“Million, billion, trillion … all big numbers. A trillion is just a bigger number, right? True … but it may be bigger than you think. A million is hard enough to imagine, much less a billion. We need some perspective on what these unimaginable numbers really mean.” — If I Had a Trillion Dollars

Q: How many millions are there in a trillion?

Q: If you were a star newsanchor earning $1 million, how many years would it take you to earn $1 billion?

Task:

1) Identify a number that you believe is important for a New York journalist to know, roughly, by heart. Don’t reveal the number. The class will first attempt to identify it.

2) Once the class has responded, put that number in context. For example, if you were talking about population you might give a sense of growth by offering a number from an earlier decade.

3) Cite the source for your number and why you believe it is credible.

Exercise #2: Harper’s Index

Review the numbers presented in your edition of the Harper’s Index handout and identify one number or set of numbers which you think is particularly revealing. Explain why and whether it/they might be worthy of a more in-depth exploration.

Exercise #3
Operations order
Percent
Mean, Mode, Median
Math for Journalists Quiz

Percent

Pizza Hut overwhelming contributed to Republicans

number / total number x 100 = percent

A percent is part of one hundred, or part of a whole. Percents describe relationships between two numbers that, when used literally, might lack context or be hard to understand. To write, “Jane Brown received 7,542 votes out of a total of 12,165″ may be accurate, but the relationship between the numbers is more clear and meaningful when the percent tool is used: “Jane Brown received 62 percent of the vote.”

Percent of total can show you patterns or disparities in sets of numbers. For example, if you have a data set that tells you that state employees got a total of $3.5 million in raises this year, and you know the governor’s salary for this year and for last year, you can figure out what percent of the total in raises given out went to the governor. You can then compare that with the percent of total that was awarded in raises to, say, clerks at the unemployment office. Thus, by calculating percent of total for a category of numbers, such as salaries, you will identify each employee’s or each department’s share of the budget “pie.” In fact, a pie chart can help you analyze these data or illustrate your news report.

Percent Change

To calculate the percent change:

new number – original number / original number × 100 = percent change

1) (New – old) / old = decimal value
2) Decimal value x 100 = percent change

If your answer is a negative number then this is a percentage decrease.

Rate

total events / total population x “per” value = rate

For the “per” value, choose a round number like 100 or 1,000 or 100,000 or even 1 million, depending on the size of the population. — Poynter U: Math for Journalists

Rate is a very handy math tool when you want to make a comparison between quantities of two things that are not alike. Let’s take an example: murder rates in U.S. cities in a given year. What do we need to know to apply the rate tool to this sort of data? Since murders happen to people, we’ll need to obtain (1) city population in that year and, (2) number of murders in that city in that year. Most data about homicide is published as the rate per 100,000 residents, which allows us to make meaningful comparisons. “Per capita” is Latin, “for each head.”

Normalization vs. Absolute numbers examples

Crime in the subway

“The difference between the total and the per–100,000 number is important not just because it changes the picture. It’s the much more interesting number. The total number of crimes means nothing for the individual. What is much more relevant is how many crimes happen per person, because that provides a much closer estimate of each individual’s risk of becoming a victim. Number 2, 125th St, has over six times the rate per 100,000, and number 5, Broadway Junction, has almost ten times the rate (but less than half the total number) compared to Times Square.”

Daily riders are a good first step when looking at the subway crime data, but there are caveats. The analysis would be much better with more, and more fine-grained, data. How should two cities be compared? Is population the right metric? What about GDP? — “Putting Data into Context”

NYTimes: Geek Appeal – NY vs Seattle

Seattle 139 Starbucks in 2013 but New York had 271 85,000 IT workers in Seattle, compared to 168,400 in NYC

Adjusting for inflation

Generally, if dollar values differ over time by three years or more, a cost-of-living adjustment should be calculated to ensure inflation has been accounted for and the comparisons being made are fair. Change in cost of living adds incredibly useful and accurate depth to news reports.

