Vector Operations Part 1I

HES 505 Fall 2022: Session 9

Matt Williamson

Your final project

  • At least 5 datasets total (1 tabular, 1 vector, 1 raster, and 2 of your choosing)

  • Choose 1 statistical approach to address your research question

  • Visualizations - minimum of 3. 1 location map; the others should help address your question

  • Submission formats

  • A note about your discussion

Today’s Plan

Image Source: Wikimedia Commons

Objectives

By the end of today, you should be able to:

  • Complete a workflow for identifying and remedying invalid geometries

  • Describe the various unary, binary, and n-ary transformers

  • Use predicates and dplyr::filter to subset spatial data

Revisiting predicates and measures

  • Predicates: evaluate a logical statement asserting that a property is TRUE

  • Measures: return a numeric value with units based on the units of the CRS

  • Unary, binary, and n-ary distinguish how many geometries each function accepts and returns

Transformations

  • Transformations: create new geometries based on input geometries

Unary Transformations

transformer returns a geometry …
centroid of type POINT with the geometry’s centroid
buffer that is this larger (or smaller) than the input geometry, depending on the buffer size
jitter that was moved in space a certain amount, using a bivariate uniform distribution
wrap_dateline cut into pieces that do no longer cover the dateline
boundary with the boundary of the input geometry
convex_hull that forms the convex hull of the input geometry
line_merge after merging connecting LINESTRING elements of a MULTILINESTRING into longer LINESTRINGs.
make_valid that is valid
node with added nodes to linear geometries at intersections without a node; only works on individual linear geometries
point_on_surface with a (arbitrary) point on a surface
polygonize of type polygon, created from lines that form a closed ring

Unary Transformations (cont’d)

transformer returns a geometry …
segmentize a (linear) geometry with nodes at a given density or minimal distance
simplify simplified by removing vertices/nodes (lines or polygons)
split that has been split with a splitting linestring
transform transformed or convert to a new coordinate reference system (chapter @ref(cs))
triangulate with Delauney triangulated polygon(s) (figure @ref(fig:vor))
voronoi with the Voronoi tessellation of an input geometry (figure @ref(fig:vor))
zm with removed or added Z and/or M coordinates
collection_extract with subgeometries from a GEOMETRYCOLLECTION of a particular type
cast that is converted to another type
+ that is shifted over a given vector
* that is multiplied by a scalar or matrix

Common uses of Unary Transformers

  • Creating valid geometries

  • Reprojecting your data

  • Combining or changing geometries

Binary Transformers

function returns infix operator
intersection the overlapping geometries for pair of geometries &
union the combination of the geometries; removes internal boundaries and duplicate points, nodes or line pieces |
difference the geometries of the first after removing the overlap with the second geometry /
sym_difference the combinations of the geometries after removing where they intersect; the negation (opposite) of intersection %/%
crop crop an sf object to a specific rectangle

Binary Transformers

Common Uses of Binary Transformers

  • Relating partially overlapping datasets to each other

  • Reducing the extent of vector objects

N-ary Transformers

  • Similar to Binary (except st_crop)

  • union can be applied to a set of geometries to return its geometrical union

  • intersection and difference take a single argument, but operate (sequentially) on all pairs, triples, quadruples, etc.

Subsetting Data

Subsetting Data

  • Often want to restrict analyses to particular locations

  • Can combine predicates with [] to subset based on geography

  • Can also use dplyr::filter and dplyr::select to subset using attributes

Using predicates

  • Can combine predicates with [] to subset based on topological relations

  • x[y, , op = st_intersects]

  • st_filter( x = x, y = y, .predicate = st_intersects)

Using dplyr

  • filter returns rows that match a criteria
  • select returns columns
library(tidyverse)
durham.cty <- nc %>% 
  filter(., NAME == "Durham")
## We can also use the bracket approach
durham.cty2 <- nc[nc$NAME == "Durham",]

plot(st_geometry(nc))
plot(st_geometry(durham.cty), add=TRUE, col="blue")

nc.select <- nc %>% 
  select(., c("BIR79", "SID79"))
plot(nc.select)