Hidden Patterns in Street Names: a Data Science Story [Part 1] | by Dea Bardhoshi | Jan, 2023
Hello!
I have been spending some time recently compiling datasets and reading up on research on my home country of Albania, attempting to understand it from a data-driven lens. In addition to my own anecdotal experiences, I wanted some systematic method of analysing the country’s social, political and lived contexts. One aspect of Albania that recently piqued my curiosity were street names and their patterns. Looking online, I could find no pre-existing dataset that looked at how the roads and streets of the country are named, so I set out to create and analyze one.
For this story, I am going to be using Open Street Map data to analyse gender distributions in street names in Albania’s capital Tirana, as well as other historical or geospatial patterns. To do this, I’ll also use pandas, seaborn and a mapping library called contextily for prettier maps. This first part will focus on gender and street names, but I will be working on a part two looking at years of activity as well as areas of contribution over the next few weeks.
So, let’s get started!
Data Labeling and Visualizations
Here is a peek at how the data from Open Street Map was provided, with a specified set of coordinates to cover the entire area of Tirana:
The data contains 26950 rows, most with a geometry and other associated road type attributes. Interestingly, the same street showed up multiple times in the data, presumably with different sections of a street being mapped at one time. Since the data did not contain a variable classifying names according to their gender, and there is no straightforward way of matching names in Albanian to a particular gender using code, I used my own knowledge of the language to hand-label about half of the dataframe’s rows. The column contained one of the following labels:
- W = women names
- M = men names
- O = other types of names (such as historical events, abstract concepts or last names of famous families)
Here is a look at the counts for each gender:
As you can see, women street names make up about 3.3% of the total street names in Tirana, with men names comprising about 71% of the names. This situation is not unique to Albania: 2% of Paris’ streets are named after women and in Rome the number is 3.5%. In these major cities, part of the reason is that city councils which make these decisions historically tended to be overwhelmingly male and white. Indeed, only an average of 36% of local government members across the world are women, with many countries falling significantly short of even this value: for instance only about 25 countries have a 40% women representation in local governments. That being said, the most recent Tirana city council legislature has a 50–50 men to women percentage, which is a step in the right direction.
Street Types and Lengths
OpenStreetMap data also includes tags describing the type of each street row in the data frame, and here is a visualization of how the types compare for genders:
It is interesting to note that there are so many “residential” streets. According to OpenStreetMap the residential tag is “used on roads that provide access to, or within, residential areas but which are not normally used as through routes”. “Living” streets are also very common, defined as roads that “have lower speed limits, and special traffic and parking rules compared to streets tagged using residential”. These are the two types of streets that dominate in Tirana even though they are not main streets, but rather narrower and with less traffic. So, it could be interesting to look into the processes of how they get named.
Let’s take a look at the total lengths of all streets grouped by gender. To do this, I projected the linestring geometries to a projected coordinate system that used meters and averaged their length by gender:
Interestingly, “Other” streets are the longest on average, and streets named after women are slightly shorter than those named after men. It also appears that “Other” streets are found in highways or peripheral streets that are away from the urban core of Tirana:
This can be explained by the fact that highways or other inter-city streets had names such as “Tirane-Durres” signifying the two urban centers they were connecting or simply highway codes such as “SH1”. Given that, I labelled these streets as “Other”.
And here are the maps for streets named after men vs. women:
Professions and Work
In what area has each of these women’s contribution been? I divided the professions in a couple of categories and here are the results (some of the figures had no available information online):
My categories were (broadly):
- Art, Teacher/Writer/Researcher, Politics, Humanitarian and Religious and War (for some context, many of these women fought alongside men in WWII and they represent the majority of women belonging to the “War” category):
Neighborhoods
In addition to fields of contribution, are there any patterns in how the street names are divided by neighborhood? Tirana has 14 administrative areas dividing the city into distinct zones. Using a GeoJSON file from OpenStreetMaps that shows the polygons of each of these areas, we can perform a spatial join of this dataset with that of the street names. Here is a map of the 14 areas:
I’m interested to see what proportion of the street names in each administrative area are women:
There are some interesting findings (at least to me 🙂 ) . There are quite a few discrepancies in the proportions of women’s names, with areas like no. 4 having almost 10% of streets named after women and no. 8 having 0.08%. On the other hand, two of the areas (12, 14) had no streets named after women. Again, it would be interesting to look into the decision-making processes behind these naming choices.
Conclusion
To conclude, this story looked into how Tirana’s (Albania) streets were named, focusing on the gender composition as well as patterns in different areas of the city and areas of contribution. A later second part will look at other aspects of the historical figures represented on the street names but for now here are links to the Jupyter Notebook and dataset.
Thank you for reading!
Hello!
