使用R中的纬度和经度计算观测值

Question

Noob question. I can't figure out what is wrong with this code. I am trying to find the number of observations that occur in a 400 meter radius circle. I have the lat and long for each observation. I am trying to create a new column that will show the number of competing restaurants within in a 400 meter radius circle. I included a sample of the data the code I am working with, and the STR of the dataframe. Thanks in advance.

for (i in seq(nrow(expandedDataFrame2)))
{
  # circle's centre
  xcentre <- df[i,'latitude']
  ycentre <- df[i,'longitude']

  # checking how many restaurants lie within 400 m of the above centre, noofcloserest   column will contain this value
  expandedDataFrame2[i,'noofcloserest'] <- sum(
(expandedDataFrame2[,'latitude'] - xcentre)^2 + 
  (expandedDataFrame2[,'longitude'] - ycentre)^2 
<= 400^2
) - 1

# logging part for deeper analysis
cat(i,': ')

 cat((expandedDataFrame2[,'latitude'] - xcentre)^2 + 
    (expandedDataFrame2[,'longitude'] - ycentre)^2 
  <= 400^2)

cat('\n')

}

Sample:

              business_id             restaurantType                                           full_address open       city
1 --5jkZ3-nUPZxUvtcbr8Uw                Greek             1336 N Scottsdale Rd\nScottsdale, AZ 85257    1 Scottsdale
2 --BlvDO_RG2yElKu9XA1_g           Sushi Bars 14870 N Northsight Blvd\nSte 103\nScottsdale, AZ 85260    1 Scottsdale
3 -_Ke8q969OAwEE_-U0qUjw Beer, Wine & Spirits                   18555 N 59th Ave\nGlendale, AZ 85308    0   Glendale
4 -_npP9XdyzILAjtFfX8UAQ           Vietnamese          6025 N 27th Avenue\nSte 24\nPhoenix, AZ 85073    1    Phoenix
5 -2xCV0XGD9NxfWaVwA1-DQ                Pizza                      9008 N 99th Ave\nPeoria, AZ 85345    1     Peoria
6 -3WVw1TNQbPBzaKCaQQ1AQ              Chinese                     302 E Flower St\nPhoenix, AZ 85012    1    Phoenix
   review_count                       name longitude state stars latitude     type      categories1          categories2
1           11 George's Gyros Greek Grill -111.9269    AZ   4.5 33.46337 business       Greek                 <NA>
2           37               Asian Island -111.8983    AZ   4.0 33.62146 business  Sushi Bars             Hawaiian
3            6    Jug 'n Barrel Wine Shop -112.1863    AZ   4.5 33.65387 business        <NA> Beer, Wine & Spirits
4           15          Thao's Sandwiches -112.0739    AZ   3.0 33.44990 business  Vietnamese           Sandwiches
5            4          Nino's Pizzeria 2 -112.2766    AZ   4.0 33.56626 business       Pizza                 <NA>
6          145                China Chili -112.0692    AZ   3.5 33.48585 business     Chinese                 <NA>
  categories3 categories4 categories5 categories6 categories7 categories8 categories9 categories10 isRestaurant Freq
1        <NA>        <NA>        <NA>        <NA>        <NA>        <NA>        <NA>         <NA>         TRUE   66
2     Chinese        <NA>        <NA>        <NA>        <NA>        <NA>        <NA>         <NA>         TRUE   58
3        <NA>        <NA>        <NA>        <NA>        <NA>        <NA>        <NA>         <NA>         TRUE    8
4        <NA>        <NA>        <NA>        <NA>        <NA>        <NA>        <NA>         <NA>         TRUE   44
5        <NA>        <NA>        <NA>        <NA>        <NA>        <NA>        <NA>         <NA>         TRUE  166
6        <NA>        <NA>        <NA>        <NA>        <NA>        <NA>        <NA>         <NA>         TRUE  166
     avgRev  avgStar  duration delta
1 31.32836 3.694030  381 days     0
2 68.62712 3.661017  690 days     0
3 34.33333 3.555556  604 days     1
4 63.22222 3.577778 1916 days     0
5 30.84431 3.482036  226 days     0
6 23.79042 3.535928 2190 days     0

The structure of the data is;

str(expandeddataframe2)

