From c2cef517c5751741aa7d7c435e0dd6b026999581 Mon Sep 17 00:00:00 2001 From: Aoboafo <101887879+Aoboafo@users.noreply.github.com> Date: Tue, 16 Jan 2024 12:54:18 -0500 Subject: [PATCH 01/22] Update week1.md --- week1.md | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/week1.md b/week1.md index e69de29..551bdaa 100644 --- a/week1.md +++ b/week1.md @@ -0,0 +1,11 @@ +Link to article :https://www.reuters.com/graphics/ALASKAAIR-BOEING/klvydkrlopg/ +Entitled : How a panel blew off a Boeing plane in mid-air + +In the article “How a panel blew off a Boeing plane in mid-air” it discusses the recent Alaska Airlines jet that experienced a fuselage panel tearing off mid flight. +The torn panel left a huge hole in the plane and it was 16,000 feet in the air. Thankfully the plane landed with 171 passengers with only minor injuries. +As of now there is plenty of on going investigations about how this happened and how it could have been prevented. +The U.S. National Transportation Safety Board is investigating the MAX9 planes and there has been plenty of data created to explore the safety of these aircrafts. +The graph I decided to look at illustrates the timeline of 737 MAX 9 aircraft operation by various airlines from 2017 to 2024. +According to data from FlightRadar24 the majority of flights conducted by Boeing 737 MAX 9s since Dec. 15 were in the United States. +This raises concerns as most Americans fly out for the holiday season. Also we expect planes to be safe and throughly inspected but it seems like that is not the case here. +This graph peaked my interest because I love to stay up to date on current news and as someone who likes to travel this is a concern and a fear a lot of people have when flying on a plane. From 2dde4c7f8bd72c8d6f9154f2ec9640834dee2b87 Mon Sep 17 00:00:00 2001 From: Aoboafo <101887879+Aoboafo@users.noreply.github.com> Date: Tue, 16 Jan 2024 12:58:18 -0500 Subject: [PATCH 02/22] Update week1.md --- week1.md | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) diff --git a/week1.md b/week1.md index 551bdaa..a75e02a 100644 --- a/week1.md +++ b/week1.md @@ -3,9 +3,5 @@ Entitled : How a panel blew off a Boeing plane in mid-air In the article “How a panel blew off a Boeing plane in mid-air” it discusses the recent Alaska Airlines jet that experienced a fuselage panel tearing off mid flight. The torn panel left a huge hole in the plane and it was 16,000 feet in the air. Thankfully the plane landed with 171 passengers with only minor injuries. -As of now there is plenty of on going investigations about how this happened and how it could have been prevented. -The U.S. National Transportation Safety Board is investigating the MAX9 planes and there has been plenty of data created to explore the safety of these aircrafts. -The graph I decided to look at illustrates the timeline of 737 MAX 9 aircraft operation by various airlines from 2017 to 2024. -According to data from FlightRadar24 the majority of flights conducted by Boeing 737 MAX 9s since Dec. 15 were in the United States. -This raises concerns as most Americans fly out for the holiday season. Also we expect planes to be safe and throughly inspected but it seems like that is not the case here. -This graph peaked my interest because I love to stay up to date on current news and as someone who likes to travel this is a concern and a fear a lot of people have when flying on a plane. +As of now there is plenty of on going investigations about how this happened and how it could have been prevented.The U.S. National Transportation Safety Board is investigating the MAX9 planes and there has been plenty of data created to explore the safety of these aircrafts. +The graph I decided to look at illustrates the timeline of 737 MAX 9 aircraft operation by various airlines from 2017 to 2024.According to data from FlightRadar24 the majority of flights conducted by Boeing 737 MAX 9s since Dec. 15 were in the United States.This raises concerns as most Americans fly out for the holiday season. Also we expect planes to be safe and throughly inspected but it seems like that is not the case here. This graph peaked my interest because I love to stay up to date on current news. Also as someone who likes to travel this is a concern and a fear a lot of people have when flying on a plane. From e48aa9a35f95aa6bc9d51547b69c5748c548bd63 Mon Sep 17 00:00:00 2001 From: Aoboafo <101887879+Aoboafo@users.noreply.github.com> Date: Tue, 23 Jan 2024 09:42:55 -0500 Subject: [PATCH 03/22] Aoboafo week2.md --- week2.