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<h1 class="title toc-ignore">More Raster</h1>
</div>
<p><a href="06_RasterTwo.R"><i class="fa fa-file-code-o fa-3x" aria-hidden="true"></i> The R Script associated with this page is available here</a>. Download this file and open it (or copy-paste into a new script) with RStudio so you can follow along.</p>
<div id="libraries" class="section level2">
<h2>Libraries</h2>
<pre class="r"><code>library(knitr)
library(dplyr)
library(tidyr)
library(ggplot2)
library(raster)
library(rasterVis)
library(scales)
library(rgeos)</code></pre>
</div>
<div id="todays-question" class="section level2">
<h2>Today’s question</h2>
<div id="how-will-future-projected-sea-level-rise-affect-bangladesh" class="section level3">
<h3>How will future (projected) sea level rise affect Bangladesh?</h3>
<ol style="list-style-type: decimal">
<li>How much area is likely to be flooded by rising sea level?</li>
<li>How many people are likely to be displaced?</li>
<li>Will sea level rise affect any major population centers?</li>
</ol>
</div>
</div>
<div id="bangladesh" class="section level2">
<h2>Bangladesh</h2>
<pre class="r"><code>getData("ISO3")%>%
as.data.frame%>%
filter(NAME=="Bangladesh")</code></pre>
<pre><code>## ISO3 NAME
## 1 BGD Bangladesh</code></pre>
<div id="download-bangladesh-border" class="section level3">
<h3>Download Bangladesh Border</h3>
<p>Often good idea to keep data in separate folder. You will need to edit this for your machine!</p>
<pre class="r"><code>datadir="~/Downloads/data"
if(!file.exists(datadir)) dir.create(datadir, recursive=T)</code></pre>
<p>Download country border.</p>
<pre class="r"><code>bgd=getData('GADM', country='BGD', level=0,path = datadir)
plot(bgd)</code></pre>
<p><img src="06_RasterTwo_files/figure-html/unnamed-chunk-5-1.png" /><!-- --></p>
</div>
</div>
<div id="topography" class="section level2">
<h2>Topography</h2>
<p>SRTM Elevation data with <code>getData()</code> as 5deg tiles.</p>
<pre class="r"><code>bgdc=gCentroid(bgd)%>%coordinates()
dem1=getData("SRTM",lat=bgdc[2],lon=bgdc[1],path=datadir)
plot(dem1)
plot(bgd,add=T)</code></pre>
<p><img src="06_RasterTwo_files/figure-html/unnamed-chunk-6-1.png" /><!-- --></p>
<div id="mosaicingmerging-rasters" class="section level3">
<h3>Mosaicing/Merging rasters</h3>
<p>Download the remaining necessary tiles</p>
<pre class="r"><code>dem2=getData("SRTM",lat=23.7,lon=85,path=datadir)</code></pre>
<p>Use <code>merge()</code> to join two aligned rasters (origin, resolution, and projection). Or <code>mosaic()</code> combines with a function.</p>
<pre class="r"><code>dem=merge(dem1,dem2)
plot(dem)
plot(bgd,add=T)</code></pre>
<p><img src="06_RasterTwo_files/figure-html/unnamed-chunk-8-1.png" /><!-- --></p>
</div>
</div>
<div id="savingexporting-rasters" class="section level2">
<h2>Saving/exporting rasters</h2>
<p>Beware of massive temporary files!</p>
<pre class="r"><code>inMemory(dem)</code></pre>
<pre><code>## [1] FALSE</code></pre>
<pre class="r"><code>dem@file@name</code></pre>
