st_meteo
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Spatial and spatio-temporal data in ifgi Institute for Geoinformatics University of Münster Edzer Pebesma DailyMeteo, Belgrade, 24-27 Jun 2014 http://ifgi.uni-muenster.de/~epebe_01/R/ 1/26
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Transcript of st_meteo
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Spatial and spatio-temporal data in
1. Das neue IfGI-Logo 1.6 Logovarianten
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ifgi
ifgi
Institute for GeoinformaticsUniversity of Mnster
ifgi
Institut fr GeoinformatikUniversitt Mnster
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deutschsprachigen Prsentationen.
Einsatzbereiche: Briefbogen, Visitenkarte,
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Edzer Pebesma
DailyMeteo, Belgrade, 24-27 Jun 2014http://ifgi.uni-muenster.de/~epebe_01/R/
1 / 26
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Overview
1. vector, matrix, array; lists, data.frame
2. selection on data.frame
3. spatial classes
4. selection on spatial objects
5. aggregation, in general
6. aggregation on spatial classes
7. spatio-temporal classes
8. selection, overlay, aggregation on spatio-temporal objects
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All data are spatio-temporal
1. There are no pure-spatial data. Maps reflect either
I a snapshot in time (remote sensing image)
I an aggregate over a time period (e.g., interpolated yearlyaverage temperature, or yearly aggregated daily interpolations)
I something that is constant over a period of time (politicalboundary)
I a seemingly non-changing phenomenon (geology)
2. There are no pure-temporal data. Time series reflect either
I spatially aggregated values (global temperature curves)
I a single spatial location (air quality sensor DEUB032, at8.191934E,50.93033N)
I vaguely located, or universal aggregates (world market prices,stock quotes)
3 / 26
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Vector, matrix, array> a = vector(3, mode = "numeric")
> a
[1] 0 0 0
> length(a)
[1] 3
or simply by initialisation
> c = 1:3
> c
[1] 1 2 3
> typeof(c)
[1] "integer"
> d = 1.5:3.5
> d
[1] 1.5 2.5 3.5
> typeof(c)
[1] "integer"
> m = matrix(rnorm(6), 2, 3)
> print(m, digits=3)
[,1] [,2] [,3]
[1,] 0.415 0.735 -0.171
[2,] -1.115 -0.540 -1.029
> dim(m)
[1] 2 3
> a = array(1:(5*7*9), c(5,7,9))
> dim(a)
[1] 5 7 9
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lists, data.frameLists can contain anything:
> a = list(1:3, x = c("foo", "bar"), c(TRUE, FALSE))
> a
[[1]]
[1] 1 2 3
$x
[1] "foo" "bar"
[[3]]
[1] TRUE FALSE
> a[1]
[[1]]
[1] 1 2 3
> a[[1]]
[1] 1 2 3
> a$x
[1] "foo" "bar"
data.frame is a column store, butmimics records of mixed type
> a[[1]] = 1:2
> b = as.data.frame(a)
> names(b) = c("NR", "what", "cond")
> b
NR what cond
1 1 foo TRUE
2 2 bar FALSE
> is.list(b)
[1] TRUE
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Selection on data.frame
> b
NR what cond
1 1 foo TRUE
2 2 bar FALSE
> b[[1]]
[1] 1 2
> b[["NR"]]
[1] 1 2
> b$NR
[1] 1 2
> b[1]
NR
1 1
2 2
> b[1,]
NR what cond
1 1 foo TRUE
> b[,1:2]
NR what
1 1 foo
2 2 bar
> b[,1]
[1] 1 2
> b[,1,drop=FALSE]
NR
1 1
2 2
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Deletion, negative selection, replacement
> b
NR what cond
1 1 foo TRUE
2 2 bar FALSE
> b$NR = NULL
> b
what cond
1 foo TRUE
2 bar FALSE
> b[-1,]
what cond
2 bar FALSE
> b$cond2 = ! b$cond
> b
what cond cond2
1 foo TRUE FALSE
2 bar FALSE TRUE
> b[1,1] = NA
> b
what cond cond2
1 TRUE FALSE
2 bar FALSE TRUE
> class(b$what)
[1] "factor"
> b$what
[1] bar
Levels: bar foo
> as.numeric(b$what)
[1] NA 17 / 26
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Spatial classes (sp)Class Spatial provides a coordinate reference system and abounding box.I Objects deriving from Spatial consist of a geometry:
