Advances in next-generation sequencing technologies contribute to the identification of (candidate) disease genes for movement disorders and other neurological diseases at an increasing speed. However, little is known about the molecular mechanisms that underlie these disorders. The genetic, molecular, and behavioral toolbox of Drosophila melanogaster makes this model organism particularly useful to characterize new disease genes and mechanisms in a high-throughput manner. Nevertheless, high-throughput screens require efficient and reliable assays that, ideally, are cost-effective and allow for the automatized quantification of traits relevant to these disorders. The island assay is a cost-effective and easily set-up method to evaluate Drosophila locomotor behavior. In this assay, flies are thrown onto a platform from a fixed height. This induces an innate motor response that enables the flies to escape from the platform within seconds. At present, quantitative analyses of filmed island assays are done manually, which is a laborious undertaking, particularly when performing large screens. This manuscript describes the "Drosophila Island Assay" and "Island Assay Analysis" algorithms for high-throughput, automated data processing and quantification of island assay data. In the setup, a simple webcam connected to a laptop collects an image series of the platform while the assay is performed. The "Drosophila Island Assay" algorithm developed for the open-source software Fiji processes these image series and quantifies, for each experimental condition, the number of flies on the platform over time. The "Island Assay Analysis" script, compatible with the free software R, was developed to automatically process the obtained data and to calculate whether treatments/genotypes are statistically different. This greatly improves the efficiency of the island assay and makes it a powerful readout for basic locomotion and flight behavior. It can thus be applied to large screens investigating fly locomotor ability, Drosophila models of movement disorders, and drug efficacy.