<pre><code>## [1] "/private/var/folders/lg/gz_jcfk5617dlpzdtg23x3rh0000gn/T/RtmpNAxz5l/raster/r_tmp_2016-10-23_204503_5034_04978.grd"</code></pre>
<pre class="r"><code>file.size(sub("grd","gri",dem@file@name))*1e-6</code></pre>
<pre><code>## [1] 144.036</code></pre>
<pre class="r"><code>showTmpFiles()</code></pre>
<pre><code>## r_tmp_2016-10-23_204503_5034_04978</code></pre>
<pre class="r"><code>rasterOptions()</code></pre>
<pre><code>## format : raster
## datatype : FLT8S
## overwrite : FALSE
## progress : none
## timer : FALSE
## chunksize : 1e+07
## maxmemory : 1e+08
## tmpdir : /var/folders/lg/gz_jcfk5617dlpzdtg23x3rh0000gn/T//RtmpNAxz5l/raster//
## tmptime : 168
## setfileext : TRUE
## tolerance : 0.1
## standardnames : TRUE
## warn depracat.: TRUE
## header : none</code></pre>
<p>Set with <code>rasterOptions(tmpdir = "/tmp")</code></p>
<p>Saving raster to file: <em>two options</em></p>
<p>Save while creating</p>
<pre class="r"><code>dem=merge(dem1,dem2,filename=file.path(datadir,"dem.tif"),overwrite=T)</code></pre>
<p>Or after</p>
<pre class="r"><code>writeRaster(dem, filename = file.path(datadir,"dem.tif"))</code></pre>
<div id="writeraster-formats" class="section level3">
<h3>WriteRaster formats</h3>
<table>
<thead>
<tr class="header">
<th>Filetype</th>
<th>Long name</th>
<th>Default extension</th>
<th>Multiband support</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>raster</td>
<td>‘Native’ raster package format</td>
<td>.grd</td>
<td>Yes</td>
</tr>
<tr class="even">
<td>ascii</td>
<td>ESRI Ascii</td>
<td>.asc</td>
<td>No</td>
</tr>
<tr class="odd">
<td>SAGA</td>
<td>SAGA GIS</td>
<td>.sdat</td>
<td>No</td>
</tr>
<tr class="even">
<td>IDRISI</td>
<td>IDRISI</td>
<td>.rst</td>
<td>No</td>
</tr>
<tr class="odd">
<td>CDF</td>
<td>netCDF (requires <code>ncdf</code>)</td>
<td>.nc</td>
<td>Yes</td>
</tr>
<tr class="even">
<td>GTiff</td>
<td>GeoTiff (requires rgdal)</td>
<td>.tif</td>
<td>Yes</td>
</tr>
<tr class="odd">
<td>ENVI</td>
<td>ENVI .hdr Labelled</td>
<td>.envi</td>
<td>Yes</td>
</tr>
<tr class="even">
<td>EHdr</td>
<td>ESRI .hdr Labelled</td>
<td>.bil</td>
<td>Yes</td>
</tr>
<tr class="odd">
<td>HFA</td>
<td>Erdas Imagine Images (.img)</td>
<td>.img</td>
<td>Yes</td>
</tr>
</tbody>
</table>
<p><code>rgdal</code> package does even more…</p>
</div>
<div id="crop-to-coastal-area-of-bangladesh" class="section level3">
<h3>Crop to Coastal area of Bangladesh</h3>
<pre class="r"><code># Crop using border polygon
# dem=crop(dem,bgd,filename=file.path(datadir,"dem_bgd.tif"),overwrite=T)
# Or crop to a lat-lon box
dem=crop(dem,extent(89,91.5,21.5,24),filename=file.path(datadir,"dem_bgd.tif"),overwrite=T)
plot(dem); plot(bgd,add=T)</code></pre>
<p><img src="06_RasterTwo_files/figure-html/unnamed-chunk-13-1.png" /><!-- --></p>
</div>
</div>
<div id="use-ggplot" class="section level1">
<h1>Use ggplot</h1>
<pre class="r"><code>gplot(dem,max=1e5)+geom_tile(aes(fill=value))+
scale_fill_gradientn(
colours=c("red","yellow","grey30","grey20","grey10"),
trans="log1p",breaks= log_breaks(n = 5, base = 10)(c(1, 1e3)))+
coord_equal(ylim=c(21.5,24))+
geom_path(data=fortify(bgd),
aes(x=long,y=lat,order=order,group=group),size=.5)</code></pre>
