plotKML

plotKML: Visualization of Spatial and Spatio-Temporal Objects in Google Earth

https://github.com/envirometrix/plotkml

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plotKML: Visualization of Spatial and Spatio-Temporal Objects in Google Earth

README.Rmd

---
output: github_document
---


```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```



# plotKML
plotKML: Visualization of Spatial and Spatio-Temporal Objects in Google Earth



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How to cite:

- Hengl, T., Roudier, P., Beaudette, D., & Pebesma, E. (2015). **plotKML: Scientific visualization of spatio-temporal data**. Journal of Statistical Software, 63(5), 1-25. https://www.jstatsoft.org/article/view/v063i05

## Installing

Install development versions from github:

```{r, eval = FALSE}
library(devtools)
install_github("envirometrix/plotKML")
# install_github("envirometrix/plotKML", ref = "stars")
```

## Integration with sf

We will now present new `sf` methods and compare them with current `sp` approach. Start with POINTS

```{r}
suppressPackageStartupMessages({
  library(plotKML)
  library(sp)
  library(rgdal)
  library(sf)
})
# Load data
data(eberg)
coordinates(eberg) <- ~ X + Y
proj4string(eberg) <- CRS("+init=epsg:31467")
## subset to 20 percent:
eberg <- eberg[runif(nrow(eberg)) < .1, ]
# sp methods
plotKML(eberg["CLYMHT_A"], open.kml = FALSE)
plotKML(eberg["CLYMHT_A"], colour_scale = rep("#FFFF00", 2), points_names = "", open.kml = FALSE)

# convert eberg to sf format
eberg_sf <- st_as_sf(eberg)

# and apply sf methods
plotKML(eberg_sf["CLYMHT_A"], open.kml = FALSE)
plotKML(eberg_sf["CLYMHT_A"], points_names = "", colour_scale = SAGA_pal[[2]], open.kml = FALSE) #Change palette
plotKML(eberg_sf["CLYMHT_A"], colour_scale = rep("#FFFF00", 2), points_names = "", open.kml = FALSE) # Degenerate palette
```

Recent version of `sf` could show a warning message relative to the old PROJ4string specified by `get("ref_CRS", envir = plotKML.opts)`. 



We can now compare the two implementations: 

```{r}
all.equal(readLines("eberg__CLYMHT_A__.kml"), readLines("eberg_sf__CLYMHT_A__.kml"))
```


The two string mismatches are simply caused by the different names and classes: 

```{r, warning = FALSE}
id_mismatches <- readLines("eberg__CLYMHT_A__.kml") != readLines("eberg_sf__CLYMHT_A__.kml")
readLines("eberg__CLYMHT_A__.kml")[id_mismatches]
readLines("eberg_sf__CLYMHT_A__.kml")[id_mismatches]
```








We can also use `kml_layer` functions: 

```{r}
data(eberg_grid)
gridded(eberg_grid) <- ~ x + y
proj4string(eberg_grid) <- CRS("+init=epsg:31467")
eberg_grid_sf <- st_as_sf(eberg_grid)

# sfc objects
kml_open("eberg_grids.kml")
kml_layer(st_geometry(eberg_grid_sf))
kml_close("eberg_grids.kml")

# sf objects
kml_open("eberg_grid_sf.kml")
kml_layer(eberg_grid_sf, colour = DEMSRT6, colour_scale = R_pal[["terrain_colors"]])
kml_layer(eberg_grid_sf, colour = TWISRT6, colour_scale = SAGA_pal[[1]])
kml_close("eberg_grid_sf.kml")
```

This is also a more complex example where we add a legend to the plot: 

```{r, eval = FALSE}
shape = "http://maps.google.com/mapfiles/kml/pal2/icon18.png" 
kml_file = "kml_file_point.kml"
legend_file = "kml_legend.png"
kml_open(kml_file)
kml_layer(
  eberg_sf["CLYMHT_A"], 
  colour = CLYMHT_A, 
  points_names = eberg_sf[["CLYMHT_A"]], 
  size = 1, 
  shape = shape, 
  alpha = 0.75, 
  colour_scale = SAGA_pal[[2]]
)
kml_legend.bar(eberg_sf$CLYMHT_A, legend.file = legend_file, legend.pal = SAGA_pal[[1]])
kml_screen(image.file = legend_file)
kml_close(kml_file)
kml_View(kml_file)
```

Then we present `sf` methods for MULTIPOINT objects. 
The idea is exactly the same, but MULTIPOINT object are converted to POINT. 

