| data science and gis: Applied Geospatial Data Science with Python David S. Jordan, 2023-02-28 Intelligently connect data points and gain a deeper understanding of environmental problems through hands-on Geospatial Data Science case studies written in Python The book includes colored images of important concepts Key Features Learn how to integrate spatial data and spatial thinking into traditional data science workflows Develop a spatial perspective and learn to avoid common pitfalls along the way Gain expertise through practical case studies applicable in a variety of industries with code samples that can be reproduced and expanded Book DescriptionData scientists, when presented with a myriad of data, can often lose sight of how to present geospatial analyses in a meaningful way so that it makes sense to everyone. Using Python to visualize data helps stakeholders in less technical roles to understand the problem and seek solutions. The goal of this book is to help data scientists and GIS professionals learn and implement geospatial data science workflows using Python. Throughout this book, you’ll uncover numerous geospatial Python libraries with which you can develop end-to-end spatial data science workflows. You’ll learn how to read, process, and manipulate spatial data effectively. With data in hand, you’ll move on to crafting spatial data visualizations to better understand and tell the story of your data through static and dynamic mapping applications. As you progress through the book, you’ll find yourself developing geospatial AI and ML models focused on clustering, regression, and optimization. The use cases can be leveraged as building blocks for more advanced work in a variety of industries. By the end of the book, you’ll be able to tackle random data, find meaningful correlations, and make geospatial data models.What you will learn Understand the fundamentals needed to work with geospatial data Transition from tabular to geo-enabled data in your workflows Develop an introductory portfolio of spatial data science work using Python Gain hands-on skills with case studies relevant to different industries Discover best practices focusing on geospatial data to bring a positive change in your environment Explore solving use cases, such as traveling salesperson and vehicle routing problems Who this book is for This book is for you if you are a data scientist seeking to incorporate geospatial thinking into your workflows or a GIS professional seeking to incorporate data science methods into yours. You’ll need to have a foundational knowledge of Python for data analysis and/or data science. |
| data science and gis: Geographical Data Science and Spatial Data Analysis Lex Comber, Chris Brunsdon, 2020-12-02 We are in an age of big data where all of our everyday interactions and transactions generate data. Much of this data is spatial – it is collected some-where – and identifying analytical insight from trends and patterns in these increasing rich digital footprints presents a number of challenges. Whilst other books describe different flavours of Data Analytics in R and other programming languages, there are none that consider Spatial Data (i.e. the location attached to data), or that consider issues of inference, linking Big Data, Geography, GIS, Mapping and Spatial Analytics. This is a ‘learning by doing’ textbook, building on the previous book by the same authors, An Introduction to R for Spatial Analysis and Mapping. It details the theoretical issues in analyses of Big Spatial Data and developing practical skills in the reader for addressing these with confidence. |
| data science and gis: Geospatial Data Science Quick Start Guide Abdishakur Hassan, Jayakrishnan Vijayaraghavan, 2019-05-31 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. |
| data science and gis: Applied Spatial Data Analysis with R Roger S. Bivand, Edzer Pebesma, Virgilio Gómez-Rubio, 2013-06-21 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. |
| data science and gis: Geographic Data Science with Python Sergio Rey, Dani Arribas-Bel, Levi John Wolf, 2023-06-14 This book provides the tools, the methods, and the theory to meet the challenges of contemporary data science applied to geographic problems and data. In the new world of pervasive, large, frequent, and rapid data, there are new opportunities to understand and analyze the role of geography in everyday life. Geographic Data Science with Python introduces a new way of thinking about analysis, by using geographical and computational reasoning, it shows the reader how to unlock new insights hidden within data. Key Features: ● Showcases the excellent data science environment in Python. ● Provides examples for readers to replicate, adapt, extend, and improve. ● Covers the crucial knowledge needed by geographic data scientists. It presents concepts in a far more geographic way than competing textbooks, covering spatial data, mapping, and spatial statistics whilst covering concepts, such as clusters and outliers, as geographic concepts. Intended for data scientists, GIScientists, and geographers, the material provided in this book is of interest due to the manner in which it presents geospatial data, methods, tools, and practices in this new field. |
| data science and gis: Geocomputation with R Robin Lovelace, Jakub Nowosad, Jannes Muenchow, 2019-03-22 Geocomputation with R is for people who want to analyze, visualize and model geographic data with open source software. It is based on R, a statistical programming language that has powerful data processing, visualization, and geospatial capabilities. The book equips you with the knowledge and skills to tackle a wide range of issues manifested in geographic data, including those with scientific, societal, and environmental implications. This book will interest people from many backgrounds, especially Geographic Information Systems (GIS) users interested in applying their domain-specific knowledge in a powerful open source language for data science, and R users interested in extending their skills to handle spatial data. The book is divided into three parts: (I) Foundations, aimed at getting you up-to-speed with geographic data in R, (II) extensions, which covers advanced techniques, and (III) applications to real-world problems. The chapters cover progressively more advanced topics, with early chapters providing strong foundations on which the later chapters build. Part I describes the nature of spatial datasets in R and methods for manipulating them. It also covers geographic data import/export and transforming coordinate reference systems. Part II represents methods that build on these foundations. It covers advanced map making (including web mapping), bridges to GIS, sharing reproducible code, and how to do cross-validation in the presence of spatial autocorrelation. Part III applies the knowledge gained to tackle real-world problems, including representing and modeling transport systems, finding optimal locations for stores or services, and ecological modeling. Exercises at the end of each chapter give you the skills needed to tackle a range of geospatial problems. Solutions for each chapter and supplementary materials providing extended examples are available at https://geocompr.github.io/geocompkg/articles/. |
