Switching between space and time: Spatio-temporal analysis with cubble

Roadmap

Follow along with the slides at https://sherryzhang-rladiesmelb2022.netlify.app

Go to the slide repo to run the code in the index.R yourself

Spatio-temporal data

People can talk about a whole range of differnt things when they only refer to their data as spatio-temporal!

The focus of today will be on vector data

Data with different spatial and temporal characteristics can all sit under the big umbrella of spatio-temporal data.

Here we have three different types: vector, raster, trajectory data (explain each)

vector data have time series measured at a collection of locations

raster data use gridded cells to represent a continuous space and each cell has variables measured at different time point. You can think of it as take snapshots of space at various time points and stack them on top of each other

trajectory data, on the other hand, have points moving in the space and time in the same time. Here each point on these line would have a different long, lat and time marker.

These are three different representation of spatio-temporal data and they have their own tools for wrangling and visualisation

That’s why I always feel

Examples of vector data

Physical sensors that measure the temperature, rainfall, wind speed & direction, water level, etc

Represent spatial data in R?

A pair of longitude/ latitude (stations_dt)

# A tibble: 30 × 6
  id           long   lat  elev name                       wmo_id
  <chr>       <dbl> <dbl> <dbl> <chr>                       <dbl>
1 ASN00060139  153. -31.4   4.2 port macquarie airport aws  94786
2 ASN00068228  151. -34.4  10   bellambi aws                94749
# … with 28 more rows

Simple features with sf (stations_sf)

Simple feature collection with 30 features and 4 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 114.0967 ymin: -41.8739 xmax: 152.8655 ymax: -11.6502
Geodetic CRS:  GDA94
# A tibble: 30 × 5
  id           elev name                       wmo_id            geometry
  <chr>       <dbl> <chr>                       <dbl>         <POINT [°]>
1 ASN00060139   4.2 port macquarie airport aws  94786 (152.8655 -31.4336)
2 ASN00068228  10   bellambi aws                94749 (150.9291 -34.3691)
# … with 28 more rows

Geometrical operations with sf

Ploting an sf object

ggplot() + 
  geom_sf(data = oz_simp) + 
  geom_sf(data = stations_sf)

set.seed(1234)
dt_lbl <- stations_sf %>% arrange(id) %>% sample_n(5)
ggplot() +
  geom_sf(
    data = oz_simp, 
    fill = "grey95", color = "white") +
  geom_sf_label(
    data = dt_lbl, 
    aes(label = name)) + 
  ggthemes::theme_map()

Look into an sf object - POINTS

dt # simple feature (sf)

Simple feature collection with 2 features and 6 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 150.9291 ymin: -34.3691 xmax: 152.8655 ymax: -31.4336
Geodetic CRS:  GDA94
# A tibble: 2 × 7
  id            lat  long  elev name            wmo_id            geometry
  <chr>       <dbl> <dbl> <dbl> <chr>            <dbl>         <POINT [°]>
1 ASN00060139 -31.4  153.   4.2 port macquarie…  94786 (152.8655 -31.4336)
2 ASN00068228 -34.4  151.  10   bellambi aws     94749 (150.9291 -34.3691)

(pnt_sfc <- dt$geometry) # simple feature column (sfc)

Geometry set for 2 features 
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 150.9291 ymin: -34.3691 xmax: 152.8655 ymax: -31.4336
Geodetic CRS:  GDA94
POINT (152.8655 -31.4336)
POINT (150.9291 -34.3691)

(pnt_sfg <- pnt_sfc[[1]]) # simple feature geometry (sfg) - POINT

POINT (152.8655 -31.4336)

Look into an sf object - POINTS (2)

The point sfg is a paired vector with special class labels

pnt_sfg # simple feature geometry (sfg) - POINTS

POINT (152.8655 -31.4336)

typeof(pnt_sfg)

[1] "double"

attributes(pnt_sfg)

$class
[1] "XY"    "POINT" "sfg"

unclass(pnt_sfg)

[1] 152.8655 -31.4336

The POINT (152.8655 -31.4336) format is called well-known text, which is a human-readable encoding used by sf

Look into an sf object - POINTS (3)

The sfc is a list of sfg with special attributes

pnt_sfc # simple feature column (sfc)

