英文:
How to group data points together based on distance between coordinates in R?
问题
I have a set of data where each sample taken has coordinates associated with it. I also have regulatory language that states that any samples taken within 200 meters of each other are representative of the same site.
My data set contains 159 unique coordinates that I am hoping will translate to fewer sites, which I can then analyze together.
The coordinate matrix looks like this:
> as.matrix(unique(raw_data[, c("lat","long")]))
lat long
[1,] 25.75038 -80.96646
[2,] 25.78788 -81.09702
[3,] 25.75816 -80.99451
[4,] 25.85593 -80.89979
[5,] 25.93371 -80.81229
[6,] 25.95037 -80.83312
[7,] 25.86704 -81.09979
[8,] 25.84482 -80.93035
[9,] 25.83371 -80.88312
[10,] 25.87538 -81.22480
...
I have already mapped this out using leaflet and know that there are several samples taken very close together, but I am hoping there is a way to test if any coordinates are 200 meters apart or closer from any other coordinates and assign a group to them as a third column in the matrix (sites 1-xx). From there I could use dplyr's group_by function to summarize the 6,000+ samples by site instead of coordinate pairs to get an analysis with far fewer groups of data.
英文:
I have a set of data where each sample taken has coordinates associated with it. I also have regulatory language that states that any samples taken within 200 meters of each other are representative of the same site.
My data set contains 159 unique coordinates that I am hoping will translate to fewer sites, which I can then analyze together.
The coordinate matrix looks like this:
> as.matrix(unique(raw_data[, c("lat","long")]))
lat long
[1,] 25.75038 -80.96646
[2,] 25.78788 -81.09702
[3,] 25.75816 -80.99451
[4,] 25.85593 -80.89979
[5,] 25.93371 -80.81229
[6,] 25.95037 -80.83312
[7,] 25.86704 -81.09979
[8,] 25.84482 -80.93035
[9,] 25.83371 -80.88312
[10,] 25.87538 -81.22480
[11,] 25.88729 -81.26125
[12,] 25.87676 -81.22787
[13,] 26.16820 -81.08820
[14,] 25.74760 -80.94979
[15,] 25.79030 -80.89110
[16,] 25.77390 -80.93390
[17,] 25.88780 -81.26170
[18,] 25.87664 -81.22823
[19,] 26.22222 -81.17222
[20,] 25.88764 -81.26188
[21,] 25.89092 -81.26972
[22,] 25.99452 -81.26270
[23,] 26.19736 -81.26716
[24,] 25.90036 -81.26199
[25,] 26.17218 -81.26681
[26,] 26.09577 -81.26506
[27,] 26.16925 -81.08729
[28,] 25.77806 -80.84444
[29,] 25.88778 -81.26167
[30,] 25.87639 -81.21778
[31,] 25.85190 -80.98100
[32,] 25.85192 -80.98103
[33,] 25.85222 -80.98083
[34,] 25.87222 -81.01861
[35,] 25.86385 -81.10096
[36,] 25.86361 -81.10111
[37,] 25.84341 -80.91720
[38,] 25.84306 -80.91778
[39,] 25.89030 -81.27030
[40,] 25.89028 -81.27025
[41,] 25.89056 -81.27000
[42,] 25.88650 -81.26210
[43,] 25.88653 -81.26208
[44,] 25.88694 -81.26194
[45,] 25.76190 -80.85330
[46,] 25.78544 -80.85119
[47,] 26.04472 -81.29972
[48,] 26.04430 -81.29990
[49,] 26.04431 -81.29992
[50,] 26.09280 -81.05390
[51,] 26.09278 -81.05392
[52,] 26.16000 -81.22639
[53,] 26.19600 -81.28870
[54,] 26.19597 -81.28869
[55,] 26.19639 -81.28861
[56,] 26.16250 -81.24170
[57,] 26.16390 -81.17360
[58,] 26.16530 -81.09310
[59,] 26.09440 -81.26670
[60,] 25.78890 -80.85690
[61,] 25.85280 -81.02920
[62,] 25.84440 -80.97080
[63,] 25.87360 -81.22920
[64,] 25.94720 -81.26250
[65,] 25.91917 -80.83639
[66,] 25.84310 -80.91770
[67,] 25.78611 -81.20056
[68,] 25.71350 -81.02190
[69,] 25.71347 -81.02192
[70,] 25.71389 -81.02167
[71,] 25.72080 -80.87220
[72,] 25.76390 -81.07500
[73,] 26.19130 -81.08680
[74,] 26.19128 -81.08675
[75,] 26.19167 -81.08667
[76,] 25.68722 -80.91972
[77,] 26.20500 -81.16833
[78,] 25.78861 -81.09991
[79,] 25.76027 -81.04831
[80,] 25.76050 -80.99626
[81,] 25.75065 -80.96644
[82,] 25.76126 -80.90782
[83,] 25.74659 -80.95390
[84,] 25.81790 -81.10038
[85,] 25.86360 -81.10120
[86,] 25.95730 -81.10380
[87,] 25.95725 -81.10383
[88,] 25.95750 -81.10361
[89,] 26.15640 -81.22190
[90,] 26.15644 -81.22192
[91,] 26.15694 -81.22167
[92,] 26.15560 -81.26650
[93,] 25.78371 -81.19146
[94,] 25.77861 -80.91194
[95,] 25.78389 -80.92528
[96,] 25.77820 -80.91220
[97,] 25.77822 -80.91222
[98,] 25.96840 -80.92640
[99,] 25.96839 -80.92636
[100,] 25.96889 -80.92611
[101,] 25.78944 -81.10000
[102,] 25.78510 -81.08313
[103,] 25.78910 -81.10010
[104,] 25.78908 -81.10011
[105,] 25.78760 -81.09896
[106,] 25.87777 -81.23770
[107,] 25.84714 -80.93604
[108,] 25.82152 -80.89180
[109,] 25.83346 -80.84754
[110,] 25.90154 -81.31661
[111,] 26.08724 -81.26474
[112,] 25.93417 -80.83276
[113,] 25.86557 -80.84374
[114,] 25.85072 -80.97178
[115,] 25.85074 -80.97178
[116,] 26.16790 -81.16497
[117,] 25.86968 -81.15835
[118,] 26.04585 -81.26362
[119,] 26.16682 -81.22861
[120,] 25.76117 -80.88047
[121,] 25.75742 -80.98732
[122,] 26.15562 -81.29818
[123,] 25.98263 -81.26223
[124,] 25.79000 -80.87640
[125,] 25.77560 -80.90440
[126,] 25.86361 -81.10117
[127,] 25.78857 -81.09992
[128,] 25.82480 -80.89610
[129,] 25.87244 -81.18669
[130,] 25.76427 -80.83034
[131,] 25.90145 -81.32419
[132,] 25.80220 -80.86970
[133,] 25.83288 -80.90423
[134,] 25.85157 -80.98093
[135,] 25.82538 -80.89562
[136,] 25.86427 -81.09979
[137,] 25.80566 -80.87312
[138,] 25.90361 -81.31417
[139,] 25.78816 -80.85507
[140,] 25.90060 -81.30420
[141,] 25.89121 -81.27008
[142,] 25.85177 -80.98035
[143,] 25.88772 -81.26163
[144,] 25.84954 -80.95590
[145,] 25.89080 -81.26980
[146,] 25.89080 -81.26981
[147,] 26.16564 -81.24702
[148,] 25.89843 -81.26480
[149,] 25.82481 -80.89611
[150,] 25.87639 -81.22811
[151,] 25.85185 -80.98065
[152,] 26.05667 -81.15583
[153,] 25.86503 -80.84377
[154,] 25.79065 -80.85630
[155,] 25.87581 -81.21880
[156,] 25.87585 -81.21883
[157,] 25.80406 -80.85364
[158,] 25.89177 -81.26959
[159,] 25.89185 -81.26954
I have already mapped this out using leaflet and know that there are several samples taken very close together, but I am hoping there is a way to test if any coordinates are 200 meters apart or closer from any other coordinates and assign a group to them as a third column in the matrix (sites 1-xx). From there I could use dplyr's group_by function to summarize the 6,000+ samples by site instead of coordinate pairs to get an analysis with far fewer groups of data.
