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

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

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Difficulties when working with historical content

When applying Named Entity Recognition (NER) with Spacy to historical content, we encountered significant challenges. The main problem was the frequent misnaming of locations, which is a result of place names changing over time, especially in historical and biblical contexts. These names often have varied across multiple languages, adding to the complexity. Furthermore, even by reading, it was occasionally challenging to determine the current significance or identity of these names due to their changing nature over centuries. This complexity highlighted how essential it is to understand the relationships between locations and what they mean within the book's narrative along with to representing a linguistic and technical challenge. It is critical to comprehend these relationships because they have a significant impact on how the text is interpreted and understood overall.

Preliminary Analysis for Model Selection - Assessment Focusing on Chapter 3

Manual detection

In our preliminary analysis for model selection, we focused on Chapter 3 for a detailed assessment. The initial step involved manually detecting and labeling named entities, specifically locations mentioned in the text. This was achieved by highlighting relevant text segments and subsequently gathering these identified locations into a spreadsheet. To ensure the accuracy of our location identification, each location was cross-referenced with its corresponding Wikipedia page. This was an important step, especially in cases where the context of the book made it challenging to understand the exact nature of the places mentioned, even after analyzing the entire paragraph. This method provided a solid foundation for our named entity recognition approach.

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.

Matching Wikipedia Pages

Using the Wikipedia API , locations identified by GPT were searched in Wikipedia, and the first relevant result was recorded. Additionally, the first image found on the page of the recorded link was retrieved. This approach was primarily used to verify the accuracy of manually determined locations. Subsequently, after all locations were obtained, it was used both for visualizing the author's path and for acquiring coordinates for locations without coordinates.

Tracking Author's Route on Maps

Finalizing the List of Coordinates

1. Fuzzy matching GPT-4 results with an existing location list

1. Retrieving coordinates by matched Wikipedia pages

Visualization by GeoPandas

1. QGIS
2. GeoPandas

Limitations and Further Work

- API for a smoother process - Improve with the full book instead of chapter by chapter

Project Timeline & Milestones

Timeframe	Task	Completion
Week 4	Exploring literature of NER Finding textual data of the book	✓
Week 5	Pre-processing text Quality assessment of the data	✓
Week 6	Applying NER using Spacy	✓
Week 7	Manually labelling chapter 3 GPT-4 Prompt Engineering	✓
Week 8	Working on mapping with QGIS	✓
Week 9	Finalizing GPT-4 Prompt Automating Wikipedia Page Search	✓
Week 10	Finalizing the list of manually detected locations Evaluation of GPT-4 and Spacy Results for chapter 3	✓
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 Retrieving coordinates from matched Wikipedia pages QGIS Mapping of the locations from the entire book Visualizing the full journey
Week 14	Complete GitHub repository Complete Wiki page Complete presentation

GitHub Repository

https://github.com/jiaming-jiang/FDH-G8.git