```{r}
eberg_sf_MULTIPOINT <- eberg_sf %>% 
  dplyr::mutate(random_ID = sample(1:4, size = dplyr::n(), replace = TRUE)) %>% 
  dplyr::group_by(random_ID) %>% 
  dplyr::summarise()
plotKML(eberg_sf_MULTIPOINT["random_ID"], open.kml = FALSE)
```

Then we present LINESTRING methods, starting from the `sp` example: 

```{r}
# sp
data(eberg_contours)
plotKML(eberg_contours, open.kml = FALSE)
plotKML(eberg_contours, colour = Z, altitude = Z, open.kml = FALSE)
# sf
eberg_contours_sf <- st_as_sf(eberg_contours)
plotKML(eberg_contours_sf, open.kml = FALSE)
plotKML(eberg_contours_sf, colour = Z, altitude = Z, open.kml = FALSE)
```

Again, the kml files are identical but for super small differences (if they are present) due to rounding errors: 

```{r, warning = FALSE}
all.equal(readLines("eberg_contours.kml"), readLines("eberg_contours_sf.kml"))

id_mismatches <- which(readLines("eberg_contours.kml") != readLines("eberg_contours_sf.kml"))
readLines("eberg_contours.kml")[id_mismatches[1:5]]
readLines("eberg_contours_sf.kml")[id_mismatches[1:5]]
```

The same ideas can be applied to MULTILINESTRING objects. 
The only problem is that  some of the features in `eberg_contous_sf` are not valid according to the simple feature definition of LINESTRING. 
For example

```{r}
eberg_contours@lines[[4]]
```

since it would represent a LINESTRING with only 1 point: 

```{r, error = TRUE}
st_is_valid(st_geometry(eberg_contours_sf)[[4]], NA_on_exception = FALSE, reason = TRUE)
```

So we need to exclude these elements and create a MULTILINESTRING object, 

```{r}
ID_valid <- vapply(
  st_geometry(eberg_contours_sf), 
  function(x) isTRUE(st_is_valid(x)), 
  logical(1)
)
eberg_contous_sf_multi <- eberg_contours_sf %>% 
  dplyr::filter(ID_valid) %>% 
  dplyr::group_by(Z) %>% 
  dplyr::summarise()
plotKML(eberg_contous_sf_multi, colour = Z, altitude = Z, open.kml = FALSE)
```

We can also use `kml_layer` functions: 

```{r}
# sfc LINESTRING object
kml_open("eberg_contours_sfc.kml")
kml_layer(st_geometry(eberg_contours_sf))
kml_close("eberg_contours_sfc.kml")
```

Then we can work with POLYGON geometry, starting from `sp` example: 

```{r}
# sp 
set.seed(1) # I set the seed to compare the kml files
data(eberg_zones)
plotKML(eberg_zones["ZONES"], open.kml = FALSE)
## add altitude:
zmin = 230
plotKML(eberg_zones["ZONES"], altitude = zmin + runif(length(eberg_zones)) * 500, open.kml = FALSE)

# sf objects with sfc_POLYGON geometry
eberg_zones_sf <- st_as_sf(eberg_zones)
set.seed(1)
plotKML(eberg_zones_sf["ZONES"], open.kml = FALSE)
plotKML(eberg_zones_sf["ZONES"], altitude = zmin + runif(length(eberg_zones)) * 500, open.kml = FALSE)
```

and compare the results:  

```{r, warning = FALSE}
all.equal(readLines("eberg_zones__ZONES__.kml"), readLines("eberg_zones_sf__ZONES__.kml"))
```

The differences here (if they are present) are caused by extremely small rounding problems: 

