March 29, 2018

Zooplankton Diversity Project

Zooplankton Diversity ProjectZooplankton Diversity Project

The Savannah River Site (SRS) is a US Department of Energy owned facility located in South Carolina. Located within the SRS property are a series of ephemeral wetlands known as Carolina Bays, home to numerous zooplankton species. The specific species found in the bays (their community composition) changes throughout the seasons as bays fill with rain water or dry down during the hot summer months.

Data Collection: Members of the Drake lab in the Odum School of Ecology at the University of Georgia sampled 14 bays monthly between January 2009 and spring 2016. Marcus Zokan spent many hours identifying the species (and even discovering a few) in the samples collected between January 2009 and December 2010. For more about the sampling methods see Marcus’ dissertation.1

This week, our lab at the Odum School of Ecology is announcing the Zooplankton Diversity Project, a datavisualization toolkit and open dataset of 485,047 zooplankton specimens representing 133 taxa, collected from Carolina Bays of the Savannah River Site.

Data Visualizations…

Drake Lab researchers constructed a full suite of data exploration tools in D3. The tools can be used to subselect data for download or generate SVG plots of the selected data. You can use the SVG Crowbar chrome browser extension to automatically download SVG plots generated on the site.

On various pages, you will find tools for exploring species richness and species density, by taxonomic group, environmnetal covariates, species distribution by bay, species co-occurrence, population dynamics, community similarity over time, and the taxonomic tree.

Here’s a screenshot of the Community Similarity tool. You can explore zooplankton comminity similarity over time here.

Example of an "NMDS" plot (Non-metric multidimensional scaling), representing community similarity, that expresses how much a zooplankton community changes over time. The high dimensional data representing a snapshot of a zooplankton community is reduced through NMDS to 2 dimensions, such that the distances between these 2D coordinates (over time) mirror as much as possible the distances between the coordinates in high-dimensional space. The 2D dimensions do not represent individual variables; rather they are a mapping of the high dimensional space onto 2 dimensions.Example of an "NMDS" plot (Non-metric multidimensional scaling), representing community similarity, that expresses how much a zooplankton community changes over time. The high dimensional data representing a snapshot of a zooplankton community is reduced through NMDS to 2 dimensions, such that the distances between these 2D coordinates (over time) mirror as much as possible the distances between the coordinates in high-dimensional space. The 2D dimensions do not represent individual variables; rather they are a mapping of the high dimensional space onto 2 dimensions.

The project was spearheaded by Drew Kramer, now an Assistant Professor in the Department of Integrative Biology at the University of South Florida.


  1. Marcus Zokan. Zooplankton species diversity in the temporary wetland system of the Savannah River Site, South Carolina, USA. 2015, University of Georgia.

May 20, 2016

Shell Oil Spill - Gulf Of Mexico

(Google Earth) Animated flight over Shell Oil Spill with photos (scroll down to download .kmz)(Google Earth) Animated flight over Shell Oil Spill with photos (scroll down to download .kmz)

On May 12, 2016, an oil spill occurred in the Gulf of Mexico, originating in Royal Dutch Shell’s Glider” Oil Field, at about 1000 m.

The leak, which according to Shell has been stopped, emanated from the infrastructure that ties the oilfield to its Brutus” floating oil platform, about 10km (or 5 1/2 nautical miles) away.

Map of the leak area. The leak originated somehwere in the Glider field, circled in yellow, which is connected via pipelines to the Brutus platform. Areas outlined in dark red are communities of chemosynthetic organisms associated with methane hydrates and natural oil and gas seepage. Orange crosses are wellheads on the seafloor. DATA: BOEMMap of the leak area. The leak originated somehwere in the Glider field, circled in yellow, which is connected via pipelines to the Brutus platform. Areas outlined in dark red are communities of chemosynthetic organisms associated with methane hydrates and natural oil and gas seepage. Orange crosses are wellheads on the seafloor. DATA: BOEM

According to Shell, about 88,000 gallons were realeased (compare to the 206 Million gallons released in the Deepwater Horizon accident).

Visual estimates suggest the spill could be much bigger than 88,000 gallons…

ECOGIG researchers have flown over the area twice, on 5/15 and 5/18, the second flight coordinated with ECOGIG researchers on board the R/V Tommy Munro.

