User requirements for geo-collaborative work with spatio-temporal data in a web-based virtual globe environment

2013 ◽  
Vol 44 (6) ◽  
pp. 929-939 ◽  
Author(s):  
Zornitza Yovcheva ◽  
Corné P.J.M. van Elzakker ◽  
Barend Köbben
Keyword(s):  
Collaborative Work ◽  
User Requirements ◽  
Temporal Data ◽  
Web Based ◽  
Virtual Globe ◽  
Spatio Temporal

scholarly journals Creating and evaluating web-based visualizations of multi-dimensional spatiotemporal data

2019 ◽  
Vol 2 ◽  
pp. 1-8
Author(s):  
Jan Wilkening ◽  
Keni Han ◽  
Mathias Jahnke
Keyword(s):  
Spatiotemporal Data ◽  
Weather Data ◽  
Temporal Data ◽  
Web Based ◽  
Animated Maps ◽  
Different Types ◽  
Weather Parameters ◽  
Spatio Temporal ◽  
The Web

<p><strong>Abstract.</strong> In this article, we present a method for visualizing multi-dimensional spatio-temporal data in an interactive web-based geovisualization. Our case study focuses on publicly available weather data in Germany. After processing the data with Python and desktop GIS, we integrated the data as web services in a browser-based application. This application displays several weather parameters with different types of visualisations, such as static maps, animated maps and charts. The usability of the web-based geovisualization was evaluated with a free-examination and a goal-directed task, using eye-tracking analysis. The evaluation focused on the question how people use static maps, animated maps and charts, dependent on different tasks. The results suggest that visualization elements such as animated maps, static maps and charts are particularly useful for certain types of tasks, and that more answering time correlates with less accurate answers.</p>

scholarly journals AtmoVis: Visualization of Air Quality Data

2021 ◽  
Author(s):  
◽  
Benjamin Powley
Keyword(s):  
Air Quality ◽  
User Study ◽  
Poor Quality ◽  
Quality Data ◽  
Temporal Data ◽  
Multiple Sources ◽  
Web Based ◽  
Visualization System ◽  
Spatio Temporal ◽  
Temporal Data Analysis

<p>Air quality has an adverse impact on the health of people living in areas with poor quality air. Monitoring is needed to understand the effects of poor air quality. It is difficult to compare measurements to find trends and patterns between different monitoring sites when data is contained in separate data stores. Data visualization can make analyzing air quality more effective by making the data more understandable. The purpose of this research is to design and build a prototype for visualizing spatio-temporal data from multiple sources related to air quality and to evaluate the effectiveness of the prototype against criteria by conducting a user study. The prototype web based visualization system, AtmoVis, has a windowed layout with 6 different visualizations: Heat calendar, line plot, monthly rose, site view, monthly averages and data comparison. A pilot study was performed with 11 participants and used to inform the study protocol before the main user study was performed on 20 participants who were air quality experts or experienced with Geographic Information Systems (GIS). The results of the study demonstrated that the heat calendar, line plot, site view, monthly averages and monthly rose visualizations were effective for analyzing the air quality through AtmoVis. The line plot and the heat calendar were the most effective for temporal data analysis. The interactive web based interface for data exploration with a window layout, provided by AtmoVis, was an effective method for accessing air quality visualizations and inferring relationships among air quality variables at different monitoring sites. AtmoVis could potentially be extended to include other datasets in the future.</p>

scholarly journals A WEB-BASED INTERACTIVE PLATFORM FOR CO-CLUSTERING SPATIO-TEMPORAL DATA

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 175-179
Author(s):  
X. Wu ◽  
A. Poorthuis ◽  
R. Zurita-Milla ◽  
M.-J. Kraak
Keyword(s):  
Clustering Algorithm ◽  
Temporal Patterns ◽  
Temporal Data ◽  
Web Based ◽  
Server Side ◽  
Exploratory Data ◽  
Spatio Temporal ◽  
Client Side ◽  
Concurrent Analysis ◽  
Selection Of

Since current studies on clustering analysis mainly focus on exploring spatial or temporal patterns separately, a co-clustering algorithm is utilized in this study to enable the concurrent analysis of spatio-temporal patterns. To allow users to adopt and adapt the algorithm for their own analysis, it is integrated within the server side of an interactive web-based platform. The client side of the platform, running within any modern browser, is a graphical user interface (GUI) with multiple linked visualizations that facilitates the understanding, exploration and interpretation of the raw dataset and co-clustering results. Users can also upload their own datasets and adjust clustering parameters within the platform. To illustrate the use of this platform, an annual temperature dataset from 28 weather stations over 20 years in the Netherlands is used. After the dataset is loaded, it is visualized in a set of linked visualizations: a geographical map, a timeline and a heatmap. This aids the user in understanding the nature of their dataset and the appropriate selection of co-clustering parameters. Once the dataset is processed by the co-clustering algorithm, the results are visualized in the small multiples, a heatmap and a timeline to provide various views for better understanding and also further interpretation. Since the visualization and analysis are integrated in a seamless platform, the user can explore different sets of co-clustering parameters and instantly view the results in order to do iterative, exploratory data analysis. As such, this interactive web-based platform allows users to analyze spatio-temporal data using the co-clustering method and also helps the understanding of the results using multiple linked visualizations.

