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ArcPy and ArcGIS: Geospatial Analysis with Python

ArcPy and ArcGIS - Geospatial Analysis with Python

ArcPy and ArcGIS: Geospatial Analysis with Python
by Silas Toms

ArcGIS allows for complex analyses of geographic information. The ArcPy module is used to script these ArcGIS analyses, providing a productive way to perform geo-analyses and to automate map production.

This book will guide you from basic Python scripting to advanced ArcPy script tools. This book starts off with setting up your Python environment, demonstrates a complex ArcPy script tool with multiple iterations, illustrates data access module cursors, and explains how to use ArcPy Geometry classes. Then, you will learn how to output maps using ArcPy.Mapping, and how to create ArcGIS script tools.

With the help of this book, you will be able to create repeatable analyses reducing the time-consuming nature of GIS, making you into a GIS professional as powerful as a whole team.

What You Will Learn

  • Understand how to integrate Python into ArcGIS and make GIS analysis faster and easier
  • Model an analysis and export it to Python for further improvement
  • Create Python functions from exported scripts using ArcToolbox tools to avoid repetitive code
  • Update the records of interest in your existing geospatial data automatically using data cursors
  • Add new geospatial data to existing datasets automatically from field-collected data or data produced during analysis
  • Export formatted analysis results to spreadsheets automatically
  • Update map documents with analysis-generated data and export maps to PDF or image formats
  • Create geometric networks and analyze routes using scripts

Who This Book Is For

If you are a GIS student or professional who needs an understanding of how to use ArcPy to reduce repetitive tasks and perform analysis faster, this book is for you. It is also a valuable book for Python programmers who want to understand how to automate geospatial analyses.

What are geospatial technologies

About the technologies

Geospatial technologies is a term used to describe the range of modern tools contributing to the geographic mapping and analysis of the Earth and human societies. These technologies have been evolving in some form since the first maps were drawn in prehistoric times. In the 19th century, the long important schools of cartography and mapmaking were joined by aerial photography as early cameras were sent aloft on balloons and pigeons, and then on airplanes during the 20th century. The science and art of photographic interpretation and map making was accelerated during the Second World War and during the Cold War it took on new dimensions with the advent of satellites and computers. Satellites allowed images of the Earth’s surface and human activities therein with certain limitations. Computers allowed storage and transfer of imagery together with the development of associated digital software, maps, and data sets on socioeconomic and environmental phenomena, collectively called geographic information systems (GIS). An important aspect of a GIS is its ability to assemble the range of geospatial data into a layered set of maps which allow complex themes to be analyzed and then communicated to wider audiences. This ‘layering’ is enabled by the fact that all such data includes information on its precise location on the surface of the Earth, hence the term ‘geospatial’.

Especially in the last decade, these technologies have evolved into a network of national security, scientific, and commercially operated satellites complemented by powerful desktop GIS. In addition, aerial remote sensing platforms, including unmanned aerial vehicles (e.g. the GlobalHawk reconnaissance drone), are seeing increased non-military use as well. High quality hardware and data is now available to new audiences such as universities, corporations, and non-governmental organizations. The fields and sectors deploying these technologies are currently growing at a rapid pace, informing decision makers on topics such as industrial engineering, biodiversity conservation, forest fire suppression, agricultural monitoring, humanitarian relief, and much more.

There are now a variety of types of geospatial technologies potentially applicable to human rights, including the following:

  • Remote Sensing: imagery and data collected from space- or airborne camera and sensor platforms. Some commercial satellite image providers now offer images showing details of one-meter or smaller, making these images appropriate for monitoring humanitarian needs and human rights abuses.
  • Geographic Information Systems (GIS): a suite of software tools for mapping and analyzing data which is georeferenced (assigned a specific location on the surface of the Earth, otherwise known as geospatial data). GIS can be used to detect geographic patterns in other data, such as disease clusters resulting from toxins, sub-optimal water access, etc.
  • Global Positioning System (GPS): a network of U.S. Department of Defense satellites which can give precise coordinate locations to civilian and military users with proper receiving equipment (note: a similar European system called Galileo will be operational within the next several years while a Russian system is functioning but restricted).
  • Internet Mapping Technologies: software programs like Google Earth and web features like Microsoft Virtual Earth are changing the way geospatial data is viewed and shared. The developments in user interface are also making such technologies available to a wider audience whereas traditional GIS has been reserved for specialists and those who invest time in learning complex software programs.

