02-AREAS

02-AREAS

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ColorBrewer: Color Advice for Maps
ColorBrewer: Color Advice for Maps
1. Sequential schemes are suited to ordered data that progress from low to high. Lightness steps dominate the look of these schemes, with light colors for low data values to dark colors for high data values.
X TYPES OF COLOR SCHEMES 1. Sequential schemes are suited to ordered data that progress from low to high. Lightness steps dominate the look of these schemes, with light colors for low data values to dark colors for high data values. 2. Diverging schemes put equal emphasis on mid-range critical values and extremes at both ends of the data range. The critical class or break in the middle of the legend is emphasized with light colors and low and high extremes are emphasized with dark colors that have contrasting hues.
3. Qualitative schemes do not imply magnitude differences between legend classes, and hues are used to create the primary visual differences between classes. Qualitative schemes are best suited to representing nominal or categorical data.
The appearance and robustness of a color scheme is in part a product of what else goes on the map and the background over which you are trying to show your colors. Small differences in the color of linework or the presence of other map items (like labels) really has a big impact on the appearance of a color scheme, so be sure to try these options here before settling on a final color scheme. Overlay cities and roads for a first look at how well text and symbols can be read with the area colors you select. Though the examples we have chosen are highways and cities, they should give you a good idea of how other linework or typography will function on the map. We have also provided a grayscale DEM so you can see what happens to your colors when you combine them with other underlying map data: Generally speaking, colors become harder to distinguish and you will need to user fewer classes of data.
TIP: Try turning off the county borders or making them white; notice a big difference? Try changing the background surrounding the map to see how colors are changed by their surroundings.
Choosing the number of data classes is an important part of map design. Increasing the number of data classes will result in a more "information rich" map by decreasing the amount of data generalization. However, too many data classes may overwhelm the map reader with information and distract them from seeing general trends in the distribution. In addition, a large numbers of classes may compromise map legibility—more classes require more colors that become increasingly difficult to tell apart. Many cartographers advise that you use five to seven classes for a choropleth map. Isoline maps, or choropleth maps with very regular spatial patterns, can safely use more data classes because similar colors are seen next to each other, making them easier to distinguish.
·colorbrewer2.org·
ColorBrewer: Color Advice for Maps
Highlight AI | Master your world
Highlight AI | Master your world
Get instant answers about anything you've seen, heard or said. Download free: highlightai.com
·highlightai.com·
Highlight AI | Master your world
MCP.Link | Connect APIs to AI Assistants
MCP.Link | Connect APIs to AI Assistants
Transform OpenAPI specifications into Model-Context-Protocol Protocol (MCP) endpoints for seamless AI integration.
·mcp-link.vercel.app·
MCP.Link | Connect APIs to AI Assistants
RealEstateAPI | Public APIs | Postman API Network
RealEstateAPI | Public APIs | Postman API Network
Explore public APIs from RealEstateAPI, exclusively on the Postman API Network. Find everything you need to quickly get started with RealEstateAPI APIs.
·postman.com·
RealEstateAPI | Public APIs | Postman API Network
Ref.
Ref.
Documentation for your agent.
·ref.tools·
Ref.
RealEstateAPI Developer Documentation
RealEstateAPI Developer Documentation
THE Property Data Solution. Our revolutionary tech allows us to get you property and owner data (and lots of it!) faster and cheaper than you've ever been able to before. Slow or buggy applications due to unreliable third party data APIs are a problem of the past.
·developer.realestateapi.com·
RealEstateAPI Developer Documentation
Agentic Engineer - Build LIVING software
Agentic Engineer - Build LIVING software
Build LIVING software. Your guide to mastering prompts, ai coding, ai agents, and agentic workflows.
·agenticengineer.com·
Agentic Engineer - Build LIVING software
AI Model & API Providers Analysis | Artificial Analysis
AI Model & API Providers Analysis | Artificial Analysis
Comparison and analysis of AI models and API hosting providers. Independent benchmarks across key performance metrics including quality, price, output speed & latency.