Update: The flight reports and images from On Wings of Care (linked to here) are not available at the moment. It looks like On Wings of Care is in the process of transfering files to a new webserver. This inlcudes the images of the spill in the kmz file below.

In her 5/15 flight report, Dr. Bonny L. Schumaker (On Wings Of Care), flying with ECOGIG scientist Ian MacDonald, wrote:

Even if the average thickness of the visible oil were a mere 100 micron (0.1 millimeter, vastly smaller than the areas of emulsified oil that stretch across the area), the visible surface oil would represent about 500,000 gallons of oil. We haven’t seen images like this since the BP disaster of 2010.” - source

That’s much less than DWHs 206 Million gallons, but a lot more than Shell’s estimate of 88,000 gallons for this spill. Here’s an image of the spill from the air, with a skimming operation going on tin the upper left.

You can read the full flight reports, with LOTS of photos here and here

If you’d like to see where all these photos were taken, check out the 5/18 flight path and browse images in Google Earth:

OWOC20160518_hires.kmz - Download the .kmz file and open in Google Earth.

April 26, 2016

Intro to Sound and Data Sonification

Definitions

Acoustics: (1) The branch of physics concerned with sound. (2) The properties of a concert hall with respect to the way sound interacts with it.

Psychoacoustics: The branch of psychophysics that studies the sense of hearing. Psychoacoustics defines, qualifies and quantifies sensations in relation to the stimuli (sounds) that cause them.

Electroacoustics: The intersection of acoustics and electronics. Electroacoustics studies the conversion of sound into an electronic signal (called transduction), the manipulation of the electronic signal, and the conversion of the signal back into sound (also transduction).

Sound: a mechanical vibration transmitted through a medium (usually air) to the ear, with an amplitude and frequency capable of being perceived by the auditory system.


IF A TREE FALLS IN THE WOODS with no one around, it does make a sound.

IF A TREE FALLS ON THE MOON, even with someone around, it does not make a sound. (Sound does not travel in a vacuum.)

Bats produce ultra-sound (sound too high for humans to hear). Elephants produce infra-sound (sound too low for humans to hear).


Signal: any other vibration or energy variation that does not fit the definition of sound, even if the vibration or variation represents a sound. We commonly refer to electric and digital signals.

Analog Signal: A smoothly varying signal. In other words, a direct analog” for sound. An electric signal is an analog signal. The grooves on an LP are also a type of analog signal. A cassette tape stares an analog signal magnetically.

Digital Signal: A signal that varies in discreet steps. A digital signal can be created is created by sampling an analog signal at regular interval, called the sampling rate. A digital signal is like a rasterized image: It is a series of numbers, or each number (or sample”) representing the intensity of a signal at a given moment in time. (Whereas a raster image is a series of numbers each representing the color of an image at a given point on the screen or page.) A digital signal can be stored in a variety of ways, including magnetically (on a digital audio tape, or DAT), and optically (on a CD). A digital signal can be transmitted electrically (in a computer chip, or on a specially built electric cable) or optically (fiber optics).


Analog-to-Digital Conversion (ADC): The process of sampling an analog signal in order to create a digital signal.

SOUNDELECTRIC (ANALOG) SIGNALDIGITAL SIGNAL

Digital-to-Analog Conversion (DAC): The process of converting a series of samples to a continuously varying (analog) electric signal.

DIGITAL SIGNALELECTRIC (ANALOG) SIGNALSOUND

(NOTE: technically, in the diagrams above, the transition from electric to sound is transduction.”

Sample: (1) An individual number in a digital signal. The sample represents the intensity of a signal at a given time. The sample is to a digital signal as a pixel is to a digital image. (2) The length of time it takes for one sample to go by (depends on the sampling rate, but is usually a small fraction of a millisecond). (3) An entire bit of digitally recorded sound, stored as a series of numbers (A.K.A. a stored digital signal). This is also the popular usage of the term.

Sampling Rate (in Samples per Second): (1) The rate at which an analog signal is sampled in order to create a digital signal. The most common sampling rate is 44,100 samples per second. This is the rate used by CD players. Other common sampling rates are 22,050 samples per second and 48,000 samples per second. Sampling rate is analogous to the resolution of a raster image.


