GI Product Development Gerhard Navratil Workshop in connection with Geomatics 2011, Teheran, May 14...

GI Product Development

Gerhard Navratil

Workshop in connection withGeomatics 2011, Teheran, May 14 2011

Gerhard NavratilWorkshop GI Product Development

Contents

• Motivating Examples

• GI Product Design– Value of a GI Product– Data Requirements– Legal Aspects– Costs of a GI Product

• Implementation?

Motivating Examples

• Course concept used for several years at– Vienna University of Technology – Surveyors– Technikum Wien (University of Applied

Science) – Intelligent Transportation Systems

• Results from student work

Mobile GIS to Optimize Information Flow in Case of Emergency (1)

• Fast reaction can save lives

• Information in essential

• Requires cooperation between organizations

Use of new communication and information technology

Philipp Nitsche

CLIENT SERVER

XML, CAPalert

XML, GMLinfo

XML, GMLjob form

Terrestrial trunked radio TEDS: up to 500 KBit/s For emergency- and

rescue services 2-way authentification Direct connection

without telephone network possible

Potential use of different frequencies

Push-to-Talk

Technical Concept

Benefits:

Emergency Calls for Elderly People

• Increasing percentage of elderly people in Europe

• Many living alone

• How to help them when they need help?

Susanne Pröstl

Existing System 1

• Bracelet with sender• Connection to telephone line• Pushing a button creates connection to

emergency response center• Handsfree equipment for ommnunication• If not possible: Person of trust informed

Costs: Rent: 27.50€/month

Existing System 2

• Additional sensor around the neck

• Automatic detection of fall

• Automatic alarm

Costs: 33.90€/month

Extensions

• Built like a mobile phone• Positioning by GSM-Positioning• Relaying position to person of trust or

ambulance• Finding shortest route• Communication via telephone

But: Is it allowed to transfer the position to a third party?

Personalized Traffic Information System (1)

What is the current situation?

• many traffic information services available– anachb.at– ÖAMTC– ASFINAG (road pilot)– ÖBB (Scotty), Wiener Linien (Qando) etc.

a lot of information available

but not personalized

Peter Votzi

Personalized Traffic Information System (2)

• provides information depending on the user needs.

• filters “unnecessary information”– users need only information affecting their

trips– trip routes are often the same, e.g., family

visitstell the user only what he wants to know

How does it work

• user data needed:– trip routes– trip times– service channels

• traffic events affecting the routes are transmitted to the user– route edges subtend affected edges– travel time exceeds a threshold

GIS operations, functions needed

from where to where to you go and when?

how do you want to receive the information?

What data are needed?

• a common reference system (GIS)– road network, ways, points of interest,

addresses

• traffic information

• travel time

What functions are needed?

• geocoding, routing, a map

• service interface

• service broker

easy user interface

provide the information

receive traffic information,compute personalized results

Where to get data?

• Requirements– up to date, multimodal– can be used as reference system

• ALERT-C/TMC, chain age (linear reference)

– routing graph

• commercial data providers?

• solution: OpenStreetMap– up to date, open license (attribution, share alike)– Replication

too expensive, old, not multi modal, license issues

How do design? Architecture

• PostgreSQL + PostGIS

• pgrouting, osm2pgrouting

• osmosis

• UMN MapServer

• Openlayers

• Apache Web Server, Asterisk, Sendmail

storage

routing

map rendering, OGC services

replication

client map

web server, phone & sms gateway, email gateway

all components are open source no license fees!

ArchitectureTraffic Information

Source

Service Broker

datex2 push

DatabasePostgreSQL, PostGIS

reference system, user data, gis data

MapServer

Web Server

Replication osm

SMS gateway E-Mail gateway

routing engine

Website

Which standards to use ?

• OGC– Web Map Service (WMS)– Web Feature Service (WFS)– Simple Features (database)

• EASYWAY – Datex2 or TPEG

Tons of other examples

• Tourist guides

• Support for Alzheimer patients

• Finding paths without crosswind

• Bus-stop optimization

• Fire protection system – are there still humans in a burning building?

• …

GI Product Design

• What is the information content for the user?

• What is the value for the user?

• What data are necessary?

• Access method?

• How can the information be extracted?

• What are the costs of this service?For the producer and for the user!

Information Content

Amount of data transferred through a channel can be measured (e.g., in Bits – one binary decision) (Shannon and Weaver 1949)

Useful to determine storage space etc.

But is it useful for information content?