You can use an automatic Web calculator to adjust for inflation. Just plug in the dollar amount ($1) and the year (1913) and poof, your answer — what that dollar was worth in 2012 — shows up on the screen. There are a few calculators on the Bureau of Labor Statistics website. If you plug in the $1 and enter 1913 as your starting point, the calculator will tell you that the 1913 dollar was worth $23.19 in 2012.

– Poynter U: Math for Journalists

Ratio

Ratio is a way of explaining how two similar things relate. Ratios crop up in daily life all the time — in recipes, measurements — and in journalism in stories on elections, sports, education, health and much more. For instance, in a city council race, Candidate Brown got 75,347 votes and Candidate Smith only got 24,994, you could accurately tell your audience that Candidate Brown won by a ratio of 3-to-1. That means that, for every vote that Smith got, Brown got 3 votes.

Order is important in expressing a ratio. If you were in a class that had 5 men and 15 women, you could say that the ratio of men to women was 5-to-15. Or that the ratio of women to men was 15-to-5 or reduce it to 3-to-1. But it’s crucial to keep the objects you are counting or measuring in the correct order.

Ratios are easily confused with fractions and percents, but the numbers mean very different things. For example, if candidate A won an election by a 5:4 margin, that does not mean that candidate A got 4 out of 5 votes. It means that for every 4 votes that candidate B got, candidate A received 5. So, candidate A got 5 out of every 9 votes, or about 55 percent. — Poynter U: Math for Journalists

Distribution — Robert Niles “Stats Guide”

A normal distribution of data means that most of the examples in a set of data are close to the “average,” while relatively few examples tend to one extreme or the other. Let’s say you are writing a story about nutrition. You need to look at people’s typical daily calorie consumption. Like most data, the numbers for people’s typical consumption probably will turn out to be normally distributed. That is, for most people, their consumption will be close to the mean, while fewer people eat a lot more or a lot less than the mean.

Standard Deviation — Robert Niles “Stats Guide”

If you are comparing test scores for different schools, the standard deviation will tell you how diverse the test scores are for each school. Let’s say Springfield Elementary has a higher mean test score than Shelbyville Elementary. Your first reaction might be to say that the kids at Springfield are smarter. But a bigger standard deviation for one school tells you that there are relatively more kids at that school scoring toward one extreme or the other. By asking a few follow-up questions you might find that, say, Springfield’s mean was skewed up because the school district sends all of the gifted education kids to Springfield. Or that Shelbyville’s scores were dragged down because students who recently have been “mainstreamed” from special education classes have all been sent to Shelbyville.

Margin of Error Robert Niles “Stats Guide”

The margin of error in a sample = 1 divided by the square root of the number of people in the sample.

You’ve probably heard that term — “margin of error” — a lot before. Reporters throw it around like a hot potato — like if they linger with it too long (say, by trying to explain what it means), they’ll just get burned. That’s because many reporters have no idea what a “margin of error” really represents.

If a poll has a margin of error of 2.5 percent, that means that if you ran that poll 100 times — asking a different sample of people each time — the overall percentage of people who responded the same way would remain within 2.5 percent of your original result in at least 95 of those 100 polls.

Math for specific story types

Source: Poynter U: Math for Journalists

Calculate Percentages

https://percentagecalculator.net/

Sources:

Poynter U: Math for Journalists
Robert Niles Stats
Data journalism handbook
The process: The inverted pyramid of data journalism
Finding Stories in Spreadsheets

Data & Numeracy Resources

Math for Journalists: Help with Numbers
Beginner’s Pre-Guide to Data Journalism
Data Journalism Isn’t Just a Technical Skill – It’s a Cultural One Too
The Data Journalism Handbook
Mining Census Data for Reporting
American Community Survey Guide
A Compass for Understanding and Using American Community Survey Data
Which Data Tool Should I Use?
Community District Resources & NYC.gov
How Census Race Categories Have Changed Over Time
IRE: A Case for Why Journalists Should Learn Statistics
Math Basics for Journalists: Working with Averages and Percentages
Why Math Matters
5 Ways Statistics Are Used to Lie to You Every Day
10 Rules for Writing Numbers and Numerals
Poynter: Writing With Numbers
Number and Numeral Writing Tips

Google Fusion

Storymaps JS

Story Map – ArcGIS

MapBox

II. Exercise: Point map

Geocoding is just a fancy term for the process of converting a location to latitude and longitude coordinates. Google Fusion Tables automatically geocodes addresses, although you may have to provide some help to specify locations that it can’t find or figure out. There’s also a daily limit to the number of geocodings it can do for you.