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/week2.md b/week2.md index e69de29..d2c8b33 100644 --- a/week2.md +++ b/week2.md @@ -0,0 +1,4 @@ +Link to article: https://www.nytimes.com/interactive/2024/01/20/world/middleeast/houthi-red-sea-shipping.html +Title: How Houthi Attacks Have Upended Global Shipping +Many ships are not going through the Suez Canal route because of Houthi militia attacks in the Red Sea. Instead they are opting for a longer route around Africa. These on going attacks have disrupted shipping, leading to increased costs, delayed arrivals, and rising insurance premiums for companies. Shipping companies increased container prices from Asia to Europe, which can lead to inflation. Plenty of retailers have warned that there will be delays in product shipment and rise in consumer prices. The Red Sea's significance in global trade, especially in oil and LNG shipments, adds complexity to the situation. The us navy has tried to help the situation by shooting down drones and missiles. However, this process is costly. +The map data illustrates the changing paths of ships navigating the Red Sea. It shows the positions of 3,461 cargo vessels recorded at entrances to the Red Sea over the last three months. The shipping routes before the Houthi attacks, covering Nov. 1 to Nov. 15, and the regular paths of the vessels. This can be seen with green lines. With purple lines the data shoes after the attacks, positions from Jan. 1 to Jan. 15 depict altered routes as ships detour around the Red Sea, highlighting the impact of the disruptions on global shipping patterns. From 4ed740876263ca5272087f00bd86afff014e2138 Mon Sep 17 00:00:00 2001 From: Aoboafo <101887879+Aoboafo@users.noreply.github.com> Date: Tue, 23 Jan 2024 09:44:06 -0500 Subject: [PATCH 04/22] Update week2.md From 9504ae8e0ac27a8500523822752571f13383bf16 Mon Sep 17 00:00:00 2001 From: Aoboafo <101887879+Aoboafo@users.noreply.github.com> Date: Tue, 23 Jan 2024 09:48:04 -0500 Subject: [PATCH 05/22] Update week2.md From 09d91f6e7e83e74cdaf0dfa04452f9627598d205 Mon Sep 17 00:00:00 2001 From: Aoboafo <101887879+Aoboafo@users.noreply.github.com> Date: Tue, 23 Jan 2024 09:55:57 -0500 Subject: [PATCH 06/22] aoboafo week2.md From 8c2dda0a17fcdadadab8ee4627741fa2825742e6 Mon Sep 17 00:00:00 2001 From: Aoboafo <101887879+Aoboafo@users.noreply.github.com> Date: Tue, 30 Jan 2024 10:09:59 -0500 Subject: [PATCH 07/22] Update week3.md --- week3.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/week3.md b/week3.md index e69de29..aa0adda 100644 --- a/week3.md +++ b/week3.md @@ -0,0 +1,4 @@ +Title: How have Americans' drinking habits changed? +Link to article: https://usafacts.org/data-projects/beverages + +What Americans drink has changed drastically overtime. With more than 500 options to choose from there has been a significant shift in Americans' beverage consumption patterns. There has been a 56% rise in bottled water consumption. This shift has been accompanied by a decline in the consumption of soft drinks, with a 60% decrease among teenagers over the last 15 years. Water both bottled and unbottled is the most consumed drink followed by Coffee (14%), soft drinks (10%), milk (7%), and tea (7%). Milk used to be really popular among children 12 years and younger but this is declining leading to a rise in imitation milk, including plant-based alternatives like soy, rice, and oat milk. Soft drinks, which were the top beverage for teenagers in the mid-2000s, experienced a notable decline, dropping by about 60% in the last 15 years. Lastly, coffee and alcohol consumption for adults remained relatively steady. On average, adults consumed about 1.3 cups of coffee a day, and alcohol consumption showed little change. However, there was a shift in the type of alcohol consumed, with beer and liquor declining and wine and cocktails experiencing increases. All of these changes makes sense due the increase popularity in reusable cups such as Stanleys and Owalas. Amongst young people its trendy to have these water bottles