Hands-on_Ex04
Erika Aldisa
9/6/2020
Spatial Point Patterns Analysis: First-order Analysis Methods
IS415 Geospatial Analytics and Application
Instructor: Dr. Kam Tin Seong.
Assoc. Professor of Information Systems (Practice)
1. Getting Started
Research aim
To discover the spatial point processes of childcare centres in Singapore
Questions to answer
- Are childcare centres in Singapore randomly distributed?
- If not, then where are the locations with higher concentration of childcare centres?
2. Load/Install necessary R packages
- spatstat - useful functions for point pattern analysis
- raster - reads, writes, manipulates, analyses and model raster data (gridded spatial data)
- maptools - in this hands-on ex, mainly use it to convert Spatial objects into ppp format of spatstat
packages = c('rgdal','maptools','raster', 'spatstat', 'tmap')
for (p in packages){
if(!require(p, character.only = T)){
install.packages(p)
}
library(p, character.only = T)
}3. Spatial Data Wrangling
Importing shapefiles
We use readOGR() of the rgdal package to import the 3 shapefiles in R’s spatialpolygonsdataframe.
childcare <- readOGR(dsn = "data", layer= "CHILDCARE")## OGR data source with driver: ESRI Shapefile
## Source: "D:\SMU Year 3 Sem 1\IS415 Geospatial Analytics and Application\Week 4\Hands-on_Ex04\data", layer: "CHILDCARE"
## with 1312 features
## It has 18 fieldssg <- readOGR(dsn = "data", layer= "CostalOutline")## OGR data source with driver: ESRI Shapefile
## Source: "D:\SMU Year 3 Sem 1\IS415 Geospatial Analytics and Application\Week 4\Hands-on_Ex04\data", layer: "CostalOutline"
## with 60 features
## It has 4 fieldsmpsz <- readOGR(dsn = "data", layer= "MP14_SUBZONE_WEB_PL")## OGR data source with driver: ESRI Shapefile
## Source: "D:\SMU Year 3 Sem 1\IS415 Geospatial Analytics and Application\Week 4\Hands-on_Ex04\data", layer: "MP14_SUBZONE_WEB_PL"
## with 323 features
## It has 15 fieldsEnsure same projection system (CRS)
crs(childcare)## CRS arguments:
## +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1
## +x_0=28001.642 +y_0=38744.572 +datum=WGS84 +units=m +no_defscrs(sg)## CRS arguments:
## +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1
## +x_0=28001.642 +y_0=38744.572 +datum=WGS84 +units=m +no_defscrs(mpsz)## CRS arguments:
## +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1
## +x_0=28001.642 +y_0=38744.572 +datum=WGS84 +units=m +no_defsVisualize the 3 shapefiles
par(mfrow = c(1,3))
plot(childcare)
plot(mpsz)
plot(sg)
Or another way to plot all 3 shapefiles into one map is to use add=TRUE
plot(sg, border= 'lightgrey')
plot(sg, add = TRUE)
plot(childcare, add = TRUE)
plot(mpsz, add = TRUE)
Interactive map:
tmap_mode('plot')## tmap mode set to plottingtm_shape(childcare) +
tm_dots()
Converting spatial point data frame into sp format
we need to convert SpatialDataFrame -> Spatial object (sp) -> ppp object form for spatstat
childcare_sp <- as(childcare, "SpatialPoints")
sg_sp <- as(sg, "SpatialPolygons")The visualisation looks the same
plot(childcare_sp)
So, what’s the difference?
childcare## class : SpatialPointsDataFrame
## features : 1312
## extent : 11203.01, 45404.24, 25667.6, 49300.88 (xmin, xmax, ymin, ymax)
## crs : +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +datum=WGS84 +units=m +no_defs
## variables : 18
## names : OBJECTID, ADDRESSBLO, ADDRESSBUI, ADDRESSPOS, ADDRESSSTR, ADDRESSTYP, DESCRIPTIO, HYPERLINK, LANDXADDRE, LANDYADDRE, NAME, PHOTOURL, ADDRESSFLO, INC_CRC, FMEL_UPD_D, ...
