Setup: sample data

For more on setting up the environment and sample data, see the preparation document.

Table 0.1: Sample data
Data File(s) Format Source
“Nafot” nafot.shp (+7) Shapefile https://www.gov.il/he/Departments/Guides/info-gis
Railways RAIL_STRATEGIC.shp (+7) Shapefile https://data.gov.il/dataset/rail_strategic
Statistical areas statisticalareas_demography2018.gdb GDB https://www.cbs.gov.il/he/Pages/geo-layers.aspx

The data for this tutorial can be downloaded from:

https://github.com/michaeldorman/R-Spatial-Workshop-at-CBS-2021/raw/main/data.zip

A script with the R code of this document is available here:

https://github.com/michaeldorman/R-Spatial-Workshop-at-CBS-2021/raw/main/main.R

All of the materials are also available on GitHub.

Please feel free to ask questions as we go along!

1 R for Spatial Data Analysis

1.1 Software for analysis of spatial data

Software in general, and software for spatial analysis in particular, is characterized by two types of interfaces:

  • Graphical User Interface (GUI) (Figure 1.1)
  • Command Line Interface (CLI) (Figure 1.2)

In a GUI, our interaction with the computer is restricted to the predefined set of input elements, such as buttons, menus, and dialog boxes. In a CLI, we interact with the computer by writing code, which means that our instructions are practically unconstraned. In other words, with a CLI, we can give the computer specific instructions to do anything we want.

R, which we talk about today, is an example of CLI software for working with (among other things) spatial data.