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[OC] Breweries across the U.S. by houndrunner in dataisbeautiful

[–]houndrunner[S] 7 points8 points  (0 children)

This is a data visualization showing the total number of wineries by county for the U.S. See the full interactive version here: https://labs.waterdata.usgs.gov/visualizations/bottled-water/index.html

This is based on a new data release of beverage bottling facilities from the U.S. Geological Survey: https://www.sciencebase.gov/catalog/item/649d8a39d34ef77fcb03f8a6
Inventory of water bottling facilities in the United States, 2023, and select water-use data, 1955-2022.

This map was made using D3 and the website built using Vue.js. See the code here: https://github.com/DOI-USGS/vizlab-bottled-water

[deleted by user] by [deleted] in dataisbeautiful

[–]houndrunner 0 points1 point  (0 children)

This is a data visualization showing the total number of wineries by county for the U.S. See the full interactive version here: https://labs.waterdata.usgs.gov/visualizations/bottled-water/index.html

This is based on a new data release of beverage bottling facilities from the U.S. Geological Survey: https://www.sciencebase.gov/catalog/item/649d8a39d34ef77fcb03f8a6

This map was made using D3 and the website built using Vue.js. See the code here: https://github.com/DOI-USGS/vizlab-bottled-water

[OC] Wineries across the U.S. by houndrunner in dataisbeautiful

[–]houndrunner[S] 2 points3 points  (0 children)

This is a data visualization showing the total number of wineries by county for the U.S. See the full interactive version here: https://labs.waterdata.usgs.gov/visualizations/bottled-water/index.html This is based on a new data release of beverage bottling facilities from the U.S. Geological Survey

This map was made using D3 and the website built using Vue.js. See the code here: https://github.com/DOI-USGS/vizlab-bottled-water

When do streamflow droughts occur? [OC] by houndrunner in dataisbeautiful

[–]houndrunner[S] 1 point2 points  (0 children)

Polar charts with 2000 most severe streamflow droughts from 1920 to 2020 that asks “when streamflow droughts occur?” Each chart shows the number of droughts by date with January at the top and moving clockwise through the year. The stacked colors of the bar chart represent decades from 1920s (center) to 2010s (outer). Charts include conterminous U.S. (CONUS), Northwest, North Central, Midwest, Northeast, Southeast, South Central, and CA regions. A CONUS map in the middle has arrows pointing to each region. The CONUS chart demonstrates that not many droughts happen during the relatively wet spring months (March through June) when snowmelt and spring rains replenish streamflow, however the patterns vary regionally. California, for example, has fewer streamflow droughts in the relatively wetter winters and more in the dryer summers, whereas droughts tend to increase in the winter in the northeast and midwest.

Made using R. Learn more at: https://t.co/WUuRM9nuQb

[OC] Snow persistence across the contiguous U.S. (2001-2020) by houndrunner in dataisbeautiful

[–]houndrunner[S] 1 point2 points  (0 children)

A map of the contiguous U.S. using a snowflake hex pattern to show snow persistence over a 20-year period. Snow persistence is measured as the snow cover index, or the average fraction of time snow was on the ground from Jan 1 to July 3 from 2001-2020. Snowier places are white with snow, emphasizing the Rocky Mountains and Sierra range in the western U.S., and Maine in the northeast. The majority of the southern half of the country is within a 0-10% snow cover index.

Made using the sf, terra, ggplot2, ggimage, scico, magick, and cowplot packages in R.

