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TECHNISCHEUNIVERSITÄTWIENVienna University of Technology

DEPARTMENT FORGEODESY ANDGEOINFORMATION

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RESEARCH GROUPGEOINFORMATION

SUPPORTING SPATIAL PLANNING WITH

QUALITATIVE CONFIGURATION ANALYSIS

20.05.2013CORP 2013

Paolo Fogliaroni and Gerhard Navratil

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SPATIAL PLANNING TODAY SNAPSHOT

• Spatial planning strongly relies on Spatial Information Systems (SIS):

• Computer-Aided Design

• Geographic Information Systems

• SIS offer continuously more powerful spatial data analysis and management options

• Yet, SIS largely lack support for natural human-computer interaction

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SIS VS HUMANS

• SIS → quantitative representations (raster/vector) + numerical operationsE.g.: The angular lake-park distance is 158.9°

• Humans → qualitative representations + reasoning E.g.: The lake is to the west of the park

• Today, translation efforts to map qualitative spatial representation into a quantitative (numerical/geometric) one is up to SIS user

158.9°Lake Park

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QUALITATIVE INFORMATION

•Qualitative information is, by its own nature, somewhat “vague” and “imprecise”

• It is not a substitute of quantitative information

• Yet, when it comes to interaction with human beings, it plays a fundamental role

• How to embody qualitative models into SIS?

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QUALITATIVE SPATIAL REPRESENTATION AND REASONING• Qualitative Spatial Representation and Reasoning (QSR) is a subfield of Artificial Intelligence providing

so-called qualitative spatial calculi

• A qualitative calculus

• is cognitive suitable

• focuses on a single aspect of space (e.g. topology, direction, or distance)

• consists of two main items:

• a finite set of symbols called qualitative spatial relations: used for representational purposes

• a finite set of inference rules that allow for symbolic reasoning

o1 Disjoint o2

o1 Meet o2

o1 Overlap o2

o1 Covers o2

o1CoveredBy o2

o1 Equal o2

o1 Contains o2

o1 Inside o2 The 9-intersection model defines the 8 topological relations that can hold

between two spatial objects;on the left depicted and

arranged according to their conceptual neighborhood.

Egenhofer, M.J. (1989).

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QUALITATIVE SPATIAL INFORMATION SYSTEM

Query/Analysis/Editing

Spatial Information System

Geometry

Raster/Vector

Low-level operations

Quantitative Data storage

Spatial DB

Web/Local visualization

Fogliaroni, P. (2012).

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QUALITATIVE SPATIAL INFORMATION SYSTEM

Query/Analysis/Editing

Spatial Information System

Geometry

Raster/Vector

Low-level operations

Quantitative Data storage

Spatial DB

Pool of Qualitative

Calculi

+ Qualitative Extension

Web/Local visualization

Fogliaroni, P. (2012).

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QUALITATIVE SPATIAL INFORMATION SYSTEM

Query/Analysis/Editing

Spatial Information System

Geometry

Raster/Vector

TopologyDistanceDirectionVisibility

⋮Low-level operations

Quantitative Data storage

Spatial DB

Pool of Qualitative

Calculi

Qual.Rels

+ Qualitative Extension

Web/Local visualization

Fogliaroni, P. (2012).

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QUALITATIVE SPATIAL INFORMATION SYSTEM

Query/Analysis/Editing

Spatial Information System

Geometry

Raster/Vector

TopologyDistanceDirectionVisibility

⋮Low-level operations

Quantitative Data storage

Spatial DB

Pool of Qualitative

Calculi

Qualitative Data storage

Qualitative Representation

Qual.Rels

+ Qualitative Extension

Qual.Repr.

Web/Local visualization

Fogliaroni, P. (2012).

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QUALITATIVE SPATIAL INFORMATION SYSTEM

Query/Analysis/Editing

Spatial Information System

Geometry

Raster/Vector

TopologyDistanceDirectionVisibility

⋮Low-level operations

Quantitative Data storage

Spatial DB

Pool of Qualitative

Calculi

Qualitative Data storage

Qualitative Representation

Qual.Rels

+ Qualitative Extension

Qual.Repr.

Web/Local visualization

Qualitative query/

Reasoning

Fogliaroni, P. (2012).

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QUALITATIVE REPRESENTATION

o1

o2

o3

Quantitative representationVector

o1

o3

o2

Disjoint,West,...

Disjoint,East,...

Disj

oint

,East,

...

Disj

oint

,Wes

t,...

Meet,East

Meet,West

Qualitative representationQualitative Constraint Network (QCN)

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QUALITATIVE REPRESENTATION

o1

o3

o2

Disjoint,West,...

Disjoint,East,...

Disj

oint

,East,

...

Disj

oint

,Wes

t,...

Meet,East

Meet,West

Qualitative representationQualitative Constraint Network (QCN)

Qualitative QueriesNatural language sentence

I’m looking for a pair of objects such that they are in touch and one lies west of the other!

