Geospatial analysis is not special; it is just different when compared to other types of analysis such as financial market analysis. We work with geometry objects, such as lines, points, and polygons, and connect these geometries to attributes such as business data. We ask "where" question, such as "Where is the nearest pub?", "Where are all my customers located?", and "Where is my competition located?". The other location questions include, "Will this new building cast a shadow over the park?", "What is the shortest way to school?", "What is the safest way to school for my kids?", "Will this building block my view of the mountains?", and "Where is the optimal place to build my next store?". Identify the areas that fire trucks can reach from their station in 5 min, 10 min, or 20 min, and so on.

One thing all these questions have in common is the fact that you need to know where certain objects are located in order to answer them. Without the spatial component, you cannot answer such questions and this is what geospatial analysis is all about.

Geospatial features are laid over each other and patterns or trends are easily identified. This ability to see a pattern or trend is geospatial analysis in its simplest form.

Throughout this book, simple and complex code recipes are provided as small working models that can easily be integrated or expanded into a larger project or model.

Analysis is the fun part of GIS, and involves visualizing relationships, identifying trends, and seeing patterns that are not visible in a spreadsheet.

The Python programming language is clean, clear, and concise, making it great for beginners. It also has advanced powers for professionals to help them quickly code solutions to complex problems. Python makes visualization quick and easy for experts or beginners who work with geospatial data. It's that simple.