<pre><code>## Regions defined for each Polygons</code></pre>
<p><img src="06_RasterTwo_files/figure-html/unnamed-chunk-14-1.png" /><!-- --></p>
</div>
<div id="terrain-analysis-an-aside" class="section level1">
<h1>Terrain analysis (an aside)</h1>
<div id="terrain-analysis-options" class="section level2">
<h2>Terrain analysis options</h2>
<p><code>terrain()</code> options:</p>
<ul>
<li>slope</li>
<li>aspect</li>
<li>TPI (Topographic Position Index)</li>
<li>TRI (Terrain Ruggedness Index)</li>
<li>roughness</li>
<li>flowdir</li>
</ul>
<p>Use a smaller region:</p>
<pre class="r"><code>reg1=crop(dem1,extent(93.8,94,21.05,21.15))
plot(reg1)</code></pre>
<p><img src="06_RasterTwo_files/figure-html/unnamed-chunk-15-1.png" /><!-- --></p>
<p>The terrain indices are according to Wilson et al. (2007), as in <a href="http://www.gdal.org/gdaldem.html">gdaldem</a>.</p>
<div id="calculate-slope" class="section level3">
<h3>Calculate slope</h3>
<pre class="r"><code>slope=terrain(reg1,opt="slope",unit="degrees")
plot(slope)</code></pre>
<p><img src="06_RasterTwo_files/figure-html/unnamed-chunk-16-1.png" /><!-- --></p>
</div>
<div id="calculate-aspect" class="section level3">
<h3>Calculate aspect</h3>
<pre class="r"><code>aspect=terrain(reg1,opt="aspect",unit="degrees")
plot(aspect)</code></pre>
<p><img src="06_RasterTwo_files/figure-html/unnamed-chunk-17-1.png" /><!-- --></p>
</div>
<div id="tpi-topographic-position-index" class="section level3">
<h3>TPI (Topographic Position Index)</h3>
<p>Difference between the value of a cell and the mean value of its 8 surrounding cells.</p>
<pre class="r"><code>tpi=terrain(reg1,opt="TPI")
gplot(tpi,max=1e6)+geom_tile(aes(fill=value))+
scale_fill_gradient2(low="blue",high="red",midpoint=0)+
coord_equal()</code></pre>
<p><img src="06_RasterTwo_files/figure-html/unnamed-chunk-18-1.png" /><!-- --> Negative values indicate valleys, near zero flat or mid-slope, and positive ridge and hill tops</p>
<div class="well">
<h2 id="your-turn">Your turn</h2>
<ul>
<li>Identify all the pixels with a TPI less than -15 or greater than 15.</li>
<li>Use <code>plot()</code> to:
<ul>
<li>plot elevation for this region</li>
<li>overlay the valley pixels in blue</li>
<li>overlay the ridge pixels in red</li>
</ul></li>
</ul>
<p>Hint: use <code>transparent</code> to plot a transparent pixel and <code>add=T</code> to add a layer to an existing plot.</p>
<button data-toggle="collapse" class="btn btn-primary btn-sm round" data-target="#demo1">
Show Solution
</button>
<div id="demo1" class="collapse">
<pre class="r"><code>plot(reg1)
plot(tpi>15,col=c("transparent","red"),add=T,legend=F)
plot(tpi<(-15),col=c("transparent","blue"),add=T,legend=F)</code></pre>
<p><img src="06_RasterTwo_files/figure-html/unnamed-chunk-19-1.png" /><!-- --></p>
<pre class="r"><code>#OR (ggplot solution, sort of)
rcl=matrix(c(-Inf,-15,1,
-15,15,2,
15,Inf,3),byrow=T,nrow=3)
regclass=reclassify(tpi,rcl)
gplot(regclass,max=1e6)+geom_tile(aes(fill=value))+
scale_fill_gradient2(low="blue",high="red",midpoint=2)+
coord_equal()</code></pre>
