来自两个不同数据集的点之间的距离

Question

我有两个不同的数据集（下面的示例）：

我试图找到数据集 1 和数据集 2 中每个站点之间的最小距离。因此，数据集 1 中的每个位置都会有一列显示与数据集 2 中存在的最近站点的距离。

到目前为止我有这个，但我无法让它工作。任何建议如何进行表示赞赏。

from geopy import distance
import pandas as pd


s = {
    'site_id': dataset1['site_id'],
    'latitude' : dataset1['latitude'], 
    'longitude' : dataset1['longitude']
    }

d = {
    'site_id': dataset2['site_id'],
    'latitude' : dataset2['latitude'], 
    'longitude' : dataset2['longitude']
    }

#s = pd.DataFrame(s)
#d = pd.DataFrame(d)




for (ss, a) in s.items():
    best = None
    dist = None
    for (dd, b) in d.items():
        km = distance.distance(a, b).km
        if dist is None or km < dist:
            best = dd
            dist = km

    print(f'{ss} is nearest {best}: {dist} km')

Answer 1

您可以使用 Haversine 公式计算给定经纬度坐标的两点之间的距离。下面是一个示例，说明如何修改代码以使用 Haversine 公式：

from math import radians, sin, cos, sqrt, atan2

def haversine(lat1, lon1, lat2, lon2):
    # convert decimal degrees to radians
    lat1, lon1, lat2, lon2 = map(radians, [lat1, lon1, lat2, lon2])

    # haversine formula
    dlat = lat2 - lat1
    dlon = lon2 - lon1
    a = sin(dlat / 2) ** 2 + cos(lat1) * cos(lat2) * sin(dlon / 2) ** 2
    c = 2 * atan2(sqrt(a), sqrt(1 - a))
    km = 6371 * c

    return km

使用以下内容更新您的代码

for (ss, a) in s.items():
    best = None
    dist = None
    for (dd, b) in d.items():
        km = haversine(a['latitude'], a['longitude'], b['latitude'], b['longitude'])
        if dist is None or km < dist:
            best = dd
            dist = km

    print(f'{ss} is nearest {best}: {dist} km')

Answer 2

看起来数据框中还有另一列称为

'site_id'

，因为您正在将其读入

s

和

d

变量

s = {
    'site_id': dataset1['site_id'],
    'latitude' : dataset1['latitude'], 
    'longitude' : dataset1['longitude']
    }

所以看起来你会在公式中比较

site_id

km = distance.distance(a, b).km

此外，

a

和

b

需要是纬度/经度的元组，这似乎不太可能是从

s

和

d

系列中提取的情况。 你的数据框更像这样吗？

DF1                                     DF2
| site_id | latitude | longitude |      | site_id | latitude | longitude |
|---------|----------|-----------|      |---------|----------|-----------|
| Site1   |51.8236   | -3.019610 |      | SiteA   | 51.8313  | -2.27422  |
| Site2   |52.4157   | -4.083580 |      | SiteB   | 50.4891  | -3.55259  |
| Site3   |57.1478   | -2.098000 |      | SiteC   | 56.5792  | -3.34735  |
| Site4   |56.4617   | -2.991410 |      | SiteD   | 57.1492  | -2.08277  |
| Site5   |51.2490   | -0.764848 |      | SiteE   | 57.2875  | -2.37346  |
| Site6   |57.1438   | -2.109280 |      | SiteF   | 57.1558  | -2.11278  |
| Site7   |51.6707   | -1.282660 |      | SiteG   | 57.1967  | -2.09314  |
                                        | SiteH   | 57.1538  | -2.27820  |
                                        | SiteI   | 53.7527  | -2.36054  |
                                        | SiteJ   | 55.8659  | -3.97845  |

如果是这样，您想创建两个元组字典，其中

site_id

是键，纬度/经度的元组是值，如下面的

s_dict

和

d_dict

所示，例如；

s_dict = {
    'Site1': (51.8236, -3.01961), 
    'Site2': (52.4157, -4.08358), 
    'Site3': (57.1478, -2.098), 
    'Site4': (56.4617, -2.99141), 
    ...

}

然后您可以提取每个站点的源纬度/经度元组并与目标元组进行比较并获得最佳距离。

from geopy import distance
import pandas as pd

# Dataframes...dataset1 and dataset2 sourced

s_dict = {x[0]: (x[1:]) for x in dataset1.itertuples(index=False)}
d_dict = {x[0]: (x[1:]) for x in dataset2.itertuples(index=False)}

for s_site, s in s_dict.items():
    print(f'Checking site: {s_site} Co-Ords: {s}')
    best = None
    dist = None
    for d_site, d in d_dict.items():
        km = distance.distance(s, d).km
        print(f'Comparing {s_site} to {d_site}, Co-ords: {d}, Distance: {km}')
        if dist is None or km < dist:
            best = d_site
            dist = km
    print(f'{s_site}: The nearest site is {best}: {dist} km')

这应该给出如下输出，并为每个比较添加打印行；

Checking site: Site1 Co-Ords: (51.8236, -3.01961)
Comparing Site1 to SiteA, Co-ords: (51.8313, -2.27422), Distance: 51.39541157179988
Comparing Site1 to SiteB, Co-ords: (50.4891, -3.55259), Distance: 153.07461731514346
Comparing Site1 to SiteC, Co-ords: (56.5792, -3.34735), Distance: 529.7691869147437
Comparing Site1 to SiteD, Co-ords: (57.1492, -2.08277), Distance: 595.8983925872216
Comparing Site1 to SiteE, Co-ords: (57.2875, -2.37346), Distance: 609.6418219993236
Comparing Site1 to SiteF, Co-ords: (57.1558, -2.11278), Distance: 596.4352710313524
Comparing Site1 to SiteG, Co-ords: (57.1967, -2.09314), Distance: 601.0900671080333
Comparing Site1 to SiteH, Co-ords: (57.1538, -2.2782), Distance: 595.2575358222653
Comparing Site1 to SiteI, Co-ords: (53.7527, -2.36054), Distance: 219.22839170745868
Comparing Site1 to SiteJ, Co-ords: (55.8659, -3.97845), Distance: 454.30856091686644
Site1: The nearest site is SiteA: 51.39541157179988 km