| data science and gis: An Introduction to R for Spatial Analysis and Mapping Chris Brunsdon, Lex Comber, 2014-04-30 In an age of big data, data journalism and with a wealth of quantitative information around us, it is not enough for students to be taught only 100 year old statistical methods using ′out of the box′ software. They need to have 21st-century analytical skills too. This is an excellent and student-friendly text from two of the world leaders in the teaching and development of spatial analysis. It shows clearly why the open source software R is not just an alternative to commercial GIS, it may actually be the better choice for mapping, analysis and for replicable research. Providing practical tips as well as fully working code, this is a practical ′how to′ guide ideal for undergraduates as well as those using R for the first time. It will be required reading on my own courses. - Richard Harris, Professor of Quantitative Social Science, University of Bristol R is a powerful open source computing tool that supports geographical analysis and mapping for the many geography and ‘non-geography’ students and researchers interested in spatial analysis and mapping. This book provides an introduction to the use of R for spatial statistical analysis, geocomputation and the analysis of geographical information for researchers collecting and using data with location attached, largely through increased GPS functionality. Brunsdon and Comber take readers from ‘zero to hero’ in spatial analysis and mapping through functions they have developed and compiled into R packages. This enables practical R applications in GIS, spatial analyses, spatial statistics, mapping, and web-scraping. Each chapter includes: Example data and commands for exploring it Scripts and coding to exemplify specific functionality Advice for developing greater understanding - through functions such as locator(), View(), and alternative coding to achieve the same ends Self-contained exercises for students to work through Embedded code within the descriptive text. This is a definitive ′how to′ that takes students - of any discipline - from coding to actual applications and uses of R. |
| data science and gis: Geospatial Data Science Techniques and Applications Hassan A. Karimi, Bobak Karimi, 2017-10-24 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. |
| data science and gis: An Introduction to Spatial Data Analysis Martin Wegmann, Jakob Schwalb-Willmann, Stefan Dech, 2020-09-14 This is a book about how ecologists can integrate remote sensing and GIS in their research. It will allow readers to get started with the application of remote sensing and to understand its potential and limitations. Using practical examples, the book covers all necessary steps from planning field campaigns to deriving ecologically relevant information through remote sensing and modelling of species distributions. An Introduction to Spatial Data Analysis introduces spatial data handling using the open source software Quantum GIS (QGIS). In addition, readers will be guided through their first steps in the R programming language. The authors explain the fundamentals of spatial data handling and analysis, empowering the reader to turn data acquired in the field into actual spatial data. Readers will learn to process and analyse spatial data of different types and interpret the data and results. After finishing this book, readers will be able to address questions such as “What is the distance to the border of the protected area?”, “Which points are located close to a road?”, “Which fraction of land cover types exist in my study area?” using different software and techniques. This book is for novice spatial data users and does not assume any prior knowledge of spatial data itself or practical experience working with such data sets. Readers will likely include student and professional ecologists, geographers and any environmental scientists or practitioners who need to collect, visualize and analyse spatial data. The software used is the widely applied open source scientific programs QGIS and R. All scripts and data sets used in the book will be provided online at book.ecosens.org. This book covers specific methods including: what to consider before collecting in situ data how to work with spatial data collected in situ the difference between raster and vector data how to acquire further vector and raster data how to create relevant environmental information how to combine and analyse in situ and remote sensing data how to create useful maps for field work and presentations how to use QGIS and R for spatial analysis how to develop analysis scripts |
| data science and gis: GIS and Spatial Analysis for the Social Sciences Robert Nash Parker, Emily K. Asencio, 2009-09-10 This is the first book to provide sociologists, criminologists, political scientists, and other social scientists with the methodological logic and techniques for doing spatial analysis in their chosen fields of inquiry. The book contains a wealth of examples as to why these techniques are worth doing, over and above conventional statistical techniques using SPSS or other statistical packages. GIS is a methodological and conceptual approach that allows for the linking together of spatial data, or data that is based on a physical space, with non-spatial data, which can be thought of as any data that contains no direct reference to physical locations. |