Geometry set for 1 feature 
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 152.8655 ymin: -31.4336 xmax: 152.8655 ymax: -31.4336
Geodetic CRS:  GDA94
POINT (152.8655 -31.4336)
POINT (150.9291 -34.3691)

typeof(pnt_sfc)

[1] "list"

names(attributes(pnt_sfc))

[1] "class"     "precision" "bbox"      "crs"       "n_empty"

unclass(pnt_sfc)[1:2]

[[1]]
POINT (152.8655 -31.4336)

[[2]]
POINT (150.9291 -34.3691)

Other geoms (1/3) - LINESTRING

The linestring sfg is a matrix of paired doubles

(ls_sfg <- ls_sfc[[1]])

LINESTRING (149.2317 -35.222, 149.2716 -35.2708, 149.3153 -35.27623, 149.3972 -35.32425, 149.3363 -35.33988, 149.2493 -35.33013, 149.2045 -35.34761, 149.1464 -35.4153, 149.1352, ...)

typeof(ls_sfg)

[1] "double"

unclass(ls_sfg)

          [,1]      [,2]
 [1,] 149.2317 -35.22200
 [2,] 149.2716 -35.27080
 [3,] 149.3153 -35.27623
 [4,] 149.3972 -35.32425
 [5,] 149.3363 -35.33988
 [6,] 149.2493 -35.33013
 [7,] 149.2045 -35.34761
 [8,] 149.1464 -35.41530
 [9,] 149.1352 -35.45369
[10,] 149.1516 -35.49320
[11,] 149.1325 -35.54950
[12,] 149.1405 -35.58829
[13,] 149.0781 -35.58642
[14,] 149.0950 -35.67682
[15,] 149.1094 -35.69659
[16,] 149.0907 -35.76562
[17,] 149.1011 -35.80506
[18,] 149.0488 -35.92041
[19,] 149.0122 -35.89970
[20,] 148.9584 -35.89179
[21,] 148.9127 -35.85546
[22,] 148.8894 -35.72272
[23,] 148.8347 -35.74051
[24,] 148.7884 -35.69781
[25,] 148.7985 -35.66698
[26,] 148.7689 -35.60426
[27,] 148.7628 -35.49550
[28,] 148.7858 -35.40891
[29,] 148.8087 -35.38246
[30,] 148.7935 -35.33912
[31,] 148.8101 -35.30745
[32,] 149.1209 -35.12442
[33,] 149.1891 -35.16552
[34,] 149.1903 -35.19435
[35,] 149.2317 -35.22200

Other geoms (2/3) - POLYGONS

POLYGON sfg is a list where each element is a matrix of paired vectors

(pol_sfg <- pol_sfc[[1]])

POLYGON ((149.2317 -35.222, 149.2716 -35.2708, 149.3153 -35.27623, 149.3972 -35.32425, 149.3363 -35.33988, 149.2493 -35.33013, ...))

typeof(pol_sfg)

[1] "list"

unclass(pol_sfg)

[[1]]
          [,1]      [,2]
 [1,] 149.2317 -35.22200
 [2,] 149.2716 -35.27080
 [3,] 149.3153 -35.27623
 [4,] 149.3972 -35.32425
 [5,] 149.3363 -35.33988
 [6,] 149.2493 -35.33013
 [7,] 149.2045 -35.34761
 [8,] 149.1464 -35.41530
 [9,] 149.1352 -35.45369
[10,] 149.1516 -35.49320
[11,] 149.1325 -35.54950
[12,] 149.1405 -35.58829
[13,] 149.0781 -35.58642
[14,] 149.0950 -35.67682
[15,] 149.1094 -35.69659
[16,] 149.0907 -35.76562
[17,] 149.1011 -35.80506
[18,] 149.0488 -35.92041
[19,] 149.0122 -35.89970
[20,] 148.9584 -35.89179
[21,] 148.9127 -35.85546
[22,] 148.8894 -35.72272
[23,] 148.8347 -35.74051
[24,] 148.7884 -35.69781
[25,] 148.7985 -35.66698
[26,] 148.7689 -35.60426
[27,] 148.7628 -35.49550
[28,] 148.7858 -35.40891
[29,] 148.8087 -35.38246
[30,] 148.7935 -35.33912
[31,] 148.8101 -35.30745
[32,] 149.1209 -35.12442
[33,] 149.1891 -35.16552
[34,] 149.1903 -35.19435
[35,] 149.2317 -35.22200

And lastly … MULTIPOLYGONS!

