Database created for replication of GeoStoryTelling. Our life stories evolve in specific and contextualized places. Although our homes may be our primarily shaping environment, our homes are themselves situated in neighborhoods that expose us to the immediate “real world” outside home. Indeed, the places where we are currently experiencing, and have experienced life, play a fundamental role in gaining a deeper and more nuanced understanding of our beliefs, fears, perceptions of the world, and even our prospects of social mobility. Despite the immediate impact of the places where we experience life in reaching a better understanding of our life stories, to date most qualitative and mixed methods researchers forego the analytic and elucidating power that geo-contextualizing our narratives bring to social and health research. From this view then, most research findings and conclusions may have been ignoring the spatial contexts that most likely have shaped the experiences of research participants. The main reason for the underuse of these geo-contextualized stories is the requirement of specialized training in geographical information systems and/or computer and statistical programming along with the absence of cost-free and user-friendly geo-visualization tools that may allow non-GIS experts to benefit from geo-contextualized outputs. To address this gap, we present GeoStoryTelling, an analytic framework and user-friendly, cost-free, multi-platform software that enables researchers to visualize their geo-contextualized data narratives. The use of this software (available in Mac and Windows operative systems) does not require users to learn GIS nor computer programming to obtain state-of-the-art, and visually appealing maps. In addition to providing a toy database to fully replicate the outputs presented, we detail the process that researchers need to follow to build their own databases without the need of specialized external software nor hardware. We show how the resulting HTML outputs are capable of integrating a variety of multi-media inputs (i.e., text, image, videos, sound recordings/music, and hyperlinks to other websites) to provide further context to the geo-located stories we are sharing (example https://cutt.ly/k7X9tfN). Accordingly, the goals of this paper are to describe the components of the methodology, the steps to construct the database, and to provide unrestricted access to the software tool, along with a toy dataset so that researchers may interact first-hand with GeoStoryTelling and fully replicate the outputs discussed herein. Since GeoStoryTelling relied on OpenStreetMap its applications may be used worldwide, thus strengthening its potential reach to the mixed methods and qualitative scientific communities, regardless of location around the world. Keywords: Geographical Information Systems; Interactive Visualizations; Data StoryTelling; Mixed Methods & Qualitative Research Methodologies; Spatial Data Science; Geo-Computation.

Researchers are frequently using visualization tools to visualize large amounts of data. Data visualizations are a quick way for researchers to illustrate trends or concepts found in survey data. This hands-on workshop will give participants experience with creating visual summaries of various types of survey questions using Tableau.

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.

Snapshot img

Data Visualization Tools Market Size 2025-2029

The data visualization tools market size is forecast to increase by USD 7.95 billion at a CAGR of 11.2% between 2024 and 2029.

The market is experiencing significant growth due to the increasing demand for business intelligence and AI-powered insights. Companies are recognizing the value of transforming complex data into easily digestible visual representations to inform strategic decision-making. However, this market faces challenges as data complexity and massive data volumes continue to escalate. Organizations must invest in advanced data visualization tools to effectively manage and analyze their data to gain a competitive edge. The ability to automate data visualization processes and integrate AI capabilities will be crucial for companies to overcome the challenges posed by data complexity and volume. By doing so, they can streamline their business operations, enhance data-driven insights, and ultimately drive growth in their respective industries.

What will be the Size of the Data Visualization Tools Market during the forecast period?

Request Free SampleIn today's data-driven business landscape, the market continues to evolve, integrating advanced capabilities to support various sectors in making informed decisions. Data storytelling and preparation are crucial elements, enabling organizations to effectively communicate complex data insights. Real-time data visualization ensures agility, while data security safeguards sensitive information. Data dashboards facilitate data exploration and discovery, offering data-driven finance, strategy, and customer experience. Big data visualization tackles complex datasets, enabling data-driven decision making and innovation. Data blending and filtering streamline data integration and analysis. Data visualization software supports data transformation, cleaning, and aggregation, enhancing data-driven operations and healthcare. On-premises and cloud-based solutions cater to diverse business needs. Data governance, ethics, and literacy are integral components, ensuring data-driven product development, government, and education adhere to best practices. Natural language processing, machine learning, and visual analytics further enrich data-driven insights, enabling interactive charts and data reporting. Data connectivity and data-driven sales fuel business intelligence and marketing, while data discovery and data wrangling simplify data exploration and preparation. The market's continuous dynamism underscores the importance of data culture, data-driven innovation, and data-driven HR, as organizations strive to leverage data to gain a competitive edge.

