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Currently, spatial geographic data can be collected for many applications that involve data on the planet earth. These collected data typically have coordinates (x, y), or longitude and latitude in map space, and thus can be located and displayed on maps. Data alone represents facts and has no meaning on its own but becomes meaningful when it is associated with application knowledge, such as elections, crimes, disease, etc. For example, there is no meaning behind those numbers (1, 23, 125, 355, . . .), yet they are data that can get meaning when correlated with the total number of cases of COVID-19 in Texas per day starting on a certain date. Many devices can provide sequences of object location data over time (GPS in vehicles or mobile devices, etc.). However, no device can visualize or display them on its own without a visualization App. Both numeric and location data are raw data that need to be pre-processed and cleaned to become meaningful. Currently, collected data is a very valuable source of information which, after collection, can be processed, stored, analyzed, and visualized. In this paper, the available techniques of spatial data visualization will be overviewed. Moreover, a case study of COVID-19 spatio-temporal data visualization, using one of the techniques will be demonstrated. The COVID-19 data will be spatially visualized when data on a specific date is queried for analysis. On the other hand, spatio-temporal visualization will be displayed when a time series of COVID-19 data is queried for analysis.

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Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.