Edit
To answer some questions:
If sample A is <200m from sample B, which is <200m from sample C, while A is >200m from C, they should all be put into one group.
I have been able to use geodist to make a distance matrix of all sites, including self-matches, but I am unsure how to use that matrix to assign a new column value to all isolated samples and grouped samples.
This is what I have now:
map.dat = unique(raw_data[, c("station name","lat","long")])
geo.dist = geodist(map.dat)
colnames(geo.dist) = as.vector(map.dat$`station name`)
rownames(geo.dist) = as.vector(map.dat$`station name`)
> data.frame(map.dat)
station.name lat long
1 10B CULVERT 24 ON LOOP ROAD NR PINECREST F 25.75038020 -80.96645810
2 10B CYPRESS STRD OFF SR 94 NR PINECREST FL 25.78787950 -81.09701680
3 10B CYPRESS SWP DR AT SR 94 NR PINCREST FL 25.75815780 -80.99451440
4 10B CYPRESS SWP NR JETPORT BORROW PIT 3 NR 25.85593150 -80.89978890
5 10B L-28 EAST CA NR PINECREST FLA 25.93370590 -80.81228610
6 10B LAKE OKEECHOBEE AT OKEECHOBEE FLA 25.95037200 -80.83311990
7 10B TAMIAMI CA AT BR 96 AT MONROE FLA 25.86704310 -81.09979410
8 10B TAMIAMI CA AT JETPORT ENTRANCE NR MIAM 25.84482090 -80.93034540
9 10B TAMIAMI CANAL AT BR 115 NEAR MIAMI FLA 25.83371000 -80.88312190
10 10B TAMIAMI CANAL AT BRIDGE 86 NR OCHOPPE 25.87537680 -81.22479730
11 21FLSFWMBC16 25.88729000 -81.26125000
12 21FLSFWMBC17 25.87676000 -81.22787000
13 AABR265 26.16820000 -81.08820000
14 AT BR 115 COLLIER COUNTY, FLA 25.83371000 -80.88312190
15 AT BR 96 MONROE, FLA 25.86704310 -81.09979410
16 BARROW CA AT SR 94 PINECREST FLA 25.74760250 -80.94979100
17 BASIN, CONCH KEY, BAYSIDE 25.79030000 -80.89110000
18 BAY, FLORIDA, TOM@S HARBOR 25.77390000 -80.93390000
19 BC16 25.88780000 -81.26170000
20 BC17 25.87664160 -81.22823330
21 BCAP2 26.22222220 -81.17222220
22 Bear Island Loop - at Williams Wayside Prkk and Turner Riv 25.88764417 -81.26188444
23 Bear Island Loop - BCA8; on US 41 at Turner River 25.89092417 -81.26972167
24 Bear Island Loop - corner of Wagon Wheel and Turner River Rd 25.99452160 -81.26269700
25 Bear Island Loop - On Perocchi Grade Road at Et Hinson Marsh 26.19736306 -81.26715889
26 Bear Island Loop - On Turner Riv Rd @ Turner River Headwater 25.90035583 -81.26198917
27 Bear Island Loop; BR030169 On Turner River Road 26.17217970 -81.26681100
28 Bear Island Loop; On Turner River Road at Fire Prairie trail 26.09576583 -81.26506444
29 BIG CYPRESS WATERSHED EVERGLADES PARKWAY, NR. BI 26.16925350 -81.08729240
30 BIG CYPRESS WATERSHED NEAR SUNNILAND,FLA 26.16925350 -81.08729240
31 BRIDGE #25 ON U.S.41 2 MILES WEST OF S-12A 25.77805560 -80.84444440
32 Bridge #84 on US 41E 25.88777780 -81.26166670
33 Bridge #86 on US 41E 25.87638890 -81.21777780
34 BRIDGE 105 25.85190000 -80.98100000
35 BRIDGE 105 25.85191670 -80.98102780
36 BRIDGE 105 25.85222220 -80.98083330
37 Bridge 30090 on US41E 25.87222220 -81.01861110
38 Bridge 30096 at intersection of US41 and Loop Road 25.86385000 -81.10096000
39 Bridge 30096 at intersection of US41 and Loop Road 25.86361110 -81.10111110
40 Bridge 30105 on US41 East 25.84341000 -80.91720000
41 Bridge 30105 on US41 East 25.84305560 -80.91777780
42 BRIDGE 83 25.89030000 -81.27030000
43 BRIDGE 83 25.89027780 -81.27025000
44 BRIDGE 83 25.89055560 -81.27000000
45 BRIDGE 84 25.88650000 -81.26210000
46 BRIDGE 84 25.88652780 -81.26208330
47 BRIDGE 84 25.88694440 -81.26194440
48 C-4 TAMIAMI CANAL ABOVE S-12A 25.76190000 -80.85330000
49 Dad-L29-1 25.78544440 -80.85119400
50 DEEP LAKE 26.04472220 -81.29972220
51 DEEP LAKE STRAND 26.04430000 -81.29990000
52 DEEP LAKE STRAND 26.04430560 -81.29991670
53 EAST CROSSING STRAND 26.09280000 -81.05390000
54 EAST CROSSING STRAND 26.09277780 -81.05391670
55 EAST CROSSING STRAND 26.16000000 -81.22638890
56 EAST HINSON MARSH 26.19600000 -81.28870000
57 EAST HINSON MARSH 26.19597220 -81.28869440
58 EAST HINSON MARSH 26.19638890 -81.28861110
59 EV-BR. ALL ALLEY HWY 84 MI 127 26.16250000 -81.24170000
60 EV-BR. ALL ALLEY HWY 84 MI 132 26.16390000 -81.17360000
