Spatiotemporal
Spatiotemporal Definition
Spatiotemporal, or spatial temporal, relates to space and time. Spatial refers to space and temporal refers to time.
FAQs
What is Spatial Temporal?
Spatial refers to space. Temporal refers to time. Spatiotemporal, or spatial temporal, is used in data analysis when data is collected across both space and time. It describes a phenomenon in a certain location and time — for example, shipping movements across a geographic area over time (see above example image). A person uses spatial-temporal reasoning to solve multi-step problems by envisioning how objects move in space and time.
What is Spatio-temporal Data Analysis?
Spatio-temporal data analysis is a growing area of research with the development of powerful computing processors like graphic processing units (GPUs) used for big data analysis. Spatio-temporal databases host data collected across both space and time that describe a phenomenon in a particular location and period of time.
Applications for spatio-temporal data analysis include the study of biology, ecology, meteorology, medicine, transportation and forestry.
Spatio-temporal data visualization and analysis can be challenging because space has unlimited directions — up, down, sideways, North, South, East, West — while time can only go forward. Combining and assessing the two factors of space and time can be complex.
The data can produce different results depending on how space is defined — a zip code, a census tract or a state. Time can also provide conflicting answers depending on whether it is measured in seconds, minutes, hours, days or years.
The primary goals of spatiotemporal analysis are prediction and description.
Difference Between Temporal and Spatial Databases
A spatial database stores and allows queries of data defined by geometric space. Many spatial databases can represent simple coordinates, points, lines and polygons. Some can handle more complex data like 3D objects, topological coverage and linear networks.
A temporal database stores data relating to time whether past, present or future. It can establish at what times certain entries are accurate. The data is divided into three types:
- Valid time — the time period when a fact is true in the real world.
- Transaction time — the time period when a fact stored in the database was known.
- Bitemporal data — combines both valid and transaction time.
The storage and access of spatial and temporal data is required in areas like geographical information systems, environmental information systems and neuroinformatics. A spatio-temporal database focuses on data related to moving objects.
What is Spatial Temporal Reasoning?
Spatial temporal reasoning is what a person or a robot with artificial intelligence uses to understand how items fit together in a space. It is accomplished by picturing a spatial pattern. This includes visualizing a step-by-step process and how objects can be manipulated into different patterns.
Spatiotemporal reasoning is key for problem-solving and organizational skills. It is used in computer science to help robots understand and navigate time and space. Spatial temporal reasoning is also used in cognitive psychology to explore how a mind processes its knowledge of time and space.
Spatial-temporal Reasoning Examples
Some examples of spatial-temporal reasoning include job application aptitude tests. Spatial reasoning tests assess a candidate’s ability to manipulate, arrange and rotate objects in their mind’s eye without touching physical objects. The tests are often used for technical engineering and military recruits.
In everyday life, spatial-temporal reasoning examples include using a map or compass, merging in traffic while driving and determining how many objects can fit in a box.
Spatial-temporal reasoning applies to the healthcare industry by determining the best distribution and performance of medical care. Spatio temporal analyses can examine patterns of illness over time and show anything unusual.
Spatial-temporal reasoning is also important in the development of artificial intelligence. It has many applications in natural language processing, geographic information systems, computational biology and document interpretation.
Does HEAVY.AI Offer Tools for Spatio Temporal Analysis?
Yes. HEAVY.AI gives analysts transformational new ways to interact with very large datasets, especially those that include spatiotemporal data. Interacting with geospatial data on a map, or as a time-series chart, provides context and visual signals that HEAVY.AI can help you understand clearly and intuitively.