## min values : 1, NA, NA, 038983, 1 & 3 Stratton Road SINGAPORE 806787, NA, Child Care Services, http://www.childcarelink.gov.sg/ccls/chdcentpart/ChdCentPartLnk.jsp?centreCd=EB0001, 0, 0, 3-IN-1 FAMILY CENTRE, NA, NA, 000FD4E317754866, 2016/12/23, ...
## max values : 1312, NA, NA, 829646, UPPER BASEMENT LEVEL WEST WING TERMINAL 1 SINGAPORE CHANGI AIRPORT SINGAPORE 819642, NA, Child Care Services, http://www.childcarelink.gov.sg/ccls/chdcentpart/ChdCentPartLnk.jsp?centreCd=YW0150, 0, 0, ZEE SCHOOLHOUSE PTE LTD, NA, NA, FFC5A1F137748668, 2017/03/16, ...childcare_sp## class : SpatialPoints
## features : 1312
## extent : 11203.01, 45404.24, 25667.6, 49300.88 (xmin, xmax, ymin, ymax)
## crs : +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +datum=WGS84 +units=m +no_defsConverting sp format to spatstat’s ppp format
childcare_ppp <- as(childcare_sp, "ppp")
childcare_ppp## Planar point pattern: 1312 points
## window: rectangle = [11203.01, 45404.24] x [25667.6, 49300.88] unitsplot(childcare_ppp)
summary(childcare_ppp)## Planar point pattern: 1312 points
## Average intensity 1.623186e-06 points per square unit
##
## *Pattern contains duplicated points*
##
## Coordinates are given to 3 decimal places
## i.e. rounded to the nearest multiple of 0.001 units
##
## Window: rectangle = [11203.01, 45404.24] x [25667.6, 49300.88] units
## (34200 x 23630 units)
## Window area = 808287000 square unitsHandling duplicated points
check for duplicated points in a ppp object
any(duplicated(childcare_ppp))## [1] TRUEwe can use multiplicity() function to count the number of coincidence point
multiplicity(childcare_ppp)## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
## 1 1 4 1 1 1 1 1 1 1 1 1 1 1 1 1
## 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
## 1 1 1 1 1 1 1 1 1 1 3 1 1 1 1 1
## 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64
## 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1
## 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
## 1 1 2 1 1 1 1 1 1 7 1 1 1 1 1 1
## 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96
## 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1
## 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112
## 2 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1
## 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128
## 1 1 1 1 1 1 2 1 1 1 1 5 1 1 1 1
## 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
## 2 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1
## 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160
## 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1
## 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192
## 1 1 1 1 1 1 1 1 7 1 1 1 1 1 1 1
## 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208
## 1 1 1 1 5 1 1 1 1 1 1 2 1 1 1 1
## 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224
## 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1
## 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240
## 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1
## 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256
## 1 1 1 1 1 1 1 1 1 2 1 1 1 1 2 2
## 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272
## 1 2 1 1 3 1 1 1 1 1 2 1 1 1 1 1
## 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288
## 1 1 7 1 1 1 1 2 1 2 1 1 2 1 1 1
## 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368
## 4 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1
## 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384
## 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1
## 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448
## 1 7 1 2 1 1 1 1 1 1 1 1 1 1 1 1
