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如何从给定的经纬度信息中提取加州县的位置[已关闭]

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  • Nishant  · 技术社区  · 7 年前

    我有以下加州住房数据集:

    head(calif_cluster,15)
       MedianHouseValue MedianIncome MedianHouseAge TotalRooms TotalBedrooms Population
    1            190300      4.20510             16    2697.00        490.00       1462
    2            150800      2.54810             33    2821.00        652.00       1206
    3            252600      6.08290             17    6213.20       1276.05       3288
    4            269700      4.03680             52     919.00        213.00        413
    5             91200      1.63680             28    3072.00        790.00       1375
    6             66200      2.18980             30     744.00        156.00        410
    7            148800      2.63640             39     620.95        136.00        348
    8            384800      4.46150             20    2270.00        498.00       1070
    9            153200      2.75000             22    1931.00        445.00       1009
    10            66200      1.60057             36     973.00        219.00        613
    11           461500      3.78130             43    3070.00        668.00       1240
    12           144600      2.85000             22    5175.00       1213.00       2804
    13           143700      5.09410              8    6213.20       1276.05       3288
    14           195500      5.30620             16    2918.00        444.00       1697
    15           268800      2.42110             22     620.95        136.00        348
       Households Latitude Longitude cluster_kmeans gender_dom marital     race edu_level rental
    1         515    38.48   -122.47              1          M   other    black jrcollege rented
    2         640    38.00   -122.13              1          F   other hispanic doctorate  owned
    3        1162    33.88   -117.79              3          M   other    white jrcollege  owned
    4         193    37.85   -122.25              1          M  single   others jrcollege  owned
    5         705    38.13   -122.26              1          F  single    white doctorate rented
    6         165    38.96   -122.21              1          F  single   others jrcollege  owned
    7         125    34.01   -118.18              2          M married   others  postgrad  owned
    8         521    33.83   -118.38              2          F  single    white  graduate rented
    9         407    38.95   -121.04              1          M married   others  postgrad leased
    10        187    35.34   -119.01              2          M  single hispanic doctorate  owned
    11        646    33.76   -118.12              2          F   other   others  highschl leased
    12       1091    37.95   -122.05              3          M   other    white  graduate rented
    13       1162    36.87   -119.75              3          M   other   others  postgrad leased
    14        444    32.93   -117.13              2          M   other    asian jrcollege  owned
    15        125    37.71   -120.98              1          F  single    asian  postgrad leased
    

    因为我有纬度&在数据集中的经度信息,我想提取相应的 county 对于使用R的给定地理信息,也可以获得每个提取县的首都(或最大城市)。这些可以使我的分层分析更具洞察力;打算做一些聚类/映射练习。

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  •   moodymudskipper    7 年前

    看看 ggmap::revgeocode

    密码

    library(ggmap)
    revgeocode(c(-122.47,38.48)) # longitude then latitude
    # [1] "2233 Sulphur Springs Ave, St Helena, CA 94574, USA"
    
    library(dplyr)
    library(magrittr)
    df12 %<>% rowwise %>% mutate(address = revgeocode(c(Longitude,Latitude))) %>% ungroup        # add full address using google api through ggmap
    df12 %<>% separate(address,c("street_address", "city","county","country"),remove=F,sep=",")  # structure all the info you need
    

