Python For ArcGIS
This book introduces Python scripting for geographic information science (GIS) workflow optimization using ArcGIS. It builds essential programming skills for automating GIS analysis. Over 200 sample Python scripts and 175 classroom-tested exercises reinfo
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Python For ArcGIS
Python For ArcGIS
Laura Tateosian
Python For ArcGIS
Laura Tateosian North Carolina State University Raleigh, NC, USA
ISBN 978-3-319-18397-8 ISBN 978-3-319-18398-5 DOI 10.1007/978-3-319-18398-5
(eBook)
Library of Congress Control Number: 2015943490 Springer Cham Heidelberg New York Dordrecht London © Springer International Publishing Switzerland 2015 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Esri images are used by permission. Copyright © 2015 Esri. All rights reserved. Python is copyright the Python Software Foundation. Used by permission. PyScripter is an OpenSource software authored by Kiriakos Vlahos. Printed on acid-free paper Springer International Publishing AG Switzerland is part of Springer Science+Business Media (www.springer.com)
Preface
Imagine... You’ve just begun a new job as a GIS specialist for the National Park Service. Your supervisor has asked you to analyze some wildlife data. She gives you a specific example to start with: One data table (Bird Species) contains a list of over 600 bird species known to be native to North Carolina. Another data table (Bird Inventory) contains over 5,000 records, each corresponding to a sighting of a particular bird. Your task is to clean the data, reformat the file for GIS compatibility, and summarize the data by determining what percent of the native Bird Species appear in the inventory and map the results. Once you complete this, she would like
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Preface
you to repeat this process for historical datasets for the last 10 years of monthly records. After that, the next assignment will be to answer the same question based on monthly species inventory datasets for fish and invertebrates. Performing this process manually for one dataset could be time consuming and error prone. Performing this manually for numerous datasets is completely impractical. Common GIS tasks such as this provide a strong motivation for learning how to automate workflows. Python programming to the rescue! This analysi
