Now that we have learned that location data is beyond mapping, and specifically is manipulation and processing of geographic data and applying analytical methods, we will move into the interdisciplinarity of location data science. We have also studied location data intelligence and how insights are derived from location data by illustrating this with diagrams. But how is location data science (spatial data science) different than data science? How do they relate to each other? In this section, we will cover the commonalities as well as differences between location data science and data science as a discipline.
Location data science versus data science
Data science
What is data science? Data science as a field consists of computer science, mathematics and statistics, and domain expertise and is generally referred to as the process of extracting insights and useful information from data. Mostly, it involves importing data and tidying it to make it ready for analysis. An iterative process of data science also implies transforming, visualizing, and modeling data to understand phenomena and hidden patterns within the data. The final process in data science which is often explored less, is to communicate the insights. Now you may realize from what we have covered so far that this is not far from location intelligence, and that is right. The location dimension is critical in many domains and applications with data science. Next, let's look at what spatial data science.
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.
