Qualitative representation of change

Hornsby, Kathleen; Egenhofer, Max J.

doi:10.1007/3-540-63623-4_40

Cited by 44 publications

(29 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The study of this paper is on the perspective of continuants that exist at a given time at a given level of granularity and undergo different types of changes over time. Hornsby and Egenhofer [5] suggest a qualitative representation of change. Their notion of change is based on object identity, and a set of operations that either preserve or change identity.…”

Section: Related Workmentioning

confidence: 99%

“…If the message contains only one segment, i.e., msg(v).p 1 = ∅ and msg(v).p 2 = ∅, then node v detects an appearance or a disappearance (lines [3][4][5]. If the segment is on inserted edges, i.e., msg(v).p 1 = I, then node v detects an appearance (line 4).…”

Section: Detecting Changementioning

confidence: 99%

“…Many spatial phenomena are continuously changing in time and space [5]. Environmental monitoring, for example, focuses especially on areas of environmental disturbance, such as grazing, fertilizing, pollution, and logging.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Detecting Change in Snapshot Sequences

Shi

Winter

2010

Geographic Information Science

View full text Add to dashboard Cite

Abstract. Wireless sensor networks are deployed to monitor dynamic geographic phenomena, or objects, over space and time. This paper presents a new spatiotemporal data model for dynamic areal objects in sensor networks. Our model supports for the first time the analysis of change in sequences of snapshots that are captured by different granularity of observations, and our model allows both incremental and nonincremental changes. This paper focuses on detecting qualitative spatial changes, such as merge and split of areal objects. A decentralized algorithm is developed, such that spatial changes can be efficiently detected by in-network aggregation of decentralized datasets.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Detecting Changementioning

confidence: 99%

See 1 more Smart Citation

Detecting Change in Snapshot Sequences

Shi

Winter

2010

Geographic Information Science

View full text Add to dashboard Cite

show abstract

“…Examples focusing on (cognitive) spatial questions include: linguists who employ the '(spatial) language as window to (spatial) cognition' metaphor to learn about the conceptualization of spatial information through the analysis of linguistic expressions and language use (Lakoff, 1987;Talmy, 1983); psychologists who designed controlled experiments to shed light on questions such as the validity of qualitative spatial calculi (Knauff et al, 1997). The spatial sciences, including geographical information science, address the human understanding of conceptualizations of spatiotemporal information through the development of formal models inspired by cognitive models (Galton, 2000;Hornsby & Egenhofer, 1997;Peuquet, 2001;Worboys, 2005).…”

Section: Introductionmentioning

confidence: 99%

Analyzing Cognitive Conceptualizations Using Interactive Visual Environments

Klippel

Weaver²,

Robinson

2011

Cartography and Geographic Information Science

View full text Add to dashboard Cite

ABSTRACT. The conceptualization of spatio-temporal information is an interdisciplinary research area. The focus of this article is on human conceptualizations of spatio-temporal geographic phenomena (also referred to as events). Identifying and understanding human conceptualizations is a crucial component in defining the semantics of spatiotemporal information. However, most research focuses primarily on how humans imbue dynamic phenomena with meaning on a general level. In contrast, this article is concerned with contextual factors (specifically: individual differences) that are too often neglected in general theories and in the analysis of behavioral data. In other words, we are interested in individual or group strategies of participants that are not detected by classical analysis methods. Research on individual difference is gaining widespread attention in cognitive and spatial sciences and it is time to consider individual differences in the area of conceptualizing spatio-temporal information. To be able to shed light on individual differences in behavioral data on how people conceptualize events, we have developed software solutions and combined them with established similarity measures. These software solutions allow analysts to effectively explore individual differences. We demonstrate the feasibility of our approach, its usefulness in analyzing behavioral data, and results that can be obtained through this individualized analysis by reanalyzing four sets of experimental data we previously collected.

show abstract

“…In order to integrate phenomena that change over space and time in real-world phenomenon, a better understanding of the underlying components of change and how people reason about change are needed. In paper [12], the authors proposed a qualitative representation of changes. It offers a classification of changes based on object identity and the set of operations that either preserve or change identity.…”

Section: Introductionmentioning

confidence: 99%

Modeling Herds and Their Evolvements from Trajectory Data

Huang

Chen

Dong

2008

Geographic Information Science

View full text Add to dashboard Cite

Abstract. A trajectory is the time-stamped path of a moving entity through space. Given a set of trajectories, this paper proposes new conceptual definitions for a spatio-temporal pattern named Herd and four types of herd evolvements: expand, join, shrink, and leave based on the definition of a related term flock. Herd evolvements are identified through measurements of Precision, Recall, and F-score. A graph-based representation, Herd Interaction Graph, or Herding, for herd evolvements is described and an algorithm to generate the graph is proposed and implemented in a Geographic Information System (GIS) environment. A data generator to simulate herd movements and their interactions is proposed and implemented as well. The results suggest that herds and their interactions can be effectively modeled through the proposed measurements and the herd interaction graph from trajectory data.

show abstract

Qualitative representation of change

Cited by 44 publications

References 19 publications

Detecting Change in Snapshot Sequences

Detecting Change in Snapshot Sequences

Analyzing Cognitive Conceptualizations Using Interactive Visual Environments

Modeling Herds and Their Evolvements from Trajectory Data

Contact Info

Product

Resources

About