Does object 1 touch object 2?

o1 o2

or a sketch

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SPATIAL PLANNING SUPPORT• Qualitative Spatial Information System +

• spatial object ontology: e.g. lake, park, building, house...

• spatial plan ontology: e.g. public green, residential area...

• Each designed plan is stored in the database together with the corresponding qualitative config

Lake(o1)

Park (o2)

Road (o3)

Geometric Plan Design Qualitative Configuration

Lake(o1)

Road(o3)

Park(o2)

Disjoint,West,...

Disjoint,East,...

Disj

oint

,East,

...

Disj

oint

,Wes

t,...

Meet,East

Meet,West

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SPATIAL PLANNING SUPPORT• Qualitative Spatial Information System +

• spatial object ontology: e.g. lake, park, building, house...

• spatial plan ontology: e.g. public green, residential area...

• Each designed plan is stored in the database together with the corresponding qualitative config

Lake(o1)

Park (o2)

Road (o3)

Geometric Plan Design Qualitative ConfigurationLake(o1)

Road(o3)

Park(o2)

Disjoint,West,...

Disjoint,East,...

Disj

oint

,East,

...

Disj

oint

,Wes

t,...

Meet,East

Meet,West

Ge inf

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SPATIAL PLANNING SUPPORT• Qualitative Spatial Information System +

• spatial object ontology: e.g. lake, park, building, house...

• spatial plan ontology: e.g. public green, residential area...

• Each designed plan is stored in the database together with the corresponding qualitative config

Lake(o1)

Park (o2)

Road (o3)

Geometric Plan Design Qualitative ConfigurationLake(o1)

Road(o3)

Park(o2)

Disjoint,West,...

Disjoint,East,...

Disj

oint

,East,

...

Disj

oint

,Wes

t,...

Meet,East

Meet,West

Park (o2)

Lake(o1)

Road (o3) Lake(o1)

Road(o3)

Park(o2)

Contains

Inside

Disj

oint

Disj

oint

Meet

Meet

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SPATIAL PLANNING SUPPORT• Qualitative Spatial Information System +

• spatial object ontology: e.g. lake, park, building, house...

• spatial plan ontology: e.g. public green, residential area...

• Each designed plan is stored in the database together with the corresponding qualitative config

Lake(o1)

Park (o2)

Road (o3)

Geometric Plan Design Qualitative ConfigurationLake(o1)

Road(o3)

Park(o2)

Disjoint,West,...

Disjoint,East,...

Disj

oint

,East,

...

Disj

oint

,Wes

t,...

Meet,East

Meet,West

Park (o2)

Lake(o1)

Road (o3)Lake(o1)

Road(o3)

Park(o2)

Contains

Inside

Disj

oint

Disj

oint

Meet

Meet

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SPATIAL PLANNING SUPPORT• Qualitative Spatial Information System +

• spatial object ontology: e.g. lake, park, building, house...

• spatial plan ontology: e.g. public green, residential area...

• Each designed plan is stored in the database together with the corresponding qualitative config

Lake(o1)

Park (o2)

Road (o3)

Geometric Plan Design Qualitative ConfigurationLake(o1)

Road(o3)

Park(o2)

Disjoint,West,...

Disjoint,East,...

Disj

oint

,East,

...

Disj

oint

,Wes

t,...

Meet,East

Meet,West

Park (o2)

Lake(o1)

Road (o3)Lake(o1)

Road(o3)

Park(o2)

Contains

Inside

Disj

oint

Disj

oint

Meet

Meet

Park (o2)

Lake(o1)Road (o3)

Lake(o1)

Road(o3)

Park(o2)

Covers

Covered By

Disj

oint

Disj

oint

Meet

Meet

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SPATIAL PLANNING SUPPORT• Qualitative Spatial Information System +

• spatial object ontology: e.g. lake, park, building, house...

• spatial plan ontology: e.g. public green, residential area...

• Each designed plan is stored in the database together with the corresponding qualitative config

Lake(o1)

Park (o2)

Road (o3)

Geometric Plan Design Qualitative ConfigurationLake(o1)

Road(o3)

Park(o2)

Disjoint,West,...

Disjoint,East,...

Disj

oint

,East,

...

Disj

oint

,Wes

t,...

Meet,East

Meet,West

Park (o2)

Lake(o1)

Road (o3)Lake(o1)

Road(o3)

Park(o2)

Contains

Inside

Disj

oint

Disj

oint

Meet

Meet

Park (o2)

Lake(o1)Road (o3)

Lake(o1)

Road(o3)

Park(o2)

Covers

Covered By

Disj

oint

Disj

oint

Meet

Meet

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COMPUTING OPTIMAL PLANS

1.Normalize  qualitative  configs  by

1.1.computing  maximum  common  set  of  spatial  objects

1.2.removing  non-­‐common  objects  (and  relations  they  are  involved  in)  

2.Compare  normalized  configs  via  graph  matching  techniques  to  find  the  minimum  common  subgraph

