In this paper, we describe the methodology being used to develop certain aspects of ISO-Space, an annotation language for encoding spatial and spatiotemporal information as expressed in natural language text. After reviewing the requirements of a specification for capturing such knowledge from linguistic descriptions, we describe how ISO-Space has developed to meet the needs of the specification. ISO-Space is an emerging resource that is being developed in the context of an iterative effort to test the specification model with annotation, a methodology called MAMA (Model-Annotate-Model-Annotate) (Pustejovsky and Stubbs, 2012). We describe the genres of text that are being used in a pilot annotation study, in order to both refine and enrich the specification language by way of crowd sourcing simple annotation tasks with Amazon's Mechanical Turk Service.