---
title:  "dggridR: Discrete Global Grids for R"
author:
- Richard Barnes
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
bibliography: bibliography.bib
vignette: >
  %\VignetteIndexEntry{dggridR: Discrete Global Grids for R}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---


```{r, fig.width=5, fig.height=5, results='hide', warning=FALSE, error=FALSE, message=FALSE, echo=FALSE, fig.align='center'}
#Generate cover picture
library(dggridR)
#Generate grids of various sizes
hdggses <- lapply(3:5, function(res) dgconstruct(res=res))
hgrids  <- lapply(hdggses, function(dggs) dgearthgrid(dggs))

countries <- map_data("world")

orthobase <-ggplot() + 
    geom_polygon(data=countries,  aes(x=long, y=lat, group=group), fill=NA, color="black")   +
    scale_fill_gradient(low="blue", high="red")+
    coord_map("ortho", orientation = c(0, 90, 0))+
    #coord_map("ortho", orientation = c(-89.49831, -179.9223, 0))+
    xlab('')+ylab('')+
    theme(axis.ticks.x=element_blank())+
    theme(axis.ticks.y=element_blank())+
    theme(axis.text.x=element_blank())+
    theme(axis.text.y=element_blank())

for(i in 1:3) {
  cellstosave <- (hgrids[[i]] %>% group_by(group) %>% summarise(long=max(long)) %>% filter((i-1)*70-5<=long & long<=i*70+5))$group
  hgrids[[i]] <- hgrids[[i]] %>% filter(group %in% cellstosave)
}

orthobase+
 geom_path(data=hgrids[[1]], aes(x=long, y=lat, group=group), alpha=1, color="#1B9E77")+
 geom_path(data=hgrids[[2]], aes(x=long, y=lat, group=group), alpha=1, color="#D95F02")+
 geom_path(data=hgrids[[3]], aes(x=long, y=lat, group=group), alpha=1, color="#7570B3")
```



#_Spatial Analysis Done Right_

You want to do spatial statistics, and it's going to involve binning.

Binning with a rectangular grid introduces messy distortions. At the macro-scale
using a rectangular grid does things like making Greenland bigger than the
United States and Antarctica the largest continent.

![Mercator Projection](mercator.png)

But this kind of distortion is present no matter what the resolution is.

What you want are bins of equal size, regardless of where they are on the globe,
regardless of their resolution.

dggridR solves this problem.

dggridR builds discrete global grids which partition the surface of the Earth
into hexagonal, triangular, or diamond cells, **all of which have the same
size.** (There are some minor caveats which are detailed in the vignettes.)

![Discrete Global Grid in use](dggrid.png)

This package includes everything you need to make spatial binning great again.

Many details are included in the vignette.




#Grids

The following grids are available: 

 * ISEA3H:    Icosahedral Snyder Equal Area Aperture 3 Hexagonal Grid
 * ISEA4H:    Icosahedral Snyder Equal Area Aperture 4 Hexagonal Grid
 * ISEA43H:   Icosahedral Snyder Equal Area Mixed Aperture 4,3 Hexagonal Grid
 * ISEA4T:    Icosahedral Snyder Equal Area Aperture 4 Triangular Grid
 * ISEA4D:    Icosahedral Snyder Equal Area Aperture 4 Diamond Grid
 * FULLER3H:  Fuller Aperature 3 Hexagonal Grid
 * FULLER4H:  Fuller Aperature 4 Hexagonal Grid
 * FULLER43H: Fuller Mixed Aperature 4,3 Hexagonal Grid
 * FULLER4T:  Fuller Aperature 4 Triganular Grid
 * FULLER4D:  Fuller Aperature 4 Diamond Grid

Unless you are using cells with very large areas (significant fractions of
Earth's hemispheres), I recommend the ISEA3H be your default grid.