1) Go to NYC Open Data and download this file: https://data.cityofnewyork.us/Housing-Development/Projects-in-Construction-Map/dzgh-ja44

2) In your Google documents/Google drive home page, select Create > New > Table to create a new Google Fusion Table.

3) Choose the CSV file you just downloaded to your computer and click Next.

4) Google uploads the XLS file and shows you a preview of the fields.

5) Click Next to create your new Google Fusion Table. Information highlighted in yellow, indicates that it has not yet been geocoded.

6) As with all spreadsheets, columns contain specific kinds of data. Sometimes it’s numeric data, sometimes it’s text, sometimes it’s a date. In some cases, you will want to make sure that Google knows that the Location column contains location data. In this file, there is a latitude and longitude provided so that is not necessary for the mapping.

7) Now, simply choose Map of Latitude.

8) Go to Change Info Window and uncheck all but the relevant data.

9) Go to Change Map Feature Styles > Buckets. Select 5 and the column Construction Award. Be sure to click “Use this range”

10) Edit the numbers on the key so that they are rounded. Round up at the end to 100,000,000.

11) Click on “Automatic Legend,” Show Marker Legend and select a location for the key.

Link to map

III. Exercise: Boundary data, shapefiles and KML files

Displaying areas on maps requires a bit more information. If you want to color the different NY neighborhoods shades of red, depending on their population size, then you need to provide Google Fusion Tables with the boundary information for those neighborhoods.

Google Fusion Tables requires the boundary information be in the form of a KML file. A KML file (Keyhole Markup Language) is simply a text file that has vector information structured like an XML document. Vectors describe the points and paths of any shape in coordinates so it can be plotted on a map. Google Earth uses KML files.

Where do you get KML files for boundaries that you need? Fortunately, many common political boundaries (states, congressional districts, school districts, etc.) are available online from many diverse sources.

Some may be provided by local governmental agencies, such as the New York Department of City Planning. Others may be provided by the federal government or specific organizations (the UN, Census), and still others you may be able to find online in the Google Fusion Tables Public Search.

However, the geographic data is not always in the KML format. A common format for geographic data is the Shapefile, which is used by commercial GIS software applications such as ArcviewGIS, or its open-source version, QGIS. A shapefile comes in a .zip package, and when unpacked, there are multiple files that describe the geography. Fortunately, you can fairly easily translate shapefiles to KML files for use in Google Fusion Tables. One online tool you can use is Shape Escape.

Download the boundary data for the Neighborhood Tabulation Areas from the NYC Department of City Planning. You’ll get a zip file called, “nynta_17.zip”. Go to Shape Escape. Upload the zipped file.

IV. Exercise: Mapping de Blasio’s Supporters in NYC

1) Find the de Blasio contributions CSV file you saved last week. Review it to make sure you don’t have any Grand Totals or other random data and that your top row is your column labels.

2) Upload it to Google Fusion tables.

3) Delete the zip code 083 in Central Park by clicking on the trash can in the first row of the de Blasio file.

4) If you haven’t already, Download zip file of NYC boundary files and find the NYC_zipcode.kml. Upload it to Google Fusion Tables.

5) Merge it with the de Blasio boundary file.
6) Match the zip code columns.

7) Click on Map of geometry. It should look something like this.

8) Change info window

Click on Tools > Change Map > Change info window. Uncheck everything but zip code and check amount and contributor.

9) Change map styles

Now you need to modify the map features. Go to Tools > Change Map > Change feature styles

We are going to use Polygon > Fill color > Buckets >

- Select 6 buckets.
- In Column select “Amount”
- Next “use this range”
-Select the six shades of red from light to dark
- Modify the numbers by rounding them off.

