Develop Reference

Struggling to iterate through a list with a list

i am in need of help. The idea is to calculate distance between two points via lat and long. That being between customer's lat and long and the store's lat and long. Repeat this for other stores. This seems to me, i have to iterate through a list with another list. I have been struggling to implement this for several hours now.
EXCEL VIEW
# Backup.xlsx/AccountLocation.xlsx
# column 0 = account numbers
# column 1 = postcodes
# Column 2 = latitude
# Column 3 = longitude
# Column 4 = order total (men)
# Column 5 = order total (women)
# Column 6 = order total (children)
# location.xlsx
# Column 1 = City name
# Column 2 = latitude
# Column 3 = longitude
# Income.xlsx
# Column 1 = City name
# Column 2 = estimated income (Men)
# Column 3 = estimated income (Woman)
# Column 4 = estimated income (Children)
Code
import pandas as pd
import math
from openpyxl import load_workbook
def distance2Point(inputLat1, inputLon1, inputLat2, inputLon2):
lat1 = float(inputLat1)
lon1 = float(inputLon1)
lat2 = float(inputLat2)
lon2 = float(inputLon2)
R = 6371 #metres
radLat1 = lat1 * (numpy.pi)/180 #φ, λ in radians
radLat2 = lat2 * (numpy.pi)/180
diffLat = (lat2-lat1) * (numpy.pi)/180
diffLong = (lon2-lon1) * (numpy.pi)/180
a = numpy.sin(diffLat/2) * numpy.sin(diffLat/2) + numpy.cos(radLat1) * numpy.cos(radLat2) * numpy.sin(diffLong/2) * numpy.sin(diffLong/2)
c = 2 * math.atan((math.sqrt(a))/(math.sqrt(1-a)))
d = R * c #in metres
return d
accountLocation = pd.read_excel("Backup.xlsx", header=None)
storeLocation = pd.read_excel("locations.xlsx", header=None)
accountLatitude = (accountLocation.iloc[:,2]).tolist()
accountLongitude = (accountLocation.iloc[:,3]).tolist()
storeLatitude = (storeLocation.iloc[:,1]).tolist()
storeLongitude = (storeLocation.iloc[:,2]).tolist()
londonDistance = []
for list in a:
for number in list:
print number
Edit: Sorry, i forgot to mention. Considering i had to use my own haversine formula. Which i got to work, but i am just simply struggling to iterate through this list.

There's a lot I would change about this code. For starters, do not provide lat and lon as separate variables. It's confusing. Instead use a tuple which is a standard way of dealing with coordinates.
Secondly, there's no reason to implement your own version of the haversine distance when reputable packages have it implemented already, for example, https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.haversine_distances.html.
So the code in the end could look something like:
from __future__ import annotations
from math import radians
from sklearn.metrics.pairwise import haversine_distances
def distance2point(point1: tuple[float, float], point2: tuple[float, float]):
lats = [radians(point1[0]), radians(point2[0])]
lons = [radians(point1[1]), radians(point2[1])]
return haversine_distances(lats, lons)
or even simpler for multiple points:
def distance2point(points: list[tuple[float, float]]):
lats = [radians(p[0]) for p in points]
lons = [radians(p[1]) for p in points]
return haversine_distances(lats, lons)
EDIT: Oopsie, forgot that sklearn expects radians. Fixed now.

