Geographical Information System(GIS) & LBS

Augsburg 2019

Anto Aasa

http://aasa.ut.ee/augsburg

Important factors for LBS

• Location

• Spatial data & GIS

• Wireless communication

• Positioning– Satellite

– Mobile phone

– Indoor (WLAN, RFID)

• Functioning of LBS (location only is not theLBS)

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• Everything, what happends in real world hasgeographical coordinates

– X

– Y

– Z

– time

• (also in virtual space)

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Location

• Map and database must be in samecoordinate system and datum

– Surface of the earth

• Land

• Sea

– Geoid

– Ellipsoid

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Location:

• Describing

• Spatial

• Network

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Spatial databases & GIS

• Location: spatial vs describing?

– N 48° 21’ 17’’ E 11° 47’ 15’’

– Germany, Munich airport

• Relation between points => distances– Geography in general!!!

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Geographic information system (GIS)

• Mutually related complex of software and data

– Watching geographical information,

– Administration of geographical information,

– Analysis of spatial relationships and patterns,

– Modelling of spatial processes.

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GIS

• Hardware

• Software

• Database– Geographical space

– Theme (attributes)

• Operations

• Human resource– Knowledge

– Experience

• …http://aasa.ut.ee/augsburg

GIS functions

• Mapping and visualization;

• administration of geographical information;

• data collecting and updating;

• geographical analysis.

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History

• First person who placed different layers on top of each other?

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Cholera deaths in London

John Snow 1854 http://aasa.ut.ee/augsburg

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History• 1963: first nation-wide GIS - Canada• 1966: first raster-GIS• 1972: first civil use remote sensing satellite Landsat 1 • 1978: first satellites of NAVSTAR (development of GPS technology)• 1979: first vector-GIS –ODYSSEY GIS• 1981: Esri ARC/INFO • 1986: MapInfo – first desktop GIS• 1994: beginning of standardization of spatial data and infrastructure

(OpenGIS consortium)• 1996: first Internet based GIS products• 1996: first Internet based map service MapQuest• 2000: over 1 million professional GIS users in world, over 5 million

„average“ GIS users• 2016: starts GNSS Galileo

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Area of use

• Land survey, cartography• Logistics• Aviation• Real estate• Military forces• Trade• Local authority• Science• Infrastructure management• …• Location Based Services

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Representation of spatial data

• Real world is too complex

• Simplified models

– Maps

– Cartography

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Modelling of the real world

• Discrete objects

• Continuous fields

Visualization of invisible objects (Augmented, Mixed Reality)

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Representation of geoinformation inGIS

• objects (set of points, lines and polygons);

• raster;

• attributes.

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VectorPoint

Line

Polygon

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Raster

slope Elevation shading Population density

ortophoto Landuse concentration

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• Attributes table:

– Rows: map objects

– Columns: attributes

– Queries

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• Layer based model:

– One theme for every layer

– One data type for every layer (point, line, polygon, raster)

Elevation

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GIS resolution

http://nas-sites.org/climatemodeling/page_3_2.phpModel vs visualization http://aasa.ut.ee/augsburg

• Use of color

– Traditions of visualising certain object types

• Symbols

• Colors

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• Map scale

– the ratio of a distance on the map to the corresponding distance on the ground (e.g. 1:400 000)

• Generalization

– Simplifying of objects

Measurements accuracyModel accuracyAmount of data

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Generalization

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GIS output

• Table

• Graph

• Report

• Thematic map

• Something else?

What is the aim?

Mobile device display!

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Internet maps

• Different API’s for LBS:

– Open Street Map

– Google Map

– Bing Map

• WMS

• …

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• Earth is not ideal sphere but geoid (potato-shaped)

– Earth model: ellipsoid

• Map projection – method of representing the surface of Earth on a plane

– All map projections distort the surface in some fashion

• Error minimization

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Distortions

– area,

– direction,

– scale,

– distance.

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Creating GIS

• Reality model (description of the real world)

• Data model (database structure and technology)

• Representation model (rules for datarepresentation)

– e.g. Roads on top of rivers

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Management of spatial data

• Raster model

– Rectangular regular grid of pixels

• Vector model

– Points, lines, polygons (functions determining theshape and form of objects)

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Processing of geographical data

• Processing of the initial data for achievementof goals

– Queries (response to relevant conditions)

– Spatial analysis (description of place, attribues and relationships between them)

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Quality of spatial data

• Completeness (missing, redundant data)

• Consistence

• Location correctness

• Up-to-date

• Thematic correctness

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For tomorrow

• How to visualize flow in time and space?

• Movement of parcel

• Data download

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