This grid, along with the other Icosahedral grids ensures that all cells are of
equal area, with a notable exception. At every resolution, the Icosahedral grids
contain 12 pentagonal cells which each have an area exactly 5/6 that of the
hexagonal cells. But you don't need to worry about this too much for two
reasons. (1) As the table below shows, these cells are a small, small minority
of the total number of cells. (2) The grids are orientated so that these cells
are in out-of-the-way places. Future versions of this package will allow you to
reorient the grids, if need be. (TODO)

For more complex applications than simple spatial binning, it is necessary to
consider trade-offs between the different grids. Good references for
understanding these include [@Kimerling1999; @Gregory2008].

Users attempting multi-scale analyses should be aware that in the hexagonal
grids cells from one resolution level are partially contained by the cells of
other levels.

![Nested hexagonal grid](hex_grid_nested.png)

At present, there is no convenient way to convert grid cell ids at one
resolution level to another. In the future, I hope to add this capability to the
package. (TODO)



##ISEA3H Details

The following chart shows the number of cells, their area, and statistics
regarding the spacing of their center nodes for the ISEA3H grid type.

|Res |Number of Cells  | Cell Area (km^2) |    Min      |     Max     |    Mean     |    Std    |
|---:|----------------:|-----------------:|------------:|------------:|------------:|----------:|
|  0 |              12 | 51,006,562.17241 |             |             |             |           |
|  1 |              32 | 17,002,187.39080 | 4,156.18000 | 4,649.10000 | 4,320.49000 | 233.01400 |
|  2 |              92 |  5,667,395.79693 | 2,324.81000 | 2,692.72000 | 2,539.69000 | 139.33400 |
|  3 |             272 |  1,889,131.93231 | 1,363.56000 | 1,652.27000 | 1,480.02000 |  89.39030 |
|  4 |             812 |    629,710.64410 |   756.96100 |   914.27200 |   855.41900 |  52.14810 |
|  5 |           2,432 |    209,903.54803 |   453.74800 |   559.23900 |   494.95900 |  29.81910 |
|  6 |           7,292 |     69,967.84934 |   248.80400 |   310.69300 |   285.65200 |  17.84470 |
|  7 |          21,872 |     23,322.61645 |   151.22100 |   187.55000 |   165.05800 |   9.98178 |
|  8 |          65,612 |      7,774.20548 |    82.31100 |   104.47000 |    95.26360 |   6.00035 |
|  9 |         196,832 |      2,591.40183 |    50.40600 |    63.00970 |    55.02260 |   3.33072 |
| 10 |         590,492 |        863.80061 |    27.33230 |    35.01970 |    31.75960 |   2.00618 |
| 11 |       1,771,472 |        287.93354 |    16.80190 |    21.09020 |    18.34100 |   1.11045 |
| 12 |       5,314,412 |         95.97785 |     9.09368 |    11.70610 |    10.58710 |   0.66942 |
| 13 |      15,943,232 |         31.99262 |     5.60065 |     7.04462 |     6.11367 |   0.37016 |
| 14 |      47,829,692 |         10.66421 |     3.02847 |     3.90742 |     3.52911 |   0.22322 |
| 15 |     143,489,072 |          3.55473 |     1.86688 |     2.35058 |     2.03789 |   0.12339 |
| 16 |     430,467,212 |          1.18491 |     1.00904 |     1.30335 |     1.17638 |   0.07442 |
| 17 |   1,291,401,632 |          0.39497 |     0.62229 |     0.78391 |     0.67930 |   0.04113 |
| 18 |   3,874,204,892 |          0.13166 |     0.33628 |     0.43459 |     0.39213 |   0.02481 |
| 19 |  11,622,614,672 |          0.04389 |     0.20743 |     0.26137 |     0.22643 |   0.01371 |
| 20 |  34,867,844,012 |          0.01463 |     0.11208 |     0.14489 |     0.13071 |   0.00827 |



#How do I use it?

