Map or Gantt? Which Diagram Helps Viewers Best in Spatio-Temporal Data Exploration Tasks?

In this paper we investigate the effectiveness and efficiency of two two-dimensional static visual representations of spatio-temporal data, a map-based and a Gantt-based diagram, in their support of various information retrieval tasks. The map-based diagram is characterized by a natural spatial arrangement of locations on a schematic map. The Gantt-based one represents time naturally as a linearly ordered set of time intervals from left to right. A within-subject empirical experiment has been conducted, in which participants were asked to verify queries about persons, locations, and time intervals. The formulation of the queries was based on (i) Bertin’s three reading levels, (ii) certain cognitive operations, and (iii) different syntactic orders of expressions denoting persons, locations and times. Response correctness and response time were recorded. With respect to response accuracy, both diagrams support viewers well in nearly all information retrieval tasks. Regarding efficiency, the map-based diagram elicited significantly faster response times than the Gantt-based one, except for queries with time in focus. The results suggest that map-based diagrams require less search and reasoning effort of viewers to retrieve the information asked for in the task types used in this study.</p

Visualized exploratory spatiotemporal analysis of hand-foot-mouth disease in southern China

Objectives: In epidemiological research, major studies have focused on theoretical models; however, few methods of visual analysis have been used to display the patterns of disease distribution.Design: For this study, a method combining the space-time cube (STC) with space-time scan statistics (STSS) was used to analyze the pattern of incidence of hand-foot-mouth disease (HFMD) in Guangdong Province from May 2008 to March 2009. In this research, STC was used to display the spatiotemporal pattern of incidence of HFMD, and STSS were used to detect the local aggregations of the disease.Setting: The hand-foot-mouth disease data were obtained from Guangdong Province from May 2008 to March 2009, with a total of 68,130 cases.Results: The STC analysis revealed a differential pattern of HFMD incidence among different months and cities and also showed that the population density and average precipitation are correlated with the incidence of HFMD. The STSS analysis revealed that the most likely aggregation includes the Shenzhen, Foshan and Dongguan populations, which are the most developed regions in Guangdong Province.Conclusion: Both STC and STSS are efficient tools for the exploratory data analysis of disease transmission. STC clearly displays the spatiotemporal patterns of disease. Using the maximum likelihood ratio, the STSS model precisely locates the most likely aggregation

SILKNOWViz: Spatio-temporal data ontology viewer

Interactive visualization of spatio-temporal data is a very active area that has experienced remarkable advances in the last decade. This is due to the emergence of fields of research such as big data and advances in hardware that allow better analysis of information. This article describes the methodology followed and the design of an open source tool, which in addition to interactively visualizing spatio-temporal data that are represented in an ontology, allows the definition of what to visualize and how to do it. The tool allows selecting, filtering and visualizing in a graphical way the entities of the ontology with spatiotemporal data, as well as the instances related to them. The graphical elements used to display the information are specified on the same ontology, extending the VISO graphic ontology, used for mapping concepts to graphic objects with RDFS/OWL Visualization Language (RVL). This extension contemplates the data visualization on rich real-time 3D environments, allowing different modes of visualization according to the level of detail of the scene, while also emphasizing the treatment of spatio-temporal data, very often used in cultural heritage models. This visualization tool involves simple visualization scenarios and high interaction environments that allow complex comparative analysis. It combines traditional solutions, like hypercube or time-animations with innovative data selection methods.Interactive visualization of spatio-temporal data is a very active area that has experienced remarkable advances in the last decade. This is due to the emergence of fields of research such as big data and advances in hardware that allow better analysis of information. This article describes the methodology followed and the design of an open source tool, which in addition to interactively visualizing spatio-temporal data that are represented in an ontology, allows the definition of what to visualize and how to do it. The tool allows selecting, filtering and visualizing in a graphical way the entities of the ontology with spatiotemporal data, as well as the instances related to them. The graphical elements used to display the information are specified on the same ontology, extending the VISO graphic ontology, used for mapping concepts to graphic objects with RDFS/OWL Visualization Language (RVL). This extension contemplates the data visualization on rich real-time 3D environments, allowing different modes of visualization according to the level of detail of the scene, while also emphasizing the treatment of spatio-temporal data, very often used in cultural heritage models. This visualization tool involves simple visualization scenarios and high interaction environments that allow complex comparative analysis. It combines traditional solutions, like hypercube or time-animations with innovative data selection methods

GIS and spatial data analysis: Converging perspectives

We take as our starting point the state of geographic information systems (GIS) and spatial data analysis 50 years ago when regional science emerged as a new field of enquiry. In the late 1950s and 1960s advances in computing technology were making possible forms of automated cartography that in due course would lead to th

Geographic Visualization in Archaeology

Archaeologists are often considered frontrunners in employing spatial approaches within the social sciences and humanities, including geospatial technologies such as geographic information systems (GIS) that are now routinely used in archaeology. Since the late 1980s, GIS has mainly been used to support data collection and management as well as spatial analysis and modeling. While fruitful, these efforts have arguably neglected the potential contribution of advanced visualization methods to the generation of broader archaeological knowledge. This paper reviews the use of GIS in archaeology from a geographic visualization (geovisual) perspective and examines how these methods can broaden the scope of archaeological research in an era of more user-friendly cyber-infrastructures. Like most computational databases, GIS do not easily support temporal data. This limitation is particularly problematic in archaeology because processes and events are best understood in space and time. To deal with such shortcomings in existing tools, archaeologists often end up having to reduce the diversity and complexity of archaeological phenomena. Recent developments in geographic visualization begin to address some of these issues, and are pertinent in the globalized world as archaeologists amass vast new bodies of geo-referenced information and work towards integrating them with traditional archaeological data. Greater effort in developing geovisualization and geovisual analytics appropriate for archaeological data can create opportunities to visualize, navigate and assess different sources of information within the larger archaeological community, thus enhancing possibilities for collaborative research and new forms of critical inquiry

Design Patterns for Spatio-temporal Processes

Time is an essential dimension to analyse and understand real-world evolution. Although many temporal extensions to spatial models have been proposed, there is still a need to define modelling methods to describe and represent real-world phenomena. This paper presents a set of design patterns modelling spatio-temporal processes expressed in an object-relationship data model. The proposed framework is based on an analysis of spatio-temporal processes and on properties of object-oriented and entity-relationship data models. 1. Introduction Geographical processes involve entities and changes that are located in both space and time. Therefore, building a taxonomy of spatio-temporal processes (STP) implies an analysis of the representation of facts and events within a space-time framework. According to Peuquet (1994), scientists retain two complementary ontological views to define space-time structures. The absolute approach (introduced by Newton) identifies space as a collection of points..