Monash University, Australia
2023 May 02
A final year PhD student in the Department of Econometrics and Business Statistics
My research centers on exploring multivariate spatio-temporal data with data wrangling and visualisation tool.
Find me on
huizezhangsh,huizezhang-sherry, andhttps://huizezhangsh.netlify.app/
People can talk about a whole range of different things when they refer to their data as spatio-temporal!
The focus of today will be on vector data.
Physical sensors that measure the temperature, rainfall, and wind speed & direction
# A tibble: 88 × 6
id lat long elev name wmo_id
<chr> <dbl> <dbl> <dbl> <chr> <dbl>
1 ASN00001006 -15.5 128. 3.8 wyndham aero 95214
2 ASN00002032 -17.0 128. 203 warmun 94213
3 ASN00003080 -17.6 124. 77.5 curtin aero 94204
4 ASN00005007 -22.2 114. 5 learmonth airport 94302
5 ASN00006044 -25.9 114. 9 denham 94402
# … with 83 more rows
Cubble is a nested object built on tibble that allow easy pivoting between spatial and temporal form.
(weather <- as_cubble( list(spatial = stations, temporal = ts), key = id, index = date, coords = c(long, lat) ))(weather <- as_cubble( list(spatial = stations, temporal = ts), key = id, index = date, coords = c(long, lat) ))
# cubble: id [88]: nested form
# bbox: [113.53, -43.49, 153.64, -10.58]
# temporal: date [date], prcp [dbl], tmax [dbl], tmin [dbl]
id lat long elev name wmo_id ts
<chr> <dbl> <dbl> <dbl> <chr> <dbl> <list>
1 ASN00001006 -15.5 128. 3.8 wyndham aero 95214 <tibble [366 × 4]>
2 ASN00002032 -17.0 128. 203 warmun 94213 <tibble [366 × 4]>
3 ASN00003080 -17.6 124. 77.5 curtin aero 94204 <tibble [366 × 4]>
4 ASN00005007 -22.2 114. 5 learmonth airport 94302 <tibble [366 × 4]>
5 ASN00006044 -25.9 114. 9 denham 94402 <tibble [366 × 4]>
# … with 83 more rowsstations) can be an sf object and temporal data (ts) can be a tsibble object.long form
# cubble: date, id [88]: long form
# bbox: [113.53, -43.49, 153.64, -10.58]
# spatial: lat [dbl], long [dbl], elev [dbl],
# name [chr], wmo_id [dbl]
id date prcp tmax tmin
<chr> <date> <dbl> <dbl> <dbl>
1 ASN00001006 2020-01-01 164 38.3 25.3
2 ASN00001006 2020-01-02 0 40.6 30.5
3 ASN00001006 2020-01-03 16 39.7 27.2
4 ASN00001006 2020-01-04 0 38.2 27.3
5 ASN00001006 2020-01-05 2 39.3 26.7
# … with 32,203 more rowsback to the nested form:
# cubble: id [88]: nested form
# bbox: [113.53, -43.49, 153.64, -10.58]
# temporal: date [date], prcp [dbl], tmax [dbl],
# tmin [dbl]
id lat long elev name wmo_id ts
<chr> <dbl> <dbl> <dbl> <chr> <dbl> <list>
1 ASN0000… -15.5 128. 3.8 wynd… 95214 <tibble>
2 ASN0000… -17.0 128. 203 warm… 94213 <tibble>
3 ASN0000… -17.6 124. 77.5 curt… 94204 <tibble>
4 ASN0000… -22.2 114. 5 lear… 94302 <tibble>
5 ASN0000… -25.9 114. 9 denh… 94402 <tibble>
# … with 83 more rows[1] TRUEReference temporal variables with $
# cubble: id [88]: nested form
# bbox: [113.53, -43.49, 153.64, -10.58]
# temporal: date [date], prcp [dbl], tmax [dbl], tmin [dbl]
id lat long elev name wmo_id ts avg_tmax
<chr> <dbl> <dbl> <dbl> <chr> <dbl> <list> <dbl>
1 ASN00001006 -15.5 128. 3.8 wyndham aero 95214 <tibble [366 × 4]> 36.7
2 ASN00002032 -17.0 128. 203 warmun 94213 <tibble [366 × 4]> 35.8
3 ASN00003080 -17.6 124. 77.5 curtin aero 94204 <tibble [366 × 4]> 35.9
4 ASN00005007 -22.2 114. 5 learmonth airport 94302 <tibble [366 × 4]> 33.2
5 ASN00006044 -25.9 114. 9 denham 94402 <tibble [366 × 4]> 27.1
# … with 83 more rowsMove spatial variables into the long form
# cubble: date, id [88]: long form
# bbox: [113.53, -43.49, 153.64, -10.58]
# spatial: lat [dbl], long [dbl], elev [dbl], name [chr], wmo_id [dbl]
id date prcp tmax tmin long lat
<chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl>
1 ASN00001006 2020-01-01 164 38.3 25.3 128. -15.5
2 ASN00001006 2020-01-02 0 40.6 30.5 128. -15.5
3 ASN00001006 2020-01-03 16 39.7 27.2 128. -15.5
4 ASN00001006 2020-01-04 0 38.2 27.3 128. -15.5
5 ASN00001006 2020-01-05 2 39.3 26.7 128. -15.5
# … with 32,203 more rowsModified from Glyph-maps for Visually Exploring Temporal Patterns in Climate Data and Models (Wickham, 2012)
cb <- as_cubble(
list(spatial = stations, temporal = ts),
key = id, index = date,
coords = c(long, lat)
)
cb_glyph <- cb %>%
face_temporal() %>%
mutate(month = lubridate::month(date)) %>%
group_by(month) %>%
summarise(tmax = mean(tmax, na.rm = TRUE)) %>%
unfold(long, lat)
cb_glyph %>%
ggplot(aes(x_major = long,
x_minor = month,
y_major = lat,
y_minor = tmax)) +
geom_sf(data = oz_simp, fill = "grey90",
color = "white", inherit.aes = FALSE) +
geom_glyph_box(width = 1.3, height = 0.5) +
geom_glyph(width = 1.3, height = 0.5) +
ggthemes::theme_map()
Wickham, H., Hofmann, H., Wickham, C., & Cook, D. (2012). Glyph‐maps for visually exploring temporal patterns in climate data and models. Environmetrics, 23(5), 382-393: https://vita.had.co.nz/papers/glyph-maps.pdf
