I'm trying to associate a set of points (lat, long) to an edge using osmnx library in Python.
I would like to find the nearest points to an edge within a distance x.
I have an edge and I would like to draw a circle and count how many points are into the circle, with a given radius. I have tha lat and long coordinates of each point but I don't know how to calculate the lat, long coordinate of the edge. I also have the coordinates lat, long of the nodes connected by that edge.
Thank you for your help.
import pandas as pd
from shapely.ops import transform
from functools import partial
import pyproj
from shapely.geometry import Point
mid_point = Point(lon,lat) # UNPROJECTED CO-ORDINATES OF MID-POINT OF AN EDGE
node_point = Point(lon_node, lat_node)# UNPROJECTED CO-ORDINATES OF THE NODE
x = 500 #DISTANCE IN METERS
#TRANSFORM INTO PROJECTED CO-ORDINATES
project = partial(pyproj.transform,pyproj.Proj(init='epsg:4326'),pyproj.Proj(init='epsg:3112'))
mid_point_projected = transform(project, mid_point)
node_point_projected = transform(project, node_point)
#CREATE BUFFER CIRCLE WITH DISTANCE X METRES WITH CENTRE AT EDGE MID-POINT
buffer_circle = mid_point_projected.buffer(x)
#PERFORM POINT-IN-POLYGON ANALYSIS TO CHECK WHETHER THE NODE FALLS WITHIN THE BUFFER CIRCLE
print(buffer_circle.contains(node_point_projected))
POINTS TO BE NOTED:
EPSG Geodetic Parameter Dataset is a structured dataset of Coordinate Reference Systems and Coordinate Transformations, accessible through this online registry (www.epsg-registry.org)
EPSG 4326 represents World Geodetic System (WGS84) (https://epsg.io/4326)(points on the Earth's surface represented in terms of latitude and longitude)
I have transformed it into EPSG 3112 representing GDA94 / Geoscience Australia Lambert (https://epsg.io/3112). You should transform it into the corresponding EPSG code for your study area.
Related
I have 20 000 polygons in a dataset. I need to have the Euclidean Distance between all polygons, so a 20 000 x 20 000 distance matrix where for each of the polygons, the distance to all other polygons is stored.
I have read in some other threads the recommendation to use the "Near" tool in Arcmap. However, this tool only calculates the distance to the NEAREST polygon, while I need the distance from ALL polygons to ALL polygons.
Is there any solution for this?
Near tool: Calculates distance and additional proximity information between the
input features and the closest feature in another layer or feature
class.
In order to calculate the distance between the centroids of each of your polygons make sure your map is in a projected coordinate system.
Then, make sure the centroid points are calculated (detailed step-by-step here: https://support.esri.com/en/technical-article/000009381 )
Export your centroid point attribute table as a DBF (Click on Options > Export.)
Add the table to your map. Right click on the new table, Display XY Data, select Longitude for the X and Latitude for Y, and select the map's coordinate system to create an events layer.
Then, use the Point Distance tool (Details here: https://desktop.arcgis.com/en/arcmap/10.3/tools/analysis-toolbox/point-distance.htm ). The event points are both the input and near features. The output will be a table displaying distance between all polygon centroids on the map.
I am writing a program. I have, say, a grid of dots on a piece of paper. I fix one end and bend the paper toward the screen, giving me a trapezoidal shape from the camera's point of view. I have the (x,y) camera coordinate of each dot. Is there a simple way I can change these (x,y) to real life (x,y) which should give me a rectangle? I have the camera/real (x,y) of the original flat sheet of paper pre-bend if that helps.
I have looked at 3D Camera coordinates to world coordinates (change of basis?) and Transforming screen coordinates from security camera to real world coordinates.
Look up "homography". The transformation from a plane in 3D space to its image as captured by an ideal pinhole camera is a homography. It can be represented as a 3x3 matrix H that transforms the 3D coordinates X of points in the world to their corresponding homogeneous image coordinates x:
x = H * X
where X is a 3x1 vector of the world point coordinates, and x = [u, v, w]^T is the image point in homogeneous coordinates.