61 EV-BR. ALL ALLEY HWY 84 MI 137 26.16530000 -81.09310000
62 EV-BRIDGE OVER TURNER RIVER CNL 26.09440000 -81.26670000
63 EV-CANAL NR CONSERVATION AREA #3 25.78890000 -80.85690000
64 EV-TAMIAMI TRL-BRIDGE NO 100 25.85280000 -81.02920000
65 EV-TAMIAMI TRL-BRIDGE NO 105 25.84440000 -80.97080000
66 EV-TAMIAMI TRL-BRIDGE NO 86 25.87360000 -81.22920000
67 EV-TURNER R CNL NR HWY 840A 25.94720000 -81.26250000
68 GATED CULVERT 25.91916670 -80.83638890
69 GATOR 25.84310000 -80.91770000
70 Gator Hook Strand 25.78611110 -81.20055560
71 GUM SLOUGH 25.71350000 -81.02190000
72 GUM SLOUGH 25.71347220 -81.02191670
73 GUM SLOUGH 25.71388890 -81.02166670
74 JP-BRIDGE ON LOOP ROAD-STATE 94 25.72080000 -80.87220000
75 JP-GUM SLOUGH, LOOP ROAD 25.76390000 -81.07500000
76 KISSIMMEE BILLY STRAND 26.19130000 -81.08680000
77 KISSIMMEE BILLY STRAND 26.19127780 -81.08675000
78 KISSIMMEE BILLY STRAND 26.19166670 -81.08666670
79 Lime Tree Hammock 25.68722220 -80.91972220
80 LITTLE MARSK 26.20500000 -81.16833330
81 Loop Rd - Bridge 6 near BCA11; 5 mi S from Monroe Stat 25.78860833 -81.09991222
82 Loop Road - Bridge 29; 14.55 mi W of 40 Mile Bend 25.76027222 -81.04830667
83 Loop Road - Bridge 32N; 11.3 miles W of 40 Mile Bend 25.76049720 -80.99625600
84 Loop Road - Bridge 37; 9.3 miles W of 40 Mile Bend 25.75065278 -80.96644306
85 Loop Road - Loop 1; 5 miles W of 40 Mile bend 25.76125556 -80.90782361
86 Loop Road - Loop 2; Crooked Culvert ? Culvert 46; 3 8.3 m 25.74659167 -80.95390139
87 Loop Road - Robert Lake Strand Culvert 25.81789720 -81.10037780
88 MONROE 25.86360000 -81.10120000
89 MONUMENT ROAD 25.95730000 -81.10380000
90 MONUMENT ROAD 25.95725000 -81.10383330
91 MONUMENT ROAD 25.95750000 -81.10361110
92 MULLET SLOUGH 26.15640000 -81.22190000
93 MULLET SLOUGH 26.15644440 -81.22191670
94 MULLET SLOUGH 26.15694440 -81.22166670
95 NORTH SIDE OF ALLIGATOR ALLEY 15 MI. W.OF BROWARD 26.15560000 -81.26650000
96 P-6 GATOR HOOK STRAND AT MANGROVE FRINGE 25.78371360 -81.19146360
97 PINECREST 25.77861110 -80.91194440
98 Pinecrest Flowway 25.78388890 -80.92527780
99 PINECREST HAMMOCK 25.77820000 -80.91220000
100 PINECREST HAMMOCK 25.77822220 -80.91222220
101 RACCOON POINT 25.96840000 -80.92640000
102 RACCOON POINT 25.96838890 -80.92636110
103 RACCOON POINT 25.96888890 -80.92611110
104 ROBERTS LAKE 25.78944440 -81.10000000
105 ROBERTS LAKE SLOUGH NEAR MONROE, FLA. 25.78510180 -81.08312760
106 ROBERTS LAKE STRAND 25.78910000 -81.10010000
107 ROBERTS LAKE STRAND 25.78908330 -81.10011110
108 Roberts Lake Strand off Loop Road Nr Monroe St., F 25.78760000 -81.09896000
109 SF1-LR-2003 TAMIAMI CANAL 25.87776690 -81.23770300
110 SF1-LR-2013 TAMIAMI CANAL 25.84714330 -80.93604100
111 SF1-LR-2018 TAMIAMI CANAL 25.82152330 -80.89179600
112 SF1-LR-2027 UNNAMED SMALL STREAM 25.83345630 -80.84754300
113 SF1-LR-2030 TAMIAMI CANAL 25.90154440 -81.31660800
114 SF1-SS-2127 UNNAMED SMALL STREAM 26.08723800 -81.26474300
115 SF5-LR-2029 L-28 25.93417020 -80.83276100
116 SFC-HS-1004 UNKNOWN 25.86556940 -80.84373860
117 SFC-HS-1015 Unknown 25.85072220 -80.97177700
118 SFC-HS-1015 UNKNOWN 25.85073690 -80.97178420
119 SFC-HS-1017 UNKNOWN 26.16789830 -81.16496810
120 SFC-HS-1020 UNKNOWN 25.86967640 -81.15835360
121 SFC-HS-1021 UNKNOWN 26.04585440 -81.26361810
122 SFC-HS-1027 UNKNOWN 26.16681810 -81.22861080
123 SFC-HS-1029 UNKNOWN 25.76117440 -80.88047420
124 SFC-HS-1030 UNKNOWN 25.75741690 -80.98731940
125 SFC-HS-1031 UNKNOWN 26.15561610 -81.29818250
126 SFC-HS-1032 UNKNOWN 25.98263080 -81.26222940
127 SOUND, ATLANTIC, LONG KEY BRIDGE 25.79000000 -80.87640000
128 SOUND, ATLANTIC, TOM@S HARBOR CU 25.77560000 -80.90440000
129 South canal@ Monroe Station 25.86361110 -81.10116600
130 SWEETWATER STRAND AT LOOP RD. NR MONROE STATION FL 25.78857220 -81.09992220
131 TAMBR115 25.82480000 -80.89610000
132 TAMBR90 25.87243610 -81.18668880
133 TAMIAMI C AT 40 MI BEND,NR MIAMI,FLA. 25.76426800 -80.83034310
134 Tamiami Canal - Near Big Cypress Visitors Center 25.90145000 -81.32419000
