Posts for Tag: map

County-level Election Results from 2020 and 2016

The map has been updated to include the latest 2020 results and also adds the option to color the circles by the win margin rather than just looking at the winner.
Click here to view a visualization that looks more explicitly at the correlation between population density and votes by county.
This interactive map shows the election results by county and you can display the size of counties based on their land area or population size.

Previously, I created a map (cartogram) that showed the state by state electoral results from the Presidential Election by scaling the size of the states based on their electoral votes. The idea for that map was that by portraying a state as Red or Blue, your eye naturally attempts to determine which color has a greater share of the total. On a normal election map, Red states dominate, especially because a number of larger, less populated states happen to vote Republican. That cartogram changed the size of the states so that large states with low population, and thus low electoral votes tended to shrink in size, while smaller states with moderate to larger populations tended to grow in size. Thus, when your eyes attempt to discern which color prevails, the comparison is more accurate and attempts to replicate the relative ratio of electoral votes for each side.

This map looks at the 2020 and 2016 presidential election results, county by county. An interesting thing to note is that this view is even more heavily dominated by the color red, for the same reasons. Less densely populated counties tend to vote republican, while higher density, typically smaller counties tend to vote for democrats. As a result, the blue counties tend to be the smaller ones so blue is visually less represented than it should be based on vote totals. More than 75% of the land area is red, when looking at the map based on land areas, while shifting to the population view only about 46% of the map is red. Neither of these percentages is exactly correct because each county is colored fully red or blue and don’t take into account that some counties are won by a large percentage and some are essentially tied. However, the population number is certainly closer to reality as Trump won about 48.8% of the votes that went to either Trump or Clinton.

Instructions

This tool should be relatively straightforward to use. Just click around and play with it.
The map has a few different options for display:

• Hide Circles – just shows the county map
• Show land circles – where the area of the circle matches the area of the county itself, though obviously shaped like a circle. The counties are colored red or blue depending on whether Trump or Biden (in 2020) or Clinton (in 2016) won more votes in that county.
• Show population circles – where the area of the circle matches the relative population of the county itself. More populated counties will grow larger while less populated counties will shrink. The counties are colored red or blue depending on whether Trump or Biden or Clinton won more votes in that county.
• Selecting the No County Overlap checkbox will spread out all of the circles so you can see them all. The total displayed area of the county circles is the same in either land and population view, though if the circles are overlapping, you may see less total colors.
• Selecting the Color by Margin checkbox will color the each county circle by the amount that a candidate won the county. If the vote margin is small, the county will be colored light blue or red, whereas if a county strongly favors one candidate, it will be colored darker red or blue.

Visualization notes

This was my second attempt at using d3 to generate visualizations. I typically use leaflet to do web-based mapping but I wanted the power of d3 which has functions for the circles to prevent overlapping. This map was inspired by Karim Douieb’s cool visualization of 2016 election results. I modified it in a number of different ways to try to make it more interactive and useful.

This visualization does not actually simulate the collisions between the circles on your browser. It is a bit computationally taxing and causes my computer fan to turn on after awhile. So instead I ran the simulation on my computer and recorded the coordinates for where each circle ended up for each category. Then your browser is simply using d3 transitions to shift positions and sizes of the circles between each of the maps, which is simpler, though with 3142 counties, it can still tax the computer occasionally.

Data and Tools
County level election data for 2016 is from MIT Election Lab. The 2020 county level 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 d3 javascript visualization library.

This map divides up the Continental United States into different regions depending on which National Park (or other National Park Service site) is closest to it. It is based on a straight-line (‘as the crow flies’) distance between locations rather than along road networks. It is an example of a Voronoi Diagram, which is subdivided into different regions based upon the distance between points of interest. Everything within a subregion is closer to the point defining the region than any other point.

Hover over the circle points to see the name of the park. The map has a dropdown menu that lets you choose between the following types of locations in the National Park Service:

• National Parks
• National Historic Sites
• National Memorial Sites
• National Monuments
• National Seashores/Lakeshores
• National Recreation Areas
• National Battlefields
• National Military Sites
• National Scenic Areas

For National Parks, there is a high concentration of National Parks in the Western US, especially around the Southwestern US and running up the Pacific Coast. As a result, in these areas, the Voronoi regions are fairly small. The Southwest is also home to a high concentration of National Monuments. There are only few parks in the Eastern US and so the Voronoi regions are correspondingly large. Looking at National Historic Sites, the situation is flipped somewhat, with a high concentration of historic sites in the eastern US, and specifically the Northeast.

Let me know in the comments which park you are closest to and which park you last visited.

Tools and Data Sources
Locations of each of the National Park Service sites comes from the National Park Service. The map was created using the Leaflet javascript mapping library and the Voronoi diagram using the Turfjs javascript, geospatial analysis library.