10) Add a legend. Select a location, rename the title.

It should look something like this.

11) Publish and get the embed code for the map

VI. Exercise: Asthma Rates in NYC

DOWNLOAD THIS FOLDER OF DATA

1. We are going to create the data by combining borough data from health statistics. 2008-2010 Hospital Discharge Rates for Asthma, by NYC ZIP codes:
http://www.health.ny.gov/statistics/ny_asthma/hosp/zipcode/map.htm

FYI: To assemble the data set click on each county (Queens, Kings, Bronx, Richmond, Manhattan).

Click on TOTAL TABLE, and copy and paste each table into a new Excel file. The result will be all the zip codes for NYC showing asthma discharges, population, and rates.

Note the footnote!

* – Data is suppressed for confidentiality purposes if there are less than 3 discharges per ZIP code or if the average annual population in each ZIP code contains less than 33 people.
+ – Less than or equal to 10 discharges, therefore rate may not be stable (RSE>30%).

You can deal with the footnote in the map itself (you can filter out all the discharges less or equal to 10). Delete all the asterisks and Plus symbols from the spreadsheet.

2. Upload the completed CSV. It is here HospitalAsthmaDischarge.csv in your folder. You can also find it here.

3. Go to File > Merge and merge the Hospital Asthma data with the zip code file we uploaded earlier. Or you can paste this URL into the field at the bottom of the box.: https://www.google.com/fusiontables/DataSource?docid=1uUkL6MvZTpsG-Zjl6o43zVaoCLmjCPpCzOWxB00

4. Click on the “Map of Geometry” tab on the upper-right hand side of the table. This should display the map.

5. Then click on the “Change Feature Styles”. If it is not visible, click on Tools > Change Map. Click on Polygon fill. Next, navigate to the “Bucket” tab on the right. Then, choose Divide by 4 buckets. Next, select the “Rate” from the pulldown menu next to “Column.

6. Underneath the Rate, you will see a range of numbers and a link to “use this range.” Click on that link. Next go and round up the numbers to 0-25, 25-50, 50-75, 75-97.1. Hit “save”

7. In this study, some information was not usable due to confidentiality concerns. For example, in instances where there were very few hospital cases of asthma (any less than 10)

8. To eliminate this odd data, we are going to restrict our map to discharges greater than 10. So, go to Filter>2008-2010 discharges. Next insert 11 – 2261

9. Next we are going to tidy up the info window. Go to Tools > Change Info Window. Uncheck all but the following.

ZIP Code: {ZIP Code}
2008-2010 discharges: {2008-2010 discharges}
2008-2010 population: {2008-2010 population}
Rate: {Rate}

10. Add a map legend by going to Tools> Change Map Tools > Automatic Legend. Select “Polygon fill”

VI. Exercise: Stop and Frisk

Use Google Fusion Maps to Try and Answer Some of These Questions

1) Identify your data sets and what you want to illustrate. Example: I am going to show the number of 2011 stop and frisk incidents by police precinct. I am also going to include a racial breakdown of these stops.

Data source: NYPD – 2011 Reasonable Suspicion Encounters

2) Prepare your data and locate the appropriate shapefiles, depending on the geographical parameters of your graphic: borough, community district, precinct, block etc.

Data set: Reasonable Suspicion Stops (CSV, comma separated values)
Precinct boundaries: NYC Police Precincts

3) Upload your data sets to Google Fusion Tables.

4) Click on Reasonable Suspicion Stop. Go to edit>merge

5) Select Police Precincts

6) Confirm that the correct row is selected for the titles.

7) Make sure that they both are synced to Precinct

8) Click on Map View, then under Tools, select Change Info Window Layout

9) Delete all information except for the precinct number, the number of stops, the population and stops for black and Latinos.