Given a minimum of 4 matches between world and image points (e.g. the corners of a rectangle) you can estimate the parameters of the matrix H. For details, look up "DLT algorithm". In OpenCV the routine to use is findHomography.
I have a set of GPS coordinates 12.9611159,77.6362214. What exactly do these mean? How can I convert them to degrees of longitude and latitude? What formula should I use to get accurate distance between two sets of coordinates when the order of distance is 10km.
Most likely 12.9611159 is the latitude in degrees, 77.6362214 the longitude. In that case, the coordinate is in India. If latitude and longitude are reversed, you end up in the Greenland Sea.
You can easily check this by entering the coordinate pair in the Google maps search box. Google expects latitude first.
For the distance, in python you can easily use the haversine package:
from haversine import haversine
my_coord = (12.9611159,77.6362214)
other_coord = (12.9, 77.6)
distance = haversine(my_coord, other_coord)
This will give you the distance in km.
Currently I'm trying the following: I have points from google earth (WGS84) which I want to transform to a local x,y coordinate system: a tangential plane with y positive from south to north and x positive from west to east.
There is no need for the plane to be part of a global coordinate system more than the relation (x=0, y=0) = (lat,lon). The scale at which I'm working is in the order of say 100 kilometers (maximum of for example 200 km's). Very small errors (due to for example the curvature of the earth) are acceptable.
I have relatively little understanding of this topic as of yet. Can anybody help me out? Where would I need to look for example.
Thanks!
I haven't found the answer mathematically but have found that the package basemap (of the mpl_toolkit) should help with this respect (from wgs84 to a transverse mercator projection).
I am trying to figure out if a latitude/longitude point is contained within a polygon defined by vertexes that represent points on the earth (also lat/lon's, in clockwise order). This is trivial for polygons that can be mapped to the 2D lat/lon space.
Where this becomes increasingly difficult is circle's (now switching back to 3D) that may go from pole to pole covering half the earth. The translation to lat/lon looks like a sine wave. The 2D point in polygon test no longer applies to this case. Is there an algorithm that exists that solves this problem?
================== Clarifications on comments below: ===================
The polygon is defined as (lon, lat) pairs in degrees, i.e., (60, 90), (60, 110), (-30, 110), (-30, 90).
I do have code that implements the ray casting algorithm, and that works. however, certain polygons on the surface of the earth do not translate to closed polygons in the 2D space.
As stated by denniston.t, if you are only interested in circles, and you have a radius, you can simply check if the Great Circle Distance between the center point and the point is less than the radius. To find the great circle distance you typically use the Haversine Formula. The following is my implementation in python:
from math import radians, sin, cos, asin, sqrt
def haversine(point1, point2):
"""Gives the distance between two points on earth.
The haversine formula, given two sets of latitude and longitude,
returns the distance along the surface of the earth in miles,
ignoring potential changes in elevation. The points must be in
decimal degrees.
"""
earth_radius_miles = 3956
lat1, lon1 = (radians(coord) for coord in point1)
lat2, lon2 = (radians(coord) for coord in point2)
dlat, dlon = (lat2 - lat1, lon2 - lon1)
a = sin(dlat/2.0)**2 + cos(lat1) * cos(lat2) * sin(dlon/2.0)**2
great_circle_distance = 2 * asin(min(1,sqrt(a)))
d = earth_radius_miles * great_circle_distance
return d
If you have the center point and radius of your circle drawn on the surface of the sphere, calculate the Great-circle distance between the center point and target point. If it's less than the radius of the circle, the target point lies in the circle.
This will not generalize to arbitrary polygons drawn on your sphere, but you only asked about circles, so I don't know if it matters to you.
containsLocation(point:LatLng, polygon:Polygon)