I have a high frequency of gps data which i want to downsample to every 50 meters ie keep gps latitude and longitude every 50 meter and discard inbetween points. I found a python code on the internet which basically calculates the distance between two points. But i am not sure how to basically read from a csv the lat and long values and feed it into the function and calculate the distance. If the distance reaches 50 meter i simply save that gps coordinates. So far, i have the following python code
from math import radians, cos, sin, asin, sqrt
def haversine(lon1, lat1, lon2, lat2):
lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])
# haversine formula
dlon = lon2 - lon1
dlat = lat2 - lat1
a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
c = 2 * asin(sqrt(a))
r = 6371 # Radius of earth in kilometers. Use 3956 for miles
return c * r
x1 = 52.19421607
x2 = 52.20000327
y1 = -1.484984011
y2 = -1.48533465
result = haversine(x1,y1,x2,y2) #need to give input from a csv
#if result is greater than 50m , save the coordinates
print(result)
How can i solve the problem?Any direction would be appreciated.
Here is a outline and a working code example - where I made some assumptions about which to keep/drop. I assume the dataframe is sorted.
First calculate distance to next point, indeed use haversine for lat/long pairs. This part is not fast in my implementation - you can find faster.
Use cumsum() of distances, to create distance groups, where group 1 is all distances below 50, group 2 between 50 and 100, etc...
Within each group, keep for instance only the first()
Note that this is approximately each 50 units based on group, so be aware this is different than take a point and jump to next point which is closest to 50 units away and repeat. But for data reduction purposes it should be fine.
Generate some random data around London.
import numpy as np
import sklearn
import pandas as pd
LONDON = (51.509865, -0.118092)
random_gps = np.random.random( (10000,2) ) / 25
random_gps[:,0] += np.arange(random_gps.shape[0]) / 25
random_gps[:,0] += LONDON[0]
random_gps[:,1] += LONDON[1]
gps_data = pd.DataFrame( random_gps, columns=["lat","long"] )
Shift the data to get the lat/long of the next point
gps_data['next_lat'] = gps_data.lat.shift(1)
gps_data['next_long'] = gps_data.long.shift(1)
gps_data.head()
Define the distance metric. This part can be improved in terms of speed by using vector expressions with numpy, so if speed is important change this part.
from sklearn.neighbors import DistanceMetric
dist = DistanceMetric.get_metric('haversine')
EARTH_RADIUS = 6371.009
def haversine_distance(row):
point_a = np.array([[row.lat, row.long]])
point_b = np.array([[row.next_lat, row.next_long]])
return EARTH_RADIUS * dist.pairwise(np.radians(point_a), np.radians(point_b) )[0][0]
and apply our distance function (slow part, which can be improved)
gps_data["distance_to_next"] = gps_data.apply( haversine_distance, axis=1)
gps_data["distance_cumsum"] = gps_data.distance_to_next.cumsum()
Finally, create groups and drop. (!) The haversine is returning the distance in KM - so here i wrongly did an example of 50 km instead of meters.
gps_data["distance_group"] = gps_data.distance_cumsum // 50
filtered = gps_data.groupby(['distance_group']).first()
Related
I have address data and shapefiles with polygons, and am trying to determine the closest distance (in miles) of each address from each polygon, then create a nested dict containing all the info, with this format:
nested_dict = {poly_1: {address1: distance, address2 : distance},
poly2: {address1: distance, address2: distance}, etc}
The full, applicable code I'm using is:
import pandas as pd
from shapely.geometry import mapping, Polygon, LinearRing, Point
import geopandas as gpd
from math import radians, cos, sin, asin, sqrt
address_dict = {k: [] for k in addresses_geo.input_string}
sludge_dtc = {k: [] for k in sf_geo.unique_name}
def haversine(lon1, lat1, lon2, lat2):
"""
Calculate the great circle distance between two points
on the earth (specified in decimal degrees)
"""
# convert decimal degrees to radians
lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])
# haversine formula
dlon = lon2 - lon1
dlat = lat2 - lat1
a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
c = 2 * asin(sqrt(a))
r = 3956 # Radius of earth in miles. Use 6371 for kilometers
return c * r
# Here's the key loop that isn't working correctly
for unique_name, i in zip(sf_geo.unique_name, sf_geo.index):
for address, pt in zip(addresses_geo.input_string, addresses_geo.index):
pol_ext = LinearRing(sf_geo.iloc[i].geometry.exterior.coords)
d = pol_ext.project(addresses_geo.iloc[pt].geometry)
p = pol_ext.interpolate(d)
closest_point_coords = list(p.coords)[0]
# print(closest_point_coords)
dist = haversine(addresses_geo.iloc[pt].geometry.x,
addresses_geo.iloc[pt].geometry.y,
closest_point_coords[0], closest_point_coords[1])
address_dict[address] = dist
sludge_dtc[unique_name] = address_dict
# Test results on a single address
addresses_with_sludge_distance = pd.DataFrame(sludge_dtc)
print(addresses_with_sludge_distance.iloc[[1]].T)
If I break this code out and try and calculate the distances for a single polygon, it seems to work fine. However, when I create the DataFrame and check an address, it lists the same distance for every single polygon.
