A month of data viz for the #30DayChartChallenge
A round up of @USGS_Datasci tweets for the
For the month of April, the #30DayChartChallenge challenged twitter users to create daily data visualizations that fit within 5 major categories: comparisons, distributions, relationships, timeseries, and uncertaintites. Each day had a unique prompt to inspire participants to use a variety of chart types and get creative with their data.
The USGS Vizlab was excited to take this challenge on! With support and contributions from the USGS Water Data Science Branch and CDI Data Visualization Collaboration Area , we participated using the @USGS_DataSci twitter account .
See below for our contributions throughout the month.
Day 2: comparisons, pictogram
Day 2 of #30DayChartChallenge – Pictograms! This chart by Ellen Bechtel features the @USGS_Water Science School (https://t.co/TewTcdyOwM) cute lil’ friend Drippy to guide us through the total volume of #water stored on Earth. #usgs #waterscience #scicomm pic.twitter.com/5e5E9uEoMQ
— USGS Data Science (@USGS_DataSci) April 2, 2021
Day 3: comparisons, historical
Day 3 of the #30DayChartChallenge: historical comparisons. #Hydrologist Sam Oliver created this ridgeplot using #rstats pkgs #ggplot2 and ggridges to explore trends in daily #streamflow from 1990-2018 at the #Yahara #River in #Wisconsin (site info at https://t.co/RmjCPt6dtz) pic.twitter.com/mig66Uy8xF
— USGS Data Science (@USGS_DataSci) April 3, 2021
Day 4: comparisons, magical
Day 4 of the #30DayChartChallenge: magical comparisons! Hayley Corson-Dosch was inspired by the ✨Magic✨ #Reservoir in #Idaho. Here, we can see how reservoir operations impact downstream #streamflow by comparing flow above (upstream) and below (downstream) the reservoir. pic.twitter.com/F8MYwyoMzB
— USGS Data Science (@USGS_DataSci) April 4, 2021
Day 6: comparisons, experimental
Day 6 of the #30DayChartChallenge: experimental comparisons. Today, we revisit Lindsay Platt’s experimental #streamgraph #dataviz comparing #flood durations at multiple #rivers since 2000. Notably, the James River in #SouthDakota endured a 534-day long flood that ended last Sept pic.twitter.com/xRU3Xkq87F
— USGS Data Science (@USGS_DataSci) April 6, 2021
Day 7: distributions, physical
Day 7 of the #30DayChartChallenge: physical distributions. This chart shows projected changes to lake ice cover and snow depth in Northern WI & MI, USA under future climate scenarios. These physical changes can influence lake #biogeochemistry - https://t.co/yVyc73Z5DY pic.twitter.com/NAgRNLuug1
— USGS Data Science (@USGS_DataSci) April 7, 2021
Day 8: distributions, animals
More #30DayChartChallenge: Introduced species in Ohio from 4 major taxonomic groups. Data are from the Nonindigenous Aquatic Species Database (https://t.co/IyMtF6lpYE), aggregated by HUC8, & includes all reported species. Points = major cities. From Matthew Neilson @USGS_CDI pic.twitter.com/tKh1TFEt4H
— USGS Data Science (@USGS_DataSci) April 8, 2021
Day 8 of the #30DayChartChallenge: animal distribution. The Tagged Animal Movement Explorer combines the spatial distributions of animals (blue whales below!) with interactive distribution charts to cross filter multiple related variables. From Jeff Walker & Ben Letcher @USGS_CDI pic.twitter.com/qpatnXGIZN
— USGS Data Science (@USGS_DataSci) April 8, 2021
Because it's #spring time out in the Rockies, here’s the GAP Predicted Habitat for the charismatic Pronghorn (Antilocapra americana) in the southern Rockies Ecoregion (level III). From the National Biogeographic Map, provided by Steven Aulenbach #30DayChartChallenge pic.twitter.com/FE7TKZoGCy
— USGS Data Science (@USGS_DataSci) April 8, 2021
Day 9: distributions, statistics
Day 9 #30DayChartChallenge: distributions/statistics. Here's a sneak peek of a #dataviz project about using process-guided #DeepLearning to predict water temperature. The chart shows the distribution of model RMSEs across stream reaches in the Delaware River Basin. By C. Nell #D3 pic.twitter.com/9x8StBSpLC
— USGS Data Science (@USGS_DataSci) April 9, 2021
Day 10: distributions, abstract
