faculty of civil engineering institute of construction informatics, prof. dr.-ing. scherer institute...

Faculty of Civil EngineeringInstitute of Construction Informatics , Prof. Dr.-Ing. Scherer

Institute of Construction Informatics, Prof. Dr.-Ing. Scherer

TechnischeUniversitätDresden

GIS1

Geo Information SystemsPart 1 Introduction and Overview

Prof. Dr.-Ing. Raimar J. SchererInstitute of Construction Informatics

Dresden, 04.07.2006

Institute of Construction Informatics, Prof. Dr.-Ing. SchererGIS


2

Quality of engineering studies

The quality of an engineering study is maximal as high as the quality of the used data base (input data).

The loss of quality of an engineering study in relation to its maximal achievable quality is determined by the quality of the engineering model (approach), i.e. of the applied engineering knowledge

quality of data

wrong data

wrongknowledge

knowledgeapplied

quality of study



3

Steps of Modelling

1 Problem Wind exposure to a building

2 Physical Model Linear vibration 1. Approx.

3 Mathematical Model 2. Approx.

4 Numerical Approximation of the Mathematical Model 3. Approx.

5 Computer: Numerics of finite floating-point numbers

finite domain of floating-point numbers π = 3.142857

4. Approx.

tftkxtxctxm...

h

htxtx2htx

The following steps of modelling are necessary in order to be able to calculate or simulate a scientific or engineering problem on a computer



4

Steps of Modelling

1. Given is a physical / engineering problem, e.g. the stream around a building and hence the wind exposure of a building

2. In order to solve the problem, one builds a physical model corresponding to the reality, e.g. linear vibration. The problem will be described qualitatively.

3. The physical model will be transformed to a mathematical model. Now the problem can be described quantitatively and is able to be solved objectively and comprehensively.

4. In principle, a computer is only able to carry out additions, i.e. all mathematical operations must be reduced to that. In case of a differentiation, this means that the derivative will be replaced by the difference quotient and hence the problem will be linearised.

5. In contrast to formal mathematics, computer provide only a finite number of numbers. There does not exist ∞, but only a largest INTEGER and a largest REAL number, e.g. 1E99=1099. Furthermore a floating point number can only comprise a finite number (usually 8, 16 or 32) of decimal places. This causes the need of rounding.



5

Model Errors

Model Error: The transition from reality to the mathematical model contains the Model Error, that arises e.g. by simplifications or approximations in order to make the problem solvable, e.g. by modelling a building as a linear oscillator. Model Errors often are also the consequence of the current state-of-the art, which may not afford a better model.

Methodological Error: Arises from the fact, that every mathematical operation must be traced back to additions. E.g. for finite element analysis this leads to a linear system of equations.

Rounding Error: Due to the transition from the infinite number of real numbers to the finite domain of floating-point numbers, each number must be truncated from a certain digit. This leads to an error in the last digit. If one has a complex physical system (e.g. multi-storey buildings) and hence a large mathematical system the number of operations is very high. Every operation causes a rounding error. The accumulation of these rounding errors can cause wrong results and maybe lead to uncertain interpretations.



6

Error Checking

During a simulation the three kinds of errors mentioned above may add up. This leads to the question, how to check the results of a computer calculation. One possibility is to monitor the real model for some input values – as far as this is possible in reality – and compare them with the computed values if these values are coincident, then the chance is high, that

simulation errors are low, but it is also possible, that the different kind of errors erased mutually for the particular test case and hence errors are high for other cases

if there is no coincidence, at least one of the above mentioned errors occurred. The model error and methodological error can be checked analytically, i.e. error bounds can be specified. To check the rounding error, a special arithmetic of the computer is needed, which controls the rounding operation. This leads to the principle of interval computation, for which special computers are available.



7

Information System Definition

An Information System is in its simplest form a request-response system based on a data source

An Information System consists of Data base

data storage data management

system user interface

to formulate request provides answers

data interface acquisition of data continuous updating

of data

Data base

datastorage

datamgmt.

requestresponse

representation:-graphical-alphanum.

investigation-analysis-simulation

collection

scanningmonitoring

Server

Clients

Applications

standardized

individual adapted



8

Space Information System Definition

It is an information system managing information of spacially

distributed objects and the relationships between each other.

