About Spatial
Spatial explained
Spatial science, and the industry it supports, is at its core about positioning and location. Traditionally it has been represented by cartography and surveying. Over the last century, photogrammetry, Geographic Information Systems (GIS), remote sensing through earth observation and PNT, through GNSS and RNSS, have come to characterise what is commonly known today as ‘spatial’.
Spatial data gives the location of something, usually defined by coordinates, like the location of a road, or through the identification of area with a place name, together with some understanding of what is happening there (i.e. the characteristics of the object, event, or phenomena concerned, such as the size of an earthquake or the number of children living in a suburb), and often how it changes through time (e.g. the position of a moving vehicle or the spread of an infectious disease). Spatial data gives us a more complete picture of our ever-changing world so that we can better understand and manage it. Examples include; satellite positioning, earth observation and digital mapping of the features around us.
Spatial embraces both the collection of information related to position and location and its analysis to produce information products that include metric information about position. These information products span the production of simple analogue or digital maps to highly complex derivative products in 3D, time stamped to render them in 4D and value added with many other data sources to take them into the nth dimension. In fact with continuous streaming of data from sources like geostationary satellites, the data is real-time and persistent.
Spatial information products are now ubiquitously used by society; Google Maps, Bing and Open Street Map. Most industries use spatial technologies; agriculture to monitor crops and plan the transport logistics for harvest to market; mining for exploration and robotics in autonomous mining; banking and finance for GNSS atomic clock based timing for transactions; health for analysing population demographics; water industry through the use of digital elevation models to aid in catchment management. These are just a small fraction of the uses to which spatial is being put.