·artificialanalysis.ai·
AI Model & API Providers Analysis | Artificial Analysis
shinymgr: A Framework for Building, Managing, and Stitching Shiny Modules into Reproducible Workflows
shinymgr: A Framework for Building, Managing, and Stitching Shiny Modules into Reproducible Workflows
The R package shinymgr provides a unifying framework that allows Shiny developers to create, manage, and deploy a master Shiny application comprised of one or more "apps", where an "app" is a tab-based workflow that guides end-users through a step-by-step analysis. Each tab in a given "app" consists of one or more Shiny modules. The shinymgr app builder allows developers to "stitch" Shiny modules together so that outputs from one module serve as inputs to the next, creating an analysis pipeline that is easy to implement and maintain. Apps developed using shinymgr can be incorporated into R packages or deployed on a server, where they are accessible to end-users. Users of shinymgr apps can save analyses as an RDS file that fully reproduces the analytic steps and can be ingested into an RMarkdown or Quarto report for rapid reporting. In short, developers use the shinymgr framework to write Shiny modules and seamlessly combine them into Shiny apps, and end-users of these apps can execute reproducible analyses that can be incorporated into reports for rapid dissemination. A comprehensive overview of the package is provided by 12 learnr tutorials.
·journal.r-project.org·
shinymgr: A Framework for Building, Managing, and Stitching Shiny Modules into Reproducible Workflows
Futureverse
Futureverse
A Unifying Parallelization Framework in R for Everyone
·futureverse.org·
Futureverse
Sparrow API Platform
Sparrow API Platform
Sparrow is your one-stop API testing solution. Supercharge your API workflow with Sparrow—the ultimate ally for agile teams and individual devs. Test, organize, and share APIs with finesse, revolutionizing your API game.
·sparrowapp.dev·
Sparrow API Platform
Introduction
Introduction
Build production-ready Copilots and Agents effortlessly.
·docs.copilotkit.ai·
Introduction
RealEstateAPI Developer Documentation
RealEstateAPI Developer Documentation
THE Property Data Solution. Our revolutionary tech allows us to get you property and owner data (and lots of it!) faster and cheaper than you've ever been able to before. Slow or buggy applications due to unreliable third party data APIs are a problem of the past.
·developer.realestateapi.com·
RealEstateAPI Developer Documentation
How to make data pipelines idempotent
How to make data pipelines idempotent
Unable to find practical examples of idempotent data pipelines? Then, this post is for you. In this post, we go over a technique that you can use to make your data pipelines professional and data reprocessing a breeze.
·startdataengineering.com·
How to make data pipelines idempotent
Shell and A.I - Steven Bucher - PSConfEU 2024
Shell and A.I - Steven Bucher - PSConfEU 2024
In this extensive lecture, I, Steven Bucher, a product manager on the PowerShell team, discuss the integration of AI into the shell environment. Over the pas...
·youtu.be·
Shell and A.I - Steven Bucher - PSConfEU 2024
autodb: Automatic Database Normalisation for Data Frames
autodb: Automatic Database Normalisation for Data Frames
Automatic normalisation of a data frame to third normal form, with the intention of easing the process of data cleaning. (Usage to design your actual database for you is not advised.) Originally inspired by the 'AutoNormalize' library for 'Python' by 'Alteryx' (<a href="https://github.com/alteryx/autonormalize" target="_top"https://github.com/alteryx/autonormalize/a>), with various changes and improvements. Automatic discovery of functional or approximate dependencies, normalisation based on those, and plotting of the resulting "database" via 'Graphviz', with options to exclude some attributes at discovery time, or remove discovered dependencies at normalisation time.
·cran.r-project.org·
autodb: Automatic Database Normalisation for Data Frames
Data Pipeline Design Patterns - #1. Data flow patterns
Data Pipeline Design Patterns - #1. Data flow patterns
Data pipelines built (and added on to) without a solid foundation will suffer from poor efficiency, slow development speed, long times to triage production issues, and hard testability. What if your data pipelines are elegant and enable you to deliver features quickly? An easy-to-maintain and extendable data pipeline significantly increase developer morale, stakeholder trust, and the business bottom line! Using the correct design pattern will increase feature delivery speed and developer value (allowing devs to do more in less time), decrease toil during pipeline failures, and build trust with stakeholders. This post goes over the most commonly used data flow design patterns, what they do, when to use them, and, more importantly, when not to use them. By the end of this post, you will have an overview of the typical data flow patterns and be able to choose the right one for your use case.
·startdataengineering.com·
Data Pipeline Design Patterns - #1. Data flow patterns