So, inner-dict-key '123 Main Street' will have 5.25 miles for each of the polygon keys in the outer dict, and '456 South Street' will have 6.13 miles for each of the polygon keys in the outer dict. (Made up examples.)
I realize I must be doing something dumb in the way I have the for loops set up, but I can't figure it out. I've reversed the order of the for statements, messed with indents-- all the same result.
To make it clear, what I want to happen is:
Take a single polygon, then
For each address in the address data, find the distance from that polygon and add to the address_dict dictionary with the address as the key and the distance as the value
When all addresses have been calculated, add the entire address dict as the value for the polygon key in sludge_dtc
Move on to the next polygon and continue
Any ideas what I'm missing?
The problem is very simple, you are always using the same address_dict instance.
You just need to recreate it inside every key loop.
import pandas as pd
from shapely.geometry import mapping, Polygon, LinearRing, Point
import geopandas as gpd
from math import radians, cos, sin, asin, sqrt
def haversine(lon1, lat1, lon2, lat2):
"""
Calculate the great circle distance between two points
on the earth (specified in decimal degrees)
"""
# convert decimal degrees to radians
lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])
# haversine formula
dlon = lon2 - lon1
dlat = lat2 - lat1
a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
c = 2 * asin(sqrt(a))
r = 3956 # Radius of earth in miles. Use 6371 for kilometers
return c * r
sludge_dtc = {k: [] for k in sf_geo.unique_name}
# Here's the key loop that isn't working correctly
for unique_name, i in zip(sf_geo.unique_name, sf_geo.index):
address_dict = {k: [] for k in addresses_geo.input_string}
for address, pt in zip(addresses_geo.input_string, addresses_geo.index):
pol_ext = LinearRing(sf_geo.iloc[i].geometry.exterior.coords)
d = pol_ext.project(addresses_geo.iloc[pt].geometry)
p = pol_ext.interpolate(d)
closest_point_coords = list(p.coords)[0]
# print(closest_point_coords)
dist = haversine(addresses_geo.iloc[pt].geometry.x,
addresses_geo.iloc[pt].geometry.y,
closest_point_coords[0], closest_point_coords[1])
address_dict[address] = dist
sludge_dtc[unique_name] = address_dict
# Test results on a single address
addresses_with_sludge_distance = pd.DataFrame(sludge_dtc)
print(addresses_with_sludge_distance.iloc[[1]].T)
Another consideration:
Your are creating empty dictionaries with empty lists as values, but after you set values directly (empty list are replaced). If you need to collect a list of values you should append values to the existing list, eg:
address_dict[address].append(dist)
and
sludge_dtc[unique_name].append(address_dict)
I need to find the distance between two gps trajectories, from US 101 dataset, which covers totally 2000ft distance.
"Vehicle ID","Frame ID","Total Frames","Global Time","Local X","Local Y","Global X","Global Y","V_Len","V_Width","V_Class","V_Vel","V_Acc","Lane_ID","Pre_Veh","Fol_Veh","Spacing","Headway"
2,13,437,1118846980200,16.467,35.381,6451137.641,1873344.962,14.5,4.9,2,40.00,0.00,2,0,0,0.00,0.00
2,14,437,1118846980300,16.447,39.381,6451140.329,1873342.000,14.5,4.9,2,40.00,0.00,2,0,0,0.00,0.00
2,15,437,1118846980400,16.426,43.381,6451143.018,1873339.038,14.5,4.9,2,40.00,0.00,2,0,0,0.00,0.00
2,16,437,1118846980500,16.405,47.380,6451145.706,1873336.077,14.5,4.9,2,40.00,0.00,2,0,0,0.00,0.00
2,17,437,1118846980600,16.385,51.381,6451148.395,1873333.115,14.5,4.9,2,40.00,0.00,2,0,0,0.00,0.00
But when I am trying to find the distance between two adjacent points of the same vehicle, Its giving in more than 20 kms..
