Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Object storage wikipedia , lookup
Data Protection Act, 2012 wikipedia , lookup
Data center wikipedia , lookup
Database model wikipedia , lookup
Data analysis wikipedia , lookup
Forecasting wikipedia , lookup
Information privacy law wikipedia , lookup
Data vault modeling wikipedia , lookup
Spatiotemporal Visualization Andrew Christie, Adam Kosakoski, Craig Evans, Charles Schmidt, David Altieri, Peter Schooley, Richard Johnson Advisor: Dr. Terry Mason, Dr. Wan Bae Universty of Wisconsin - Stout About The Project The Spatiotemporal Visualization project is to help analyze movement patterns, behaviors and trends by: • Taking cleaned GPS data from the Data Mining Team and displaying it in a 3D or 2D visual environment. •Allowing selection of areas of interest •Separating different object types. • Sorting objects by time. • Using navigational tools to better visualize the data in the visual environment. Filtering Data The researcher is allowed to filter which points should be displayed based upon data values or the researcher’s selection using a mouse. We have implemented filtering algorithms to filter data efficiently and accurately. Currently, we are investigating data structures and algorithms such as R-Trees and hash trees to further increase the efficiency of the filtering while preserving ease-of-use for the end user. Handling Missing Data Since we are getting our data from GPS devices, there’s bound to be places where points don’t exist, so we had to take this into account when visualizing it. The first attempt we made to accurately estimate where the object would be based on its position prior to losing connection and after reestablishing it, was to use B-splines, a Numerical Analysis technique of estimating a curve, or in this case points at a certain interval on a curve. This works very well in practice, however it suffers from quite a long run time, as we have to run it on entire files of data every time we want to open a new file to view the points of. Pre-filtered 2D and 3D visualizations Filtered 2D and 3D visualizations Distinguishing Data Types The Applet Since all of the lines are going to be relatively hard to distinguish differences in them, we are using a color coding system. Similar to electronic wiring, wires are often striped. Since we are using color to distinguish between multiple attributes, this seemed the most reasonable. One object will consist of a large line, colored for object type, and a small line, cycling through multiple colors to distinguish between objects of same type. Each line will also have a gradient to represent change in time. We are also displaying different shapes at each data point. For example, people could be spheres, and cars could be cubes. In a flattened 2D image, the 3D shapes become 2D as well, e.g. cubes become squares. The applet promotes online collaboration and provides an easy interface for the researcher to view and manipulate the spatiotemporal data. The user can open csv data files to analyze on the applet and save various views of interest. The applet provides information to the user about what they are viewing and what data is selected. Here you can see the line striping in random data (Red-people, Blue-Automobiles) Displaying different shapes for different object types