'data.frame':   2833 obs. of  28 variables:
 $ business_id   : chr  "--5jkZ3-nUPZxUvtcbr8Uw" "--BlvDO_RG2yElKu9XA1_g" "-_Ke8q969OAwEE_-U0qUjw" "-_npP9XdyzILAjtFfX8UAQ" ...
 $ restaurantType: chr  "Greek" "Sushi Bars" "Beer, Wine & Spirits" "Vietnamese" ...
 $ full_address  : chr  "1336 N Scottsdale Rd\nScottsdale, AZ 85257" "14870 N Northsight Blvd\nSte 103\nScottsdale, AZ 85260" "18555 N 59th Ave\nGlendale, AZ 85308" "6025 N 27th Avenue\nSte 24\nPhoenix, AZ 85073" ...
 $ open          : Factor w/ 2 levels "0","1": 2 2 1 2 2 2 2 2 2 2 ...
 $ city          : chr  "Scottsdale" "Scottsdale" "Glendale" "Phoenix" ...
 $ review_count  : num  11 37 6 15 4 145 255 35 7 7 ...
 $ name          : chr  "George's Gyros Greek Grill" "Asian Island" "Jug 'n Barrel Wine Shop" "Thao's Sandwiches" ...
 $ longitude     : num  -112 -112 -112 -112 -112 ...
 $ state         : chr  "AZ" "AZ" "AZ" "AZ" ...
 $ stars         : num  4.5 4 4.5 3 4 3.5 4.5 4 2.5 4.5 ...
 $ latitude      : num  33.5 33.6 33.7 33.4 33.6 ...
 $ type          : chr  "business" "business" "business" "business" ...
 $ categories1   : chr  "Greek" "Sushi Bars" NA "Vietnamese" ...
 $ categories2   : chr  NA "Hawaiian" "Beer, Wine & Spirits" "Sandwiches" ...
 $ categories3   : chr  NA "Chinese" NA NA ...
 $ categories4   : chr  NA NA NA NA ...
 $ categories5   : chr  NA NA NA NA ...
 $ categories6   : chr  NA NA NA NA ...
 $ categories7   : chr  NA NA NA NA ...
 $ categories8   : chr  NA NA NA NA ...
 $ categories9   : chr  NA NA NA NA ...
 $ categories10  : chr  NA NA NA NA ...
 $ isRestaurant  : logi  TRUE TRUE TRUE TRUE TRUE TRUE ...
 $ Freq          : num  66 58 8 44 166 166 98 35 45 166 ...
 $ avgRev        : num [1:2833(1d)] 31.3 68.6 34.3 63.2 30.8 ...
  ..- attr(*, "dimnames")=List of 1
  .. ..$ : chr  "Greek" "Sushi Bars" "Beer, Wine & Spirits" "Vietnamese" ...
 $ avgStar       : num [1:2833(1d)] 3.69 3.66 3.56 3.58 3.48 ...
  ..- attr(*, "dimnames")=List of 1
  .. ..$ : chr  "Greek" "Sushi Bars" "Beer, Wine & Spirits" "Vietnamese" ...
 $ duration      :Class 'difftime'  atomic [1:2833] 381 690 604 1916 226 ...
  .. ..- attr(*, "units")= chr "days"
 $ delta         : num  0 0 1 0 0 0 0 0 0 0 ...

Answer 1

So here is one way, which uses the function spDistsN1(...) in package sp . Calling your dataframe df ,

library(sp)

get.dists <- function(i) {
  ref.pt <- with(df[i,],c(longitude,latitude))
  points <- as.matrix(with(df[-i,],cbind(longitude,latitude)))
  dists  <- spDistsN1(points, ref.pt, longlat=T)
  return(length(which(dists<0.4)))
}
df$count <- sapply(1:nrow(df),get.dists)

spDistsN1(points, ref.pt) calculates the Great-Circle distance from ref.pt to each of the points in points . If longlat=T the distance is returned in km. So the function get.dists generates a vector of distances from the reference row to every other row, and then counts how many are < 0.4km using length(which(dists<0.4)) . This function is called for each row in df using sapply(...) .

Note that in your sample dataset, none of the restaurants are within 400m of each other.