Ontology and spatial planning

Ontology and spa-al planning.

Beniamino Murgante, Francesco Scorza

Università della Basilicata – Laoratorio di Ingegneria dei Sistemi Urbani e Territoriali (LISUT) University of Basilicata – Laboratory of Urban and Regional Systems Engineering (LISUT),

Viale dell’Ateneo Lucano 10, 85100, Potenza – Italy [email protected]

LISUT

Laboratorio di Ingegneria dei Sistemi Urbani e Territoriali

Laboratory of Urban and Regional Systems Engineering

University of Basilicata

Faculty of Engineering

Department of Architecture, Planning and Transport Infrastructure

ICCSA 2011 – University of Cantabria, Santander, SPAIN Ing. Francesco Scorza

Index:

•  Lack of data vs interoperability •  SemanRc barriers and ontology •  Broad ontology •  Conclusions

LISUT






Lack of data vs interoperability

“Isolated” data set

Recent trends: •  Wiki approach in data production •  Volunteered geographic information •  Crowdsourcing •  Neogeography (geotg, blogs, etc.) •  …

LISUT






Lack of data vs interoperability

Three main barriers:

•  Bureaucratic: poor practice in sharing data •  Technological: differences between systems

and data formats •  Semantic: lack of correspondance in

meanings.

LISUT






SemanRc barriers

LISUT






… the main issue in usign GIS is “where is located an object” without thinking in deep about “what you are looking for” …..

SemanRc barriers

LISUT






… the main issue in usign GIS is “where is located an object” without thinking in deep about “what you are looking for” …..

Obj.

Ontology

A tool for achieving database integration and interoperability

LISUT






Planning issues: •  Complexity •  Detailed analysis •  Domains specialists •  Perceived visions •  Conflicting interests •  Uncertainty •  …

SemanRc agreement

Broad ontology

LISUT






DefiniRon of …

DefiniRon of …

DefiniRon of …

DefiniRon of … Obj.

Ontology

DefiniRon of …

AYributes …

RelaRons …

Broad ontology

LISUT






Broad ontology

LISUT






a)

c)

b)

Broad ontology

LISUT






!9-intersection model

Broad ontology

LISUT






Broad boundaries model !

Information System A) (U, IS =

Rough set

! a"A # Va= value set (domain of attribute)

Let U be a nonempty finite set of objects called the universe

Let A be a nonempty finite set of attributes

{ }nxxxxxxx ,...,......... , , , ,,U 654321=

{ }3 2 1 ,, A AAA=

{ }{ }{ }4 ,3,2, 1V

2, 1V3,2, 1V

3

2

1

=

=

=

Revue des Nouve l l es Techno log ies de l'Information, Éditions Cépaduès, France

LISUT






Information System

U a1 a2 a3

x1 2 1 3

X2 3 2 1

X3 2 1 3

X4 2 2 3

X5 1 1 4

X6 1 1 2

X7 3 2 1

X8 1 1 4

X9 2 1 3

x10 3 2 1

function ninformatio V U:f aa →

Murgante B., Las Casas G., Sansone A., (2008) “ A spatial rough set for extracting periurban fringe”


Rough set

LISUT






U a1 a2 a3 d1

x1 2 1 3 1 X2 3 2 1 4

X3 2 1 3 5

X4 2 2 3 2

X5 1 1 4 2

X6 1 1 2 4

X7 3 2 1 1

X8 1 1 4 2

X9 2 1 3 3

x10 3 2 1 2

Decision System A decision system is an

information system in which the values of a special decision attribute classify the cases

other attributes a ! A- d{ } Conditional Attributes

Ad )A (U, DS ≠∪= d


Rough set

LISUT






[ ]{ } )( XxUxLX Bindii ⊂∈=

LXUXBX −=

[ ]{ } )( ∅≠∩∈= XxUxUX Bindii

Equivalence classes

Lower Approximation

Boundary region

Upper Approximation

)(/)()( UXcardLXcardXB =µ

If BX = ∅ then the set X is Crisp If BX ≠ ∅ then the set X is Rough

Accuracy

Murgante B., Las Casas G., Sansone A., (2008) “ A spatial rough set for extracting periurban fringe”