    后果

    df12 %>% select(Longitude,Latitude,address,county)
    # A tibble: 15 x 4
    # Longitude Latitude                                            address    county
    # *     <dbl>    <dbl>                                              <chr>     <chr>
    #  1   -122.47    38.48 2233 Sulphur Springs Ave, St Helena, CA 94574, USA  CA 94574
    #  2   -122.13    38.00    3400-3410 Brookside Dr, Martinez, CA 94553, USA  CA 94553
    #  3   -117.79    33.88  19721 Bluefield Plaza, Yorba Linda, CA 92886, USA  CA 92886
    #  4   -122.25    37.85             6365 Florio St, Oakland, CA 94618, USA  CA 94618
    #  5   -122.26    38.13              119 Mimosa Ct, Vallejo, CA 94589, USA  CA 94589
    #  6   -122.21    38.96              Unnamed Road, Arbuckle, CA 95912, USA  CA 95912
    #  7   -118.18    34.01    4360-4414 Noakes St, Los Angeles, CA 90023, USA  CA 90023
    #  8   -118.38    33.83    903 Serpentine St, Redondo Beach, CA 90277, USA  CA 90277
    #  9   -121.04    38.95        14666-14690 Musso Rd, Auburn, CA 95603, USA  CA 95603
    # 10   -119.01    35.34           800 Ming Ave, Bakersfield, CA 93307, USA  CA 93307
    # 11   -118.12    33.76   6211-6295 E Marina Dr, Long Beach, CA 90803, USA  CA 90803
    # 12   -122.05    37.95              1120 Carey Dr, Concord, CA 94520, USA  CA 94520
    # 13   -119.75    36.87        1815-1899 E Pryor Dr, Fresno, CA 93720, USA  CA 93720
    # 14   -117.13    32.93      9010-9016 Danube Ln, San Diego, CA 92126, USA  CA 92126
    # 15   -120.98    37.71       748-1298 Claribel Rd, Modesto, CA 95356, USA  CA 95356
    

    df1 <- read.table(text = "MedianHouseValue MedianIncome MedianHouseAge TotalRooms TotalBedrooms Population
        1            190300      4.20510             16    2697.00        490.00       1462
                          2            150800      2.54810             33    2821.00        652.00       1206
                          3            252600      6.08290             17    6213.20       1276.05       3288
                          4            269700      4.03680             52     919.00        213.00        413
                          5             91200      1.63680             28    3072.00        790.00       1375
                          6             66200      2.18980             30     744.00        156.00        410
                          7            148800      2.63640             39     620.95        136.00        348
                          8            384800      4.46150             20    2270.00        498.00       1070
                          9            153200      2.75000             22    1931.00        445.00       1009
                          10            66200      1.60057             36     973.00        219.00        613
                          11           461500      3.78130             43    3070.00        668.00       1240
                          12           144600      2.85000             22    5175.00       1213.00       2804
                          13           143700      5.09410              8    6213.20       1276.05       3288
                          14           195500      5.30620             16    2918.00        444.00       1697
                          15           268800      2.42110             22     620.95        136.00        348",header=T,stringsAsFactors=F)
    
    
    df2 <- read.table(text = "Households Latitude Longitude cluster_kmeans gender_dom marital     race edu_level rental
                      1         515    38.48   -122.47              1          M   other    black jrcollege rented
                      2         640    38.00   -122.13              1          F   other hispanic doctorate  owned
                      3        1162    33.88   -117.79              3          M   other    white jrcollege  owned
                      4         193    37.85   -122.25              1          M  single   others jrcollege  owned
                      5         705    38.13   -122.26              1          F  single    white doctorate rented
                      6         165    38.96   -122.21              1          F  single   others jrcollege  owned
                      7         125    34.01   -118.18              2          M married   others  postgrad  owned
                      8         521    33.83   -118.38              2          F  single    white  graduate rented
                      9         407    38.95   -121.04              1          M married   others  postgrad leased
                      10        187    35.34   -119.01              2          M  single hispanic doctorate  owned
                      11        646    33.76   -118.12              2          F   other   others  highschl leased
                      12       1091    37.95   -122.05              3          M   other    white  graduate rented
                      13       1162    36.87   -119.75              3          M   other   others  postgrad leased
                      14        444    32.93   -117.13              2          M   other    asian jrcollege  owned
                      15        125    37.71   -120.98              1          F  single    asian  postgrad leased",header=T,stringsAsFactors=F)
    
    df12 <- cbind(df1,df2)
    

    我不认为图书馆提供了一个选择,以获得该县的首都或最大的城市,但我认为你不会有太多的麻烦建立一个从网上信息查表。