## 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464
## 1 3 1 1 1 1 1 1 1 1 1 2 2 2 1 1
## 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480
## 1 1 1 1 1 1 1 2 1 1 1 1 4 1 1 1
## 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496
## 1 1 1 1 1 1 3 1 1 1 1 1 1 1 1 1
## 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512
## 1 1 1 1 1 1 1 1 1 1 3 1 1 1 1 1
## 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528
## 1 1 1 1 1 1 1 1 4 1 4 1 1 1 2 1
## 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560
## 1 1 1 1 1 1 1 1 1 3 1 1 1 1 1 1
## 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576
## 1 1 1 1 4 1 1 1 1 1 1 1 1 1 1 1
## 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592
## 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1
## 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608
## 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1
## 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624
## 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1
## 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640
## 1 1 1 1 1 1 1 1 1 1 2 1 1 7 1 1
## 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656
## 1 1 4 1 1 1 2 1 1 1 1 1 1 1 1 1
## 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672
## 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 3
## 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704
## 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752
## 4 1 1 1 1 1 1 7 1 1 1 1 1 1 1 1
## 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768
## 1 1 1 1 1 1 1 1 1 4 1 2 2 1 1 1
## 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784
## 1 1 2 1 1 1 2 1 1 1 1 1 1 1 2 1
## 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816
## 1 1 1 1 5 1 1 1 1 1 1 1 1 1 1 1
## 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848
## 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1
## 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864
## 3 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1
## 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3
## 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960
## 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1
## 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976
## 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1
## 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008
## 1 1 1 5 1 1 1 1 1 1 1 1 1 1 1 1
## 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056
## 1 1 1 1 1 1 1 1 1 1 1 1 5 1 1 1
## 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072
## 1 1 1 1 1 1 1 1 1 1 1 1 4 1 1 1
## 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168
## 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200
## 1 1 4 1 1 1 1 1 1 1 2 1 1 1 1 1
## 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264
## 2 1 1 1 1 4 2 1 1 1 1 1 1 1 1 1
## 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280
## 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296
## 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1
## 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312
## 1 1 1 1 1 2 1 1 1 1 1 1 1 1 7 2we can get the locations that have more than one point event by applying conditions to the multiplicity function
sum(multiplicity(childcare_ppp) > 1)## [1] 853 ways to handle duplicate points in visualization:
- Delete the duplicates - this will mean that some useful points events will be lost
- Jittering - add small perturbation to the duplicate points so they do not occupy the same space
- Make each point “unique” + attach duplicates of points to the patterns as marks, as attributes of the points