<p><img src="06_RasterTwo_files/figure-html/unnamed-chunk-19-2.png" /><!-- --></p>
</div>
</div>
</div>
<div id="tri-terrain-ruggedness-index" class="section level3">
<h3>TRI (Terrain Ruggedness Index)</h3>
<p>Mean of the absolute differences between the value of a cell and the value of its 8 surrounding cells.</p>
<pre class="r"><code>tri=terrain(reg1,opt="TRI")
plot(tri)</code></pre>
<p><img src="06_RasterTwo_files/figure-html/unnamed-chunk-20-1.png" /><!-- --></p>
</div>
<div id="roughness" class="section level3">
<h3>Roughness</h3>
<p>Difference between the maximum and the minimum value of a cell and its 8 surrounding cells.</p>
<pre class="r"><code>rough=terrain(reg1,opt="roughness")
plot(rough)</code></pre>
<p><img src="06_RasterTwo_files/figure-html/unnamed-chunk-21-1.png" /><!-- --></p>
</div>
<div id="hillshade-pretty" class="section level3">
<h3>Hillshade (pretty…)</h3>
<p>Compute from slope and aspect (in radians). Often used as a backdrop for another semi-transparent layer.</p>
<pre class="r"><code>hs=hillShade(slope*pi/180,aspect*pi/180)
plot(hs, col=grey(0:100/100), legend=FALSE)
plot(reg1, col=terrain.colors(25, alpha=0.5), add=TRUE)</code></pre>
<p><img src="06_RasterTwo_files/figure-html/unnamed-chunk-22-1.png" /><!-- --></p>
</div>
<div id="flow-direction" class="section level3">
<h3>Flow Direction</h3>
<p><em>Flow direction</em> (of water), i.e. the direction of the greatest drop in elevation (or the smallest rise if all neighbors are higher).</p>
<p>Encoded as powers of 2 (0 to 7). The cell to the right of the focal cell ‘x’ is 1, the one below that is 2, and so on:</p>
<table>
<thead>
<tr class="header">
<th align="left">32</th>
<th align="left">64</th>
<th>128</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">16</td>
<td align="left"><strong>x</strong></td>
<td>1</td>
</tr>
<tr class="even">
<td align="left">8</td>
<td align="left">4</td>
<td>2</td>
</tr>
</tbody>
</table>
<pre class="r"><code>flowdir=terrain(reg1,opt="flowdir")
plot(flowdir)</code></pre>
<p><img src="06_RasterTwo_files/figure-html/unnamed-chunk-23-1.png" /><!-- --> Much more powerful hydrologic modeling in <a href="https://grass.osgeo.org">GRASS GIS</a></p>
</div>
</div>
</div>
<div id="sea-level-rise" class="section level1">
<h1>Sea Level Rise</h1>
<div id="global-slr-scenarios" class="section level2">
<h2>Global SLR Scenarios</h2>
<pre class="r"><code>slr=data.frame(year=2100,
scenario=c("RCP2.6","RCP4.5","RCP6.0","RCP8.5"),
low=c(0.26,0.32,0.33,0.53),
high=c(0.54,0.62,0.62,0.97))
kable(slr)</code></pre>
<table>
<thead>
<tr class="header">
<th align="right">year</th>
<th align="left">scenario</th>
<th align="right">low</th>
<th align="right">high</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="right">2100</td>
<td align="left">RCP2.6</td>
<td align="right">0.26</td>
<td align="right">0.54</td>
</tr>
<tr class="even">
<td align="right">2100</td>
<td align="left">RCP4.5</td>
<td align="right">0.32</td>
<td align="right">0.62</td>
</tr>
<tr class="odd">
<td align="right">2100</td>
<td align="left">RCP6.0</td>
<td align="right">0.33</td>
<td align="right">0.62</td>
</tr>
<tr class="even">