135 TAMIAMI CANAL 4 M WEST OF 40 M BEND 25.80220000 -80.86970000
136 TAMIAMI CANAL AT 40-MILE BEND, NEAR MIAMI, FLA. 25.76426800 -80.83034310
137 TAMIAMI CANAL AT BR 86 NEAR OCHOPEE FLA (AUX) 25.87537680 -81.22479730
138 tamiami canal at bridge 030114 nr miami, fl 25.83288000 -80.90423000
139 TAMIAMI CANAL AT BRIDGE 105 NR MONROE, FL 25.85156670 -80.98093330
140 TAMIAMI CANAL AT BRIDGE 115, NEAR MIAMI, FLA. 25.82537710 -80.89562230
141 TAMIAMI CANAL AT BRIDGE 96, AT MONROE, FLA. 25.86426540 -81.09979420
142 TAMIAMI CANAL AT COLLIER CO. LINE 25.80565550 -80.87312180
143 TAMIAMI CANAL AT INTERSECTION OF S.R. 839 AND U.S. 41 EAST 25.90361110 -81.31416670
144 tamiami canal culvert below s343b nr miami, fl 25.78816000 -80.85507000
145 TAMIAMI CANAL OCHOPEE 25.90060000 -81.30420000
146 TAMIAMI CANAL OUTLETS AT BRIDGE 83 25.89120970 -81.27007620
147 TAMIAMI CANAL OUTLETS, 40-MILE BEND TO MONROE, FL 25.85176530 -80.98034670
148 TAMIAMI CANAL OUTLETS, MONROE TO CARNESTOWN, FLA 25.88772090 -81.26163430
149 TAMIAMI CN AT HIGHWAY 25.84954300 -80.95590160
150 TURNER 25.89080000 -81.26980000
151 Turner R. @ US 41 25.89079900 -81.26981100
152 TURNER RIVER CANAL AT ALLIGATOR ALLEY 26.16564300 -81.24701870
153 TURNER RIVER NORTH OF US-41 25.89843160 -81.26479820
154 US 41 Bridge #030115 25.82480550 -80.89611100
155 US 41 Canal 2.6 mi. E. of Turner River 25.87638880 -81.22811100
156 WATER QUALITY MONITORING STATION 25.85185333 -80.98064944
157 WEST MUD LAKE 26.05666670 -81.15583330
158 Z5-CN-11014 UNNAMED CANAL 25.86503330 -80.84377500
159 Z5-CN-14007 UNNAMED CANAL 25.79064721 -80.85629750
160 Z5-LR-3013 Tamiami Canal 25.87581130 -81.21879600
161 Z5-LR-3013R Tamiami Canal 25.87584970 -81.21883100
162 Z5-LR-3014R L-28 25.80405520 -80.85364100
163 Z5-SS-4079 TURNER RIVER 25.89176970 -81.26959000
164 Z5-SS-4079R TURNER RIVER 25.89184940 -81.26953600
All station names are unique, but a few station names have the same coordinates.
Ultimately, I would like the output to be the same as the map.dat object, but with an extra $group column that has an ID number for each isolated station and each group/chain of nearby stations.
答案1
得分: 2
以下是您提供的代码的翻译：
library(geodist)
# swap order for lon-lat
df2 <- data.frame(lon = df1$long, lat = df1$lat)
dist_matrix <- geodist(df2)
library(tidyverse)
as.data.frame(dist_matrix) %>%
mutate(row = paste0("V", row_number())) %>%
pivot_longer(-row, names_to = "match") %>%
filter(value < 200, row != match) %>%
filter(row < match) # if we only want one row per link
这段代码计算了所有距离小于200米的点对。
# A tibble: 143 × 3
row match value
<chr> <chr> <dbl>
1 V1 V81 30.1
2 V11 V17 72.3
3 V11 V20 74.0
4 V11 V29 68.8
5 V11 V42 122.
6 V11 V43 118.
7 V11 V44 79.2
8 V11 V143 61.0
9 V12 V18 38.4
10 V12 V150 47.6
# ℹ 133 more rows
# ℹ Use `print(n = ...)` to see more rows
这是计算结果，包含了所有距离小于200米的点对的距离数据。
对于您提到的关于如何分组的问题，是否将A与B和B与C视为一个群组还是两个群组，这取决于您的需求。如果您想根据连接来分组，您可以考虑使用 igraph 或 tidygraph 来基于链接分配群组。可以参考以下链接：https://igraph.org/r/doc/cliques.html
最后，您提供的代码中还包含了示例数据（df1），这是用于计算距离的经纬度数据。
英文:
library(geodist)
# swap order for lon-lat
df2 <- data.frame(lon = df1$long, lat = df1$lat)
dist_matrix <- geodist(df2)
library(tidyverse)
as.data.frame(dist_matrix) %>%
mutate(row = paste0("V", row_number())) %>%
pivot_longer(-row, names_to = "match") %>%
filter(value < 200, row != match) %>%
filter(row < match) # if we only want one row per link
This outputs all the pairs within 200 meters:
# A tibble: 143 × 3
row match value
<chr> <chr> <dbl>
1 V1 V81 30.1
2 V11 V17 72.3
3 V11 V20 74.0
4 V11 V29 68.8
5 V11 V42 122.
6 V11 V43 118.
7 V11 V44 79.2
8 V11 V143 61.0
9 V12 V18 38.4
10 V12 V150 47.6
# ℹ 133 more rows
# ℹ Use `print(n = ...)` to see more rows
I'm unclear on how you want the grouping to work if, say, A is 150 meters from B, and B is 150 meters from C, but A is >200 meters from C. Are those one group or two? I would probably turn to igraph/tidygraph to assign clusters ("cliques") based on links, but not sure how that should be implemented.
