Latitude et Longitude jusqu'à l'Adresse

Question

J'ai environ 8000 + Latitudes et Longitudes. Je dois extraire (code inverse), les à l'emplacement, éventuellement la ville. Au début, j'ai vérifié avec Google Maps, mais selon leurs termes, nous ne devrions pas utiliser leurs scripts.

Même les OpenStreetMaps ne nous permettent pas de frapper leurs serveurs à plusieurs reprises. Ils ont une limite de temps. J'ai donc téléchargé les latitudes et les logitudes pour les emplacements. J'ai écrit un script python,

import tabular as tb 
import csv 



citiesLatLongData = tb.tabarray(SVfile="D:/latitude-longitude/citieslatlong.csv") 

allData = tb.tabarray(SVfile="C:/Users/User/Desktop/alldata.csv") 

latlonglocs = {'a1':"Car Nicobar",'a2':"Port Blair",'a3':"Hyderabad",'a4':"Kadapa",'a5':"Puttaparthi", 
'a6':"Rajahmundry",'a7':"Tirupati",'a8':"Vijayawada",'a9':"Vishakhapatnam",'a10':"Itanagar", 
'a11':"Dibrugarh",'a12':"Dispur",'a13':"Guwahati",'a14':"North Lakhimpur",'a15':"Silchar", 
'a16':"Gaya",'a17':"Patna",'a18':"Chandigarh",'a19':"Raipur",'a20':"Silvassa", 
'a21':"Daman",'a22':"Bawana",'a23':"New Delhi",'a24':"Mormugao",'a25':"Panaji", 
'a26':"Ahmedabad",'a27':"Bhavnagar",'a28':"Bhuj",'a29':"Gandhinagar",'a30':"Jamnagar", 
'a31':"Kandla",'a32':"Rajkot",'a33':"Vadodara",'a34':"Hisar",'a35':"Bilaspur", 
'a36':"Dharamsala",'a37':"Kulu",'a38':"Shimla",'a39':"Jammu",'a40':"Srinagar",'a41':"Jamshedpur", 
'a42':"Ranchi",'a43':"Bangalore",'a44':"Belgaum",'a45':"Bellary",'a46':"Hubli Dharwad", 
'a47':"Mandya",'a48':"Mangalore",'a49':"Mysore",'a50':"Cochin",'a51':"Kozhikode", 
'a52':"Thiruvananthapuram",'a53':"Bingaram Island ",'a54':"Kavaratti",'a55':"Bhopal",'a56':"Gwalior", 
'a57':"Indore",'a58':"Jabalpur",'a59':"Khandwa",'a60':"Satna",'a61':"Ahmadnagar", 
'a62':"Akola",'a63':"Aurangabad",'a64':"Jalna",'a65':"Kolhapur",'a66':"Mumbai", 
'a67':"Nagpur",'a68':"Nasik",'a69':"Pimpri",'a70':"Pune",'a71':"Solapur", 
'a72':"Imphal",'a73':"Shillong",'a74':"Aizawl",'a75':"Kohima",'a76':"Bhubaneswar", 
'a77':"Jharsuguda",'a78':"Karaikal",'a79':"Mahe",'a80':"Pondicherry",'a81':"Yanam", 
'a82':"Amritsar",'a83':"Pathankot",'a84':"Jaipur",'a85':"Jodhpur",'a86':"Kota", 
'a87':"Udaipur",'a88':"Gangtok",'a89':"Chennai",'a90':"Coimbatore",'a91':"Madurai", 
'a92':"Nagercoil",'a93':"Thiruchendur",'a94':"Thiruvannaamalai",'a95':"Thoothukudi", 
'a96':"Tiruchirappalli",'a97':"Tirunelveli",'a98':"Vellore",'a99':"Agartala", 
'a100':"Agra",'a101':"Allahabad",'a102':"Bareilly",'a103':"Gorakhpur",'a104':"Jhansi", 
'a105':"Kanpur",'a106':"Lucknow",'a107':"Varanasi",'a108':"Dehradun",'a109':"Pantnagar", 
'a110':"Kolkata",'a111':"Siliguri"} 



