Check out my California Reservoir Levels Dashboard
I based this graph off of my California Reservoir marimekko graph, because many folks were interested in seeing a similar figure for the Colorado river reservoirs.
This is a marimekko (or mekko) graph which may take some time to understand if you aren’t used to seeing them. Each “row” represents one reservoir, with bars showing how much of the reservoir is filled (blue) and unfilled (brown). The height of the “row” indicates how much water the reservoir could hold. Lake Mead is the reservoir with the largest capacity (at almost 29,000 kaf) and so it is the tallest row. The proportion of blue to brown will show how full it is. As with the California version of this graph, there are also lines that represent historical levels, including historical median level for the day of the year (in red) and the 1 year ago level, which is shown as a dark blue line. I also added the “Deadpool” level for the two largest reservoirs. This is the level at which water cannot flow past the dam and is stuck in the reservoir.
Lake Mead and Lake Powell are by far the largest of these reservoirs and also included are several smaller reservoirs (relative to these two) so the bars will be very thin to the point where they are barely a sliver or may not even show up.
Historical data comes from https://www.water-data.com/ and differs for each reservoir.
The daily data for each reservoir was captured in this time period and median value for each day of the calendar year was calculated and this is shown as the red line on the graph.
If you are on a computer, you can hover your cursor over a reservoir and the dashboard at the top will provide information about that individual reservoir. If you are on a mobile device you can tap the reservoir to get that same info. It’s not possible to see or really interact with the tiniest slivers. The main goal of this visualization is to provide a quick overview of the status of the main reservoirs along the Colorado River (or that provide water to the Colorado).
Units are in kaf, thousands of acre feet. 1 kaf is the amount of water that would cover 1 acre in one thousand feet of water (or 1000 acres in water in 1 foot of water). It is also the amount of water in a cube that is 352 feet per side (about the length of a football field). Lake Mead is very large and could hold about 35 cubic kilometers of water at full (but not flood) capacity.
Data and Tools
This visualization lets you divide the US into 1,2,3,4,5,8 and 10 different segments with equal population and across different dimensions. The divisions are made using counties as the building blocks (of which there are 3143 in the US). There are numerous different ways to make the divisions. This lets you make the divisions by different types of geographic directions and divisions by population density.
If you can think of other interesting ways to divide up the US, please let me know and I can try to add them to this visualization.
Sources and Tools:
I added a share button (arrow button) that lets you send a graph with specific name. It copies a custom URL to your clipboard which you can paste into a message/tweet/email.
Use this visualization to explore statistics about names, specifically the popularity of different names throughout US history (1880 until 2020). This is a useful tool for seeing the rise (and fall) of popularity of names. Look at names that we think of as old-fashioned, and names that are more modern.
This visualization is not my original idea, but rather a re-creation of the Baby Name Voyager (from the Baby Name Wizard website) created by Laura Wattenberg. The original visualization disappeared (for some unknown reason) from the web, and I thought it was a shame that we should be deprived of such a fun resource.
It started about a week ago, when I saw on twitter that the Baby Name Wizard website was gone. Here’s the blog post from Laura. I hadn’t used it in probably a decade, but it flashed me back to many years ago well before I got into web programming and dataviz and I remember seeing the Baby Name Voyager and thinking how amazing it was that someone could even make such a thing. Everyone I knew played with it quite a bit when it first came out. It got me thinking that it should still be around and that I could probably make it now with my programming skills and how cool that would be.
So I downloaded the frequency data for Baby Names from the US Social Security Administration and set to work trying to create a stacked area graph of baby names vs time. I started with my go to library for fast dataviz (Plotly.js) but eventually ended up creating the visualization in d3.js which is harder for me, but made it very responsive. I’m not an expert in d3, but know enough that using some similar examples and with lots of googling and stack overflow, I could create what I wanted.
I emailed Laura after creating a sample version, just to make sure it was okay to re-create it as a tribute to the Baby Name Wizard / Voyager and got the okay from her.
Some info about Data (from SSA Baby Names Website):
All names are from Social Security card applications for births that occurred in the United States after 1879. Note that many people born before 1937 never applied for a Social Security card, so their names are not included in our data.
Name data are tabulated from the “First Name” field of the Social Security Card Application. Hyphens and spaces are removed, thus Julie-Anne, Julie Anne, and Julieanne will be counted as a single entry.