pint: convert geographic CRS degrees to nautical miles

I would like to use pint to convert degrees (distance in a geographic CRS) into nautical miles.
https://geopandas.org/docs/reference/api/geopandas.GeoDataFrame.sjoin_nearest.html outputs distance in degree for epsg:4326.
Given distance (in nm) varies from equator to pole i'm not sure if this is possible.
I could use a rule of thumb of 1 deg ~= 111 km ~= 60 nm.
Perhaps it can be calculated using the starting point and distance using something like: https://github.com/anitagraser/movingpandas/blob/master/movingpandas/geometry_utils.py#L38
This code is also useful: https://geopy.readthedocs.io/en/stable/#module-geopy.distance
Here's some code to test:
import pandas as pd
import geopandas as gpd
df = pd.DataFrame({"lon": [0], "lat": [0]})
gdf_pt = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df["lon"], df["lat"]), crs="epsg:4326")
df2 = pd.DataFrame({"lon": [1, 2], "lat": [0, 0]})
gdf_pts = gpd.GeoDataFrame(df2, geometry=gpd.points_from_xy(df2["lon"], df2["lat"]), crs="epsg:4326")
value = gdf_pt.sjoin_nearest(gdf_pts, distance_col="distances")["distances"].values[0]
import pint
l = value * ureg.arcdegree

Probably best to throw it to Mercator and use that if you can
import pint_pandas
gdf = gdf_pt.to_crs("EPSG:3395").sjoin_nearest(gdf_pts.to_crs("EPSG:3395"), distance_col="distances")
gdf["distance"] = gdf["distance"].astype("pint[meter]").pint.to("nautical_mile")

This function, which I've pulled from existing code, computes the distance in meters between two lat/long sets. "rlat" and "rlong" are expressed in radians; you'll have to do the conversion from degrees. To get nm instead of meters, just set R to 3440.
from math import *
# Radius of the earth, in meters.
R = 6371000
# Return distance between two lat/longs.
def distance( pt1, pt2 ):
rlat1 = pt1.rlat
rlat2 = pt2.rlat
dlat = pt2.rlat - pt1.rlat
dlong = pt2.rlong - pt1.rlong
a = sin(dlat/2) * sin(dlat/2) + cos(rlat1) * cos(rlat2) * sin(dlong/2) * sin(dlong/2)
c = 2 * atan2(sqrt(a), sqrt(1-a))
return R * c

Calculating distances in TSPLIB

Hello i have a problem with calculating distances between cities from tsp library: http://www.math.uwaterloo.ca/tsp/world/countries.html. I have this set of data (cities in djibouti): http://www.math.uwaterloo.ca/tsp/world/dj38.tsp. I used this function to calculate distaces in this QaA here: http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/TSPFAQ.html. i programed this in python and now it looks like this, here is my code:
cityCoords = {
1:(11003.611100,42102.500000),
2:(11108.611100,42373.888900),
3:(11133.333300,42885.833300),
4:(11155.833300,42712.500000),
5:(11183.333300,42933.333300),
6:(11297.500000,42853.333300),
7:(11310.277800,42929.444400),
8:(11416.666700,42983.333300),
9:(11423.888900,43000.277800),
10:(11438.333300,42057.222200),
11:(11461.111100,43252.777800),
12:(11485.555600,43187.222200),
13:(11503.055600,42855.277800),
14:(11511.388900,42106.388900),
15:(11522.222200,42841.944400),
16:(11569.444400,43136.666700),
17:(11583.333300,43150.000000),
18:(11595.000000,43148.055600),
19:(11600.000000,43150.000000),
20:(11690.555600,42686.666700),
21:(11715.833300,41836.111100),
22:(11751.111100,42814.444400),
23:(11770.277800,42651.944400),
24:(11785.277800,42884.444400),
25:(11822.777800,42673.611100),
26:(11846.944400,42660.555600),
27:(11963.055600,43290.555600),
28:(11973.055600,43026.111100),
29:(12058.333300,42195.555600),
30:(12149.444400,42477.500000),
31:(12286.944400,43355.555600),
32:(12300.000000,42433.333300),
33:(12355.833300,43156.388900),
34:(12363.333300,43189.166700),
35:(12372.777800,42711.388900),
36:(12386.666700,43334.722200),
37:(12421.666700,42895.555600),
38:(12645.000000,42973.333300)
}
def calcCityDistances(coordDict):
cities = list(coordDict.keys())
n = len(cities)
distances = {}
latitude = []
longitude = []
RRR = 6378.388;
PI = 3.141592;
for i in range(1,n+1):
cityA = cities[i-1]
latA, longA = coordDict[cityA]
deg = int(latA)
Min = latA - deg
latitude.append(PI * (deg + 5 * Min / 3) / 180)
deg = int(longA);
Min = longA - deg;
longitude.append(PI * (deg + 5 * Min / 3) / 180)
for i in range(1,n+1):
for j in range(i + 1, n + 1):
q1 = cos(longitude[i-1] - longitude[j-1]);
q2 = cos(latitude[i-1] - latitude[j-1]);
q3 = cos(latitude[i-1] + latitude[j-1]);
key = frozenset((i, j))
distances[key] = {}
dist = RRR * acos(0.5 * ((1.0 + q1) * q2 - (1.0 - q1) * q3)) + 1.0
distances[key]['dist'] = dist
distances[key]['pher'] = init_fer
distances[key]['vis'] = 0
return distances
distances = calcCityDistances(cityCoords)
My problem is that the distances calculated in this algorithm are off mark in huge scale. average lenght of one route between cities is 10 000 km and the problem is that the optimal TSP route is 6635. you can imagine that when i apply this to my Ant Colony System algorithm the result is around 110 000 km. this is really different from 6 thousand. Can someone explain what am i doing wrong please ?

