# ENGTYPE NL_NAME VARNAME Shp_Lng Shap_Ar # 10 102 HKG Hong Kong 8 Sai Kung HK.SK NA District # 9 102 HKG Hong Kong 3 Islands HK.IS NA District # 8 102 HKG Hong Kong 9 Sha Tin HK.ST NA District # 7 102 HKG Hong Kong 3 Islands HK.IS NA District # 6 102 HKG Hong Kong 4 Kowloon City HK.KC NA District # 5 102 HKG Hong Kong 14 Tuen Mun HK.TM NA District # 4 102 HKG Hong Kong 3 Islands HK.IS NA District # 3 102 HKG Hong Kong 18 Yuen Long HK.YL NA District # 2 102 HKG Hong Kong 8 Sai Kung HK.SK NA District # 1 102 HKG Hong Kong 14 Tuen Mun HK.TM NA District
Vector vs raster sai iso#
# ID_0 ISO NAME_0 ID_1 NAME_1 HASC_1 CCN_1 CCA_1 TYPE_1 # proj4string: +proj=longlat +datum=WGS84 +no_defs # Simple feature collection with 35 features and 14 fields # although coordinates are longitude/latitude, st_intersects assumes that they are planar Set up our environment and read in the data
Vector vs raster sai how to#
To begin today, we are going to demonstrate how to sf and tidyverse libraries together to manipulate spatial vector data. Reading, Visualizaing, and Manipulating Spatial Data in R
Later when we work with movement data we may find a need for other spatial packages in R such as: spatial, the adehabitat packages, maptools, mapview, and the developers version of ggplot2. The sf library is used to store vector data but when working with raster data we will use operations from packages raster and velox. Often, Rasters are stored as “GeoTIFFs” (.tif) These are useful for storing data that varies continuously, as in an aerial photograph, a satellite image, a surface of chemical concentrations, or an elevation surface Raster models are a representation of the world as a surface divided into a regular grid of cells. Often, vector data is stored as “shapefiles” (.shp) This class is useful for storing data that has discrete boundaries, such as country borders, land parcels, and streets. Vector models are a representation of the world using points, lines, and polygons. With important differences across classes. More information regarding this shift here However, as this package is new and under developement there are times were we will switch back to the S4 class structure to play nice with our movement packages. The sf library replaces the S4 class structure used in sp with simple feature access - the current standard across industry for organizing spatial data – extending R’s ame structure directly to accept spatial geometry attributes and making it easier to manipulate spatial datasets using tools like dplyr and the tidyverse. Ultimately this seeks to replace the older sp, rgdal, rgeos packages which formed the original toolset for working with spatial data in R. tools for spatial operations on vectors.
The sf library is an R implementation of: Primarily we will be introducing the sf (“simple features”) package for working with simple spatial data. I have adapted it here for the specific purposes of our workshop. Much of the content and structure of this tutorial was inspired by Jamie Afflerbach’s own introduction to the sf library and spatial analysis in R (see her spatial-analysis-R repo). In this section we will review some of the R packages available for handling spatial data, discuss the format of different spatial data types, and explore how we can manipulate and visualize these in R. Today we are going to get our first taste of working with movement data in R, but we will begin by introducing you to spatial data analysis in R more generally.
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