https://igraph.org/r/doc/cliques.html
Sample data
df1 <- data.frame(
lat = c(25.75038,25.78788,25.75816,25.85593,
25.93371,25.95037,25.86704,25.84482,25.83371,25.87538,
25.88729,25.87676,26.1682,25.7476,25.7903,25.7739,25.8878,
25.87664,26.22222,25.88764,25.89092,25.99452,26.19736,
25.90036,26.17218,26.09577,26.16925,25.77806,25.88778,25.87639,
25.8519,25.85192,25.85222,25.87222,25.86385,25.86361,
25.84341,25.84306,25.8903,25.89028,25.89056,25.8865,25.88653,
25.88694,25.7619,25.78544,26.04472,26.0443,26.04431,
26.0928,26.09278,26.16,26.196,26.19597,26.19639,26.1625,
26.1639,26.1653,26.0944,25.7889,25.8528,25.8444,25.8736,
25.9472,25.91917,25.8431,25.78611,25.7135,25.71347,25.71389,
25.7208,25.7639,26.1913,26.19128,26.19167,25.68722,26.205,
25.78861,25.76027,25.7605,25.75065,25.76126,25.74659,
25.8179,25.8636,25.9573,25.95725,25.9575,26.1564,26.15644,
26.15694,26.1556,25.78371,25.77861,25.78389,25.7782,
25.77822,25.9684,25.96839,25.96889,25.78944,25.7851,25.7891,
25.78908,25.7876,25.87777,25.84714,25.82152,25.83346,
25.90154,26.08724,25.93417,25.86557,25.85072,25.85074,26.1679,
25.86968,26.04585,26.16682,25.76117,25.75742,26.15562,
25.98263,25.79,25.7756,25.86361,25.78857,25.8248,25.87244,
25.76427,25.90145,25.8022,25.83288,25.85157,25.82538,
25.86427,25.80566,25.90361,25.78816,25.9006,25.89121,25.85177,
25.88772,25.84954,25.8908,25.8908,26.16564,25.89843,
25.82481,25.87639,25.85185,26.05667,25.86503,25.79065,
25.87581,25.87585,25.80406,25.89177,25.89185),
long = c(-80.96646,-81.09702,-80.99451,-80.89979,
-80.81229,-80.83312,-81.09979,-80.93035,-80.88312,
-81.2248,-81.26125,-81.22787,-81.0882,-80.94979,-80.8911,
-80.9339,-81.2617,-81.22823,-81.17222,-81.26188,-81.26972,
-81.2627,-81.26716,-81.26199,-81.26681,-81.26506,-81.08729,
-80.84444,-81.26167,-81.21778,-80.981,-80.98103,-80.98083,
-81.01861,-81.10096,-81.10111,-80.9172,-80.91778,-81.2703,
-81.27025,-81.27,-81.2621,-81.26208,-81.26194,-80.8533,
-80.85119,-81.29972,-81.2999,-81.29992,-81.0539,-81.05392,
-81.22639,-81.2887,-81.28869,-81.28861,-81.2417,-81.1736,
-81.0931,-81.2667,-80.8569,-81.0292,-80.9708,-81.2292,
-81.2625,-80.83639,-80.9177,-81.20056,-81.0219,-81.02192,
-81.02167,-80.8722,-81.075,-81.0868,-81.08675,-81.08667,
-80.91972,-81.16833,-81.09991,-81.04831,-80.99626,-80.96644,
-80.90782,-80.9539,-81.10038,-81.1012,-81.1038,-81.10383,
-81.10361,-81.2219,-81.22192,-81.22167,-81.2665,-81.19146,
-80.91194,-80.92528,-80.9122,-80.91222,-80.9264,-80.92636,
-80.92611,-81.1,-81.08313,-81.1001,-81.10011,-81.09896,
-81.2377,-80.93604,-80.8918,-80.84754,-81.31661,-81.26474,
-80.83276,-80.84374,-80.97178,-80.97178,-81.16497,-81.15835,
-81.26362,-81.22861,-80.88047,-80.98732,-81.29818,
-81.26223,-80.8764,-80.9044,-81.10117,-81.09992,-80.8961,
-81.18669,-80.83034,-81.32419,-80.8697,-80.90423,-80.98093,
-80.89562,-81.09979,-80.87312,-81.31417,-80.85507,-81.3042,
-81.27008,-80.98035,-81.26163,-80.9559,-81.2698,-81.26981,
-81.24702,-81.2648,-80.89611,-81.22811,-80.98065,-81.15583,
-80.84377,-80.8563,-81.2188,-81.21883,-80.85364,-81.26959,
-81.26954)
)
答案2
得分: 1
string = "
[1,] 25.75038 -80.96646
[2,] 25.78788 -81.09702
[3,] 25.75816 -80.99451
[4,] 25.85593 -80.89979
[5,] 25.93371 -80.81229
[6,] 25.95037 -80.83312
[7,] 25.86704 -81.09979
[8,] 25.84482 -80.93035
[9,] 25.83371 -80.88312
[10,] 25.87538 -81.22480
[11,] 25.88729 -81.26125
[12,] 25.87676 -81.22787
[13,] 26.16820 -81.08820
[14,] 25.74760 -80.94979
[15,] 25.79030 -80.89110
[16,] 25.77390 -80.93390
[17,] 25.88780 -81.26170
[18,] 25.87664 -81.22823
[19,] 26.22222 -81.17222
[20,] 25.88764 -81.26188
[21,] 25.89092 -81.26972
[22,] 25.99452 -81.26270
[23,] 26.19736 -81.26716
[24,] 25.90036 -81.26199
[25,] 26.17218 -81.26681
[26,] 26.09577 -81.26506
[27,] 26.16925 -81.08729
[28,] 25.77806 -80.84444
[29,] 25.88778 -81.26167
[30,] 25.87639 -81.21778
[31,] 25.85190 -80.98100
[32,] 25.85192 -80.98103
[33,] 25.85222 -80.98083
[34,] 25.87222 -81.01861
[35,] 25.86385 -81.10096
[36,] 25.86361 -81.10111
[37,] 25.84341 -80.91720
[38,] 25.84306 -80.91778
[39,] 25.89030 -81.27030
[40,] 25.89028 -81.27025
[41,] 25.89056 -81.27000
[42,] 25.88650 -81.26210
[43,] 25.88653 -81.26208
[44,] 25.88694 -81.26194
[45,] 25.76190 -80.85330
[46,] 25.78544 -80.85119
[47,] 26.04472 -81.29972
[48,] 26.04430 -81.29990
[49,] 26.04431 -81.29992
[50,] 26.09280 -81.05390