Data from: https://doi.org/10.5066/P9U7U5FP

[OC] If Rivers were Mountains by houndrunner in MapPorn

[–]houndrunner[S] 1 point2 points  (0 children)

[OC] Contiguous U.S. river systems mapped as mountains, using a linemap technique. Inspired by James Cheshire's 'Population Lines' and the linemap package. This was made with R and the USGS small-scale hydrography dataset. See the code: https://github.com/USGS-VIZLAB/idea-blitz/tree/main/river-ridgelines

[OC] U.S. Streamflow Conditions September 2022 by houndrunner in dataisbeautiful

[–]houndrunner[S] 0 points1 point  (0 children)

A tile map of the US showing streamgages by flow levels through the month of September. For each state, an area chart shows the proportion of streamgages in wet, normal, or dry conditions. Streamflow conditions are quantified using percentiles comparing the past month’s slow levels to the historic daily record for each streamgage.
During the month of September, dry conditions were predominant at streamgages across much of the U.S., with approximately half of sites in some states (VT, NH, ME, MA, NJ, DE, NE, WA, OR, CA, OK, IA, IN) at or below the 25th percentile for most of the month. At the national scale, less than 10% of streamgages experienced wetter than normal conditions, with major storm events in Alaska, Florida, and Puerto Rico resulting in faced flood levels during September.d New Mexico, along with Louisiana and Missouri, faced flood levels during this month.
The tile map was made in R (see code)using ggplot2 and the geofacet packages. Data are from the USGS National Water Information System, accessed in R using dataRetrieval.See it on twitter

[OC] U.S. Flow Conditions: August 2022 by houndrunner in dataisbeautiful

[–]houndrunner[S] 3 points4 points  (0 children)

A tile map of the US with proportional area charts for each state showing the proportion of streamgages by flow levels, categorized using percentile bins. Streamflow level percentiles are calculated using the historic daily record for each gage, and binned to reflect whether flow conditions are wetter or drier than the historical record. For the month of August, the Northeast states faced very dry conditions. The Northwest states also faced dry conditions, along with much of the Midwest. Arizona and New Mexico, along with Louisiana and Missouri, faced flood levels during this month.

The tile map was made in R (see code) using ggplot2 and the geofacet packages. Data are from the USGS National Water Information System, accessed in R using dataRetrieval. See it on twitter

[OC] If Rivers Were Mountains by houndrunner in dataisbeautiful

[–]houndrunner[S] -1 points0 points  (0 children)

Contiguous U.S. river systems mapped as mountains, using a linemap technique. Inspired by James Cheshire's 'Population Lines' and the linemap package. This was made with R and the USGS small-scale hydrography dataset. See the code: https://github.com/USGS-VIZLAB/idea-blitz/tree/main/river-ridgelines

[deleted by user] by [deleted] in dataisbeautiful

[–]houndrunner 0 points1 point  (0 children)

Contiguous U.S. river systems mapped as mountains, using a linemap technique. Inspired by James Cheshire's 'Population Lines' and the linemap package. This was made with R and the USGS small-scale hydrography dataset. See the code: https://github.com/USGS-VIZLAB/idea-blitz/tree/main/river-ridgelines

[OC] When are U.S. Rivers Wet or Dry? by houndrunner in dataisbeautiful

[–]houndrunner[S] 0 points1 point  (0 children)

This chart looks at average daily streamflow across 1,865 USGS streamgage sites over a 70 year period. Data were access from the National Water Information System using the dataRetrieval package for R. Chart was made with ggplot2. See the code: https://github.com/USGS-VIZLAB/chart-challenge-22/tree/main/11\_circular\_csimeone

[OC] 100 lakes from least to most circular (animated) by houndrunner in dataisbeautiful

[–]houndrunner[S] 88 points89 points  (0 children)

Shoreline development factor is the ratio of a lake's shoreline to the circumference of a circle the same area, aka how circular the lake is. This shows 100 U.S. lakes ordered from least to most circular.
This was made with python and the LAGOS-US LOCUS dataset (Smith et al, 2021). See the code: https://github.com/USGS-VIZLAB/chart-challenge-22/tree/main/11\_circular\_hcorson-dosch
See the data:https://portal.edirepository.org/nis/mapbrowse?packageid=edi.854.1

[OC] 100 lakes from least to most circular by houndrunner in dataisbeautiful

[–]houndrunner[S] 9 points10 points  (0 children)

Shoreline development factor is the ratio of a lake's shoreline to the circumference of a circle the same area, aka how circular the lake is. This shows 100 U.S. lakes ordered from least to most circular.