latlongs = {'a1':[9.15,92.8167],'a2':[11.6667,92.7167],'a3':[17.45,78.4667],'a4':[14.4833,78.8333], 
      'a5':[14.1333,77.7833],'a6':[16.9667,81.7667],'a7':[13.65,79.4167],'a8':[16.5333,80.8], 
      'a9':[17.7,83.3],'a10':[27.0833,93.5667],'a11':[27.4833,95.0167],'a12':[26.0833,91.8333], 
      'a13':[26.1667,91.5833],'a14':[27.2333,94.1167],'a15':[24.8167,92.8],'a16':[24.75,84.95], 
      'a17':[25.6,85.1],'a18':[30.7333,76.75],'a19':[21.2333,81.6333],'a20':[20.2833,73], 
      'a21':[20.4167,72.85],'a22':[28.7833,77.0333],'a23':[28.5667,77.1167],'a24':[15.3833,73.8167],  
      'a25':[15.3833,73.8167],'a26':[23.0333,72.6167],'a27':[21.75,72.2],'a28':[23.25,69.6667], 
      'a29':[23.3333,72.5833],'a30':[22.4667,70.0667],'a31':[23.0333,70.2167],'a32':[22.3,70.7833], 
      'a33':[22.3,73.2667],'a34':[29.1667,75.7333],'a35':[31.25,76.6667],'a36':[32.2,76.4], 
      'a37':[31.9667,77.1],'a38':[31.1,77.1667],'a39':[32.7,74.8667],'a40':[34.0833,74.8167], 
      'a41':[22.8167,86.1833],'a42':[23.3167,85.3167],'a43':[12.9833,77.5833],'a44':[15.85,74.6167], 
      'a45':[15.15,76.85],'a46':[15.35,75.1667],'a47':[12.55,76.9],'a48':[12.9167,74.8833], 
      'a49':[12.3,76.65],'a50':[9.95,76.2667],'a51':[11.25,75.7667],'a52':[8.46667,76.95], 
      'a53':[10.9167,72.3333],'a54':[10.5833,72.65],'a55':[23.2833,77.35],'a56':[26.2333,78.2333], 
      'a57':[22.7167,75.8],'a58':[23.2,79.95],'a59':[21.8333,76.3667],'a60':[24.5667,80.8333], 
      'a61':[19.0833,74.7333],'a62':[20.7,77.0667],'a63':[19.85,75.4],'a64':[19.8333,75.8833], 
      'a65':[16.7,74.2333],'a66':[19.1167,72.85],'a67':[21.1,79.05],'a68':[19.8933,73.8], 
      'a69':[18.55,73.8167],'a70':[18.5333,73.8667],'a71':[17.6667,75.9],'a72':[24.7667,93.9], 
      'a73':[25.55,91.85],'a74':[23.6667,92.6667],'a75':[25.6667,94.1167],'a76':[20.25,85.8333], 
      'a77':[21.5833,84.08333],'a78':[10.95,79.7833],'a79':[11.7,75.5333],'a80':[11.9333,79.8833], 
      'a81':[16.7333,82.2167],'a82':[31.6333,74.8667],'a83':[32.2833,75.65],'a84':[26.8167,75.8], 
      'a85':[29.1667,75.7333],'a86':[25.15,75.85],'a87':[24.5667,73.6167],'a88':[27.3333,88.6167], 
      'a89':[13,80.1833],'a90':[11.0333,77.05],'a91':[9.83333,78.0833],'a92':[8.16667,77.4333], 
      'a93':[8.48333,78.1167],'a94':[12.2167,79.0667],'a95':[8.78333,78.1333],'a96':[10.7667,78.7167], 
      'a97':[8.73333,77.7],'a98':[12.9167,79.15],'a99':[23.8833,91.25],'a100':[27.15,77.9667], 
      'a101':[25.45,81.7333],'a102':[28.3667,79.4],'a103':[26.75,83.3667],'a104':[29.1667,75.7333], 
      'a105':[26.4,80.4],'a106':[26.75,80.8833],'a107':[25.45,83],'a108':[30.3167,78.0333], 
      'a109':[29.0833,79.5],'a110':[22.65,88.45],'a111':[26.6333,88.3167] 
      } 

for eachOne in allData: 
    for eachTwo in latlongs: 
     eachOne_Coordinates_Latitude = eachOne['COORDINATES-Latitude'] 
     latlongs_eachTwo_Latitude_Plus = int(latlongs[eachTwo][0]) + 0.18 
     latlongs_eachTwo_Latitude_Minus = int(latlongs[eachTwo][0]) - 0.18 

     eachOne_Coordinates_Longitude = eachOne['COORDINATES-Longitude'] 
     latlongs_eachTwo_Longitude_Plus = int(latlongs[eachTwo][1]) + 0.18 
     latlongs_eachTwo_Longitude_Minus = int(latlongs[eachTwo][1]) - 0.18 

     if ((eachOne_Coordinates_Latitude < latlongs_eachTwo_Latitude_Plus) and (latlongs_eachTwo_Latitude_Plus > latlongs_eachTwo_Latitude_Minus)) and ((eachOne_Coordinates_Longitude < latlongs_eachTwo_Longitude_Plus) and (eachOne_Coordinates_Longitude > latlongs_eachTwo_Longitude_Minus)): 
      someDict.setdefault((eachOne_Coordinates_Latitude,eachOne_Coordinates_Longitude),[]).append(latlongs[eachTwo]) 


for each in someDict: 
    print each,':', min(someDict[each]) 

MON PROBLÈME: Comme vous le savez, les latitudes et longitudes que nous recevons de sources externes ne correspond pas exactement aux latitudes et longitudes que nous avons. J'ai entendu quelque part qu'ils ne correspondent pas et il y aura un pourcentage d'erreur ou quelque chose.

J'ai besoin de conseils de quiconque. Je demande à quelqu'un de me diriger dans la bonne direction ou si vous connaissez des paquets ou des scripts qui le font.

Je serais extrêmement reconnaissant envers vous.

Answer 1

Cela ressemble beaucoup à un "problème de point le plus proche". Vous avez N points (villes) et M emplacements (vos coordonnées 8000). Pour chacun des M emplacements, vous voulez classer l'emplacement par sa ville la plus proche. Il y a un certain nombre de solutions pour la Nearest Neighbor Search, mais le plus simple est une recherche linéaire:

function getClosestCity(Coordinate location){ 
    bestCity = cities[0]; 
    foreach(city in cities){ 
     if (distance(bestCity.location, location) < distance(city.location, location)){ 
      bestCity = city; 
     } 
    } 
    return bestCity; 
}