How is this Data Visualization Tools Industry segmented?

The data visualization tools industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. DeploymentOn-premisesCloudCustomer TypeLarge enterprisesSMEsComponentSoftwareServicesApplicationHuman resourcesFinanceOthersEnd-userBFSIIT and telecommunicationHealthcareRetailOthersGeographyNorth AmericaUSMexicoEuropeFranceGermanyUKMiddle East and AfricaUAEAPACAustraliaChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.The market has experienced notable expansion as businesses across diverse sectors acknowledge the significance of data analysis and representation to uncover valuable insights and inform strategic decisions. Data visualization plays a pivotal role in this domain. On-premises deployment, which involves implementing data visualization tools within an organization's physical infrastructure or dedicated data centers, is a popular choice. This approach offers organizations greater control over their data, ensuring data security, privacy, and adherence to data governance policies. It caters to industries dealing with sensitive data, subject to regulatory requirements, or having stringent security protocols that prohibit cloud-based solutions. Data storytelling, data preparation, data-driven product development, data-driven government, real-time data visualization, data security, data dashboards, data-driven finance, data-driven strategy, big data visualization, data-driven decision making, data blending, data filtering, data visualization software, data exploration, data-driven insights, data-driven customer experience, data mapping, data culture, data cleaning, data-driven operations, data aggregation, data transformation, data-driven healthcare, on-premises data visualization, data governance, data ethics, data discovery, natural language processing, data reporting, data visualization platforms, data-driven innovation, data wrangling, data-driven s

Research dissemination and knowledge translation are imperative in social work. Methodological developments in data visualization techniques have improved the ability to convey meaning and reduce erroneous conclusions. The purpose of this project is to examine: (1) How are empirical results presented visually in social work research?; (2) To what extent do top social work journals vary in the publication of data visualization techniques?; (3) What is the predominant type of analysis presented in tables and graphs?; (4) How can current data visualization methods be improved to increase understanding of social work research? Method: A database was built from a systematic literature review of the four most recent issues of Social Work Research and 6 other highly ranked journals in social work based on the 2009 5-year impact factor (Thomson Reuters ISI Web of Knowledge). Overall, 294 articles were reviewed. Articles without any form of data visualization were not included in the final database. The number of articles reviewed by journal includes : Child Abuse & Neglect (38), Child Maltreatment (30), American Journal of Community Psychology (31), Family Relations (36), Social Work (29), Children and Youth Services Review (112), and Social Work Research (18). Articles with any type of data visualization (table, graph, other) were included in the database and coded sequentially by two reviewers based on the type of visualization method and type of analyses presented (descriptive, bivariate, measurement, estimate, predicted value, other). Additional revi ew was required from the entire research team for 68 articles. Codes were discussed until 100% agreement was reached. The final database includes 824 data visualization entries.

