Hidden Patterns in Street Names [Part 2] | by Dea Bardhoshi | Mar, 2023
Analyzing streets’ names in Tirana, Albania using data science
Hello!
This is part two of my story on street names in Tirana (you can find part 1 here: Hidden Patterns in Street Names: a Data Science Story [Part 1]). In the first part, we looked into overall gender distributions and how they changed based on neighborhood and road type. For this follow-up post, I will be focusing on occupations and the historical period these figures lived in. Let’s get started!
The Data and Tools
As with the first blog, in order to match figures with their birth and death dates and their occupations, I hand-labeled part of the data set obtained from OpenStreetMaps (licensing: CC BY-SA 2.0). Specifically, I used the primary occupation listed for a particular historical figure in Wikipedia and relied on the birth/death dates listed there to add these details into the data. In total, I labeled 643 unique streets divided into segments throughout the data, of which a significant proportion had no information online (more on that at the end).
Again for this second part, I’ll be using pandas, seaborn and a few geospatial Python libraries to analyze the resulting dataset and create various visualizations.
Occupations and Historical Periods
When labelling the data, I classified each person into a few broad categories depending on their Wikipedia page. It turns out there are 62 unique occupations among the street names! Here is a bar plot of the top 20 and their respective counts:
Note the prominence of politicians, fighters and writers: these 3 categories together make up approximately 40 percent of all labelled street names in the data. Artists follow suit, rounding out the rest of the top 10. I made the choice to label partisan figures (8% of labelled total) separately from the rest of fighters, because of their direct associations with the period of the Communist regime in Albania (1946–1991) and the propaganda they were made a part of during this time. Therefore, I believe this deserves its own consideration as part of a broader conversation on how we reckon with our history as a country.
Let’s take a better look into the top 3 categories: politicians, writers and fighters. Specifically, what are the historical periods they lived and worked in? Here is a distribution of the birth and death years, as well as the mean values for both of them:
- Mean birth year: Politicians (1882), Writers (1858), Fighters (1853)
- Mean death year: Politicians (1936), Writers (1925), Fighters (1900)
There are some interesting patterns to be noticed in the graphs and the average statistics above: firstly, it seems the majority of these figures were active in the period between the 1850s to 1930s. This seems to be less of the case for writers, which are represented in the 17th and 18th centuries as well. There is also a peak in the “Fighter” birth and death years, corresponding to the Skanderbeg-led period of rebellion during the Ottoman Empire invasion. Skanderbeg (1405–1468) is recognized as our National Hero and is widely honored in many monuments, squares and institutions to this day. The 19th-20th century, on the other hand, corresponds to the period of the Albanian National Awakening which was a cultural and political movement seeking to establish an independent country of Albania.
Streets named after women
Last time, we looked at how women-named streets made up approximately 3% of the total names, but let’s take a look at these streets in their historical context and occupation as well. Filtering by gender and taking a look at the same birth/death distributions:
- Median birth year: 1912
- Median death year: 1949
Here, there are some interesting patterns too: first, I chose to take the median instead of the mean because it would have been greatly influenced by the small spike around the 1400s. This results in a shift in time of the era of women’s activity, compared to the overall distributions. In part 1, we saw that many of these women were in the arts or had fought in various wars, which can explain the median death year.
Mapping it out, Neighborhoods and Road Types
Let’s take a look at how all of this looks from a geography perspective. Here are three maps for each of the top 3 categories:
I would say I can’t pick out any immediate patterns just by looking at the data. To start to see if there are any, here is also a look into the most common occupations in each of Tirana’s 14 administrative areas (the polygon data for the administrative areas which is merged with the street names also comes from OpenStreetMaps):
Not surprisingly, politicians and writers still feature prominently: what I think might merit more thinking is how certain areas have religious or partisan figures as their most common occupations. This might point at particular figures originating from those neighborhoods as a form of local representation, or other interesting patterns to look into.
Names with no information on the internet
As I mentioned in the beginning, a large part of these streets’ names had no readily available information about them online. According to the labelled data, they comprised about 55% of the streets named after people. I found this to be an interesting phenomenon as it could mean many things such as these names being locally important figures but perhaps not widely recognized, or the (obvious) fact that the internet has a limited amount of information and one would need to look into other sources. Here is a visualization of where all of these names were located (note how these names feature in the periphery, rather than in the more urban core of the city):
Final Thoughts + the Code
Overall, this story looked at Tirana’s streets landscape from a more historical lens. We delved deeper into how different work areas and historical periods were represented and uncovered patterns across both these aspects. In addition, there’s a few other directions to take this project: some questions that come to mind are how the names have changed over the years as the city has grown and changed, or even what specifics to the naming process lead to the distributions we saw. For now, here is the Jupyter Notebook and the dataset for this post. Hope you enjoyed!
If you like these urban planning-themed posts, you might like my newsletter in which I talk about these topics even further: The Zoned Out Chronicles!