Rough set

LISUT






ReDO

LISUT






!"#$$%$&'"(!)*''"$#!*'+&+',%!*&"#-&+'.+#/%-0(+'-/%-*

!" !"##$%&'(!# )*+,-'&.,!$ /-'&"%!% !"##&'#,%'!& 0,1%!' 2"13!( 4'51',6(!) 7$*3&-!* !"8'!"+ 918:

4;0/<9=!>?;@

ReDO

LISUT






ReDO

LISUT






!"#$$%$ !" !# !$ !% !& !' !( !) !* !"+&'"(!) ! " # ! " !*''"$ ! "#!*'+ !&+',%!* ! !&"#- ! ! "&+'.+#/ # " ! " !%-0(+'-/%-* # " # !

1%)23'+4$2!+(*%+(#

!" !"##$%&'(!# )*+,-'&.,!$ /-'&"%!% !"##&'#,%'!& 0,1%!' 2"13!( 4'51',6(!) 7$*3&-!* !"8'!"+ 918:

4;0/<9=!>?;@

ReDO

LISUT






ROSE 2

ReDO

LISUT






# LEM2 # C:\Documents and Settings\LT\Desktop\ReDO.ISF # objects = 7 # attributes = 11 # decision = a6 # classes = {1, 3} # 0 s rule 1. (a2 = 0) & (a5 = 0) => (a6 = 0); [2, 2, 66.67%, 100.00%][2, 0] [{3, 4}, {}] rule 2. (a1 = 3) => (a6 = 0); [1, 1, 33.33%, 100.00%][1, 0] [{7}, {}] rule 3. (a1 = 0) & (a8 = 0) & (a10 = 0) => (a6 = 2); [3, 3, 75.00%, 100.00%][0, 3] [{}, {2, 5, 6}] rule 4. (a1 = 1) => (a6 = 2); [1, 1, 25.00%, 100.00%][0, 1] [{}, {1}] **END

ReDO

LISUT






!"#$$%$ !" !# !$ !% !& !' !( !) !* !"+&'"(!) ! " # ! " !*''"$ ! "#!*'+ !&+',%!* ! !&"#- ! ! "&+'.+#/ # " ! " !%-0(+'-/%-* # " # !

1%)23'+4$2!+(*%+(#

!" !"##$%&'(!# )*+,-'&.,!$ /-'&"%!% !"##&'#,%'!& 0,1%!' 2"13!( 4'51',6(!) 7$*3&-!* !"8'!"+ 918:

4;0/<9=!>?;@ rule 1. (a2 = 0) & (a5 = 0) => (a6 = 0); [2, 2, 66.67%, 100.00%][2, 0] rule 2. (a1 = 3) => (a6 = 0); [1, 1, 33.33%, 100.00%][1, 0] rule 3. (a1 = 0) & (a8 = 0) & (a10 = 0) => (a6 = 2); [3, 3, 75.00%, 100.00%][0, 3] rule 4. (a1 = 1) => (a6 = 2); [1, 1, 25.00%, 100.00%][0, 1]

Conclusions

•  Interoperability -‐> produced “unified legends” ?? •  SDI -‐> semanRc approach could reinforce the usability of the systems

•  SemanRc correspondence brings to interoperability not only between data sets but also in the process of management plans and intervenRon programs.

LISUT






Thanks

Ing. Francesco Scorza

PhD Student at University of Basilicata -‐ Faculty of Engineering Department of Architecture, Planning and Transport Infrastructure (DAPIT) Laboratory of Urban and Regional Systems Engineering (LISUT) Viale dell’Ateneo Lucano, 10 85100 Potenza – Italy

www.lisut.org [email protected] Skype: francescoscorza Tel: 0039.347.7001363

Education

Ontology and spatial planning