The resource is a practical worksheet that can guide the integration of eye-tracking capabilities into visualization or visual analytic systems by helping identify opportunities, challenges, and benefits of doing so. The resource also includes guidance for its use and three concrete examples. Importantly, this resource is meant to be used in conjunction with the design framework and references detailed in section 4 of: ���Gaze-Aware Visualization: Design Considerations and Research Agenda��� by R. Jianu, N. Silva, N. Rodrigues, T. Blascheck, T. Schreck, and D. Weiskopf (in Transactions on Visualization and Computer Graphics). The worksheet encourages designers who wish to integrate eye-tracking into visualization or visual analytics systems to carefully consider 18 fundamental facets that can inform the integration process and whether it is likely to be valuable. Broadly, these relate to: M1-M3: Measurable data afforded by eye trackers (and other modalities and context data that could be used together with such data) I1-I6: Inferences that can be made from measured data about users��� interests, tasks, intent, and analysis process S1-S7: Opportunities to use such inferences to support visual search, interaction, exploration, analysis, recall, collaboration, and onboarding B1-B9: Limitations to beware that arise from eye-tracking technology and the sometimes inscrutable ways in which human perception and cognition work, and which may constrain support possibilities. To apply the worksheet to inform the design of a gaze-aware visualization or visual analytic system one would: Progress through its sections and consider the facets they contain step-by-step. For each facet: Refer to the academic paper mentioned above (in particular section 4) for a more detailed discussion about the facet and for supporting references that provide further depth, inspiration, and concrete examples Consider carefully how these details apply to the specific visualization under analysis and its context of use. Consider both opportunities that eye-tracking affords (M, I, S) but also limitations and challenges (B) Use the specific questions under each facet (e.g., ���Are lighting conditions too variable for accurate gaze tracking?��� ) to further guide the thought process and capture rough yes/no assessments (if this is possible) Summarize a design rationale at the end of each worksheet section. This should capture design decisions or options and the motivation behind them, as informed by thought processes and insights facilitated by the design considerations in the section. The format and level of detail of such summaries are up to the designer (a few different options are shown in our examples). We exemplify this use of the worksheet by conjecturing how eye-tracking could be integrated in three visualizations systems (included in the resource). We chose three systems that span a broad range of domains and contexts to exemplify different challenges and opportunities. We also exemplify different ways of capturing design rationales ��� more detailed/verbose or as bullet points.

Companion data for the creation of a banksia plot:Background:In research evaluating statistical analysis methods, a common aim is to compare point estimates and confidence intervals (CIs) calculated from different analyses. This can be challenging when the outcomes (and their scale ranges) differ across datasets. We therefore developed a plot to facilitate pairwise comparisons of point estimates and confidence intervals from different statistical analyses both within and across datasets.Methods:The plot was developed and refined over the course of an empirical study. To compare results from a variety of different studies, a system of centring and scaling is used. Firstly, the point estimates from reference analyses are centred to zero, followed by scaling confidence intervals to span a range of one. The point estimates and confidence intervals from matching comparator analyses are then adjusted by the same amounts. This enables the relative positions of the point estimates and CI widths to be quickly assessed while maintaining the relative magnitudes of the difference in point estimates and confidence interval widths between the two analyses. Banksia plots can be graphed in a matrix, showing all pairwise comparisons of multiple analyses. In this paper, we show how to create a banksia plot and present two examples: the first relates to an empirical evaluation assessing the difference between various statistical methods across 190 interrupted time series (ITS) data sets with widely varying characteristics, while the second example assesses data extraction accuracy comparing results obtained from analysing original study data (43 ITS studies) with those obtained by four researchers from datasets digitally extracted from graphs from the accompanying manuscripts.Results:In the banksia plot of statistical method comparison, it was clear that there was no difference, on average, in point estimates and it was straightforward to ascertain which methods resulted in smaller, similar or larger confidence intervals than others. In the banksia plot comparing analyses from digitally extracted data to those from the original data it was clear that both the point estimates and confidence intervals were all very similar among data extractors and original data.Conclusions:The banksia plot, a graphical representation of centred and scaled confidence intervals, provides a concise summary of comparisons between multiple point estimates and associated CIs in a single graph. Through this visualisation, patterns and trends in the point estimates and confidence intervals can be easily identified.This collection of files allows the user to create the images used in the companion paper and amend this code to create their own banksia plots using either Stata version 17 or R version 4.3.1

Visualizing research data can be an important science communication tool. In recent decades, 3D data visualization has emerged as a key tool for engaging public audiences. Such visualizations are often embedded in scientific documentaries screened on giant domes in planetariums or delivered through video streaming services such as Amazon Prime. 3D data visualization has been shown to be an effective way to communicate complex scientific concepts to the public. With its ability to convey information in a scientifically accurate and visually engaging way, cinematic-style 3D data visualization has the potential to benefit millions of viewers by making scientific information more understandable and interesting. Maximizing the effectiveness of 3D data visualization can benefit millions of viewers. To support a wider shift in this professional field towards more evidence-based practice in 3D data visualization to enhance science communication impact, we have conducted a survey experiment comparing audience responses to two versions of 3D data visualizations from a scientific documentary film on the theme of ‘solar superstorms’ (n = 577). This study was conducted using a single (with two levels: labeled and unlabeled), between-subjects, factorial design. It reveals key strengths and weaknesses of communicating science using 3D data visualization. It also shows the limited power of strategically deployed informational labels to affect audience perceptions of the documentary film and its content. The major difference identified between experimental and control groups was that the quality ratings of the documentary film clip were significantly higher for the ‘labeled’ version. Other outcomes showed no statistically significant differences. The limited effects of informational labels point to the idea that other aspects, such as the story structure, voiceover narration and audio-visual content, are more important determinants of outcomes. This study concludes with a discussion of how this new research evidence informs our understanding of ‘what works and why’ with cinematic-style 3D data visualizations for the public.

