Posts for Tag: data

California Rainfall Totals

Posted In: Environment | Water
California Rainfall

How do current California rainfall and precipitation totals compare with Historical Averages?

Check out the California reservoir dashboard.

It’s winter in California and that means the rainy season (snowy in the mountains). This year has been a relatively dry year and wanted to visualize how this year compares with historical levels for this time of year. I used data for California rainfall totals from the California Department of Water Resources. Other California water-related visualizations include reservoir levels in the state as well.

There are three sets of stations that are tracked in the data and these plots:

  • Northern Sierra 8-station index
  • Tulare Basin 6-station index
  • San Joaquin 5-station index

These stations are tracked because they provide important information about the state’s water supply (most of which originates from the Sierra Nevada Mountains). Data from the CDEC website appears to be updated at around 8:30am PST each day.

The visualization consists of two primary graphs both of which show the range of historical values for precipitation. The top graph is a histogram of water year precipitation totals on the specified date (in blue) as well as the precipitation total for the current water year in red.
The second graph shows the percentiles of precipitation over the course of the historical water year, spreading out like a cone from the start of the water year (October 1). You can see the current water year plotted on this to show how it compares to historical values. It also shows the present precipitation level and its percentile within the historical data for the day of the water year.

You can hover (or click) on the graph to audit the data a little more clearly.

Sources and Tools

Data is downloaded from the California Data Exchange Center website of the California Department of Water Resources using a python script. The data is processed in javascript and visualized here using HTML, CSS and javascript and the open source Plotly javascript graphing library.

California Rainfall

Countries Mapped onto Solar System Bodies

Posted In: Maps
US on the Moon

We can compare the sizes of countries and continents to planets and moons by projecting a map of a specific country onto another planet. Select a country and planet or moon to find out.

In one of my kid’s favorite books, there’s a picture demonstrating how Pluto is the same size as Australia. It has a satellite image of the country and an image of the former ninth planet superimposed on top as if it were hovering above the country. That image has stuck with me and I thought it would be interesting to see how other countries would compare with other planets and bodies in our solar system. As I’ve been working with javascript graphing/mapping library, D3.js and making maps/globes, I realized I should try to “project” individual countries onto these planets to see what they looked like.

Instructions

This visualization should be pretty self explanatory. You can select a country or continent and a planet or moon (or the sun) in the solar system. The visualization will then project the land onto the body and you have a simple visual comparison of the size of the country/continent and the planet or moon. You can drag on the visualization to rotate the planet.

There are some combinations that are not possible because the country/continent is too large to be projected onto the body without overlap. In these cases, the planet or country will be greyed out in the selection menu. You can click the “Get URL” button and share a specific map combination (country and planet) by copying the address in the url address bar.

Calculations

The calculations to project a country onto another body involves starting with a set of coordinates (made up of longitude and latitude values) which define the border of the country, in the geojson format. To display them on Earth, the coordinates are modified so that the center of the country is centered at the intersection between the equator and prime meridian [0 deg latitude, 0 deg longitude].

To display them projected on a different planet or moon, it is necessary to change the latitude and longitude values of each point of the polygon country border so that it represents the same distance away from the polygon center. I used the Haversine formula to calculate the distance and bearing between two points on a sphere and then used the inverse to find the coordinates that were that distance and bearing from the center point on a sphere of a different size. These formulas can be found here. The main idea is that the distance representing one degree of latitude on Earth will be half as large on a planet that is half the size of Earth (like Mars). Thus, the distance between the center of a country and a point on the border will be a different number of degrees latitude and longitude from the center point on a different planet than on Earth. And this calculatin is done using these formulae.

Sources and Tools:
This visualization was made using the open-source, d3 javascript dataviz library and UI are made using HTML, CSS and javascript.

Mapping Countries onto Planets

Animation of Coronavirus Cases and Deaths in US

Posted In: Health | Maps

Visualize the large number of coronavirus cases and deaths in the US each day/hour in about 10 seconds

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:

  • https://engaging-data.com/animation-of-us-covid/?cat=cases
  • https://engaging-data.com/animation-of-us-covid/?cat=deaths
  • Sources and Tools:

    The coronavirus data comes from the covidtracking.com API. The data is parsed daily using a custom python script and visualizations are made using the open-source Leaflet javascript mapping library and the interface and animation are made using HTML/CSS/javascript.

    US covid case death animation

    Election Results and Population Density

    Posted In: Elections
    election county population density

    How do 2020 presidential election results correlate with population density?

    The visualization I made about county election results and comparing land area to population size was very popular around the time of the 2020 presidential election. As the counties were represented by population, it was clear that democratic-leaning areas on that map tended to grow in size, while republican-leaning areas tended to shrink. This raised the question of exactly how population density correlates with election results.

    Hover over (or click on) the bubbles to see information about the county.

