Using ArcGIS, anyone can quickly make and share a map-but creating an effective map requires knowing a few design fundamentals. Enroll in this plan to learn techniques to appropriately symbolize and label map features, apply settings that enhance user interaction with your maps, and create impactful data visualizations that resonate with your intended audience.Goals Choose appropriate map symbols to represent your data. Create attractive labels to provide information about map features. Visualize data in 2D and 3D.

This dataset belongs to the Taiwan-building-footprints project. It contains a example of the visualization code and the data needed to run the code. More code and information can be found on the Github Repo and Juputer Book.

The ZIP file contains 80 images showcasing the result various visualization options, with 4 images for each county. These images are the same to those showed in the Jupyter Book, but this Zip file contains the original .png files without compression.

This article discusses how to make statistical graphics a more prominent element of the undergraduate statistics curricula. The focus is on several different types of assignments that exemplify how to incorporate graphics into a course in a pedagogically meaningful way. These assignments include having students deconstruct and reconstruct plots, copy masterful graphs, create one-minute visual revelations, convert tables into “pictures,” and develop interactive visualizations, for example, with the virtual earth as a plotting canvas. In addition to describing the goals and details of each assignment, we also discuss the broader topic of graphics and key concepts that we think warrant inclusion in the statistics curricula. We advocate that more attention needs to be paid to this fundamental field of statistics at all levels, from introductory undergraduate through graduate level courses. With the rapid rise of tools to visualize data, for example, Google trends, GapMinder, ManyEyes, and Tableau, and the increased use of graphics in the media, understanding the principles of good statistical graphics, and having the ability to create informative visualizations is an ever more important aspect of statistics education. Supplementary materials containing code and data for the assignments are available online.

Dataset

This dataset was created by Nadeem Qamar

Released under MIT

Contents

Many scientists have the common need of visualizing data in a collaborative and interactive manner. In a modern environment, these data are often stored across a widely distributed network and the researchers themselves are just as often separated by large geographical distances. Traditional visualization and collaboration approaches require the local installation of software specific to each end user as well as the downloading of data to each local machine. The proposed innovation would provide researchers with an environment that allows them to visualize remote data using the standard and familiar web browser as the application platform. No proprietary software need be installed and no data has to be downloaded to local machines. Furthermore, multiple researchers can interactively explore data via visualization in a joint session where changes by one researcher are seamlessly seen by the others. The architecture is based on technologies underlying state of the art web applications such as Google Maps. Employing a modular design using web services as means to connect the modules, the environment is easy to modify and improve as new data access, rendering, and client-side display technologies mature and become available.

A complete list of live websites using the Visualize technology, compiled through global website indexing conducted by WebTechSurvey.

Data Visualization 2 (trail)

## Overview

Data Visualization 2 (trail) is a dataset for object detection tasks - it contains Food 5Sze annotations for 7,580 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Detect, Count, And Visualize Object Detection

## Overview

Detect, Count, And Visualize Object Detection is a dataset for object detection tasks - it contains Items annotations for 495 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

VisIt is an Open Source, interactive, scalable, visualization, animation and analysis tool. From Unix, Windows or Mac workstations, users can interactively visualize and analyze data ranging in scale from small (<101 core) desktop-sized projects to large (>105 core) leadership-class computing facility simulation campaigns. Users can quickly generate visualizations, animate them through time, manipulate them with a

variety of operators and mathematical expressions, and save the resulting images and animations for presentations. VisIt contains a rich set of visualization features to enable users to view a wide variety of data including scalar and vector fields defined on two- and three-dimensional (2D and 3D) structured, adaptive and unstructured meshes. Owing to its customizeable plugin design, VisIt is capabable of visualizing data from over 120 different scientific data formats

Maps are currently experiencing a paradigm shift from static representations to dynamic platforms that capture, visualize and analyse new data, bringing different possibilities for exploration and research. The first objective of this paper is to present a map that illustrates, for the first time, the real flow of casual cyclists and bike messengers in the city of Madrid. The second objective is to describe the development and results of the Madrid Cycle Track initiative, an online platform launched with the aim of collecting cycling routes and other information from volunteers. In the framework of this initiative, different online maps are presented and their functionalities described. Finally, a supplemental video visualizes the cyclist flow over the course of a day.