MULTIPOLYGON sfg is a nested list where each list element can contain multiple matrices of paired vectors

(mpol_sfg <- mpol_sfc[[1]])

MULTIPOLYGON (((145.2859 -38.3962, 145.2903 -38.28367, 145.4109 -38.37078, 145.2859 -38.3962)), ((140.9657 -38.05599, 140.9739 -37.46209, 140.9693 -36.79305, 140.9631 -35.74853, 140.9656 -35.00826, 140.9617 -34.09582, ...)))

typeof(mpol_sfg)

[1] "list"

length(unclass(mpol_sfg))

[1] 2

unclass(mpol_sfg)

[[1]]
[[1]][[1]]
         [,1]      [,2]
[1,] 145.2859 -38.39620
[2,] 145.2903 -38.28367
[3,] 145.4109 -38.37078
[4,] 145.2859 -38.39620


[[2]]
[[2]][[1]]
           [,1]      [,2]
  [1,] 140.9657 -38.05599
  [2,] 140.9739 -37.46209
  [3,] 140.9693 -36.79305
  [4,] 140.9631 -35.74853
  [5,] 140.9656 -35.00826
  [6,] 140.9617 -34.09582
  [7,] 141.0089 -34.04466
  [8,] 141.2610 -34.07898
  [9,] 141.3576 -34.11069
 [10,] 141.5176 -34.20645
 [11,] 141.7049 -34.09569
 [12,] 141.8424 -34.13253
 [13,] 141.8991 -34.11149
 [14,] 142.0745 -34.13049
 [15,] 142.0821 -34.17299
 [16,] 142.2203 -34.18167
 [17,] 142.2385 -34.30782
 [18,] 142.3337 -34.33930
 [19,] 142.3684 -34.52709
 [20,] 142.4347 -34.57615
 [21,] 142.5262 -34.75981
 [22,] 142.6968 -34.72016
 [23,] 142.6995 -34.60315
 [24,] 142.7635 -34.59674
 [25,] 143.0400 -34.70251
 [26,] 143.1041 -34.69400
 [27,] 143.3515 -34.79600
 [28,] 143.3196 -34.96870
 [29,] 143.3470 -35.10478
 [30,] 143.3964 -35.19311
 [31,] 143.5639 -35.20263
 [32,] 143.5637 -35.31612
 [33,] 143.6423 -35.40104
 [34,] 143.7465 -35.38860
 [35,] 143.8666 -35.48032
 [36,] 144.0479 -35.55617
 [37,] 144.1175 -35.62593
 [38,] 144.3757 -35.80121
 [39,] 144.4082 -35.90422
 [40,] 144.5833 -36.03903
 [41,] 144.8058 -36.12384
 [42,] 144.9236 -35.98895
 [43,] 144.9750 -35.88343
 [44,] 145.0729 -35.82776
 [45,] 145.2465 -35.83041
 [46,] 145.3524 -35.86580
 [47,] 145.5340 -35.80529
 [48,] 145.6847 -35.93075
 [49,] 145.7934 -35.98282
 [50,] 145.9080 -35.95356
 [51,] 145.9891 -36.01308
 [52,] 146.2618 -36.01580
 [53,] 146.3877 -36.03527
 [54,] 146.4143 -35.97545
 [55,] 146.5179 -35.96006
 [56,] 146.6870 -36.04082
 [57,] 146.8363 -36.08219
 [58,] 147.0162 -36.08950
 [59,] 147.1372 -36.02862
 [60,] 147.2043 -36.04989
 [61,] 147.3522 -36.03229
 [62,] 147.4043 -35.94356
 [63,] 147.4986 -35.94664
 [64,] 147.5552 -36.00321
 [65,] 147.7084 -35.92854
 [66,] 147.9140 -35.99795
 [67,] 147.9976 -36.04777
 [68,] 148.0289 -36.14008
 [69,] 148.0383 -36.36556
 [70,] 148.1247 -36.46484
 [71,] 148.1244 -36.55098
 [72,] 148.2174 -36.59885
 [73,] 148.1851 -36.69222
 [74,] 148.1949 -36.79625
 [75,] 149.1998 -37.20119
 [76,] 149.9763 -37.50503
 [77,] 149.7393 -37.58451
 [78,] 149.6747 -37.68584
 [79,] 149.4948 -37.76847
 [80,] 149.2516 -37.78179
 [81,] 148.8087 -37.78642
 [82,] 148.6260 -37.80814
 [83,] 148.3662 -37.80395
 [84,] 148.1792 -37.83164
 [85,] 147.7884 -37.95525
 [86,] 147.4672 -38.15915
 [87,] 147.0672 -38.47512
 [88,] 146.9320 -38.59602
 [89,] 146.7857 -38.64632
 [90,] 146.4438 -38.69492
 [91,] 146.2675 -38.68791
 [92,] 146.1856 -38.74344
 [93,] 146.2656 -38.80531
 [94,] 146.2919 -38.90428
 [95,] 146.4170 -38.84892
 [96,] 146.4873 -38.90615
 [97,] 146.4367 -38.97907
 [98,] 146.4209 -39.12585
 [99,] 146.2739 -38.99612
[100,] 146.1842 -38.86951
[101,] 146.0632 -38.81177
[102,] 145.9189 -38.90688
[103,] 145.8715 -38.77944
[104,] 145.7308 -38.64813
[105,] 145.5647 -38.65419
[106,] 145.4237 -38.53648
[107,] 145.4329 -38.42871
[108,] 145.5525 -38.35457
[109,] 145.4883 -38.23615
[110,] 145.3442 -38.21445
[111,] 145.2550 -38.23743
[112,] 145.1299 -38.39045
[113,] 145.0257 -38.47967
[114,] 144.9244 -38.49740
[115,] 144.8369 -38.37040
[116,] 144.9750 -38.32697
[117,] 145.1213 -38.13339
[118,] 145.0906 -38.01495
[119,] 145.0307 -37.99351
[120,] 144.9638 -37.85576
[121,] 144.8085 -37.88033
[122,] 144.6502 -38.00202
[123,] 144.4874 -38.07828
[124,] 144.3979 -38.07382
[125,] 144.3561 -38.13840
[126,] 144.4807 -38.16449
[127,] 144.6318 -38.10734
[128,] 144.7197 -38.14612
[129,] 144.6142 -38.29214
[130,] 144.4357 -38.28130
[131,] 144.2516 -38.38841
[132,] 144.0365 -38.47578
[133,] 143.9731 -38.56636
[134,] 143.8421 -38.67908
[135,] 143.6854 -38.73536
[136,] 143.5557 -38.85494
[137,] 143.3569 -38.75435
[138,] 143.2203 -38.76029
[139,] 143.0217 -38.62625
[140,] 142.8611 -38.60563
[141,] 142.5633 -38.42200
[142,] 142.3722 -38.34942
[143,] 142.1482 -38.38969
[144,] 141.8849 -38.26629
[145,] 141.7420 -38.25311
[146,] 141.5992 -38.32107
[147,] 141.5575 -38.42235
[148,] 141.4067 -38.36934
[149,] 141.3574 -38.27460
[150,] 141.2278 -38.17065
[151,] 140.9657 -38.05599

