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GIS

GIS. Basic Principles. What is GIS?. Geographical Information Systems…GI Science (NB rebrand taking hold: Spatial Data Science ) Conceptualising reality in a computer model Not just maps or manipulation Why is GI important? Everything happens somewhere

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GIS

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  1. GIS Basic Principles

  2. What is GIS? • Geographical Information Systems…GI Science (NB rebrand taking hold: Spatial Data Science) • Conceptualising reality in a computer model • Not just maps or manipulation • Why is GI important? • Everything happens somewhere • Ability to attach multitude of information • Relationship between phenomena • spatial co-incidence • relate/transfer info (attributes) between layers where co-incide

  3. Functionality • Data acquisition/integration • Data management/database management • ‘electronic filing cabinet’ • Data analysis • Decision making • Visualisation/cartography

  4. Data Types I: Vector • Discrete entities with specific location • Multiple attributes for each feature Point Polyline Polygon Longley et al., 2005: 77.

  5. Data Types II: Raster • Continuous surface with changing values • Elevation and derivatives • Satellite imagery • Photographs • Scanned maps • Attributes • Each cell has single value • This can relate to Value Attributes

  6. Examples: Vector • Mapping schools, hospitals, retail outlets, etc. • Points • Multiple attributes can be recorded • Type of facility • Number of users, staff, etc. • Turnover, vol. of sales, success rates, infection rate, etc. • Date built, condition of buildings • Record number • And of course implicitly – spatial reference/location • Permits spatial analysis, incl. of all the attributes above • Point-pattern analysis

  7. Examples: Raster • Display density of alcohol outlets in Scotland • Grid (of usually square cells) • Each grid cell can show value relating to density of outlets per unit square • Aggregate (zonal) measures of one variable per grid

  8. GIS – Data Capture • In the field – data capture and recording; primary data • Vector • Fieldwalking • Point locations • Incidents • Street Furniture • Line Surveys • Transects • Elevation Profiles • Polygons • Land Parcel Capture • Exclusion Zones • Conservation Areas • Contour surveys • Full Plans • In the lab – (automated) digitising, geo’l text analysis www.english-heritage.org.uk

  9. GIS – Data Sources • Maps • Modern • Can be vector or raster • OS (OpenData, MasterMap) • Enviro, e.g. CEH, BGS • Historical • Old OS • BGS? • Other Historic Maps/Plans • Topography • Geophysical imagery • Remote sensing • Optical/RADAR/LiDAR Caracol LiDAR (www.britannica.com)

  10. Databasing • Asset, Data, Facilities, Resource management • Land and Property Gazetteers (National/Local) • Large Environmental/Scientific Datasets • e.g. Climate, Flood risk, Land Capability, Forestry • More flexible storage and querying • Topology preserved • Sophisticated querying/spatial testing • Handling of large data volumes (Big Data?) • Attaching detailed attribute data to spatial objects • Analysis of sites/processes within broader context

  11. Databases – Public Access • Public dissemination • Search databases online • Examples • Data.gov.uk – goldmine! • Met Office Rainfall, DTI/BERR Windspeeds, BGS Geology Data • ONS • OS – GPS network, OpenData, OpenSpace API • Or restricted to Academia e.g. • UK (Census) Data Service • EDINA Digimap (OS, CEH), MIMAS Landmap • CEH/NERC datasets • Lack of standardisation. Not all may qualify as DBMS.

  12. Data Extraction – APIs/ Screenscraping • Many services publicly accessible but limited • In number of points that can be retrieved/processed • In number of tasks which can be achieved, thus… • APIs/Screenscraping: • Automation of data retrieval across web • Using a mashup (our custom web page code) we can 'run' a target web service with different inputs and extract desired information from the returned web page • E.g. Twitter, Foursquare, Facebook…. "Big Data" • See: TwitterMap, TweetMap, etc. • But alsogov, enviro, health – see OpenCensusproj in R • Smart Cities/City Science/Sensor Networks • VGI/Crowd-Sourcing

  13. The one million tweet map

  14. Trendsmap – mapping the #hashtags

  15. Visualising Movement/Flow

  16. Interesting Maps on the Web • These just appeared as I was preparing today’s materials: • http://matadornetwork.com/life/57-worlds-interesting-maps/ • And, we can have interactivity • http://earth.nullschool.net/ • Or, even a 'full' GIS in a browser…

  17. Distributed GIS – GIS online BGS Geology of Britain viewer

  18. GIS in your Browser • Google Maps (and Google Earth) • Basic Functionality Online • Full Power via JavaScript powered web pages • Over-reliance on commercial megabusiness? • OpenLayers • Free and Open alternative (NB Open <> Free) • Other web map tools: • E.g. MapServer, GeoServer

  19. Full GIS Software – ESRI’s ArcMap • Market leader in GIS software • One of several ESRI ArcGIS packages – ArcScene, ArcCatalog… • Can handle most data types • At a basic level, is helpful for cartography and visualisation • Many forms of analysis available • Multi-criteria analysis (prediction modelling) • Visibility • Cost surfaces • Networks… • However – • Closed, proprietary software; (tho ArcGIS Online to compete with Google) • Not the only program available, and expensive (thosome other free components) • Alternatives: MapInfo, GeoMedia, FME, CadCorp • Open Source GIS • Much open source software available; modifiable, extensible, fixable! • GRASS, Quantum GIS, gvSIG – many make use of GDAL/OGR libraries • http://opensourcegis.org/ • Or e.g. R or Python – stats, programming – both oft used for maps

  20. Büyük Bedesten http://www.shc.ed.ac.uk/projects/longwalls/Methodology/Visualisation.htm Casal de Freiria Rua and Altivo, 2011: 3302 USGS (Sept 2011) – LiDAR particularly good for trees (top and bot = first and last)

  21. (Now) We’ll Have Manhattan… U.S. Army JPSD/NOAA / www.britannica.com – a decade earlier (2001)

  22. 3D (2.5D) fly-through animations • Previous examples may require GIS data/model be fed to 3D modelling software for detailed work (or that model be constructed in dedicated CAD/3D software) • We can however very quickly create effective visualisations of 2.5D landscapes in e.g. ArcScene (and we can import 3D models from CAD/3D software) • We can also very quickly render animated visualisations of landscapes in GIS software • One example! (NB may not be made with Arc!): • http://www.satimagingcorp.com/gallery/quicktime-north-korea.html

  23. Think! • We must be cautious when using computers • Demonstrate how models are developed, and provide information indicating our depth/lack of knowledge • Full publication (data, methods, limitations) • ‘Knowledge representation’-what we think we know • Don’t be blinded by their ‘scientific aura’ • Don’t use them for the sake of it: question them • Technological determinism • Don’t tacitly accept their results…

  24. Questions to ask before starting GIS analysis • What am I trying to get the GIS to do? How does this relate to my aim? • What data are available and will more need to be created? • What about data quality? Completeness? • What is the state of scientific/area knowledge? • Therefore, how appropriate are the data? • What is the most appropriate scale at which to work? And what scale/resolution are the data?

  25. Limitations of GIS • Data quality • Different bodies hold data; different standards • Currency, Completeness • Representation of reality; and not 3D but 2.5D • Technological determinism • Packages can only perform certain operations • Algorithms themselves can be restricting • Experiential/Subjective difficult to analyse… • GIS/RS works fast over large areas, but final decisions made at local scale/scale of day-day human experience • Big Data Volumes may allow behavioural analysis, modelling of supply and demand, etc. but temporal GIS still limited (as is true 3D, 4D, 5D, etc…)

  26. Questions? • If not, then…

  27. Practicals!

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