Spatialising Sarah Barclay Johnson's travelogue around Jerusalem (1858)

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Project Timeline

Timeframe Task Completion
Week 11
  • Matching the coordinates of the locations from chapter 3
  • QGIS mapping of the locations from chapter 3
Week 12
  • Visualizing the full chapter 3 journey
  • Retrieving the locations from the entire book
Week 13
  • Matching the coordinates of the locations from the entire book
  • QGIS Mapping of the locations from the entire book
  • Visualizing the full journey
Week 14
  • Complete GitHub repository
  • Complete Wiki page
  • Complete presentation

Abstract

Introduction

Delving into the pages of "Hadji in Syria: or, Three years in Jerusalem" by Sarah Barclay Johnson, this project sets out to digitally map the toponyms embedded in Johnson's 19th-century exploration of Jerusalem with the wish to connect the past and the present. By visualizing Johnson's recorded toponyms, this project aims to offer a dynamic tool for scholars and enthusiasts, contributing to the ongoing dialogue on the city's historical evolution.

This spatialization, in its attempt, pays homage to Johnson's literary contribution, serving as a digital window into the cultural crossroads: Jerusalem. The project invites users to engage with the city's history, fostering a deeper understanding of its rich heritage and the interconnected narratives that have shaped the city. In this fusion of literature, history, and technology, we hope to embark on a digital odyssey, weaving a narrative tapestry that transcends time and enriches our collective understanding of Jerusalem's intricate past.

Methodology

Working with the Book / Extracting Book Information

Detecting Locations

NER with Spacy

//LIST or visual

Spacy API

So, we can see the problem is mislabeling: in theory we only need to retrieve the toponyms, i.e. “GPE” & “LOC”, but SpaCy labelled some of them as “PERSON” or “ORG”. In other words, if we only select “GPE” and “LOC”, we’ll lose some toponyms; if we also select “ORG” and “PERSON”, we’ll get some non-toponyms.

Difficulties when working with historical content

Too many mis-labels due to historical and biblical references place names changing over time multiple languages

Difficult to understand even by reading sometimes

Importance of understanding how places relate to the meaning in the book

GPT-4

Matching Wikipedia Pages

Preliminary Analysis for Model Selection - Assessment Focusing on Chapter 3

Manual detection

Spacy Results

GPT-4 Results

Since the GPT-4 Results outperformed the results of Spacy NER, presented GPT-4 prompt has been used to retrieve the locations in the book.


Tracking Author's Route on Maps

Adding coordinates to the JSON

  1. Fuzzy matching GPT-4 results with an existing location list
  2. QGIS
  3. GeoPandas

Results

Limitations and Further Work

Conclusion

Project Timeline & Milestones

GitHub Repository

References