I'm not familiar with the distance calculation listed in the TSP FAQ. Here's the resource I've used in the past: http://www.movable-type.co.uk/scripts/latlong.html
He gives two great circle distance calculation methods. Neither one looks like the one TSP provided. But, they both produced a distance that seemed to match reality (that Diksa and Dikhil are about 31k apart).
The input data is in 1000ths of a degree, and I'm not sure if the conversion to radians given takes that into account.
Here's an implementation that might give you better results: note I updated the input data to degrees:
import cmath
import math
cityCoords = {
1:(11.0036111,42.1025),
2:(11.1086111,42.3738889)
}
def spherical_cosines(coordDict):
R = 6371; # kilometers
cities = list(coordDict.keys())
n = len(cities)
for i in range(1,n+1):
for j in range(i + 1, n + 1):
cityA = cities[i-1]
lat1, lon1 = coordDict[cityA]
cityB = cities[j-1]
lat2, lon2 = coordDict[cityB]
lat1_radians = math.radians(lat1)
lat2_radians = math.radians(lat2)
lon1_radians = math.radians(lon1)
lon2_radians = math.radians(lon2)
print('A={},{} B={},{}'.format(lat1_radians, lon1_radians, lat2_radians, lon2_radians))
delta_lon_radians = math.radians(lon2-lon1)
distance = cmath.acos(cmath.sin(lat1_radians) * cmath.sin(lat2_radians) + cmath.cos(lat1_radians) *
math.cos(lat2_radians) * cmath.cos(delta_lon_radians)) * R;
print('spherical_cosines distance={}'.format(distance))
spherical_cosines(cityCoords)
update:
The code you posted is not producing the correct distance values. Here's the first two cities using calcCityDistances and sperical cosines:
input loc=11003.6111, 42102.5
input loc=11108.6111, 42373.8889
radians A = 192.05631381917777,734.8329132074075
B=193.88890915251113,739.5740671363777
calcCityDistances distance = 8078.816781077703
input degrees A=11.0036111,42.1025 B=11.1086111,42.3738889
radians A=0.19204924330399503,0.7348272483209126
B=0.19388183901858905,0.7395638781792782
spherical_cosines> distance=(31.835225475974934+0j)
Units is kilometers. Spherical cosines produces approximately the right value. Is the code you're using the same as what you posted? Notice the radians conversion doesn't seem to take into account that the input is thousandths of a degree

Calculating geographic distance between a list of coordinates (lat, lng)

