Ontology of Geographic Representation
Kejin Cui
1
The scope of the ontology
The designed ontology focuses on geographic representation, namely spatial data
models used in GIS (Geographic Information Systems). First, my ontology will
discuss about the nature and relationships between different spatial data models,
especially the essence of field models and object models, which is the most important
dichotomy in geographic representation.
Moreover, my ontology will include the
relationships about how to use appropriate data models to describe natural entities –
material entities.
In addition, the relation between data models and two kinds of
occurrent entities – geographic process and spatial operations – are discussed.
Existed geographic ontology mainly discusses categories of geographic entities,
such as the nature and relationship among mountains, hills, valleys, and rock.
However, there are few discussion on geographic representation and spatial data
models.
On the other hand, currently there are many research on spatial data
models, but there is lack about exploration from the perspective of ontology.
Hence, the designed ontology project aims at finding links between geographic
entities and geographic data models, and giving people better understanding on
reasoning geographic representation.
2
The structure of designed ontology
2.1 General structure
According to the hierarchy structure of BFO2.0, my designed ontology is placed
in four major branches.
First, for the major part of my ontology, spatial data models
should be placed under “Two-dimensional Spatial Region” of “Spatial Region”.
This is because spatial data models are results of people’s spatial cognition, so they
should be classified as spatial regions.
Also for simplifying, this ontology only
discusses the 2-dimensional condition.
Second, all of the geographic entities are
belong to “Material Entities”. Also geographic events, processes and states should
be classified as “Process” as parts of “Occurrent”.
In addition, there are also some
generically dependent continuants, which are parts of the concept “Geo-atoms”.
Thus, based on the consideration above, my designed ontology is successfully
integrated in the BFO2.0 standard, as shown in Figure 1, Figure 2 and Figure 3.
Figure 1: the structure of BFO2.0
Figure 2: overall structure of spatial data models and geographic entities
Figure 3: overall structure of geographic events and dependent continuants
Next, some important terms used in this paper will be defined and explained.
Then the corresponding ontology implementation will be given.
2.2 Spatial data models
This part will discuss the ontology of spatial data models themselves, including
the field/object dichotomy, their different categories, their common primitive – geoatoms, and their relationships.
First, spatial data models are the abstraction of geographic information. They
are the products of people’s spatial cognition, and also the carriers of analyzing and
visual in geographic information systems.
In research field of spatial data models, there are two dual categories of models –
continuous field models and discrete object models.
spatial cognition concepts.
They represents two dual
In current exploration on geographic representation,
much research focuses on their nature, their relationship and how to unify and
integrate them.
The design of this ontology will use one of the ideas from papers to
integrate these two different models.
Geo-atom: A geo-atom is defined as an association between a point location in
space-time and a property. (Goodchild, 2007)
A geo-atom can be written as a tuple
<x, Z, z(x)>, where x defines a point in space or space-time (PointX in my ontology),
Z identifies a property (the PropertyZ in my ontology), and z(x) defines the particular
value of the property at that point (the ValueZ(x) in my ontology).
Figure 4 and 5
show the structure on ontology of Geo_atom.
For example, a geo-atom can describe the temperature of one point in Buffalo.
At this time, the tuple <x, Z, z(x)> of the geo-atom can indicate that at the point
42.9°N, 78.8°W (a two-dimentional definition of x), the temperature (the property
Z) is 41℉ (the value Z(x)).
Figure 4: ontology of Geo_atom
Figure 5: ontology graph of Geo_atom,
green lines: is_part_of property
yellow lines: aggregate property
purple line: sub-class property
Geo-field: A geo-field is a continuous data model to describe geographic entities.
It defines the variation of one or more properties over a domain of space D
(Goodchild, 2007).
Thus, in my ontology the geo-field is defined by the aggregation
of geo-atoms over space by property Z, irrespective of value.
Geo-fields can be used to conceptual many geographic factors, such as
temperature, precipitation, soil type, population density, etc. On the other hand, on a
finite geographic space D, there would be infinite geo-atoms aggregated to form a
geo-field.
So in reality we can only sample some points in the geographic space and
use mathematical simplified methods to approximate or calculate other points in the
space. This condition will bring several discretizing methods to represent geo-fields.
In current GIS practice, there appear to be six commonly used ways of representing
geo-fields (Goodchild 1993):
F1: piecewise constant, such that the variable is constant within each of a set of
non-overlapping, space-exhausting polygons (the choropleth map, and he area-class
map in the nominal case);
F2: piecewise linear with continuity of value over triangular elements (the
triangulated irregular network or TIN), and where there is continuity of value across
triangle edges;
F3: piecewise constant in a regular grid of cells (normally rectangular), a special
case of F1;
F4: sampled at a set of irregularly spaced points;
F5: sampled at a set of points in a regular array (normally rectangular), a special
case of F4;
F6: sampled along a set of isolines of the property Z.
Figure 6 shows the structure of Geo_field ontology.
Figure 6: ontology graph of Geo_field
Geo-object: a geo-object is a discrete model to describe geographic entities.
uses points, lines and areas to form different object models.
It
In this designed ontology,
a geo-object is defined as an aggregation of points in space-time whose geo-atoms meet
certain requirements, such as having specified values for certain properties (Goodchild,
2007). For example, to represent Ellicot Complex in 2-dimentional geographic space,
we can use a polygon area object to describe it.
This polygon area can be seen as geo-
atoms having the value “Ellicot Complex” for the property “Building Name”.