```{r, warning = FALSE}
id_mismatches <- readLines("eberg_zones__ZONES__.kml") != readLines("eberg_zones_sf__ZONES__.kml")
readLines("eberg_zones__ZONES__.kml")[id_mismatches][1:5]
readLines("eberg_zones_sf__ZONES__.kml")[id_mismatches][42]
```

Again, the same ideas can be applied to MULTIPOLYGON objects: 

```{r}
eberg_zones_sf_MULTI <- eberg_zones_sf %>% 
  dplyr::group_by(ZONES) %>% 
  dplyr::summarise() %>% 
  st_cast("MULTIPOLYGON") 
plotKML(eberg_zones_sf_MULTI["ZONES"], open.kml = FALSE)
```

We will now present `plotKML` methods applied to `stars` objects. 
We extended the `plotKML()` function to `stars` objects representing Raster Data Cubes creating a wrapper to RasterLayer method:  

```{r}
library(stars)
# Start with a raster data cube
(g <- read_stars(system.file("external/test.grd", package = "raster")))
plotKML(g, open.kml = FALSE)
```

Default methods do not work if the third dimension does not represent a `Date` or `POSIXct` object: 

```{r, error = TRUE}
# Test with another example: 
(L7 <- read_stars(system.file("tif/L7_ETMs.tif", package = "stars")))
plotKML(L7, open.kml = FALSE)
```


It fails. The problems is that there is no method definition for `plotKML()` function for objects of class `RasterBrick`.
The code behind `as(x, "Raster")` is defined [here](https://github.com/r-spatial/stars/blob/e0c8506d8e00413721a1dfe83cc7f3d45b16c912/R/raster.R#L116-L124) and it says that if `length(dim(x)) > 2` (i.e. there is an extra dimension other than `x` and `y`), then it creates a `RasterBrick` object. 
We can still plot the raster associated to one of the layers in the `RasterBrick` with a little bit of extra work:

```{r}
plotKML(raster::raster(as(L7, "Raster"), layer = 1), open.kml = FALSE)
```

We are working on defining an extension to `stars` object with a temporal dimension as a wrapper around `RasterBrickTimeSeries` methods. 

Let's focus now on Vector Data cubes: 

```{r}
# Vector data cube
data(air, package = "spacetime")
d = st_dimensions(station = st_as_sfc(stations), time = dates)
(aq = st_as_stars(list(PM10 = air), dimensions = d))
(agg = aggregate(aq, "months", mean, na.rm = TRUE))
plotKML(agg, open.kml = FALSE) 
```

Unfortunately there is a known bug in `plotKML`/`spacetime` and the following example fails:  

```{r, error = TRUE}
# Spatial aggregation: 
(a = aggregate(agg, st_as_sf(DE_NUTS1), mean, na.rm = TRUE))
plotKML(a) # error

# The error is in as(obj, "STIDF"), i.e. the conversion between STFDF and STIDF
```

Moreover, all the examples fail if the third dimension is not a temporal object (since the methods we defined as wrappers around STFDF class): 

```{r, error = TRUE}
nc = read_sf(system.file("gpkg/nc.gpkg", package="sf"))
nc.df = st_set_geometry(nc, NULL)
mat = as.matrix(nc.df[c("BIR74", "SID74", "NWBIR74", "BIR79", "SID79", "NWBIR79")])
dim(mat) = c(county = 100, var = 3, year = 2) # make it a 3-dimensional array
dimnames(mat) = list(county = nc$NAME, var = c("BIR", "SID", "NWBIR"), year = c(1974, 1979))
(nc.st = st_as_stars(pop = mat))
(nc.geom <- st_set_dimensions(nc.st, 1, st_geometry(nc)))
plotKML(nc.geom)
```

We can somehow fix this problem converting the `stars` object into `sf` format: 

```{r}
# We can use the following approach
plotKML(st_as_sf(nc.geom), open.kml = FALSE)
```

In some cases, it is still possible to redefine a temporal dimension (but unfortunately the example fails for the same bug as the previous case): 

```{r, error = TRUE}
(nc.geom <- st_set_dimensions(nc.geom, 3, as.Date(c("1974-01-01","1979-01-01"))))
(nc.geom <- split(aperm(nc.geom, c(1,3,2))))
plotKML(nc.geom)
```

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