1. Construct a discrete global grid system (dggs) object using `dgconstruct()`

2. Modify this dggs object using:
    * `dg_closest_res_to_area()`
    * `dg_closest_res_to_cls()`
    * `dg_closest_res_to_spacing()`
    * `dgsetres()`

3. Get information about your dggs object using:

    * `dggetres()`
    * `dginfo()`
    * `dgmaxcell()`

4. Get the grid cells of some lat-long points with:

    * `dgtransform()`

5. Get the boundaries of the associated grid cells for use in plotting with:

    * `dgearthgrid()`
    * `dgrectgrid()`
    * `dgcellstogrid()`
    * `dgshptogrid()`

6. Check that your dggs object is valid (if you've mucked with it) using:

    * `dgverify()`


#Examples

##Binning Lat-Long Points

The following example demonstrates converting lat-long locations (the epicenters
of earthquakes) to discrete global grid locations (cell numbers), binning based
on these numbers, and plotting the result.

```{r, results='hide', warning=FALSE, error=FALSE, message=FALSE}
#Include libraries
library(dggridR)
library(dplyr)

#Construct a global grid with cells approximately 1000 miles across
dggs          <- dgconstruct(spacing=1000, metric=FALSE, resround='down')

#Load included test data set
data(dgquakes)

#Get the corresponding grid cells for each earthquake epicenter (lat-long pair)
dgquakes$cell <- dgtransform(dggs,dgquakes$lat,dgquakes$lon)

#Get the number of earthquakes in each cell
quakecounts   <- dgquakes %>% group_by(cell) %>% summarise(count=n())

#Get the grid cell boundaries for the whole Earth using this dggs in a form
#suitable for plotting with ggplot2
grid          <- dgcellstogrid(dggs,quakecounts$cell,frame=TRUE,wrapcells=TRUE)

#Update the grid cells' properties to include the number of earthquakes
#in each cell
grid          <- merge(grid,quakecounts,by.x="Name",by.y="cell")

#Make adjustments so the output is more visually interesting
grid$count    <- log(grid$count)
cutoff        <- quantile(grid$count,0.9)
grid          <- grid %>% mutate(count=ifelse(count>cutoff,cutoff,count))

#Get polygons for each country of the world
countries <- map_data("world")
```

Okay, let's draw the plot. Notice how the hexagons appear to be all different
sizes. Really, though, they're not: that's just the effect of trying to plot a
sphere on a flat surface! And that's what would happen to your data if you
didn't use this package :-)

```{r, fig.width=6, fig.height=4}
#Plot everything on a flat map
p<- ggplot() + 
    geom_polygon(data=countries, aes(x=long, y=lat, group=group), fill=NA, color="black")   +
    geom_polygon(data=grid,      aes(x=long, y=lat, group=group, fill=count), alpha=0.4)    +
    geom_path   (data=grid,      aes(x=long, y=lat, group=group), alpha=0.4, color="white") +
    scale_fill_gradient(low="blue", high="red")
p
```

If we replot things on a sphere, it's easy to see that all of the hexagons are
the same size, as they should be. Note how they deal easily with the
longitudinal convergence towards Antarctica, as well as with crossing -180/180
degrees.

```{r, fig.width=6, fig.height=6}
#Replot on a spherical projection
p+coord_map("ortho", orientation = c(-38.49831, -179.9223, 0))+
  xlab('')+ylab('')+
  theme(axis.ticks.x=element_blank())+
  theme(axis.ticks.y=element_blank())+
  theme(axis.text.x=element_blank())+
  theme(axis.text.y=element_blank())+
  ggtitle('Your data could look like this')
```

You can also write out a KML file with your data included for displaying in,
say, Google Earth:

```{r, eval=FALSE}
library(rgdal)

#Get the grid cell boundaries for the whole Earth using this dggs in a form
#suitable for printing to a KML file
grid          <- dgearthgrid(dggs,frame=FALSE)

#Update the grid cells' properties to include the number of earthquakes
#in each cell
grid@data$count <- merge(grid@data, quakecounts, by.x="Name", by.y="cell", all.x=TRUE)$count

#Write out the grid
writeOGR(grid, "quakes_per_cell.kml", "quakes", "KML")
```


