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# -*- encoding: utf-8; -*-
"""
cities.py - Locations and population values for many cities of the world
"""
#
# This code is derived from a list of cities provided by MaxMind at
# http://www.maxmind.com/en/worldcities. Here is the license under
# which we use and redistribute the data:
#
# OPEN DATA LICENSE for MaxMind WorldCities and Postal Code Databases
#
# Copyright (c) 2008 MaxMind Inc. All Rights Reserved.
#
# The database uses toponymic information, based on the Geographic
# Names Data Base, containing official standard names approved by the
# United States Board on Geographic Names and maintained by the
# National Geospatial-Intelligence Agency. More information is
# available at the Maps and Geodata link at www.nga.mil. The National
# Geospatial-Intelligence Agency name, initials, and seal are
# protected by 10 United States Code Section 445.
#
# It also uses free population data from Stefan Helders www.world-gazetteer.com.
# Visit his website to download the free population data. Our database
# combines Stefan's population data with the list of all cities in the world.
#
# All advertising materials and documentation mentioning features or
# use of this database must display the following acknowledgment:
# "This product includes data created by MaxMind, available from
# http://www.maxmind.com/"
#
# Redistribution and use with or without modification, are permitted provided
# that the following conditions are met:
#
# 1. Redistributions must retain the above copyright notice, this list of
# conditions and the following disclaimer in the documentation and/or other
# materials provided with the distribution.
#
# 2. All advertising materials and documentation mentioning features or use of
# this database must display the following acknowledgement:
# "This product includes data created by MaxMind, available from
# http://www.maxmind.com/"
#
# 3. "MaxMind" may not be used to endorse or promote products derived from this
# database without specific prior written permission.
#
# THIS DATABASE IS PROVIDED BY MAXMIND.COM ``AS IS'' AND ANY EXPRESS
# OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL MAXMIND.COM BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
# GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER
# IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS DATABASE, EVEN
# IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from __future__ import print_function, absolute_import, division
import logging
from math import inf
from tracktable.core.geomath import latitude, longitude, distance
from tracktable.domain.terrestrial import TrajectoryPoint, BasePoint
CITY_TABLE = None
CITY_HEADERS = None
# ----------------------------------------------------------------------
[docs]class CityInfo(object):
"""Brief information about a city.
Attributes:
country_code (str): 2-character abbreviation for country
name (str): City name
population (int): Estimated population
location (TerrestrialBasePoint): location of city
"""
def __init__(self):
"""Initialize an empty city."""
self.country_code = None
self.name = None
self.population = None
self.location = None
# ----------------------------------------------------------------------
[docs]def cities_in_bbox(bbox_min=(-180, -90),
bbox_max=(180, 90),
minimum_population=0):
"""Return all the cities in a bounding box.
Keyword Args:
bbox_min (TrajectoryPoint): Southwest corner of bounding box,
(Default: (-180, -90))
bbox_max (TrajectoryPoint): Northeast corner of bounding box,
(Default: (180, 90))
minimum_population (int): Cities with lower population than
this will not be returned. (Default: 0).
Returns:
List of CityInfo objects.
"""
global CITY_TABLE
if not CITY_TABLE:
from tracktable.info.data.city_table import city_table as cities
CITY_TABLE = cities
result = []
logger = logging.getLogger(__name__)
logging.debug(logger, ("cities_in_bbox: bbox_min is {}, "
"bbox_max is {}".format(bbox_min, bbox_max)))
logging.debug(
logger,
("DEBUG: min_longitude is {}, min_latitude is {}, "
"max_longitude is {}, max_latitude is {}").format(
longitude(bbox_min),
latitude(bbox_min),
longitude(bbox_max),
latitude(bbox_max)))
min_longitude = longitude(bbox_min)
max_longitude = longitude(bbox_max)
min_latitude = latitude(bbox_min)
max_latitude = latitude(bbox_max)
for row in CITY_TABLE:
lat = row[3]
lon = row[4]
if ( lon >= min_longitude and
lon <= max_longitude and
lat >= min_latitude and
lat <= max_latitude and
((not minimum_population) or (row[2] >= minimum_population)) ):
info = CityInfo()
info.country_code = row[0]
info.name = row[1]
info.population = row[2]
info.latitude = row[3]
info.longitude = row[4]
result.append(info)
return result
# ----------------------------------------------------------------------
[docs]def largest_cities_in_bbox(bbox_min=(-180, -90),
bbox_max=(180, 90),
count=10):
"""Return the largest N cities in a bounding box.
A city's size is measured by its population.
Keyword Args:
bbox_min (TrajectoryPoint): Southwest corner of bounding box.
(Default: to (-180, -90)).
bbox_max (TrajectoryPoint): Northeast corner of bounding box.
(Default: to (180, 90)).
count (int): How many cities to return. (Default: 10).
Returns:
A list of CityInfo objects.
"""
all_cities = cities_in_bbox(bbox_min, bbox_max)
sorted_cities = sorted(all_cities,
key=lambda city: city.population,
reverse=True)
return sorted_cities[0:count]
# ----------------------------------------------------------------------
[docs]def get_city(name, country=None, location=None):
"""
Returns a city that most closely matches the input data.
In the case that multiple cities with the same name are found,
the closest to the given location will be used. In the case that
multiple cities are found and location is not used, the city
with the largest population will be returned.
Valid:
London, UK
Springfield, US, with one of the many valid lats or longs
Springfield, None, with one of the many valid lats or longs
Qandahar (there’s only one Qandahar in the world)
Arguments:
name (str): City name to search for
Keyword Arguments:
country (str): two character country code. (Default: None).
location (tuple, TrajectoryPoint, BasePoint): location to search near
(Default: None).
Returns:
A CityInfo object or None if no records are found.
"""
global CITY_TABLE
if not CITY_TABLE:
from tracktable.info.data.city_table import city_table as cities
CITY_TABLE = cities
city_list = CITY_TABLE
COUNTRY = 0
CITY = 1
POP = 2
LAT = 3
LONG = 4
# Convert location input if needed
location_point = location
print(type(location))
if type(location) is not BasePoint and location is not None:
location_point = BasePoint(location)
city_info = CityInfo()
name_lc = name.lower()
if country is None:
# Get cities with this name
city_list = [
city for city in city_list
if (city[CITY].lower() == name_lc)
]
else:
# Get cities with this name AND this country code
country_lc = country.lower()
city_list = [
city for city in city_list
if ((city[CITY].lower() == name_lc) and
(city[COUNTRY].lower() == country_lc))
]
if len(city_list) == 0:
return None
if location_point is not None:
# Find the distance of each town from the target lat/long
closest_city_distance = inf
closest_city = None
for city in city_list:
city_distance = distance(
BasePoint(city[LAT], city[LONG]),
location_point)
if city_distance < closest_city_distance:
closest_city = city
closest_city_distance = city_distance
city_info.country_code = closest_city[COUNTRY]
city_info.name = closest_city[CITY]
city_info.population = closest_city[POP]
city_info.location = BasePoint(closest_city[LAT], closest_city[LONG])
return city_info
# Get the biggest city
biggest_city_population = -inf
biggest_city = None
for city in city_list:
if city[POP] > biggest_city_population:
biggest_city = city
biggest_city_population = city[POP]
city_info.country_code = biggest_city[COUNTRY]
city_info.name = biggest_city[CITY]
city_info.population = biggest_city[POP]
city_info.location = BasePoint(biggest_city[LAT], biggest_city[LONG])
return city_info
HEADINGS = [
u'Country', u'AccentCity', u'Population', u'Latitude', u'Longitude'
]