This statistic shows the global data visualization market revenue in 2017 and 2023. In 2017, the total value of this market was estimated to be 4.51 billion US dollars. The market is expected to increase to 7.76 billion U.S. dollars by 2023, with a CAGR of 9.47 percent over the forecast period.

The Data Visualization market size reached USD 9.48 Billion in 2020 and revenue is forecasted to reach USD 20.16 Billion in 2028 registering a CAGR of 10.2%. Data Visualization industry report classifies global market by share, trend, growth and on the basis of component, deployment, enterprise, end...

The financially constrained element of Visualize 2045 identifies all the regionally significant capital improvements to the region’s highway and transit systems that transportation agencies expect to make and to be able to afford through 2045.For more information on Visualize 2045, visit https://www.mwcog.org/visualize2045/.To view the web map, visit https://www.mwcog.org/maps/map-listing/visualize-2045-project-map/.Download the ZIP file that contains a File Geodatabase

Taking projections of high-dimensional data is a common analytical and visualization technique in statistics for working with high-dimensional problems. Sectioning, or slicing, through high dimensions is less common, but can be useful for visualizing data with concavities, or nonlinear structure. It is associated with conditional distributions in statistics, and also linked brushing between plots in interactive data visualization. This short technical note describes a simple approach for slicing in the orthogonal space of projections obtained when running a tour, thus presenting the viewer with an interpolated sequence of sliced projections. The method has been implemented in R as an extension to the tourr package, and can be used to explore for concave and nonlinear structures in multivariate distributions. Supplementary materials for this article are available online.

ViTSel is and R based software to visualize results of multi-environmental multi-trait analysis for selection in plant breeding. Given a matrix of genotype by environments in rows and traits in columns it produces several descriptive statistics and figures to explore results. It has the capability of define different criteria to identify the best genotypes.

This site collates and visualizes critical indicators within the aerospace and defense markets to enable firms to develop export strategies and identify target markets. These data include trade flows (exports and imports) of aerospace products, M3 shipment values of defense and non-defense aircraft and parts, annual employment data, and defense exports delineated by country and products. Users can download the data themselves or use the onscreen tools to visualize the data.

Data analytics as a field is currently at a crucial point in its development, as a commoditization takes place in the context of increasing amounts of data, more user diversity, and automated analysis solutions, the latter potentially eliminating the need for expert analysts. A central hypothesis of the present paper is that data visualizations should be adapted to both the user and the context. This idea was initially addressed in Study 1, which demonstrated substantial interindividual variability among a group of experts when freely choosing an option to visualize data sets. To lay the theoretical groundwork for a systematic, taxonomic approach, a user model combining user traits, states, strategies, and actions was proposed and further evaluated empirically in Studies 2 and 3. The results implied that for adapting to user traits, statistical expertise is a relevant dimension that should be considered. Additionally, for adapting to user states different user intentions such as monitoring and analysis should be accounted for. These results were used to develop a taxonomy which adapts visualization recommendations to these (and other) factors. A preliminary attempt to validate the taxonomy in Study 4 tested its visualization recommendations with a group of experts. While the corresponding results were somewhat ambiguous overall, some aspects nevertheless supported the claim that a user-adaptive data visualization approach based on the principles outlined in the taxonomy can indeed be useful. While the present approach to user adaptivity is still in its infancy and should be extended (e.g., by testing more participants), the general approach appears to be very promising.

Dataset

This dataset was created by Vishnuca

Contents

All 311 Service Requests from 2010 to present. This information is automatically updated daily.

Click here to download data from 2011 - https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2011/fpz8-jqf4

Click here to download data from 2012 - https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2012/as38-8eb5

Click here to download data from 2013 - https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2013/hybb-af8n

Click here to download data from 2014 - https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2014/vtzg-7562

Click here to download data from 2015 - https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2015/57g5-etyj

This is a step-by-step walkthrough of creating a visualization on the Open Data Portal.