in different colors and bring them with you everywhere as part of your everyday style. This has led to the average bottled water consumption doubled, and overall, bottled water consumption rose 56% between 2005-2006 and 2017-2020. Also, there has been a rise in mock-tails and not consuming as much alcohol in millennial which is causing the shift in the type of alcohol consumed from beer and liquor declining to wine and cocktails on the rise. From aee62c128f4c1221288d7ea351e6f949d3cf06f1 Mon Sep 17 00:00:00 2001 From: Aoboafo <101887879+Aoboafo@users.noreply.github.com> Date: Tue, 30 Jan 2024 10:16:03 -0500 Subject: [PATCH 08/22] Update week3.md From 5d90bd24581b7ddb3aa036718134abe1b228f952 Mon Sep 17 00:00:00 2001 From: Aoboafo <101887879+Aoboafo@users.noreply.github.com> Date: Tue, 30 Jan 2024 10:19:01 -0500 Subject: [PATCH 09/22] Update week3.md From 84658e86115310ff6f40278162db4fda2c56385a Mon Sep 17 00:00:00 2001 From: Aoboafo <101887879+Aoboafo@users.noreply.github.com> Date: Tue, 30 Jan 2024 10:28:48 -0500 Subject: [PATCH 10/22] Update week3.md From 8a9f1f95c26a40d6f81addf0d657e9119cfcecd4 Mon Sep 17 00:00:00 2001 From: Aoboafo <101887879+Aoboafo@users.noreply.github.com> Date: Tue, 6 Feb 2024 09:48:28 -0500 Subject: [PATCH 11/22] Update week4.md --- week4.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/week4.md b/week4.md index e69de29..365e029 100644 --- a/week4.md +++ b/week4.md @@ -0,0 +1,5 @@ +Link to article: https://usafacts.org/articles/how-much-does-text-message-fraud-cost-americans/ + +How much does text message fraud cost Americans? + +Text message scams has been on the rise with the emergence of new technologies. In 2022, text message fraud has cost Americans a median loss of $1,000 per incident, compared to the overall consumer fraud median loss of $650. The Federal Trade Commission (FTC) reported that Americans collectively lost nearly $330 million to text message scams. Out of the 2.56 million fraud reported 22% was from text fraud. The graph showed that Georgia had the highest rate of fraud reports in 2022, with 1,611 reports per 100,000 people. While North Dakota had the lowest at 568 per 100,000 people. Fraud has caused Americans nearly $9 billion. Text message fraud incidents were mainly related to business impostors. Which was 31.8% of all reported cases, resulting in losses of $35.1 million. Followed by investment scams, led to the most significant losses, totaling $149.53 million. In the past phone calls were the most common type of fraud report. This graph peaked my interest because my family is always telling me to be aware of these scams and during this season the text scams are at an all time high. From 45ed2fa44c4c5f42f5a857f8262bb1f0fec4b642 Mon Sep 17 00:00:00 2001 From: Aoboafo <101887879+Aoboafo@users.noreply.github.com> Date: Tue, 6 Feb 2024 09:53:36 -0500 Subject: [PATCH 12/22] Update week4.md From e5451b61a55a7dc377cb6ea47f56d8dc8734f2b5 Mon Sep 17 00:00:00 2001 From: Aoboafo <101887879+Aoboafo@users.noreply.github.com> Date: Tue, 6 Feb 2024 09:53:49 -0500 Subject: [PATCH 13/22] Update week4.md --- week4.md | 1 + 1 file changed, 1 insertion(+) diff --git a/week4.md b/week4.md index 365e029..f65b904 100644 --- a/week4.md +++ b/week4.md @@ -3,3 +3,4 @@ Link to article: https://usafacts.org/articles/how-much-does-text-message-fraud- How much does text message fraud cost Americans? Text message scams has been on the rise with the emergence of new technologies. In 2022, text message fraud has cost Americans a median loss of $1,000 per incident, compared to the overall consumer fraud median loss of $650. The Federal Trade Commission (FTC) reported that Americans collectively lost nearly $330 million to text message scams. Out of the 2.56 million fraud reported 22% was from text fraud. The graph showed that Georgia had the highest rate of fraud reports in 2022, with 1,611 reports per 100,000 people. While North Dakota had the lowest at 568 per 100,000 people. Fraud has caused Americans nearly $9 billion. Text message fraud incidents were mainly related to business impostors. Which was 31.8% of all reported cases, resulting in losses of $35.1 million. Followed by investment scams, led to the most significant losses, totaling $149.53 million. In the past phone calls