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Joe Spagnoletti commented on the post, Progress Report for Week Ending, February 23, on the site 7 years, 8 months ago
Seems like balance of population is balancing out over time.
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Joe Spagnoletti commented on the post, Weekly Question #3: Complete by February 8, 2017, on the site 7 years, 8 months ago
Looks like Temple loses a lot of great potential students.
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Joe Spagnoletti commented on the post, Progress Report for Week Ending, February 23, on the site 7 years, 8 months ago
So I guess the key to making a hit movies is spend less money and make it as scary as possible.
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Joe Spagnoletti commented on the post, Progress Report for Week Ending, February 23, on the site 7 years, 8 months ago
Great too for finding just the right place
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Joe Spagnoletti wrote a new post on the site MIS 0855: Data Science Spring 2017 7 years, 8 months ago
Here is the assignment.
Here is the worksheet as a Word document to make it easy to fill in and submit (along with your Tableau file).
And here is the data file you will need to complete the assignment […]
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Joe Spagnoletti wrote a new post on the site MIS 0855: Data Science Spring 2017 7 years, 8 months ago
Here is the exercise.
And here is the spreadsheet you’ll need to complete the exercise [In-Class Exercise 4.2 – FoodAtlas.xlsx].
Make sure you right-click on the Excel file link and select “Sa […]
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Joe Spagnoletti commented on the post, Progress Report for Week Ending, February 9, on the site 7 years, 8 months ago
AWESOME interactive tool! I could waste lots of time here.
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Joe Spagnoletti commented on the post, Happy Birthday SNL // the typists from the Carol Burnett show, on the site 7 years, 8 months ago
I guess my 25 yr old son could be with me for another 9 years ugh.
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Joe Spagnoletti commented on the post, Happy Birthday SNL // the typists from the Carol Burnett show, on the site 7 years, 8 months ago
Nate, any interesting hypotheses you could consider or test looking at the graphs?
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Joe Spagnoletti posted a new activity comment 7 years, 8 months ago
This map could have been a much better tool in the election for telling the story of health coverage.
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Joe Spagnoletti commented on the post, Happy Birthday SNL // the typists from the Carol Burnett show, on the site 7 years, 8 months ago
No surprise that politics closely follows culture. Love using TV shows as the metaphor.
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Joe Spagnoletti commented on the post, Happy Birthday SNL // the typists from the Carol Burnett show, on the site 7 years, 8 months ago
Teams are definitely overpaying for talent
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Joe Spagnoletti commented on the post, Happy Birthday SNL // the typists from the Carol Burnett show, on the site 7 years, 8 months ago
Visualizations sparks lots of questions.
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Joe Spagnoletti commented on the post, Happy Birthday SNL // the typists from the Carol Burnett show, on the site 7 years, 8 months ago
Love the Home Alone visual ..
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Joe Spagnoletti commented on the post, Happy Birthday SNL // the typists from the Carol Burnett show, on the site 7 years, 8 months ago
Cool interactive feature. Good example of letting you keep asking and answering questions
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Joe Spagnoletti commented on the post, Happy Birthday SNL // the typists from the Carol Burnett show, on the site 7 years, 8 months ago
Reading this while eating a Wawa hoagie. I guess it’s a bad idea.
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Joe Spagnoletti wrote a new post on the site MIS 0855: Data Science Spring 2017 7 years, 8 months ago
Here is the exercise
Here are the links in case you cannot click from the document.
History, Economics and Social Issues
Science and Health
English, Fine Arts and Entertainment
Remember to […]
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Joe Spagnoletti wrote a new post on the site MIS 0855: Data Science Spring 2017 7 years, 8 months ago
Some quick instructions:
You must complete the quiz by the start of class on February 7, 2017. The quiz is based on the readings for the whole week.
