ISPRS, Vol.34, Part 2W2, “Dynamic and Multi-Dimensional GIS”, Bangkok, May 23-25, 2001
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PRIMARY SPATIAL CHANGES
Hong SHU 1 , Christopher GOLD 2 and Jun CHEN 3
1 National Lab for Information Engineering in Surveying, Mapping & Remote Sensing
Wuhan Technical University of Surveying and Mapping
129 Luoyu Road, Wuhan 430079
China
Tel: +86-27-87881292
Fax: +86-27-87863229
Shu_Hong@hotmail.com
2 Department of Land Surveying and Geo-Informatics
Hong Kong Polytechnic University
Hung Horn, Kowloon
Hong Kong
Tel: + 852 - 27665975
Fax: + 852 - 23302994
E-mail: LsCMGold®polyu.edu.hk
3 National Geomatics Center of China
No. 1, Bei Shengcun, Zi Zhuyuan, Beijing 100044
China
Jchen@nsdi.gov.cn
KEY WORDS: Spatio-temporal Database, Spatio-temporal Semantics, Spatial Changes.
ABSTRACT: Spatio-temporal semantic modeling and spatio-temporal data storage/access are two fundamental aspects of spatio-
temporal database management systems. In this paper, spatio-temporal semantic modeling is emphasized. Essentially, spatio-temporal
semantics refer to two perspectives of spatio-temporal scale and spatial change over time, or say, absolute space-time and relative
space-time perspectives. In the perspective of spatio-temporal scale, geographic objects are positioned with time and spatial
coordinates, and some measurement functions are accompanied. In the perspective of spatial change, a sequence of changes present
human an image schema of time. In human cognition, relative knowledge is first apprehended, then absolute knowledge is to refine
relative knowledge. Likewise, human beings first perceive changes or events, then estimate absolute time, i.e., assign time or date
numbers to a sequence of events. In spatio-temporal databases, spatio-temporal scale and spatial change are viewed as two kinds of
equally important semantics. Spatio-temporal coordinates are easy for computer to calculate, but spatio-temporal changes are suited for
human beings to develop spatio-temporal reasoning. To enrich spatial change semantics, we have completed three parts of work here.
First, the existing taxonomies of spatial changes are reviewed. Second, three levels of spatial change, spatial property, individual and
scene changes, are proposed. Primary changes of spatial property include location change, distance change, direction change, size
change and shape change. Six kinds of primary individual change are functionally given in terms of mapping relationships between
source objects and result objects. The primary scene change is spatial distribution type change, which can partly be described with
different statistic quantities. Finally, we attempt to cognitively understand our spatial change categorization. At the same time, it is
believed that complex changes are composed of primary changes.
1 MOTIVATION
In the early 1990s, Gail Langran, in her recognized book 'Time
in Geographical Information Systems”, began initial studies of
temporal GIS (TGIS) from concepts to implementations. In 1996,
CHOROCHRONOS project started up the studies of spatio-
temporal databases (STDB) in aspects of space-time concepts,
data models, query languages, physical data structures,
indexing methods and DBMS frameworks. In the fields of
database, artificial intelligence and GIS, an increasing number of
papers are devoted to TGIS or STDB. However, so far few
results have truly been brought into practice (Timos Sellis,
1999). This somewhat indicates that a gap exists between our
wide theories and a large number of spatio-temporal phenomena
modeling requirements. In our opinion, one efficient way to
bridge this gap is to further research of spatio-temporal semantic
modeling, because semantics root in our understanding of
practical problems. Furthermore, only from the viewpoint of
geometry, time has no difference with space, and it can be
regarded as another dimension. A lot of past spatial or
geometrical database techniques can be easily extended to
spatio-temporal databases in the form. One of our research
motivations is to shorten distance between our theoretical
studies and practical requirements.
On the other hand, among ongoing research, two topics are
receiving more attentions recently. They are moving object data
types (Sistla, A.P., O. Wolfson, et al., 1997; Erwig, M., R.H.
Gueting, et al., 1997) and constraint-based spatio-temporal data
models (Chomicki, J., P.Z. Revesz, 1997; Grumbach, S., P.
Rigaus, L. Segoufin, 1998). Moving object types consist of
moving points and moving regions, which have been embedded
in Gueting’s geo-relational algebra. Mathematically, moving
object types are defined as mappings of time to spatial extent. It
is easy to see that moving object types directly aim at location
change modeling. A typical example is moving vehicles, which
change their locations over time continuously. In constraint-
based spatio-temporal data models, space and time are
modeled as two variables in a linear constraint equality or
inequality, denoted by f(x,y,t)>=0. f(x,y,t)>=0 represents an
object (identifier and thematic values) with space-time
constraints. Alternatively, f(x,y,t)>=0 is transformed into h(x,
y)>=g(t), where h(x,y) represents the spatial extent of an object
and g(t) is a function of time. Usually, h(x,y) itself is treated as a
constraint of coordinate variables x and y. h(x, y)>=g(t) means
spatial extent change over time as well as thematic value
change. Likewise, constraint-based spatio-temporal data models
basically describe the location change of an object. The
difference between moving object types and constraint-based
spatio-temporal data models is that the former is at the high level
