问题
Basically, my experimental program is trying to find the number of points that fall within a (e.g., 50km) radius of a valid point at a given time. My data is structured (but I can restructure if need-be) in three separate arrays such:
1_LAT,1_LON,1_TIM
Where 1_LAT,1_LON,1_TIM all contain roughly ~250 values corresponding to Latitude, Longitude (decimal degrees), and time respectively.
I have 20 sets of these arrays (i.e., 1_LAT,1_LON,1_TIM...20_LAT,20_LON,20_TIM).
Here is what I would like to accomplish:
1) Figure out the number of lat/lon sets that fall within a particular radius of each set. For example, how many points fall within a 50km radius of 1_LAT,1_LON at the valid time of 1_TIM from the other 19 sets of points. I would then like to iterate through each valid time to figure out the number of points in the valid radius at each specific point and valid time.
I have attached a picture below to help visually describe.
The black squares represent all the points in the LAT_1/LON_1 arrays. The blue squares represent all the points in the LAT_n/LAT_n arrays.
I would like to count the number of points in each radius at each valid time for each set of lat/lon arrays. The final display would be a summed raster or meshgrid of the denisty (i.e., number of counts / 20) for each grid spot on a geographic basemap image.
I have a feeling that a KDEtree may be the best way to accomplish this, but I have little/no experience with such. Any ideas or suggestions would be greatly appreciated.
回答1:
You would do something like the following... First, group your (x, y) coordinates for each group in a single points_x array:
points_1 = np.column_stack((LAT_1, LON_1))
...
points_n = np.column_stack((LAT_n, LON_n))
It may be a good idea to store them in a list of arrays:
points = [point_1, points_2, ..., points_n]
Now, make a kdTree out of each set of points:
import scipy.spatial as spsp
kdtrees = [spsp.cKdTree(p) for p in point]
And you are ready to go. If you now run the following code:
r = whatever_your_threshold_value_is
points_within_r = np.zeros((len(kdtrees), len(kdtrees)), dtype=np.int)
for j in xrange(len(kdtrees)):
for k in xrange(j+1, len(kdtrees)):
points_within_r[j, k] = kdtrees[j].count_neighbors(kdtrees[k], r, 2)
points_within_r = points_within_r + points_within_r.T
You should now find that points_within_r[j, k] holds how many points in points_j are within radius r of a point in points_k.
Keep in mind that distances here are the euclidean distance of the coordinates, disregarding the fact that what they measure are spherical angles.
来源：https://stackoverflow.com/questions/17956149/python-sum-lat-lon-points-within-geographic-radius-and-sum-to-grid