Everything is becoming smarter: we have smartphones, vacuums that remember our house layouts, smart homes that let us transform manual tasks into automatic ones, smart maps, etc. GeoDjango is a module of Django, a popular Python framework. It helps developers create location-based applications (for instance, Tinder).
In this GeoDjango tutorial, we will be working in a Linux environment (Ubuntu Linux 16.04) to create a smart map. The latter concept refers to a map that presents information. If you are using a different operating system, you can run Linux alongside it. Another option is that you can download a virtual machine such as Vmware Player or Virtual Box.
GeoDjango is the module to use when you need to create a data-driven map. It makes it rather easy to create Python GIS (geographic information system). The latter refers to using and managing location-based information. While there are many opinions on GIS, I describe GIS as the way of visualizing data as a map.
Such representation makes data more approachable, with people more likely to examine data in a map rather than in a long spreadsheet. While some Python GIS projects might be full-fledged, this tutorial shows you how to create a basic data-driven map.
In this GeoDjango tutorial, we will be representing data in the form of a map. The idea behind this project is that we will use a variety of different resources to display the differences between the water intake in the suburbs of Cape Town.
This course on GeoDjango and the development of GIS systems takes a very practical approach, helping you learn through a real-life project. Therefore, it will serve you as the baseline for your future GIS projects. Let’s start building this GeoDjango application and learning how to use maps as one of the data visualization options!
Edwin Bomela is a Big Data Engineer and Consultant, involved in multiple projects ranging from Business Intelligence, Software Engineering, IoT and Big data analytics. Expertise are in building data processing pipelines in the Hadoop and Cloud ecosystems and software development.
He is currently a consulting at one of the top business intelligence consultancies helping clients build data warehouses, data lakes, cloud data processing pipelines and machine learning pipelines. The technologies he uses to accomplish client requirements range from Hadoop, Amazon S3, Python, Django, Apache Spark, MSBI, Microsoft Azure, SQL Server Data Tools, Talend and Elastic MapReduce.