  Dynamic Mode Decomposition (DMD) is a data-driven and model-free
decomposition technique. It is suitable for revealing spatio-temporal features
of both numerically and experimentally acquired data. Conceptually, DMD
performs a low-dimensional spectral decomposition of the data into the
following components: The modes, called DMD modes, encode the spatial
contribution of the decomposition, whereas the DMD amplitudes specify their
impact. Each associated eigenvalue, referred to as DMD eigenvalue,
characterizes the frequency and growth rate of the DMD mode. In this paper, we
demonstrate how the components of DMD can be utilized to obtain temporal and
spatial information from time-dependent flow fields. We begin with the
theoretical background of DMD and its application to unsteady flow. Next, we
examine the conventional process with DMD mathematically and put it in
relationship to the discrete Fourier transform. Our analysis shows that the
current use of DMD components has several drawbacks. To resolve these problems
we adjust the components and provide new and meaningful insights into the
decomposition: We show that our improved components describe the flow more
adequately. Moreover, we remove redundancies in the decomposition and clarify
the interplay between components, allowing users to understand the impact of
components. These new representations ,which respect the spatio-temporal
character of DMD, enable two clustering methods that segment the flow into
physically relevant sections and can therefore be used for the selection of DMD
components. With a number of typical examples, we demonstrate that the
combination of these techniques allow new insights with DMD for unsteady flow.