| data science and gis: GIS and Machine Learning for Small Area Classifications in Developing Countries Adegbola Ojo, 2020-12-29 Since the emergence of contemporary area classifications, population geography has witnessed a renaissance in the area of policy related spatial analysis. Area classifications subsume geodemographic systems which often use data mining techniques and machine learning algorithms to simplify large and complex bodies of information about people and the places in which they live, work and undertake other social activities. Outputs developed from the grouping of small geographical areas on the basis of multi- dimensional data have proved beneficial particularly for decision-making in the commercial sectors of a vast number of countries in the northern hemisphere. This book argues that small area classifications offer countries in the Global South a distinct opportunity to address human population policy related challenges in novel ways using area-based initiatives and evidence-based methods. This book exposes researchers, practitioners, and students to small area segmentation techniques for understanding, interpreting, and visualizing the configuration, dynamics, and correlates of development policy challenges at small spatial scales. It presents strategic and operational responses to these challenges in cost effective ways. Using two developing countries as case studies, the book connects new transdisciplinary ways of thinking about social and spatial inequalities from a scientific perspective with GIS and Data Science. This offers all stakeholders a framework for engaging in practical dialogue on development policy within urban and rural settings, based on real-world examples. Features: The first book to address the huge potential of small area segmentation for sustainable development, combining explanations of concepts, a range of techniques, and current applications. Includes case studies focused on core challenges that confront developing countries and provides thorough analytical appraisal of issues that resonate with audiences from the Global South. Combines GIS and machine learning methods for studying interrelated disciplines such as Demography, Urban Science, Sociology, Statistics, Sustainable Development and Public Policy. Uses a multi-method approach and analytical techniques of primary and secondary data. Embraces a balanced, chronological, and well sequenced presentation of information, which is very practical for readers. |
| data science and gis: Spatial Data Modelling for 3D GIS Alias Abdul-Rahman, Morakot Pilouk, 2007-09-23 This book covers fundamental aspects of spatial data modelling specifically on the aspect of three-dimensional (3D) modelling and structuring. Realisation of true 3D GIS spatial system needs a lot of effort, and the process is taking place in various research centres and universities in some countries. The development of spatial data modelling for 3D objects is the focus of this book. |
| data science and gis: Spatial Analysis Methods and Practice George Grekousis, 2020-06-11 An introductory overview of spatial analysis and statistics through GIS, including worked examples and critical analysis of results. |
| data science and gis: GIS Algorithms Ningchuan Xiao, 2015-11-09 Geographic information systems (GIS) have become increasingly important in helping us understand complex social, economic, and natural dynamics where spatial components play a key role. The critical algorithms used in GIS, however, are notoriously difficult to both teach and understand, in part due to the lack of a coherent representation. GIS Algorithms attempts to address this problem by combining rigorous formal language with example case studies and student exercises. Using Python code throughout, Xiao breaks the subject down into three fundamental areas: Geometric Algorithms Spatial Indexing Spatial Analysis and Modelling With its comprehensive coverage of the many algorithms involved, GIS Algorithms is a key new textbook in this complex and critical area of geography. |
| data science and gis: The ArcGIS Book Christian Harder, Clint Brown, 2017 This is a hands-on book about ArcGIS that you work with as much as read. By the end, using Learn ArcGIS lessons, you'll be able to say you made a story map, conducted geographic analysis, edited geographic data, worked in a 3D web scene, built a 3D model of Venice, and more. |
| data science and gis: Urban Analytics Alex D. Singleton, Seth Spielman, David Folch, 2017-11-27 The economic and political situation of cities has shifted in recent years in light of rapid growth amidst infrastructure decline, the suburbanization of poverty and inner city revitalization. At the same time, the way that data are used to understand urban systems has changed dramatically. Urban Analytics offers a field-defining look at the challenges and opportunities of using new and emerging data to study contemporary and future cities through methods including GIS, Remote Sensing, Big Data and Geodemographics. Written in an accessible style and packed with illustrations and interviews from key urban analysts, this is a groundbreaking new textbook for students of urban planning, urban design, geography, and the information sciences. |
| data science and gis: Advances in Spatial Data Handling and GIS Anthony G.O. Yeh, Wenzhong Shi, Yee Leung, Chenghu Zhou, 2012-06-06 This book provides a cross-section of cutting-edge research areas being pursued by researchers in spatial data handling and geographic information science (GIS). It presents selected papers on the advancement of spatial data handling and GIS in digital cartography, geospatial data integration, geospatial database and data infrastructures, geospatial data modeling, GIS for sustainable development, the interoperability of heterogeneous spatial data systems, location-based services, spatial knowledge discovery and data mining, spatial decision support systems, spatial data structures and algorithms, spatial statistics, spatial data quality and uncertainty, the visualization of spatial data, and web and wireless applications in GIS. |
| data science and gis: Geographic Information Systems for the Social Sciences Steven J. Steinberg, Sheila L. Steinberg, 2005-08-04 The Steinbergs have produced a very relevant book for the times. . . . While many books have emerged on the details of GIS, few resources exist to help teach the merger of GIS with more standard research methods. The Steinbergs accomplish this goal in a way that is readily accessible even to undergraduates. —Theodore Wagenaar, Miami University The Steinbergs take the reader through all of the essential foundations of GIS... using examples drawn from the social sciences throughout. This book will be essential reading for any social scientist looking for a straightforward introduction to GIS. —Mike Goodchild, University of California, Santa Barbara Geographic Information Systems for the Social Sciences: Investigating Space and Place is the first book to take a cutting-edge approach to integrating spatial concepts into the social sciences. In this text, authors Steven J. Steinberg and Sheila L. Steinberg simplify GIS (Geographic Information Systems) for practitioners and students in the social sciences through the use of examples and actual program exercises so that they can become comfortable incorporating this research tool into their repertoire and scope of interest. The authors provide learning objectives for each chapter, chapter summaries, links to relevant Web sites, as well as suggestions for student research projects. Key Features: Presents step-by-step guidance for integrating GIS with both quantitative and qualitative