Latitude and Longitude to Address

Question

I have about 8000 + Latitudes and Longitudes. I need to extract(reverse code), them to location, possibly city.

Initially I checked with Google Maps, but according to their terms we should not hit their services with our scripts.

Even the OpenStreetMaps doesnt allow us to hit their servers repeatedly. They have some time limit.

So, I downloaded the latitudes and logitudes for locations. I wrote a python script,

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])

MY PROBLEM: As you know, the latitudes and longitudes that we get from external sources does not exactly match with the latitudes and longitudes that we have. I heard somewhere that they wont match and there will be some error percentage or something.

I need some guidance from anyone. I request someone to please point me in the right direction or if you know any packages or scripts that does this.

I would be extremely thankful to you.

Answer 1

This sounds a lot like a "Closest point problem". You have N points (cities) and M locations (your 8000 coordinates). For each of the M locations, you want to categorize the location by its closest city. There are a number of solutions for the Nearest Neighbor Search, but the simplest one is a linear search:

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