I SpatialPointsI SpatialLinesI SpatialPolygonsI SpatialPixelsI SpatialGrid
I from these, Spatial*DataFrame objects derive, and haveattributes (a data slot, of class data.frame)
> library(sp)
> library(rgdal)
> p = SpatialPoints(cbind(lon = 8, lat = 52), CRS("+init=epsg:4326"))
> p
SpatialPoints:
lon lat
[1,] 8 52
Coordinate Reference System (CRS) arguments: +init=epsg:4326
+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84
+towgs84=0,0,0 8 / 26
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Meuse data set
> library(sp)
> data("meuse")
> coordinates(meuse) spplot(meuse["zinc"],
+ col.regions = bpy.colors())
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[113,458.2](458.2,803.4](803.4,1149](1149,1494](1494,1839]
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Getting spatial data in RUsually, we dont create spatial objects from scratch, but fromexternal files (readGDAL, readOGR), or from data.frame objects:> library(sp)
> data(meuse)
> class(meuse)
[1] "data.frame"
> dim(meuse)
[1] 155 14
> names(meuse)[1:6]
[1] "x" "y" "cadmium" "copper" "lead" "zinc"
> coordinates(meuse) = c("x", "y")
> # which is short for:
> data(meuse)
> pts = SpatialPoints(meuse[c("x", "y")])
> m = SpatialPointsDataFrame(pts, meuse)
> class(m)
[1] "SpatialPointsDataFrame"
attr(,"package")
[1] "sp"10 / 26
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Selection on spatial objects
Selecting the data.frame metaphor:I selection of records (features), attributes (columns):
> meuse[1:3,1:6]
x y cadmium copper lead zinc
1 181072 333611 11.7 85 299 1022
2 181025 333558 8.6 81 277 1141
3 181165 333537 6.5 68 199 640
I extraction of variables:> meuse$zinc[1:3]
[1] 1022 1141 640
I replacement:> meuse$zinc[1:2] = NA
> meuse[1:3,1:6]
x y cadmium copper lead zinc
1 181072 333611 11.7 85 299 NA
2 181025 333558 8.6 81 277 NA
3 181165 333537 6.5 68 199 640
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Aggregation, in general> d = data.frame(x = 1:6, grp1 = c(rep("A",3), rep("B",3)))
> d$grp2 = rep(c("P","Q","R"), each = 2)
> d
x grp1 grp2
1 1 A P
2 2 A P
3 3 A Q
4 4 B Q
5 5 B R
6 6 B R
> aggregate(d[1], list(d$grp1), mean)
Group.1 x
1 A 2
2 B 5
> aggregate(d[1], list(d$grp1, d$grp2), mean)
Group.1 Group.2 x
1 A P 1.5
2 A Q 3.0
3 B Q 4.0
4 B R 5.512 / 26
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Aggregation, needs:
## S3 method for class 'data.frame'
aggregate(x, by, FUN, ..., simplify = TRUE)
I an object to aggregate (x)
I an aggregation predicate (by)
I an aggregation function (FUN)
NOTE that
I we pass functions as arguments R is a functionalprogramming language
I we can write our own function, and pass it to FUN
I ... is passed on to this function
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Aggregation of spatial classes
> library(sp)
> data("meuse")
> coordinates(meuse) offset = c(178460, 329620)+20
> gt = GridTopology(offset, c(400,400),
+ c(8,11))
> SG = SpatialGrid(gt)
> agg = aggregate(meuse["zinc"], SG)
> spplot(agg["zinc"],
+ col.regions=bpy.colors(),
+ sp.layout = list("sp.points",
+ meuse, col=3))200
400
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Aggregating interpolated values
> library(sp)
> data("meuse")
> coordinates(meuse) data("meuse.grid")
> coordinates(meuse.grid) gridded(meuse.grid) library(gstat)
> x = idw(log(zinc)~1, meuse,
+ meuse.grid, debug.level=0)[1]
> spplot(x[1],col.regions=bpy.colors()) 5.0
5.5
6.0
6.5
7.0
7.5
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Aggregating interpolated values, spatially
> agg = aggregate(x["var1.pred"], SG)
> spplot(agg, col.regions=bpy.colors(),
+ sp.layout = list("sp.points",
+ meuse.grid, col=grey(.5), cex=.5))
5.2
5.4
5.6
5.8
6.0
6.2
6.4
6.6
6.8
NOTE: the aggregation predicate (SG) can be of any type: points,lines, polygons, grid.