Main Road

Park Running Track

Parking Lot

Meet

Meet

Contains

Main Road

Park Running Track

Parking Lot

Meet

Meet

Contains

Lake

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COMPUTING OPTIMAL PLANS

1.Normalize  qualitative  configs  by

1.1.computing  maximum  common  set  of  spatial  objects

1.2.removing  non-­‐common  objects  (and  relations  they  are  involved  in)  

2.Compare  normalized  configs  via  graph  matching  techniques  to  find  the  minimum  common  subgraph

Main Road

Park Running Track

Parking Lot

Meet

Meet

Contains

Main Road

Park Running Track

Parking Lot

Meet

Meet

Contains

Lake

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COMPUTING OPTIMAL PLANS

1.Normalize  qualitative  configs  by

1.1.computing  maximum  common  set  of  spatial  objects

1.2.removing  non-­‐common  objects  (and  relations  they  are  involved  in)  

2.Compare  normalized  configs  via  graph  matching  techniques  to  find  the  minimum  common  subgraph

Main Road

Park Running Track

Parking Lot

Meet

Meet

Contains

Main Road

Park Running Track

Parking Lot

Meet

Meet

Contains

Lake

Ge inf

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COMPUTING OPTIMAL PLANS

1.Normalize  qualitative  configs  by

1.1.computing  maximum  common  set  of  spatial  objects

1.2.removing  non-­‐common  objects  (and  relations  they  are  involved  in)  

2.Compare  normalized  configs  via  graph  matching  techniques  to  find  the  minimum  common  subgraph

Main Road

Park Running Track

Parking Lot

Meet

Meet

Contains

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SUPPORTING SPATIAL PLANNING

•Our system supports spatial planners in 3 different stages:

• Preliminary phase: site localization

• Plan setup: kickstart configuration

• Plan development: design assistance

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LOCALIZING A PLAN SITE

• Finding a suitable location for a new plan consists in finding a number of land parcels satisfying a number of constraints

• Part of such constraints are purely spatial: search for a spatial configuration of objects arranged in a certain manner

• Strict constraints are better expressed with mathematical equations

• Loose constraints are more easily expressed in natural language without resorting to complex equations, inequalities and conditions

• Our system allows for complementing standard search methods via hybrid quantitative-qualitative spatial queries

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DESIGN KICKSTARTER

SpatialDB

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DESIGN KICKSTARTERPlan category: public  green

Optimal configuration

Main Road

Park Running Track

Parking Lot

Meet

Meet

Contains

SpatialDB

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DESIGN KICKSTARTERMain Road

Park Running Track

Parking Lot

Meet

Meet

Contains

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Main Road

Park Running Track

Parking Lot

Meet

Meet

Contains

DESIGN ASSISTANTMain Road

Park Running Track

Parking Lot

Meet

Meet

Contains

Optimalconfiguration

Plan design

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Main Road

Park Running Track

Parking Lot

Meet

Meet

Contains

DESIGN ASSISTANT

Main Road

Parking Lot

Meet

Meet

Optimalconfiguration

Plan design

Currentconfiguration

Contains Park Running Track

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Main Road

Park Running Track

Parking Lot

Meet

Meet

Contains

DESIGN ASSISTANT

Main Road

Parking Lot

Meet

Meet

Optimalconfiguration

Plan design

Currentconfiguration

DisjointPark Running Track

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FITTING PLANS TO PUBLIC EXPECTATIONS

• Public Participatory Geographic Information Systems (PPGIS) are web platforms designed to collect public opinion of a topic of interest

• PPGIS allows for adapting optimal plans to public expectation in two ways:

• ratings collection

• public envisioning

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RATING COLLECTION

• Spatial plan will eventually affect real world

• The resulting environment is best assessed by its users

• PPGIS can be used to collect user feedbacks and generate an overall rating of a certain environment

• The rating is associated to the corresponding plan design in the database

• Ratings are used to weight plans: high-scored designs play an heavier role in the optimal plan generation process

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OPTIMAL PLAN FROM PEOPLE EXPECTATION

•Web design tools can be used to let people sketching the new environment as they expect it

•Qualitative configurations can be obtained from such sketches and associated to a certain plan category

B3 (www.geogameslab.com) is a web platform that allows laypeople to draw an environment via drag and drop

Poplin, A. (2012).

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THANK YOU FOR YOUR ATTENTION!

QUESTIONS ARE WELCOME

Egenhofer, M.J. (1989). A formal definition of binary topological relationships. In: Litwin, W., Schek, H.J. (eds.), FODO 1989, 3rd International Conference on Foundations of Data Organization and Algorithms, Lecture Notes in Computer Science, vol. 367, pp. 457–472. Springer-Verlag

Fogliaroni, P. (2012). Qualitative Spatial Configuration Queries – Towards Next Generation Access Methods for GIS. Ph.D. Thesis (http://nbn-resolving.de/urn:nbn:de:gbv:46-00102731-11)

Poplin, A. (2012). Playful public participation in urban planning: A case study for online serious games. Computers, Environment and Urban Systems.

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