Historical Geography, GIScience, and Textual Analysis: Landscapes of Time and Place eds. by Charles Travis, Francis Ludlow, and Ferenc Gyuris
Bibliographic record
Abstract
Reviewed by: Historical Geography, GIScience, and Textual Analysis: Landscapes of Time and Place eds. by Charles Travis, Francis Ludlow, and Ferenc Gyuris Zef Segal Historical Geography, GIScience, and Textual Analysis: Landscapes of Time and Place. Charles Travis, Francis Ludlow, and Ferenc Gyuris, eds. Cham, Switzerland: Palgrave Macmillan, 2021. Pp. 272. $179.99, hardcover, ISBN 978-3-0303-7568-3. $139.00, paperback, ISBN 978-3-0303-7571-3. $109.00, eBook, ISBN 978-3-0303-7569-0. Historical GIS has been a thriving research field with tremendous potential for the last two decades; nevertheless, it feels very much in its childhood stages. Scholars are still breaking new ground, and methodologies are even now being assessed and debated. Perhaps the biggest problem it faces is also its biggest advantage: its scope and interdisciplinary nature. Due to its breadth, examples always seem too narrow to exemplify the field, either by limiting the historical questions to a specific case study or by stressing the technicalities of GIS rather than the historical interpretation of the results. Historical Geography, GIScience, and Textual Analysis tackles this problem by providing a rich and varied portrayal of historical GIS in the making. The sixteen chapters offer a diverse range of case studies covering different periods and regions, as well as significantly different source materials and disciplinary backgrounds. At first glance we might even ask, for example, what is the connection between the cinematic views of Texas in the 1984 film Places of the Heart (chapter 1) and the forensic investigation of an 1832 mass grave of Irish immigrant railroaders in Pennsylvania (chapter 3)? And the answer is probably: not much. However, as we continue to read through the chapters of the book, we realize that it is much less about particular case studies, as interesting as they might be, and much more about the common questions facing those implementing historical GIS in their work: What data can be relevant for this type of research? How should this data be mined and later [End Page 135] aggregated? And the most important of all, how does one combine the results with a more nuanced hermeneutic interpretation? Although the contributions are short (seventeen pages on average), the contributors manage, surprisingly well, to provide in-depth examples of the implementation of GIS in their respected study, as well as some reflective insights concerning their work. Part I's contributions of the edited collection deal with the mapping of textual source materials. Chapter 1 juxtaposes cinematic, literary, and scientific depictions of Waxahachie, Texas. Chapter 2 outlines an ongoing digital project of mapping Iceland's medieval textual corpus. Chapter 3 combines archival, archeological, and geographic research to uncover the truth behind an 1832 mass grave in Pennsylvania. Chapter 4 uses the Green Book, the Jim Crow–era travel guide, as a form of spatial countermapping of Black resistance. The four contributions in part II deal with the mapping of social practices and mobilities. Chapter 5 uses GIS to evaluate the impact of urban renewal in San Francisco on LGBTQ hangouts on the city's waterfront. Chapter 6 explores the relationship between work and home in 1881 Ontario. This chapter is a notable example of the methodological affordances of a combined use of data mining, GIS, statistical analysis, and an eventual closer reading of the data. Chapter 7 explores political corruptness in nineteenth-century Atlanta by juxtaposing the initial routes of streetcars in the city and the actual travel patterns of the city's inhabitants. Chapter 8 studies diasporic resettlement patterns of Irish migrants in late nineteenth-century North America. The social dimensions of environmental phenomena are considered in part III. Chapter 9 shows how an 1849 farmer's diary can be used as source material for exploring historical meteorology. Chapter 10 examines the effects of water distribution in the Rio Grande on economic patterns in the region. Chapter 11 presents a geographic interpretation of the available environmental resources during the 1066 Battle of Hastings between the English and the Normans. It successfully shows the methodological value of GIScience in cases where little data is at hand. Chapter 12 explores ringfort locations in the Irish Midlands. Part IV considers the mapping of historical landscapes. Chapter...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.034 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".