import math
def distance(origin, destination):
lat1, lon1 = origin
lat2, lon2 = destination
radius = 3959 * 5280 # km
dlat = math.radians(lat2-lat1)
dlon = math.radians(lon2-lon1)
a = math.sin(dlat/2) * math.sin(dlat/2) + math.cos(math.radians(lat1)) \
* math.cos(math.radians(lat2)) * math.sin(dlon/2) * math.sin(dlon/2)
c = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a))
d = radius * c
return d
lat1 = 16.467; lat2 = 16.447; long1 = 35.381; long2 = 39.381;
print( distance((lat1, long1), (lat2, long2)) )
Can any you help me to find the distance between two adjacent trajectories
I need to segregate the dataset into subsections covering 200ft distance each..
Are you sure those coordinates are latitude and longitude?
I am not familiar with the dataset you are using. However, if I am not mistaken, this is it. And the documentation clearly states about Local X (my italics):
Lateral (X) coordinate of the front center of the vehicle in feet with respect to the left-most edge of the section in the direction of travel.
(and something similar for Local Y).
I want to compute the "MANHATTAN DISTANCE" also called "CITY BLOCK DISTANCE" among pairs of coordinates with LAT, LNG.
Following this post Manhattan Distance for two geolocations I had computed the distance using the haversine formula:
source = (45.070060, 7.663708)
target = (45.068250, 7.663492)
This is my computation:
from math import radians, sin, asin, sqrt, atan2
# convert decimal degrees to radians
lat1, lon1, lat2, lon2 = map(radians, [source[0], source[1], target[0], target[1]])
#haversine formula for delta_lat
dlat = lat2 - lat1
a = sin(dlat / 2) ** 2
c = 2 * atan2(sqrt(a), sqrt(1-a)))
r = 6371
lat_d = c * r
# haversine formula for delta_lon
dlon = lon2 - lon1
a = sin(dlon / 2) ** 2
c = 2 * atan2(sqrt(a), sqrt(1-a))
r = 6371
lon_d = c * r
print lat_d + lon_d
The problem is that my result is 225m, while Google Maps says 270m.
Trying again to compute the distance among
source = (45.070060, 7.663708)
target = (45.072800, 7.665540)
the result I obtained is 508m while Google Maps says 350m.
I will appreciate if someone can help me understanding what is wrong here and how to improve this solution which is far from being acceptable.
Thank you!
I probably got the point on myself, the answer is that if you look at these pictures, which are the same posted above in the original question, you can understand that the haversine method I have implemented, computes the distance as the RED LINE in the image. For this reason in the first case I obtain 225m instead of 270m (lower because the red line is the hypotenuse of the triangle) while in the second case I obtained 508m instead of 350 (higher because sum of legs of the triangle). Hence the way to solve this problem should be ROTATE THE CITY MAP COUNTERCLOCKWISE to align the BLUE DOTTED line with the Y-AXIS of the cartesian reference.
Any suggestion will be appreciated. Thank You
I am working on a Python project where I have two lat/long pairs and I want to calculate the distance between them. In other projects I have calculated distance in Postgres using ST_Distance_Sphere(a.loc_point, b.loc_point), but I would like to avoid having to load all of my data into Postgres just so that I can calculate distance differences. I have searched, but have not been able to find what I would like, which is a purely Python implementation of this so that I don't have to load my data into Postgres.
I know there are other distance calculations that treat the earth as a perfect sphere, but those aren't good enough due to poor accuracy, which is why I would like to use the PostGIS ST_Distance_Sphere() function (or an equivalent).
Here are a couple of sample Lat/Longs that I would like to calculate the distance of:
Lat, Long 1: (49.8755, 6.07594)
Lat, Long 2: (49.87257, 6.0784)
I can't imagine I am the first person to ask this, but does anyone know of a way to use ST_Distance_Sphere() for lat/long distance calculations purely from within a Python script?
I would recommend the geopy package - see section Measuring Distance in the documentation...
For your particular case:
from geopy.distance import great_circle
p1 = (49.8755, 6.07594)
p2 = (49.87257, 6.0784)
print(great_circle(p1, p2).kilometers)
This is a rudimentary function used to calculate distance between two coordinates on a perfect sphere with Radius = Radius of Earth
from math import pi , acos , sin , cos
def calcd(y1,x1, y2,x2):
#
y1 = float(y1)
x1 = float(x1)
y2 = float(y2)
x2 = float(x2)
#
R = 3958.76 # miles
#
y1 *= pi/180.0
x1 *= pi/180.0
y2 *= pi/180.0
x2 *= pi/180.0
#
# approximate great circle distance with law of cosines
#
x = sin(y1)*sin(y2) + cos(y1)*cos(y2)*cos(x2-x1)
if x > 1:
x = 1
return acos( x ) * R
Hope this helps!
