Draft:Time Series Trajectory Analysis: Difference between revisions
TSTA is able to compare among sites and across inconsistent time intervals by expressing results as an annual percentage of each site’s unified size. A site’s unified size is the union of where the category exists at any of the site’s time points.
TSTA is able to compare among sites and across inconsistent time intervals by expressing results as an annual percentage of each site’s unified size. A site’s unified size is the union of where the category exists at any of the site’s time points.
Key to the TSTA methods are visualizations of trajectory data. One visual output is a map showing the spatial distribution of different trajectories, as shown in ”Figure 1”. Another, unique to TSTA, is stack bar plots, as shown in ”Figure 2”. Stack bar plots show gain, loss, and stable trajectories, with or without alteration, over any degree of temporal resolution. Map ”e)” in ”Figure 2” displays very different trajectories between the first and second intervals. This is an indicator of possible map errors/inconsistencies in data collection that can be more clearly determined using TSTA and gross change analysis than with a net change analysis.
Key to the TSTA methods are visualizations of trajectory data. One visual output is a map showing the spatial distribution of different trajectories, as shown in ”Figure 1”. Another, unique to TSTA, is stack bar plots, as shown in ”Figure 2”. Stack bar plots show gain, loss, and stable trajectories, with or without alteration, over any degree of temporal resolution. Map ”e)” in ”Figure 2” displays very different trajectories between the first and second intervals. This is an indicator of possible map errors/inconsistencies in data collection.
== Example Analysis ==
== Example Analysis ==
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Introduction[edit]
Time Series Trajectory Analysis (TSTA) is a quantitative method used in land change science that quantifies and visualizes the trajectories of a binary variable during a time series. TSTA builds on existing land change analysis methods, particularly those developed by Robert Gil Pontius Jr[1][2] and is presented as a method and analytic tool in Bilintoh, 2023[3]. TSTA was first developed to better analyze trajectories in project data from Bahia, Brazil, and Long Term Ecological Research sites in the United States (Figure 1). These projects received funding from NASA and the NSF, respectively.
Many existing methods of land change analysis focus on net change between two time points, while TSTA addresses the need in Geographic Information Science to capture the complexity of land change over multiple time points. By examining the land change of a time series
over several time intervals, trends, and cyclical patterns can be identified. These patterns might be essential for understanding land degradation [4] [5] vegetation change [6] [7] or urban expansion [8] [9].
Methods[edit]
Time Series Trajectory Analysis provides a method to analyze a series of Boolean images and classify each pixel as having one of eight possible trajectories. Taken together these trajectories can describe key insights into trends of persistence and absence of a variable in an area. These…
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