Using geospatial data is easier than ever. Data can easily be found on open data portals or simply on Open Street Map. But how can we use this data? What kind of tools are there to store and analyse this data in an optimal manner? During this talk we will answer these questions. We will discuss particularly about PostGIS, an extension of PostgreSQL database, and GeoAlchemy, a Python library.
Erik Brynjolfsson, a professor at MIT, once said that "Every time we invent something, we make it easier to invent something else". That is probably especially true in the digital space, given the pace at which technology is evolving. While working on a pricing model that is evaluating the price of apartments in Italy, based on information such as location, size or status, we realized that it would be very useful to have more information about the area where the apartments are located. This helped us discover that finding open geospatial data is not very difficult these days, but processing and storing it require some particular skills and tools. PostGis is an open source extension of the PostgreSQL Database Management system that allows users to store geospatial data as specific data types (geometries and geographies). In addition, it helps the user handle this data using some specific spatial functions such as distance, area, etc. SQLAlchemy is a SQL library of Python implementing an object-relational mapping concept. The advantage of this library is that developers no longer need to write SQL queries in their Python code when working with a database, but they can write Python classes instead that are translated to SQL statements. GeoAlchemy is an extension of SQLAlchemy that facilitates working with spatial databases, such as PostGIS. During this talk we will shortly talk about the use-case that required the use of the tools mentioned above and walk you though an example using data from Open Street Map, stored in geojson files. Our aim is to raise awareness in the audience about how geospatial data can be manipulated and stored using Python and open source database management systems. The talk is aimed at scientists and developers interested in working with geospatial data using Python. There are no advanced topics in the talk, but in order to get most of the talk you should have some knowledge of Python and SQL.