This zip code map of the United States visualizes over 42,000 zip codes in the 50 states. Zip codes are five digit postal codes used for mail delivery in the US. The points on the map show the geographic center of each zip code. The interactive visualization lets you type in a zip code and will show you where that zip code lies on the map. As you begin to type in the zip code, the map will highlight all the zip codes that begin with those numbers.

For example, if you type in “0”, you will highlight all zip codes that start with the zero in the Northeastern US. This will represent about 10% of the zip codes in the US. When you type in another number, it will narrow down the zip codes that begin with those two digits (approximately 1% of zip codes). It will progressively narrow down the number of zip codes as you type in more numbers, until you get to a full 5 digit zip code that represents 1 out of almost 43,000 zip codes (0.002% of zip codes). The map will then tell you the name of the city that that zip code is in.

You can explore how zip codes are distributed across the US by typing in different 1 and 2 digit numbers. You can also click on the check box to show or hide the outlines of the states.

Sources and Tools:

Zip code data was downloaded from opendatasoft.com. And the visualization was created using javascript and the open source leaflet javascript mapping library.

Traveling by airplane produces significant greenhouse gas emissions

Flying in an airplane is likely the most greenhouse gas intensive activity you can do.  In a few short hours, you can can travel thousands of miles across the continent or ocean.  It takes a large amount of fossil-fuel energy (oil) to lift an 80+ ton airplane off the ground and propel it at 600 miles per hour through the air.  Every hour of travel (in a Boeing 737) consumes around 750 gallons of jet fuel.

Even when dividing the fuel usage across all of the passengers (and cargo) of an aircraft, airplane travel consumes a significant amount of fuel per passenger.  The fuel economy is estimated to be about the same as a fairly efficient hybrid car driven by one person (60-70 passenger miles per gallon).  However, because you can go 10 times faster and much further more easily than you would in a car, airline travel can, on an absolute basis, emit larger amounts of greenhouse gases. In fact, an individual passenger’s share of emissions from a single airplane flight can exceed the annual average greenhouse gas emissions per capita from a number of countries (and the global average).

The following flight calculator and data visualization shows the miles and emissions produced per passenger by a airplane trip that you can specify.  Choose two airports that you are interested in and click the “Calculate Flight Emissions” button to see the emissions associated with a round-trip flight between these two cities.  The map will show you the flight route and also shows you the countries in the world where this one single round-trip flight produces more emissions per passenger than the average resident does in one year from all sources (annual per capita emissions).

In addition to individual countries, the tool also compares the flight’s per passenger emissions to the global average emissions per capita in 2017 (4.91 tonnes) and the emissions required to achieve a 22℃ climate stabilization in 2030 (3.08 tonnes) and in 2050 (1.37 tonnes). These 2030 and 2050 numbers are based on an International Energy Agency scenario.

Calculations of Airplane Emissions

The emissions calculated by this calculator are based on calculations from myclimate.org, a non-profit environmental organization.
The fuel consumption of a jet depends on the size of the aircraft and distance traveled, but takeoff and climbing to cruising altitude are particularly fuel-intensive. On shorter flights, the takeoff and initial climb will constitute a greater proportion of the total flight time so fuel consumption per mile will be higher than on longer (e.g. international) flights.

The detailed methodology is described in more detail in this document.

In addition to emissions of CO₂ from the burning of jet fuel, jets also emit other gases (including methane, NOx, and water vapor) which can also contribute to warming (also known as “radiative forcing”). Because the emissions are occurring at high altitude, these gases can have different impacts than those at lower altitude. A number of studies have estimated the impact of these other gases can significantly contribute to the overall radiative forcing and have somewhere between 1.5 and 3 times the impact that the CO2 alone would. A number of studies, including the myclimate calculator use a factor of 2 to account for these non-CO2 gases and their warming impact, and that is what is used in this calculator as well.

Unlike cars, trucks and trains, it is much harder to power airplanes with batteries and electricity and producing low-carbon jet fuels from biomass is proving very challenging.

In order to achieve climate stabilization at 2 degrees C, global emissions need to basically go to zero over the next 40 years. With a growing global population, this means that the allowable emissions per person will shrink rapidly over these coming decades.

Ultimately, while aviation is a small part of global greenhouse gas emissions, it is a larger part of emissions in richer countries (i.e. if you are reading/viewing this post). And there are many in these richer countries who fly a disproportionate amount and therefore contribute a disproportionate amount of emissions. Hopefully, putting airplane travel in this context can help us better understand the impact of our actions and choices and maybe even change behavior for some.

Tools and Data Sources
The calculator estimates flight emissions based on the myclimate carbon footprint calculator. Data for CO2 emissions by country was downloaded from the European Commissions’s Emissions Database for Global Atmospheric Research. The map was built using the leaflet open-source mapping library in javascript.