I'm writing a flask application, using some data extracted from a GPS sensor. I am able to draw the route on a Map and I want to calculate the distance the GPS sensor traveled. One way could be to just get the start and end coordinates, however due to the way the sensor travels this is quite inaccurate. Therefore I do sampling of each 50 sensor samples. If the real sensor sample size was 1000 I will now have 20 samples (by extracting each 50 sample).
Now I want to be able to put my list of samples through a function to calculate distance. So far I've been able to use the package geopy, but when I take large gps sample sets I do get "too many requests" errors, not to mention I will have extra processing time from processing the requests, which is not what I want.
Is there a better approach to calculating the cumulative distance of a list element containing latitude and longitude coordinates?
positions = [(lat_1, lng_1), (lat_2, lng_2), ..., (lat_n, lng_n)]
I found methods for lots of different mathematical ways of calculating distance using just 2 coordinates (lat1, lng1 and lat2 and lng2), but none supporting a list of coordinates.
Here's my current code using geopy:
from geopy.distance import vincenty
def calculate_distances(trips):
temp = {}
distance = 0
for trip in trips:
positions = trip['positions']
for i in range(1, len(positions)):
distance += ((vincenty(positions[i-1], positions[i]).meters) / 1000)
if i == len(positions):
temp = {'distance': distance}
trip.update(temp)
distance = 0
trips is a list element containing dictionaries of key-value pairs of information about a trip (duration, distance, start and stop coordinates and so forth) and the positions object inside trips is a list of tuple coordinates as visualized above.
trips = [{data_1}, {data_2}, ..., {data_n}]

Here's the solution I ended up using. It's called the Haversine (distance) function if you want to look up what it does for yourself.
I changed my approach a little as well. My input (positions) is a list of tuple coordinates:
def calculate_distance(positions):
results = []
for i in range(1, len(positions)):
loc1 = positions[i - 1]
loc2 = positions[i]
lat1 = loc1[0]
lng1 = loc1[1]
lat2 = loc2[0]
lng2 = loc2[1]
degreesToRadians = (math.pi / 180)
latrad1 = lat1 * degreesToRadians
latrad2 = lat2 * degreesToRadians
dlat = (lat2 - lat1) * degreesToRadians
dlng = (lng2 - lng1) * degreesToRadians
a = math.sin(dlat / 2) * math.sin(dlat / 2) + math.cos(latrad1) * \
math.cos(latrad2) * math.sin(dlng / 2) * math.sin(dlng / 2)
c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))
r = 6371000
results.append(r * c)
return (sum(results) / 1000) # Converting from m to km

I'd recommend transform your (x, y) coordinates into complex, as it is computational much easier to calculate distances. Thus, the following function should work:
def calculate_distances(trips):
for trip in trips:
positions = trip['positions']
c_pos = [complex(c[0],c[1]) for c in positions]
distance = 0
for i in range(1, len(c_pos)):
distance += abs(c_pos[i] - c_pos[i-1])
trip.update({'distance': distance})
What I'm doing is converting every (lat_1, lng_1) touple into a single complex number c1 = lat_1 + j*lng_1, and creates a list formed by [c1, c2, ... , cn].
A complex number is, all in all, a 2-dimensional number and, therefore, you can make this if you have 2D coordinates, which is perfect for geolocalization, but wouldn't be possible for 3D space coordinates, for instance.
Once you got this, you can easily compute the distance between two complex numbers c1 and c2 as dist12 = abs(c2 - c1). Doing this recursively you obtain the total distance.
Hope this helped!

Detecting geographic clusters

I have a R data.frame containing longitude, latitude which spans over the entire USA map. When X number of entries are all within a small geographic region of say a few degrees longitude & a few degrees latitude, I want to be able to detect this and then have my program then return the coordinates for the geographic bounding box. Is there a Python or R CRAN package that already does this? If not, how would I go about ascertaining this information?