Examples are Facility Management Systems Construction Side Management Systems Production Management Systems, including Supply Chain Management

(e.g. car production or airplain production) car maut systems (toll collect in Germany) Animal Monitoring Systems Airspace Information Systems

in particular information Systems for moving objects



9

Geo-Information System Definition

It is an Information System, managing information of objects, which are part of the earth or which do have a strong relation to the earth, namely which are stationary, non-moving objects.

The information are preferably to be managed through a "cartographic" representation, i.e. on a 2D basis. This means data management, as well as request and the representation of responses are outstanding good in cartographic from.

Usually the information system do have a very high information density concerning the observed earth surface.

Other representation forms are not excluded, but are complementary.

Complementary representation forms are: any statistical representation, bar chart, pie chart cross section digital terrain model in 3D with buildings

iso-lines of terrain, snow height, CO2 concentration



10

Example: Hurricane

We have to distinguish between

1. Investigation of the hurricane non – GISA hurricane is a moving object. Therefore it is not appropriate to manage the hurricane information by a GIS, but through a space information management system. The air and the objects in a hurricane are highly moving objects and even their relationships are highly time-dependent

2. Consequences of a hurricane GISLooking on the consequences, we are only interested what happens with the objects on the earth, because of the hurricane. All those objects are stationary, non-time-dependent, hence it is appropriate to manage the information through GIS. We may only be interested in 2 time spots, namely before and after the hurricane. We are interested in the destructiveness zone of the hurricane and there in the strength of destructiveness, which can be represented by iso-lines (lines or coloured area), the priority of help, etc.



11

Investigation of the hurricane non – GIS

Satellite picture of Hurricane Juan (2003)



12

Consequences of a hurricane GIS

facility

Wind velocity in km/hSS: Saffir-Simpson-Hurricane Scale

Path and damaged area after Hurricane Bertha (1996), USADuring or after a natural disaster GIS helps to analyse the damage. Storm data (wind areas, fragility curves, etc.) may be associated and hence damage distributions can be estimated.

Distribution of offshore business in the Gulf of Mexico. Overlaying simulated hurricanes’ wind speeds gives an indication of the exploration fields and offshore structures that will be most seriously affected and the losses that are to be expected.



13

Map-orientation, Space relations

GIS is a cartographic, i.e. map-oriented representation and hence a 2D representation. Therefore one of its big advantages is the layer structure. This contrasts with the modern (3D) design and configuration systems, where an aggregation (assembly) structure is prefered.

GIS is hence very geometric-centred.

The necessary space relation will be achieved through primary metric

a 2D co-ordination system secondary metric

parameters (postal codes, code numbers (phone), district numbers, premises numbers)

names (name of town, boundary, lea) addresses



14

Space relation through primary and secundary metrics

x y z

4695.74 3685.12 123.76

4623.54 3626.87 125.64

4593.34 3653.26 122.75

4695.74 3685.12 121.75

x y

4695.74 3685.12

4623.54 3626.87

4593.34 3653.26

4695.74 3685.12

Public Services DresdenType of Report: Wires - overviewDate: 3rd February 1991District: 1Street: Kurvenstraße

Wire No. Voltage Length Material

Distribution Lines:

4-7001 0.4 20.90 CU

4-7002 0.4 15.80 CU

4-7050 0.4 10.90 CU

4-7060 0.4 11.50 CU

Glock Manfred 75 Isegrimmweg 25 2 44 72 10Glock Udo 1 Filder-29 6 59 10 25Glocke Eckhard 0 Heuweg 9A 77 92 15- Gerhard 70 Reginen-44 77 19 20-Karl-Josef 70 Welfen-66B 4 93 27 11Glockenbring Gerhard 1 Schellberg-342 64 54 55Glocker H. 50 Einstein-29 62 66 23-Thomas 1 Herder-9 57 91 69Glockgether Erika 70 Im Asemwald 28 76 75 81

80

70

75

61

60

5030

20 1

31

40

a) Coordinates b) District Numbers

c) Names of Streets

d) Addresses



15



16



17

Themes

The information of the themes are stored in the attributes of the geo-objects

Basic themessecondary metric like: ownership (real estate register) digital terrain (data from land surveying office)

Visual themes

Any information that is acquirable from light, i.e. photography (scanning) and also infrared (heat). This is often represented by false colour representation.