How Acoustic Parameters of Sound Map to Perception (and possible data types)

physical parameter perceptual parameter possible data mapping (Q=Quantatiative, O=Ordinal, N=Nominal)
Frequency (Hz) Pitch, or height” QON
Intensity Loudness (Q)ON
Waveform (spectrum) Tone Color (Q)(O)N
Intensity+Frequency+Waveform in Time Timbre (Q)(O)N

Notice, the term volume” is not used. Loudness” and Intensity are more precise. Volume” is used in psychoacoustics to refer to an esoteric characteristic of sound, which could be described as its fullness.” We will avoid the term volume” for now.


More Definitions

Noise:

  1. any undesirable, uncomfortable or dangerous sound. Sound pollution refers to this meaning. This is the popular meaning.
  2. The opposite of signal. Parasitic vibrations accompanying a signal that interfere with its clear transmission. The Signal-Noise ratio” refers to this meaning.
  3. an a-periodic sound (a sound without a definable frequency, hence without a definite pitch). This is the opposite of Musical Sound.”

Musical sound or pitched sound: a periodic sound (a sound with a definable frequency, hence with a definite pitch).

Unpitched sound: Noise (definition 3.)


Auditory Perception

As graphic perception must be taken into account when designing scales for visualization, auditory perception must be taken into account when designing scales for sonification of data.

One notable example of how auditory perception should influence scale design is in the construction of pitch scales.

For a discussion, see: http://blog.ericmarty.com/7/perceptually-uniform-pitch-loudness-scales-for-data-sonification


Sonification Examples

General Interest

http://www.huffingtonpost.com/mark-ballora/sound-the-music-universe_b_2745188.html

Simple Mapping of Single Variables

Nick Bearman: temperature to pitch (map mousover)

altitude to pitch (map mouseover)

integers mapped to integer frequency bins
Sorting algorithms (computer science) - scanned to frequency (integers mapped to integer frequencies)

Redundant Mapping of Single Vairables

price to speed+pitch+loudness
https://www.foreignaffairs.com/audios/2015-07-22/sound-economy

Multiple 1:1 Mappings

Listen to wikipedia: Hatnote

Arctic Ice

Sonification of LHC data

Complex Mappings of Single Variables

Brian Foo http://brianfoo.com

smog levels to granular synthesis parameters http://www.theatlantic.com/technology/archive/2012/09/soundscapes-of-smog-researchers-let-you-hear-the-pollution-of-cities-literally/262152/

February 25, 2016

Visualization Tools Built on D3

The D3 / Vega stack” (from the creators of D3):

The in-house family of higher-level tools built on top of Mike Bostock’s D3. Mike Bostock developped D3 at the Stanford Visualization Group, led by Jedff Heer. The lab moved to the University of Washington and became the Interactive Data Lab (IDL). IDL/Stanford Vis Group built the Vega declarative visusaliation language on top of D3, Vega-Lite (a simplified declarative language) on top of that, and is building a small suite of exploratory data analysis and design tools on top of Vega and Vega Lite. (IDL is also behind Tableau and the spinoff company Trifacta that makes it.)

My introduction

The Vega family on GitHub


Third-Party Tools built on D3

Here are a numer of third-party languages, environments and tools built on top of D3.

• Mid-level (js)

nvd3.js — many standard chart types

c3.js — many standard chart types

dimple.js — for business

xcharts.js — simple charts, few options

• Specialized Mid-level (js)

Crossfilter — large, cross-linked multivariate datasets in the browser

cubism.js — scalable, realtime animated, time series visualisations

JSNetworkX — networks

• High-level data exploration

raw by Density Design. Drag and drop editor outputs d3 code.

• Visual Programming Environments

vvvv.js — in-browser version of the VVVV visual programming environment (built on D3).

• Full GUI Web Apps

Plot.ly
Web interface for D3 (see below). Free (public charts only). Can export to SVG. Powerful. Private charts require a paid subscirption. tutorials

Dashboards.ly
Layout multiple Plotly charts on a single page and publish.

Compare to:
Tableau - desktop + online drag/drop visualization editor. Publish to web. Pro version is $1000. Free student license.). Tableau Public (also free). tutorials Tableau is NOT built on top of D3, but came out of the same group that made D3, and is based on Grammar of Graphics. So, it is conceptually similar to Plotly (and Vega). Orginally called Polaris, it was commercialized as Tableau when the Stanford/IDL group created the company Trifacta.