research Provides an introduction to the use of GIS technology written at an accessible level for individuals without GIS experience while providing depth and guidance appropriate to experienced GIS users Offers an associated interactive Web site—http://www.socialsciencegis.org—to provide a forum for sharing experience and ideas, input to the authors, and a variety of other examples, data, and information related to the topics covered in the text Geographic Information Systems for the Social Sciences offers a nuts-and-bolts introduction to GIS for undergraduate and graduate students taking methods courses across the social sciences. It is an excellent textbook for courses dedicated to GIS research and its applications in the fields of Sociology, Criminology, Public Health, Geography, Anthropology, Political Science, and Environmental Studies. It is also a valuable resource for any social scientist or practitioner interested in applying GIS technology to his or her work. An Instructor′s Resource CD, containing PowerPoint slides, test questions, and suggested Web site links, among other items, is also available to all professors adopting this text. |
| data science and gis: Key Concepts and Techniques in GIS Jochen Albrecht, 2007-08-20 Key Concepts and Techniques in GIS is a concise overview of the fundamental ideas that inform geographic information science. It provides detailed descriptions of the concepts and techniques that anyone using GIS software must fully understand to analyse spatial data. Short and clearly focussed chapters provide explanations of: spatial relationships and spatial data the creation of digital data, the use and access of existing data, the combination of data the use of modelling techniques and the essential functions of map algebra spatial statistics and spatial analysis geocomputation - including discussion of neural networks, cellular automata, and agent-based modelling Illustrated throughout with explanatory figures, the text also includes a glossary, cross referenced to discussion in the text. Written very much from a user′s perspective, Key Concepts and Techniques in GIS is highly readable refresher course for intermediate level students and practitioners of GIS in the social and the natural sciences. |
| data science and gis: Qualitative GIS Meghan Cope, Sarah Elwood, 2009-07-09 Geographic Information Systems are an essential tool for analyzing and representing quantitative spatial data. Qualitative GIS explains the recent integration of qualitative research with Geographical Information Systems With a detailed contextualising introduction, the text is organised in three sections: Representation: examines how researchers are using GIS to create new types of representations; working with spatial data, maps, and othervisualizations to incorporate multiple meanings and to provide texture and context. Analysis: discusses the new techniques of analysis that are emerging at the margins between qualitative research and GIS, this in the wider context of a critical review of mixed-methods in geographical research Theory: questions how knowledge is produced, showing how ideas of ′science′ and ′truth′ inform research, and demonstrates how qualitative GIS can be used to interrogate discussions of power, community, and social action Making reference to representation, analysis, and theory throughout, the text shows how to frame questions, collect data, analyze results, and represent findings in a truly integrated way. An important addition to the mixed methods literature, Qualitative GIS will be the standard reference for upper-level students and researchers using qualitative methods and Geographic Information Systems. |
| data science and gis: Remote Sensing and GIS for Ecologists Martin Wegmann, Benjamin Leutner, Stefan Dech, 2016-02-08 This is a book about how ecologists can integrate remote sensing and GIS in their daily work. It will allow ecologists to get started with the application of remote sensing and to understand its potential and limitations. Using practical examples, the book covers all necessary steps from planning field campaigns to deriving ecologically relevant information through remote sensing and modelling of species distributions. All practical examples in this book rely on OpenSource software and freely available data sets. Quantum GIS (QGIS) is introduced for basic GIS data handling, and in-depth spatial analytics and statistics are conducted with the software packages R and GRASS. Readers will learn how to apply remote sensing within ecological research projects, how to approach spatial data sampling and how to interpret remote sensing derived products. The authors discuss a wide range of statistical analyses with regard to satellite data as well as specialised topics such as time-series analysis. Extended scripts on how to create professional looking maps and graphics are also provided. This book is a valuable resource for students and scientists in the fields of conservation and ecology interested in learning how to get started in applying remote sensing in ecological research and conservation planning. |
| data science and gis: Spatial Modeling in GIS and R for Earth and Environmental Sciences Hamid Reza Pourghasemi, Candan Gokceoglu, 2019-01-18 Spatial Modeling in GIS and R for Earth and Environmental Sciences offers an integrated approach to spatial modelling using both GIS and R. Given the importance of Geographical Information Systems and geostatistics across a variety of applications in Earth and Environmental Science, a clear link between GIS and open source software is essential for the study of spatial objects or phenomena that occur in the real world and facilitate problem-solving. Organized into clear sections on applications and using case studies, the book helps researchers to more quickly understand GIS data and formulate more complex conclusions. The book is the first reference to provide methods and applications for combining the use of R and GIS in modeling spatial processes. It is an essential tool for students and researchers in earth and environmental science, especially those looking to better utilize GIS and spatial modeling. - Offers a clear, interdisciplinary guide to serve researchers in a variety of fields, including hazards, land surveying, remote sensing, cartography, geophysics, geology, natural resources, environment and geography - Provides an overview, methods and case studies for each application - Expresses concepts and methods at an appropriate level for both students and new users to learn by example |
| data science and gis: The GIS Guide to Public Domain Data Joseph J. Kerski, Jill Clark, 2012 Readers will understand how to find, evaluate, and analyze data to solve location-based problems. This guide covers practical issues such as copyrights, cloud computing, online data portals, volunteered geographic information, and international data with supplementary exercises. |