were the most common type of fraud report. This graph peaked my interest because my family is always telling me to be aware of these scams and during this season the text scams are at an all time high. + From e3d02802c9ceb783f886ac317309190a07869be9 Mon Sep 17 00:00:00 2001 From: Aoboafo <101887879+Aoboafo@users.noreply.github.com> Date: Tue, 6 Feb 2024 10:04:06 -0500 Subject: [PATCH 14/22] Update week4.md --- week4.md | 1 - 1 file changed, 1 deletion(-) diff --git a/week4.md b/week4.md index f65b904..365e029 100644 --- a/week4.md +++ b/week4.md @@ -3,4 +3,3 @@ Link to article: https://usafacts.org/articles/how-much-does-text-message-fraud- How much does text message fraud cost Americans? Text message scams has been on the rise with the emergence of new technologies. In 2022, text message fraud has cost Americans a median loss of $1,000 per incident, compared to the overall consumer fraud median loss of $650. The Federal Trade Commission (FTC) reported that Americans collectively lost nearly $330 million to text message scams. Out of the 2.56 million fraud reported 22% was from text fraud. The graph showed that Georgia had the highest rate of fraud reports in 2022, with 1,611 reports per 100,000 people. While North Dakota had the lowest at 568 per 100,000 people. Fraud has caused Americans nearly $9 billion. Text message fraud incidents were mainly related to business impostors. Which was 31.8% of all reported cases, resulting in losses of $35.1 million. Followed by investment scams, led to the most significant losses, totaling $149.53 million. In the past phone calls were the most common type of fraud report. This graph peaked my interest because my family is always telling me to be aware of these scams and during this season the text scams are at an all time high. - From 435dc80ed13411b7ab52bc5348d2e7111af3dc13 Mon Sep 17 00:00:00 2001 From: Aoboafo <101887879+Aoboafo@users.noreply.github.com> Date: Tue, 13 Feb 2024 10:28:07 -0500 Subject: [PATCH 15/22] week 5 reflection - Abigail Boafo --- week5.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/week5.md b/week5.md index e69de29..0cfeef4 100644 --- a/week5.md +++ b/week5.md @@ -0,0 +1,4 @@ +Big Game Census: 2024 +https://www.census.gov/library/visualizations/interactive/big-game-census-2024.html + +The article provides insights into the Super Bowl and the history of winners. It highlights that 22 unique teams have won the championship, with the Pittsburgh Steelers and the New England Patriots leading by winning the Super Bowl six times each. It stars off by shoeing the history of the first Super Bowl in 1967, where the Green Bay Packers defeated the Kansas City Chiefs. This years super bowl was held in nevada featuring the San Francisco 49ers and Kansas City Chiefs. It was a close game and the Kansas City Chiefs won making them 3 time winners of the super bowl. The Big Game Census displays the players, position, exp, height, weight,team and college. The visualization offers multiple interactive features for users to explore and analyze data related to the birthplaces of players in the Super Bowl. Users can click on a city or state within the map, which then filters the table and highlights the bar chart. Providing detailed insights into the origins of the players participating in the big game. Users have the option to click on a specific team, narrowing down the displayed information to players from that particular team. From 4cf9d695f17e759ee8a108df6d20944c565ea277 Mon Sep 17 00:00:00 2001 From: Aoboafo <101887879+Aoboafo@users.noreply.github.com> Date: Tue, 13 Feb 2024 10:29:05 -0500 Subject: [PATCH 16/22] Update week5.md From c3c4937bd7235b8cc91376aec77a93921d7cd999 Mon Sep 17 00:00:00 2001 From: Aoboafo <101887879+Aoboafo@users.noreply.github.com> Date: Tue, 13 Feb 2024 10:37:39 -0500 Subject: [PATCH 17/22] week5 reflection --- week5.md | 1 + 1 file changed, 1 insertion(+) diff --git a/week5.md b/week5.md index 0cfeef4..c1c0eca 100644 --- a/week5.md +++ b/week5.md @@ -1,4 +1,5 @@ Big Game Census: 2024 https://www.census.gov/library/visualizations/interactive/big-game-census-2024.html + The article provides insights into the Super Bowl and the history of winners. It highlights that 22 unique teams have won the championship, with the Pittsburgh