##Randomly Sampling the Earth: Method 1

Say you want to sample `N` areas of equal size uniformly distributed on the
Earth. dggridR provides two possible ways to accomplish this. The conceptually
simplest is to choose `N` uniformly distributed lat-long pairs and retrieve
their associated grid cells:

```{r, results='hide', warning=FALSE, error=FALSE, message=FALSE}
#Include libraries
library(dggridR)
library(rgdal)
library(dplyr)

N <- 100    #How many cells to sample

#Distribute the points uniformly on a sphere using equations from
#http://mathworld.wolfram.com/SpherePointPicking.html
u     <- runif(N)
v     <- runif(N)
theta <- 2*pi*u      * 180/pi
phi   <- acos(2*v-1) * 180/pi
lon   <- theta-180
lat   <- phi-90

df    <- data.frame(lat=lat,lon=lon)

#Construct a global grid in which every hexagonal cell has an area of
#100,000 km^2. You could, of course, choose a much smaller value, but these
#will show up when I map them later.

#Note: Cells can only have certain areas, the `dgconstruct()` function below
#will tell you which area is closest to the one you want. You can also round
#up or down.

#Note: 12 cells are actually pentagons with an area 5/6 that of the hexagons
#But, with millions and millions of hexes, you are unlikely to choose one
#Future versions of the package will make it easier to reject the pentagons
dggs    <- dgconstruct(area=100000, metric=FALSE, resround='nearest')

#Get the corresponding grid cells for each randomly chosen lat-long
df$cell <- dgtransform(dggs,df$lat,df$lon)

#Get the hexes for each of these cells
gridfilename <- dgcellstogrid(dggs,df$cell,savegrid=TRUE)
```

The resulting distribution of cells appears as follows:

```{r, fig.width=6, fig.height=4}
#Get the grid in a more convenient format
grid           <- dgcellstogrid(dggs,df$cell,frame=TRUE,wrapcells=TRUE)

#Get polygons for each country of the world
countries      <- map_data("world")

#Plot everything on a flat map
p<- ggplot() + 
    geom_polygon(data=countries, aes(x=long, y=lat, group=group), fill=NA, color="black")   +
    geom_polygon(data=grid,      aes(x=long, y=lat, group=group), fill="green", alpha=0.4)    +
    geom_path   (data=grid,      aes(x=long, y=lat, group=group), alpha=0.4, color="white")
p
```








##Randomly Sampling the Earth: Method 2

Say you want to sample `N` areas of equal size uniformly distributed on the
Earth. dggridR provides two possible ways to accomplish this. The easiest way to
do this is to note that grid cells are labeled from 1 to `M`, where `M` is the
largest cell id at the resolution in question. Therefore, we can sample cell ids
and generate a grid accordingly.

```{r, results='hide', warning=FALSE, error=FALSE, message=FALSE}
#Include libraries
library(dggridR)
library(dplyr)

N <- 100    #How many cells to sample

#Construct a global grid in which every hexagonal cell has an area of
#100,000 km^2. You could, of course, choose a much smaller value, but these
#will show up when I map them later.

#Note: Cells can only have certain areas, the `dgconstruct()` function below
#will tell you which area is closest to the one you want. You can also round
#up or down.

#Note: 12 cells are actually pentagons with an area 5/6 that of the hexagons
#But, with millions and millions of hexes, you are unlikely to choose one
#Future versions of the package will make it easier to reject the pentagons
dggs    <- dgconstruct(area=100000, metric=FALSE, resround='nearest')

maxcell <- dgmaxcell(dggs)                     #Get maximum cell id
cells   <- sample(1:maxcell, N, replace=FALSE) #Choose random cells
grid    <- dgcellstogrid(dggs,cells,frame=TRUE,wrapcells=TRUE) #Get grid
```

