xDist = Math.abs(centerPt.x - pt.x);
double yDist = Math.abs(centerPt.y - pt.y);
if (xDist > radius || yDist > radius)
continue;
if (xDist > innerBound || yDist > innerBound)
continue;
if (distSq(centerPt, pt) < radiusSq)
nearbyPtsSet.add(pt);
}
return nearbyPtsSet;
}
I have two points (x1, y1) and (x2,y2) which represent the location of two entities in my space. I calculate the Euclidian distance between them using Pythagoras' theorem and everything is wonderful. However, if my space becomes finite, I want to define a new shortest distance between the points that "wraps around" the seams of the map. For example, if I have point A as (10, 10) and point B as (90,10), and my map is 100 units wide, I'd like to calculate the distance between A and B as 20 (out the right edge of the map and back into the left edge), instead of 80, which is the normal Euclidian distance.
I think my issue is that I'm using a coordinate system that isn't quite right for what I'm trying to do, and that really my flat square map is more of a seamless doughnut shape. Any suggestions for how to implement a system of this nature and convert back and forth from Cartesian coordinates would be appreciated too!
Toroidal plane? Okay, I'll bite.
var raw_dx = Math.abs(x2 - x1);
var raw_dy = Math.abs(y2 - y1);
var dx = (raw_dx < (xmax / 2)) ? raw_dx : xmax - raw_dx;
var dy = (raw_dy < (ymax / 2)) ? raw_dy : ymax - raw_dy;
var l2dist = Math.sqrt((dx * dx) + (dy * dy));
There's a correspondence here between the rollover behavior of your x and y coordinates and the rollover behavior of signed integers represented using the base's complement representation in the method of complements.
If your coordinate bounds map exactly to the bounds of a binary integer type supported by your language, you can take advantage of the two's complement representation used by nearly all current machines by simply performing the subtraction directly, ignoring overflow and reinterpreting the result as a signed value of the same size as the original coordinate. In the general case, you're not going to be that lucky, so the above dance with abs, compare and subtract is required.