DAAD climapAfrica - Climate change research in Africa

©Francisco Maiato Gonçalves

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Contents

• Definition of a raster data

• Utility of a raster data

• Hands on session

What is a raster data?

©National Ecological Observatory Network (NEON)

Resolution matters

©National Ecological Observatory Network (NEON)

You say multi-band raster data?

©National Ecological Observatory Network (NEON)

Why store data as a raster?

The advantages of storing your data as a raster are as follows:

• A simple data structure—A matrix of cells with values representing a coordinate and sometimes linked to an attribute table

• A powerful format for advanced spatial and statistical analysis

• The ability to represent continuous surfaces and perform surface analysis

• The ability to uniformly store points, lines, polygons, and surfaces

• The ability to perform fast overlays with complex datasets

©ArcGIS

Why store data as a raster?

• There can be spatial inaccuracies due to the limits imposed by the raster dataset cell dimensions

• Raster datasets are potentially very large. Resolution increases as the size of the cell decreases

• However, normally cost also increases in both disk space and processing speeds. For a given area, changing cells to one-half the current size requires as much as four times the storage space, depending on the type of data and storage techniques used.

©ArcGIS

Applications of raster data

Hands on session

Packages loading

library(ggplot2)
library(raster)
library(tidyr)
library(rnaturalearth)
library(rgeos)
library(cowplot)

Data acquisition

climate = getData('worldclim', var = 'bio', res = 2.5)

See the data

plot(climate)

Crop the data to the African continent

climate = crop(climate, extent(-20, 60, -40, 40))

Check the data

plot(climate)

Get the raster 12 relative to the rainfall

raster_rainfall = climate$bio12

Get countries borders shapefiles from the package rnaturalearth

africa = rnaturalearth::ne_countries(continent = 'africa', returnclass = 'sf')

Plot it

plot(africa)

Raster data conversion into dataframe

rasdf = as.data.frame(raster_rainfall, xy = TRUE)%>%drop_na()

Check it

head(rasdf)

##           x        y bio12
## 1 -8.937500 39.97917   838
## 2 -8.895833 39.97917   856
## 3 -8.854167 39.97917   863
## 4 -8.812500 39.97917   882
## 5 -8.770833 39.97917   888
## 6 -8.729167 39.97917   872

Plotting

m = ggplot() +
  geom_tile(aes(x=x, y=y, fill=bio12), data = rasdf) +
  geom_sf(fill = 'transparent', data = africa) +
  scale_fill_viridis_c(name= 'mm/yr', direction = -1 ) +
  labs(x= 'Longitute' , y = 'Latitude',
       title = "Africa's climate map",
       subtitle = "Annual precipitation",
       caption = 'Source: WordClim, 2020') +
  cowplot::theme_cowplot() +
  theme(panel.grid.major = element_line(color = "black",
                                         linetype = 'dashed',
                                         size = .5),
         panel.grid.minor = element_blank(),
         panel.ontop = TRUE,
         panel.background = element_rect(fill = NA, color = 'black'))

Should get this

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