Presentation Date: Friday, March 15, 2019. Location: Barnstable, MA. Abstract: A presentation to a crowd of Barnstable High "AstroJunkies," about how we use physics, statistics, and visualizations to turn information from large, public, astronomical data sets, across many wavelengths into a better understanding of the structure of the Milky Way.

A good statistical graph for a randomized experiment simultaneously conveys the study's design, analysis, and results. It reveals the experimental design by mapping design elements to aesthetic parameters. It illuminates the analysis by plotting the statistical model in data-space.'' When the design and analysis of an experiment are encoded in a plot, the interpretation of the experimental results is clarified.Analyze as you randomize'' is a dictum attributed to Fisher that guides interpretations of experimental data. This chapter extends that principle to visualizations of randomized experiments. While not every experiment requires a visualization, those that do should be visualized in ways that communicate the design and results together.

Penumbral imaging is a technique used in plasma diagnostics in which a radiation source shines through one or more large apertures onto a detector. To interpret a penumbral image, one must reconstruct it to recover the original source. The inferred source always has some error due to noise in the image and uncertainty in the instrument geometry. Interpreting the inferred source thus requires quantification of that inference’s uncertainty. Markov chain Monte Carlo algorithms have been used to quantify uncertainty for similar problems but have never been used for the inference of the shape of an image. Because of this, there are no commonly accepted ways of visualizing uncertainty in two- dimensional data. This paper demonstrates the application of the Hamiltonian Monte Carlo algorithm to the reconstruction of penumbral images of fusion implosions and presents ways to visualize the uncertainty in the reconstructed source. This methodology enables more rigorous analysis of penumbral images than has been done in the past.

The Data Visualization Corpus

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1430847%2F29f7950c3b7daf11175aab404725542c%2FGettyImages-1187621904-600x360.jpg?generation=1601115151722854&alt=media" alt="">

Data Visualization

Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

In the world of Big Data, data visualization tools and technologies are essential to analyze massive amounts of information and make data-driven decisions

The Data Visualizaion Copus

The Data Visualization corpus consists:

• 32 cheat sheets: This includes A-Z about the techniques and tricks that can be used for visualization, Python and R visualization cheat sheets, Types of charts, and their significance, Storytelling with data, etc..

• 32 Charts: The corpus also consists of a significant amount of data visualization charts information along with their python code, d3.js codes, and presentations relation to the respective charts explaining in a clear manner!

• Some recommended books for data visualization every data scientist's should read:

  1. Beautiful Visualization by Julie Steele and Noah Iliinsky
  2. Information Dashboard Design by Stephen Few
  3. Knowledge is beautiful by David McCandless (Short abstract)
  4. The Functional Art: An Introduction to Information Graphics and Visualization by Alberto Cairo
  5. The Visual Display of Quantitative Information by Edward R. Tufte
  6. storytelling with data: a data visualization guide for business professionals by cole Nussbaumer knaflic
  7. Research paper - Cheat Sheets for Data Visualization Techniques by Zezhong Wang, Lovisa Sundin, Dave Murray-Rust, Benjamin Bach

Suggestions:

In case, if you find any books, cheat sheets, or charts missing and if you would like to suggest some new documents please let me know in the discussion sections!

Resources:

• Charts: I personally recommend data viz catalogue, it's easy to understand with their explanation!
• Python codes: Plotly for python and Python graph gallery
• R codes for charts:Plotly for R
• d3 codes: Visualization codes using d3

Request to kaggle users:

• A kind request to kaggle users to create notebooks on different visualization charts as per their interest by choosing a dataset of their own as many beginners and other experts could find it useful!

