Location (spatial) data science

Adding location data and the underlying spatial science entails additional challenges and opportunities. It will form a combination of the interdisciplinary field consisting of computer science, mathematics and statistics, domain expertise, and spatial science. This does not only indicate the addition of spatial science but also whole new concepts, theories, and the application of spatial and location analysis, including spatial patterns, location clusters, hot spots, location optimization, and decision-making, as well as spatial autocorrelation and spatial exploratory data analysis. For example, in data science, histograms and scatter plots are used for data distributions analysis, but this won't help with location data analysis, as it requires specific methods, such as spatial autocorrelation and spatial distribution to get location insights.

To get the reader up and running quickly and without burdening the local setup of Python environments, we will use Google Colab Jupyter Notebooks in this book. In the next section, we will cover a primer on how to use Google Colab and Jupyter Notebooks.