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Convivial Ground : Stories from Collaborative Spatial Practices
We are all improvising. We are all making decisions. And we are all watching each other improvise. Together, we are improvising. Decisions become shared ones. We will have built this city together. Convivial Ground invites the reader to discover the work of the transdisciplinary European design-build network Constructlab piece by piece.Taking Ivan Illich’s understanding of conviviality as departure point, the essays, conversations, stories, and images in the book reflect on the specificities of collaborative practices as situated experiments, as well as on their possible roles in the creation of convivial societies.Exploring contemporary conditions of togetherness, learning, and working, the book is not a "how-to" guide, but an invitation to cooperatively write new convivial narratives. The situational working methods of the transnational collaborative network Constructlab in book form Offers reflections on the collective design of cities, institutions, and everyday life in the context of growing scarcity Aimed at city planners and all those with an interest in the subject Selected for the shortlist of The Best German Book Design 2024 (Stiftung Buchkunst)
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Disease Mapping : From Foundations to Multidimensional Modeling
Disease Mapping: From Foundations to Multidimensional Modeling guides the reader from the basics of disease mapping to the most advanced topics in this field.A multidimensional framework is offered that makes possible the joint modeling of several risks patterns corresponding to combinations of several factors, including age group, time period, disease, etc.Although theory will be covered, the applied component will be equally as important with lots of practical examples offered.Features: Discusses the very latest developments on multivariate and multidimensional mapping. Gives a single state-of-the-art framework that unifies most of the previously proposed disease mapping approaches. Balances epidemiological and statistical points-of-view. Requires no previous knowledge of disease mapping. Includes practical sessions at the end of each chapter with WinBUGs/INLA and real world datasets. Supplies R code for the examples in the book so that they can be reproduced by the reader. About the Authors:Miguel A. Martinez Beneito has spent his whole career working as a statistician for public health services, first at the epidemiology unit of the Valencia (Spain) regional health administration and later as a researcher at the public health division of FISABIO, a regional bio-sanitary research center.He has been also the Bayesian Hierarchical Models professor for several seasons at the University of Valencia Biostatics Master.Paloma Botella Rocamora has spent most of her professional career in academia although she now works as a statistician for the epidemiology unit of the Valencia regional health administration.Most of her research has been devoted to developing and applying disease mapping models to real data, although her work as a statistician in an epidemiology unit makes her develop and apply statistical methods to health data, in general.
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Data Analytics & Visualization All-in-One For Dummies
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Business Intelligence Guidebook : From Data Integration to Analytics
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What is spatial visualization ability?
Spatial visualization ability refers to the capacity to mentally manipulate and comprehend spatial relationships between objects. Individuals with strong spatial visualization skills can easily visualize and understand how objects relate to each other in space, such as rotating or manipulating shapes in their mind. This ability is crucial in various fields such as engineering, architecture, and mathematics, as it allows individuals to solve complex problems and understand spatial concepts more effectively. Improving spatial visualization ability can enhance problem-solving skills and overall cognitive performance.
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Is spatial visualization important for engineers?
Yes, spatial visualization is important for engineers as it allows them to mentally manipulate and understand complex 3D objects and structures. Engineers often need to design and analyze various components and systems, and spatial visualization skills help them to conceptualize and communicate their ideas effectively. Whether it's designing a new product, creating blueprints for a building, or solving complex problems, spatial visualization is a crucial skill that allows engineers to think critically and innovate in their field.
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Can one improve their spatial visualization skills?
Yes, it is possible to improve spatial visualization skills through practice and training. Engaging in activities such as puzzles, building models, and playing spatial reasoning games can help develop these skills. Additionally, practicing mental rotation exercises and regularly challenging oneself with spatial tasks can also contribute to improvement. With consistent effort and dedication, individuals can enhance their spatial visualization abilities over time.
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What are problems with spatial visualization skills?
Some problems with spatial visualization skills include difficulty in understanding and interpreting maps, graphs, and diagrams. Individuals with poor spatial visualization skills may struggle with tasks such as navigating through unfamiliar environments, understanding 3D objects, and mentally rotating objects. This can impact their performance in subjects such as math, science, and engineering, as well as in everyday activities such as driving and assembling furniture. Additionally, poor spatial visualization skills can lead to frustration and decreased confidence in one's abilities.
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Google Analytics Breakthrough : From Zero to Business Impact
A complete, start-to-finish guide to Google Analytics instrumentation and reporting Google Analytics Breakthrough is a much-needed comprehensive resource for the world's most widely adopted analytics tool.Designed to provide a complete, best-practices foundation in measurement strategy, implementation, reporting, and optimization, this book systematically demystifies the broad range of Google Analytics features and configurations.Throughout the end-to-end learning experience, you'll sharpen your core competencies, discover hidden functionality, learn to avoid common pitfalls, and develop next-generation tracking and analysis strategies so you can understand what is helping or hindering your digital performance and begin driving more success. Google Analytics Breakthrough offers practical instruction and expert perspectives on the full range of implementation and reporting skills: Learn how to campaign-tag inbound links to uncover the email, social, PPC, and banner/remarketing traffic hiding as other traffic sources and to confidently measure the ROI of each marketing channelAdd event tracking to capture the many important user interactions that Google Analytics does not record by default, such as video plays, PDF downloads, scrolling, and AJAX updatesMaster Google Tag Manager for greater flexibility and process control in implementationSet up goals and Enhanced Ecommerce tracking to measure performance against organizational KPIs and configure conversion funnels to isolate drop-offCreate audience segments that map to your audience constituencies, amplify trends, and help identify optimization opportunitiesPopulate custom dimensions that reflect your organization, your content, and your visitors so Google Analytics can speak your languageGain a more complete view of customer behavior with mobile app and cross-device trackingIncorporate related tools and techniques: third-party data visualization, CRM integration for long-term value and lead qualification, marketing automation, phone conversion tracking, usability, and A/B testingImprove data storytelling and foster analytics adoption in the enterprise Millions of organizations have installed Google Analytics, including an estimated 67 percent of Fortune 500 companies, but deficiencies plague most implementations, and inadequate reporting practices continue to hinder meaningful analysis.By following the strategies and techniques in Google Analytics Breakthrough, you can address the gaps in your own still set, transcend the common limitations, and begin using Google Analytics for real competitive advantage. Critical contributions from industry luminaries such as Brian Clifton, Tim Ash, Bryan and Jeffrey Eisenberg, and Jim Sterne – and a foreword by Avinash Kaushik – enhance the learning experience and empower you to drive consistent, real-world improvement through analytics.