• To create interactive EDA using animation with a combination of data visualization charts to give an idea about how to tackle data and extract the insights from the data

Suggestion and queries:

Feel free to use the discussion platform of this data set to ask questions or any queries related to the data visualization corpus and data visualization techniques

Kindly upvote the dataset if you find it useful or if you wish to appreciate the effort taken to gather this corpus! Thank you and have a great day!

Presentation Date: Tuesday, September 10, 2019. Location: Institute for Advanced Study, Princeton, NJ. Abstract: It has been nearly 100 years since the "Great Debate," where Heber Curtis correctly argued that Thomas Wright's 1750 ideas about our Milky Way being one of many "galaxies," each a flattish disk of a multitude of stars, was correct. Since then, astronomers have made sharper and sharper images of galaxies beyond our own, often revealing intricate sprial structure. But, for the mostpart, our potentially super-close-up view of our own Galaxy's structure has been ruined by our unfortunate vantage point within its disk. Work over the past century indicates that the Milky Way is a barred spiral, but even the Galaxy's number of arms is still at-issue. In this talk, I will discuss how four techniques are being combined to tease out the true structure of the Milky Way. In particular, our collaboration* is combining 3D-dust mapping, searches for extraordinarily long galactic filaments called "Bones," position-position-velocity observations of gas, and numerical simulations to create a new, and sometimes very surprising, view of our Galaxy. Unexpected results to be presented include: several-hundred-pc long, ~1-pc wide, gaseous "Bones" lying in, and likely defining, the gravitational mid-plane of the Milky Way; a 2.5 kpc-long damped sine wave with 200-pc amplitude that seems to be the Local Arm (and the undoing of "Gould's Belt"); and simulations that suggest the need for feedback and/or magnetic fields, and/or stranger physics (dark matter in the disk?) in order to explain the Bones and/or the Local Arm's Wave.

ABSTRACT Even though data visualization is a common analytical tool in numerous disciplines, it has rarely been used in agricultural sciences, particularly in agronomy. In this paper, we discuss a study on employing data visualization to analyze a multiplicative model. This model is often used by agronomists, for example in the so-called yield component analysis. The multiplicative model in agronomy is normally analyzed by statistical or related methods. In practice, unfortunately, usefulness of these methods is limited since they help to answer only a few questions, not allowing for a complex view of the phenomena studied. We believe that data visualization could be used for such complex analysis and presentation of the multiplicative model. To that end, we conducted an expert survey. It showed that visualization methods could indeed be useful for analysis and presentation of the multiplicative model.

Transparency in data visualization is an essential ingredient for scientific communication. The traditional approach of visualizing continuous quantitative data solely in the form of summary statistics (i.e., measures of central tendency and dispersion) has repeatedly been criticized for not revealing the underlying raw data distribution. Remarkably, however, systematic and easy-to-use solutions for raw data visualization using the most commonly reported statistical software package for data analysis, IBM SPSS Statistics, are missing. Here, a comprehensive collection of more than 100 SPSS syntax files and an SPSS dataset template is presented and made freely available that allow the creation of transparent graphs for one-sample designs, for one- and two-factorial between-subject designs, for selected one- and two-factorial within-subject designs as well as for selected two-factorial mixed designs and, with some creativity, even beyond (e.g., three-factorial mixed-designs). Depending on graph type (e.g., pure dot plot, box plot, and line plot), raw data can be displayed along with standard measures of central tendency (arithmetic mean and median) and dispersion (95% CI and SD). The free-to-use syntax can also be modified to match with individual needs. A variety of example applications of syntax are illustrated in a tutorial-like fashion along with fictitious datasets accompanying this contribution. The syntax collection is hoped to provide researchers, students, teachers, and others working with SPSS a valuable tool to move towards more transparency in data visualization.

This resource contains Jupyter Python notebooks which are intended to be used to learn about the U.S. National Water Model (NWM). These notebooks explore NWM forecasts in various ways. NWM Notebooks 1, 2, and 3, access NWM forecasts directly from the NOAA NOMADS file sharing system. Notebook 4 accesses NWM forecasts from Google Cloud Platform (GCP) storage in addition to NOMADS. A brief summary of what each notebook does is included below:

Notebook 1 (NWM1_Visualization) focuses on visualization. It includes functions for downloading and extracting time series forecasts for any of the 2.7 million stream reaches of the U.S. NWM. It also demonstrates ways to visualize forecasts using Python packages like matplotlib.