[51,] 26.09278 -81.05392
[52,] 26.16000 -81.22639
[53,] 26.19600 -81.28870
[54,] 26.19597 -81.28869
[55,] 26.19639 -81.28861
[56,] 26.16250 -81.24170
[57,] 26.16390 -81.17360
[58,] 26.16530 -81.09310
[59,] 26.09440 -81.26670
[60,] 25.78890 -80.85690
[61,] 25.85280 -81.02920
[62,] 25.84440 -80.97080
[63,] 25.87360 -81.22920
[64,] 25.94720 -81.26250
[65,] 25.91917 -80.83639
[66,] 25.84310 -80.91770
[67,] 25.78611 -81.20056
[68,] 25.71350 -81.02190
[69,] 25.71347 -81.02192
[70,] 25.71389 -81.02167
[71,] 25.72080 -80.87220
[72,] 25.76390 -81.07500
[73,] 26.19130 -81.08680
[74,] 26.19128 -81.08675
[75,] 26.19167 -81.08667
[76,] 25.68722 -80.91972
[77,] 26.20500 -81.16833
[78,] 25.78861 -81.09991
[79,] 25.76027 -81.04831
[80,] 25.76050 -80.99626
[81,] 25.75065 -80.96644
[82,] 25.76126 -80.90782
[83,] 25.74659 -80.95390
[84,] 25.81790 -81.10038
[85,] 25.86360 -81.10120
[86,] 25.95730 -81.10380
[87,] 25.95725 -81.10383
[88,] 25.95750 -81.10361
[89,] 26.15640 -81.22190
[90,] 26.15644 -81.22192
[91,] 26.15694 -81.22167
[92,] 26.15560 -81.26650
[93,] 25.78371 -81.19146
[94,] 25.77861 -80.91194
[95,] 25.78389 -80.92528
[96,] 25.77820 -80.91220
[97,] 25.77822 -80.91222
[98,] 25.96840 -80.92640
[99,] 25.96839 -80.92636
[100,] 25.96889 -80.92611
[101,] 25.78944 -81.10000
[102,] 25.78510 -81.08313
[103,
英文:
string = "
[1,] 25.75038 -80.96646
[2,] 25.78788 -81.09702
[3,] 25.75816 -80.99451
[4,] 25.85593 -80.89979
[5,] 25.93371 -80.81229
[6,] 25.95037 -80.83312
[7,] 25.86704 -81.09979
[8,] 25.84482 -80.93035
[9,] 25.83371 -80.88312
[10,] 25.87538 -81.22480
[11,] 25.88729 -81.26125
[12,] 25.87676 -81.22787
[13,] 26.16820 -81.08820
[14,] 25.74760 -80.94979
[15,] 25.79030 -80.89110
[16,] 25.77390 -80.93390
[17,] 25.88780 -81.26170
[18,] 25.87664 -81.22823
[19,] 26.22222 -81.17222
[20,] 25.88764 -81.26188
[21,] 25.89092 -81.26972
[22,] 25.99452 -81.26270
[23,] 26.19736 -81.26716
[24,] 25.90036 -81.26199
[25,] 26.17218 -81.26681
[26,] 26.09577 -81.26506
[27,] 26.16925 -81.08729
[28,] 25.77806 -80.84444
[29,] 25.88778 -81.26167
[30,] 25.87639 -81.21778
[31,] 25.85190 -80.98100
[32,] 25.85192 -80.98103
[33,] 25.85222 -80.98083
[34,] 25.87222 -81.01861
[35,] 25.86385 -81.10096
[36,] 25.86361 -81.10111
[37,] 25.84341 -80.91720
[38,] 25.84306 -80.91778
[39,] 25.89030 -81.27030
[40,] 25.89028 -81.27025
[41,] 25.89056 -81.27000
[42,] 25.88650 -81.26210
[43,] 25.88653 -81.26208
[44,] 25.88694 -81.26194
[45,] 25.76190 -80.85330
[46,] 25.78544 -80.85119
[47,] 26.04472 -81.29972
[48,] 26.04430 -81.29990
[49,] 26.04431 -81.29992
[50,] 26.09280 -81.05390
[51,] 26.09278 -81.05392
[52,] 26.16000 -81.22639
[53,] 26.19600 -81.28870
[54,] 26.19597 -81.28869
[55,] 26.19639 -81.28861
[56,] 26.16250 -81.24170
[57,] 26.16390 -81.17360
[58,] 26.16530 -81.09310
[59,] 26.09440 -81.26670
[60,] 25.78890 -80.85690
[61,] 25.85280 -81.02920
[62,] 25.84440 -80.97080
[63,] 25.87360 -81.22920
[64,] 25.94720 -81.26250
[65,] 25.91917 -80.83639
[66,] 25.84310 -80.91770
[67,] 25.78611 -81.20056
[68,] 25.71350 -81.02190
[69,] 25.71347 -81.02192
[70,] 25.71389 -81.02167
[71,] 25.72080 -80.87220
[72,] 25.76390 -81.07500
[73,] 26.19130 -81.08680
[74,] 26.19128 -81.08675
[75,] 26.19167 -81.08667
[76,] 25.68722 -80.91972
[77,] 26.20500 -81.16833
[78,] 25.78861 -81.09991
[79,] 25.76027 -81.04831
[80,] 25.76050 -80.99626
[81,] 25.75065 -80.96644
[82,] 25.76126 -80.90782
[83,] 25.74659 -80.95390
[84,] 25.81790 -81.10038
[85,] 25.86360 -81.10120
[86,] 25.95730 -81.10380
[87,] 25.95725 -81.10383
[88,] 25.95750 -81.10361
[89,] 26.15640 -81.22190
[90,] 26.15644 -81.22192
[91,] 26.15694 -81.22167
[92,] 26.15560 -81.26650
[93,] 25.78371 -81.19146
[94,] 25.77861 -80.91194
[95,] 25.78389 -80.92528
[96,] 25.77820 -80.91220
[97,] 25.77822 -80.91222
[98,] 25.96840 -80.92640
[99,] 25.96839 -80.92636
[100,] 25.96889 -80.92611
[101,] 25.78944 -81.10000
[102,] 25.78510 -81.08313
[103,] 25.78910 -81.10010
[104,] 25.78908 -81.10011
[105,] 25.78760 -81.09896
[106,] 25.87777 -81.23770
[107,] 25.84714 -80.93604
[108,] 25.82152 -80.89180
[109,] 25.83346 -80.84754
[110,] 25.90154 -81.31661
[111,] 26.08724 -81.26474
[112,] 25.93417 -80.83276
[113,] 25.86557 -80.84374
[114,] 25.85072 -80.97178
[115,] 25.85074 -80.97178
[116,] 26.16790 -81.16497
[117,] 25.86968 -81.15835
[118,] 26.04585 -81.26362
