Full Course 3014 Notes Modules: 3 weeks on each
o Introduction to GIS o Spatial Modelling o Remote sensing
o Real-world Applications Week 1
UoS Aims:
• Spatial analysis of geographical and temporal processes
• GIS skills for decision-making in environmental contexts - data sources and acquisition, processing spatial data, practical exercises
ArcGIS Pro & Google Earth Engine: two main software packages we will use
• Data stories:
1. 2019/20 Australian mega-fires 2. Coastal management
3. Food security
• Heaps of free GIS courses offered through this website (GIS & Remote sensing Dictionary/glossaries)
Geographic Information Systems (GIS): Geographic refers to Earth's surface or near surface. GIS are tools.
Spatial Information Systems (SIS): Spatial refers to Space - could be space in any part of the universe
What is GIS?
• Difference between GIS and computer-aided mapping = analytical capacity --> used to efficiently allocate resources and effectively manage/conserve the land e.g., fertilisers in precision agriculture
• GIS is a predictability and forecasting measure - takes into account SCALEABILITY OF AN ISSUE - local, regional, national, continental or global.
• Shows WHAT has changed in the environment + WHERE it has changed GIS: geo-spatial data
• Data linked to a specific location on Earth
• Geographic Information System: subset of information science that deals with spatially or geographically referenced data (multi billionaire dollar global business) --> POWERFUL TOOL (military and marine have entire GIS jobs/departments)
• set of computer programs and form different functions (capturing, storing, retrieving, transforming, analysing and displaying data)
• 1854: John Snow (doctor) created one of the first spatial analysis maps to overlay cholera deaths on a map of London
• 1960s-70s: first GIS developed in Canada
• 1980s: GIS commercialised + first global datasets e.g., ArcGIS
• 2000s: open source GIS software e.g., QGIS and SAGA
Five components of a GIS:
1. Hardware: computers, networks, cloud computing etc.
2. Software: Database
3. Data: vector, point, raster, image, attribute 4. People/Stakeholders
5. Methods Data representation:
• Can turn a google maps (normal map) into a vector (with more simple keys e.g. turn the two line road into ONE LINE, houses into CUBES, and trees into POLYGONS)
• Or can turn into Raster model --> into a series of grids (you have cells inside which you can assign a value e.g., H for house, R for roads, T for trees
Vector Format: ArcGIS Vector data are represented in 2 ways:
• Shape-file (common version) --> is the basic, non-topological data format
o Treats a point as a pair of X,Y coordinates, a line as a series of points and a polygon as a series of line segments (East-West)
o Most common is a point (dot)
o Then build up the complexity of these points to create other shapes e.g., a line or a polygon (refer to pic below)
o Avd: Precision of data is only limited by quality of data originally collected, highly space efficient, makes spatial analysis easy, high-quality output
o Disadv: doesn't capture any SPATIAL RELATIONSHIPS BETWEEN THE POINTS (we don't know the rate of change between the two or the topography between the two locational points), not suitable for continuous surfaces e.g., rainfall data.
o Attribute Table: split into SPATIAL attributes + DESCRIPTIVE attributes (describe and add more value e.g., census data to the spatial data e.g., shape of the land)
Raster Format: e.g., google earth, satellite imagery, land cover data etc.
• Map is divided into cells of equal size - each cell is supposedly homogenous
• Each cell has ONE attribute with ONE value --> taking a cell and giving it a value (grey scale between 0-255 in black and white --> 0 is pure black and 255 is pure white and everything in between is different shades of grey - provides analysis on a plethora of data e.g., the density of population in a specific area of the map - classify different parts of the map into land-use type).
• Adv: can represent a continuous surface, can also represent points by single cells, lines by a sequence of neighbouring cells, areas by a collection of contiguous/touching/sharing a common border cells
o Simple concept
o Easy management with computers
o Map overlay and algebra is simple: cell-by-cell (can start to add cells together and track them - useful for remote sensing)
o Suitable for scanned images
o Modelling and interpolation is simple
o Original format for satellite imagery and aerial photos (cubed mapped look)
• Diadv: fixed resolution (generally cannot be improved), information loss at any resolution, expensive storage and processing requirement to improve resolution, large amount of data especially at high resolution e.g., if you want to identify minute habitat changes in a forest, this is not the best mapping format (i.e., zooming in significantly is not very compatible with this format).
Overall (how to determine whether to use a VECTOR or RASTER): depends on
1. LEVEL OF DETAIL you’re trying to map which depends on land-use you're examining e.g., a forest, agricultural field or population of a city
2. SCALE of the land
• E.g. streams and roads are better delineated using a vector - represents the stream curves and waves more accurately - when you turn a stream's curves into cubes, it doesn't accurately map the original shape
• Raster tends to overlook the subtle changes in the environment e.g., contour lines which are better revealed by the flexibility of shapes created in a vector model
• While raster is better to show larger scale geographic trends e.g., population density with colour - this is not a detailed depiction of land use change but can be mapped by satellite, hence the term, larger-scale (colour mapping)
• Can OVERLAY DATASETS (vector and raster; complement each other, building an artwork LAYER-BY-LAYER)