scholarly journals AtmoVis: Visualization of Air Quality Data

2021 ◽  
Author(s):  
◽  
Benjamin Powley
Keyword(s):  
Air Quality ◽  
User Study ◽  
Poor Quality ◽  
Quality Data ◽  
Temporal Data ◽  
Multiple Sources ◽  
Web Based ◽  
Visualization System ◽  
Spatio Temporal ◽  
Temporal Data Analysis

<p>Air quality has an adverse impact on the health of people living in areas with poor quality air. Monitoring is needed to understand the effects of poor air quality. It is difficult to compare measurements to find trends and patterns between different monitoring sites when data is contained in separate data stores. Data visualization can make analyzing air quality more effective by making the data more understandable. The purpose of this research is to design and build a prototype for visualizing spatio-temporal data from multiple sources related to air quality and to evaluate the effectiveness of the prototype against criteria by conducting a user study. The prototype web based visualization system, AtmoVis, has a windowed layout with 6 different visualizations: Heat calendar, line plot, monthly rose, site view, monthly averages and data comparison. A pilot study was performed with 11 participants and used to inform the study protocol before the main user study was performed on 20 participants who were air quality experts or experienced with Geographic Information Systems (GIS). The results of the study demonstrated that the heat calendar, line plot, site view, monthly averages and monthly rose visualizations were effective for analyzing the air quality through AtmoVis. The line plot and the heat calendar were the most effective for temporal data analysis. The interactive web based interface for data exploration with a window layout, provided by AtmoVis, was an effective method for accessing air quality visualizations and inferring relationships among air quality variables at different monitoring sites. AtmoVis could potentially be extended to include other datasets in the future.</p>

scholarly journals An interactive web-based geovisual analytics platform for co-clustering spatio-temporal data

2020 ◽  
Vol 137 ◽  
pp. 104420
Author(s):  
Xiaojing Wu ◽  
Ate Poorthuis ◽  
Raul Zurita-Milla ◽  
Menno-Jan Kraak
Keyword(s):  
Temporal Data ◽  
Web Based ◽  
Geovisual Analytics ◽  
Spatio Temporal

Web-based GIS technologies for monitoring and analysis of spatio-temporal processes

2016 ◽  
Vol 12 (1) ◽  
pp. 102-124 ◽  
Author(s):  
Valery Gitis ◽  
Alexander Derendyaev ◽  
Arkady Weinstock
Keyword(s):  
Critical Incidents ◽  
Temporal Data ◽  
Web Based ◽  
Content Type ◽  
Spatial Properties ◽  
Environmental Processes ◽  
Gis Technologies ◽  
Spatial Aspect ◽  
Temporal Aspect ◽  
Spatio Temporal

Purpose This paper aims to describe two Web-based technologies of geographic information systems (GIS) to be used in monitoring and analysis of environmental processes, proposed by the authors. The technologies analyze the temporal aspect of the process together with the spatial aspect, which defers them from most other works on environmental processes, as these are usually limited either to spatial statistics or to temporal statistics. The approach is instrumental in dynamically finding the relationships between the processes and predicting critical incidents. Design/methodology/approach Often, the study of natural processes is limited to the analysis of their spatial properties presented by individual time series. The principal idea of this approach consists in supplementing this traditional analysis with the analysis of time fields. In this way, the authors are able to analyze temporal and spatial properties of environmental processes together. Findings The paper presents two technologies which provide the analysis of spatial and temporal data obtained in natural environment monitoring. The discussed spatio-temporal data mining methods are shown to enable the research into environmental processes, and the solution of practical issues of critical situation forecasts. Originality/value The paper discussed Web-based GIS technologies for the analysis of the temporal aspect of the environmental process together with the spatial aspect. Application examples demonstrate the ability of this approach to find the relationships in dynamics of the processes and to predict critical incidents.

Developing Food Sensory Analysis Information System using Waterfall Software Engineering Model

2017 ◽  
Vol 4 (2) ◽  
pp. 63-81
Author(s):  
Stefanus Oliver ◽  
Abdullah Muzi Marpaung ◽  
Maulahikmah Galinium
Keyword(s):  
Software Engineering ◽  
Sensory Analysis ◽  
Acceptance Testing ◽  
Design Activity ◽  
User Requirements ◽  
Human Errors ◽  
Web Based ◽  
Entity Relationship ◽  
Entity Relationship Diagram ◽  
Analysis System

Food sensory analysis is the terms from the field of Food Technology that has a meaning which means sensory evaluation of food that is conducted by the food sensory evaluators. Currently, food sensory analysis is conductedmanually. It can caus e human errors and consume much ti me. The objective of this research is to build a web based application that is specific for food sensory analysis using PHP programming language. This research followsfour first steps of waterfall software engineering mod el which are user requirements ana lysis (user software and requirements analysis), system design (activity, use cases, architecture, and entity relationship diagram),implementation (software development), and testing (software unit, functionality, validit y, and user acceptance testing). T he software result is well built. It is also acceptable for users and all functionality features can run well after going through those four software testing. The existence of the software brings easiness to deal with the manual food sensory analysis exper iment. It is considered also for the future it has business value by having open source and premium features.

Sign in / Sign up

Export Citation Format

Share Document