childcare_ppp_jit <- rjitter(childcare_ppp, retry = TRUE, nsim = 1, drop = TRUE)
plot(childcare_ppp_jit)
Creating owin
In spatstat, owin object is designed to represent polygonal region to confine analysis within a geographical area like Singapore boundary
Convert sg SpatialPolygon (sp) object into owin object
sg_owin <- as(sg_sp, "owin")
plot(sg_owin)
Combine childcare points and study area (sg_owin)
childcareSG_ppp <- childcare_ppp_jit[sg_owin]
plot(childcareSG_ppp)
summary(childcareSG_ppp)## Planar point pattern: 1312 points
## Average intensity 1.752274e-06 points per square unit
##
## Coordinates are given to 3 decimal places
## i.e. rounded to the nearest multiple of 0.001 units
##
## Window: polygonal boundary
## 60 separate polygons (no holes)
## vertices area relative.area
## polygon 1 38 1.56140e+04 2.09e-05
## polygon 2 735 4.69093e+06 6.27e-03
## polygon 3 49 1.66986e+04 2.23e-05
## polygon 4 76 3.12332e+05 4.17e-04
## polygon 5 5141 6.36179e+08 8.50e-01
## polygon 6 42 5.58317e+04 7.46e-05
## polygon 7 67 1.31354e+06 1.75e-03
## polygon 8 15 4.46420e+03 5.96e-06
## polygon 9 14 5.46674e+03 7.30e-06
## polygon 10 37 5.26194e+03 7.03e-06
## polygon 11 53 3.44003e+04 4.59e-05
## polygon 12 74 5.82234e+04 7.78e-05
## polygon 13 69 5.63134e+04 7.52e-05
## polygon 14 143 1.45139e+05 1.94e-04
## polygon 15 165 3.38736e+05 4.52e-04
## polygon 16 130 9.40465e+04 1.26e-04
## polygon 17 19 1.80977e+03 2.42e-06
## polygon 18 16 2.01046e+03 2.69e-06
## polygon 19 93 4.30642e+05 5.75e-04
## polygon 20 90 4.15092e+05 5.54e-04
## polygon 21 721 1.92795e+06 2.57e-03
## polygon 22 330 1.11896e+06 1.49e-03
## polygon 23 115 9.28394e+05 1.24e-03
## polygon 24 37 1.01705e+04 1.36e-05
## polygon 25 25 1.66227e+04 2.22e-05
## polygon 26 10 2.14507e+03 2.86e-06
## polygon 27 190 2.02489e+05 2.70e-04
## polygon 28 175 9.25904e+05 1.24e-03
## polygon 29 1993 9.99217e+06 1.33e-02
## polygon 30 38 2.42492e+04 3.24e-05
## polygon 31 24 6.35239e+03 8.48e-06
## polygon 32 53 6.35791e+05 8.49e-04
## polygon 33 41 1.60161e+04 2.14e-05
## polygon 34 22 2.54368e+03 3.40e-06
## polygon 35 30 1.08382e+04 1.45e-05
## polygon 36 327 2.16921e+06 2.90e-03
## polygon 37 111 6.62927e+05 8.85e-04
## polygon 38 90 1.15991e+05 1.55e-04
## polygon 39 98 6.26829e+04 8.37e-05
## polygon 40 415 3.25384e+06 4.35e-03
## polygon 41 222 1.51142e+06 2.02e-03
## polygon 42 107 6.33039e+05 8.45e-04
## polygon 43 7 2.48299e+03 3.32e-06
## polygon 44 17 3.28303e+04 4.38e-05
## polygon 45 26 8.34758e+03 1.11e-05
## polygon 46 177 4.67446e+05 6.24e-04
## polygon 47 16 3.19460e+03 4.27e-06
## polygon 48 15 4.87296e+03 6.51e-06
## polygon 49 66 1.61841e+04 2.16e-05
## polygon 50 149 5.63430e+06 7.53e-03
## polygon 51 609 2.62570e+07 3.51e-02
## polygon 52 8 7.82256e+03 1.04e-05
## polygon 53 976 2.33447e+07 3.12e-02
## polygon 54 55 8.25379e+04 1.10e-04
## polygon 55 976 2.33447e+07 3.12e-02
## polygon 56 61 3.33449e+05 4.45e-04
## polygon 57 6 1.68410e+04 2.25e-05
## polygon 58 4 9.45963e+03 1.26e-05
## polygon 59 46 6.99702e+05 9.35e-04
## polygon 60 13 7.00873e+04 9.36e-05
## enclosing rectangle: [2663.93, 56047.79] x [16357.98, 50244.03] units
## (53380 x 33890 units)
## Window area = 748741000 square units
## Fraction of frame area: 0.4144. Quadrat Analysis
Complete Spatial Randomness (CSR) for a given point pattern, based on quadrat counts by using quadrat.test() of spatstat
- H0 = The distribution of childcare services are randomly distributed
- H1 = The distribution of childcare services are not randomly distributed
- Confident interval = 95%
(Default) Chi-squared test of CSR using quadrat counts