Notebook 2 (NWM2_Xarray) explores methods for slicing and dicing NWM NetCDF files using the python library, XArray.

Notebook 3 (NWM3_Subsetting) is focused on subsetting NWM forecasts and NetCDF files for specified reaches and exporting NWM forecast data to CSV files.

Notebook 4 (NWM4_Hydrotools) uses Hydrotools, a new suite of tools for evaluating NWM data, to retrieve NWM forecasts both from NOMADS and from Google Cloud Platform storage where older NWM forecasts are cached. This notebook also briefly covers visualizing, subsetting, and exporting forecasts retrieved with Hydrotools.

NOTE: Notebook 4 Requires a newer version of NumPy that is not available on the default CUAHSI JupyterHub instance. Please use the instance "HydroLearn - Intelligent Earth" and ensure to run !pip install hydrotools.nwm_client[gcp].

The notebooks are part of a NWM learning module on HydroLearn.org. When the associated learning module is complete, the link to it will be added here. It is recommended that these notebooks be opened through the CUAHSI JupyterHub App on Hydroshare. This can be done via the 'Open With' button at the top of this resource page.

In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California’s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map California’s State Waters with consistent methods at a consistent scale. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. This approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of “lands” from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth. This data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013). The California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bay’s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Service–Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey. These web services for the Santa Barbara Channel map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://walrus.wr.usgs.gov/mapping/csmp/index.html. Each published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and photographic imagery; these “ground-truth” surveying data are available from the CSMP Video and Photograph Portal at https://doi.org/10.5066/F7J1015K. The “seafloor character” data layer shows classifications of the seafloor on the basis of depth, slope, rugosity (ruggedness), and backscatter intensity and which is further informed by the ground-truth-survey imagery. The “potential habitats” polygons are delineated on the basis of substrate type, geomorphology, seafloor process, or other attributes that may provide a habitat for a specific species or assemblage of organisms. Representative seismic-reflection profile data from the map area is also include and provides information on the subsurface stratigraphy and structure of the map area. The distribution and thickness of young sediment (deposited over the past about 21,000 years, during the most recent sea-level rise) is interpreted on the basis of the seismic-reflection data. The geologic polygons merge onshore geologic mapping (compiled from existing maps by the California Geological Survey) and new offshore geologic mapping that is based on integration of high-resolution bathymetry and backscatter imagery seafloor-sediment and rock samplesdigital camera and video imagery, and high-resolution seismic-reflection profiles. The information provided by the map sheets, pamphlet, and data catalog has a broad range of applications. High-resolution bathymetry, acoustic backscatter, ground-truth-surveying imagery, and habitat mapping all contribute to habitat characterization and ecosystem-based management by providing essential data for delineation of marine protected areas and ecosystem restoration. Many of the maps provide high-resolution baselines that will be critical for monitoring environmental change associated with climate change, coastal development, or other forcings. High-resolution bathymetry is a critical component for modeling coastal flooding caused by storms and tsunamis, as well as inundation associated with longer term sea-level rise. Seismic-reflection and bathymetric data help characterize earthquake and tsunami sources, critical for natural-hazard assessments of coastal zones. Information on sediment distribution and thickness is essential to the understanding of local and regional sediment transport, as well as the development of regional sediment-management plans. In addition, siting of any new offshore infrastructure (for example, pipelines, cables, or renewable-energy facilities) will depend on high-resolution mapping. Finally, this mapping will both stimulate and enable new scientific research and also raise public awareness of, and education about, coastal environments and issues. Web services were created using an ArcGIS service definition file. The ArcGIS REST service and OGC WMS service include all Santa Barbara Channel map area data layers. Data layers are symbolized as shown on the associated map sheets.