NO: Measures the amount of data, not the effect of the message

Example: Same Route Information? [1]

Kirchberg am Wechsel to Gloggnitz

• Follow the road to Otterthal

• In Otterthal turn right towards Gloggnitz

• Follow the road through Schlagl and Graben

• Cross under the Semmering highway

• Follow the road into the town of Gloggnitz

Example: Same Route Information? [2]

Your route from Kirchberg am Wechsel to Gloggnitz: The total distance is 13.1 km. To drive this distance will probably take 00:21 (hh:mm).

• 00:00 On LH134\Markt 4,1 km 4,1 km• 00:06 Turn right on LH134\Otterthal 6,6 km 10,6 km• 00:16 Turn right on LH134\Graben 430 m 11,0 km• 00:16 Turn right on LH134\Graben 770 m 11,8 km• 00:18 Turn right on B27\Semmeringstrasse 650 m 12,5 km• 00:19 Turn right on Hoffeldstrasse 500m 13,0 km• 00:20 Turn right on Sparkassenplatz 50 m 13,0 km• 00:21 Turn left on Sparkassenplatz 128 m 13,1 km

Pragmatic Information Content

Shannon & Weaver: Transmission

Frank: Pragmatic Information Content

Assessment of information contentmust include the decision that isbased on the data.Relative measure:better decision = more (better) information

Necessary Discussion for a GI-Product

• What is the information necessary for good decisions?

• Level of redundancy?

Value for the User (1)

Products must have a benefitBenefit of GI-product may be (Krek & Frank 1999)

• Reduced time to make a decision• Improved decision• Reduced risk

Problem: Cost of data increases with quality

Quality

Quantification of value? Value chain:The value chain concept according to Porter (1985) suggests

that the activities in transforming raw materials and other inputs to final goods can be viewed as a collection of complementary and sequential tasks, each adding value to the final product.

Producer 1 data collector

Producer 2 Producer N dataintegrator

specialistGeoinformationproduct

(Krek & Frank 1999)

Costs in the Value Chain

fixed cost + marginal cost = total cost

high low, zero(sunk cost)

The Value Chain Paradox

VALUElow high

How much is a client willing to pay?

Maximum is determined by

• the benefit

• reduced by the costs of accessing the GI product

Price = Maximum No gain for the user

Price < Maximum

What Data are Necessary

Analysis of information leads to required data

Example: Car navigation requires at least• Street network• Speed limit• Restrictions (turning, weight, height, etc.)• Address information

Missing data leads to services that do not work – how often do you specify your destination by coordinates?

Why is Data Quality Important?

• Data deviate from the ‚correct‘ values

• Reason (Morgan & Henrion, 1990)

– Incompleteness– Disaccord between different data sources– Linguistic uncertainty– Variability– Registration of physical data

Description of Data Quality

• Lineage

• Accuracy

• Completeness

• Logical consistency

• Currency

• Semantic accuracy (Guptil & Morrison, 1995)

Storage of Data Quality: Metadata

Metadata = Data on DataDefined in standards (e.g., ISO TC 211, 2002)

Should be connected with data and contain

• Identification

• Data quality (without semantics)

• Data organization (e.g., vector - raster)

• Information on classification, attributes

• Distribution path, contact, liability

Problems With Metadata

• Typically not collected or not updated

• User do not understand them (Boin & Hunter, 2007)

• No rules for 3D-data (compare Sargent, Harding & Freeman,

What is Quality?

„degree to which a set of inherent characteristics fulfils requirements “ (ISO9000)

Requirements depend on application

Simple if only one type of application is possible (e.g., fire arms)

What about geographic data?

Observation and Measurement

Observations used to determine properties of the physical world

Result measured in different scales– Nominal scale: Names only– Ordinal scale: Includes order– Interval scale: Includes differences– Rational scale: Includes absolute zero

Total error

Difference between a measurement value and the real value.

The total error consists of systematic and random error (bias).

Systematic error(relates to instrument or conditions of measurements)

Difference between the mean of measurements and the true value.

Systematic errors are determinate errors which affect the accuracy of measurements.

They are caused by instrumental-, human-, environmental- or other effects and can be reduced by using control observations and specific observation methods.Instrumental effects e.g. can be reduced by calibration.

Random error(relates to set of measurements)

Difference between a measurement and the mean of measurements.

The random error is an indeterminate error (noise) and affects the precision of measurements.