In my ontology, basic components, such as points, lines, areas and ST_Objects
(Spatiotemporal Objects) are given.
Moreover, when considering the geographic
object varying through time, some more categories of geo-objects models can be found.
According to the paper of Goodchild (2007), there are three conditions of common
characteristics of geo-objects through time, as shown in Figure X.
First, the geo-
object itself can be stationary or moving; Second, the boundary and shape of the geoobject can be changed or not, which is represented by Elastic and Rigid; Third, the
intern structure and value of a geo-object may be changed, especially in some fieldobjects, which is described by Uniform and Evolving.
Thus, using the three
conditions above, we can divide the geo-objects into the following eight categories
according to their behaviors with time (Goodchild, 2007):
Figure 7: 3 dimensions of dynamic objects (Goodchild, 2007)
Uniform Stationary Rigid: buildings and roads in a city.
Uniform Stationary Elastic: the seasonal expansion or contraction of a lake when
only the extent of the lake is considered.
Uniform Moving Rigid: moving vehicles and planes.
Uniform Moving Elastic: a spreading wildfire when only the burn scar is
considered.
Evolving Stationary Rigid: soils in a watershed and digital elevation models.
Evolving Stationary Elastic: heat-island effects in an urban area, and vegetation
cover during desertification.
Evolving Moving Rigid: changing landscapes on moving, rigid tectonic plates.
Evolving Moving Elastic: hurricanes and cloud clusters.
Continuous-field and discrete-object conceptualizations constitute the foundation
of geospatial data modeling at the conceptual level of abstraction (Liu, 2008).
They
reflect the two dual perspective of spatial cognition.
To describe the relationship between Geo_atom and Geo_field & Geo_object, two
new object properties are created. One is Aggregate property, indicating both field
models and object models are formed by aggregation of geo-atoms.
The other
property is Aggregate_by, which indicates the field models are aggregated by the
element PropertyZ in geo_atom, while the object models are aggregated by the element
ValueZ(x), according to their definitions.
Moreover, much research in recent years are focusing on their relationships,
specially how to combine and unify them.
The introduced concept, geo-atom, is one
method to unify the two distinguish categories of models.
In addition, several other
methods to link object and field are presented, such as field-object and object-field.
Object-fields: It is defined by Cova and Goodchild (2002). On the object field,
each point maps to a geo-object, not a property value. For instance, at each point on
the topographic surface, we can define the visible area, which we can see the surface of
geographic space from that point. There can be four categories of object-fields: point
object-field, corridor object-field, areal object-field and network object-field. Figure 8
shows the ontology of object-fields.
Figure 8: ontology graph of Object-field
Field-objects: It is defined by Yuan (1999).
heterogeneity conceptualized as a field.
It is a geo-object with internal
For example, to represent the precipitation
zones, although we can describe them as geo-objects based on the boundary of clouds,
other important information such as air pressure should be represented by fields.
So
using field-objects to represent precipitation zones is a better solution.
2.3 Building the relationship of geographic representation
This section will discuss how to show the relationship of geographic representation
between geographic entities and data models.
property, “is_represented_by”, is created.
To describe this relation, a new object
Using this object property, it is possible to
link different geographic entities to the corresponding spatial data models, describing
the usage of varied data models. Below is an example to use defined data models to
describe moving cars.
Figure 9: ontology description of using data models to represent moving vehicles
2.4 Geographic events
This chapter is an extension of geographic representation ontology to represent
more complex geographic phenomena – geographic events.
From the perspective of
ontology, geographic events should be under the category of occurrent entities.
A
geographic event, such as floods, hurricane and precipitation, consists some geographic
processes, and a geographic process also contains several geographic states, which
indicate the condition of related geographic phenomena. A geographic process may
be related to several different geographic events, and a state may also be related to
several geographic process.
associated processes.
Thus, a geographic event is an aggregation of its
The ontology graph below is an example of ontology
description on a geographic event – hurricane.
A geographic event of hurricane
contains several geographic process, such as its forming, enhancement, landing and
dying out.
Moreover, each process of a hurricane also consists of several geographic
state to represent the condition such as air pressure in each important time point.
Figure 10 shows the ontology of one hurricane event.
Figure 10: ontology of hurricane event
3 Summary and future work
This project gives a preliminary implement on how to use ontology to describe
spatial data models.
Firstly, the different essence of field and object models is
represented, and their relationship is defined with ontology.
In addition, some
research achievements on how to integrate fields and objects, such as object-field and
field-object, are also created in this ontology.
Moreover, in the second part, the
relationship between geographic entities and data models is built, using a new object
property is_represented_by.
Then in the third part, geographic events, as parts of
occurrent processes, are ontological defined.
A geographic event contains several
related geographic process, and a geographic process also aggregates several
geographic states. An application of describing a hurricane event is designed.
Future work will be focused on several aspects.
ontology should be added and improved.
First, some detailed of this
For instance, when describing the six
categories of field models, more methods to differentiate them should be presented,
which can represent their differences of inherent structure. Another example is about
describing geographic events.
My ontology is a little simple and it needs more details
to represent more complex geographic phenomena.
Second, an important
phenomenon, scale effect, can be one direction for my ontology.
For example, a city
can be described as a point a large scale, and it can also be represented as an area in a
small scale.
Furthermore, future ontology can also consider describing spatial
operation, which is also a kind of occurrent entities.
For instance, geographic
boundary analysis is a spatial operation, which can extract object models from a field
model. Thus, new ontology can be designed to describe this relationship.
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