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Philippe Marchand, Université du Québec en Abitibi-Témiscamingue
August 18, 2019
Becoming increasingly common in various fields (e.g. aerial photos, satellite imagery, census data, geolocated posts on social networks, etc.).
Raster data: variables defined at every point of a grid covering the full extent of the data, e.g. satellite images.
Vector data: variables (attributes) associated to discrete geometrical objects (features) located in space, e.g. position of cities and highways on a road map.
At first, using programming commands to process geographical data can seem less intuitive, compared with the graphical user interface of GIS software.
A few advantages of scripted analyses:
It is easy to repeat the analysis for new data by re-running the script.
It is easy for other researchers to reproduce the methods if they have access to the same programming language.
When using R specifically, the spatial data can be extracted and merged with other datasets for statistical analyses in a single programming environment.
Become familiar with the main packages for processing and simple visualization of vector and raster data in R (the sf and stars packages, respectively).
Perform common data transformation operations using functions from those packages.
Create more complex static maps (with ggplot2) and interactive maps (with mapview).
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Read vector datasets into R.
Visualize variables on a map.
Manipulate spatial data frames with dplyr.
Spatial vector data associate data fields to localized geometric features such as points, lines and polygons. The sf package can be used to work with those datasets in R.
To read a vector dataset: st_read.
To convert a regular data.frame into a spatial object: st_as_sf.
All basic data.frame operations, as well as dplyr package operations, also apply to sf objects.
The plot function applied to an sf object displays one or many data fields on a map.
Geographic coordinates (longitude and latitude, in degrees): require a geodetic reference or datum.
Planar or projected coordinates (in metres): require a geographic coordinate system as well as a projection.
Impossible to provide exact representation of curved surface on plane. Different projections are adapted to specific regions or particular needs.
Example:
Red circles represent regions of the same size and shape on the Earth’s surface.
Mercator projection (left): preserves shape, but distorts size.
Lambert equal-area projection (right); preserves area, but distorts shape.
Check the coordinate system: st_crs.
Transform coordinates from one CRS to another: st_transform.
Basic structure of ggplot graphics.
Display one or more spatial layers on a map.
Change coordinate systems and add labels to the map.
Elements of a ggplot2 graph: call to the ggplot() function, specific geom defining each graph layer, optional customization functions.
The data argument specifies a dataset and the aes function associates variables in that dataset to graphical elements.
data and aes can be defined in the ggplot function (if they apply to all layers) on in specific geom layers.
geom_sf: create map from sf object.
geom_sf_text or geom_sf_label: add text to each spatial feature on a map.
coord_sf: define axis limits and the CRS to use, transforming all spatial features to that CRS.
Three main types:
predicates, which output TRUE or FALSE (e.g. is geometry A inside B?);
measures, which produce a scalar quantity (e.g. length of a line, area of a polygon, distance between two geometries);
geometry-generating functions, which produce output geometries based on input (e.g. distance buffer around a point, centroid of a polygon, intersection of two lines or polygons).
Measure functions for the area of polygons (st_area), the length of a line (st_length) or the distance between pairs of geometric features (st_distance): those functions work with either geographic (long, lat) or projected coordinate systems.
All other geometric operations in sf are based on planar geometry.
Spatial predicate e.g. st_intersects(A, B): for each feature in A, the function returns the indices of features with B that intersect with it.
st_join(A, B) takes an sf object A and appends the data fields from a second object B for each case where the feature in A intersects with a feature in B.
st_intersection(A, B) produces a dataset containing all regions where features in A and B overlap.
st_difference(A, B) produces a dataset containing the set differences (portion of A not in B) for each pair of features in A and B.
st_union(A, B) produces a dataset containing the unions (area covered by either A or B) for each pair of features in A and B; or, with a single input object, merges all features in that object.
st_buffer produces new geometric features that buffer the input features by a given distance.
Read and visualize raster data.
Crop and transform rasters.
Extract variables from rasters based on a set of input points.
read_stars loads a raster file in R. Specify proxy = TRUE to avoid loading the full raster in memory.
A stars object can be plotted by itself with plot, or added to a ggplot with geom_stars.
filter crops a stars object along the specified dimensions, st_crop crops it within the boundaries of an sf object.
Arithmetic (+, -, etc.) and comparison operators (<, ==, etc.) are applied to each pixel of the stars object.
The extract function from the raster package extracts raster values at specific points specified by an sf object.
mapview(data) where data is a raster or vector dataset.
Add multiple layers as mapview(data1) + mapview(data2).
For vector datasets, specify data field to show on plot with zcol argument.