Watch the United States assemble state by state based on statistics of interest

Based on earlier popularity of the country-by-country animation, this map lets you watch as the world is built-up one state at a time. This can be done along a large range of statistical dimensions:

These statistics can be sorted from small to large or vice versa to get a view of the US and its constituent states plus DC in a unique and interesting way. It’s a bit hypnotic to watch as the states appear and add to the country one by one.

You can use this map to display all the states that have higher life expectancy than the Texas:
select “Life expectancy”, sort from “high to low” and use the scroll bar to move to the Texax and you’ll get a picture like this:

or this map to display all the states that have higher population density than California:
select “Population density, sort from “high to low” and use the scroll bar to move to the United States and you’ll get a picture like this:

I hope you enjoy exploring the United States through a number of different demographic, economic and physical characteristics through this data viz tool. And if you have ideas for other statistics to add, I will try to do so.

Data and tools: Data was downloaded from a variety of sources:

• Population https://en.wikipedia.org/wiki/List_of_states_and_territories_of_the_United_States_by_population
• Admission to union https://simple.wikipedia.org/wiki/List_of_U.S._states_by_date_of_admission_to_the_Union
• Educational attainment https://nces.ed.gov/programs/digest/d18/tables/dt18_104.88.asp
• Highest points https://geology.com/state-high-points.shtml
• Life expectancy https://en.wikipedia.org/wiki/List_of_U.S._states_and_territories_by_life_expectancy
• Median Age http://www.statemaster.com/graph/peo_med_age-people-median-age
• Land area https://statesymbolsusa.org/symbol-official-item/national-us/uncategorized/states-size
• Mean elevation https://www.census.gov/library/publications/2011/compendia/statab/131ed/geography-environment.html
• Electricity price https://www.chooseenergy.com/electricity-rates-by-state/
• Gasoline price https://gasprices.aaa.com/state-gas-price-averages/
• GDP https://www.bea.gov/data/gdp/gdp-state
• Sunlight North America Land Data Assimilation System (NLDAS) Daily Sunlight (insolation) for years 1979-2011 on CDC WONDER Online Database, released 2013. Accessed at http://wonder.cdc.gov/NASA-INSOLAR.html on Jun 14, 2019 1:37:15 PM
• Births United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS), Division of Vital Statistics, Natality public-use data 2007-2017, on CDC WONDER Online Database, October 2018. Accessed at http://wonder.cdc.gov/natality-current.html on Jun 14, 2019 1:53:58 PM
• Precipitation North America Land Data Assimilation System (NLDAS) Daily Precipitation for years 1979-2011 on CDC WONDER Online Database, released 2013. Accessed at http://wonder.cdc.gov/NASA-Precipitation.html on Jun 26, 2019 3:30:40 PM
• Temperature http://www.usa.com/rank/us–average-temperature–state-rank.htm

The map was created with the help of the open source leaflet javascript mapping library

Americans are known for loving cars and driving quite a bit. Drivers in the United States own more cars and drive more than those in any other country. So what kinds of vehicles do Americans drive? This visualization looks at the types of vehicles (by body type and country of origin) across the 50 States and Washington DC.

You can view two different attributes about the types of vehicles in use in the United States:

• Body type of passenger vehicles
• Manufacturer/Brand region of origin

The different categories of passenger vehicles include:

• Cars – includes sedans, hatchbacks, wagons and sports cars
• Pickup trucks
• SUVs
• Vans – includes Minivans and full-size vans

Classification of the vehicles manufacturer (US, Asia or Europe) is based on the company’s headquarters and not the place of vehicle manufacturing. So a Toyota here is an Asian vehicle even if it was assembled in Mississippi.

It is pretty interesting to see the regional differences in vehicle types (cars vs trucks and SUVs) and vehicle brand (domestic vs foreign). Michigan, especially, stands out with their very high domestic ownership. It makes sense as Detroit is the home of the big three US auto manufacturers (Ford, GM and Chrysler). And I hear there’s a very strong culture of owning American cars there (and employee, friends and family discounts as well).

The data is derived from a survey by the US Department of Transportation called the National Household Travel Survey (NHTS) released in 2017. The following is a quote from the NHTS webpage:

The National Household Travel Survey (NHTS) is the source of the Nation’s information about travel by U.S. residents in all 50 States and Washington, DC. This inventory of travel behavior includes trips made by all modes of travel (i.e., private vehicle, public transportation, pedestrian, and cycling) and for all purposes (e.g., travel to work, school, recreation, and personal/family trips). It provides information to assist transportation planners and policymakers who need comprehensive data on travel and transportation patterns in the United States.

Data and Tools:
Data, as stated before, comes from the US Department of Transportation’s National Household Travel Survey (NHTS). That data was processed to identify vehicle characteristics by state and plotted using javascript and the open-source leaflet map library.