By the end of this talk, audience members will be empowered with the tools they need to help identify and bring light to important problems affecting their cities. To achieve this, I show how to combine data on urban structure from OpenStreetMap and demographic data from the German Census in PostgreSQL. Once the data is gathered, I then show how to do the actual analysis and present the findings with Python.

The presentation will be broken up into the following sections:

Laying the foundation

The first step is creating an organized database that will serve as the data source for the rest of the study. I show how to use "PgOSM Flex" for this plus a tool that I wrote in Python to make it easy to import German Census data into PostgreSQL.

Asking meaningful questions

With all the data in place, it's time to formulate a research question to drive our analysis. Formulating a meaningful research question can keep our analysis on track and much better organized. To get there, we explore the data we have available and consider the types of questions we can actually answer.

Analyze and present

Now that we have a clear question in mind, we'll construct the queries we need to generate the data necessary for our analysis. Once exported from PostgreSQL, we perform the analysis and generate the final reports using popular scientific libraries in Python.

Final thoughts

To conclude the talk, I share how this analysis could be extended by including even more datasets. I also discuss the limitations of these types of studies while offering practical advice on how you can make a positive impact with your research.