Geospatial Innovation in the Digital Humanities:Implementation and Evaluation of Deep Mapping in the Lake District

This thesis explores the concept of Deep Mapping and how it can be implemented and evaluated using historic texts from the Lake District region. Deep Mapping is a new way to approach, understand and analyze the relationship between geography, history and literature through a variety of media. Deep Mapping has been discussed in the literature, but few practical implementations of the concept have been created. This thesis works to bridge the gap between the theoretical concept of Deep Mapping and its real-world application and use. The primary data used for this thesis is the Corpus of Lake District Writing, a collection of texts collected by researchers. This corpus contains 80 manually digitized texts about the Lake District region from 1622 to 1900. The corpus is made up of several different genres, including travel journals, essays, novels, and poetry. These texts, along with other multimedia, were brought together with interdisciplinary methods drawn from the fields of history, literature, corpus linguistics, spatial and digital humanities and computer science to create Deep Mapping prototypes. A series of case studies, discussed in chapters 3-7, were undertaken to explore Deep Mapping. Each case study focuses on developing a different Deep Map prototype addressing a new problem and set of objectives. Each prototype was then evaluated based on three sets of criteria: addressing the problem and objectives (design), this thesis’s research questions (research), and classifications of digital and spatial humanities and Deep Mapping (data). The last case study, chapter 7, focuses on processing data not related to the Lake District in order to explore if the methods used to develop the prototypes in chapters 3-6 are repeatable with other data sources. The final chapters discuss the implementation and evaluation of Deep Mapping. This thesis produced four Deep Mapping prototypes that can be used as exemplars for future research. The evaluation of these prototypes led to the development of the Evaluation Rubric, that can be used in the evaluation of applications to help determine their fit as a Deep Map and to assist in the comparison in Deep Mapping applications developed in contrasting forms and based on differing content