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 group dynamics and in predicting the patterns that will emerge under altered agent interactions. In the case of spatial patterns, 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. My main focus throughout this tutorial is spatial point-cloud patterns, but many of these methods are flexible and can also be applied to temporal patterns arising from self-organization. I conclude by highlighting some places that I see as especially promising for quantitative data, modeling, and data-driven approaches to continue meeting in the future.