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GEOM2001 L1-4

GIS Integrates hardware, software and data for capturing, managing, analysing and displaying all forms of geographically referenced info
Spatial data unique geographic coords or other spatial identifiers that allow the data to be located in geographic space
6 parts of GIS Hardware, software, people, data, procedures, all connected to a network
Normative GIS questions and examples Practical and decision making/design applications Eg. managing traffic flows
Positive GIS questions and examples Discovery or the advancement of science, incorporates normative thinking Eg. Where is global warming having the greatest impact on natural systems
4 GIS architecture types Desktop, centralised desktop, client-server and centralised server
What are some major challenges in representing landscapes in a GIS 1. The world is infinitely complex and GIS can't represent all of the infinite complexities 2. Hard to try and abstract the real world
Continuous field Vary continuously over a landscape eg. temp, humidity,rainfall, elevation
Discrete objects Have discrete boundaries and are homogenous within the boundary eg. streets, buildings
Data model set of constructs for representing objects and processes in the digital environment of a computer Reality -> conceptual model -> logical model -> physical model
Raster Grid cells, each cell contains info about spatial location and cell value
Benefits of raster -Simpler to do calculations -Cells are the same size -Easy to find cells -Uniformity of grid makes it simpler to work with -Useful for representing continuous fields
Vector Points, lines or polygons used to represent discrete data Each point/line/polygon contains info on its spatial location and info about what it is
4 types of raster data Base layers - aerial photos Thematic layers - land use Surface layers -elevation Attribute features - geotagged pictures
Limitations of raster -Have to put in data for each cell, can be tedious with large datasets
Run length encoding Simple way of reducing/compacting amount of numbers needed in a raster dataset
Spatial attribute data Where something is on the earth's surface, location
Non-spatial attribute data What an object actually is
Topology Geometric characteristics that don't change under transformations
3 elements to topography Adjacency, connectivity and containment
Georeferencing Concerned with identifying where geographic features are on the earth’s surface
Geographic coordinate system Longitude and latitude define unique points on earth's surface and referenced to a Datum
Longitude -180 to +180 Measured from Prime Meridian
Latitude -90 to +90 Geodetic latitude requires knowledge of the earth's shape
Projected coordinate system transforms a 3D GCS to a flat 2D coord system
Conformal projection Preserves shapes of features - useful for navigation
Equidistant projection Preserves distances - useful for calculating distances
Equal area projection Preserves area of features - useful for calculating areas
True direction (azimuthal) projection Preserves direction with respect to the centre - useful for navigation
Universal Transverse Mercator (UTM) Minimal distortion of areas and distances Cylindrical projection based on Transverse Mercator World split into 60 zones
Data capture Collection of geographic info from the real world and representing that digitally in GIS
5 step data collection process Planning, preparation, digitizing/transfer, editing, evaluation
Primary data capture collect data directly into digital form, involves direct measurement of spatial info
Secondary data capture taking spatial data used for other purposes and then using that data to be represented in a GIS
In-situ data capture Collecting data actually at the location
Remotely sensed data capture Not at location when collecting data
Examples of primary raster data Digital remote sensing images, digital aerial photos Mainly remote sensing
Examples of primary vector data GPS measurements, survey measurements, ground surveying
Examples of secondary vector data Topographic surveys, Placename data sets available from atlases, manual digitising
Examples of secondary raster data Scanned maps, DEMs from maps
Describe how GPS works Measures time the signal takes between location to satellite and measures the distance and narrows it down to a location on the earth’s surface Trilateration - requires 3 satellites to get an approximate location and 4 for accurate location
3 types of primary raster data resolution Spatial - measure of the smallest angular or linear separation between 2 objects Spectral - describe the specific wavelengths that the sensor can record within the electromagnetic spectrum Temporal-how often a sensor can obtain imagery of an area
Passive primary raster data Detect natural radiation that is emitted or reflected by the object or surrounding areas eg.photography, infrared
Active primary raster data uses its own energy source for illumination, sensor emits radiation which is directed toward the target and the radiation reflected from the target is detected and measured by the sensor eg LiDAR
Georeferencing Process of connecting a spatial data set to a coord system
Positional accuracy Measures how close the geographic coords of features in a spatial data layer are to their real world geographic coords
Attribute accuracy accuracy of the non-spatial attributes of geographic features
Logical consistency having the same rules and logic applied through a dataset
Completeness How complete a data set is
Metadata Data about the data Gives info on how, when, where, who collected data, coord system, data quality and accuracy
Difference between GIS maps and traditional maps Differ in terms of mode, objective, scope of communication and level of interaction between map and user GIS maps store data, provides real-time decision support, simulations and integrates non-spatial stat analyses
Bias in maps Reflect the dominant power at the time Map projection can cause distortions Data accuracy errors Human errors
Thematic mapping Represent and illustrate spatial structure, patterns and interrelationships rather than just the location of geographical phenomena
Nominal data unordered, qualitative categories eg land use, land cover classes, soil type
Ordinal data ordered categories eg high, medium, low
Interval data quantitative, represent positions along continuous number lines Values are on a linear calibrated scale but not relative to a zero point in time and space Eg Temp in C & F, year, pH value scale
Ratio data quantitative, values are derived to a fixed zero point on a linear scale There’s an absolute zero which allows the use of maths operations (+,-,/,*) Eg distance, age, weight, length, temp in Kelvin and area
Equal interval same intervals in each category
Quantile break equal amount of members in each category
Natural breaks plot as a chart, grouping close characteristics
Limitations of different data classification Inappropriate classification may hide meaningful patterns and anomalies or give misleading info
Map design principles Legibility, visual contrast, figure-ground organisation and hierarchical structure
Created by: cmw001001