| data science and gis: Spatial Analysis And GIS S Fotheringham, Peter Rogerson, 2013-04-08 Geographic information systems represent an exciting and rapidly expanding technology via which spatial data may be captured, stored, retrieved, displayed, manipulated and analysed. Applications of this technology include detailed inventories of land use parcels. Spatial patterns of disease, geodemographics, environmental management and macroscale inventories of global resources. The impetus for this book is the relative lack of research into the integration of spatial analysis and GIS, and the potential benefits in developing such an integration. From a GIS perspective, there is an increasing demand for systems that do something other than display and organize data. From a spatial analytical perspective, there are advantages to linking statistical methods and mathematical models to the database and display capabilities of a GIS. Although the GIS may not be absolutely necessary for spatial analysis, it can facilitate such an analysis and moreover provide insights that might otherwise have been missed. The contributions to the book tell us where we are and where we ought to be going. It suggests that the integration of spatial analysis and GIS will stimulate interest in quantitative spatial science, particularly exploratory and visual types of analysis and represents a unique statement of the state-of-the-art issues in integration and interface. |
| data science and gis: Geographic Information Analysis David O'Sullivan, David Unwin, 2010-03-29 Geographic Information Analysis provides up-to-date coverage of the foundations of spatial data analysis through visualization and maps. This book covers key spatial concepts, including point pattern, line objects and networks, area objects, and continuous fields, as well as such new subjects as local statistics. With crucial methods for analyzing geographical information, this is an essential reference for professionals as well as a useful text for the classroom. |
| data science and gis: Placing History Anne Kelly Knowles, Amy Hillier, 2008 CD-ROM contains: Four Microsoft PowerPoint presentations and interactive mapping exercises, some of which extend the scholarly material and addresses new issues related to historical GIS. |
| data science and gis: Foundations of Geographic Information Science Matt Duckham, Michael F. Goodchild, Michael Worboys, 2003-01-30 As the use of geographical information systems develops apace, a significant strand of research activity is being directed to the fundamental nature of geographic information. This volume contains a collection of essays and discussions on this theme. What is geographic information? What fundamental principles are associated with it? How can |
| data science and gis: Geoprocessing with Python Christine Garrard, 2016-05-05 Summary Geoprocessing with Python teaches you how to use the Python programming language, along with free and open source tools, to read, write, and process geospatial data. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology This book is about the science of reading, analyzing, and presenting geospatial data programmatically, using Python. Thanks to dozens of open source Python libraries and tools, you can take on professional geoprocessing tasks without investing in expensive proprietary packages like ArcGIS and MapInfo. The book shows you how. About the Book Geoprocessing with Python teaches you how to access available datasets to make maps or perform your own analyses using free tools like the GDAL, NumPy, and matplotlib Python modules. Through lots of hands-on examples, you’ll master core practices like handling multiple vector file formats, editing geometries, applying spatial and attribute filters, working with projections, and performing basic analyses on vector data. The book also covers how to manipulate, resample, and analyze raster data, such as aerial photographs and digital elevation models. What's Inside Geoprocessing from the ground up Read, write, process, and analyze raster data Visualize data with matplotlib Write custom geoprocessing tools Three additional appendixes available online About the Reader To read this book all you need is a basic knowledge of Python or a similar programming language. About the Author Chris Garrard works as a developer for Utah State University and teaches a graduate course on Python programming for GIS. Table of Contents Introduction Python basics Reading and writing vector data Working with different vector file formats Filtering data with OGR Manipulating geometries with OGR Vector analysis with OGR Using spatial reference systems Reading and writing raster data Working with raster data Map algebra with NumPy and SciPy Map classification Visualizing data Appendixes A - Installation B - References C - OGR - online only D - OSR - online only E - GDAL - online only |
| data science and gis: Python Data Science Handbook Jake VanderPlas, 2016-11-21 For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms |
| data science and gis: GIS and the Social Sciences Dimitris Ballas, Graham Clarke, Rachel S. Franklin, Andy Newing, 2017-09-18 GIS and the Social Sciences offers a uniquely social science approach on the theory and application of GIS with a range of modern examples. It explores how human geography can engage with a variety of important policy issues through linking together GIS and spatial analysis, and demonstrates the importance of applied GIS and spatial analysis for solving real-world problems in both the public and private sector. The book introduces basic theoretical material from a social science perspective and discusses how data are handled in GIS, what the standard commands within GIS packages are, and what they can offer in terms of spatial analysis. It covers the range of applications for which GIS has been primarily used in the social sciences, offering a global perspective of examples at a range of spatial scales. The book explores the use of GIS in crime, health, education, retail location, urban planning, transport, geodemographics, emergency planning and poverty/income inequalities. It is supplemented with practical activities and datasets that are linked to the content of each chapter and provided on an eResource page. The examples are written using ArcMap to show how the user can access data and put the theory in the textbook to applied use using proprietary GIS software. This book serves as a useful guide to a social science approach to GIS techniques and applications. It provides a range of modern applications of GIS with associated practicals to work through, and demonstrates how researcher and policy makers alike can use GIS to plan services more effectively. It will prove to be of great interest to geographers, as well as the broader social sciences, such as sociology, crime science, health, business and marketing. |
