Map Visualization using Spatial and Spatio-Temporal Data: Application to COVID-19 Data
Abstract
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.