Data
The data for this markdown file are taken from https://openplaques.org/data/ see also (programmingr.com (2024))
Introduction
This project utilizes the dataset provided by openplaques.org, an extensive online repository that catalogues, curates, and promotes commemorative plaques installed on buildings and landmarks worldwide. This dataset offers a unique glimpse into the historical figures and events deemed worthy of public recognition through the installation of over 50,000 plaques. For this analysis, my focus has been narrowed to plaques located in London. Ian Sinclair being one of my favourite authors, I have always been interested in the urban life of London and the idea of how we can tell stories through urban landmarks. Blue plaques, for me, is a great way to tell the story of buildings. I believe that people shape and change the meaning of the spaces through their interaction and everyday life. Who is chosen to be included in the narrative is also one of my interests. While walking in London streets, I play a game where I spot the female figures in the blue plaques since my arrival in London in September. Therefore, this project gave me a chance to explore the the distribution of sexes among blue plaques in London, shedding light on potential biases in whom history chooses to remember and celebrate in one of the most advanced and leading cities in the world. The choice to examine the distribution of sexes is driven by a broader societal interest in understanding gender representation in historical contexts. Previous studies and discussions in various media, including academic contributions from fields such as data feminism, suggest that public commemoration has traditionally been skewed towards male figures (see Results section). By analyzing this dataset, this project seeks to quantify and discuss these potential biases within the specific context of London’s commemorative blue plaques. Through the visualizations and analysis that follow, this report will attempt to highlight any evident disparities in the recognition of men and women, discussing the implications of these findings.
Methods
Data Preparation:
The dataset, pre-filtered and provided for London, was sourced from openplaques.org. This dataset underwent several key preparation steps. To focus on gender distribution in London, the dataset was filtered to include only plaques within the London area. This was further processed to identify and categorize plaques by the sex of the individuals commemorated. Due to the nature of the dataset, where the sex is not categorised, the categorisation of the plaques was based on the description such as ‘places’ and ‘things’.
Purpose of Visualizations:
The primary aim of the visualizations in this report is to illustrate the distribution of sexes among the commemorative plaques in London. Each visualization serves a specific purpose, clarified through the implementation of various ggplot functions:
Bar Chart of Sex Distribution: Implemented using ggplot(plaques, aes(x = gender, fill = gender)) + geom_bar(), this visualization aims to show the disparity in the number of plaques dedicated to different sexes. It provides a clear understanding of potential gender biases in historical recognition.
Timeline of Sex Distribution Over Time: This line graph tracks changes in gender representation over time, illustrating shifts that may align with broader societal changes.
Bar Chart of Wikipedia Page Presence by Sex: This visualization explores the extension of historical recognition into the digital realm, examining whether the individuals commemorated on plaques have corresponding Wikipedia entries. It aims to uncover any discrepancies between physical and digital recognition across different sexes.
Results
Analysis of Sex Distribution in Plaques: The bar chart visualizing the distribution of sexes among London’s plaques reveals a clear dominance of males over females and other categories. We can also say that for a considerable amount of plaques we have no information for the sexes- they could either be male, female, non-binary or objects. The amount of plaques dedicated to males is 8 times higher than females according to my chart. What was interesting to find out was that the amount of Object data is just slightly higher than the plaques dedicated to females. This might reveal the general bias in the society against the success and accomplishments of women. This issue was taken up by The Guardian last year as well. Accordingly, “the disparity between the numbers of women’s and men’s plaques starkly illustrates the historical exclusion of women: of more than 990 plaques currently in place in London, about 85% commemorate men. Even if only women were to be honoured from now on (a prospect that English Heritage stresses would never happen), it would take 50 years to reach numerical parity” (Addley). Women’s work have been systematically made invisible. The visualisation only makes this centuries old issue more concrete. It shows the gender bias in collecting data and reaffirms that patriarchal practices are still quite dominant in our everyday lives since we interact with the cities we live in. It is also nominally clear that women are almost non-existent in the memory of London city compared to men.