| data science and gis: Geographic Information Science Rick Bunch, Elisabeth Nelson, James Nelson, 2020-01-09 |
| data science and gis: Data Science For Dummies Lillian Pierson, 2021-08-20 Monetize your company’s data and data science expertise without spending a fortune on hiring independent strategy consultants to help What if there was one simple, clear process for ensuring that all your company’s data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that’s most prime for achieving profitability while also moving your company closer to its business vision? There is. Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework – A simple, proven process for leading profit-forming data science projects. Not sure what data science is yet? Don’t worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you’re already a data science expert? Then you really won’t want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book. Data Science For Dummies demonstrates: The only process you’ll ever need to lead profitable data science projects Secret, reverse-engineered data monetization tactics that no one’s talking about The shocking truth about how simple natural language processing can be How to beat the crowd of data professionals by cultivating your own unique blend of data science expertise Whether you’re new to the data science field or already a decade in, you’re sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company’s data by picking up your copy today. |
| data science and gis: Learning ArcGIS Pro Tripp Corbin, GISP, 2015-12-04 Create, analyze, maintain, and share 2D and 3D maps with the powerful tools of ArcGIS Pro About This Book Visualize GIS data in 2D and 3D maps Create GIS projects for quick and easy access to data, maps, and analysis tools A practical guide that helps to import maps, globes, and scenes from ArcMap, ArcScene, or ArcGlobe Who This Book Is For This book is for anyone wishing to learn how ArcGIS Pro can be used to create maps and perform geospatial analysis. It will be especially helpful for those that have used ArcMap and ArcCatalog in the past and are looking to migrate to Esri's newest desktop GIS solution. Though previous GIS experience is not required, you must have a solid foundation using Microsoft Windows. It is also helpful if you understand how to manage folders and files within the Microsoft Windows environment. What You Will Learn Install ArcGIS Pro and assign Licenses to users in your organization Navigate and use the ArcGIS Pro ribbon interface to create maps and perform analysis Create and manage ArcGIS Pro GIS Projects Create 2D and 3D maps to visualize and analyze data Author map layouts using cartographic tools and best practices to show off the results of your analysis and maps Import existing map documents, scenes, and globes into your new ArcGIS Pro projects quickly Create standardized workflows using Tasks Automate analysis and processes using ModelBuilder and Python In Detail ArcGIS Pro is Esri's newest desktop GIS application with powerful tools for visualizing, maintaining, and analyzing data. ArcGIS Pro makes use of the modern ribbon interface and 64-bit processing to increase the speed and efficiency of using GIS. It allows users to create amazing maps in both 2D and 3D quickly and easily. This book will take you from software installation to performing geospatial analysis. It is packed with how-to's for a host of commonly-performed tasks. You will start by learning how to download and install the software including hardware limitations and recommendations. Then you are exposed to the new Ribbon interface and how its smart design can make finding tools easier. After you are exposed to the new interface, you are walked through the steps to create a new GIS Project to provide quick access to project resources. With a project created, you will learn how to construct 2D and 3D maps including how to add layers, adjust symbology, and control labeling. Next you will learn how to access and use analysis tools to help you answer real-world questions. Lastly, you will learn how processes can be automated and standardized in ArcGIS Pro using Tasks, Models, and Python Scripts. This book will provide an invaluable resource for all those seeking to use ArcGIS Pro as their primary GIS application or for those looking to migrate from ArcMap and ArcCatalog. Style and approach This book includes detailed explanations of the GIS functionality and workflows in ArcGIS Pro. These are supported by easy-to-follow exercises that will help you gain an understanding of how to use ArcGIS Pro to perform a range of tasks. |
| data science and gis: Multicriteria Decision Analysis in Geographic Information Science Jacek Malczewski, Claus Rinner, 2015-02-02 This book is intended for the GIS Science and Decision Science communities. It is primarily targeted at postgraduate students and practitioners in GIS and urban, regional and environmental planning as well as applied decision analysis. It is also suitable for those studying and working with spatial decision support systems. The main objectives of this book are to effectivley integrate Multicriteria Decision Analysis (MCDA) into Geographic Information Science (GIScience), to provide a comprehensive account of theories, methods, technologies and tools for tackling spatial decision problems and to demonstrate how the GIS-MCDA approaches can be used in a wide range of planning and management situations. |
| data science and gis: GIS and the 2020 Census Amor Laaribi, Linda Peters, 2018-07-13 Census workers need to capture and analyze information at the finest geographic level with mobile and geospatial-based technology. GIS and the 2020 Census: Modernizing Official Statistics provides statistical organizations with the most recent GIS methodologies and technological tools to support census workers' needs at all the stages of a census. Learn how to plan and carry out census work with GIS using new technologies for field data collection and operations management. After planning and collecting data, apply innovative solutions for performing statistical analysis, data integration and dissemination. Additional topics cover cloud computing, big data, Location as a Service (LaaS), and emerging data sources. While GIS and the 2020 Census focuses on using GIS and other geospatial technology in support of census planning and operations, it also offers guidelines for building a statistical-geospatial information infrastructure in support of the 2020 Round of Censuses, evidence-based decision making, and sustainable development. Case studies illustrate concepts in practice. |
