1 / 30

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

micheal
Télécharger la présentation

GIS

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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!

More Related