Random errors scatter measurements above and below the mean and can be reduced by averaging a set of measurements if the sample set is large enough.Random errors are assumed to be normally distributed.

Random and Systematic Errors

precision:random errors

correctness:systematic errors

resolution

Gross error

Undetected mistakes that cause a measurement to be very much farther from the mean measurement when compared to other measurements.

Gross error

Accuracy

Correctness of a single measurement, calculated from total error.

In spatial datasets we can distinguish between positional accuracy and attribute accuracy. Positional accuracy is often divided into vertical and horizontal accuracy (which can differ significantly) and between relative and absolute positional accuracy.

Attributes can be measured on four measurement scales: nominal, ordinal, interval and ratio. Error descriptions are different dependent on the used scale.

Precision

Reproducibility of the same measurement value.

A statistical measure for precision is the standard deviation.

Other common definition for precision:

number of digits used to report a measurement, not necessarily related to accuracy!

Accuracy and precision

Measurements taken from the same position represented by the center of the circle.

Left: successive measurements have similar values, they are precise. But measurements are far from the real value and are therefore inaccurate.

Right: Precision is lower but accuracy is higher.

Vagueness

Arises due to poor definition and can be caused by poor documentation fuzziness of objects.

Criteria:“Is boundary crisp and well defined?”

“Is the assignment of a particular label to a given zone robust and defensible?”

Fitness for Use (1)

A function that allows the user to evaluate the fitness of the data for his particular application.

Product use decision

Difficult: definition of useful output

Fitness for Use (2)

Can I use a data set to solve a specific problem? (Chrisman, 1984)

Fitness for Use (3)

A data set is useful if its use leads to a feasible decision

What is the definition of ‚feasible‘?

Example: Shop size

Classification

Data are typically classified (organized in classes)– e.g., land use, language

regions, etc.

Requires a definition– What is the definition of

a forest?

(Comber, 2007)

Uncertainty

Reasons for uncertainty? (Fisher, 1999, 2003)

• Vagueness (spatial extent of a mountain?)

• Ambiguity (variations in the interpretation of classification rules)

• Discord (different classifications – e.g., land use/land cover)

Not considered in standards! (Goodchild, 2007)

Where are the single buildings?

(Förstner, 2007)

Semantic Loop

Increasingly Challenging

• Data sets for the personal need

• Data transfer to colleagues

• Data transfer to colleagues outside of the subject area

(Goodchild 2007)

Problem

Traditionally: Map scale describes qualityreduction in scale = reduction in quality

Typical map scales for different applications

Digital maps: Data separated from map scale

Idea: Scale introduced during the observation process – carried forward as quality indication?

Tiered Ontology (1)

Ontology: Describes the conceptualization of the world in a particular context(Guarino 1995, Gruber 2005)

Ontology for information system: Description of conceptualization and processes (reality and information p.)

Ontology used here: Tiered ontology

Tiered Ontology (2)

• Tier 1: Point observationvalue from the properties found at a point in space and time: v=p(x,t)

• Tier 2: (Physical) ObjectsRegions with uniformity in property

• Tier 3: Social ConstructionsConstructs relating physical objects to abstract concepts, e.g., money (Searle 1995)

Information Process

Transforms information

obtained at a lower tier

to a higher tier

Observation of physical properties at a point

Physical process

Links reality (tier 0) to tier 1

Realization is imperfect– Random disturbance– Area observation, not point observation

Systematic bias can be eliminated no further consideration

Object Formation (1)

Also called granulation (Zadeh 2002)

2 Steps:– Form boundaries– Summarize properties

Meaningful things (Gibson 1986)

Object Formation (2)

Often spatially cohesive solids which move as a single piece

In Geography objects do not move uniform properties used to define (overlapping) objects

Increases the imperfection of data – summary instead of detailed description

Boundary Identification

Uniform regions

Select property and property value, define threshold Object boundary

Descriptive Summary

Summary of properties of the space within the object boundaries

Typical functions: Sum, maximum, minimum, average

Examples: Weight of a movable object, rainfall on a watershed, maximum elevation in a country (Tomlin 1983, Egenhofer & Frank 1986)

Classification

Mental classification – relating objects to actions, e.g., using affordances (Gibson 1986, Raubal 2002)

Interactions assert conditionsDifferentiation between objects that fulfill

the conditions and those that do not: Distinction

Distinctions are partially ordered – form a taxonomic lattice (Frank 2006)