Analyzing streets’ names in Tirana, Albania using data science
Hello!
This is part two of my story on street names in Tirana (you can find part 1 here: Hidden Patterns in Street Names: a Data Science Story [Part 1]). In the first part, we looked into overall gender distributions and how they changed based on neighborhood and road type. For this follow-up post, I will be focusing on occupations and the historical period these figures lived in. Let’s get started!
The Data and Tools
As with the first blog, in order to match figures with their birth and death dates and their occupations, I hand-labeled part of the data set obtained from OpenStreetMaps (licensing: CC BY-SA 2.0). Specifically, I used the primary occupation listed for a particular historical figure in Wikipedia and relied on the birth/death dates listed there to add these details into the data. In total, I labeled 643 unique streets divided into segments throughout the data, of which a significant proportion had no information online (more on that at the end).
Again for this second part, I’ll be using pandas, seaborn and a few geospatial Python libraries to analyze the resulting dataset and create various visualizations.
Occupations and Historical Periods
When labelling the data, I classified each person into a few broad categories depending on their Wikipedia page. It turns out there are 62 unique occupations among the street names! Here is a bar plot of the top 20 and their respective counts:
Note the prominence of politicians, fighters and writers: these 3 categories together make up approximately 40 percent of all labelled street names in the data. Artists follow suit, rounding out the rest of the top 10. I made the choice to label partisan figures (8% of labelled total) separately from the rest of fighters, because of their direct associations with the period of the Communist regime in Albania (1946–1991) and the propaganda they were made a part of during this time. Therefore, I believe this deserves its own consideration as part of a broader conversation on how we reckon with our history as a country.
Let’s take a better look into the top 3 categories: politicians, writers and fighters. Specifically, what are the historical periods they lived and worked in? Here is a distribution of the birth and death years, as well as the mean values for both of them:
- Mean birth year: Politicians (1882), Writers (1858), Fighters (1853)
- Mean death year: Politicians (1936), Writers (1925), Fighters (1900)
There are some interesting patterns to be noticed in the graphs and the average statistics above: firstly, it seems the majority of these figures were active in the period between the 1850s to 1930s. This seems to be less of the case for writers, which are represented in the 17th and 18th centuries as well. There is also a peak in the “Fighter” birth and death years, corresponding to the Skanderbeg-led period of rebellion during the Ottoman Empire invasion. Skanderbeg (1405–1468) is recognized as our National Hero and is widely honored in many monuments, squares and institutions to this day. The 19th-20th century, on the other hand, corresponds to the period of the Albanian National Awakening which was a cultural and political movement seeking to establish an independent country of Albania.
Streets named after women
Last time, we looked at how women-named streets made up approximately 3% of the total names, but let’s take a look at these streets in their historical context and occupation as well. Filtering by gender and taking a look at the same birth/death distributions:
- Median birth year: 1912
- Median death year: 1949
Here, there are some interesting patterns too: first, I chose to take the median instead of the mean because it would have been greatly influenced by the small spike around the 1400s. This results in a shift in time of the era of women’s activity, compared to the overall distributions. In part 1, we saw that many of these women were in the arts or had fought in various wars, which can explain the median death year.
Mapping it out, Neighborhoods and Road Types
Let’s take a look at how all of this looks from a geography perspective. Here are three maps for each of the top 3 categories:
I would say I can’t pick out any immediate patterns just by looking at the data. To start to see if there are any, here is also a look into the most common occupations in each of Tirana’s 14 administrative areas (the polygon data for the administrative areas which is merged with the street names also comes from OpenStreetMaps):
Not surprisingly, politicians and writers still feature prominently: what I think might merit more thinking is how certain areas have religious or partisan figures as their most common occupations. This might point at particular figures originating from those neighborhoods as a form of local representation, or other interesting patterns to look into.
Names with no information on the internet
As I mentioned in the beginning, a large part of these streets’ names had no readily available information about them online. According to the labelled data, they comprised about 55% of the streets named after people. I found this to be an interesting phenomenon as it could mean many things such as these names being locally important figures but perhaps not widely recognized, or the (obvious) fact that the internet has a limited amount of information and one would need to look into other sources. Here is a visualization of where all of these names were located (note how these names feature in the periphery, rather than in the more urban core of the city):
Final Thoughts + the Code
Overall, this story looked at Tirana’s streets landscape from a more historical lens. We delved deeper into how different work areas and historical periods were represented and uncovered patterns across both these aspects. In addition, there’s a few other directions to take this project: some questions that come to mind are how the names have changed over the years as the city has grown and changed, or even what specifics to the naming process lead to the distributions we saw. For now, here is the Jupyter Notebook and the dataset for this post. Hope you enjoyed!
If you like these urban planning-themed posts, you might like my newsletter in which I talk about these topics even further: The Zoned Out Chronicles!
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