# arguments:
# x - point pattern, object of class ppp to be subjected to the goodness-of-fit test
# nx, ny = number of quadrats in the x and y directions
qt <- quadrat.test(childcareSG_ppp, nx = 20, ny = 15)
qt##
## Chi-squared test of CSR using quadrat counts
##
## data: childcareSG_ppp
## X2 = 2020, df = 184, p-value < 2.2e-16
## alternative hypothesis: two.sided
##
## Quadrats: 185 tiles (irregular windows)Since p-value is smaller than the significance level of 0.05 (since confidence interval is 0.95), we can reject the null hypothesis. Thus, the distribution of childcare services are not randomly distributed
plot(childcareSG_ppp)
plot(qt, add = TRUE, cex = 0.1)
Conditional Monte Carlo test of CSR using quadrat counts
quadrat.test(childcareSG_ppp, nx = 20, ny = 15, method = "M", nsim = 999)##
## Conditional Monte Carlo test of CSR using quadrat counts
## Test statistic: Pearson X2 statistic
##
## data: childcareSG_ppp
## X2 = 2020, p-value = 0.002
## alternative hypothesis: two.sided
##
## Quadrats: 185 tiles (irregular windows)Since p-value is smaller than the significance level of 0.05 (since confidence interval is 0.95), we can reject the null hypothesis. Thus, the distribution of childcare services are not randomly distributed
5. Nearest Neighbour Analysis
Clark-Evans test of aggregation for a spatial point pattern by using clarkevans.test() of spatstat
- H0 = The distribution of childcare services are randomly distributed
- H1 = The distribution of childcare services are not randomly distributed
- Confident interval = 95%
Testing spatial point patterns using Clark and Evans test
clarkevans.test(childcareSG_ppp, correction = "none", clipregion = 'sg_owin', alternative = c('two.sided'), nsim = 99)##
## Clark-Evans test
## No edge correction
## Monte Carlo test based on 99 simulations of CSR with fixed n
##
## data: childcareSG_ppp
## R = 0.5603, p-value = 0.02
## alternative hypothesis: two-sidedSince p-value is smaller than the significance level of 0.05 (since confidence interval is 0.95), we can reject the null hypothesis. Thus, the distribution of childcare services are not randomly distributed
6. Kernel Density Estimation
Computing KDE using automatic bandwidth selection method
We use density() function of spatstat, whereby the congif is bw.diggle() for automatic bandwidth selection method.
edge argument - intensity estimate corrected for edge effect bias, default = FALSE
kde_childcareSG_bw <- density(childcareSG_ppp, sigma= bw.diggle, edge=TRUE, kernel= 'gaussian')plot(kde_childcareSG_bw)
You can see that the density values are too small (from 0 to 0.000035), because svy21 is in meter so the density values computed is in number of points per square meter.
childcareSG_ppp.km <- rescale(childcareSG_ppp, 1000, "km")kde_childcareSG.bw <- density(childcareSG_ppp.km, sigma = bw.diggle, edge = TRUE, kernel = 'gaussian')
plot(kde_childcareSG.bw)
Working with diff automatic bandwidth methods
Other selection methods are:
- bw.CvL()
- bw.scott()
- bw.ppl()
Bandwidth return by these automatic bandwidth calculation methods
bw.CvL(childcareSG_ppp.km)## sigma
## 4.376167bw.scott(childcareSG_ppp.km)## sigma.x sigma.y
## 2.302779 1.493379bw.ppl(childcareSG_ppp.km)## sigma
## 0.3570812bw.diggle(childcareSG_ppp.km)## sigma
## 0.3066986Baddeley et. (2016) suggested the use of the bw.ppl() algorithm because in ther experience it tends to produce the more appropriate values when the pattern consists predominantly of tight clusters. But they also insist that if the purpose of once study is to detect a single tight cluster in the midst of random noise then the bw.diggle() method seems to work best.
