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.
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. 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."--Provided by publisher.
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 2019-05-31. 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 Analytics and Urban Applications
Geospatial Data Analytics and Urban 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 Analytics and Urban Applications write by Sandeep Narayan Kundu. This book was released on 2022-01-03. Geospatial Data Analytics and Urban Applications available in PDF, EPUB and Kindle. This book highlights advanced applications of geospatial data analytics to address real-world issues in urban society. With a connected world, we are generating spatial at unprecedented rates which can be harnessed for insightful analytics which define the way we analyze past events and define the future directions. This book is an anthology of applications of spatial data and analytics performed on them for gaining insights which can be used for problem solving in an urban setting. Each chapter is contributed by spatially aware data scientists in the making who present spatial perspectives drawn on spatial big data. The book shall benefit mature researchers and student alike to discourse a variety of urban applications which display the use of machine learning algorithms on spatial big data for real-world problem solving.
Emerging Trends, Techniques, and Applications in Geospatial Data Science
Emerging Trends, Techniques, and Applications in Geospatial Data Science - 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 Emerging Trends, Techniques, and Applications in Geospatial Data Science write by Gaur, Loveleen. This book was released on 2023-04-24. Emerging Trends, Techniques, and Applications in Geospatial Data Science available in PDF, EPUB and Kindle. With the emergence of smart technology and automated systems in today’s world, big data is being incorporated into many applications. Trends in data can be detected and objects can be tracked based on the real-time data that is utilized in everyday life. These connected sensor devices and objects will provide a large amount of data that is to be analyzed quickly, as it can accelerate the transformation of smart technology. The accuracy of prediction of artificial intelligence (AI) systems is drastically increasing by using machine learning and other probability and statistical approaches. Big data and geospatial data help to solve complex issues and play a vital role in future applications. Emerging Trends, Techniques, and Applications in Geospatial Data Science provides an overview of the basic concepts of data science, related tools and technologies, and algorithms for managing the relevant challenges in real-time application domains. The book covers a detailed description for readers with practical ideas using AI, the internet of things (IoT), and machine learning to deal with the analysis, modeling, and predictions from big data. Covering topics such as field spectra, high-resolution sensing imagery, and spatiotemporal data engineering, this premier reference source is an excellent resource for data scientists, computer and IT professionals, managers, mathematicians and statisticians, health professionals, technology developers, students and educators of higher education, librarians, researchers, and academicians.