10) Now, in the Tools menu, select Change Map Styles. Here you are going to select Fill color > Buckets. Select five buckets and the column should be Reasonable Stops. Once you select that, it will show you the range of numbers and ask if you want to use that data set. This will spread the data evenly over the five buckets. You can adjust it to make the statement you wish to make. In this case, we want to round up the numbers, with 31,005 as the final bucket.

Analysis

- How do the absolute numbers compare to the normalized?

- Example. Check out the top two precincts for stop and frisk, the 73rd and the 75th, in terms of absolute numbers. By this measure, they fall into the same dark shade of red, but when you normalize the data they are quite different.

- Use filters to determine what precincts have a disproportionate number of stops for minorities.

A precinct where a disproportionate number of African Americans are stopped:

A precinct where a disproportionate number of Latinos are stopped:

Other information found in the 2011 report. How could this data be used to further illuminate the stop and frisk topic?

Top crime suspected reason during stop.
All known suspects
Known violent crime suspects
Radio runs
Total crime complaints
Arrests and criminal court summonses issued

Things to consider:

- What biases may exist in your data source? Crosscheck these with a neutral expert. You might also consider the opposing point of view. In the case of stop and frisk, the NYCLU has been working on this issue, what does its data say?

- If you are entering data yourself, double-check all of your work to make sure you haven’t make errors.

- Use your common sense. Does something seem unrealistic? If so, maybe the math is wrong.

FFusion Tables Layer Wizard

Student Examples:

Lottery
Hunger and Food Stamps
Illegal Occupancy
311 Complaints

Source: Carl V Lewis

Another process: The inverted pyramid of data journalism

Step #1: Seek Inspiration

- Reporting on a story
- Breaking news
- News folo
- Browsing data sets

An Interactive Visualization of NYC Street Trees

Using data provided by NYC Open Data, this visualization shows the variety and quantity of street trees in all five New York City boroughs.

Step #2: Find Relevant Data

CUNY J-school List of Data Sources
This is a very thorough, constantly updated, list of data sources.

Government (city, county, state and national) and nonprofits
- NYC Open Data NYC
- NYC Factfinder
- Census/ACS – American Factfinder
- Data.gov: U.S. data portal
- World Bank
- United Nations

Data collections, portals, hubs

- DataPortal.org: Data portals around the world
- Guardian World Government Data
- The Data Hub
- Census Reporter

Data is Plural – Collection of interesting data sets for journalists

Searching for data using Google

Tips from the Data Journalism Handbook

When searching for data, make sure that you include both search terms relating to the content of the data you’re trying to find as well as some information on the format or source that you would expect it to be in. Google and other search engines allow you to search by file type. For example, you can look only for spreadsheets (by appending your search with ‘filetype:XLS filetype:CSV’), geodata (‘filetype:shp’), or database extracts (‘filetype:MDB, filetype:SQL, filetype:DB’). If you’re so inclined, you can even look for PDFs (‘filetype:pdf’).

You can also search by part of a URL. Googling for ‘inurl:downloads filetype:xls’ will try to find all Excel files that have “downloads” in their web address (if you find a single download, it’s often worth just checking what other results exist for the same folder on the web server). You can also limit your search to only those results on a single domain name, by searching for, e.g. ‘site:agency.gov’.

Another popular trick is not to search for content directly, but for places where bulk data may be available. For example, ‘site:agency.gov Directory Listing’ may give you some listings generated by the web server with easy access to raw files, while ‘site:agency.gov Database Download’ will look for intentionally created listings.

Data formats
.CSV: CSV stands for Comma Separated Values – this means that the value in each column is separated by a comma like so: Name, Date of birth, Address. When the spreadsheet software opens this, it replaces each comma with a new column.
.TSV: Tab Separated Values – values are separated by tabs
Fixed width: values are separated by spaces
.XLS: Excel document
XML: Extensible markup language – values arranged using open/closing tags like HTML
JSON: Used for exporting data for use in Web javascript
PDF: If data is in PDF, there are tools to extricate it like Tabula.
.KML or .SHP: Geographic data

Step #3: Download data, import into Excel, clean

Many data sets can be downloaded in Excel or CSV formats. NYC Open Data allows you to filter out the data sets for particular information.