Day 10 of the #30DayChartChallenge, abstract distributions. These streamgraphs show the distribution of #river #flow during major water events in the U.S., across @USGS_Water streamgages from the Hydro-Climatic Data Network https://t.co/GBU4sNFcB5 #dataviz by Ellen Bechtel pic.twitter.com/z9FLVBVua4
— USGS Data Science (@USGS_DataSci) April 10, 2021
Day 12: distributions, strips
Day 12 of the #30DayChartChallenge, strip distributions, we’re plotting the distribution of ❄️#snowmelt timing since 1981 across @USDA_NRCS snow telemetry (SNOTEL) sites. Snowmelt timing is shown as the difference in days from the median melt date at each site. #rstats #ggplot2 pic.twitter.com/JfTkGlEKMq
— USGS Data Science (@USGS_DataSci) April 12, 2021
Day 13: relationships, correlation
Catching up with the #30DayChartChallenge, correlation, to look at the total area burned by #wildfire & federal #fire suppression costs through time (1985-2020). Inspired by past #dataviz on wildfire impacts to water supplies https://t.co/qZoCffbm9h. Data from @NIFC_fire #rstats pic.twitter.com/Rx53Qi20yi
— USGS Data Science (@USGS_DataSci) April 14, 2021
Day 14: relationships, space
Now the model can better leverage information across the network. It decides what data from other reaches to use to predict temperature in a given reach based on proximity. This heatmap shows the pairwise distances between all stream reaches in the #DelawareRiverBasin. #dataArt pic.twitter.com/PpY2brBOr7
— USGS Data Science (@USGS_DataSci) April 15, 2021
Day 15: relationships, multivariate
We missed some #30DayChartChallenge while working on a #dataviz project about snow. For day15 (relationships, multivariate), we cleaned up an early sketch where we looked at how this year’s snow is related to the past. Data: @USGS_NRCS
— USGS Data Science (@USGS_DataSci) April 28, 2021
See the final page: https://t.co/RkzZGhxEp6 pic.twitter.com/wHHLvIzVfq
Day 18: relationships, connections
Warming rates are dependent on climate & local traits with the most rapidly warming lakes distributed globally. This shows spatial & temporal coherence in lake temperature variability, drawing connections between lakes (& continents) w/ temporal correlation. #30DayChartChallenge pic.twitter.com/7n9mBgXX6V
— USGS Data Science (@USGS_DataSci) April 18, 2021
Day 19: timeseries, global change
The shrinking ice season on Lake Mendota in #Madison, WI. For day 19 of the #30DayChartChallenge (#timeseries, #GlobalChange). Made using ice cover data from 1855-2021 and #rstats by Sam Oliver, USGS #longtermdata
— USGS Data Science (@USGS_DataSci) April 19, 2021
Code: https://t.co/hW5YFiQhPT pic.twitter.com/s0OyGOE1r5
Day 20: timeseries, upwards
The #USGS tool #dataRetrieval has reach 100,000 downloads on CRAN! Released in 2014, this package makes it easy to retrieve @USGS_Water hydrologic data with #rstats. #hydrology #openscience #opengov #30DayChartChallenge pic.twitter.com/sZbHkxpyhk
— USGS Data Science (@USGS_DataSci) April 20, 2021
Day 21: timeseries, downwards
Ice duration from 1855 through this past winter on Lake Mendota in Madison, WI. Spoiler alert: it’s going #downwards #30DayChartChallenge. A remix on the same dataset from our #GlobalChange #timeseries by Jake Zwart, USGS
— USGS Data Science (@USGS_DataSci) April 21, 2021
Code: https://t.co/3lMMdftZMc pic.twitter.com/YE0hNLjkS5
Day 22: timeseries, animation
But what does it mean? Here we show the #timeseries of snow (#SWE) and streamflow (discharge) at a single site. The magnitude, “Peak SWE”, and timing, “SM50” of snowmelt matters for streamflow. #D3js pic.twitter.com/vJ8S6K8jlu
— USGS Data Science (@USGS_DataSci) May 2, 2021
Day 23: timeseries, tiles
The Delaware River Basin is one of the most well-observed stream networks when it comes to water #temperature, but when and where do we have data? Temperature matrix #tiles for the #30DayChartChallenge #dataviz pic.twitter.com/i2Spd4bLLJ
— USGS Data Science (@USGS_DataSci) April 23, 2021
Even though we did not make it to every day, we had a lot of fun along the way exploring new datasets, revisiting old datasets, and trying out new visualization tools in R. Many thanks to the #30DayChartChallenge organizers, Cédric Scherer and Dominic Royé.
To see the full collection of contributions, check out the #30DayChartChallenge github repo .
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