如何使用纬度/经度坐标根据与所有其他要素的地理距离为每个要素创建一个包含平均索引值的新列？

Question

我正在分析相邻组织在创新方面如何相互影响。我有一个数据集，其中包含创新

Index

和

Lat

/

Lon

数据的值。我需要获取一定距离（例如 50 公里）内所有情况的指数平均值。

如何为数据框中的每个条目创建一个新值，该值是根据纬度/经度从一定地理距离内的其他条目派生的？

作为一个具体示例，我的数据集如下所示：

df <- data.frame("Name" = c("A","B","C","D","E","F"), 
                 "Index" = c(5,2,8,3,5,9), 
                 "Lat" = c(42.1234, 41.0192, 40.9988, 51.0175, 50.6523, 50.9214), 
                 "Lon" = c(26.5462, 25.9967, 27.0001, 31.1542, 31.8924, 32.1025))

df
>   Name Index     Lat     Lon
> 1    A     5 42.1234 26.5462
> 2    B     2 41.0192 25.9967
> 3    C     8 40.9988 27.0001
> 4    D     3 51.0175 31.1542
> 5    E     5 50.6523 31.8924
> 6    F     9 50.9214 32.1025

我想要得到的是一个看起来像这样的数据框，其中

NearbyIndex

列显示附近案例的平均

Index

值：

df2
>   Name Index     Lat     Lon NearbyIndex
> 1    A     5 42.1234 26.5462         5.0
> 2    B     2 41.0192 25.9967         6.5
> 3    C     8 40.9988 27.0001         3.5
> 4    D     3 51.0175 31.1542         7.0
> 5    E     5 50.6523 31.8924         6.0
> 6    F     9 50.9214 32.1025         4.0

Answer 1

好吧，如果您的

df

有很多行，也许我的解决方案在效率方面不是最好的，但作为第一次尝试它可能很有用。

# Your data
df <- data.frame("Name" = c("A","B","C","D","E","F"), 
                 "Index" = c(5,2,8,3,5,9), 
                 "Lat" = c(42.1234, 41.0192, 40.9988, 51.0175, 50.6523, 50.9214), 
                 "Lon" = c(26.5462, 25.9967, 27.0001, 31.1542, 31.8924, 32.1025))

# Setting the distance threshold (I decided to change to 80 km because 50 km was 
# too low for apprecaiting correctly the example)
dist_threshold_km <- 80


# Installing terra package
# install.packages("terra")

# Creating a matrix of distances
# 'lonlat = TRUE' is for applying a Great Circle (WGS84 ellipsoid) distance.
# 'unit = km' do exist but it is not working properly right now, so it'd
# be better to require the values in meters and then divide them by 1e3
distMat <- terra::distance(x = as.matrix(df[,c("Lon", "Lat")]),
                           y = as.matrix(df[,c("Lon", "Lat")]),
                           lonlat = TRUE, unit = "m")/1e3

# 'distMat <= dist_threshold_km' converts your matrix of distance in a boolean
# matrix where the only the values that are less or equal to the threshold will 
# be TRUE. Then every row is used for indexing your df$Index and calculates the 
# mean.
df$NearbyIndex <- apply(X = distMat <= dist_threshold_km, MARGIN = 1, 
                        FUN = \(x, df) mean(df$Index[x]), df = df)