<td align="right">2100</td>
<td align="left">RCP8.5</td>
<td align="right">0.53</td>
<td align="right">0.97</td>
</tr>
</tbody>
</table>
<p><a href="https://www.ipcc.ch/pdf/assessment-report/ar5/wg1/drafts/fgd/WGIAR5_WGI-12Doc2b_FinalDraft_Chapter13.pdf">IPCC AR5 WG1 Section 13-4</a></p>
</div>
<div id="storm-surges" class="section level2">
<h2>Storm Surges</h2>
<p>Range from 2.5-10m in Bangladesh since 1960 <a href="http://www.sciencedirect.com/science/article/pii/S0959378008000447">Karim & Mimura, 2008</a>.</p>
<pre class="r"><code>ss=c(2.5,10)</code></pre>
</div>
<div id="raster-area" class="section level2">
<h2>Raster area</h2>
<p>1st Question: How much area is likely to be flooded by rising sea levels?</p>
<p>WGS84 data is unprojected, must account for cell area (in km^2)…</p>
<pre class="r"><code>area=raster::area(dem)
plot(area)</code></pre>
<p><img src="06_RasterTwo_files/figure-html/unnamed-chunk-26-1.png" /><!-- --></p>
<div class="well">
<h2 id="your-turn-1">Your Turn</h2>
<ol style="list-style-type: decimal">
<li>How much area is likely to be flooded by rising sea levels for two scenarios:</li>
</ol>
<ul>
<li>0.26m SLR and 2.5m surge (2.76 total)</li>
<li>0.97 SLR and 10m surge (10.97 total)</li>
</ul>
<p>Steps:</p>
<ul>
<li>Identify which pixels are below thresholds</li>
<li>Multiply by cell area</li>
<li>Use <code>cellStats()</code> to calculate potentially flooded areas.</li>
</ul>
<button data-toggle="collapse" class="btn btn-primary btn-sm round" data-target="#demo2">
Show Solution
</button>
<div id="demo2" class="collapse">
<h2 id="identify-pixels-below-thresholds">Identify pixels below thresholds</h2>
<pre class="r"><code>flood1=dem<=2.76
flood2=dem<=10.97
plot(flood2,col=c("transparent","darkred"))
plot(flood1,col=c("transparent","red"),add=T)</code></pre>
<p><img src="06_RasterTwo_files/figure-html/unnamed-chunk-27-1.png" /><!-- --></p>
<h2 id="multiply-by-area-and-sum">Multiply by area and sum</h2>
<pre class="r"><code>flood1_area=flood1*area
flood2_area=flood2*area
cellStats(flood1_area,sum)</code></pre>
<pre><code>## [1] 3843.93</code></pre>
<pre class="r"><code>cellStats(flood2_area,sum)</code></pre>
<pre><code>## [1] 40818.03</code></pre>
</div>
</div>
</div>
<div id="reclassification" class="section level2">
<h2>Reclassification</h2>
<p>Another useful function for raster processing is <code>reclass()</code>.</p>
<pre class="r"><code>rcl=matrix(c(-Inf,2.76,1,
2.76,10.97,2,
10.97,Inf,3),byrow=T,ncol=3)
rcl</code></pre>
<pre><code>## [,1] [,2] [,3]
## [1,] -Inf 2.76 1
## [2,] 2.76 10.97 2
## [3,] 10.97 Inf 3</code></pre>
<pre class="r"><code>regclass=reclassify(dem,rcl)
gplot(regclass,max=1e5)+
geom_tile(aes(fill=as.factor(value)))+
scale_fill_manual(values=c("red","orange","blue"),
name="Flood Class")+
coord_equal()</code></pre>
<p><img src="06_RasterTwo_files/figure-html/unnamed-chunk-29-1.png" /><!-- --></p>
<p>Or, do reclassification ’on the fly in the plotting function</p>
<pre class="r"><code>gplot(dem,max=1e5)+
geom_tile(aes(fill=cut(value,c(-Inf,2.76,10.97,Inf))))+
scale_fill_manual(values=c("red","orange","blue"),
