Concepts and Functions of

Geographic Information Systems(2/5)

MSc GIS - Alexander Mogollon Diaz

Department of Agronomy

2009

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Concepts and Functions of GIS

.PPT Topic #1 Topic #2 Topic #31 A GIS is an information

systemGIS is a technology

2 Spatial Data modelling Sources of data for geodatasets

Metadata

3 Geo-referencing Coordinate transformations

4 Database management

5 Spatial Analysis

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Modelling of geographic reality

• Geographic reality (GR) can be conceived as

– a collection of discrete, crisp spatial entities (water courses, built up zones, parcel boundaries, …)

occurring within

– a spatial continuum, the terrain (topographic elevation, bathimetric depth, groundwater depth, cadmium concentration, noise intensity, ... )

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Modelling of geographic reality with GIS

1. Spatial delineation of that part of geographic reality that is of interest

2. Selection of entity classes and terrain characteristics which are of interest

matching the project, the problem at hand or the mission of the organisation

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Spatial data modelling with GIS

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The gDB as a spatial model• The spatial model is materialised in the geographic

database (gDB). The gDB is the CORE of every GIS

• The gDB is a collection of vertically integrated geodatasets

• Every geodataset contains (semi-)structured and interpreted data regarding 1 (or a few) entity classes (as object classes) or 1 terrain characteristic (as a surface/DTM)

• Most common geodataset contains object classes reduced to 2 dimensions (planimetric)

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The gDB as a spatial model

• Geodatasets in a gDB must be vertically integrated

– 1 spatial reference system for all geodatasets

– similar level of spatial detail for every geodataset

– coinciding objects or parts of objects stored in separate geodatasets must spatially match

• a river line in geodataset 1 which is also a municipality boundary in geodataset 2 must coincide

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Geographic database

Geodataset 1

Geodataset 3

Geodataset 2

X

Y

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Two types of geodatasets

• There are two types of fundamentally different geodatasets:– Geodatasets containing vectorial descriptions of

objects;Geometric primitive = point

– Geodatasets containing cell-based descriptions of locations;Geometric primitive = cell

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Vectorial versus cell-based spatial entity modelling

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Vectorial objects

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The vectorial data structure• Well suited to model discrete spatial entities as discrete objects • Close to traditional cartography• Recognises objects with:

– geometric characteristics (locations - positions- pairs of X-Y coordinates)– descriptive attributes

• Spatial resolution (polygon or point representation, polygon or line representation, minimal polygon area and line minimal length, geometric detail of boundaries and lines) is determined by:– The nature of the spatial entities to be modelled – Intended applications; “Scale”– Available memory, storage and computing capacity

• Geometric precision is limited only by the algorithms used and the computer capabilities

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Cell-based objects

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Cell-based equivalent of vectorial objects

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The RASTER data structure• Regular array of cells, each having a primary attribute

• Objects are not explicitly defined– labeling of pre-defined cells as part of or covering completely an

entity– Implicit identification of objects as 1 cell or a connected set of

cells having the same attribute

• Position of the cell is determined in an indirect way from– the absolute X-Y coordinates of the origin of the grid– the row- and column number of the cell– the width (and height) of the cell

• A cell has an area which corresponds to the resolution of the raster or grid

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Spatial resolution of raster-geodatasets

• Dimensions of elementary cell• Predefined choice depending upon

– Nature of the spatial entities to be modelled– Detail of the data which are available about the

entities to be modelled – Intended applications– Available memory and computing capacity

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Vectorial versus cell-based objects

Vector

• Exact modelling of entities “Vector is corrector”• Close to traditional cartography• Compact geodatasets• Efficient coding of spatial relationships• Complex structure• Complex overlay• Complex maintenance

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Vectorial versus cell-based objects Raster

• Simple structure• Simpler analysis “Raster is faster”• Simple representation of complex realities• Geometric accuracy is determined by pre-

defined spatial resolution• Less compact, compression may be needed• Spatial relationships are not explicitly stored• Graphic presentation may be less attractive

(resolution)

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Vector-to-Raster and Raster-to-Vector-conversion

• Spatial resolution (cell size) is important

• Cel-size can be choosen freely but small cellslead to voluminous (bytes) geodatasets

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Spatial (entity) modelling

= Design and elaboration of a gDB which is suitable for the intended applications, fit for purpose

1. Delineation of territory of interest2. Selection –from geographic reality- of the relevant entity classes

(terrain characteristics come later)3. Choice of the coordinate reference system 4. Choice between the vector- or raster-datastructure for the

geodatasets that will accomodate the data about the selected entity classes

5. Choice of the spatial resolution (Poly or Point, Poly or Line, minimal area/length; cell size) – Must be comparable for all geodatasets in the gDB

6. Acquisition / Creation of the geodatasets by structuring and transformation of the source data

7. Determiniation of the reference-geodataset8. Integration of the geodatasets: vertical, horizontal, semantical

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Each gDB contains a reference geodataset (a base map)

•Geodataset with the highest geometric quality, covering the full study area•Providing anchor points for vertical and horizontal integration

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Geographic database

polygon

line

point

TIN

categoric raster

image

lattice

X

Y

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Sources of data for geodatasets

• Analogue and digital

• Primary and secundary

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Earth Observation

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The Global Positioning System

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Sources of data for geodatasets

• Analogue, printed maps: A/D conversion required • Imaging remote observations from earth, air or space of

reflected electromagnetic radiation– Photographical images– Multi- en hyperspectral images– Non-optical radiation (infrared, thermal, radar, …) images– Laser-based images

• Imaging remote observations from earth, air or space of reflected mechanic pressure waves

• Data obtained by surveying• Field inventories • GPS measurements• Enquiries with an indirect spatial component -> national census• Traditional databases -> register of inhabitants

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Documentation of available geo-datasets

• By means of METADATA: • METADATA contain description of

– Source of the data, author– Purpose of acquisition or production– Quality

• related to pre-processing• positional, thematic, temporal accuracy• consistency, completeness

– Spatial reference system– Definition of included object classes with attributes or

terrain characteristics; assessment methods, value domain and units used for attributes

– Explanation of codes– Format, availability and conditions/restrictions for use

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Summary of important items

• The gDB is a data model of geographic reality

• Spatial data modelling = designing and building an appropriate gDB

• gDB’s/spatial models are collections of vertically integrated geodatasets

• Geodatasets describe 1 object class or 1 terrain aspect

• A geodataset can be vector- or raster-structured

• Geodatasets can be built from diverse data sources

• Metadata of geodatasets are required for efficient use and re-use

Questions or remarks ?

Thank you …