[119,] 26.16682 -81.22861
[120,] 25.76117 -80.88047
[121,] 25.75742 -80.98732
[122,] 26.15562 -81.29818
[123,] 25.98263 -81.26223
[124,] 25.79000 -80.87640
[125,] 25.77560 -80.90440
[126,] 25.86361 -81.10117
[127,] 25.78857 -81.09992
[128,] 25.82480 -80.89610
[129,] 25.87244 -81.18669
[130,] 25.76427 -80.83034
[131,] 25.90145 -81.32419
[132,] 25.80220 -80.86970
[133,] 25.83288 -80.90423
[134,] 25.85157 -80.98093
[135,] 25.82538 -80.89562
[136,] 25.86427 -81.09979
[137,] 25.80566 -80.87312
[138,] 25.90361 -81.31417
[139,] 25.78816 -80.85507
[140,] 25.90060 -81.30420
[141,] 25.89121 -81.27008
[142,] 25.85177 -80.98035
[143,] 25.88772 -81.26163
[144,] 25.84954 -80.95590
[145,] 25.89080 -81.26980
[146,] 25.89080 -81.26981
[147,] 26.16564 -81.24702
[148,] 25.89843 -81.26480
[149,] 25.82481 -80.89611
[150,] 25.87639 -81.22811
[151,] 25.85185 -80.98065
[152,] 26.05667 -81.15583
[153,] 25.86503 -80.84377
[154,] 25.79065 -80.85630
[155,] 25.87581 -81.21880
[156,] 25.87585 -81.21883
[157,] 25.80406 -80.85364
[158,] 25.89177 -81.26959
[159,] 25.89185 -81.26954"
library(stringr)
df_coords <- data.frame(
loc = paste0("loc",1:159),
lat = as.numeric(str_extract_all(string,"\\d+\\.\\d+(?= \\-)")[[1]]),
long = as.numeric(str_extract_all(string,"-\\d+\\.\\d+")[[1]])
)
library(plyr)
d = expand.grid(loc1 = df_coords$loc,loc2 = df_coords$loc)
library(dplyr)
d <- d %>%
inner_join(
df_coords, by = c("loc1"="loc"), suffix=c("","loc1")
) %>%
inner_join(
df_coords, by = c("loc2"="loc"), suffix=c("","loc2")
)
library(geosphere)
d <- d %>%
rowwise() %>%
mutate(
dist = distm(c(long, lat), c(longloc2, latloc2), fun = distHaversine)
) %>%
filter(dist>0,dist<200)
答案3
得分: 1
spdep::dnearneigh() 可以找到基于距离的邻居，n.comp.nb() 处理集群 ID。
library(spdep)
library(sf)
library(dplyr)
library(ggplot2)
# 将数据框转换为 sf 对象，
# 找到基于距离的邻居，上限距离设置为0.2公里，
# 识别连接的子图/集群并将集群大小存储在n中
dat_sf <- st_as_sf(df1, coords = c("long", "lat"), crs = "WGS84", remove = FALSE) %>%
mutate(comp_id = dnearneigh(geometry, 0, 0.2) %>% n.comp.nb() %>% getElement("comp.id")) %>%
add_count(comp_id)
dat_sf
#> 简单要素集合，包含 159 个要素和 4 个字段
#> 几何类型: 点
#> 坐标系: WGS 84
#> 元素: 159
#> Bounding box: xmin: -81.32419 ymin: 25.68722 xmax: -80.81229 ymax: 26.22222
#> 经度/纬度坐标系 (WGS 84):
#> 纬度 经度 comp_id n geometry
#> 1 25.75038 -80.96646 1 2 POINT (-80.96646 25.75038)
#> 2 25.78788 -81.09702 2 7 POINT (-81.09702 25.78788)
#> 3 25.75816 -80.99451 3 1 POINT (-80.99451 25.75816)
#> 4 25.85593 -80.89979 4 1 POINT (-80.89979 25.85593)
#> 5 25.93371 -80.81229 5 1 POINT (-80.81229 25.93371)
#> 6 25.95037 -80.83312 6 1 POINT (-80.83312 25.95037)
#> ... 以下省略部分内容
所有点，按簇大小分类为 > 1
dat_sf %>%
ggplot() +
geom_sf(aes(color = n > 1), size = 2, shape = 4) +
ggspatial::annotation_scale()
半随机子集，按簇 ID (comp_id) 分类
st_crop(dat_sf, xmin = -81.30, ymin = 25.80, xmax = -81.20, ymax = 25.90) %>%
ggplot() +
geom_sf(aes(color = as.factor(comp_id)), size = 2, shape = 4) +
ggspatial::annotation_scale()
#> 警告: 属性变量被假定在所有几何体中是空间上恒定的
# 如果需要，将数据还原为常规数据框
st_drop_geometry(dat_sf) %>% head()
#> 纬度 经度 comp_id n
#> 1 25.75038 -80.96646 1 2
#> 2 25.78788 -81.09702 2 7
#> 3 25.75816 -80.99451 3 1
#> 4 25.85593 -80.89979 4 1
#> 5 25.93371 -80.81229 5 1
#> 6 25.95037 -80.83312 6 1
输入数据:
df1 <- structure(list(lat = c(25.75038, 25.78788, 25.75816, 25.85593,
25.93371, 25.95037, 25.86704, 25.84482, 25.83371, 25.87538, 25.88729,
25.87676, 26.1682, 25.7476, 25.7903, 25.7739, 25.8878, 25.87664,
26.22222, 25.88764, 25.89092, 25.99452, 26.19736, 25.90036, 26.17218,
26.09577, 26.16925, 25.77806, 25.88778, 25.87639, 25.8519, 25.85192,
25.85222, 25.87222, 25.86385, 25.86361, 25.84341, 25.84306, 25.8903,
25.89028, 25.89056, 25.8865, 25.88653, 25.88694, 25.7619, 25.78544,
26.04472, 26.0443, 26.04431, 26.0928, 26.09278, 26.16, 26.196,
26.19597, 26.19639, 26.1625, 26.1639, 26.1653, 26.0944, 25.7889,
25.8528, 25.8444, 25.8736, 25.9472, 25.91917, 25.8431, 25.78611,
25.7135, 25.71347, 25.71389, 25.7208, 25.7639, 26.1913, 26.19128,
26.19167, 25.68722, 26.205, 25.78861, 25.76027, 25.7605, 25.75065,
25.76126, 25.74659, 25.8179, 25.8636, 25.9573, 25.95725, 25.9575,
26.1564, 26.15644, 26.15694, 26.1556, 25.78371, 25.77861, 25.78389,
25.7782, 25.77822, 25.9684, 25.96839, 25
<details>
<summary>英文:</summary>
[`spdep::dnearneigh()`][1] can find distance-based neighbours and `n.comp.nb()` deals with cluster id-s.
