Book Image

Programming ArcGIS 10.1 with Python Cookbook

By : Donald Eric Pimpler, Eric Pimpler
Book Image

Programming ArcGIS 10.1 with Python Cookbook

By: Donald Eric Pimpler, Eric Pimpler

Overview of this book

ArcGIS is an industry standard geographic information system from ESRI.This book will show you how to use the Python programming language to create geoprocessing scripts, tools, and shortcuts for the ArcGIS Desktop environment.This book will make you a more effective and efficient GIS professional by showing you how to use the Python programming language with ArcGIS Desktop to automate geoprocessing tasks, manage map documents and layers, find and fix broken data links, edit data in feature classes and tables, and much more."Programming ArcGIS 10.1 with Python Cookbook" starts by covering fundamental Python programming concepts in an ArcGIS Desktop context. Using a how-to instruction style you'll then learn how to use Python to automate common important ArcGIS geoprocessing tasks.In this book you will also cover specific ArcGIS scripting topics which will help save you time and effort when working with ArcGIS. Topics include managing map document files, automating map production and printing, finding and fixing broken data sources, creating custom geoprocessing tools, and working with feature classes and tables, among others.In "Python ArcGIS 10.1 Programming Cookbook" you'll learn how to write geoprocessing scripts using a pragmatic approach designed around an approach of accomplishing specific tasks in a Cookbook style format.
Table of Contents (21 chapters)
Programming ArcGIS 10.1 with Python Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Introduction


Selecting features from a geographic layer or rows from a standalone attribute table is one of the most common GIS operations. Queries are created to enable these selections, and can be either attribute or spatial queries. Attribute queries use SQL statements to select features or rows through the use of one or more fields or columns in a dataset. An example attribute query would be "Select all land parcels with a property value greater than $500,000". Spatial queries are used to select features based on some type of spatial relationship. An example might be "Select all land parcels that intersect a 100 year floodplain" or perhaps "Select all streets that are completely within Travis County, Texas". It is also possible to combine attribute and spatial queries. An example might be "Select all land parcels that intersect the 100 year floodplain and have a property value greater than $500,000".