Steelers and the New England Patriots leading by winning the Super Bowl six times each. It stars off by shoeing the history of the first Super Bowl in 1967, where the Green Bay Packers defeated the Kansas City Chiefs. This years super bowl was held in nevada featuring the San Francisco 49ers and Kansas City Chiefs. It was a close game and the Kansas City Chiefs won making them 3 time winners of the super bowl. The Big Game Census displays the players, position, exp, height, weight,team and college. The visualization offers multiple interactive features for users to explore and analyze data related to the birthplaces of players in the Super Bowl. Users can click on a city or state within the map, which then filters the table and highlights the bar chart. Providing detailed insights into the origins of the players participating in the big game. Users have the option to click on a specific team, narrowing down the displayed information to players from that particular team. From 565de5e8127106f51e6f2c0cdb16828070bf68e9 Mon Sep 17 00:00:00 2001 From: Aoboafo <101887879+Aoboafo@users.noreply.github.com> Date: Tue, 20 Feb 2024 11:58:35 -0500 Subject: [PATCH 18/22] Update week6.md --- week6.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/week6.md b/week6.md index e69de29..b38a289 100644 --- a/week6.md +++ b/week6.md @@ -0,0 +1,5 @@ +How shrinking populations fuel divisive politics +https://www.ekathimerini.com/nytimes/1230849/how-shrinking-populations-fuel-divisive-politics/ +https://www.nytimes.com/2024/02/02/world/europe/interpreter-shrinking-populations-fuel-divisive-politics.html + +The article discusses how shrinking populations, especially the decline in working-age populations, can fuel divisive politics and have negative consequences for societies. The map illustrates changes in working-age populations worldwide by 2050. With a focus on regions like Europe, Brazil, China, Chile, Japan, and Russia experiencing a decline. The decline in working-age populations has been attributed to lower birthrates as countries become wealthier. Because of the decline birth rates there has been efforts to boost fertility through various programs. However, some countries like Australia, Canada, and the United States show modest growth due to immigration.Immigration as a partial solution to demographic challenges causes political barriers to arise, particularly fueled by anti-immigrant sentiments. As depopulation occurs, the state tends to withdraw services from these regions, leading to difficulties and neglect among residents. This leads to far right parties blaming immigrants for societal problems and creating a cycle where depopulation issues fuel divisive politics that hinder effective policy solutions. The article ends with an emphasis on a need for a shift in public debate, addressing the reality of depopulating regions and aging populations, urging policymakers to address these challenges sooner rather than later. From afcebdd3e22e829425ff0809e06da858334cc02e Mon Sep 17 00:00:00 2001 From: Aoboafo <101887879+Aoboafo@users.noreply.github.com> Date: Tue, 20 Feb 2024 12:04:01 -0500 Subject: [PATCH 19/22] week6 Abigail Boafo From 803023a54efc9ea38d93fdefe1737815d5491f71 Mon Sep 17 00:00:00 2001 From: Aoboafo <101887879+Aoboafo@users.noreply.github.com> Date: Tue, 27 Feb 2024 12:29:23 -0500 Subject: [PATCH 20/22] aoboafo week7.md --- week7.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/week7.md b/week7.md index e69de29..682d45b 100644 --- a/week7.md +++ b/week7.md @@ -0,0 +1,5 @@ +Sleep Schedule, From the Inconsistent Teenage Years to Retirement +https://flowingdata.com/2019/09/13/sleep-schedule-and-age/ + +This chart highlights the escalating issue of sleeplessness with age, leveraging insights from the American Time Use Survey data spanning from 2014 to 2018. The author uses both real data and personal experinces on the challenges of maintaining a sound sleep cycle, citing reasons such as bathroom visits, children's nightmares, and temperature discomfort. The chart shows a clear trend in reported sleeplessness percentages. Especially after the age of 30, signifying an age-related increase in nocturnal disruption. The first chart displays the binary color scheme (yellow for awake, magenta for asleep) and reveals