A client-based visual analytics framework for large spatiotemporal data under architectural constraints

AbstractVisual analytics science develops principles and methods for efficient human–computer collaboration in solving complex problems. Visual and interactive techniques are used to create conditions in which human analysts can effectively utilize their unique capabilities: the power of seeing, interpreting, linking, and reasoning. Visual analytics research deals with various types of data and analysis tasks from numerous application domains. A prominent research topic is analysis of spatiotemporal data, which may describe events occurring at different spatial locations, changes of attribute values associated with places or spatial objects, or movements of people, vehicles, or other objects. Such kinds of data are abundant in urban applications. Movement data are a quintessential type of spatiotemporal data because they can be considered from multiple perspectives as trajectories, as spatial events, and as changes of space-related attribute values. By example of movement data, we demonstrate the utilization of visual analytics techniques and approaches in data exploration and analysis.

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Spatiotemporal Analysis of Circulation Behaviors Using Path And Residing Time displaY (PARTY)

International Journal of Virtual Reality ◽

10.20870/ijvr.2013.12.1.2857 ◽

2013 ◽

Vol 12 (1) ◽

pp. 44-56

Author(s):

Kingkarn Sookhanaphibarn ◽

Ruck Thawonmas ◽

Frank Rinaldo ◽

Kuan-Ta Chen

Keyword(s):

Visual Analytics ◽

Spatiotemporal Analysis ◽

Spatiotemporal Data ◽

World's Fair ◽

Time Interval ◽

Visualization Technique ◽

Art Galleries ◽

Time Segment ◽

Virtual Spaces ◽

Circulation Patterns

Spatiotemporal data displayed in a spatial layout are not the best visualization for finding similarities of visitor paths and extracting patterns of visitor interest to placed items. A challenging problem is the visual analytics of circulation patterns in varying layouts commonly found in a museum with many exhibition rooms. This paper proposes a layout-independent visualization approach to represent a visitor path and his/her time spent residing near the closest item. In this approach, we encode a time interval residing in an item boundary into a color-shaded line segment. Color shade is used as an indicator to the proximity distance to the nearest item. The length of a segment is in proportion to the total time spent in the layout. The time segment is placed in the row corresponding to its item boundary. A path of visited items is illustrated by connecting the time segments with vertical lines. The resulting visualization technique, called Path And Residing Time displaY (PARTY), enables users to find trends of circulation behaviors in a consistent fashion regardless of the targeted layout. We demonstrate the effectiveness of PARTY on two datasets: one showing circulation behaviors of visiting styles in a 3D virtual museum and the other showing a flow of people escaped from an explosion in a building. PARTY is applicable for analyzing data in real and virtual spaces. While the focus of this paper is to apply PARTY to discovering circulation patterns in museums or art galleries, the utilization of this approach covers also visual analytics of customer circulation in a number of environments (e.g. convenient store, department store, World's Fair, etc.). PARTY provides useful information about the number of visitors to items, flow patterns, crowded areas, items not visited, and other aspects of visitor behaviors.

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AN INTERACTIVE PLATFORM FOR ENVIRONMENTAL SENSORS DATA ANALYSES

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-viii-4-w1-2021-57-2021 ◽

2021 ◽

Vol VIII-4/W1-2021 ◽

pp. 57-64

Author(s):

S. Harbola ◽

V. Coors

Keyword(s):

Visual Analytics ◽

Urban System ◽

Environmental Data ◽

Human Interaction ◽

Spatiotemporal Data ◽

Improve Energy Efficiency ◽

Depth Sensors ◽

Environmental Monitoring System ◽

The Future ◽

Pm2.5 And Pm10

Abstract. The increased usage of the environmental monitoring system and sensors, installed on a day-to-day basis to explore information and monitor the cities’ environment and pollution conditions, are in demand. Sensor networking advancement with quality and quantity of environmental data has given rise to increasing techniques and methodologies supporting spatiotemporal data interactive visualisation analyses. Moreover, Visualisation (Vis) and Visual Analytics (VA) of spatiotemporal data have become essential for research, policymakers, and industries to improve energy efficiency, environmental management, and cities’ air pollution planning. A platform covering Vis and VA of spatiotemporal data collected from a city helps to portray such techniques’ potential in exploring crucial environmental inside, which is still required. Therefore, this work presents Vis and VA interface for the spatiotemporal data represented in terms of location, including time, and several measured attributes like Particular Matter (PM) PM2.5 and PM10, along with humidity, and wind (speed and direction) to assess the detailed temporal patterns of these parameters in Stuttgart, Germany. The time series are analysed using the unsupervised HDBSCAN clustering on a series of (above mentioned) parameters. Furthermore, with the in-depth sensors nature understanding and trends, Machine Learning (ML) approach called Transformers Network predictor model is integrated, that takes successive time values of parameters as input with sensors’ locations and predict the future dominant (highly measured) values with location in time as the output. The selected parameters variations are compared and analysed in the spatiotemporal frame to provide detailed estimations on how average conditions would change in a region over the time. This work would help to get a better insight into the urban system and enable the sustainable development of cities by improving human interaction with the spatiotemporal data. Hence, the increasing environmental problems for big industrial cities could be alarmed and reduced for the future with proposed work.

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Functional significance of preserved color patterns of mollusks from the Gosport Sand (Eocene) of Alabama

Journal of Paleontology ◽

10.1017/s0022336000058911 ◽

1988 ◽

Vol 62 (01) ◽

pp. 83-87 ◽

Cited By ~ 4

Author(s):

Patricia H. Kelley ◽

Charles T. Swann

Keyword(s):

Functional Morphology ◽

Functional Significance ◽

Intraspecific Variability ◽

Color Pattern ◽

Color Patterns ◽

Historical Factors ◽

Life Mode ◽

Molluscan Fauna ◽

Excellent Preservation ◽

Architectural Constraints

The excellent preservation of the molluscan fauna from the Gosport Sand (Eocene) at Little Stave Creek, Alabama, has made it possible to describe the preserved color patterns of 15 species. In this study the functional significance of these color patterns is tested in the context of the current adaptationist controversy. The pigment of the color pattern is thought to be a result of metabolic waste disposal. Therefore, the presence of the pigment is functional, although the patterns formed by the pigment may or may not have been adaptive. In this investigation the criteria proposed by Seilacher (1972) for testing the functionality of color patterns were applied to the Gosport fauna and the results compared with life mode as interpreted from knowledge of extant relatives and functional morphology. Using Seilacher's criteria of little ontogenetic and intraspecific variability, the color patterns appear to have been functional. However, the functional morphology studies indicate an infaunal life mode which would preclude functional color patterns. Particular color patterns are instead interpreted to be the result of historical factors, such as multiple adaptive peaks or random fixation of alleles, or of architectural constraints including possibly pleiotropy or allometry. The low variability of color patterns, which was noted within species and genera, suggests that color patterns may also serve a useful taxonomic purpose.

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