Hello!
This is an element 2 of the urban resilience project (part 1 here) specializing in demographic trends in Tirana! In the primary part, we checked out power law distributions and built spatial markov models to grasp population changes over time. On this second part, I desired to delve a bit deeper into these predictions and have a look at what they mean for specific neighborhoods in Tirana. Let’s start!
Last time, I used Tirana Open Data demographics information (data license: Creative Commons Attribution) to acquire this spatial Markov model matrix:
Let’s take a have a look at what these results entail within the context of specific neighborhoods. As of 2021, probably the most populated areas of the town are Area 5, 2, 7, 4 and 11 followed closely by Kashar, a municipality outside of the bounds of Tirana proper with many latest developments. Here’s a quick visualization:
Kashar is an interesting example of periurban growth with firms like Coca-Cola, Vodafone, Top Channel and smaller businesses organising shop there. In 2009, its population was just 20829 but as of 2021, it has almost tripled to 58664 people. These areas of very rapid growth are also some with the best need for sustainable solutions: Kashar grows with about 11 latest people a day and has a comparatively young median age of 33 (source).
The opposite highest population areas have seen their very own growth previously 12 years:
Its interesting that these areas are neighboring one another: this enforces the intuition that the trends happening in places around a neighborhood likely influence the character of that neighborhood as well.
Some Examples
Let’s focus a bit on admin area #5. Its immediate neighbors are areas 7, 10 and a couple of which have populations of 77124, 27637 and 83827 respectively. In keeping with the spatial Markov results, given these neighbors, area #5 has a likelihood of about 90% of staying in the best population bin. It also has a likelihood of about 5% of falling in the primary and second bins.
Area #10 is one other neighborhood in Tirana encompassing the town square, business district (Blloku/The Block) in addition to a few of the most bustling streets of Tirana. Its 2021 population is 27637 and its neighbors have populations of 77000–87000. Based on the Markov results, it could have around a 93% likelihood of staying in its current population bin.
With regards to resilient development, cities should work towards providing high-quality resources to people living across all neighborhoods. The concept of a geographical availability of resources can be often known as spatial equity: in a city working to supply all residents access to similar opportunities, which means people would have equal access to public spaces, a clean environment and institutions similar to schools.
On this context, I need to explore the distribution of faculties as a marker of spatial equity. Are all children throughout Tirana served with accessible, high-quality schools? Are there areas which are disadvantaged? What are some school trends and patterns? For this, I’ll be using data for Tirana’s middle and first schools (together often known as “9-vjecare”) (link, licensed with a Creative Commons Attribution license). Here’s a visualization of faculty density in each of Tirana’s administrative areas:
And here is similar visualization, only specializing in the 11 urban areas:
At a look, evidently the areas with the best density are in reality those outside of the 11 most important admin areas. Namely, places like Shengjergj, Zall Bastar and Peze transform the highest 3. What does this mean for the children who attend these schools? Is it necessarily easier for them to go to highschool safely or reliably?
Here’s a street network visualization for walking from one among Kashar’s schools, “Sadik Stavileci”. The graph shows isochrones for a way far you’ll be able to travel from the college if walking in 5, 10 or quarter-hour (assuming a speed of 4.5 kilometers/hour).
As you’ll be able to see, the space kids can cover in a number of minutes might be not that great. This tool, nonetheless, is beneficial when planning out constructing projects in order that a spot is well accessible by the people meant to make use of it. What’s an inexpensive time to walk to and from school? How can we improve services like transit or biking in order that children are in a position to go to their schools safely? As a place to begin on these, it could be interesting to calculate isochrones for all of Tirana’s schools and compare them to what number of children can be inside walking distance.
Sidebar: I made these graphs using OSMnx, a network evaluation package that mixes OpenStreetMaps data in addition to network metrics. Here is the source notebook for doing this operation (isochrones).
Measuring Inequality: Spatial Autocorrelation
To measure inequalities within the spatial distribution, there’s a number of other metrics we will use. Spatial Autocorrelation is one, and it consists of computing Moran’s I (which we did in for population counts partially 1). This is completed to check the null hypothesis that schools in Tirana are distributed uniformly. The result from the test is 0.186 (p-value of 0.111).
PySAL also gives us two ways of visualizing autocorrelation: Moran’s plot and the distribution of Moran’s I under the null hypothesis:
Moran’s plot shows the # of faculties plotted agains a lagged # of faculties (obtained by multiplying the number of faculties and a spatial weights matrix). Qualitatively, we interpret the plot as showing positive spatial autocorrelation when the info points exhibit a high correlation. The distribution, alternatively, is an empirical one: it’s obtained by simulating a series of maps with randomly distributed schools counts after which calculating Moran’s I for every of them. (blue line: mean of distribution, red line: observed statistic in Tirana’s data)
📔 Conclusions + Notebook
This concludes part 2 of this project! Overall, I feel using spatial data science tools is something relatively unexplored, especially within the Albanian context, but definitely very useful. This project could possibly be enriched with more fine-grained data (as in the colleges example). Until then, here is the updated notebook.
Thanks for reading!
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