     

    It’s clear there is a very strong correlation between the vote margin and population density.  Vote margin is the percentage amount that one candidate beat the other candidate by in the county (0% means a tie while 50% means that one candidate got 75% and the other got 25% of the voteshare).  Population density is calculated as people per square mile in the county and is shown in the graph on a log scale, where each major grid line is 10 time greater than the previous one.  This is done because there is one to two orders of magnitude difference in the densest counties (in New York City) and even moderately dense counties.  There are also several counties with population density below 1 person per square mile (several in Alaska because of the size of their counties) but these are excluded from the graph.

    Richmond County, NY (i.e. the Borough of Staten Island) is the densest county (17th densest) in the country that Trump won. The densest counties favored Biden quite heavily as he won 45 of the 50 densest counties in the country, which also tend to have a fairly high population.

    This second graph is a histogram that specifically categorizes counties into discreet bins by population density. Note that they are on a log scale as well. You can toggle the graph to show the number of counties won by each candidate or the number of votes won in each of the population density bins. The black line shows the percentage of counties (or votes) won by the democratic candidate (Joe Biden) in each of those bins.

    Hover over (or click on) the bars to see information about each county bin.

    It’s pretty clear in these graphs that low population density areas clearly favor the republican while the denser areas favor the democrat.

     

    Data and Tools
    The 2020 county-level election data is downloaded from the New York Times county election data API and processed using a python script. Population data used is for 2018. The visualization was created using the open-source plotly javascript graphing library.

    How many Americans have contracted Coronavirus?

    Posted In: Health | Maps

    The number of US coronavirus cases is equal to the population of several states put together.

    click on the buttons below to see a new set of states. The number of Americans who have contracted the Coronavirus keeps going up with little indication of slowing down. This is an amazingly large number of cases is the highest in the world and I wanted to visualize how many people this actually is. While the number of US COVID-19 cases is very large, comparing these number to the size of the populations in several states helps to provide more context. The visualization shows a random collection of states whose total population is equal to the latest coronavirus numbers. If you click the button you can see a different set of states that have a population equal to the current number of coronavirus cases.
    The graph is updated daily using data from covidtracking.com. It’s important to note that the number of people with COVID-19 is an underestimate as many coronavirus cases are asymptomatic (i.e. people don’t get sick or show any symptoms) and the positivity rate of tests is quite high. Stay safe out there: stay away from people and wear your mask!

    Sources and Tools:

    Data on coronavirus cases was obtained from covidtracking.com. The visualization was created using javascript and the open source leaflet javascript mapping library.

    US coronavirus cases

    US Senate Representation

    Posted In: Government

    Each state has two senators in the Senate, even though there is a great disparity in the populations of the states. This was a compromise that the framers of the Constitution dealt with in creating the framework of the US government. While the US House of Representatives is based on proportional representation, the Senate was designed to have two senators per state regardless of population. This leads to some interesting variations in the number of votes that some senators get relative to other senators (and how many people they represent).

    Graph of Total Votes for Each Current Senator (2014, 2016 and 2018)


    This graph is called a treemap and shows the total number of votes cast for the winner of each senate race of the current sitting senators. They are shown in order from largest to smallest vote totals, where the area of the rectangle is proportional to the number of votes. The treemap can be organized by party if desired. This graph does not show the number of votes that their opponents got.
    If you hover over (click, on mobile) one of the boxes in the treemap, you can compare the number of votes received by that senator to the number of senators that received the same number of votes combined. This helps highlight the disparities in the representation of voters in large states in the Senate relative to that of voters in states with low populations.

    For example, Kamala Harris, Democratic senator of my home state of California, received 7.5 million votes when she won her senate race in 2016. This large number of votes is larger than the combined votes for 22 of her Republican colleagues in small states. This is even more impressive since, as noted before, she ran against another Democrat Loretta Sanchez, in the election.

    Note that some of the recently elected senators shown in the table are no longer serving in the Senate:

    • John McCain’s seat is currently held by Martha McSally
    • Johnny Isakson’s seat is currently held by Kelly Loeffler

    Because of the large variation in population sizes and a tendency for more populous states to vote for democrats, Democratic Senators received many more votes in their elections than their Republican colleagues did, despite having fewer numbers. The 47 Democratic (and Independent) senators received a total of 67.5 million votes while the 53 Republican senators received 59.5 million votes.

    Graph of Margin of Victory over Opposing Party for Each Current Senator (2014, 2016 and 2018)


    This graph shows a slightly different set of data. Instead of total votes for the winning candidate, it shows the vote margin (i.e. the number of votes the winner received vs the opponent of a different party). The reason I specify it this way is that the two Democratic California senators defeated other democrats to win their elections (i.e. no republican was on the ballot in the general election because no republican got enough votes in the primary). This comparison is interesting because not only do some senators receive very few votes (because they live in small states), but they may only win by a small margin over their opponents. Comparing margins of victory, shows how few votes it would take to “flip” a Senate seat between the two parties.

    If you take Kamala Harris’s margin of victory over Republicans to be her vote total (7.5 million votes) since there was no Republican running against her, her margin of victory is greater than the margin of victory of 43 of her Republican Senate colleagues combined.

    Sources and Tools:

    Senate election data was downloaded from MIT election lab. The data was processed using python/pandas and the visualization was created using javascript and plotly.js, the open source javascript graphing library.

    state borders