scholarly journals Multi-Purpose Ontology-Based Visualization of Spatio-Temporal Data: A Case Study on Silk Heritage

2021 ◽  
Vol 11 (4) ◽  
pp. 1636
Author(s):  
Javier Sevilla ◽  
Pablo Casanova-Salas ◽  
Sergio Casas-Yrurzum ◽  
Cristina Portalés
Keyword(s):  
Ad Hoc ◽  
Computer Architectures ◽  
Visualization Tool ◽  
Temporal Data ◽  
Use Of Data ◽  
Source Data ◽  
Visualization Tools ◽  
Spatio Temporal ◽  
Interactive Map

Due to the increasing use of data analytics, information visualization is getting more and more important. However, as data get more complex, so does visualization, often leading to ad hoc and cumbersome solutions. A recent alternative is the use of the so-called knowledge-assisted visualization tools. In this paper, we present STMaps (Spatio-Temporal Maps), a multipurpose knowledge-assisted ontology-based visualization tool of spatio-temporal data. STMaps has been (originally) designed to show, by means of an interactive map, the content of the SILKNOW project, a European research project on silk heritage. It is entirely based on ontology support, as it gets the source data from an ontology and uses also another ontology to define how data should be visualized. STMaps provides some unique features. First, it is a multi-platform application. It can work embedded in an HTML page and can also work as a standalone application over several computer architectures. Second, it can be used for multiple purposes by just changing its configuration files and/or the ontologies on which it works. As STMaps relies on visualizing spatio-temporal data provided by an ontology, the tool could be used to visualize the results of any domain (in other cultural and non-cultural contexts), provided that its datasets contain spatio-temporal information. The visualization mechanisms can also be changed by changing the visualization ontology. Third, it provides different solutions to show spatio-temporal data, and also deals with uncertain and missing information. STMaps has been tested to browse silk-related objects, discovering some interesting relationships between different objects, showing the versatility and power of the different visualization tools proposed in this paper. To the best of our knowledge, this is also the first ontology-based visualization tool applied to silk-related heritage.

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>

A spatio-temporal data model for administrative division place names: a case study of Xiamen

2009 ◽  
Author(s):  
Liping Yang ◽  
Guangfa Lin ◽  
Ailing Chen ◽  
Youfei Chen ◽  
Xiaohuan Wen
Keyword(s):  
Data Model ◽  
Temporal Data ◽  
Place Names ◽  
Administrative Division ◽  
Spatio Temporal

Design of Archival Traffic Databases: Quantitative Investigation into Application of Advanced Data Modeling Concepts

2003 ◽  
Vol 1836 (1) ◽  
pp. 126-131 ◽  
Author(s):  
Brian L. Smith ◽  
David C. Lewis ◽  
Ryan Hammond
Keyword(s):  
Intelligent Transportation Systems ◽  
Ad Hoc ◽  
Data Warehousing ◽  
Transportation Systems ◽  
Hampton Roads ◽  
Use Of Data ◽  
Data User ◽  
Freeway Management ◽  
Smart Traffic

Given the enormous quantities of data collected by intelligent transportation systems (ITS), transportation professionals recently have focused on developing archived data user services (ADUS) to facilitate efficient use of these data in myriad transportation analyses. Most ITS systems were designed by using a transactional database design, which is not well suited to support the ad hoc queries required by ADUS. Research investigated the application of data-warehousing concepts to better support the requirements of ADUS. A case study is presented in which an ADUS for the Hampton Roads Smart Traffic Center, the regional freeway management system, was redesigned from a transactional approach to one based on data warehousing. Test queries run by using both approaches demonstrated that dramatic increases in efficiency are achievable through the use of data-warehousing concepts in ADUS.

scholarly journals Characterizing an epidemiological geography of the United States: influenza as a case study

2021 ◽  
Author(s):  
Grant E. Rosensteel ◽  
Elizabeth C. Lee ◽  
Vittoria Colizza ◽  
Shweta Bansal
Keyword(s):  
United States ◽  
Ad Hoc ◽  
The United States ◽  
Public Health Approach ◽  
Significant Heterogeneity ◽  
Monitoring And Control ◽  
Epidemiological Risk ◽  
Insurance Claims Data ◽  
Spatio Temporal

AbstractThe prediction, prevention, and management of infectious diseases in the United States is either geographically homogeneous or is coordinated through ad-hoc administrative regions, ignoring the intense spatio-temporal heterogeneity displayed by most outbreaks. Using influenza as a case study, we characterize a regionalization of the United States. Based on influenza time series constructed from fine-scale insurance claims data from 2002-2009, we apply a complex network approach to characterize regions of the U.S. which experience comparable influenza dynamics. Our results identify three to five epidemiologically distinct regions for each flu season, with all locations within each region experiencing synchronous epidemics, and with an average of a two week delay in peak timing between regions. We find that there is significant heterogeneity across seasons in the identity of the regions and the relative timing across regions, making predictability from one season to the next challenging. Within a given season, however, our approach shows the potential to inform on the shaping of regions over time, to improve resources mobilization and targeted communication. Our epidemiologically-driven regionalization approach could allow for disease monitoring and control based on epidemiological risk rather than geopolitical boundaries, and provides a tractable public health approach to account for vast heterogeneity that exists in respiratory disease dynamics.

scholarly journals Case Study: Spark GPU-Enabled Framework to Control COVID-19 Spread Using Cell-Phone Spatio-Temporal Data

2020 ◽  
Vol 65 (2) ◽  
pp. 1303-1320
Author(s):  
Hussien SH. Abdallah ◽  
Mohamed H. Khafagy ◽  
Fatma A. Omara
Keyword(s):  
Cell Phone ◽  
Temporal Data ◽  
Spatio Temporal
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