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Monthly Archives: March 2016

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.

Python Geospatial Analysis Cookbook

Geospatial development links your data to places on the Earth’s surface. Its analysis is used in almost every industry to answer location type questions. Combined with the power of the Python programming language, which is becoming the de facto spatial scripting choice for developers and analysts worldwide, this technology will help you to solve real-world spatial problems. This book begins by tackling the installation of the necessary software dependencies and libraries needed to perform spatial analysis with Python. From there, the next logical step is to prepare our data for analysis; we will do this by building up our tool box to deal with data preparation, transformations, and projections. Now that our data is ready for analysis, we will tackle the most common analysis methods for vector and raster data. To check or validate our results, we will explore how to use topology checks to ensure top-quality results. This is followed with network routing analysis focused on constructing indoor routes within buildings, over different levels. Finally, we put several recipes together in a GeoDjango web application that demonstrates a working indoor routing spatial analysis application. The round trip will provide you all the pieces you need to accomplish your own spatial analysis application to suit your requirements.

Building Mapping Applications with QGIS

QGIS is one of the premiere open source Geographical Information Systems. While developing Python geospatial applications can be challenging, QGIS simplifies the process by combining the necessary geoprocessing libraries with a sophisticated user interface, all of which can be directly controlled using Python code. Starting with an introduction to QGIS and how to use the built-in QGIS Python Console, we will teach you how to write Python code that makes use of the geospatial capabilities of QGIS. Building on this, you will ultimately learn how to create your own sophisticated standalone mapping applications built on top of QGIS. You will learn how to use the Python Console as a window into the QGIS programming environment, and then use that environment to create your own Python scripts and plugins to customize QGIS.

Python Geospatial Development

Geospatial development links your data to places on the Earth’s surface. Writing geospatial programs involves tasks such as grouping data by location, storing and analyzing large amounts of spatial information, performing complex geospatial calculations, and drawing colorful interactive maps. In order to do this well, you’ll need appropriate tools and techniques, as well as a thorough understanding of geospatial concepts such as map projections, datums and coordinate systems. Python Geospatial Development – 2nd Edition teaches you everything you need to know about writing geospatial applications using Python. No prior knowledge of geospatial concepts, tools or techniques is required. The book guides you through the process of installing and using various toolkits, obtaining geospatial data for use in your programs, and building complete and sophisticated geospatial applications in Python.

Microsoft Mapping

Geospatial mapping applications have become hugely popular in recent years. With smart-phone and tablet numbers snow-balling this trend looks set to continue well into the future. Indeed, it is true to say that in today’s mobile world location-aware apps are becoming the norm rather than the exception. In Microsoft Mapping author Ray Rischpater showcases Microsoft’s Bing Maps API and demonstrates how its integration features make it by far the strongest mapping candidate for business that are already using Windows 8 or the .NET Framework. Whether you want to build a new app from scratch of add a few modest geospatial features to your existing website Ray’s carefully chosen examples will provide you with both the inspiration and the code you need to achieve your goals.

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.