name="Flood Class")+
coord_equal()</code></pre>
<p><img src="06_RasterTwo_files/figure-html/unnamed-chunk-30-1.png" /><!-- --></p>
</div>
<div id="socioeconomic-data" class="section level2">
<h2>Socioeconomic Data</h2>
<p>Socioeconomic Data and Applications Center (SEDAC) <a href="http://sedac.ciesin.columbia.edu" class="uri">http://sedac.ciesin.columbia.edu</a> <img src="06_assets/sedac.png" alt="alt text" width="70%"></p>
<ul>
<li>Population</li>
<li>Pollution</li>
<li>Energy</li>
<li>Agriculture</li>
<li>Roads</li>
</ul>
<div id="gridded-population-of-the-world" class="section level3">
<h3>Gridded Population of the World</h3>
<p>Data <em>not</em> available for direct download (e.g. <code>download.file()</code>)</p>
<ul>
<li>Log into SEDAC with an Earth Data Account <a href="http://sedac.ciesin.columbia.edu" class="uri">http://sedac.ciesin.columbia.edu</a></li>
<li>Download Population Density Grid for 2015</li>
</ul>
<p><img src="06_assets/sedacData.png" alt="alt text" width="80%"></p>
</div>
<div id="load-population-data" class="section level3">
<h3>Load population data</h3>
<p>Use <code>raster()</code> to load a raster from disk.</p>
<pre class="r"><code>pop_global=raster(file.path(datadir,"gpw-v4-population-density-2015/gpw-v4-population-density_2015.tif"))
plot(pop_global)</code></pre>
<p><img src="06_RasterTwo_files/figure-html/unnamed-chunk-31-1.png" /><!-- --></p>
<p>A nicer plot…</p>
<pre class="r"><code>gplot(pop_global,max=1e5)+geom_tile(aes(fill=value))+
scale_fill_gradientn(
colours=c("grey90","grey60","darkblue","blue","red"),
trans="log1p",breaks= log_breaks(n = 5, base = 10)(c(1, 1e5)))+
coord_equal()</code></pre>
<p><img src="06_RasterTwo_files/figure-html/unnamed-chunk-32-1.png" /><!-- --></p>
</div>
<div id="crop-to-region-with-the-dem-object" class="section level3">
<h3>Crop to region with the <code>dem</code> object</h3>
<pre class="r"><code>pop=pop_global%>%
crop(dem)
gplot(pop,max=1e5)+geom_tile(aes(fill=value))+
scale_fill_gradientn(colours=c("grey90","grey60","darkblue","blue","red"),
trans="log1p",breaks= log_breaks(n = 5, base = 10)(c(1, 1e5)))+
coord_equal()</code></pre>
<p><img src="06_RasterTwo_files/figure-html/unnamed-chunk-33-1.png" /><!-- --></p>
</div>
<div id="resample-to-dem" class="section level3">
<h3>Resample to DEM</h3>
<p>Compare the resolution and origin of <code>pop2</code> and <code>dem</code>.</p>
<pre class="r"><code>pop</code></pre>
<pre><code>## class : RasterLayer
## dimensions : 300, 300, 90000 (nrow, ncol, ncell)
## resolution : 0.008333333, 0.008333333 (x, y)
## extent : 89, 91.5, 21.5, 24 (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
## data source : in memory
## names : gpw.v4.population.density_2015
## values : 97.13571, 154258.4 (min, max)</code></pre>
<pre class="r"><code>dem</code></pre>
<pre><code>## class : RasterLayer
## dimensions : 3000, 3000, 9e+06 (nrow, ncol, ncell)
## resolution : 0.0008333333, 0.0008333333 (x, y)
## extent : 88.99958, 91.49958, 21.49958, 23.99958 (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
## data source : /Users/adamw/Downloads/data/dem_bgd.tif
## names : dem_bgd
## values : -27, 152 (min, max)</code></pre>
<pre class="r"><code>res(pop)</code></pre>
