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

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.

A Visual Analytics Framework for Big Spatiotemporal Data

2018 ◽  
Author(s):  
Shaohua Wang ◽  
Ershun Zhong ◽  
Wenwen Cai ◽  
Qiang Zhou ◽  
Hao Lu ◽  
...  
Keyword(s):  
Visual Analytics ◽  
Spatiotemporal Data

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

2017 ◽  
Author(s):  
Guizhen Wang ◽  
Abish Malik ◽  
Chittayong Surakitbanharn ◽  
Jose Florencio de Queiroz Neto ◽  
Shehzad Afzal ◽  
...  
Keyword(s):  
Visual Analytics ◽  
Spatiotemporal Data ◽  
Architectural Constraints

scholarly journals Visual Analytics for Characterizing Mobility Aspects of Urban Context

2021 ◽  
pp. 727-755
Author(s):  
Gennady Andrienko ◽  
Natalia Andrienko ◽  
Fabian Patterson ◽  
Siming Chen ◽  
Robert Weibel ◽  
...  
Keyword(s):  
Visual Analytics ◽  
Research Topic ◽  
Data Exploration ◽  
Spatiotemporal Data ◽  
Multiple Perspectives ◽  
Numerous Application ◽  
Urban Context ◽  
Spatial Objects ◽  
Movement Data ◽  
Spatial Locations

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.

scholarly journals The Spatiotemporal Analysis of Odorants at the Level of the Olfactory Receptor Sheet

1966 ◽  
Vol 50 (1) ◽  
pp. 25-41 ◽  
Author(s):  
Maxwell Mark Mozell
Keyword(s):  
Flow Direction ◽  
Spatiotemporal Analysis ◽  
Olfactory Nerve ◽  
Time Interval ◽  
Lateral Nerve ◽  
Gas Chromatograph ◽  
Nerve Branch ◽  
Relative Response ◽  
The Difference ◽  
Medial Nerve

Activity in two separate regions of the frog olfactory mucosa was sampled by simultaneously recording the summated neural discharges from the olfactory nerve branches originating from them. The difference in the activity from these two regions in response to a stimulus was measured by: (a) the ratio of the response amplitude recorded from the lateral nerve branch to that recorded from the medial nerve branch (LB/MB ratio), (b) the latency difference (or time interval) between these two responses. Equal concentrations of four different odorants were drawn into the nose by an artificially produced sniff of known dimensions. At each concentration in every animal the four chemicals were ranked in order of the magnitudes of their LB/MB ratios and again in order of their latency differences. Regardless of their concentration, the same chemicals fell into the same ranks in different animals. In addition, for each chemical the magnitudes of the ratios and latency differences showed only minimal changes with concentration. Thus, spatiotemporal patterns of relative response magnitudes and latency differences across the mucosa differentially represented the odorants. Such a spatiotemporal code, together with physicochemical considerations, suggested that the nose separates vapors in a manner similar to a gas chromatograph. This is further supported by the previously observed reversal of the ratio patterns with reversal of air flow direction through the olfactory sac.

scholarly journals Visualization of spatiotemporal data with different topology

2020 ◽  
Vol 2 ◽  
pp. 1-2
Author(s):  
Alena Vondráková ◽  
Vít Pászto
Keyword(s):  
Spatial Data ◽  
Spatiotemporal Analysis ◽  
Spatiotemporal Data ◽  
Identification Code ◽  
The Czech Republic ◽  
Advanced Technologies ◽  
Long Term Trends ◽  
Administrative Units

Abstract. The availability of advanced technologies, the time of information society, and also the development of geographic information systems, have brought a lot of spatial data in most disciplines, which are carefully stored in recent decades and allow us spatiotemporal analysis and visualization. However, for long-term analyses and the synthesis of analysed data, including cartographic synthesis processes, it is a big problem when spatiotemporal data have different topologies at different times. And this does not only mean the correction of borders in the sense of clarification or other distinction. The problem is when, for example, the small administrative units is subject to significant temporal changes. Municipalities are divided and merged, while data are always stored for the topology of a particular year or period.The contribution presents a way to solve such a situation on the example of the Czech Republic. Data from 25 years’ period are adjusted so that it is possible to calculate and visualize long-term trends and analyses. In the case of the case study, these are spatial data of more than 6,000 municipalities, with changes in more than 200 cases during the observed period. In addition to the spatial component, there were also changes in identifiers, which are a common means of joining data. For example, when the name of the municipality was changed and the newly "created" municipality also received new identification code. All these problems are solved by the so-called "super layer", which represents aggregation to the smallest possible extent so that the analyses performed are carried out on the most detailed possible scale without missing data in partial periods.The project goal is to explore various geodemographic processes at a very detailed level, specifically at NUTS (Nomenclature of Territorial Units for Statistics) local administrative units 2 (LAU2) commonly used in European Union for statistical purposes. Our presented dataset/approach unified municipal administrative units allowing analyses of data as they change over time. We used a principle of "common spatial denominator", i.e. we used data aggregation into larger units with stable boundary.

scholarly journals Fast Visualization Technique and Visual Analytics System for Real-time Analyzing Stream Data

2016 ◽  
Vol 22 (4) ◽  
pp. 21-30
Author(s):  
Seongmin Jeong ◽  
유상봉 ◽  
김석연 ◽  
장윤 ◽  
연한별 ◽  
...  
Keyword(s):  
Real Time ◽  
Visual Analytics ◽  
Stream Data ◽  
Visualization Technique

Disk Diagram: An Interactive Visualization Technique of Fuzzy Set Operations for the Analysis of Fuzzy Data

2010 ◽  
Vol 9 (3) ◽  
pp. 220-232 ◽  
Author(s):  
Yeseul Park ◽  
Jinah Park
Keyword(s):  
Fuzzy Sets ◽  
Fuzzy Set ◽  
Visual Analytics ◽  
Interactive Visualization ◽  
Data Sets ◽  
Fuzzy Data ◽  
Visualization Technique ◽  
Data Set ◽  
Set Operations ◽  
Data Elements

Fuzzy set refers to the data set which does not have separate, distinct clusters, and they contain data elements whose membership degrees are between 0.0 and 1.0. Many fuzzy sets exist in the real world, and one of the important issues is to make a decision from the fuzzy sets using visual analytics tools by extracting information in the data set intuitively. To analyze the element data in fuzzy sets, the visualization of fuzzy sets needs to show an overview of the data with membership degree and the relationship among the sets. In this article, we suggest an interactive visualization technique of fuzzy set operations, called Disk Diagram, which offers distribution of fuzzy data and two scenarios to allow users to interpret inter-dependency among fuzzy sets. A Disk Diagram enables to depict complexity of fuzzy sets by showing the degree of resemblance between the sets with the layout of star coordinates. This article describes the use of a Disk Diagram with two different data sets such as fuzzy disease set and terror related words set. Lastly, we report the results of heuristic evaluation to show that our technique supports visual perception, usability, and knowledge discovery process in the areas of visual representation and interaction.

scholarly journals AN INTERACTIVE PLATFORM FOR ENVIRONMENTAL SENSORS DATA ANALYSES

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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