| data science and gis: Spatial Analysis Paul A. Longley, Michael Batty, 1997-04-17 Spatial Analysis: Modelling in a GIS Environment Edited by PaulLongley and Michael Batty Digital data and information are usedincreasingly by academics, professionals, local authorities, andgovernment departments. Powerful new technologies, such asgeographic information systems (GIS), are being developed toanalyse such data, and GIS technologies are rapidly becoming partof the emergent world digital infrastructure. This book shows howcomputer methods of analysis and modelling, built around GIS, canbe used to identify ways in which our cities and regions might bebetter planned and understood. The contributors to this book areall actively involved in research using geographic informationsystems. This book will be valuable reading for: * Geographers, researchers, and regional analysts * Population theorists and regional economists with interests inlarge-scale demographic and employment data * Planners and policy-makers who wish to use GIS to improve theirdecision making * Business analysts who wish to explore markets using the mostrecent advances in digital spatial data technology * All those interested in geodemographics Paul Longley is Professor of Geography at the Department ofGeography, University of Bristol, United Kingdom. Michael Batty isProfessor of Spatial Analysis and Planning at the UniversityCollege London. United Kingdom. |
| data science and gis: GIS Research Methods Sheila L. Steinberg, Steven J. Steinberg, 2015 This book presents a spatially-based multiple methods approach to research serving academic and organizational researchers from across a wide variety of disciplines. For many, consideration of spatial relationships is an important component of their research questions, including those who may not have yet recognized GIS as a valuable tool. The book will provide readers essential steps to conceptualize and implement research and analysis, develop meaningful quantitative and qualitative geographic results and to communicate their findings using the visualization capabilities of GIS to assist decision-makers and affect policy. Furthermore it offers researchers a deeper understanding of social, economic and environmental questions considering spatial relationships in their data.The broad subject area of the project is the integration of spatial analysis as a research methodology. More specifically the book provides practical guidance for the identification, collection and analysis of appropriate research data for analysis in an Esri/ArcGIS context without being specific to a particular version of the software. The objective is to present ArcGIS with an eye towards incorporating spatial analysis as a fundamental component of mixed methods research. Because GIS is, by nature, an integrative technology which can draw together multiple data sources via a common spatial attribute, it is a natural fit for mixed-methods research. GIS provides the researcher an unparalleled ability to enhance their research incorporating a geographic perspective. |
| data science and gis: Geospatial Health Data Paula Moraga, 2019-11-26 Geospatial health data are essential to inform public health and policy. These data can be used to quantify disease burden, understand geographic and temporal patterns, identify risk factors, and measure inequalities. Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny describes spatial and spatio-temporal statistical methods and visualization techniques to analyze georeferenced health data in R. The book covers the following topics: Manipulate and transform point, areal, and raster data, Bayesian hierarchical models for disease mapping using areal and geostatistical data, Fit and interpret spatial and spatio-temporal models with the Integrated Nested Laplace Approximations (INLA) and the Stochastic Partial Differential Equation (SPDE) approaches, Create interactive and static visualizations such as disease maps and time plots, Reproducible R Markdown reports, interactive dashboards, and Shiny web applications that facilitate the communication of insights to collaborators and policy makers. The book features fully reproducible examples of several disease and environmental applications using real-world data such as malaria in The Gambia, cancer in Scotland and USA, and air pollution in Spain. Examples in the book focus on health applications, but the approaches covered are also applicable to other fields that use georeferenced data including epidemiology, ecology, demography or criminology. The book provides clear descriptions of the R code for data importing, manipulation, modeling and visualization, as well as the interpretation of the results. This ensures contents are fully reproducible and accessible for students, researchers and practitioners. |
| data science and gis: Learning ArcGIS Pro 2 Tripp Corbin, 2020-07-24 Create 2D maps and 3D scenes, analyze GIS data, and share your results with the GIS community using the latest ArcGIS Pro 2 features Key FeaturesGet up to speed with the new ribbon-based user interface, projects, models, and common workflows in ArcGIS Pro 2Learn how to visualize, maintain, and analyze GIS dataAutomate analysis and processes with ModelBuilder and Python scriptsBook Description Armed with powerful tools to visualize, maintain, and analyze data, ArcGIS Pro 2 is Esri's newest desktop geographic information system (GIS) application that uses the modern ribbon interface and a 64-bit processor to make using GIS faster and more efficient. This second edition of Learning ArcGIS Pro will show you how you can use this powerful desktop GIS application to create maps, perform spatial analysis, and maintain data. The book begins by showing you how to install ArcGIS and listing the software and hardware prerequisites. You’ll then understand the concept of named user licensing and learn how to navigate the new ribbon interface to leverage the power of ArcGIS Pro for managing geospatial data. Once you’ve got to grips with the new interface, you’ll build your first GIS project and understand how to use the different project resources available. The book shows you how to create 2D and 3D maps by adding layers and setting and managing the symbology and labeling. You’ll also discover how to use the analysis tool to visualize geospatial data. In later chapters, you’ll be introduced to Arcade, the new lightweight expression language for ArcGIS, and then advance to creating complex labels using Arcade expressions. Finally, you'll use Python scripts to automate and standardize tasks and models in ArcGIS Pro. By the end of this ArcGIS Pro book, you’ll have developed the core skills needed for using ArcGIS Pro 2.x competently. What you will learnNavigate the user interface to create maps, perform analysis, and manage dataDisplay data based on discrete attribute values or range of valuesLabel features on a GIS map based on one or more attributes using ArcadeCreate map books using the map series functionalityShare ArcGIS Pro maps, projects, and data with other GIS community membersExplore the most used geoprocessing tools for performing spatial analysisCreate Tasks based on common workflows to standardize processesAutomate processes using ModelBuilder and Python scriptsWho this book is for If you want to learn ArcGIS Pro to create maps and, edit and analyze geospatial data, this ArcGIS book is for you. No knowledge of GIS fundamentals or experience with any GIS tool or ArcGIS software suite is required. Basic Windows skills, such as navigating and file management, are all you need. |
