[SOLVED] CS data structure SPATIAL MAPS CONT.

SPATIAL MAPS CONT.
STA465: Theory and Methods for Complex Spatial Data
Instructor: Dr. Vianey Leos Barajas

QUICKLY BACK TO MAP PROJECTIONS
We saw that there are ways to map the Earth in 2D
We can only preserve one to two elements (), so we always introduce biases of some sort into the maps we make.

A BIG PART OF BEING A STATISTICIAN: IDENTIFYING BIASES
The True Size Of
In mathematical statistics, you learn about biases of estimators, asymptotic behaviour, etc.
From a non-mathematical perspective, individuals introduce biases into an analysis by the choices they make:
The models they choose
The visualizations that are made like maps! How results are presented

MANIPULATING SPATIAL DATA STRUCTURES

COMBINING DATA WITH DIFFERENT CRS/STRUCTURE OAHU, HI, USA
Hawaii Soil Atlas: https://gis.ctahr.hawaii.edu/SoilAtlas

SOIL FERTILITY CLASS DATA SET
What kind of file is it? Simple feature or raster? CRS?
Geometry type?

WHAT IT LOOKS LIKE IN R

MAPPING SOIL FERTILITY CLASS ACROSS OAHU

USA ELEVATION
usa_elev <- raster::getData(‘alt’, country=’USA’, level=1) MAPPING ELEVATIONS ACROSS HAWAII WHAT I NEEDED FOR MY WORKAt specific points across, Oahu, I wanted to identify soil fertility class, elevation, etc.How do you put it all together?It takes a few steps that, for me, were not very intuitive! TO START, WHAT POINTS DO I NEED INFORMATION FOR?I first started out with coordinates that I extracted from the USA elevation source:rasterToPoints(usa_elev[[4]])This is a matrix, with two columns x and y for longitude and latitude not a spatial object, yet. THEN WHAT?dsp <- sf::st_as_sf(data.frame(oahu), coords=c(“x”, “y”), crs=4326)dsp <- sf::st_transform(dsp, crs=st_crs(fertility))oahufertility <- sf::st_join(x = dsp, y=fertility[dsp,c(“FertClass”)], join=st_intersects) [1+2*(0:1912),]st_transform(); st_as_sf(); st_intersects() FINAL DATA SET TO MAKE THE MAP OF FERTILITY CLASS:tm_shape(fertility[oahufertility,]) +tm_fill(“FertClass”, pal=”-viridis”, title=”Fertility Class”) CLIPPING, CROPPING AND MORE WHAT DOES THE DATA LOOK LIKE WHEN COMBINED? CLIPPING AND CROPPING (+ MASKING)Spatial clipping: spatial subsetting that changes the geometry columns of at least some of the affected featuresRaster cropping + masking:Often a raster may be bigger than the area of study, cropping is one way to reduce the dimension.Masking sets values outside of a boundary to NA CLIPPING TYPES CLIPPING EXAMPLE CROPPINGCROPPING GEOMETRY UNIONS: US_STATES DATA SET SPDATA R PACKAGE GEOMETRY UNIONSRASTERIZATION AND VECTORIZATIONSometimes you will want/need to switch between rasters and vector objectsIn R, its possible to change between types to suit your needsRasterization: conversion of vector objects into their representation in raster objectsVectorization: converts spatially continuous raster data into spatially discrete data such as points, lines or polygons RASTERIZATION: 5.4.3 IN GEOCOMPUTATION WITH RIn the raster R package, we have the function: rasterize() IN THE PREVIOUS EXAMPLE, TO RASTERIZE….There were a few steps to making a raster.First, we need to make a raster template – it contains no values Then, we can combine the points and raster template to create the new rasterized objects! RASTERIZATION: 5.4.3 IN GEOCOMPUTATION WITH R VECTORIZATION (OF RASTER OBJECTS) VECTORIZATION TO POLYGONS! RASTER TO….POINTS, POLYGONS, AND CONTOURSIn the `raster R package, there exist a few more options: rasterToPoints() rasterToPolygons() rasterToContour() MAPS + INTERACTIVE VISUALIZATIONS UP NEXT INTERACTIVE MAPSThere are lots of ways to make maps interactive in RInteractivity allows for greater exploration of the dataWe can work with different R packages to make maps interactive in different ways:Shiny leaflet tmap mapview