# 2 Glossary

**Raster data** is a collection of cells (or pixels) organised into rows and columns (called a grid), defined by its location in space. The raster is the pattern of locations that together - as a grid - cover a geographic area. Cells have values that carry information about those locations, such as temperature or elevation. Values can be continuous (e.g. a range of temperature values), or categorical (e.g. different categories of land use).

**Vector data** represents structures on the Earth’s surface that are defined by discrete locations (x,y) called vertices. Depending on how the vertices (x,y) are organised, we can divide vector data types into: **points, lines, and polygons**. Vector data is commonly used to represent structures such as: road networks, rivers, bridges, buildings, administrative areas.

**Extent**: the maximum and minimum values of longitude and latitude that define the boundaries of our spatial structure.

**Map projections** try to portray the surface of the earth or a portion of the earth on a flat piece of paper or computer screen.

**Coordinate Reference System (CRS)** - defines, with the help of coordinates, how the two-dimensional projected map in our GIS is related to real places on Earth.

**Geographic Information System (GIS)** - a system that creates, manages, analyses, and maps data. GIS connects data to a map, integrating location data (where things are), with all types of descriptive information (what things are like there).

**Spatial resolution** of a raster describes the area that each pixel (or observation) covers. For example, a 1km x 1km spatial resolution will be a grid where each grid cell covers and area of 1km x 1km. The smaller the area covered, the higher the spatial resolution (i.e. the more we “zoom in”).

**Resampling** - changing the spatial resolution of raster data, i.e. the size of cells that give us information about a geographical area. If we move from a lower to a higher spatial resolution, our pixels (or cells) become smaller - so that we are able to see more granularity in the data. For instance, moving from a raster where each cell covers an area of 30km x 30km to a raster where each grid cell covers only 1km x 1km would mean resampling to a higher spatial resolution, with more density points in space that carry information. There are various algorithms for resampling. Some examples we will be using, such as neareast neighbour interpolation and the “sum” algorithm, are described in Section 4.