Our results show that a visualization's effectiveness depends strongly on the task to be carried out. Tasks varied in granularity for both dimensions: time (all time steps, a subrange of steps, one step only) and space (all locations, locations in a subregion, one location only).
WORD FOR SPACE IN TIME SERIES
Participants performed a series of tasks that required them to identify if two variables were correlated over time and if there was a pattern in their evolution. We report on a study comparing three techniques that are representative of different strategies to visualize geo-temporal multivariate data: either juxtaposing all locations for a given time step, or juxtaposing all time steps for a given location and encoding thematic attributes either using symbols overlaid on top of map features, or using visual channels of the map features themselves. But how effective visualization techniques are at communicating correlation between two variables that evolve over space and time remains to be investigated. Prior work has compared geo-temporal visualization techniques for a single thematic variable that evolves over space and time, or for two variables at a specific point in time. The choice of visual representation for such multivariate data is key to enabling analysts to extract patterns and trends. For instance, looking, for different countries, at the evolution of both the life expectancy at birth and the fertility rate will give an overview of their demographics.
![word for space in time word for space in time](https://thumbs.dreamstime.com/z/time-to-learn-english-word-background-composed-colorful-abc-alphabet-block-wooden-letters-copy-space-ad-text-learning-147562809.jpg)
Observing the relationship between two or more variables over space and time is essential in many domains. Before we review methods or conceptual models that can be used to perform space–time analysis with a focus on geographical and environmental studies, we classify units of space–time analysis into the following two broad categories, and link them with the three major data types (Table. All these word- cloud findings corroborate our previous findings about what disciplines, by what methods, are contributing more to space–time analysis. More interesting to us would be those midfre- quency words that show either the methods being used (e.g., Bayesian and series), areas of study (e.g., rainfall, health, malaria, and disease), or data characteristics (e.g., correlation and cluster). It is expected that terms like data, study, and patterns are displayed as high-frequency words. The word cloud of all the papers ( Figure 3) shows the high-frequency words that are used in the corresponding abstracts.
![word for space in time word for space in time](https://ak9.picdn.net/shutterstock/videos/18342259/thumb/1.jpg)
Conceptual and mathematical models are developed to represent, explain, or predict phenomena (e.g., high-energy scattering, Hadron– Nucleus collision at high energy) in the physics world. Among these publications, mathematicians or statisticians develop formulations (e.g., Bayesian models) to express space–time dynamics in mathematical or statistical terms and to create theoretical and applied frameworks for analysis and decision making under ambiguity. (seventy-eight), biology (fifty-four), ecology (forty-four), urban studies (thirty-one), hydrology (thirty), and physics (twenty-nine) make the biggest contribution to the publications (data not shown).