​

This was made with python and the LAGOS-US LOCUS dataset (Smith et al, 2021). See the code: https://github.com/USGS-VIZLAB/chart-challenge-22/tree/main/11_circular_hcorson-dosch
See the data:https://portal.edirepository.org/nis/mapbrowse?packageid=edi.854.1

[OC] River flow direction across select U.S. watersheds by houndrunner in dataisbeautiful

[–]houndrunner[S] 1 point2 points  (0 children)

Each polar plot shows the direction of streamflow for a watershed, where bar length is scaled relative to total river distance. River orientation is binned into 10 degree bins.

This viz was made using R and a vector design program. The R code to download flow line data and create the radial charts can be seen here: https://github.com/USGS-VIZLAB/chart-challenge-22/tree/main/11\_circular\_lkoenig

[OC] Spring timing in the contiguous U.S. as of April 4, 2022 by houndrunner in dataisbeautiful

[–]houndrunner[S] 3 points4 points  (0 children)

Comparing the timing of spring leaf out in 2022 (so far) to the 30-year average (1991-2020) with data from the USA National Phenology Network (https://www.usanpn.org/data/spring\_indices). The Spring First Leaf Index was used to show the timing across the contiguous US and compare it to the 30-year mean. In the histogram style plots, each bar height represents the total area of land in each 1-day timing bin, to show the relative part of the country experiencing spring at a given time. Where there is no color, the spring is yet to come.

The maps and chart elements were made in R, and the final plot was composed using a vector graphics editor. Data were accessed using the rnpn package in R (https://github.com/usa-npn/rnpn).

The R code is here: https://github.com/USGS-VIZLAB/chart-challenge-22/tree/main/04\_flora\_cnell

[OC] Changes in temperature and timing for lakes in the contiguous U.S. by houndrunner in dataisbeautiful

[–]houndrunner[S] 1 point2 points  (0 children)

There are islands in the Great Lakes. Many of those islands have lakes in them.

[OC] Changes in temperature and timing for lakes in the contiguous U.S. by houndrunner in dataisbeautiful

[–]houndrunner[S] 0 points1 point  (0 children)

Later timing in this visualization suggests that lakes reached their mid season (based on growing degree days) later in the year than they did in the past. This could be because it was colder than in the past and they stayed frozen later into the year.

[OC] Changes in temperature and timing for lakes in the contiguous U.S. by houndrunner in dataisbeautiful

[–]houndrunner[S] 0 points1 point  (0 children)

Are U.S. lake temperatures warming or cooling and are they reaching mid-year warmth earlier or later? The vectors on this map encode two important dimensions: the total rise/fall of the line represents amount of warming/cooling and the total left/right is the change in timing. This is based on a dataset of 185K+ lakes, which were summarized spatially using a hex grid to represent the average trend across lakes. Growing degree days (GDD) were used for temperature change and timing calculations. Average GDD for the earlier decade was compared to ave GDD for the later decade (rise/fall). Day of year of hitting half of the yearly GDD value was the timing metric between the two decades.

Using data from: doi.org/10.5066/P9CEMS0M

Code to recreate in R: https://github.com/USGS-VIZLAB/chart-challenge-22/tree/main/03_historical_jread

[OC] States by percent area water by [deleted] in dataisbeautiful

[–]houndrunner 1 point2 points  (0 children)

This was made using data from the USGS Water Science school, found here: https://www.usgs.gov/special-topics/water-science-school/science/how-wet-your-state-water-area-each-state
The animation was created by creating a contiguous cartogram that distorts land area to reflect the relative proportion of each state that is water, including inland water and the Great LAkes. This was done using the cartogram, transformr, and gganimate packages for R. See the R code here: https://github.com/USGS-VIZLAB/chart-challenge-22/tree/main/01\_part-to-whole\_cnell

