  From flocking birds to schooling fish, organisms interact to form collective
dynamics across the natural world. Self-organization is present at smaller
scales as well: cells interact and move during development to produce patterns
in fish skin, and wound healing relies on cell migration. Across these
examples, scientists are interested in shedding light on the individual
behaviors informing spatial group dynamics and in predicting the patterns that
will emerge under altered agent interactions. One challenge to these goals is
that images of self-organization -- whether empirical or generated by models --
are qualitative. To get around this, there are many methods for transforming
qualitative pattern data into quantitative information. In this tutorial
chapter, I survey some methods for quantifying self-organization, including
order parameters, pair correlation functions, and techniques from topological
data analysis. I also discuss some places that I see as especially promising
for quantitative data, modeling, and data-driven approaches to continue meeting
in the future.