Watch NYC Open Data – Video Guides

At its most basic, a spreadsheet acts as a calculator. They can add, subtract, divide, and multiply, and through a combination of those can calculate averages, proportions, rates, and various other things which can provide an insight into the data. One of the most common of those stories is about change. Change is, almost by definition, newsworthy, and an integral part of storytelling. When things change, it makes the news. — Finding Stories with Spreadsheets, Paul Bradshaw

All about Excel with exercises by Miguel Paz

EXERCISE: Intro to Cleaning Data

Formulas

- Calculations are called formulas in Excel and other spreadsheet software
- A formula begins with an equals operator: =
- Formulas are most likely to use cell references like A2 or B300.

Functions

Functions are words that have special meanings in Excel and other spreadsheet software. They are shortcuts for a series of instructions. Functions are always followed by parentheses containing the ingredients it needs. These are called arguments (specifically) or parameters (in general). The function for adding up a series of cells is called SUM. =SUM(A1:A5) or =SUM(A:A) or =SUM(A1:B500) or =SUM(1:1)

- SUM: Add all these figures up’
- AVERAGE: Calculate an average for these figures
- COUNT: Count how many numbers I have in this column

Resource: Excel Spreadsheet Basics

Workflow – Tutorial: Intro To Cleaning Data, Len de Groot

- If importing CSV, retain leading zeros
- Make sure data was imported properly and column data matches the column headers
- Save a copy immediately
- Delete blank rows within the data
- Consolidate Column headers to a single row
- Format all integer columns to eliminate commas
- Find and replace special characters (&, ! , ~, etc) When in doubt, replace with a space, under-stroke or hyphen
- Check spelling (especially in columns you plan to Join)
- Check capitalization (especially in columns you plan to Join)
- Check abbreviations (especially in columns you plan to Join)
- Keep totals from original data to check against. This ensures data is not lost.
- Manipulate data in columns last.
- Delete what you don’t need

Step #4: Interview and Analyze Data

Interviewing data is very much like interviewing human sources — your goal is to find patterns that generate additional interview questions or story ideas.

- How much has something changed?
- Who or where experienced the most change? Who changed the least?
- Which company or person got the most money?
- What is typical – and who stands out the most as being atypical?
- How often does a name appear? Where?
- How much was spent in total on something?

– Finding Stories with Spreadsheets, Paul Bradshaw

How reliable, credible is the data set?

- Where did the data come from?
- Have the data been peer-reviewed?
- How were the data collected?
- If the data come from a survey, for example, you want to know that the people who responded to the survey were selected at random.
- Is it a “self-selected sample?” People who feel really passionately about one side often flood a web site poll, skewing the results from what they would have been had you polled only a random sample of people in the community.
- Have results been cherry-picked? This is the social-science equivalent of gerrymandering, where you draw up a legislative district so that all the people who are going to vote for your candidate are included in your district and everyone else is scattered among a bunch of other districts. (See Awash in Data, Thirsting for Truth.)
- When in doubt, plot the subjects of a study on map and look for yourself to see if the boundaries make sense.
- Are the numbers taken out of context? The example of weather reports citing the number of fender benders on the road in a snow storm. How do these numbers compare to a sunny day?
- 10 Foundational Quantitative Reasoning Questions

Once you have spent some time answering these questions, you should consult an expert who is familiar with both the subject and the data set.

- Have I got things right?
- Am I missing information missing?
- Where should I be looking?
- How would you evaluate this data?

Another excellent data viz video: Ted Talk- David McCandless: The beauty of data visualization

A. Insight from Edward Tufte

Tufte is the author of “The Visual Display of Quantitative Information” and “Envisioning Information.” He has been called “The Leonardo da Vinci of data,” by the New York Times and “The Galileo of graphics,” by Business Week.

1. Above all else, show the data

2. Induce the viewer to think about the substance rather than about methodology, graphic design, the tech of graphic production, or something else.