The resulting distribution of cells appears as follows:

```{r, fig.width=6, fig.height=4}
#Get the grid in a more convenient format
grid           <- dgcellstogrid(dggs,df$cell,frame=TRUE,wrapcells=TRUE)

#Get polygons for each country of the world
countries      <- map_data("world")

#Plot everything on a flat map
p<- ggplot() + 
    geom_polygon(data=countries, aes(x=long, y=lat, group=group), fill=NA, color="black")   +
    geom_polygon(data=grid,      aes(x=long, y=lat, group=group), fill="green", alpha=0.4)    +
    geom_path   (data=grid,      aes(x=long, y=lat, group=group), alpha=0.4, color="white")
p
```




##Save a grid for use in other software

Reading the grid into R, as is done in the examples above, can be expensive. You
can instead generate the grid directly to a temporary file, as follows:

```{r, results='hide', warning=FALSE, error=FALSE, message=FALSE}
library(dggridR)
#Generate a global grid whose cells are ~100,000 km^2
dggs         <- dgconstruct(area=100000, metric=FALSE, resround='nearest')
#Save the cells to a KML file for use in other software
gridfilename <- dgearthgrid(dggs,savegrid=TRUE)
```

##Get a grid that covers South Africa
```{r, results='hide', warning=FALSE, error=FALSE, message=FALSE, fig.align='center', fig.width=5, fig.height=5}
library(dggridR)

#Generate a dggs specifying an intercell spacing of ~25 miles
dggs      <- dgconstruct(spacing=25, metric=FALSE, resround='nearest')

#Use the included South Africa borders shapefile to generate a grid covering
#South Africa (including Lesotho - holes are not excluded)
sa_grid   <- dgshptogrid(dggs,dg_shpfname_south_africa)

#Read in the South Africa's borders from the shapefile
sa_border <- readOGR(dsn=dg_shpfname_south_africa, layer="ZAF_adm0")

#Plot South Africa's borders and the associated grid
p<- ggplot() + 
    geom_polygon(data=sa_border, aes(x=long, y=lat, group=group), fill=NA, color="black")   +
    geom_polygon(data=sa_grid,   aes(x=long, y=lat, group=group), fill="blue", alpha=0.4)   +
    geom_path   (data=sa_grid,   aes(x=long, y=lat, group=group), alpha=0.4, color="white") +
    coord_equal()
p
```


#Roadmap

* Method to convert between grid cell ids at different resolutions

* In the future, I plan to switch the package from using Kevin Sahr's dggrid
software to the discrete global grid system standards currently being developed
by OpenGeospatial. Those standards are being developed by a [software working
group](http://www.opengeospatial.org/projects/groups/dggsswg) right now, but
will one day be released. At that point, I expect that GDAL/OGR/PROJ4 will
incorporate the new standards making wider interoperability possible. Until that
time, Sahr's dggrid is the best option I've found.



#Credits

This R package was developed by Richard Barnes (http://rbarnes.org).

The dggrid conversion software was developed predominantly by Kevin Sahr
(http://www.discreteglobalgrids.org), with contributions from a few others.

Large portions of the above documentation are drawn from the DGGRID version 6.2b
User Documentation, which is available in its entirety
[here](dggrid_v62_manual.pdf).



#Disclaimer

This package *should* operate in the manner described here, in the package's
main documentation, and in Kevin Sahr's dggrid documentation. Unfortunately,
none of us are paid enough to make absolutely, doggone certain that that's the
case. Use at your own discretion. That said, if you find bugs or are seeking
enhancements, we want to hear about them.



#Citing this Package

Please cite this package as:

 > Richard Barnes (2016). dggridR: Discrete Global Grids for R. https://github.com/r-barnes/dggridR.



#References