Baangegevens Analyses voor voetgangers Ruimte-tijd Activiteit Study

The overall goal of this procedure is to model pedestrian space time activities through spatiotemporal analysis and visualization of human trajectory data. This is accomplished by first collecting detailed global positioning system or GPS data and loading the data into the trajectory analyzer. The second step is to pre-process and segment the trajectory data.

Next, the activity spaces of individuals are characterized. The final step is to examine spatiotemporal patterns through either density, surface mapping, density, volume rendering, or both. Ultimately, other exploratory data analysis methods and visualizations are used to show additional hidden patterns in the data.

The main advantage of this technique over existing methods such as the FGIS extension developed by SHNU for analyzing space time trajectories, is that we not only provide the interface for interactive visualization with trajectories, we focus on the processing method that cleaning up the route trajectory data segment them derive properties from the TR data and exploratory analysis to discover patterns from a large amount of traject data. This method can help answer key questions in the field of human space time activity studies related to microscale disease transmission, such as how one's space time activity affects his or her chance of infection, or which environments or space time behavior leads to higher risk Trajectory. Data can be collected with handheld GPS units.

GPS enabled smartphone tracking applications as well as assisted GPS devices, such as the one employed. In this study, which is a commercial child tracker device trajectory, data is usually saved in terms of time, latitude, longitude records. A desired time interval should be set based on application needs.

Often the most frequent interval is desired for space time activity studies convert the data to comma separated values or CSV files with separate columns for record id, latitude, longitude, and time respectively. Then convert the CSV files into commonly used geographic information systems or GIS file format. Load in a shape file of building polygons and another of the boundary of the study area with a trajectory analyzer.

Set the extrusion of the buildings properly for a 3D display and set the extrusion and transparency of the boundary layer properly to display a spacetime cube. Then open the trajectory in the spacetime cube with the XY dimensions representing space and the Z dimension. Representing time, two options are available for pre-processing.

The noisy raw trajectory data one may choose from the dropdown list of the pre-processing menu. If interactive is chosen. A 2D projection of the 3D trajectory is created for easy viewing and selection.

Manipulate the 3D display to examine the raw trajectory in space and time. Identify errors in the data based on the shape, speed, and or topology of track segments. Usually track points with unrealistic high speed or abrupt direction change signify errors, select and remove them from either the 3D trajectory or its 2D projection.

A cluster of track points with spiky shapes spatially, and a long duration temporally signify errors that are most possibly caused by indoor locations where GPS signal is weak. If a group of these points is selected, the program can calculate the spatial temporal OID of the selected points and then adjust the track to go through the oid. Alternatively, if automatic is chosen from the pre-processing menu, set the input and output locations, as well as empirical parameters that determine the abnormal high speed and abrupt turning of points.

The program searches through the loaded trajectory data and runs automatically based on an algorithm that mimics the visual error detection approach. Trajectory segmentation requires the building layer, so ensure the building shape file is ready. Click the segmentation tool in the toolbar to start the function.

Set the input and output and locate the building shape file as the reference layer. Use the building names to label the segmented trajectory. The algorithm identifies indoor segments based on set or default criteria such as speed and duration of track points, as well as the spatial topology.

With relation to buildings, click the activity space summarization tool to load in segmented trajectories and calculate selected summary attributes to characterize one's activity space such as total activity, radius, radius at a certain time period, ratio of total time spent indoors versus outdoors, and so on. The attributes can be exported to a spreadsheet for quantitative modeling uses. Density surface shows the density of activities in space with the temporal dimension collapsed.

Three options are available from the dropdown list of the density surface mapping menu. If the track point density option is selected, fill in the dialogue box with input and output information and choose to display in either 3D or 2D. All vertices from the trajectory data are used to calculate kernel densities of the points as shown here.