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Spatial Analysis with R : Statistics, Visualization, and Computational Methods
In the five years since the publication of the first edition of Spatial Analysis: Statistics, Visualization, and Computational Methods, many new developments have taken shape regarding the implementation of new tools and methods for spatial analysis with R.The use and growth of artificial intelligence, machine learning and deep learning algorithms with a spatial perspective, and the interdisciplinary use of spatial analysis are all covered in this second edition along with traditional statistical methods and algorithms to provide a concept-based problem-solving learning approach to mastering practical spatial analysis.Spatial Analysis with R: Statistics, Visualization, and Computational Methods, Second Edition provides a balance between concepts and practicums of spatial statistics with a comprehensive coverage of the most important approaches to understand spatial data, analyze spatial relationships and patterns, and predict spatial processes.New in the Second Edition: Includes new practical exercises and worked-out examples using R Presents a wide range of hands-on spatial analysis worktables and lab exercises All chapters are revised and include new illustrations of different concepts using data from environmental and social sciences Expanded material on spatiotemporal methods, visual analytics methods, data science, and computational methods Explains big data, data management, and data miningThis second edition of an established textbook, with new datasets, insights, excellent illustrations, and numerous examples with R, is perfect for senior undergraduate and first-year graduate students in geography and the geosciences.
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Hands-On Data Visualization : Interactive Storytelling From Spreadsheets to Code
Tell your story and show it with data, using free and easy-to-learn tools on the web.This introductory book teaches you how to design interactive charts and customized maps for your website, beginning with simple drag-and-drop tools such as Google Sheets, Datawrapper, and Tableau Public.You'll also gradually learn how to edit open source code templates like Chart.js, Highcharts, and Leaflet on GitHub.Hands-On Data Visualization takes you step-by-step through tutorials, real-world examples, and online resources.This hands-on resource is ideal for students, nonprofit organizations, small business owners, local governments, journalists, academics, and anyone who wants to take data out of spreadsheets and turn it into lively interactive stories.No coding experience is required. Build interactive charts and maps and embed them in your website Understand the principles for designing effective charts and mapsLearn key data visualization concepts to help you choose the right toolsConvert and transform tabular and spatial data to tell your data storyEdit and host Chart.js, Highcharts, and Leaflet map code templates on GitHub Learn how to detect bias in charts and maps produced by others
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Spatial Epidemiological Approaches in Disease Mapping and Analysis
Containing method descriptions and step-by-step procedures, the Spatial Epidemiological Approaches in Disease Mapping and Analysis equips readers with skills to prepare health-related data in the proper format, process these data using relevant functions and software, and display the results as mapped or statistical summaries.Describing the wide range of available methods and key GIS concepts for spatial epidemiology, this book illustrates the utilities of the software using real-world data.Additional topics include geographic data models, address matching, geostatistical analysis, universal kriging, point pattern analysis, kernel density, spatio-temporal display, and disease surveillance.
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What is the function of spatial visualization ability?
Spatial visualization ability allows individuals to mentally manipulate and understand spatial relationships between objects. This ability is crucial in fields such as engineering, architecture, and design, where individuals need to visualize and manipulate complex 3D objects and structures. It also plays a key role in tasks such as navigation, map reading, and understanding geometric concepts. Overall, spatial visualization ability is important for problem-solving, creativity, and understanding the physical world.
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What does it mean if someone lacks spatial visualization skills?
If someone lacks spatial visualization skills, it means that they have difficulty mentally manipulating and understanding spatial relationships between objects and shapes. This can manifest as challenges in tasks such as reading maps, understanding 3D objects, or visualizing how objects fit together in space. People with this difficulty may struggle with activities such as assembling furniture, reading graphs, or understanding geometric concepts. It's important to note that lacking spatial visualization skills does not indicate a lack of intelligence, but rather a specific cognitive challenge in this area.
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Is there an app that can help improve and train one's spatial visualization skills?
Yes, there are several apps available that can help improve and train spatial visualization skills. Apps like Lumosity, Peak, and Spatial IQ are designed to enhance spatial reasoning, mental rotation, and visualization abilities through various exercises and challenges. These apps offer a fun and interactive way to practice and develop spatial skills, which can be beneficial for tasks that require mental manipulation of objects in space, such as engineering, architecture, and mathematics.
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What is the mapping from R to R?
The mapping from R to R refers to a function that takes a real number as input and produces another real number as output. In other words, it is a function that operates on real numbers and returns real numbers. This type of mapping is commonly used in mathematics to describe relationships between real numbers or to model real-world phenomena.
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