Traditionally, zoning plans have been represented on a 2D map. However, visualizing a zoning plan in 2D has several limitations, such as visualizing heights of buildings. Furthermore, a zoning plan is abstract, which for citizens can be hard to interpret. Therefore, the goal of this research is to explore how a zoning plan can be visualized in 3D and how it can be visualized it is understandable for the public. The 3D visualization of a zoning plan is applied in a case study, presented in Google Earth, and a survey is executed to verify how the respondents perceive the zoning plan from the case study. An important factor of zoning plans is interpretation, since it determines if the public is able to understand what is visualized by the zoning plan. This is challenging, since a zoning plan is abstract and consists of many detailed information and difficult terms. In the case study several techniques are used to visualize the zoning plan in 3D. The survey shows that visualizing heights in 3D gives a good impression of the maximum heights and is considered as an important advantage in comparison to 2D. The survey also made clear including existing buildings is useful, which can help that the public can recognize the area easier. Another important factor is interactivity. Interactivity can range from letting people navigate through a zoning plan area and in the case study users can click on a certain area or object in the plan and subsequently a menu pops up showing more detailed information of a certain object. The survey made clear that using a popup menu is useful, but this technique did not optimally work. Navigating in Google Earth was also being positively judged. Information intensity is also an important factor Information intensity concerns the level of detail of a 3D representation of an object. Zoning plans are generally not meant to be visualized in a high level of detail, but should be represented abstract. The survey could not implicitly point out that the zoning plan shows too much or too less detail, but it could point out that the majority of the respondents answered that the zoning plan does not show too much information. The interface used for the case study, Google Earth, has a substantial influence on the interpretation of the zoning plan. The legend in Google Earth is unclear and an explanation of the zoning plan is lacking, which is required to make the zoning plan more understandable. This research has shown that 3D can stimulate the interpretation of zoning plans, because users can get a better impression of the plan and is clearer than a current 2D zoning plan. However, the interpretation of a zoning plan, even in 3D, still is complex.

Visualizing research data can be an important science communication tool. In recent decades, 3D data visualization has emerged as a key tool for engaging public audiences. Such visualizations are often embedded in scientific documentaries screened on giant domes in planetariums or delivered through video streaming services such as Amazon Prime. 3D data visualization has been shown to be an effective way to communicate complex scientific concepts to the public. With its ability to convey information in a scientifically accurate and visually engaging way, cinematic-style 3D data visualization has the potential to benefit millions of viewers by making scientific information more understandable and interesting. Maximizing the effectiveness of 3D data visualization can benefit millions of viewers. To support a wider shift in this professional field towards more evidence-based practice in 3D data visualization to enhance science communication impact, we have conducted a survey experiment comparing audience responses to two versions of 3D data visualizations from a scientific documentary film on the theme of ‘solar superstorms’ (n = 577). This study was conducted using a single (with two levels: labeled and unlabeled), between-subjects, factorial design. It reveals key strengths and weaknesses of communicating science using 3D data visualization. It also shows the limited power of strategically deployed informational labels to affect audience perceptions of the documentary film and its content. The major difference identified between experimental and control groups was that the quality ratings of the documentary film clip were significantly higher for the ‘labeled’ version. Other outcomes showed no statistically significant differences. The limited effects of informational labels point to the idea that other aspects, such as the story structure, voiceover narration and audio-visual content, are more important determinants of outcomes. This study concludes with a discussion of how this new research evidence informs our understanding of ‘what works and why’ with cinematic-style 3D data visualizations for the public.

Middlebury College is a private liberal arts institution that was founded in 1800. It is located in the Champlain Valley between the Green Mountains and the Adirondacks in the small town of Middlebury, Vermont. The college currently enrolls 2,526 undergraduates from all 50 states and 74 countries.

Middlebury is committed to educating students in the tradition of the liberal arts, which embodies a method of discourse as well as a group of disciplines. From their scientifically and mathematically oriented majors to their humanities, social sciences, arts, and languages, Middlebury emphasizes in reflection, discussion, and intensive interactions between students, staff, and faculty members. Their vibrant residential community, remarkable facilities, and diversity of co-curricular activities and support services, all exist primarily to serve these educational purposes.

Using Professor Peggy Nelson's Middlebury Survey, I analyzed and displayed significant outcomes that informed important aspects of Middlebury culture such as how number of siblings, race, and class can affect GPA, how class can dictate satisfaction with opportunities of meeting new people, and how year and housing can determine prospects of either looking for a short-term relationship or finding a long-term partner. It was my hope that this information would help discover insights that will lead the Middlebury community to not only reexamine the way they approach they own community culture, but to create and sustain an environment on campus that is conducive to learning, inclusivity, and engaged discourse.