If track path density is selected, the algorithm calculates and displays density of individual paths traveled. If the resampled point density option is selected, the algorithm resample the trajectory data using a set time interval and maps the densities of points spread evenly in time. This option is designed for tracking devices that collect tracking points in irregular time intervals due to varying sensitivity of the devices under various physical conditions or segmented trajectories.

2D and 3D density surface of segmented trajectories are shown here. If temporal focusing is selected for any of the options, temporal focusing can be performed to examine activity patterns at different time periods. For example, activity density surfaces at different times in a day may be visualized for easy identification of hotspots across time density volume visualization uses the notion of a spacetime cube as in the visualization of trajectories.

The core of such visualization is the disaggregation of space into voxels. The approach used here for visualizing density volume first estimates density volume in individual voxels by counting the number of space time tracks that intersect with the voxels. The same three options are available for density, volume visualization as for density surface visualization.

Next, click one of the options to launch the 3D volume visualization interface for interactive volume rendering. By setting the number of divisions along each axis, one may examine clusters at different scales. A Z factor is used to set the vertical exaggeration for better visualization.

A reference layer such as the buildings can be loaded to aid visualization as well. The results of volume rendering can be interactively adjusted by manipulating the transfer function that controls the mapping from density to color. A procedure is available to create animated series to be displayed in Google Earth.

Under other click export to KML for EDA to access this procedure, it creates a KML file that opens in Google Earth for interactive animation of the trajectory. One may follow the trajectory to travel the environment in time by scrolling along the timeline. In Google Earth, a procedure is available to visualize connections among places of interest through connection analysis.

For example, connections among different buildings on a university campus are derived from segmented trajectory data that were collected by students based on the derived connections hotspots such as those buildings with the most outbound or inbound traffic and hubs that connect the most trafficked places may be identified. Trajectory data was collected by volunteering undergraduate students from Keen University in the spring of 2010. The purpose was to study activity patterns of students who caught influenza in comparison to those who did not.

In order to illustrate the methods and procedure presented in this, the trajectories collected within the suburban campus area were used to generate representative results. The space time cube representation of a trajectory with reference to buildings on the university campus is shown here. The raw data collected by a student recording one day of his activity on campus using an A GPS device reveals that some long duration of indoor stays has resulted in noisy data indicated by the spiky portion of the track.

This is very common in pedestrian trajectory data. This figure represents the pre-processed and segmented trajectory, while the pre-processed and segmented trajectory with color-coded indoor and outdoor segments in the spacetime cube is represented here. Shown here is the density surface mapping of a set of trajectories.

The raw tracking points involved in performing a track point density mapping option and the resulting density map can be visualized. Conversely, the densities of traveled paths can also be mapped. Density mapping is particularly useful when analyzing a large number of trajectories.

This map displays a total of 470 trajectories. The density surface can also be displayed in 2D and 3D representations using resampled points from these trajectories. In addition to the interactive display of the temporal dimension in a space time cube, the time variable can be processed through temporal focusing to examine spatial patterns at different time periods.

Shown here are examples of such analysis. Using the sample dataset that contains trajectory data collected by students during the flu season. It is obvious that their activities are centered around different locations throughout the day.

To lead eventually to the composite activity density map on the bottom density volume rendering can also be performed as shown here, it is hard to detect patterns if all the space time tracks are visualized in a space time cube. Because of visual clutters here, the corresponding data is visualized as density volume rendering. The four illustrations represent different settings of the transfer function of the density rendering program, and thus highlight density volumes at different frequency ranges.

Another way of finding hotspots is through connection analysis. The straight line connections among all buildings on campus are shown here. The highlighted buildings are those with the highest outbound traffic volume.

Here, the same connections are shown with the most trafficked connections highlighted in black. While attempting this procedure, it's important to remember to start with a pre-processing step before you move to the segmentation, exploratory analysis and other visualization methods Following this procedure. Other methods like statistical analysis of the attributes, categorizing one's activity space, or sequence analysis such as sequence alignment can be performed in order to answer additional questions such as how one's activity, space, and sequences might affect the chances of infection.