# Creating a bar plot with custom colors
ggplot(plaques, aes(x = lead_subject_sex, fill = lead_subject_sex)) +
geom_bar() +
labs(title = "Distribution of Sexes in London Blue Plaques",
x = "Sex",
y = "Frequency") +
theme_minimal() + scale_x_discrete(labels=c("NA", "Female", "Male", "Object")) +
scale_fill_manual(name = 'Sex', values = c('red',"deeppink", "darkslateblue", "forestgreen"),labels = c("NA", "Female", "Male", "Object")) + theme(plot.title = element_text(hjust=0.5))
# Note for the grader, there is glitch in the rmd file hence the object bar does not appear here, please refer to the knitted version for it.Timeline of Sex Distribution Over Time: The line graph showing the distribution of sexes over time illustrates changing trends in the erection of plaques. The spikes and trends observed suggest that while there may be periods of increased recognition for females, these are often short-lived or inconsistent. Visualizing data over time can make visible the patterns that are not visible in static data. Our analysis suggests that despite some progress, the pace of change is slow, and the disparities remain pronounced, especially in recent decades as it has been since the beginning of the plaques were erected. In fact, the discrepancy has been following a divergent trend since 1950s an important year since the publishing of Simone de Beauvoir’s Second Sex which marked the start of second-wave feminism. As the chart reflects, it is indeed visible that there was a slight upward trend in the female representation in the erecting of the plaques. However, despite the ongoing struggles and centuries long women’s movement to reclaim their private and public spaces, our data also shows that women are still excluded from the public spaces.
# Creating a bar plot with custom colors
ggplot(plaques, aes(x = erected, colour = lead_subject_sex)) +
geom_freqpoly(binwidth=10) + scale_color_discrete(name = 'Sex',labels= c("Female", "Male", "Object")) + labs(title="Distribution of Sexes over Time", x="The Year Erected", y="The Count") + theme(plot.title = element_text(hjust=0.5))## Warning: Removed 1951 rows containing non-finite values (`stat_bin()`).
Bar Chart of Wikipedia Page Presence by Sex: This horizontal bar chart contrasts the presence of Wikipedia pages for individuals commemorated with plaques, categorized by sex. The visualization reveals a notable discrepancy: a greater percentage of males have associated Wikipedia pages compared to females and other groups. This disparity highlights an additional layer of recognition—or the lack thereof—which extends beyond physical plaques into the digital realm. However, the use of stacked bars illustrates the proportion of this digital recognition. As a matter of fact, with respect to the previous data I presented, the Wikipedia stacked bars demonstrating respective categorical variables indicate that women have been more widely represented online. Although compared to the previous charts women might seem to be more visible on Wikipedia entries, this chart might also suggest that the plaques erected for women may inadvertently be known and visible figures. To illustrate, I would like to mention Dr. Jess Wade who discovered that the Wikipedia entries for women have been less than men and started a project of her own to make more entries for women. According to an interview with her on The Guardian she makes it clear that ” many notable women working in science, technology, engineering and maths (Stem) and others from minority backgrounds did not have Wikipedia pages” (Ferguson 2023). To conclude, the comparison between the wikipedia entries and the plaques erected of women, although at first sight demonstrate that women have been more visible, may suggest that Wikipedia itself is biased towards women.
Plotting the variable leading_subject_sex:
# Assigning 1 to non-empty Wikipedia entries and 0 to empty entries
plaques$count_wiki <- as.numeric(plaques$lead_subject_wikipedia != '')
# Filtering out empty entries from the sex variable
plaques <- plaques[plaques$lead_subject_sex != '', ]
# Creating a ggplot for a horizontal bar plot with entries and non-entries stacked in the same bar, using specified colors
ggplot(plaques, aes(y=lead_subject_sex, fill=as.factor(count_wiki))) +
geom_bar(stat="count") + # Default stat="count" is used for counting entries
scale_fill_brewer(palette="Set2", labels=c("No Wikipedia Page", "Has Wikipedia Page")) +
ggtitle("Distribution of Wikipedia Entries by Sex") +
xlab("Count of Wikipedia Entries") + ylab("Sex") +
theme_minimal() +
theme(plot.title = element_text(hjust=0.5), axis.title.x = element_text(vjust=-0.2)) +
labs(fill="Legend") 
Conclusion
This analysis of commemorative plaques in London has illuminated significant gender disparities in historical recognition. My visualisations have highlighted that males are predominantly commemorated, with females and other groups markedly underrepresented. This trend is consistent over time, though there are minor improvements in more recent years. My findings resonate with the ongoing discussions in societal recognition and the need for broader historical inclusivity of the second-class citizens. The underrepresentation observed underscores the importance of initiatives aimed at achieving greater equity in how historical figures are remembered and celebrated.
Future explorations could benefit from a variety of additional visualizations:
Network Graphs: To examine the connections between commemorated individuals, potentially revealing influential networks or clusters that might influence plaque dedications.
Geospatial Analysis: Mapping the locations of plaques to explore geographic patterns and their correlation with historical or demographic factors could provide insights into the socio-political landscape influencing plaque placements.