When you click on the link, you may […]
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Joe Spagnoletti wrote a new post on the site MIS 0855: Data Science Spring 2017 7 years, 8 months ago
Leave your response as a comment on this post by the beginning of class on February 9, 2017. Remember, it only needs to be three or four sentences. For these weekly questions, I’m mainly interested in your o […]
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http://www.census.gov/library/visualizations/2016/comm/cb16-108_education_finance.html
This visualization is a map that details how much each state in America spends on public education based on amount spent per pupil. The darker the shade of the state the more money was spent on education per student. This graph shows that states in northeast America spend more per student than the rest of the states. There is also a bar graph which shows where the money is going overall for the US which conveys that the majority of money is spent on instructor’s salary.-
Visualizations sparks lots of questions.
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This series of charts and graphs is on Christmas movies. The first series of bar graphs is on pain level of saying different words with “FUDGE” being the most painful word to say. There is another bar graph below that that gives your chances of putting your tongue on a pole when someone says either dare, double dog dare, or triple dog dare. The graph for “All Santa- related movies” is a time series comparing the belief in Santa throughout.
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Love the Home Alone visual ..
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https://bl.ocks.org/mbostock/1341021
Parallel coordinates is the example I chose to visualize. They are used to compare a wide category of things changing over a specified time period. The parallel coordinate I used in my example explains the changes from vehicles from the 70s and the early 80s. I was impressed that there was so much information in the parallel coordinate and I could see each piece of information by clicking and dragging along the axis. For example, the chart described over twenty different brand of vehicles and over a hundred different divisions, which described the changes of economy, cylinders, displacement, power, and miles per hour of each division over the years.
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Cool interactive feature. Good example of letting you keep asking and answering questions
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http://www.glencoe.com/sec/math/studytools/books/0-07-845771-8/images/ST07-008W-821347.gif
This visualization represents a Scatterplot Matrices. The scatterplot compares the relationship between blood cholesterol levels and the number of weekly meals at fast food restaurants. There is a strong correlation shown between these two factors. The higher the number of weekly meals you eat at a fast food restaurant the higher your chances are of having high blood cholesterol. I already understood how unhealthy fast food can be for you but it was interesting to visual the relationship between health issues and fast food and interpret the correlation.-
Reading this while eating a Wawa hoagie. I guess it’s a bad idea.
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The visualization shows the 2012 payroll against wins in Major League Baseball. Data in the scatter plot compares the relationship between team payroll and total wins. Through the information represented on the plot, it reveals that the team with the highest payroll did not get the most win percentage. It appears that the middle to high payroll percentages get the most wins, but the two lowest and highest paid teams had roughly the same win percentage.-
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This link/article shows that the more money you put into your team the higher your win percentage will be.
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Teams are definitely overpaying for talent
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No surprise that politics closely follows culture. Love using TV shows as the metaphor.
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The visualization I found on the internet is a map. Here is the link of that visualization: https://www.census.gov/dataviz/visualizations/health_insurance/. This map clearly illustrates the population by states of the United States does not have health insurance coverage from 2008 to 2015. It can be seen clearly from the map that in 2008, many states had the population without health insurance coverage above 14 percent which is presented in dark blue color, especially those states locate on the West Coast of the United States. However, few years had passed, the number of people without health insurance coverage has been decreasing by changing from dark blue color to light blue color. Nevertheless, Texas was the only state that still had the population without health insurance coverage above 14 percent even after 7 years. By looking at the map like this, it helps people to realize easily that the society is changing for a better.
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This map could have been a much better tool in the election for telling the story of health coverage.
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http://pubsonline.informs.org/doi/pdf/10.1287/ited.5.1.75
This article has multiple graphs that include a multitude of sports data. One of the graphs contains the salaries from all MLB teams in 2003. The team with the lowest salary was Tampa Bay, which had less than 75 million. Another map was of eight major sports and when they were mentioned in a statistics article. Baseball led the list with appearing 91 times in a statistics article from 1960-2002. Overall, this page shows some visualization charts used to display data about different sports, players, and individual teams.-
Nate, any interesting hypotheses you could consider or test looking at the graphs?
