Using the Model-Driven Architecture Approach
for Geospatial Databases Design of
Ecological Niches and Potential Distributions
Gerardo J. Zárate (Master student)
Jugurta Lisboa Filho (Department of Informatics)
Carlos F. Sperber (General Biology Department)
Federal University of Viçosa
Minas Gerais - Brazil
Department of Informatics
2
Outline
• Introduction
▫ Ecological Domain
▫ Conceptual Models for Geospatial DB Design
▫ MDA approach
• Geospatial Databases Design of
Ecological Niches and Potential Distributions
• Study Case
• Concluding remarks
3
Introduction
4
What is an ecological niche?
Definitions
• Habitat requirements of a species
• Environmental variables that enable the survival
and reproduction of a species (Grinnell, 1917)
• N-dimensional hypervolume determined by
species requirements (Hutchinson, 1957)
5
Species requirements
• Biotic - Resources that are consumed or used
Food
Water
• Abiotic - Environmental conditions
Temperature
Precipitation
Humidity
6
Environmental variables
• Easier to collect than Biotic resources
• Can be imported into GIS Software
• Used in algorithms to calculate
Potential Geographic Distributions
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N-dimensional hypervolume
• Each environmental condition has appropriate
minimum and maximum values for the survival
of a species
• The range of each condition (variable/axis) form
the hypervolume
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Ex.: 2-dimensional hypervolume
9
Potential Geographic Distribution
• Refers to regions that have the appropriate
set of environmental conditions for a species
to live and reproduce.
• Mathematical algorithms can be used to predict
these regions
▫ Input:
 location of occurrences and
 environmental data
10
Motivation for this work
• Potential Geographic Distributions are
useful in
▫ studies about global climate change
▫ predicting the extent of invasive species
▫ finding suitable regions for endangered species
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Model-Driven Architecture (MDA)
Approach
• Use of different models level
▫ Computation Independent Model (CIM)
▫ Platform Independent Model (PIM)
▫ Platform Specific Model (PSM)
• For Geographic Database Design,
transformations use ISO and OGC standards
12
Objective
• The aim of this paper is to describe the stages
of a Model-Driven Architecture (MDA) for the
Geospatial Database design of Environmental
Niches and Potential Geographic Distributions.
13
Conceptual Data Models for
Geospatial Database Design
Conceptual Data Models for GIS
• Perceptory's model [Bédard]
• UML-GeoFrame [Lisboa Filho]
• OMT-G (Object Modeling Technique for
Geographic Applications) [Borges, Davis]
• GeoOOA (Geo Object-Oriented Analysis) [Kosters]
• MADS (Modeling of Application Data with Spatio-temporal features) [Parent]
UML Profile -> the next stop
• UML Profile is an UML extension mechanism
▫ Metamodel defines new constructors from the UML
constructors + stereotypes
• UML GeoProfile is a profile for the Geographic Domain
metamodel
UML Profile -> the next stop
• UML GeoProfile metamodel [CAiSE`2010]
17
Stereotypes
• Many conceptual data models use pictograms
• GeoProfile pictograms are defined as UML stereotypes
• You can choose the pictogram of your taste and any CASE
tool with support to UML Profile
Example of UML GeoProfile Stereotypes
18
MDA Approach
(Model-Driven Architecture)
for Geospatial Databases Design of
Ecological Niches and Potential Distributions
Font: http://caminao.wordpress.com/system-engineering
19
MDA CIM Level
• Modeled with UML GeoProfile
• Consists of 3 packages
▫ Environmental Niche
▫ Abiotic conditions
▫ Potential Geographic Distribution
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MDA CIM Level
Environmental Niche package (1)
• Organisms of multiple species
• Occurrences modeled as a spatiotemporal class
• Spatial constraint between Occurrences and Occupied
Area
• Multiple environmental conditions defined for each
species
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MDA CIM Level
Environmental Niche package (1)
22
MDA CIM Level
Abiotic conditions package (2)
• Includes the environmental layers
• Multiple representations for environmental
conditions
• Spatiotemporal representations
23
MDA CIM Level
Abiotic conditions package (2)
24
MDA CIM Level
Potential Geographic Distribution Package (3)
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MDA CIM Level
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CIM –> PIM Transformation
• Integrated with standards
▫ Geographic Information of the International Organization
for Standardization (ISO) or OGC GML
27
MDA PIM Level
28
MDA PSM Level (PostGIS)
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Study Case
• Potential Geographic Distributions for
Aroeira tree (Myracrodruon urundeuva)
• It can be an invasive species
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Study Case
• Data Schema was implemented in PostGIS
• Input
▫ Evaluated Region = Brazil’s map
▫ Occurrences of Aroeira (Point)
• 2 algorithms for prediction
▫ GARP - Genetic Algorithm for Rule-Set Prediction
(D. Stockwell, 1999)
▫ Climate Space Model
31
PostGIS data types
• GEOMETRY for geospatial objects
• RASTER for geospatial fields
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Table creation
CREATE TABLE public.occurrence(
id_organism
INTEGER NOT NULL,
id_species
INTEGER,
id_area
INTEGER,
instant
TIMESTAMP WITHOUT TIME ZONE,
geom
GEOMETRY(POINT),
CONSTRAINT ocurrence_pkey
PRIMARY KEY (id_ organism, instant),
CONSTRAINT ocurrence_id_area_fkey
FOREIGN KEY (id_area)
REFERENCES public.area (id_area),
CONSTRAINT ocurrence_id_species_fkey
FOREIGN KEY (id_species)
REFERENCES public.species (id_species)
);
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Study Case Output
▫ Potential Geographic Distribution Area = World map
▫ Geospatial data were retrieved using QuantumGIS
▫ Potential geographic distributions projected with algorithms available in
openModeller. Algorithms used: GARP (a) and Climate Space Model (b)
34
Concluding remarks
• We presented the development of a data schema of
ecological niches and potential geographic distributions
of species using the MDA approach.
• The CIM level was modeled using the UML GeoProfile.
• The CIM level was transformed into a PIM that follows
the ISO standard.
• The PIM level was transformed into a PSM linked to
PostgreSQL and PostGIS specifications of geospatial
data.
35
Concluding remarks
• As ER schemas, with a UML Profile you can see the
same schema in different appearances – based on the
same metamodel.
• The data schema presented can help ecologists to start
their projects related topics involving niche-based
potential geographical distributions.
• The main contribution of this work is a interdisciplinary
contribution, showing how ecologists can benefit from
conceptual modeling.
Jugurta Lisboa Filho
jugurta@ufv.br
Universidade Federal de Viçosa
Minas Gerais - Brazil
Departamento de Informática