Concepts and Functions of

Geographic Information Systems(5/5)

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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Functionalities of GIS

INPUT

QUERY - DISPLAY - MAP

ANALYSE

STRUCTURE

MANAGE

TRANSFORM

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Spatial analysis• Creation of information / added value from the gDB by

means of:– computational algorithms applied to the geometric and attribute

data

• Finding answers to questions which are not already in the gDB– Which hotels are closer than 2 km walk from the coast line ?– Which area of arable land is located on slopes steeper than 8% ?– What is the shortest path from point A to point B ?

• Analysis often requires specific re-structuring and transformations of the geodatasets in the gDB

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Spatial analysis tools

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Spatial analysis & Topology

• Absolute location of objects / locations is important: – Where is it ? What is the shape like ? How far is it from ?

• From absolute location, relative location can be deduced– Who is the owner of the parcel next to mine ?– Which store is closest to my home ?– To which province does this municipality belong ?– Which streets are crossing at this roundabout ?

• Topology = spatial properties of objects / locations which– Are independent of the geospatial reference system, i.e.

independent of absolute location– Are dependent on relative location– Can be exploited in spatial analysis

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Topological properties of vectorial geodatasets

• Can be permanently stored in the gDB– topological vectorial data structures

• Can be derived at runtime from the (geometric raster and vector) data stored in the gDB

• Both require topologically correct geodatasets– Polygon-line– Line-node– Left-right

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Raster topology Column-/row-number of cells implicitly contains topological

information

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Spatial (topological) analysis for vectorial objects

1. Generalisation

2. Overlay-analysis

3. Proximity-analysis (buffering)

4. Multi-criteria-analysis– Search for optimal location

5. Network-analysis– Shortest, fastest, cheapest path: travelling salesman

problem– Search for optimal locations on a network

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1. Generalisation

• Line-generalisation: see 3.PPT (Structuring)

• Polygon-generalisation– Reclassification = Substitute attribute values by alternative

values, possibly followed by geometric/topologic modifications (dissolve)

– Aggregation = Incorporation of non-sense areas into surrounding polygons

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1. Generalisationof lines; of polygons

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Poly # Attribuut2 A3 B4 A5 C6 D

A

A

DISSOLVE polygons =dropping boundary lines

usingtopological info

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Aggregation - Vector

• = Eliminate operation:• Polygons with identification

codes 1, 2 and 4 are merged with the surrounding polygon with code 5 based on a threshold value for area

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3

5 4

3

5

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2. Overlay of geodatasets

• Visual overlay• Topological overlay

Both require vertically integrated geodatasets

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Topological overlay

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Topological OverlayPoly-on-Poly

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Topological overlay line-node, poly-line, left-right is modified

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Topological overlay and Boolean logic

Intersection

Union

Subtraction

Union without intersection

Applied to overlapping polygons and/or lines

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Topological overlay

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Topological OverlayLine-in-Poly

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Topological OverlayPoint (node)-in-Poly

Half-line algorithm:At uneven # intersections, Point is in last-left polygon

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3. Proximity analysis (= Buffering)

A. One ore more target objects or locations

B. Specification of a ‘neighbourhood’ or ‘buffer’ relative to the target object/location – As a final product (e.g. for cartography) – As an input for further analysis

C. Specification of the analysis to be performed within the neighbourhood

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Buffering: Steps A & BTARGET

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Buffering: Steps A & B

TARGET

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3(C). Operations on the bufferzone

• Buffers are mostly isotropic but can also be anisotropic

• Such operations need additional geodatasets. Examples: – Selection of objects (in an additional geodataset)

which are located within the buffer zone, i.e. at a distance smaller than the given threshold (buffer distance) from the target object / location

– Counting the selected objects – Computing statistics of characteristics of the selected

objects (frequency of classes, min, max, average, range, ...)

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4. Multi-criteria location analysis

• Determination of locations matching spatial criteria by combining– Overlay analysis– Proximity analysis

• Example: determine the potential locations for a multi-national company:– Within 2 km from highway– On a parcel of at least 10.000 m2– With stable sub-soil

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5. Network analysis

• Finding the shortest, fastest, cheapest path over a network of lines

• Finding the optimal location in terms of accessibility over a network

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Networks

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Topological networks

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Finding paths

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Spatial analysis of raster-geodatasets

• Complex analyses are efficient due to simple data structure1. Proximity (buffer) analysis

2. Neighbourhood analysis; Filtering

3. Cost-distance analysis

4. Map Algebra

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Distances in raster-geodatasets

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1. Buffering - Raster

TARGET = cell or group of cells

BUFFERING = selection of cells whichmatch the distance threshold. Result = WINDOW

Operations can be performed on the window, e.g. FILTERING

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2. Example: Majority filter 5 * 5

The most frequent class in each (moving, e.g. 5*5) window is atributed to the central cell

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• A convolution kernel is a matrix of numbers which is used to replace the value of each pixel with a weighed average of the values of the pixels in the neighbourhood of which the dimensions are those of the kernel

• (-1x8)+(-1x6)+(-1x6)+(-1x2)+(16x8)+(-1x6)+(-1x2)+(-1x2)+(-1x8) /(-1+ -1+ -1+-1+ 16+ -1+ -1+ -1+ -1)) = 11

• High pass filter: differences between pixel values are enhanced

1 2 3 4 5

1 2 8 6 6 6

2 2 11 5 6 6

3 2 0 11 6 6

4 2 2 2 8 6

5 2 2 2 2 8

2. Example: Convolution-filtering

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3. Map Algebra

• Applicable to vertically integrated raster geodatasets of equal spatial resolution

• 1st order computing functions– Add– Subtract – Multiply– Divide

• Relational operators– >, <, =

• Boolean logic: AND, OR, NOT, XOR

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3. Map AlgebraWEIGHT FACTOR

ACCESSIBILITY

DRAINAGE

EXPOSURE

GOOD

GOOD

SOUTH

EAST OR WEST

NORTH

FAIR

FAIR

POOR

POOR

using map addition

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4. Cost-Distance analysis using a ‘friction’ surface or friction-geodataset

Friction surface

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4. Cost-Distance analysis

(1/v in min/km)

(min; resolution = 1 km)

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Summary of important items • Analytical functions create added value with respect to the

data available in the gDB. Information is generated which is not stored in the gDB and which provide (part of) the answer to more complex questions

• Spatial analysis exploits topological relationships, both in vector and raster geodatasets

• Some analytical functions require one input geodataset only (buffer and simple network analysis, filtering, ...).

• Most analytical functions need more than one geodataset: map algebra (raster), topological overlay (vector), ...

Questions or remarks ?

Thank you …