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This is a data set that I found on PEW research that tracks the amount of young adults (aged 18-34) and their living arrangements over the modern era (1880-2014). There are two graphs for each gender and it shows that in recent years, many young adults, from both genders, are choosing to live with their parents. It is also the first time in the ‘modern era’ in which men choosing to live with their parents has eclipsed moving out. If the trend continues, women choosing to live in their parents’ home will also eclipse moving out. -
http://buckets.peterbeshai.com/app/#/playerView/201935_2015
I would say this kind of represents a scatterplot matrices. In my research during this time I focused on James Harden. This just showed how his percentage rates are with shooting. Also it shows how accurate and consistent he is in certain spots where he usually tries to score. How frequent and how far the shooting is, is also included all in these graphs. These graphs show good each player is in terms of percentages of shooting, scoring, and how often and frequently they make their shots.
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AWESOME interactive tool! I could waste lots of time here.
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I guess my 25 yr old son could be with me for another 9 years ugh.
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This data visualization from the Tableau Public Gallery highlights AirBnB listings in San Francisco by neighborhood. It presents a map and parallel coordinate chart. It allows you to manipulate the data as you utilize the tool which helps to compare and explore various data points. Some data points included are price/neighborhood, listing sized/square footage and acceptance rate/total listings. I found the residences with the highest acceptance rates had the most properties and vice versa – low acceptance rates correlate with lower number of listings. I also saw that properties close to the water were priced much higher than the average price per neighborhood. The other tab presents a time series graph to analyze AirBnB’s growth over time. The most popular type of listing is for the “Entire Home/Apartment” and it’s been on the rise since 2013 – with a 67% increase in these listings YoY from 2013-2014. Can’t wait to start using Tableau in class!
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http://www.informationisbeautiful.net/visualizations/the-hollywood-insider/
This data visualization illustrates data compiled from over 1200 major Hollywood films of the last eight years. A representative of Scatterplot Matrices. There are many Y-axis and X-axis to choose from. Selecting the % budget recovered from Y-axis and selecting the average audience from the X-axis, we can find out which movie uses low budget but earn a lot from the audience worldwide. It’s an very interesting movie data visualization website.-
So I guess the key to making a hit movies is spend less money and make it as scary as possible.
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http://collegeapps.about.com/od/GPA-SAT-ACT-Graphs/ss/temple-university-admission-gpa-sat-act.htm
The data visualization represents a scatterplot matrix. This is a data set collected by an admission expert that shows data regarding the input of Temple University’s students. The X-axis is the ACT/SAT scores, and the Y-axis is the GPA. In the scatter plot, there are dots in four colors, each represents whether the student is accepted, accepted and won’t attend, denied or waitlisted. Using this scatter plot, students who are planning to apply to Temple University can see the general requirement and decide if he or she matches the criteria to be a Temple student! I personally find this data visualization very useful and believe it conveys the important information.-
Looks like Temple loses a lot of great potential students.
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This visualization talks about the world population will reach 9.9 billion in 2050, up 33 percent from an estimated 7.4 billion now, according to projections included in the 2016 World Population Data Sheet from the Population Reference Bureau. Despite declines in fertility rates around the world, we expect population gains to remain strong enough to take us toward a global population of 10 billion,” said Jeffrey Jordan, president and CEO of PRB. “Significant regional differences remain, though. For example very low birth rates in Europe will mean population declines there while Africa’s population is expected to double.”PRB’s widely referenced World Population Data Sheet has been produced annually since 1962.The Data Sheet included measures of carbon emissions, access to electricity, power from renewable energy resources, how much land countries have set aside for protection, and population per square kilometer of arable land.
Link for PRB.org http://www.prb.org/Publications/Datasheets/2016/2016-world-population-data-sheet.aspx
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http://www.zillow.com/visuals/negative-equity/#7/40.897/-77.838
This link shows a map visualization of the percentage of homes that are underwater by U.S. county. I zoomed in on Pennsylvania. A house being underwater, or having negative equity, means that the homeowner owes more on the mortgage than the house is worth. This is typically the result of the inflated housing prices that the U.S. experienced leading up to the 2008 economic recession. Subprime mortgages meant that loans were being given to an excess of people, many of whom were not worthy of the credit they received. Because of the glut of potential home buyers, housing prices soared to all-time highs. After the bottom fell out of the economy and man homes were foreclosed on, the real estate sector became a buyers market with an overabundance of homes for sale at extreme undervaluations, particularly because many were owned by banks after foreclosure. According to the visualization, Pike and Monroe counties are the two PA counties with the highest rates of underwater mortgages. My county, Delaware, lags only slightly behind with 22% or mortgages underwater, placing it in the highest 20% of counties in the country in terms of negative equity.-
I’m surprise California didn’t look worse.