I was able to combine Joran's answer along with Dan H's comment. This is an example ouput:
The python code emits functions for R: map() and rect(). This USA example map was created with:
map('state', plot = TRUE, fill = FALSE, col = palette())
and then you can apply the rect()'s accordingly from with in the R GUI interpreter (see below).
import math
from collections import defaultdict
to_rad = math.pi / 180.0 # convert lat or lng to radians
fname = "site.tsv" # file format: LAT\tLONG
threshhold_dist=50 # adjust to your needs
threshhold_locations=15 # minimum # of locations needed in a cluster
def dist(lat1,lng1,lat2,lng2):
global to_rad
earth_radius_km = 6371
dLat = (lat2-lat1) * to_rad
dLon = (lng2-lng1) * to_rad
lat1_rad = lat1 * to_rad
lat2_rad = lat2 * to_rad
a = math.sin(dLat/2) * math.sin(dLat/2) + math.sin(dLon/2) * math.sin(dLon/2) * math.cos(lat1_rad) * math.cos(lat2_rad)
c = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a));
dist = earth_radius_km * c
return dist
def bounding_box(src, neighbors):
neighbors.append(src)
# nw = NorthWest se=SouthEast
nw_lat = -360
nw_lng = 360
se_lat = 360
se_lng = -360
for (y,x) in neighbors:
if y > nw_lat: nw_lat = y
if x > se_lng: se_lng = x
if y < se_lat: se_lat = y
if x < nw_lng: nw_lng = x
# add some padding
pad = 0.5
nw_lat += pad
nw_lng -= pad
se_lat -= pad
se_lng += pad
# sutiable for r's map() function
return (se_lat,nw_lat,nw_lng,se_lng)
def sitesDist(site1,site2):
#just a helper to shorted list comprehension below
return dist(site1[0],site1[1], site2[0], site2[1])
def load_site_data():
global fname
sites = defaultdict(tuple)
data = open(fname,encoding="latin-1")
data.readline() # skip header
for line in data:
line = line[:-1]
slots = line.split("\t")
lat = float(slots[0])
lng = float(slots[1])
lat_rad = lat * math.pi / 180.0
lng_rad = lng * math.pi / 180.0
sites[(lat,lng)] = (lat,lng) #(lat_rad,lng_rad)
return sites
def main():
sites_dict = {}
sites = load_site_data()
for site in sites:
#for each site put it in a dictionary with its value being an array of neighbors
sites_dict[site] = [x for x in sites if x != site and sitesDist(site,x) < threshhold_dist]
results = {}
for site in sites:
j = len(sites_dict[site])
if j >= threshhold_locations:
coord = bounding_box( site, sites_dict[site] )
results[coord] = coord
for bbox in results:
yx="ylim=c(%s,%s), xlim=c(%s,%s)" % (results[bbox]) #(se_lat,nw_lat,nw_lng,se_lng)
print('map("county", plot=T, fill=T, col=palette(), %s)' % yx)
rect='rect(%s,%s, %s,%s, col=c("red"))' % (results[bbox][2], results[bbox][0], results[bbox][3], results[bbox][2])
print(rect)
print("")
main()
Here is an example TSV file (site.tsv)
LAT LONG
36.3312 -94.1334
36.6828 -121.791
37.2307 -121.96
37.3857 -122.026
37.3857 -122.026
37.3857 -122.026
37.3895 -97.644
37.3992 -122.139
37.3992 -122.139
37.402 -122.078
37.402 -122.078
37.402 -122.078
37.402 -122.078
37.402 -122.078
37.48 -122.144
37.48 -122.144
37.55 126.967
With my data set, the output of my python script, shown on the USA map. I changed the colors for clarity.
rect(-74.989,39.7667, -73.0419,41.5209, col=c("red"))
rect(-123.005,36.8144, -121.392,38.3672, col=c("green"))
rect(-78.2422,38.2474, -76.3,39.9282, col=c("blue"))
Addition on 2013-05-01 for Yacob
These 2 lines give you the over all goal...
map("county", plot=T )
rect(-122.644,36.7307, -121.46,37.98, col=c("red"))
If you want to narrow in on a portion of a map, you can use ylim and xlim
map("county", plot=T, ylim=c(36.7307,37.98), xlim=c(-122.644,-121.46))
# or for more coloring, but choose one or the other map("country") commands
map("county", plot=T, fill=T, col=palette(), ylim=c(36.7307,37.98), xlim=c(-122.644,-121.46))
rect(-122.644,36.7307, -121.46,37.98, col=c("red"))
You will want to use the 'world' map...