subtle fluctuations, particularly around college age and retirement. In this graph the age range is from 0 childhood to 80+ eternity which i found very intresting. The second chart employs a gradient color scale, offering a more subtle view of sleep schedules. It indicates less consistent sleep patterns during early years, more consistent sleep among older individuals, and wider bands in later years suggesting a more consistent sleeping schedule and later wake-up times. The author was suprised to see the wider bands in later years, hinting at a more stable sleep schedule and later waking times. I choose this article because as a college student I can relate to having a messed up sleep schedule. Often times I go to bed after 12 and have to wake up early for 8am classes. + From 821425d7ee27c47e187970d99ddb0e43cce98569 Mon Sep 17 00:00:00 2001 From: Aoboafo <101887879+Aoboafo@users.noreply.github.com> Date: Tue, 27 Feb 2024 12:31:44 -0500 Subject: [PATCH 21/22] Aoboafo - week7.md From 75f9f6ca86d2337e9016de6e4b25f40910b03ead Mon Sep 17 00:00:00 2001 From: Aoboafo <101887879+Aoboafo@users.noreply.github.com> Date: Tue, 27 Feb 2024 13:28:32 -0500 Subject: [PATCH 22/22] Update week7.md --- week7.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/week7.md b/week7.md index 682d45b..005e83c 100644 --- a/week7.md +++ b/week7.md @@ -1,5 +1,4 @@ Sleep Schedule, From the Inconsistent Teenage Years to Retirement https://flowingdata.com/2019/09/13/sleep-schedule-and-age/ -This chart highlights the escalating issue of sleeplessness with age, leveraging insights from the American Time Use Survey data spanning from 2014 to 2018. The author uses both real data and personal experinces on the challenges of maintaining a sound sleep cycle, citing reasons such as bathroom visits, children's nightmares, and temperature discomfort. The chart shows a clear trend in reported sleeplessness percentages. Especially after the age of 30, signifying an age-related increase in nocturnal disruption. The first chart displays the binary color scheme (yellow for awake, magenta for asleep) and reveals subtle fluctuations, particularly around college age and retirement. In this graph the age range is from 0 childhood to 80+ eternity which i found very intresting. The second chart employs a gradient color scale, offering a more subtle view of sleep schedules. It indicates less consistent sleep patterns during early years, more consistent sleep among older individuals, and wider bands in later years suggesting a more consistent sleeping schedule and later wake-up times. The author was suprised to see the wider bands in later years, hinting at a more stable sleep schedule and later waking times. I choose this article because as a college student I can relate to having a messed up sleep schedule. Often times I go to bed after 12 and have to wake up early for 8am classes. - +This chart highlights the escalating issue of sleeplessness with age, leveraging insights from the American Time Use Survey data spanning from 2014 to 2018. The author uses both real data and personal experinces on the challenges of maintaining a sound sleep cycle, citing reasons such as bathroom visits, children's nightmares, and temperature discomfort. The chart shows a clear trend in reported sleeplessness percentages. Especially after the age of 30, signifying an age-related increase in nocturnal disruption. The first chart displays the binary color scheme (yellow for awake, magenta for asleep) and reveals subtle fluctuations, particularly around college age and retirement. In this graph the age range is from 0 childhood to 80+ eternity which i found very intresting. The second chart employs a gradient color scale, offering a more subtle view of sleep schedules. It indicates less consistent sleep patterns during early years, more consistent sleep among older individuals, and wider bands in later years suggesting a more consistent sleeping schedule and later wake-up times. The author was suprised to see the wider bands in later years, hinting at a more stable sleep schedule and later waking times. I choose this article because as a college student I can relate to having a messed up sleep schedule. Often times I go to bed after 12 and have to wake up early for 8am classes