3. Avoid distorting the data, reflect balance, a proportion, a sense of relevant scale and eliminate “chart-junk.”

4. Present many numbers in a small space.

A map of the Napoleon’s Grande Armée’s advance and retreat into Russia

Charles Joseph Minard was a French civil engineer that created what Tufte calls, “The greatest statistical graphic ever drawn.” The graphic displays six different sets of data: latitude, longitude, direction of movement, time, temperature, and size of the army.

5. Make large datasets coherent.

6. Encourage the eye to compare different pieces of data.

7. Reveal data at several levels of detail, from a broad overview to the fine structure.

8. Serve a reasonably clear purpose: description, exploration, tabulation, or decoration.

9. Use words, numbers and drawings together.

“Lousy graphics omit context, bury critical information, cherry-pick data to advance a cause and heap on “chartjunk’’—a Tufteism (and there are many) for the smiley faces, irrelevant numbers and other doodads that distract us from grasping evidence, thinking about it and drawing smart conclusions.” — Intelligent Designs

B. Selecting the right visualization for your data set

What is your goal? Show change over time, identify patterns, make comparisons, investigate outliers, personalize with databases.

- How much has something changed?
- Who or where experienced the most change? Who changed the least?
- Which company or person got the most money?
- What is typical – and who stands out the most as being atypical?
- How often does a name appear? Where?
- How much was spent in total on something?

– Finding Stories with Spreadsheets, Paul Bradshaw

Tables

Periodic Table

Periodic Table of Storytelling

Charts

Graphics

C. What chart is best for your story?

Kurt Vonnegut on the Shapes of Stories

Choosing a good chart

What is your goal: to compare? to show the parts of a whole? to show a range? to illustrate a relationship?

FT: Visual Vocabulary

NICAR: Choosing a Chart/Information Design

Bar chart: comparing data across categories

Bar charts are especially effective when you have numerical data that splits nicely into different categories so you can quickly see trends within your data.

The Aging of America

The Race Gap in America’s Police Departments

Daily Routines of Famous Creative People

Two Dramas in Slow Motion

Column Chart

Which Countries Are Under the Most Strain in the European Migration Crisis?

“Germany has received more applicants than any other European Union nation, with more than 154,000 migrants seeking asylum from January to June, up from 68,000 in the same period last year. When adjusted for population, Hungary and Sweden are among the top recipients.”– NYTimes.com

Bloomberg: Based on an analysis that included the candidate’s 92-page personal financial disclosure form, his wealth is closer to $2.9 billion.

The Language of the State of the Union

“An interactive chart reveals how the words presidents use reflect the twists and turns of American history.” – Atlantic Monthly

Is It Better to Rent or Buy?

“The choice between buying a home and renting one is among the biggest financial decisions that many adults make. But the costs of buying are more varied and complicated than for renting, making it hard to tell which is a better deal. To help you answer this question, our calculator takes the most important costs associated with buying a house and computes the equivalent monthly rent.” — NYTimes.com

Line charts: change or trends over a period of time

The Voting Habits of Americans Like You

The electorate is increasingly divided by race, education, gender and generation, and these fissures could grow even wider in the 2016 election. Below, new estimates of turnout and support for more than 8,000 different groups.

Hollywood Insider: Movies

A Day in the Life of Americans

Who Old Are You?

The Internet of Things

Propublica Workman’s Compensation

“If you suffer a permanent injury on the job, you’re typically entitled to compensation for the damage to your body and your future lost wages. But depending on the state, benefits for the same body part can differ dramatically.”

Gay rights in the US, state by state

“Gay rights laws in America have evolved to allow — but in some cases ban — rights for gay, lesbian and transgender people on a range of issues, including marriage, hospital visitation, adoption, housing, employment and school bullying. The handling of gay rights issues vary by state and follow trends by region.” — Guardian

WaPo: Top Secret America

Public opinion of OWS

Fractions of a Second: An Olympic Musical
At the Olympics, the blink of an eye can be all that separates the gold medalist from the 10th-place finisher. In some events, this is obvious. But in others, with athletes racing one by one, the closeness of the race is harder to perceive.