D3 wrappers in other languages

rCharts — extensible R wrapper. Supports many charting libraries, including NVD3, Polychart, Morris, Rickshaw, xCharts, HighCharts, and Leaflet for mapping.

Shiny - extensible web application framework for R

D3.py — python library for generating d3-based plots, using the panda module. See also: vincent, a python to Vega translator

Plotly also has APIs for the major scientific computing languages (Matlab, R, Python), so a round-about way to leverage D3 without actually using it.

• Other people’s lists of D3 based tools

Tony Hirst: Climbing the d3.js Visualisation Stack

Marielle Lange: D3lib

Mike McDearmon: Data Visualization Libraries Based on D3.JS

February 24, 2016

Vega Visualization Grammar

Vega is a visualization grammar. You can read about Vega, its relationship to D3, and the family of tools built on top of Vega in my last post: The D3 - Vega Stack”. This post is an introduction to Vega 2.5.

Vega is a declarative format for creating, saving, and sharing interactive visualization designs. A designer declares the elements of a visualization (using the Vega grammar) in a visualization specification, in JSON format, something like this:

{ 
    "data":[...], 
    "scales":[...], 
    "axes":[...], 
    "marks":[...]
}

Vega does the rest. Vega Runtime can interpret the specification and render it directly in the browser using either SVG or HTML Canvas. (Or, a simple command line application can convert it directly to an SVG file.) An online Vega Editor that shows the spec and the visualization it produces side-by-side makes it easy to write Vega. Check out the examples in the Vega Editor to see some real Vega specs and the visualizations they produce. (Like this bar chart)

Conceptually, the Vega grammar separates the elements of the visualization into these semantic areas:

DATA The data to visualize
DATA TRANSFORMS Grouping, stats, projections, etc.
SCALES Mappings of data to visual parameters
GUIDES Axes & Legends to visualize Scales
MARKS Graphic elements representing actual data

In addition, SIGNALS are dynamic variables that drive interactive behaviours.

The full Vega grammar is described in the wiki. Here is my basic Vega 2.5 grammar cheat sheet:

Top Level Visualization Properties” (container properties)

  • name (optional) - name for this visualization
  • width - width of chart
  • height - height of chart
  • viewport (optional) - [width, height] of scrollable window onto chart
  • padding (optional) - margins
  • background (optional) - background color
  • scene (optional) - stroke and fill the entire scene

Other Top Level Properties (chart properties)

  • data - data to visualize. See Data

  • scales (optional) - Scale transform definitions. See Scales

  • axes (optional) - Axis definitions. Axes are the labels (tick marcs, etc.) that show the scales on the visualization

  • legends (optional) - Legend definitions. See Legends

  • marks - Graphical mark definitions. Marks are the main graphical and text elements of the visualization.

  • signals(optional) - Signals are dynamic variables or interactive events

data - properties

  • name - unique name for the data set
  • format (optional)
  • values, source, or url - The data (manually entered values, named source or URL)
  • transform (optional) - transforms (analysis, filters, etc.) to perform on the data. See Data-Transforms
    • Data Manipulation Transforms:
      • aggregate - perform basic stats
      • bin - sort into quantatitive bins
      • countpattern - find and count occurrences of a text pattern
      • cross - cross-product of two data sets
      • facet - organize data into groups
      • filter - filter data to remove unwanted items
      • fold
      • formula - extend the data set using formulas
      • impute - perform imputation of missing values
      • lookup - extend the data set using a lookup table
      • rank - rank data
      • sort - sort data
      • treeify - compute a tree structure from table data
    • Visual Encoding Transforms:
      • force - Performs force-directed layout for network data
      • geo - Performs cartographic projection
      • geopath - Creates paths for geographic regions
      • hierarchy - Computes tidy, cluster, and partition layouts
      • linkpath - Computes path definition for connecting nodes in a node-link network or tree diagram
      • pie - Computes a pie chart layout
      • stack - Computes layout values for stacked graphs, as in stacked bar charts or stream graphs
      • treemap - Computes a squarified treemap layout for heirarchical or faceted” data.
      • voronoi - Computes voronoi diagram for a set of x,y coordinates.
      • wordcloud - Builds a word cloud from text data
  • modify (optional) - streaming operators to respond to signals. See Streaming-Data

scales - properties

  • name - unique name for the scale
  • type - type of scale
  • domain - The domain of the scale, representing the set of data values
  • domainMin - Min value for scale domain (quantitative scales only)
  • domainMax - Max value for scale domain (quantitative scales only)
  • range - The range of the scale, representing the set of visual values
  • domainMin - Min value for scale range (quantitative scales only)
  • domainMax - Max value for scale range (quantitative scales only)
  • reverse - flip scale range
  • round - round scale range to integers

other properties whose usage varieas according to scale type:

  • points - distribute ordinal values uniformly
  • padding - apply spacing around ordinal points
  • clamp - clamp out-of-range data to the ends of the scale domain
  • nice - force scale to use human-friendly values (whole numbers, minutes, hours, etc.)
  • exponent - set exponent (for exponential scales only)
  • zero - force scale to include zero (quantitative scales only)

axes - properties

  • type - type of axis: x or y
  • scale - name of the scale for this axis
  • orient - axis orientation: top, bottom, left or right (e.g. right to put a y axis on the right side.)
  • title (optional) - title text for the axis
  • titleOffset - offset (in pixels) from the axis at which to place the title
  • format (optional) - formatting pattern for axis labels (number formats, etc.)
  • fomatTyle (optional) - (time, utc, string or number)
  • ticks - number of ticks, for axes showing quantitative scales
  • values - instead of specifying number of ticks, explicitely set each tick value
  • subdivide - number of minor ticks between main ticks (e.g. 9 = decimal subdivision)
  • tickPadding - padding between ticks and text labels
  • tickSize - size of all ticks
  • tickSizeMajor - size of only the major ticks
  • tickSizeMinor - size of only the minor ticks
  • tickSizeEnd - size of only the end ticks
  • offset - offset betwwen axis and edge of the main data rectangle
  • layer - draw axes in front (default) or back of the data
  • grid - draw grid lines (true or false)
  • properties - use for custom axis styling

legends - properties

Legends link to named scales. At least one of the size, shape, fill or stroke parameters must be specified

  • size, shape, fill and/or stroke — scale name determining size, shape, fill or stroke of a data item in the visualization (at least one must be specified)
  • orient — position of legend within the scene: right (default) or left
  • offset - horizontal offset of legend (in pixels) from the data rectangle
  • title (optional) — legend title
  • format (optional) - formatting pattern for legend labels (number formats, etc.)
  • values (optional) - Explicitly set the visible legend values
  • properties - use for custom legend styling

marks - properties

Marks are the basic visual buiding blocks of a visualization. A mark” is a prototype graphic object duplicated and varied for each data point. (e.g. one single rectangle mark generates an entire bar graph.)

  • type - type of mark: rect, symbol, path, arc, area, line, rule, image, text or group.
    Also, the special group type can contain other marks, plus local scales, axes and legends. See Group Marks
  • name (optional) - unique name for the mark instance (can be used for css styling)
  • description (optional) - desciption of mark or comment
  • from — data this mark set should visualize
    • data — name of the data set to use.
    • transform (optional) — array of data transformations to apply
  • properties - object containing sets of mark properties
    • enter — set of properties to apply when data is processed for the first time and a mark instance is newly added to a scene
    • exit (optional) — set of properties to apply when the data linked to a mark instance is removed, and so the mark instance is disappearing. Seldom used.
    • update (optional) — set of properties to apply to already existing mark instances, when needed (such as when data changes or after a hover).
    • hover (optional) — set of properties evaluated when pointer hovers over a mark instance. At the end of the hover, the update property set is triggered.

Within each set, properties are defined in "name":"value" pairs, where "name" is the property and "value" is either a value reference or a production rule. See Marks for full documentation.

  • key (optional) — data field to use as a unique key for data binding for dynamic data
  • delay (optional) — transition delay (milliseconds) for mark updates. Used for animation.
  • ease (optional) — transition ease function: linear, quad, cubic, sin, exp, circle, and bounce. See here for documentation. (default = cubic-in-out)

signals - properties

Signals are dynamic variables that drive interactions.

For a description of how Signals work in Vega, see https://github.com/vega/vega/wiki/Signals


© Copyright 2015 Éric Marty