Artificial themes Deduced values Interpolated values Simulated values

All values not acquirable via light and neither by computation but have to be obtained by inspection are very expensive due to the dense information need. This means they are neither sufficently dense nor sufficently up-to-date.



18

Example for Themes



19

Example for Themes

cars

bikes

cars

bikes

trucks

260265

275270

280285

290 295 300

streets and property boarders

building stock

toxicity from traffic

topography

traffic density



20

Overlaid Themes

We eye inspection we recognized from the overlaid themes, why the concentration of CO2 is

(1) at the street crossing (2) Extending only into 2 of the 3 streets

We recognize also that there is an anomaly, because the centre of CO2 is not coincide with the crossing center, but show some shift to the right. This is either (1) the typical overlay error (not fitting coordinates)or (2) due to other influences like air movement, a theme not taking in consideration.

Final Goal:Such recognition should be possible with algorithms

cars

bikes

cars

bikes

trucks

260265

275270

280285

290 295 300



21

Advanced benefit of GIS

GIS is more than an information system. It is used to deduce new information from the documented information (facts)

through(1) empirical analysis:

recognition of relations between the different themes by- eye analysis- statistical analysis (correlation)- data miningProblem: What should be comparedpoint to point informationarea to area , point to area information?

(2) theoretical analysis / simulationthe documented information is used together with- physical- technical- sociological- psychologicalmodels to produce new information

(3) An advanced GUI to an information system (request-response system), with very powerful graphical presentation techniques



22

Requirements of GIS

(1) Ability to manage large amount of heterogeneous data which are related to points (and areas)

(2) possibility to request the data in relation to their existence (inventorial), location and themes

(3) combination of requests

(4) derive of new information through combining different theme information via the available space relation (primary, secondary metric)

(5) deduce of new information through(1) classification

building new sub areas (clusters) in order to enhance homogeneity (pre-condition for the quality of statistical analysis)

(2) correlationrecognition of trends, e.g. space and azimuth depend chances(e.g. earthquake damage patterns)

(3) combination of 5.1 + 5.2trends based on representative values of sub areas (not points) such as mean, extreme, fractal values



23

Classes of GIS(1) Real estate information systems

Use: Management of properties and assets = land registry (real estate cataster?)basis: coordinate systems (however, there are several in use in parallel)M 1:500 – 10'000 (large scale)Sometimes extended to M1:100'000 in order to add topographyRemark: scale is important, because determines the needed density of dataInformation:- ownership- cataster charges and restrictions- Debits, loansGeometry- only vector data (due to preciseness)Functionality:- acquisition, management, presentation- high security- high actuality



24

Classes of GIS(2) Space Information Systems

Use:Land development and space planningM 1:10'000 – 1'000'000 (middle-small scale)Information:- population, economy- settlement, infrastructure- use of land and resourcesFunctionality:- acquisition, management, presentation- analysis- simulation- free surface modellingGeometry:- vector- hybrid (vector + raster)- 2D – 2.5D



25

Classes of GIS(3) Environmental Information Systems

Use:

Space-, time- and content-dependent data for the description of the status of the environment and its future development

M 1:10'000 – 1'000'000 (middle-small scale)

Information:- any environmental information

Functionality:- acquisition, management, presentation- analysis- simulation- time-dependent data

Geometry:- Vector- hybrid- 2.5D – 3D



26

Classes of GIS(4) net information systems

Use:management of production support material like- supply lines and plants (e.g. water, energy, gas, oil, waste)- costumer data (supply of components, logistic, the supply chain of

productions)M 1:100'000 – 10'000'000 (very small scale) M 1:1'000 – 10'000 (large scale), e.g. in a plantInformation- supplied good- logistic data (where, when, velocity)Function- acquisition, management, presentation- net analysis (shortest path, fastest path, location, ...)Geometry:- Vector- 2.5D



27

Classes of GIS(5) specific domain information system

Use:

- Navigation: ship, airplane

- telecommunication

etc.

faculty of civil engineering institute of construction informatics, prof. dr.-ing. scherer institute...

Documents