| data science and gis: Quantitative Geography Richard Harris, 2016-09-13 Numerical data are everywhere. Charts and statistics appear not just in geography journals but also in the media, in public policy, and in business and commerce too. To engage with quantitative geography, we must engage with the quantitative methods used to collect, analyse, present and interpret these data. Quantitative Geography: The Basics is the perfect introduction for undergraduates beginning any quantitative methods course. Written in short, user-friendly chapters with full-colour diagrams, the book guides the reader through a wide range of topics from the basic to the more advanced, including: Statistics Maths Graphics Models Mapping and GIS R Closely aligned with the Q-Step quantitative social science programme, Quantitative Geography: The Basics is the ideal starting point for understanding and exploring this fundamental area of Geography. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
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The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
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Belmont Forum
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A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Geographic Information Systems (GIS)
• The acronym GIS is sometimes used for Geographic Information Science (Giscience) to refer to the academic discipline that studies GIS and is a large domain within the broader academic …
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Figure 1.1: GIS Data Types. 1.3. IMPORTING DATA AND NAVIGATING WITHIN ARC 5 1.3 Importing data and navigating within Arc To create a features flle, save data flles as .dbf flles (which …
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more data. As the volume and diversity of data expands, the need for effective visualization and analysis becomes more critical. Key Earth science disciplines benefiting from GIS include: …
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Gis And Data Science Applied Geospatial Data Science with Python David S. Jordan,2023-02-28 Intelligently connect data points and gain a deeper understanding of environmental …
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Highly skilled GIS professional with Master's in Spatial Data Science and dual Bachelor's degrees. 3+ years experience in GIS technology, including ESRI's ArcGIS and Google Earth Engine. Proficient in …
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Student will describe and explain the historical development of GIS&T and how GIS&T helps to solve problems within a spaticontext.al • 4. Student will demonstrate basic proficiency in map …
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Real Estate Postdoc at MIT
consider applicants with Ph.Ds. in Statistics, Data Science, GIS, Operations, Engineering, or Applied Math if they demonstrate compatible research interests. • Solid skills in data analytics, …
Getting Data Science with R and ArcGIS - Esri Community
R users can access organizations GIS’ data, managed in traditional GIS ways https://r-arcgis.github.io. R–ArcGIS Bridge ... Getting Data Science with R and ArcGIS, 2017 Esri Developer Summit …
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Data analysis using GIS and data mining. - shs.hal.science
and precisely uncovered. In this paper, we will discuss how to use the two data analytical tools, GIS and data mining, to analyze the data collected for the Situn district so that researchers can realize …
GIS for Science: Applying Mapping and Spatial Analyti…
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Geographic Information Systems (GIS)
Geographic Information Systems (GIS) NASA Earth science data provide a wealth of information to aid in our understanding of Earth’s processes. Our Earth Observation data are . freely and openly …
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State of Spatial Data Science in Enterprise 2020 Survey to gain new insights on the status quo and to explore what we can expect in 2020. The Spatial Data Science Discipline INTRODUCTION Nestled …
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CHAPTER 1 Get started with Web GIS - Esri
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data, data standard, data pipeline, data lake, user experience, accessibility, gdr, data science, gis, geospatial, das, distributed acoustic sensing . ABSTRACT . The Department of Energy’s (DOE’s) …
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GEOGRAPHIC INFORMATION SYSTEMS & DATA SCIENCE (GIS) Presented and defended by: Eng. Samah Termos TITLE: Analyzing Damage in South Lebanon, and Developing a Comprehensive …
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science utilizing the geographic concepts, applications and systems. Geographical Information System can be used for scientific investigations, resource ... The most important component of a GIS is …
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Editor of the International Regional Science Review from 1999-2014, editor of Geographical Analysis 2014-2017, and the president of the Western Regional Science Association. He is co-chair of the ...
Dr Larisa Golovko - Landviser
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2.1 Science/engineering expertise related to the mission’s primary anchor instrument: 2.1A Polarimetric SAR capabilities and techniques 2.1B Radar sounding 2.1C Use of other Mars remote-sensing data to …
statement of purpose guide - Oregon State University
multivariate statistics and advanced GIS raster algebra techniques. During that time I completed multiple, graduate-level courses in GIS and cartography through the Department of Geosciences, and …
LECTURE 1 - INTRODUCTION TO GIS Se…
GIS data layers have an advantage in that they can be linked to database management system (DBMS). Digital data is stored as files (called tables). ... Remember the S in GIS can also stand …