‚drinkable‘ is a subtaxon of ‚liquid‘

Random Effects in the Observations

No perfect observation by physical sensor perturbation of the observation

Modeled by Gaussian distribution

Effects on– Object formation– Classification – fuzzy membership (Zadeh 1974)

Effect on Object Formation

Statistical error propagation: Gauss‘ Law

Summary value: Similar influence

Scale in Observation

Physical observation instruments are small but not infinitely small

e.g., pixel sensor in camera: 5/1000mm – integrates photons arriving in this region

Size effects in the observations are unavoidable scale element

. ε)k(ε)dεt)f((x,=t)v(x,

ε)dεt)f((x,=t)v(x,

Physical Point-Like Observation

v=p(x,t) is not possible

with covering size and time interval

Convolution with a kernel k():

Formal model for the real observation

Convolution with a Gaussian kernel produces an average effect on the signal

Sampling Theorem

Observation density is finite Sampling

Danger of artefacts, e.g., Nyquist Law (sampling twice as frequent as highest signal frequency)

Well known for audio signals – applies to all dimensions including sampling in geographic space

Scale of Observations

• Size of smallest objects detected:Extent vs. scale

• Effects on uniformity:Variation in property value vs. threshold

• Effects on attribute values:Difference max/min vs. average

• Effects on object classification:Class distinction by size (large/small building)

What Follows?

Physical observations differ from point observations– Random perturbation of the result– Finite spatial an temporal extent

Tier ontology allows to follow the effects– Random variation probability distribution– Finite extent convolution

Well designed systems: Influences have comparable size scale

Influences on Data Quality

• Technical limitations (data capture

• Legal restrictions(society needs)

• User needs

Data quality

Technical limitations

Legal Restrictions

User needs

Technical Limitations

Higher quality higher costs

Absolute Limite.g. approximate relativeuncretainty of the definition of the unit length ‚meter‘: 2,5*10-11

Other examples: Satellite images, GPS

Quality

maximum quality

new technology

Legal Restrictions

Laws influence data quality by– Influence on data capture processes (e.g.,

cadastre, statistics)– Access restrictions

Weak constraints (laws can be broken)

Examples: Demographic data, data from spatial planning

User Needs

Assumption: Large number of users/important users higher data quality available

Indication: Data capture within communities (e.g., mountain-bikers, hikers, etc.)

Examples: Topographic maps (military), nautical charts

Legal Aspects for GI Products

• Copyright

• Data protection

• Liability

Copyright: Definitions

• Copyright: Protects the rights of the authorBasis: British/American tradition, concentration on economic rights (exploitation rights)

• Usage right: Allows possession and (personal) use

General Definition of Copyright

The right to copy; specif., a property right in an original work of authorship (including literary, musical, dramatic, choreographic, pictorial, graphic, sculptural and architectural works; motion pictures and other audiovisual works; and sound recordings) fixed in any tangible medium of expression, giving the holder the exclusive right to reproduce, adapt, distribute, perform, and display the work.

(Black‘s Law Dictionary, 2004)

Austria: Intellectual PropertyRight (1)

• Definition author: Person creating something unique (text, graphics, design, music, software, etc.)

• Several authors: co-authors• Test of co-authorship: Is the contributor‘s effort an

original expression that could quality for copyright protection? (Blacks Law Dictionary, 2004)

• Intellectual Property Rights expire– 70 years after the death of the author (USA: 50 years)– 70 years after creation if authors are unclear After that freely usable

Austria: Intellectual PropertyRight (2)

• Intellectual Property Rights include– Right of reproduction– Right of distribution– Broadcast right– Presentation right– Provision right

• Intellectual Property Right ALWAYS stay with the author!

• Only parts can be given away

Ensembles (Sammelwerke)

• Combination of individual contributions to an ensemble are copyright protected if– it constitutes a creation process– the selection of contributions could have been

done differently

• The ensemble is protected by copyright

• But: The individual contributions are still protected by copyright!

Free Work

• Not protected by copyright

• Examples: Laws, decrees

• Topographic maps of the Austrian surveying authority (BEV) are no free work!

Exploitation Right (Nutzungsrecht)

• Transfer of the rights from the author to a third party

• May happen automatically– e.g., employee writes a text for his company– Important for universities, research centres

(exclusive exploitation right? – patents)

Computer Programs

• Work as protected by copyright law if it emerged from an intellectual creation process– Code to access a TXT file is not protected– New method/algorithm to solve a specific type

of equation system is protected• Covers source code as well as materials

created while developing the program• Employer has unlimited exploitation right if

created during office hours

Copyleft (Slang)

• Specific software license model• Allows users to modify or incorporate open-source code

into larger programs on the condition that the software containing the source code is publicly distributed without restrictions

• Freeware: Software that is made generally available with express or implicit permission for anyone to use, copy, modify, and distribute for any purpose, including financial gain – includes open-sourceFree refers to usage rather than price!