map("world", plot=T )
It has been a long time since I have used this python code I have posted below so I will try my best to help you.
threshhold_dist is the size of the bounding box, ie: the geographical area
theshhold_location is the number of lat/lng points needed with in
the bounding box in order for it to be considered a cluster.
Here is a complete example. The TSV file is located on pastebin.com. I have also included an image generated from R that contains the output of all of the rect() commands.
# pyclusters.py
# May-02-2013
# -John Taylor
# latlng.tsv is located at http://pastebin.com/cyvEdx3V
# use the "RAW Paste Data" to preserve the tab characters
import math
from collections import defaultdict
# See also: http://www.geomidpoint.com/example.html
# See also: http://www.movable-type.co.uk/scripts/latlong.html
to_rad = math.pi / 180.0 # convert lat or lng to radians
fname = "latlng.tsv" # file format: LAT\tLONG
threshhold_dist=20 # adjust to your needs
threshhold_locations=20 # minimum # of locations needed in a cluster
earth_radius_km = 6371
def coord2cart(lat,lng):
x = math.cos(lat) * math.cos(lng)
y = math.cos(lat) * math.sin(lng)
z = math.sin(lat)
return (x,y,z)
def cart2corrd(x,y,z):
lon = math.atan2(y,x)
hyp = math.sqrt(x*x + y*y)
lat = math.atan2(z,hyp)
return(lat,lng)
def dist(lat1,lng1,lat2,lng2):
global to_rad, earth_radius_km
dLat = (lat2-lat1) * to_rad
dLon = (lng2-lng1) * to_rad
lat1_rad = lat1 * to_rad
lat2_rad = lat2 * to_rad
a = math.sin(dLat/2) * math.sin(dLat/2) + math.sin(dLon/2) * math.sin(dLon/2) * math.cos(lat1_rad) * math.cos(lat2_rad)
c = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a));
dist = earth_radius_km * c
return dist
def bounding_box(src, neighbors):
neighbors.append(src)
# nw = NorthWest se=SouthEast
nw_lat = -360
nw_lng = 360
se_lat = 360
se_lng = -360
for (y,x) in neighbors:
if y > nw_lat: nw_lat = y
if x > se_lng: se_lng = x
if y < se_lat: se_lat = y
if x < nw_lng: nw_lng = x
# add some padding
pad = 0.5
nw_lat += pad
nw_lng -= pad
se_lat -= pad
se_lng += pad
#print("answer:")
#print("nw lat,lng : %s %s" % (nw_lat,nw_lng))
#print("se lat,lng : %s %s" % (se_lat,se_lng))
# sutiable for r's map() function
return (se_lat,nw_lat,nw_lng,se_lng)
def sitesDist(site1,site2):
# just a helper to shorted list comprehensioin below
return dist(site1[0],site1[1], site2[0], site2[1])
def load_site_data():
global fname
sites = defaultdict(tuple)
data = open(fname,encoding="latin-1")
data.readline() # skip header
for line in data:
line = line[:-1]
slots = line.split("\t")
lat = float(slots[0])
lng = float(slots[1])
lat_rad = lat * math.pi / 180.0
lng_rad = lng * math.pi / 180.0
sites[(lat,lng)] = (lat,lng) #(lat_rad,lng_rad)
return sites
def main():
color_list = ( "red", "blue", "green", "yellow", "orange", "brown", "pink", "purple" )
color_idx = 0
sites_dict = {}
sites = load_site_data()
for site in sites:
#for each site put it in a dictionarry with its value being an array of neighbors
sites_dict[site] = [x for x in sites if x != site and sitesDist(site,x) < threshhold_dist]
print("")
print('map("state", plot=T)') # or use: county instead of state
print("")
results = {}
for site in sites:
j = len(sites_dict[site])
if j >= threshhold_locations:
coord = bounding_box( site, sites_dict[site] )
results[coord] = coord
for bbox in results:
yx="ylim=c(%s,%s), xlim=c(%s,%s)" % (results[bbox]) #(se_lat,nw_lat,nw_lng,se_lng)
# important!
# if you want an individual map for each cluster, uncomment this line
#print('map("county", plot=T, fill=T, col=palette(), %s)' % yx)
if len(color_list) == color_idx:
color_idx = 0
rect='rect(%s,%s, %s,%s, col=c("%s"))' % (results[bbox][2], results[bbox][0], results[bbox][3], results[bbox][1], color_list[color_idx])
color_idx += 1
print(rect)
print("")
main()