See this How can I quickly estimate the distance between two (latitude, longitude) points?
from math import radians, cos, sin, asin, sqrt
def haversine(lon1, lat1, lon2, lat2):
"""
Calculate the great circle distance between two points
on the earth (specified in decimal degrees)
"""
# convert decimal degrees to radians
lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])
# haversine formula
dlon = lon2 - lon1
dlat = lat2 - lat1
a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
c = 2 * asin(sqrt(a))
km = 6367 * c
return km
By Aaron D
You can modify it to return miles by adding miles = km * 0.621371
I have since found another way in addition to the answers provided here. Using the python haversine module.
from haversine import haversine as h
# Return results in meters (*1000)
print '{0:30}{1:12}'.format("haversine module:", h(a, b)*1000)
I tested all three answers plus haversine module against what I got using ST_Distance_Sphere(a, b) in Postgres. All answers were excellent (thank you), but the all math answer (calcd) from Sishaar Rao was the closest. Here are the results:
# Short Distance Test
ST_Distance_Sphere(a, b): 370.43790478
vincenty: 370.778186438
great_circle: 370.541763803
calcd: 370.437386736
haversine function: 370.20481753
haversine module: 370.437394767
#Long Distance test:
ST_Distance_Sphere(a, b): 1011734.50495159
vincenty: 1013450.40832
great_circle: 1012018.16318
calcd: 1011733.11203
haversine function: 1011097.90053
haversine module: 1011733.11203
I have a CSV file with the Lat, Long and Rainfall Information. I would like to interpolate those point and create tiff file. Can any one can suggest me the easiest way to do that.
I am trying to using gdal_grid. I am very new on using gdal in python.
This is actually several questions. Assuming you have some scattered data for lats and longs you'll to build all the location were you want to make estimation (all lats and longs for the pixels of you Tiff image).
Once you have that you can use any of the solutions around to do IWD over your data (using a recent example in another question):
class Estimation():
# IWD. Check: https://stackoverflow.com/questions/36031338/interpolate-z-values-in-a-3d-surface-starting-from-an-irregular-set-of-points/36037288#36037288
def __init__(self,lon,lat,values):
self.x = lat
self.y = lon
self.v = values
def estimate(self,x,y,using='ISD'):
"""
Estimate point at coordinate x,y based on the input data for this
class.
"""
if using == 'ISD':
return self._isd(x,y)
def _isd(self,x,y):
#d = np.sqrt((x-self.x)**2+(y-self.y)**2)
d = x.copy()
for i in range(d.shape[0]):
d[i] = haversine(self.x[i],self.y[i],x,y)
if d.min() > 0:
v = np.sum(self.v*(1/d**2)/np.sum(1/d**2))
return v
else:
return self.v[d.argmin()]
The code above is actually adapted to calculate distance with the Haversine formula (which gives great-circle distances between two points on a sphere from their longitudes and latitudes). Notice again you can find all sorts of solutions for the haversine distance like this one:
def haversine(lon1, lat1, lon2, lat2):
"""
Check: https://stackoverflow.com/questions/15736995/how-can-i-quickly-estimate-the-distance-between-two-latitude-longitude-points
Calculate the great circle distance between two points
on the earth (specified in decimal degrees)
"""
# convert decimal degrees to radians
lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])
# haversine formula
dlon = lon2 - lon1
dlat = lat2 - lat1
a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
c = 2 * asin(sqrt(a))
km = 6367 * c
return km
Finally once you have your array ready you should just build the Tiff using GDAL. For this check the following question for which I quote a part of it's solution:
driver = gdal.GetDriverByName('GTiff')
ds = driver.Create('output.tif',xsize, ysize, 1, gdal.GDT_Float32, )
# this assumes the projection is Geographic lat/lon WGS 84
srs = osr.SpatialReference()
srs.ImportFromEPSG(4326)
ds.SetProjection(srs.ExportToWkt())
gt = [ulx, xres, 0, uly, 0, yres ]
ds.SetGeoTransform(gt)
outband=ds.GetRasterBand(1)
outband.SetStatistics(np.min(mag_grid), np.max(mag_grid), np.average(mag_grid), np.std(mag_grid))
outband.WriteArray(mag_grid)