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http://www.mastersindatascience.org/blog/10-cool-big-data-visualizations/
This link shows probably all data visualizations on different type of big data like, how much was the US gun deaths in 2013,Earthquakes since 1898 in the world, field of Commemoration, the TweetPing: that shows the twitter activity in the world, Where to run like places in US for running or jogging, the Rich blocks and the Poor block in US. It shows data about “How to win an Oscar” in the form of data visualizations. That was COOL!. Also it show about Circos 16 species, what a hundred million calls to 311 reveal about New York, interactive timeline to PRISM scandal. Also the size comparison between fiction spaceships is also displayed in the form of Data Visualization.-
I could see good uses for many of these
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http://www.businessinsider.com/not-everyone-is-hurting–the-rich-get-richer-as-the-income-inequality-gap-explodes-2010-3
The visualization I found are charts and a table that talk about the widening wealth gap and its causes. The charts are showing the gap between the rich and the poor is growing at an exponential rate. The table is talking about the relationships between different household incomes and the unemployment rate.-
This really shows how the gap is widening, with no end in sight
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https://fivethirtyeight.com/features/lionel-messi-is-impossible/
In this article from FiveThirtyEight, there are a number of scatterplot matrices, a histogram and a couple maps all containing data primarily concerned with the goal scoring capabilities of the Argentinean soccer player Lionel Messi. While there is plenty of data about sports, I chose this example because it does a good job of showing how information from these visualizations changes when you account for outliers. It was made clear that this module was focused around how to visualize data to make it tell a story, and for individuals who may not find interest in soccer (or prefer Bundesliga), the visualizations used here to represent Messi clearly relay the caliber above other players that he performs at simply by how data about him looks on a graph. If a statistician were aiming to test a hypothesis on this data, in order to have an accurate value of statistical significance and meaningful correlation, Messi’s data would almost certainly be removed. What’s more, if Messi, and his almost as impressive counterpart, Cristiano Ronaldo, were not accounted for in these data, the visualizations would not resemble themselves at all, thus telling a different story.-
I wish these had interactive filtering. Looks like Messi is a much more efficient and team player.
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http://investigativereportingworkshop.org/investigations/lily-pads/htmlmulti/lily-pad-multimedia/
This is a visualization map of the growth of bases worldwide. Though the U.S. military has fewer bases that it did at the end of the Cold War, there are still more small bases around the world. Now, there are bases in around 80 countries and U.S. territories — roughly twice as many as in 1989.
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Holy cow, I had no idea there were this many locations around the world
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http://blog.minitab.com/blog/statistics-and-quality-data-analysis/time-series-plots-theres-gold-in-them-thar-hills
This is a time series chart of the price of gold over time. The chart shows the power of the free-market when introduced to a product. Up until 1968 gold prices were fixed, set by rules of the government. Then in 1968, BANG, for the better or worse prices of gold were open to the free-market and to fluctuation. The chart also pushes me to ask, what happened in 2007-2009? Maybe the ‘Great Recession’.-
Looks like late 70s to 2000 gold took a dive, but after that has skyrocketed
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https://www.statcrunch.com/5.0/viewreport.php?reportid=24348
On this website, you will find various charts and graphs comparing the number of cigarettes a person smokes a day vs death from coronary heart disease. The graphs that are used are scatter plots. Essentially, the graph is just trying to say that smoking has a direct correlation with death from coronary heart disease, as many people already know. My guess is that the visual representation was created to influence people not to smoke because of how dangerous it is for their health.-
I’m surprised by such a low number of deaths for people who smoke about 1600-1700 cigarettes per year.
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Great too for finding just the right place
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Seems like balance of population is balancing out over time.
