Book Image

Practical GIS

Book Image

Practical GIS

Overview of this book

The most commonly used GIS tools automate tasks that were historically done manually—compiling new maps by overlaying one on top of the other or physically cutting maps into pieces representing specific study areas, changing their projection, and getting meaningful results from the various layers by applying mathematical functions and operations. This book is an easy-to-follow guide to use the most matured open source GIS tools for these tasks. We’ll start by setting up the environment for the tools we use in the book. Then you will learn how to work with QGIS in order to generate useful spatial data. You will get to know the basics of queries, data management, and geoprocessing. After that, you will start to practice your knowledge on real-world examples. We will solve various types of geospatial analyses with various methods. We will start with basic GIS problems by imitating the work of an enthusiastic real estate agent, and continue with more advanced, but typical tasks by solving a decision problem. Finally, you will find out how to publish your data (and results) on the web. We will publish our data with QGIS Server and GeoServer, and create a basic web map with the API of the lightweight Leaflet web mapping library.
Table of Contents (22 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Dedication
Preface
14
Appendix

Chapter 3. Using Vector Data Effectively

In the previous chapter, we learned how vector data compares to raster data. Although every feature can only represent one coherent entity, it is a way more powerful and flexible data model. With vectors, we can store a tremendous amount of attributes linked to an arbitrary number of features. There are some limitations but only with some data exchange formats. By using spatial databases, our limitations are completely gone. If you've worked on a study area with rich data, you might have already observed that QGIS has a hard time rendering the four vector layers for their entire extent. As we can store (and often use) much more data than we need for our workflow, we must be able to select our features of interest.

Sometimes, the problem is the complete opposite--we don't have enough data. We have features which lack just the attributes we need to accomplish our work. However, we can find other datasets with the required information, possibly in a less...