Name data are not edited. For example, the sex associated with a name may be incorrect.
Different spellings of similar names are not combined. For example, the names Caitlin, Caitlyn, Kaitlin, Kaitlyn, Kaitlynn, Katelyn, and Katelynn are considered separate names and each has its own rank.
All data are from a 100% sample of our records on Social Security card applications as of March 2021.
I did notice that there was a significant under-representation of male names in the early data (before 1910) relative to female names. In the normalized data, I set the data for each sex to 500,000 male and 500,000 female births per million total births, instead of the actual data which shows approximately double the number of female names than male names. Not sure why females would have higher rates of social security applications in the early 20th century. Update: A helpful Redditor pointed me to this blog post which explains some of the wonkiness of the early data. The gist of it is that Social Security cards and numbers weren’t really a thing until 1935. Thus the names of births in 1880 are actually 55 year olds who applied for Social Security numbers and since they weren’t mandatory, they don’t include everyone. My correction basically makes the assumption that this data is actually a survey and we got uneven samples from males and female respondents. It’s not perfect (like the later data) but it’s a decent representation of name distribution.
Sources and Tools:
The biggest source of inspiration was of course, Laura Wattenberg’s original Baby Name Explorer.
I wanted to try my hand at creating 3D elevation models and thought trying to model some of the popular (and some of my favorite) national parks would be a good starting point.
Once a 3D elevation model is selected and shown you can manipulated it in multiple ways:
You can select a number of different parks from the drop down menu. If you have suggestions for additional parks, I may be able to add them to the list.
Note: the elevation files are data intensive since the visualization is downloading the elevation across in some cases, many hundreds or thousands of square miles. To keep the data needs down, I’ve reduced the resolution of the elevation data. Though the original data is 90 meter resolution (elevation is specified across every 90 x 90 m square in each park, I’ve averaged these squares together so that each park model only has about tens of thousands of these squares, regardless of the actual area of the park. This improves data loading and rendering times and makes the improves the responsiveness of the model.
Sources and Tools:
The rate of COVID-19 deaths and cases in the US is crazy high after the 2020 winter holidays and maybe still be going up. This visualization shows the number of COVID cases that occur in one hour or the COVID deaths that occur in one day based on the average of the last five days. This is another attempt to show the true scale of how many cases and deaths the US is dealing with, since it is often hard to understand large numbers. I have also attempted to show the scale of US deaths/cases here and here. Unfortunately, there are so many people getting sick and dying, it’s hard to fathom just how many people this actually is.
The 5-day averaging was done to smooth out any peaks and troughs in data reporting due to weekends/holidays, since I noticed that some states were literally reporting zero COVID cases some days while reporting many hundreds or thousands of cases other days.
The dots shown on the animation are located in the state that the cases or deaths occur but are randomly spread out within the state. This is done for visual clarity since if they were shown in their actual location, most of the dots would be overlapping in urban, high density areas. This approach lets you see which states have high COVID instances but still locate them by state.
You can share this animation by putting ?cat=deaths or ?cat=cases behind the url or copying and sharing one of these links:
Sources and Tools:
Updated to include the $1400 stimulus payment per adult and dependent in March 2021.
Use this stimulus check calculator to figure out how much you will receive in your thrid stimulus check.
On December 21, 2020, Congress passed a $900 billion dollar stimulus package in response to the COVID pandemic. The bill authorizes economic assistance to Americans in the amount of $600 per person subject to income limits. It also includes expanded unemployment benefits, rental assistance and an extension to the eviction ban. This calculator helps you calculate the amount of stimulus check that you can expect to receive based on your 2019 tax return filing status, adjusted gross income and number of dependents under 17.
Changing the inputs to the calculator, will show you how your expected stimulus check amount will change. The graph shows for a giving filing status (single, married filing jointly or head of household) how the stimulus check amount will change as a function of income and number of children. You can share a URL with specific parameters included
Sounds like some checks may even get to folks at the end of December and many more will get them in January 2021.
On March 5, congress passed the American Rescue Plan which includes $1400 payments for all Americans. The phase out of this stimulus check is different in that over a $10000 range the stimulus goes from 100% to 0% at the phase out threshold, no matter how many dependents you have. This changes things significantly as you’ll see in the calculator.
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