Geospatial Data Science Quick Start Guide - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Geospatial Data Science Quick Start Guide write by Abdishakur Hassan. This book was released on 2019-05-31. Geospatial Data Science Quick Start Guide available in PDF, EPUB and Kindle. Discover the power of location data to build effective, intelligent data models with Geospatial ecosystems Key FeaturesManipulate location-based data and create intelligent geospatial data modelsBuild effective location recommendation systems used by popular companies such as UberA hands-on guide to help you consume spatial data and parallelize GIS operations effectivelyBook Description Data scientists, who have access to vast data streams, are a bit myopic when it comes to intrinsic and extrinsic location-based data and are missing out on the intelligence it can provide to their models. This book demonstrates effective techniques for using the power of data science and geospatial intelligence to build effective, intelligent data models that make use of location-based data to give useful predictions and analyses. This book begins with a quick overview of the fundamentals of location-based data and how techniques such as Exploratory Data Analysis can be applied to it. We then delve into spatial operations such as computing distances, areas, extents, centroids, buffer polygons, intersecting geometries, geocoding, and more, which adds additional context to location data. Moving ahead, you will learn how to quickly build and deploy a geo-fencing system using Python. Lastly, you will learn how to leverage geospatial analysis techniques in popular recommendation systems such as collaborative filtering and location-based recommendations, and more. By the end of the book, you will be a rockstar when it comes to performing geospatial analysis with ease. What you will learnLearn how companies now use location dataSet up your Python environment and install Python geospatial packagesVisualize spatial data as graphsExtract geometry from spatial dataPerform spatial regression from scratchBuild web applications which dynamically references geospatial dataWho this book is for Data Scientists who would like to leverage location-based data and want to use location-based intelligence in their data models will find this book useful. This book is also for GIS developers who wish to incorporate data analysis in their projects. Knowledge of Python programming and some basic understanding of data analysis are all you need to get the most out of this book.
Geospatial Data Science Quick Start Guide
Geospatial Data Science Quick Start Guide - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Geospatial Data Science Quick Start Guide write by Abdishakur Hassan. This book was released on 2019. Geospatial Data Science Quick Start Guide available in PDF, EPUB and Kindle.
Ethics, Machine Learning, and Python in Geospatial Analysis
Ethics, Machine Learning, and Python in Geospatial Analysis - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Ethics, Machine Learning, and Python in Geospatial Analysis write by Galety, Mohammad Gouse. This book was released on 2024-04-29. Ethics, Machine Learning, and Python in Geospatial Analysis available in PDF, EPUB and Kindle. In geospatial analysis, navigating the complexities of data interpretation and analysis presents a formidable challenge. Traditional methods often need to efficiently handle vast volumes of geospatial data while providing insightful and actionable results. Scholars and practitioners grapple with manual or rule-based approaches, hindering progress in understanding and addressing pressing issues such as climate change, urbanization, and resource management. Ethics, Machine Learning, and Python in Geospatial Analysis offers a solution to the challenges faced by leveraging the extensive library support and user-friendly interface of Python and machine learning. The book’s meticulously crafted chapters guide readers through the intricacies of Python programming and its application in geospatial analysis, from fundamental concepts to advanced techniques.
Geospatial Data Science Techniques and Applications
Geospatial Data Science Techniques and Applications - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Geospatial Data Science Techniques and Applications write by Hassan A. Karimi. This book was released on 2017-10-24. Geospatial Data Science Techniques and Applications available in PDF, EPUB and Kindle. Data science has recently gained much attention for a number of reasons, and among them is Big Data. Scientists (from almost all disciplines including physics, chemistry, biology, sociology, among others) and engineers (from all fields including civil, environmental, chemical, mechanical, among others) are faced with challenges posed by data volume, variety, and velocity, or Big Data. This book is designed to highlight the unique characteristics of geospatial data, demonstrate the need to different approaches and techniques for obtaining new knowledge from raw geospatial data, and present select state-of-the-art geospatial data science techniques and how they are applied to various geoscience problems.
Applied Spatial Data Analysis with R
Applied Spatial Data Analysis with R - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Applied Spatial Data Analysis with R write by Roger S. Bivand. This book was released on 2013-06-21. Applied Spatial Data Analysis with R available in PDF, EPUB and Kindle. Applied Spatial Data Analysis with R, second edition, is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS and the handling of spatio-temporal data. The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping. The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature. All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book's own website. Compared to the first edition, the second edition covers the more systematic approach towards handling spatial data in R, as well as a number of important and widely used CRAN packages that have appeared since the first edition. This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis. It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information science and geoinformatics, the environmental sciences, ecology, public health and disease control, economics, public administration and political science. The book has a website where complete code examples, data sets, and other support material may be found: http://www.asdar-book.org. The authors have taken part in writing and maintaining software for spatial data handling and analysis with R in concert since 2003.