I have been spending some time recently compiling datasets and reading up on research on my home country of Albania, attempting to understand it from a data-driven lens. In addition to my own anecdotal experiences, I wanted some systematic method of analysing the country’s social, political and lived contexts. One aspect of Albania that recently piqued my curiosity were street names and their patterns. Looking online, I could find no pre-existing dataset that looked at how the roads and streets of the country are named, so I set out to create and analyze one.
For this story, I am going to be using Open Street Map data to analyse gender distributions in street names in Albania’s capital Tirana, as well as other historical or geospatial patterns. To do this, I’ll also use pandas, seaborn and a mapping library called contextily for prettier maps. This first part will focus on gender and street names, but I will be working on a part two looking at years of activity as well as areas of contribution over the next few weeks.
So, let’s get started!
Data Labeling and Visualizations
Here is a peek at how the data from Open Street Map was provided, with a specified set of coordinates to cover the entire area of Tirana:
The data contains 26950 rows, most with a geometry and other associated road type attributes. Interestingly, the same street showed up multiple times in the data, presumably with different sections of a street being mapped at one time. Since the data did not contain a variable classifying names according to their gender, and there is no straightforward way of matching names in Albanian to a particular gender using code, I used my own knowledge of the language to hand-label about half of the dataframe’s rows. The column contained one of the following labels:
- W = women names
- M = men names
- O = other types of names (such as historical events, abstract concepts or last names of famous families)
Here is a look at the counts for each gender:
As you can see, women street names make up about 3.3% of the total street names in Tirana, with men names comprising about 71% of the names. This situation is not unique to Albania: 2% of Paris’ streets are named after women and in Rome the number is 3.5%. In these major cities, part of the reason is that city councils which make these decisions historically tended to be overwhelmingly male and white. Indeed, only an average of 36% of local government members across the world are women, with many countries falling significantly short of even this value: for instance only about 25 countries have a 40% women representation in local governments. That being said, the most recent Tirana city council legislature has a 50–50 men to women percentage, which is a step in the right direction.
Street Types and Lengths
OpenStreetMap data also includes tags describing the type of each street row in the data frame, and here is a visualization of how the types compare for genders:
It is interesting to note that there are so many “residential” streets. According to OpenStreetMap the residential tag is “used on roads that provide access to, or within, residential areas but which are not normally used as through routes”. “Living” streets are also very common, defined as roads that “have lower speed limits, and special traffic and parking rules compared to streets tagged using residential”. These are the two types of streets that dominate in Tirana even though they are not main streets, but rather narrower and with less traffic. So, it could be interesting to look into the processes of how they get named.
Let’s take a look at the total lengths of all streets grouped by gender. To do this, I projected the linestring geometries to a projected coordinate system that used meters and averaged their length by gender:
Interestingly, “Other” streets are the longest on average, and streets named after women are slightly shorter than those named after men. It also appears that “Other” streets are found in highways or peripheral streets that are away from the urban core of Tirana:
This can be explained by the fact that highways or other inter-city streets had names such as “Tirane-Durres” signifying the two urban centers they were connecting or simply highway codes such as “SH1”. Given that, I labelled these streets as “Other”.
And here are the maps for streets named after men vs. women:
Professions and Work
In what area has each of these women’s contribution been? I divided the professions in a couple of categories and here are the results (some of the figures had no available information online):
My categories were (broadly):
- Art, Teacher/Writer/Researcher, Politics, Humanitarian and Religious and War (for some context, many of these women fought alongside men in WWII and they represent the majority of women belonging to the “War” category):
Neighborhoods
In addition to fields of contribution, are there any patterns in how the street names are divided by neighborhood? Tirana has 14 administrative areas dividing the city into distinct zones. Using a GeoJSON file from OpenStreetMaps that shows the polygons of each of these areas, we can perform a spatial join of this dataset with that of the street names. Here is a map of the 14 areas:
I’m interested to see what proportion of the street names in each administrative area are women:
There are some interesting findings (at least to me 🙂 ) . There are quite a few discrepancies in the proportions of women’s names, with areas like no. 4 having almost 10% of streets named after women and no. 8 having 0.08%. On the other hand, two of the areas (12, 14) had no streets named after women. Again, it would be interesting to look into the decision-making processes behind these naming choices.
Conclusion
To conclude, this story looked into how Tirana’s (Albania) streets were named, focusing on the gender composition as well as patterns in different areas of the city and areas of contribution. A later second part will look at other aspects of the historical figures represented on the street names but for now here are links to the Jupyter Notebook and dataset.
Thank you for reading!
Denial of responsibility! Techno Blender is an automatic aggregator of the all world’s media. In each content, the hyperlink to the primary source is specified. All trademarks belong to their rightful owners, all materials to their authors. If you are the owner of the content and do not want us to publish your materials, please contact us by email – [email protected]. The content will be deleted within 24 hours.