The visualization of temporal data is important for a variety of sciences and disciplines in understanding space-time factors and change. Analytically, showing change in a setting is important for decision-making and forecasting future events. Different sets of software and techniques have been developed recently to apply spatiotemporal visualization and analysis.
Open source tools have recently developed methods to process open formated files such as Keyhole Markup Language (KML) files. For instance, the R statistical package can use the plotKML() package to create KML files that can then be used to visualize spatiotemporal change within the XML-like data in a given area of analysis. The parsing order and user can view templates that can even be specified to display the visual output.
In the social sciences, using social media has become popular in research. Data can be geolocated and be represented in spatial cubes that encapsulate location data and unstructured information such as texts and photographs. These data can be assessed across different time intervals to demonstrate given spatial patterns of movement or interactions using social media streams. This development has been dubbed as part of CyberGIS, where it can be used, among other areas, to allow an understanding and study of social patterns as they are affected by events, such as sickness (e.g., such as a flu pandemic). In effect, these spatial cubes that are created can be useful for emergency responses to monitor how mass events could affect regions so that officials can best respond to real-time events.
One aspect of research that has seen a lot of interest is network analysis. In order to understand interaction across space and time, using networks of entities (e.g., people, fauna, etc.) interacting and determining the importance of interactions has become one focus area. Geotagged data can be imported using ArcScene, as one example, where network change and interactions are determined by looking at how information diffuses from a source onto other actors or individuals in a given networked space. In effect, this allows the measurement of the perceived importance of events on the actors themselves and how those events transpire across a network of actors.
Issues of access of spatiotemporal data have been indicated as a drawback and limitation in countries where data are needed for analysis but more difficult to obtain. This can have planning and management repercussion, for instance, in urban development. Countries that are rapidly growing in population and urbanism are facing some of the greatest change to their societies, but these countries often do not have resources to make informed decisions. One project, MEGA-WEB Geo Webservices, has developed online spatiotemporal visualization and analysis to show remote sensing and other collected data. Having a platform that can visualize multiple datasets, including remote sensing data of urban growth, facilitates spatiotemporal assessment without having to access tools that often have expensive licensing fees.
The complexity of spatiotemporal data is often evident in querying multidimensional data across space and time. Tools such as the SubVizCon framework have been developed to allow users easier access to specify subset multidimensional queries. This tool can be integrate within ArcGIS to visualize the data and is useful for investigating patterns within such data.
While spatiotemporal data and visualization have remained a challenge, sometimes due to the complexity of the data, increasingly we are seeing tools that are available on common open source platforms (e.g., R statistical package) or commercial platforms such as ArcGIS. Both the social and natural sciences, in particular, have benefited and have created tools that are potentially informative and useful for decision-making. The use of spatiotemporal visualization is likely to be more significant as major global change will require real-time data integration and visual analysis.
 For more on the use of R for spatiotemporal analysis using plotKML(), see: Hengl, T., Roudier, P., Beaudette, D., & Pebesma, E. (2015). plotKML : Scientific Visualization of Spatio-Temporal Data. Journal of Statistical Software, 63(5). https://doi.org/10.18637/jss.v063.i05
 For more on how social media can be used for spatiotemporal analysis, see: Cao, G., Wang, S., Hwang, M., Padmanabhan, A., Zhang, Z., & Soltani, K. (2015). A scalable framework for spatiotemporal analysis of location-based social media data. Computers, Environment and Urban Systems, 51, 70–82. https://doi.org/10.1016/j.compenvurbsys.2015.01.002.
 For more on using this approach on creating networks, see: Gao, S., Chen, H., Luo, W., Hu, Y., & Ye, X. (2018). Spatio-Temporal-Network Visualization for Exploring Human Movements and Interactions in Physical and Virtual Spaces. In S.-L. Shaw & D. Sui (Eds.), Human Dynamics Research in Smart and Connected Communities (pp. 67–80). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-73247-3_4
 For more on MEGA-WEB, see: Gong, H., Simwanda, M., & Murayama, Y. (2017). An Internet-Based GIS Platform Providing Data for Visualization and Spatial Analysis of Urbanization in Major Asian and African Cities. ISPRS International Journal of Geo-Information, 6(12), 257. https://doi.org/10.3390/ijgi6080257.
 For more on SubVizCon, see: Sharma, S., Tim, U. S., & Gadia, S. (2017). Visual subsetting, conversion and complex query exploitation in large spatio-temporal databases. Computers & Electrical Engineering. https://doi.org/10.1016/j.compeleceng.2017.06.015.
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