经纬度和经度

时间：2012-07-20 10:40:03

标签： python latitude-longitude

我有大约8000 +纬度和经度。 我需要提取（反向代码），将它们提取到位置，可能是城市。

最初我使用Google地图查看，但根据他们的条款，我们不应该使用我们的脚本来提供服务。

即使是OpenStreetMaps也不允许我们反复点击他们的服务器。他们有一些时间限制。

所以，我下载了地点的纬度和逻辑。我写了一个python脚本，

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])

我的问题： 如你所知，我们从外部来源获得的纬度和经度与我们所拥有的纬度和经度并不完全一致。我听说某个地方他们不匹配，会有一些错误百分比等等。

我需要任何人的指导。我请求有人请指出我正确的方向，或者如果您知道任何包或脚本执行此操作。

我非常感谢你。

1 个答案:

答案 0 :(得分：2)

这听起来很像“最近点问题”。你有N个点（城市）和M个位置（你的8000坐标）。对于M个位置中的每个位置，您希望按其最近的城市对位置进行分类。 Nearest Neighbor Search有许多解决方案，但最简单的是线性搜索：

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