See detail – http://www.aaas.org

Google Maps JavaScript API Cookbook

Day by day, the use of location data is becoming more and more popular, and Google is one of the main game changers in this area. The Google Maps JavaScript API is one of the most functional and robust mapping APIs used among Geo developers. With Google Maps, you can build location-based apps, maps for mobile apps, visualize geospatial data, and customize your own maps. Google Maps JavaScript API Cookbook is a practical, hands-on guide that provides you with a number of clear, step-by-step recipes that will help you to unleash the capabilities of the Google Maps JavaScript API in conjunction with open source or commercial GIS servers and services through a number of practical examples of real world scenarios.

Learning Geospatial Analysis with Python

Learning Geospatial Analysis with Python

Learning Geospatial Analysis with Python
by Joel Lawhead

Geospatial analysis is used in almost every field you can think of from medicine, to defense, to farming. It is an approach to use statistical analysis and other informational engineering to data which has a geographical or geospatial aspect. And this typically involves applications capable of geospatial display and processing to get a compiled and useful data.

“Learning Geospatial Analysis with Python” uses the expressive and powerful Python programming language to guide you through geographic information systems, remote sensing, topography, and more. It explains how to use a framework in order to approach Geospatial analysis effectively, but on your own terms.

“Learning Geospatial Analysis with Python” starts with a background of the field, a survey of the techniques and technology used, and then splits the field into its component speciality areas: GIS, remote sensing, elevation data, advanced modelling, and real-time data.

This book will teach you everything there is to know, from using a particular software package or API to using generic algorithms that can be applied to Geospatial analysis. This book focuses on pure Python whenever possible to minimize compiling platform-dependent binaries, so that you don’t become bogged down in just getting ready to do analysis.

“Learning Geospatial Analysis with Python” will round out your technical library with handy recipes and a good understanding of a field that supplements many a modern day human endeavors.

What you will learn from this book

  • Automate Geospatial analysis workflows using Python
  • Code the simplest possible GIS in 60 lines of Python
  • Mold thematic maps with Python tools
  • Get a hold of the various forms the geospatial data comes in
  • Produce elevation contours using Python tools
  • Create flood inundation models
  • Learn Real-Time Data tracking and apply it in storm chasing

Approach

This is a tutorial-style book that helps you to perform Geospatial and GIS analysis with Python and its tools/libraries. This book will first introduce various Python-related tools/packages in the initial chapters before moving towards practical usage, examples, and implementation in specialized kinds of Geospatial data analysis.

Who this book is written for

This book is for anyone who wants to understand digital mapping and analysis and who uses Python or another scripting language for automation or crunching data manually.This book primarily targets Python developers, researchers, and analysts who want to perform Geospatial, modeling, and GIS analysis with Python.

Learning R for Geospatial Analysis

R is a simple, effective, and comprehensive programming language and environment that is gaining ever-increasing popularity among data analysts. This book provides you with the necessary skills to successfully carry out complete geospatial data analyses, from data import to presentation of results. Learning R for Geospatial Analysis is composed of step-by-step tutorials, starting with the language basics before proceeding to cover the main GIS operations and data types. Visualization of spatial data is vital either during the various analysis steps and/or as the final product, and this book shows you how to get the most out of R’s visualization capabilities. The book culminates with examples of cutting-edge applications utilizing R’s strengths as a statistical and graphical tool.