<pre><code>## [1] 0.008333333 0.008333333</code></pre>
<pre class="r"><code>res(dem)</code></pre>
<pre><code>## [1] 0.0008333333 0.0008333333</code></pre>
<pre class="r"><code>origin(pop)</code></pre>
<pre><code>## [1] 0 0</code></pre>
<pre class="r"><code>origin(dem)</code></pre>
<pre><code>## [1] -0.000416061 -0.000416207</code></pre>
<pre class="r"><code># Look at average cell area in km^2
cellStats(raster::area(pop),"mean")</code></pre>
<pre><code>## [1] 0.7886268</code></pre>
<pre class="r"><code>cellStats(raster::area(dem),"mean")</code></pre>
<pre><code>## [1] 0.007886292</code></pre>
<p>So to work with these rasters (population and elevation), it is easiest to “adjust” them to have the same resolution. But there is no good way to do this. Do you aggregate the finer raster or resample the coarser one?</p>
<p>Assume equal density within each grid cell and resample</p>
<pre class="r"><code>pop_fine=pop%>%
resample(dem,method="bilinear")
gplot(pop_fine,max=1e5)+geom_tile(aes(fill=value))+
scale_fill_gradientn(
colours=c("grey90","grey60","darkblue","blue","red"),
trans="log1p",breaks= log_breaks(n = 5, base = 10)(c(1, 1e5)))+
coord_equal()</code></pre>
<p><img src="06_RasterTwo_files/figure-html/unnamed-chunk-35-1.png" /><!-- --></p>
<div class="well">
<h2 id="your-turn-2">Your turn</h2>
<p>How many people are likely to be displaced?</p>
<p>Steps:</p>
<ul>
<li>Multiply flooded area (<code>flood2</code>) <strong>x</strong> population density <strong>x</strong> area</li>
<li>Summarize with <code>cellStats()</code></li>
<li>Plot a map of the number of people potentially affected by <code>flood2</code></li>
</ul>
<button data-toggle="collapse" class="btn btn-primary btn-sm round" data-target="#demo3">
Show Solution
</button>
<div id="demo3" class="collapse">
<p>For the fine resolution population data</p>
<pre class="r"><code>floodpop2=flood2_area*pop_fine
cellStats(floodpop2,sum)</code></pre>
<pre><code>## [1] 50587408</code></pre>
<p>Number of potentially affected people across the region.</p>
<pre class="r"><code>gplot(floodpop2,max=1e6)+geom_tile(aes(fill=value))+
scale_fill_gradientn(
colours=c("grey90","grey60","darkblue","blue","red"),
trans="log1p",breaks= log_breaks(n = 5, base = 10)(c(1, 1e4)))+
coord_equal()</code></pre>
<p><img src="06_RasterTwo_files/figure-html/unnamed-chunk-37-1.png" /><!-- --></p>
</div>
</div>
<p>Or resample elevation to resolution of population: 1. First aggregate to approximate spatial resolution 2. Resample to align grids perfectly</p>
<pre class="r"><code>res(pop)/res(dem)</code></pre>
<pre><code>## [1] 10 10</code></pre>
<pre class="r"><code>dem_coarse=dem%>%
aggregate(fact=10,fun=min,expand=T)%>%
resample(pop,method="bilinear")</code></pre>
<p>For the coarse resolution data</p>
<pre class="r"><code>flood_coarse=dem_coarse<=10.97
dem_coarse_area=area(dem_coarse)
flood_coarse_area=flood_coarse*dem_coarse_area
floodpop_coarse=flood_coarse_area*pop
cellStats(floodpop_coarse,sum)</code></pre>
<pre><code>## [1] 71478698</code></pre>