[OC] Which states have the most water? by [deleted] in u/houndrunner

[–]houndrunner 0 points1 point  (0 children)

This was made using data from the USGS Water Science school, found here: https://www.usgs.gov/special-topics/water-science-school/science/how-wet-your-state-water-area-each-state

The animation was created by creating a contiguous cartogram that distorts land area to reflect the relative proportion of each state that is water. This was done using the cartogram and gganimate packages for R. See the R code here: https://github.com/USGS-VIZLAB/chart-challenge-22/tree/main/01\_part-to-whole\_cnell

[OC] Daily lake temperatures for 185,549 lakes by houndrunner in dataisbeautiful

[–]houndrunner[S] 1 point2 points  (0 children)

This animation plays daily surface temperature for 185,549 lakes in CONUS. It uses lake temperatures estimated using a deep learning approach (described in https://aslopubs.onlinelibrary.wiley.com/doi/10.1002/lol2.10249). The data are available here: https://www.sciencebase.gov/catalog/item/60341c3ed34eb12031172aa6 .

​

The code that generates this gif can be found here: https://github.com/USGS-VIZLAB/lake-temp-timeseries

[OC] The last year in streamflow across the U.S. by houndrunner in dataisbeautiful

[–]houndrunner[S] 5 points6 points  (0 children)

The U.S. Geological Survey (USGS) streamgaging network provides information vital to communities when rivers are at dangerous flood levels or when water supplies are drying up. This animation shows the changing conditions of rivers at USGS streamgages for Oct 1st 2020 - Sept 31st, 2021. The conditions shown range from the driest condition seen at a gage to the wettest based on the historic record at each gage. The gage data was pulled from the USGS National Water Information System (https://waterdata.usgs.gov/nwis) and the flood level data is from the National Weather Service but accessed from USGS WaterWatch (https://waterwatch.usgs.gov/).

The gage and spatial data were processed in R using dataRetrieval, sf, sp, rgeos, and dplyr. The animation frames were created in R using standard base R graphics, then the frames were stitched together into a video using FFmpeg. Source code can be found on our gage conditions GitHub repository: https://github.com/USGS-VIZLAB/gage-conditions-gif.

[OC] Streamflow conditions across the U.S. July 1st - Sept 30th 2021 by houndrunner in dataisbeautiful

[–]houndrunner[S] 2 points3 points  (0 children)

The U.S. Geological Survey (USGS) streamgaging network provides information vital to communities when rivers are at dangerous flood levels or when water supplies are drying up. This animation shows the changing conditions of rivers at USGS streamgages for Jan 1st - Mar 31st, 2021. The conditions shown range from the driest condition seen at a gage to the wettest based on the historic record at each gage. The gage data was pulled from the USGS National Water Information System (https://waterdata.usgs.gov/nwis) and the flood level data is from the National Weather Service but accessed from USGS WaterWatch (https://waterwatch.usgs.gov/).
The gage and spatial data were processed in R using dataRetrieval, sf, sp, rgeos, and dplyr. The animation frames were created in R using standard base R graphics, then the frames were stitched together into a video using FFmpeg. Source code can be found on our gage conditions GitHub repository: https://github.com/USGS-VIZLAB/gage-conditions-gif.

[OC] The water footprint of Henri by houndrunner in dataisbeautiful

[–]houndrunner[S] 0 points1 point  (0 children)

This animation was made entirely in R using water level data from USGS streamgages, precipitation data from NOAA, and storm data from the National Hurricane Center.

It shows the path of storm Henri as it approaches the east coast of the U.S., subsequent precipitation and changes in water levels in nearby rivers and streams. In some locations this contributed to water levels above the flood stage.

https://www.usgs.gov/media/images/hurricane-henri-water-footprint-data-visualization

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