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.
Chapter 1, Setting Up Your Geospatial Python Environment, explores setting up your computer to handle all software requirements in one go, such as pyproj, NumPy, and Shapely. All your development software needs to enable spatial analysis or geoprocessing on Windows and Linux are met in this chapter.
Chapter 2, Working with Projections, explains how to deal with spatial data that's projected or unprojected. You can learn and discover how to transform your data into a correct projection to prepare for an analysis.
Chapter 3, Moving Spatial Data from One Format to Another, explains how geospatial data comes in many different formats and also how messaging data from one format to another is a daily chore. In this chapter, you will find out about the most common data management tasks.
Chapter 4, Working with PostGIS, shows you how most of our geospatial data is stored in a spatial database and using, accessing, manipulating this data with Python is what this chapter is about.
Chapter 5, Vector Analysis, introduces a very common geospatial data format, that is, the vector data format. To execute analysis functions on vector data, we will explore patterns used to create new data by snapping, clipping, cutting, and overlaying vector datasets followed by determining the 3D ground distance and total elevation gain.
Chapter 6, Overlay Analysis, explains how to combine spatial data to create new data by using the process of overlaying two sets of data over each other.
Chapter 7, Raster Analysis, shows you how to create an elevation profile and quick ways to merge images to perform raster analysis functions on your data.
Chapter 8, Network Routing Analysis, shows you how finding the nearest anything is a common geospatial analysis feature. This chapter will disclose how to go about solving an indoor network type problem and demonstrate some common use cases for wayfinding inside buildings.
Chapter 9, Topology Checking and Data Validation, covers data quality and connections. In this chapter, you will learn how to verify your data for errors using custom topological functions.
Chapter 10, Visualizing Your Analysis, explains how geospatial data is inherently visual and you will learn about presenting your analysis on a web map and a 3D web.
Chapter 11, Web Analysis with GeoDjango, builds on Chapter 8, Network Routing Analysis, where you will create an indoor routing web application. You will easily be able to route a person from point A to point B within a building with real 3D network data. These key features will be presented by bringing together all the parts of the recipes you have learned so far.
Appendix A, Other Geospatial Python Libraries, explains how Python flourishes with geospatial libraries, and you will also find a listing of many popular libraries that are used for data analysis, regardless of whether they're spatial or not. This may trigger your interest.
Appendix B, Mapping Icon Libraries, quickly goes over the icon libraries out there that play a special role in the python geospatial working environment.
To work with this book, you should be familiar with the programming language Python and the concepts involved in programming. This means that you should be able to install Python 2.7.x on your machine (Windows, Linux, or OS X) if it's not already installed. The concepts related to GIS (Geographic Information Systems) are definitely helpful but not necessary. A primer to this book could be Learning Geospatial Analysis with Python, Joel Lawhead or Python Geospatial Development, Eric Westra, both by Packt Publishing.
If you are a student, teacher, programmer, geospatial or IT administrator, GIS analyst, researcher, or scientist looking to learn about spatial analysis, then this book is for you. Anyone trying to answer simple to complex spatial analysis questions will get a working demonstration of the power of Python with the help of real-world data. Some of you may be beginners but most of you will probably have a basic understanding of geospatial analysis and programming.
In this book, you will find several headings that appear frequently (Getting ready, How to do it..., How it works..., There's more..., and See also).
To give clear instructions on how to complete a recipe, we use these sections as follows:
This section tells you what to expect in the recipe, and describes how to set up any software or any preliminary settings required for the recipe.
This section usually consists of a detailed explanation of what happened in the previous section.
This section consists of additional information about the recipe in order to make the reader more knowledgeable about the recipe.
In this book, you will find a number of styles of text that distinguish between different kinds of information. Here are some examples of these styles, and an explanation of their meaning.
Code words in text are shown as follows: "If workon for some reason does not start your virtual environment, you can start it simply by executing source /home/mdiener/.venvs/pygeoan_cb/bin/activate from the command line."
A block of code is set as follows:
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from osgeo import ogr
shp_driver = ogr.GetDriverByName('ESRI Shapefile')
shp_dataset = shp_driver.Open(r'../geodata/schools.shp')
shp_layer = shp_dataset.GetLayer()
shp_srs = shp_layer.GetSpatialRef()
print shp_srsAny command-line input or output is written as follows:
$ sudo apt-get install python-setuptools python-pip
New terms and important words are shown in bold. Words that you see on the screen, in menus or dialog boxes for example, appear in the text like this: "Select Route To: and enter 2 to see the second floor options."
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