(Blacks Law Dictionary, 2004)

Databases

• Databases are treated like ensembles

• No protection if there is only one way to do it, e.g.,– Law database– Probably: addresses, contour lines

Conclusions Copyright

• Protected are display format and selection

• No protection if published in form of laws or decrees or if there is no freedom of geometry (boundaries of administrative units)

• Problematic: Contour lines

Personal Data Protection

• Right on confidentiality of personal data• Privacy law: Restricts public access to personal

information such as tax returns or medical records (Blacks Law Dictionary, 2004)

• Informational Privacy: A private person‘s right to choose to determine whether, how, and to what extent information about oneself is communicated to others, esp. sensitive and confidential information (Blacks Law Dictionary, 2004)

• Exceptions:– Publicly available data– Aggregated data (if they cannot be re-connected

with a person!)

Definitions

• Personal data: Data on persons whose identity is known or can be determined

• Indirectly personal: Connection to person cannot be made by legal methods– May be completely impossible– May be possible using illegal means

• Sensible data: Special protection (e.g., racial and ethnic origin, religion, sexual preferences, political or philosophical opinion, health status)

Liability

• The quality or state of being legally obligated or accountable; legal responsibility to another or to society, enforceable by civil remedy or criminal punishment. (Blacks Law Dictionary, 2004)

• Austria: Liability if you publicly announce that you are an expert – even if you have no adequate education!(ABGB § 1299)

Suthradhar vs. Natural EnvironmentalResearch Council (1)

• British Geological Survey (department of the NERC) works on deep wells (from 1984)

• In 1991-92 the BGS creates a hydro-geologic study on groundwater in Bangladesh

• Within the study tests of the water on bacteria and some toxins (aluminium, ferrite, iodine)

• 1993 detection of arsenic in the ground water of neighbouring areas

• Fault of the experts?

Suthradhar vs. Natural EnvironmentalResearch Council (2)

Problem: Original study not for drinking water but how to optimally design tube wells to avoid deterioration

Study described as reconnaissance study

Results of study applied to creation of wells for drinking water

Misuse of data

No liability

When We Have the Data …

• How can we access it?

• How can we make sure that the communication works?

Access Methods (1)

Communication Channels

• Textual• Graphical• Verbal

Access Methods (2)

Limitations of the users?

• Language• (Color) Blind• Deaf

Access Methods (3)

Data access

• online/offline• Mobile phone/PC• Time of access?

Information Extraction

How to perform each step?

• Collect the data

• Process the data (e.g., using a GIS)

• Create the product

• Deliver the product through the selected channel

Feasibility

• Number of potential customers?

• Market penetration?

• Earnings with each customer?

• Will the system be cost-effective?

• Return on investment?

Example: Lunch Menus (1)

Problem description• I go to lunch each day and have to make a decision for a

specific restaurant• Selection based on menu (changes daily) and personal

preference• Menu only visible when I am at the restaurant

possible detour if I do not like the menu

Solution• GI-product showing me the current menus and the

locations of the restaurants

Design• User group: people working at the Vienna

University of Technology and nearby offices• Access: online via Internet

Necessary data• List of restaurant with addresses• Menus of the restaurant (typed in manually)• City map

Costs• Simple computer (€ 1000)• Internet access (€ 30/month)• Power supply (€ 30/month)• Data input (2h/day, € 15/h € 600/month)

Number of potential customers• Employees of Vienna University of Technology:4000• Other employees: 1000

Market penetration?• Assessment: 2%

Value of the information?• Cost of a lunch € 5 – 14• Tip: € 0.5 – 1.0• Assessment of value for better decision: € 0.5

Is the service profitable?• Costs: € 1000 + 660/month• 100 customers/day 2000 queries/month

income € 1000/month

Not yet included: Programming, marketing, office rent, organization, etc. probably not profitable

Implementation

• Depends on the technology used

• Changes rapidly

• Should be based on standards and standard protocols (e.g., GML)

• Should consist of independent modules (re-use)

GI Product Development Gerhard Navratil Workshop in connection with Geomatics 2011, Teheran, May 14...

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