kde_childcareSG.ppl <- density(childcareSG_ppp.km, sigma = bw.ppl, edge=TRUE, kernel = 'gaussian')
par(mfrow = c(1,2))
plot(kde_childcareSG.bw, main='bw.diggle')
plot(kde_childcareSG.ppl, main = 'bw.ppl')
Working with diff kernel methods
Default kernel used in density.ppp() is gaussian
Smoothing kernel:
- “gaussian” (default)
- “epanechnikov”
- “quartic”
- “disc”
par(mfrow=c(1,2))
plot(density(childcareSG_ppp.km, sigma = bw.ppl, edge= TRUE, kernel = 'gaussian'), main= 'Gaussian')
plot(density(childcareSG_ppp.km, sigma = bw.ppl, edge= TRUE, kernel = 'epanechniko'), main= 'Epanechniko')
par(mfrow=c(1,2))
plot(density(childcareSG_ppp.km, sigma = bw.ppl, edge= TRUE, kernel = 'quartic'), main= 'Quartic')
plot(density(childcareSG_ppp.km, sigma = bw.ppl, edge= TRUE, kernel = 'disc'), main= 'Disc')
Computing KDE by defining bandwidth manually
kde_childcareSG_600 <- density(childcareSG_ppp.km, sigma = 0.6, edge = TRUE, kernel = 'gaussian')
plot(kde_childcareSG_600)
Computing KDE by using adaptive bandwidth
Fixed bandwidth method is very sensitive to highly skewed distribution of spatial point patterns
This is where adaptive bandwidth is used instead, by using density.adaptive() function of spatstat
kde_childcareSG_adaptive <- adaptive.density(childcareSG_ppp.km, method='kernel')
plot(kde_childcareSG_adaptive)
Visualisation comparison between fixed & adaptive kernel density estimation outputs
par(mfrow=c(1,2))
plot(kde_childcareSG.bw, main = "Fixed Bandwidth")
plot(kde_childcareSG_adaptive, main = "Adaptive Bandwidth")
Converting KDE output into grid object
gridded_kde_childcareSG_bw <- as.SpatialGridDataFrame.im(kde_childcareSG.bw)
spplot(gridded_kde_childcareSG_bw)
Converting gridded output into raster object
Using raster() function of raster package
kde_childcareSG_bw_raster <- raster(gridded_kde_childcareSG_bw)
kde_childcareSG_bw_raster## class : RasterLayer
## dimensions : 128, 128, 16384 (nrow, ncol, ncell)
## resolution : 0.4170614, 0.2647348 (x, y)
## extent : 2.663926, 56.04779, 16.35798, 50.24403 (xmin, xmax, ymin, ymax)
## crs : NA
## source : memory
## names : v
## values : -5.361e-15, 26.22644 (min, max)Assigning crs/projection systems
projection(kde_childcareSG_bw_raster) <- CRS('+init=EPSG:3414')
kde_childcareSG_bw_raster## class : RasterLayer
## dimensions : 128, 128, 16384 (nrow, ncol, ncell)
## resolution : 0.4170614, 0.2647348 (x, y)
## extent : 2.663926, 56.04779, 16.35798, 50.24403 (xmin, xmax, ymin, ymax)
## crs : +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +units=m +no_defs
## source : memory
## names : v
## values : -5.361e-15, 26.22644 (min, max)Visualizing the output in tmap
tm_shape(kde_childcareSG_bw_raster) +
tm_raster('v')+
tm_layout(legend.position = c('right', 'bottom'), frame = FALSE)## Variable(s) "v" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.
7. Comparing Spatial Point Patterns using KDE
Comparing KDE of childcare at Punggol, Tampines, Choa Chu Kang & Jurong West sub zones
Extracting planning area
pg = mpsz[mpsz@data$PLN_AREA_N == "PUNGGOL",]
tm = mpsz[mpsz@data$PLN_AREA_N == "TAMPINES",]
ck = mpsz[mpsz@data$PLN_AREA_N == "CHOA CHU KANG",]
jw = mpsz[mpsz@data$PLN_AREA_N == "JURONG WEST",]par(mfrow= c(2,2))
plot(pg, main = "Punggol")
plot(tm, main = "Tampines")
plot(ck, main = "Choa Chu Kang")
plot(jw, main = "Jurong West")
Converting spatial polygons data frame to generic sp format
pg_sp <- as(pg, "SpatialPolygons")