Geometry types - summary

The sf, sfc, and sfg objects have informative header prints but they can be boiled down to basic data structures that we’re already familiar with
There are more than just introduced geometry types: MULTIPOINTS, MULTILINESTRING, etc
In practice, you don’t have to decompose/ manipulate these vectors or matrices manually, existing geometrical operations (st_*()) and visualisation tools (geom_sf()) will do that for you.

Switching between space and time: Spatio-temporal analysis with
cubble

Hi!

Roadmap

Spatio-temporal data

Examples of vector data

Represent spatial data in R?

Geometrical operations with sf

Ploting an sf object

Look into an sf object - POINTS

Look into an sf object - POINTS (2)

Look into an sf object - POINTS (3)

Other geoms (1/3) - LINESTRING

Other geoms (2/3) - POLYGONS

And lastly … MULTIPOLYGONS!

Geometry types - summary

Geometrical operations with sf again

Aggregate time series with tsibble

Time series of weather station data

How’s the data quality from BOM?

How’s the data quality from BOM?

How’s the data quality from BOM?

Make the inexplicit NAs explicit

Space and time at the same time with cubble

Goal of today: glyph map

Cubble - a spatio-temporal vector data structure

Cast your data into a cubble

Subset on space

Summarise in time

Move coordinates into time

Glyph map transformation

Making your first glyph map

Your Time

Further reading

Acknowledgements