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Aggregating by attribute value
> i = cut(x$var1.pred, seq(4, 8, by=.5),
+ include.lowest = TRUE)
> xa = aggregate(x["var1.pred"],
+ list(i=i))
> spplot(xa[1],col.regions=bpy.colors(8)) [4,4.5](4.5,5](5,5.5](5.5,6](6,6.5](6.5,7](7,7.5](7.5,8]
(NOTE: this is still in sp on r-forge, not on CRAN)
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Spatio-temporal classes
Package spacetime tries to combine all cleverness of spatial datain sp, of temporal data in zoo and xts, and then add some. Itmainly solves:
I object creation (e.g. from tables, sp and/or xts objects),
I some I/O (RasterStack with time z; TGRASS, PostGIS)
I selection (space, time, attributes)
I aggregation (over space, over time, over space-time)
I plotting
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meteo: precipitation and stations
> library(meteo)
> data(dprec); head(dprec, 2)
staid time prec
1 2707 2011-07-01 0.2
2 67240-99999 2011-07-01 0.0
> data(stations); head(stations, 2)
staid lon lat elev_1m data_source station_name
9602 1 14.80 56.86667 166 ECA VAEXJOE
9512 10 18.05 59.35000 44 ECA STOCKHOLM
> mtch = match(dprec$staid, stations$staid)
> dprec = data.frame(dprec, stations[mtch, c("lon", "lat")])
> head(dprec, 2)
staid time prec lon lat
1 2707 2011-07-01 0.2 7.241389 62.24778
2 67240-99999 2011-07-01 0.0 7.467000 46.30000
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meteo: precipitation and stations> library(spacetime)
> m = stConstruct(dprec, c("lon", "lat"), "time")
> #, crs = CRS("+init=epsg:4326"))
> m2 = as(m, "STFDF")
> summary(m2[,,"prec"])
Object of class STFDF
with Dimensions (s, t, attr): (12923, 31, 1)
[[Spatial:]]
Object of class SpatialPoints
Coordinates:
min max
lon -179.633 179.75
lat -90.000 83.65
Is projected: NA
proj4string : [NA]
Number of points: 12923
[[Temporal:]]
Index timeIndex
Min. :2011-07-01 Min. : 1.0
1st Qu.:2011-07-08 1st Qu.: 8.5
Median :2011-07-16 Median :16.0
Mean :2011-07-16 Mean :16.0
3rd Qu.:2011-07-23 3rd Qu.:23.5
Max. :2011-07-31 Max. :31.0
[[Data attributes:]]
prec
Min. : 0.00
1st Qu.: 0.00
Median : 0.00
Mean : 2.35
3rd Qu.: 0.40
Max. :1001.00
NA's :75839
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plot: map-panel
> data(NLpol) # in meteo!
> proj4string(m2) = proj4string(NLpol)
> m2.NL = m2[NLpol,]
> stplot(m2.NL[,,"prec"],
+ col.regions = bpy.colors())
prec
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[0,5.12](5.12,10.24](10.24,15.36](15.36,20.48](20.48,25.6](25.6,30.72](30.72,35.84](35.84,40.96](40.96,46.08](46.08,51.2](51.2,56.32](56.32,61.44](61.44,66.56](66.56,71.68](71.68,76.8]
21 / 26
-
xt: space-time (Hovmoller)
> proj4string(m2) = proj4string(NLpol)
> m2.NL = m2[NLpol,]
> stplot(m2.NL[1:100,,"prec"],
+ col.regions=bpy.colors(),
+ mode="xt")
prec
sp.ID
time
Jul 04
Jul 11
Jul 18
Jul 25
910131517192127294145616365697583858791931031091191231331411431511551611631671751931951972052062072132152172192292352412452472552582592672712752892953013053083093103203243263423463503523583743823843863883903924004044064124204224304404464484504644704764824884904924945105185265320
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22 / 26
-
ts: time series
> proj4string(m2) = proj4string(NLpol)
> m2.NL = m2[NLpol,]
> stplot(m2.NL[120:140,,"prec"],
+ mode="ts")
prec
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prec
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60
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146715391807195721632169277033173427362539134345447646254761495155245730584859566356
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23 / 26
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time-panel
> proj4string(m2) = proj4string(NLpol)
> m2.NL = m2[NLpol,]
> stplot(m2.NL[120:140,,"prec"],
+ mode="tp")
prec
time
prec
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1539 1807
Jul 04 Jul 18
1957 2163
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Jul 04 Jul 18
6356
24 / 26
-
Aggregation on spatio-temporal objects
> m2.agg = aggregate(m2.NL[,,"prec"],
+ "5 days", sum)
> stplot(m2.agg[,,"prec"],
+ col.regions=bpy.colors())
prec
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20110721
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20110726
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20110731
lllllllllllllll
[0,8.84](8.84,17.68](17.68,26.52](26.52,35.36](35.36,44.2](44.2,53.04](53.04,61.88](61.88,70.72](70.72,79.56](79.56,88.4](88.4,97.24](97.24,106.1](106.1,114.9](114.9,123.8](123.8,132.6]
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-
Aggregation on spatio-temporal objects
> m2.aggNL = aggregate(m2.NL[,,"prec"],
+ NLpol, mean, na.rm=TRUE)
> plot(m2.aggNL)
Jul 012011
Jul 112011
Jul 182011
Jul 252011
Jul 312011
05
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m2.aggNL
26 / 26