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sure…easier to get probability more correct as the game changed. depending on when you placed your bet, you could have made/lost a ton of money
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There’s a lot of data to explore here. Could spend lots of time.
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I suppose Mandarin and Spanish are the 2 languages everyone should learn.
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This website discusses what data visualization is and how to create a visual of data that ordinary people can interpret. The visualization they use is a combination of different types of graphs. The middle graph is a Small Multiple / Trellis Display type graph. They also use a pie chart. And for the first graph on the graphic, I don’t believe we covered this type of visualization. But it helps display the information accurately. The main point of the graphic is to show that there isn’t just left or right wings. The data visualization shows how there are political social positions in-between the Alt-right and Alt-left parties. From the use of color, you can infer the population of each political stance. The data also goes further with the next graph and breaks down the popularity with each ideology by percent. The final data graph breaks down the popularity by age for each ideology. I don’t think the data was compiled to influence people’s political affiliations, but to just inform people about the population’s political tendencies. -
This is a maps data visualization. It shows the snowing area on the US map by spotting it. Each spot one the map has different color from red though white to blue. Red means low percentage of normal snowfall. Blue means high percentage of normal snowfall. White is between them. Readers can see where in the US is more possibly to see snowfall. It’s a good data visualization because it is easy to read and it provides the most important data of snowfall that travelers need to know.
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This website uses a mosaic plot in order to show the type of beds people around the world buy and how much of each is sold in selected countries. It isn’t technically a mosaic plot because the different figures are not directly on top of each other, but I figured that since the different types of data are categorized by color and overlap each other would kind’ve make it a mosaic plot. I especially found it interesting because it introduced new data, in the form of beds, that I did not previously know about. -
The pictograph alone shows so many different ways to think about visualizing data. Good site.
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Cool chart for helping decide where to plan your next ski trip.
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I had no idea there were Emperor, Caesar or Texas sized beds.
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The chart that I decided to go with for this is a vertical line chart which shows the amount of TV watched by different age groups. This graph starts in the 1st quarter of 2011 and finishes in the 3rd quarter of 2016. The graph shows that for ages 12-49 that there seems to be a decrease in the average TV viewership, with it fluctuating back and forth between quarters. From 50-65+ on the other hand, there is an average increase in amount of TV watched, with that fluctuation between quarters still remaining true.-
Strange why viewing swings by quarter back and forth
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http://blog.minitab.com/blog/quality-data-analysis-and-statistics/nba-lockout-part-1
This visualization is a time series graph. It displays the NBA salary cap from 1986 to 2010. It shows how the salary cap has steady risen starting at about 5 million in 1986 and increasing to 60 million in 2010.
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Seems weird to have a salary cap that keeps rising faster than inflation..kind of defeats the purpose
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https://www.bjs.gov/index.cfm?ty=tp&tid=36
The following link directs you to census data compiled from responses via a series of National Crime Victimization Surveys (NCVS) reviewed between calendar years 1992 and 2008. These particular surveys generally record testimonials provided on behalf of victims of violent crime (i.e., Homicide, Rape/Sexual Assault, Robbery, Assault, and Stalking/Intimidation), along with victims of property crime (i.e., Burglary, Larceny/Theft, Motor Vehicle theft, Cybercrime/Electronic Crime, and Identity Theft). Information collected throughout these surveys contain specific crimes reported to law enforcement, as well as the “dark figure” statistics of crime (i.e., incidents gone unreported). Analyses performed on such data spanning from 1992 to 2008 drew upon whether these cohorts of victims knew if their offender(s) belonged to some type of gang. Categorizing each set of responses into three (3) separate groups being “No”, “Don’t Know”, and “Yes”, further assessment of this data concluded that 5% of violent crime victims were able to discern whether the offender was affiliated with a gang, 4 out of 10 victims of violent crime were unsure of whether their offender(s) were part of a gang, male victims recognized their offender(s) belonged to a gang more frequently compared to what female victims disclosed, and lastly, victims of Hispanic ethnicity indicated that the offender(s) were gang members at a higher rate than all other ethnicities. The graphic used to illustrate this data was laid out using a line chart format. -
Looks like known gang violence is on the decline. That’s a good thing
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Sad to see that child mortality is linked to income.
