Discrete Spatial variables

In this lesson, you will learn to work with the following spatial data representation models:

Feature data, which represents spatial features as points, lines and polygons and is best applied to discrete objects with defined shapes and boundaries.
Raster data represents imaged or continuous data. Each grid cell in a raster is a measured quantity. The most common source for raster dataset is a remote sensing image or aerial photograph. A discrete object can be stored in a raster dataset by assigning the identifier value to the grid cell.

Raster datasets excels in storing and working with continuous data, such as elevation data, pollution concentration and temperature.

Learning Objectives

In unit 1: Introduction, you will learn the discrete and continuous nature and behavior of spatial objects and, how to describe them in image mode and feature mode based on the analytical tasks you need to perform. Actually, you have learnt these concepts in the basic module Spatial Modelling of Real World. The purpose of this unit is to recall your memory on spatial modelling.
In unit 2: Individual spatial properties of features, you will learn the spatial properties of spatial objects and how to describe these in feature mode and image or raster mode.
In unit 3: Spatial pattern and neighborhood features, you will learn different indices and implement the concept of largest proximity and minimum distance based on Euclidian distance. Moreover, you will learn how to allocate spatially in relation to the proximity of a spatial feature.
In unit 4: Weighted spatial patterns and neighborhood, you will learn and implement the concept of largest proximity and minimum weighted distance based on weighted Euclidian distance. Moreover, you will learn how to allocate spatially in relation to the proximity and weighted minimum distance of a spatial feature.