``` r
library(spdep)
library(sf)
library(dplyr)
library(ggplot2)
# convert dataframe to sf object,
# find distance-based neigbours, upper distance bound is set ti 0.2km,
# identify connected subgraphs / clusters and store cluster sizes in n
dat_sf <- st_as_sf(df1, coords = c("long", "lat"), crs = "WGS84", remove = FALSE) %>%
mutate(comp_id = dnearneigh(geometry, 0, 0.2) %>% n.comp.nb() %>% getElement("comp.id")) %>%
add_count(comp_id)
dat_sf
#> Simple feature collection with 159 features and 4 fields
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: -81.32419 ymin: 25.68722 xmax: -80.81229 ymax: 26.22222
#> Geodetic CRS: WGS 84
#> First 10 features:
#> lat long comp_id n geometry
#> 1 25.75038 -80.96646 1 2 POINT (-80.96646 25.75038)
#> 2 25.78788 -81.09702 2 7 POINT (-81.09702 25.78788)
#> 3 25.75816 -80.99451 3 1 POINT (-80.99451 25.75816)
#> 4 25.85593 -80.89979 4 1 POINT (-80.89979 25.85593)
#> 5 25.93371 -80.81229 5 1 POINT (-80.81229 25.93371)
#> 6 25.95037 -80.83312 6 1 POINT (-80.83312 25.95037)
#> 7 25.86704 -81.09979 7 1 POINT (-81.09979 25.86704)
#> 8 25.84482 -80.93035 8 1 POINT (-80.93035 25.84482)
#> 9 25.83371 -80.88312 9 1 POINT (-80.88312 25.83371)
#> 10 25.87538 -81.22480 10 1 POINT (-81.2248 25.87538)
All points, classified by cluster size being > 1
dat_sf %>%
ggplot() +
geom_sf(aes(color = n > 1), size = 2, shape = 4) +
ggspatial::annotation_scale()
<!-- -->
Semi-random subset, classified by cluster id (comp_id)
st_crop(dat_sf, xmin = -81.30, ymin = 25.80, xmax = -81.20, ymax = 25.90) %>%
ggplot() +
geom_sf(aes(color = as.factor(comp_id)), size = 2, shape = 4) +
ggspatial::annotation_scale()
#> Warning: attribute variables are assumed to be spatially constant throughout
#> all geometries
<!-- -->
# back to regular data.frame, if needed
st_drop_geometry(dat_sf) %>% head()
#> lat long comp_id n
#> 1 25.75038 -80.96646 1 2
#> 2 25.78788 -81.09702 2 7
#> 3 25.75816 -80.99451 3 1
#> 4 25.85593 -80.89979 4 1
#> 5 25.93371 -80.81229 5 1
#> 6 25.95037 -80.83312 6 1
Input data:
df1 <- structure(list(lat = c(25.75038, 25.78788, 25.75816, 25.85593,
25.93371, 25.95037, 25.86704, 25.84482, 25.83371, 25.87538, 25.88729,
25.87676, 26.1682, 25.7476, 25.7903, 25.7739, 25.8878, 25.87664,
26.22222, 25.88764, 25.89092, 25.99452, 26.19736, 25.90036, 26.17218,
26.09577, 26.16925, 25.77806, 25.88778, 25.87639, 25.8519, 25.85192,
25.85222, 25.87222, 25.86385, 25.86361, 25.84341, 25.84306, 25.8903,
25.89028, 25.89056, 25.8865, 25.88653, 25.88694, 25.7619, 25.78544,
26.04472, 26.0443, 26.04431, 26.0928, 26.09278, 26.16, 26.196,
26.19597, 26.19639, 26.1625, 26.1639, 26.1653, 26.0944, 25.7889,
25.8528, 25.8444, 25.8736, 25.9472, 25.91917, 25.8431, 25.78611,
25.7135, 25.71347, 25.71389, 25.7208, 25.7639, 26.1913, 26.19128,
26.19167, 25.68722, 26.205, 25.78861, 25.76027, 25.7605, 25.75065,
25.76126, 25.74659, 25.8179, 25.8636, 25.9573, 25.95725, 25.9575,
26.1564, 26.15644, 26.15694, 26.1556, 25.78371, 25.77861, 25.78389,
25.7782, 25.77822, 25.9684, 25.96839, 25.96889, 25.78944, 25.7851,
25.7891, 25.78908, 25.7876, 25.87777, 25.84714, 25.82152, 25.83346,
25.90154, 26.08724, 25.93417, 25.86557, 25.85072, 25.85074, 26.1679,
25.86968, 26.04585, 26.16682, 25.76117, 25.75742, 26.15562, 25.98263,
25.79, 25.7756, 25.86361, 25.78857, 25.8248, 25.87244, 25.76427,
25.90145, 25.8022, 25.83288, 25.85157, 25.82538, 25.86427, 25.80566,
25.90361, 25.78816, 25.9006, 25.89121, 25.85177, 25.88772, 25.84954,
25.8908, 25.8908, 26.16564, 25.89843, 25.82481, 25.87639, 25.85185,
26.05667, 25.86503, 25.79065, 25.87581, 25.87585, 25.80406, 25.89177,
25.89185), long = c(-80.96646, -81.09702, -80.99451, -80.89979,
-80.81229, -80.83312, -81.09979, -80.93035, -80.88312, -81.2248,
-81.26125, -81.22787, -81.0882, -80.94979, -80.8911, -80.9339,
-81.2617, -81.22823, -81.17222, -81.26188, -81.26972, -81.2627,
-81.26716, -81.26199, -81.26681, -81.26506, -81.08729, -80.84444,
-81.26167, -81.21778, -80.981, -80.98103, -80.98083, -81.01861,
-81.10096, -81.10111, -80.9172, -80.91778, -81.2703, -81.27025,
-81.27, -81.2621, -81.26208, -81.26194, -80.8533, -80.85119,
-81.29972, -81.2999, -81.29992, -81.0539, -81.05392, -81.22639,
-81.2887, -81.28869, -81.28861, -81.2417, -81.1736, -81.0931,
-81.2667, -80.8569, -81.0292, -80.9708, -81.2292, -81.2625, -80.83639,
-80.9177, -81.20056, -81.0219, -81.02192, -81.02167, -80.8722,
-81.075, -81.0868, -81.08675, -81.08667, -80.91972, -81.16833,
-81.09991, -81.04831, -80.99626, -80.96644, -80.90782, -80.9539,
-81.10038, -81.1012, -81.1038, -81.10383, -81.10361, -81.2219,
-81.22192, -81.22167, -81.2665, -81.19146, -80.91194, -80.92528,
-80.9122, -80.91222, -80.9264, -80.92636, -80.92611, -81.1, -81.08313,
-81.1001, -81.10011, -81.09896, -81.2377, -80.93604, -80.8918,
-80.84754, -81.31661, -81.26474, -80.83276, -80.84374, -80.97178,
-80.97178, -81.16497, -81.15835, -81.26362, -81.22861, -80.88047,
-80.98732, -81.29818, -81.26223, -80.8764, -80.9044, -81.10117,
-81.09992, -80.8961, -81.18669, -80.83034, -81.32419, -80.8697,
-80.90423, -80.98093, -80.89562, -81.09979, -80.87312, -81.31417,
-80.85507, -81.3042, -81.27008, -80.98035, -81.26163, -80.9559,
-81.2698, -81.26981, -81.24702, -81.2648, -80.89611, -81.22811,
-80.98065, -81.15583, -80.84377, -80.8563, -81.2188, -81.21883,
-80.85364, -81.26959, -81.26954)), class = "data.frame", row.names = c(NA,
-159L))
<sup>Created on 2023-08-05 with reprex v2.0.2</sup>
通过集体智慧和协作来改善编程学习和解决问题的方式。致力于成为全球开发者共同参与的知识库，让每个人都能够通过互相帮助和分享经验来进步。
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