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Now this is fun way to visualize data. Great site!
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http://vizhub.healthdata.org/subnational/usa
The data visualization i chose to discuss is a map of smokers across America. The map shows every single county in the US and gives info about all causes, both sexes and a standardized age of people born in 1995. However, the map is really interesting because you can change any aspect of the info to make it much more specific. The data is trying to show how staggering the number of deaths per year caused by smoking is. I found this article enticing because i personally know many people who smoke and i think it is pretty interesting that they fall into this statistic. Moreover, i can look up exactly where they are from in the US and find out how many people from their hometown die each year. Hopefully, by showing them this visualization they might reconsider their bad habit. -
I wonder what is going on in South Dakota?
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Lots of interesting stuff here. Fun site.
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http://www.kansascity.com/sports/spt-columns-blogs/for-petes-sake/article130956789.html
This is a visualization of both the Atlanta Falcons and New England Patriots’ chances to win Super Bowl LI as the game was in progress. There is a line graph with the top side leaning in New England’s favor and the bottom half leaning towards Atlanta. Many people thought the Falcons would easily win the game after going up by a few touchdowns. Even with nine minutes to go in the fourth quarter, the Falcons had a 99.6% chance to win the Super Bowl. But an almost vertical spike in the graph shows that Falcons imploded and blew their twenty-five point lead. The Patriots won the coin toss in OT and went right down the field in four minutes on a championship drive scoring the title-capturing touchdown. The final score was 34-28. -
Show just how wrong statistics can be at any time.
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This is a map of the US where each state is labeled according to the most popular google searches since the end of the election. I’m not sure if there is a key point to be made here, but it is interesting to see how some states have been googling serious topics about policy decisions and states such as Pennsylvania are googling “Donald Trump pee pee party”
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Oregon…”Punching Nazis?” That’s weird
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That’s why I don’t be on sports.
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https://www.climate.gov/news-features/features/2016-shatters-record-alaskas-warmest-year
This website use both map and scatterplot matrices to show the temperature of Alaska in 2016. The first map just show the temperature of different areas in Alaska, illustrating the dot in different color to classify the warm areas. The second chart, collecting all statistics of the temperature in recent years to show the changing trend directly. -
This website is comprised of visualizations based on social media and data. TwittEarth says that it provides a global image mapping twitter activity. It brings live feeds of twitter with a map of the world, which is super interesting to see tweets pop up all over the world. Now you can easily communicate and see news from all across the globe. There is a map and time series involved with the visualizations.
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http://www.census.gov/library/visualizations/2016/comm/cb16-ff18_single_americans.html This Data visualization Show how the gap between married and unmarried americans age 15 to and older has narrowed since 1950. That use two line graphs as well as two bar graphs. They Not only compare between married and unmarried but also widowed, divored, and never married. The key message the chart is conveying is that the gap has narrowed and will continue to narrow. More and more people are not getting married.
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https://www.census.gov/dataviz/visualizations/005/ This data visualization shows the progression of US expansion from 1790 to 1890. The visualization represents the transition from being a primarily agrarian society to a more urban society. This visualization also shows a line graph with time on the x axis and “the percent of population living in an urban community with 100,000+ people” on the y axis.
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https://corporate.morningstar.com/us/documents/SampleReports/ADV_AWO_SampleReport_RiskReward.pdf
This ScatterPlot data visualization is the combination of the decisions investors have made in a 3 year period. The Scatter Plot depicts the levels of risk and rewards these investors have encountered on a daily basis. From the information we are able to visualize which investment firms are making better decisions and if their decisions are high in risk or low in risk. To further explore this information, we can also predict which companies are doing extremely well compared to their competitors as well as which companies need to redesign for a better approach.
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Is it weird or is it good practice?
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Joe Spagnoletti wrote a new post on the site MIS 0855: Data Science Spring 2017 7 years, 8 months ago
In class we talked about a few examples of open data. Here are some others:
Business: data.gov’s “Impact” section
Science: The Genomes Unzipped project
Government: New York City parking viola […] - Load More