</div>
</div>
<div id="raster-distances" class="section level2">
<h2>Raster Distances</h2>
<p><code>distance()</code> calculates distances for all cells that are NA to the nearest cell that is not NA.</p>
<pre class="r"><code>popcenter=pop>5000
popcenter=mask(popcenter,popcenter,maskvalue=0)
plot(popcenter,col="red",legend=F)</code></pre>
<p><img src="06_RasterTwo_files/figure-html/unnamed-chunk-40-1.png" /><!-- --></p>
<p>In meters if the RasterLayer is not projected (<code>+proj=longlat</code>) and in map units (typically also meters) when it is projected.</p>
<pre class="r"><code>popcenterdist=distance(popcenter)
plot(popcenterdist)</code></pre>
<p><img src="06_RasterTwo_files/figure-html/unnamed-chunk-41-1.png" /><!-- --></p>
<div class="well">
<h2 id="your-turn-3">Your Turn</h2>
<p>Will sea level rise affect any major population centers?</p>
<p>Steps:</p>
<ul>
<li>Resample <code>popcenter</code> to resolution of <code>dem</code> using <code>method=ngb</code></li>
<li>Identify <code>popcenter</code> areas that flood according to <code>flood2</code>.</li>
</ul>
<button data-toggle="collapse" class="btn btn-primary btn-sm round" data-target="#demo4">
Show Solution
</button>
<div id="demo4" class="collapse">
<p>Will sea level rise affect any major population centers?</p>
<pre class="r"><code>popcenter2=resample(popcenter,dem,method="ngb")
floodpop2= flood2==1 & popcenter2
floodpop2=mask(floodpop2,floodpop2,maskval=0)
plot(flood2);plot(floodpop2,add=T,col="red",legend=F);plot(bgd,add=T)</code></pre>
<p><img src="06_RasterTwo_files/figure-html/unnamed-chunk-42-1.png" /><!-- --></p>
</div>
</div>
</div>
<div id="vectorize-raster" class="section level2">
<h2>Vectorize raster</h2>
<pre class="r"><code>vpop=rasterToPolygons(popcenter, dissolve=TRUE)
gplot(dem,max=1e5)+geom_tile(aes(fill=value))+
scale_fill_gradientn(
colours=c("red","yellow","grey30","grey20","grey10"),
trans="log1p",breaks= log_breaks(n = 5, base = 10)(c(1, 1e3)))+
coord_equal(ylim=c(21,25))+
geom_path(data=fortify(bgd),aes(x=long,y=lat,order=order,group=group),size=.5)+
geom_path(data=fortify(vpop),aes(x=long,y=lat,order=order,group=group),size=1,col="green")</code></pre>
<p><img src="06_RasterTwo_files/figure-html/unnamed-chunk-43-1.png" /><!-- --> Warning: very slow on large rasters…</p>
</div>
<div id="d-visualization" class="section level2">
<h2>3D Visualization</h2>
<p>Uses <code>rgl</code> library.</p>
<pre class="r"><code>plot3D(dem)
decorate3d()</code></pre>
<p><img src="06_assets/plot3d.png" alt="alt text" width="70%"></p>
<p>50 different styles illustrated <a href="https://cran.r-project.org/web/packages/plot3D/vignettes/volcano.pdf">here</a>.</p>
<p>Overlay population with <code>drape</code></p>
<pre class="r"><code>plot3D(dem,drape=pop3, zfac=1)
decorate3d()</code></pre>
</div>
<div id="raster-overview" class="section level2">
<h2>Raster overview</h2>
<ul>
<li>Perform many GIS operations</li>
<li>Convenient processing and workflows</li>
<li>Some functions (e.g. <code>distance()</code> can be slow!</li>
</ul>
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