tm_sp <- as(tm, "SpatialPolygons")
ck_sp <- as(ck, "SpatialPolygons")
jw_sp <- as(jw, "SpatialPolygons")Creating owin object
We are not converting into ppp object because they are not spatial points
- spatial points -> convert to sp -> convert to ppp object
- spatial polygons -> convert to sp -> convert to owin object
pg_owin = as(pg_sp, "owin")
tm_owin = as(tm_sp, "owin")
ck_owin = as(ck_sp, "owin")
jw_owin = as(jw_sp, "owin")Combining childcare points + study area (planning subzones)
childcare_pg_ppp = childcare_ppp_jit[pg_owin]
childcare_tm_ppp = childcare_ppp_jit[tm_owin]
childcare_ck_ppp = childcare_ppp_jit[ck_owin]
childcare_jw_ppp = childcare_ppp_jit[jw_owin]childcare_pg_ppp.km = rescale(childcare_pg_ppp, 1000, "km")
childcare_tm_ppp.km = rescale(childcare_tm_ppp, 1000, "km")
childcare_ck_ppp.km = rescale(childcare_ck_ppp, 1000, "km")
childcare_jw_ppp.km = rescale(childcare_jw_ppp, 1000, "km")Visualize the study areas with childcare points:
par(mfrow= c(2,2))
plot(childcare_pg_ppp.km, main= "Punggol")
plot(childcare_tm_ppp.km, main= "Tampines")
plot(childcare_ck_ppp.km, main= "Choa Chu Kang")
plot(childcare_jw_ppp.km, main= "Jurong West")
Computing KDE
kde_children_pg_bw <- density(childcare_pg_ppp.km, sigma = bw.diggle, edge= TRUE, kernel = 'gaussian')
plot(kde_children_pg_bw)
kde_children_tm_bw <- density(childcare_tm_ppp.km, sigma= bw.diggle, edge=TRUE, kernel = 'gaussian')
plot(kde_children_tm_bw)
kde_children_ck_bw <- density(childcare_ck_ppp.km, sigma= bw.diggle, edge=TRUE, kernel = 'gaussian')
plot(kde_children_ck_bw)
kde_children_jw_bw <- density(childcare_jw_ppp.km, sigma= bw.diggle, edge=TRUE, kernel = 'gaussian')
plot(kde_children_jw_bw)
Computing fixed bandwidth KDE
kde_children_ck_250 <- density(childcare_ck_ppp.km, sigma = 0.25, edge= TRUE, kernel = 'gaussian')
plot(kde_children_ck_250)
kde_children_jw_250 <- density(childcare_jw_ppp.km, sigma = 0.25, edge= TRUE, kernel = 'gaussian')
plot(kde_children_jw_250)
kde_children_pg_250 <- density(childcare_pg_ppp.km, sigma = 0.25, edge= TRUE, kernel = 'gaussian')
plot(kde_children_pg_250)
kde_children_tm_250 <- density(childcare_tm_ppp.km, sigma = 0.25, edge= TRUE, kernel = 'gaussian')
plot(kde_children_tm_250)
8. Analysing Spatial Point Process using Nearest Neighbour Index
Clark and Evans test: Choa Chu Kang planning area
clarkevans.test(childcare_ck_ppp, correction = "none", clipregion = NULL, alternative = c('two.sided'), nsim = 999)##
## Clark-Evans test
## No edge correction
## Monte Carlo test based on 999 simulations of CSR with fixed n
##
## data: childcare_ck_ppp
## R = 1.05, p-value = 0.754
## alternative hypothesis: two-sidedSince p-value is big, 0.852, we should not reject the null hypothesis. Thus, the distribution of childcare services are randomly distributed
Clark and Evans test: Tampines planning area
clarkevans.test(childcare_tm_ppp, correction = "none", clipregion = NULL, alternative = c('two.sided'), nsim = 999)##
## Clark-Evans test
## No edge correction
## Monte Carlo test based on 999 simulations of CSR with fixed n
##
## data: childcare_tm_ppp
## R = 0.78976, p-value = 0.002
## alternative hypothesis: two-sidedSince p-value is significantly small, 0.002, we can assume that it’s smaller than the significance level of 0.05 (since confidence interval is 0.95), we can reject the null hypothesis. Thus, the distribution of childcare services are not randomly distributed