I'm doing this on a regular basis by first creating a distance matrix and then running clustering on it. Here is my code.
library(geosphere)
library(cluster)
clusteramounts <- 10
distance.matrix <- (distm(points.to.group[,c("lon","lat")]))
clustersx <- as.hclust(agnes(distance.matrix, diss = T))
points.to.group$group <- cutree(clustersx, k=clusteramounts)
I'm not sure if it completely solves your problem. You might want to test with different k, and also perhaps do a second run of clustering of some of the first clusters in case they are too big, like if you have one point in Minnesota and a thousand in California.
When you have the points.to.group$group, you can get the bounding boxes by finding max and min lat lon per group.
If you want X to be 20, and you have 18 points in New York and 22 in Dallas, you must decide if you want one small and one really big box (20 points each), if it is better to have have the Dallas box include 22 points, or if you want to split the 22 points in Dallas to two groups. Clustering based on distance can be good in some of these cases. But it of course depend on why you want to group the points.
/Chris

A few ideas:
Ad-hoc & approximate: The "2-D histogram". Create arbitrary "rectangular" bins, of the degree width of your choice, assign each bin an ID. Placing a point in a bin means "associate the point with the ID of the bin". Upon each add to a bin, ask the bin how many points it has. Downside: doesn't correctly "see" a cluster of points that stradle a bin boundary; and: bins of "constant longitudinal width" actually are (spatially) smaller as you move north.
Use the "Shapely" library for Python. Follow it's stock example for "buffering points", and do a cascaded union of the buffers. Look for globs over a certain area, or that "contain" a certain number of original points. Note that Shapely is not intrinsically "geo-savy", so you'll have to add corrections if you need them.
Use a true DB with spatial processing. MySQL, Oracle, Postgres (with PostGIS), MSSQL all (I think) have "Geometry" and "Geography" datatypes, and you can do spatial queries on them (from your Python scripts).
Each of these has different costs in dollars and time (in the learning curve)... and different degrees of geospatial accuracy. You have to pick what suits your budget and/or requirements.

if you use shapely, you could extend my cluster_points function
to return the bounding box of the cluster via the .bounds property of the shapely geometry , for example like this:
clusterlist.append(cluster, (poly.buffer(-b)).bounds)

maybe something like
def dist(lat1,lon1,lat2,lon2):
#just return normal x,y dist
return sqrt((lat1-lat2)**2+(lon1-lon2)**2)
def sitesDist(site1,site2):
#just a helper to shorted list comprehensioin below
return dist(site1.lat,site1.lon,site2.lat,site2.lon)
sites_dict = {}
threshhold_dist=5 #example dist
for site in sites:
#for each site put it in a dictionarry with its value being an array of neighbors
sites_dict[site] = [x for x in sites if x != site and sitesDist(site,x) < threshhold_dist]
print "\n".join(sites_dict)