sophia-strano-reflection-week7

week1.md

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+![map demonstrating population density](GlobeMap.png)
+![map demonstrating global birth rates over time](BirthRates.png)
+
+The data visualization I’ve reviewed this week comes from an article that includes multiple interesting visualizations relating to the growing global population and its overall distribution. Curiously, the article includes an image of the globe with a highlighted circle centering over southern China, northern India, and indonesia. I chose to include a more detailed visualization that contextualizes the aforementioned map by providing information about the birth rate in every nation worldwide. The map uses saturation on a blue-to-red scale to represent the average ratio of children to women in a given country. The saturation is an effective means of demonstrating the birth rate of each nation comparatively so that regional averages can be easily perceived. The visualization includes two maps that use this same key, one representing global averages taken from data collected from 1955-1960, while the other includes data collected from 2005-2010. Global averages taken 60-70 years ago provide logical explanations for the current population distribution, and the article includes these modern charts and averages to make predictions about the growing world population. For example, it can be observed between the two charts that while China still has a massive population, the birth rate has dropped dramatically, from 5+ children per woman to one, while India’s population continues to grow. Based on these visualizations, the article suggests India’s population will eclipse China’s within the next 10 years, and that the global population will rise to be nearly 10 billion by 2050. The chart could be improved by including the actual national average birth rate based on the ratio of women to children as this would provide more specific detail for comparison between the many countries that have an estimated birth rate of 1-1.99 children, as many more nations have moved toward this categorization in the more modern chart makes the current saturation scale somewhat irrelevant. 
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+(article : https://www.weforum.org/agenda/2017/07/more-people-live-inside-this-egg-than-outside-of-it-and-other-overpopulation-data/ )

week2.md

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+
+![image](https://user-images.githubusercontent.com/64103447/150832830-17f6e8fa-9024-4e0b-8c71-d1bc8d6e8206.png)
+![image](https://user-images.githubusercontent.com/64103447/150832928-9fa8fa6e-96cd-4603-8c1a-a9d5b31d226f.png)
+
+
+source : https://www.reddit.com/r/dataisbeautiful/comments/saeju0/i_pulled_historical_data_from_19732019_calculated/?utm_source=share&utm_medium=web2x&context=3
+
+The visualization I've chosen this week is actually a series of four charts, representing the declining purchasing power of minimum wage. It's important to acknoweldge that this is not a representative of the median income; meaning most americans do not live this way, and this is not a representation of a 'budget'. Federal minimum wage was initially devised as enough to cover all  of the basic necessities comfortably for a family, at a time in a single-income household. The scenario remains the same for the young couple, but adjusts for inflation based on the cost of basic expensives through each period, with years noted on the x axis.
+
+To an extent, the chart may largely under-report expenses related to childcare, as the price of childcare services have increased much less dramatically than the price of housing and education. However, childcare was not a necessity for the majority of baby boomer households which were able to rely on a single-income, leaving a partner at home to watch their kids. Many millenials are the product of dual-income or single-parent households, as are a larger portion of generation X by comparison to baby boomers, meaning no parent or grandparent can be responsible for childcare. This creates an increasing demand for childcare, putting financial strain on low-income households and discouraging developing adults from having children. 
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+It's also important to note that median values were sampled for income, housing, and healthcare, as the average values for these expenses tend to be highly skewed by small percentages of wildly high costs. The biggest increases in expenses that's affected purchasing power stem from the housing and mortgage, which increased sharply in the late 70s and early 80s during a period of deregulation leading to crushing interest rates. In addition to and as a result of a general shortage of affordable housing and mortgage interest rates still suffering from deregulation, millenials making minimum wage in 2021 typically cannot afford to own property and rent their living spacce, indefinitely sacrificing large portions of income making it incredibly difficult to save money. The visualization shows the sharp increase in the cost of housing between the late 60s and early 70s as opposed to the periods following the 80s-90s to present day, indicating that we still have not recovered from the affects of the sharp inflation that occurred in the early 80s. Additionally, the cost of healthcare has increased dramatically over the last 70 years, which is a uniquely American heavy expense on minimum wage workers by comparison to other first-world countries with comparable GDPs.
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+The essential takeaway is that a few generations ago, someone born at the time could have made minimum wage, paid for health insurance, provided for a house house and still had money left over at the end of the month. Unfortunately, many people who experienced this reality are law makers and policy enforcers, who are unable to grasp the magnitude by which the cost of living has increased, leading to the delibatitng wealth disparity observed in modern America that disturbingly continues. I would say this visualization could use more descriptive captioninig to explain the implications of the chart, as my deductions required a considerable amount of additional research. 
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+This was kind of cynical, here's a kitten : ![cat](https://user-images.githubusercontent.com/64103447/150832287-4dd4d714-d760-4976-a175-4b193a383d20.png)
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week3.md

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+
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+![WordVisagain](https://user-images.githubusercontent.com/64103447/151839674-171aceb6-3c92-4746-bbe0-ec1013ad05eb.png)
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+
+source : https://www.reddit.com/r/dataisbeautiful/
+
+The data visualization I’ve chosen this week is a lighthearted representation of the frequency of different characters in 5-letter English words. The chart has a bar representative of each letter, and the colored portions of that bar are keyed to represent proportion in the overall letter frequency of the number of times a letter appears in a word once, two, three, four and five times. If I were to change this visualization, I would make the colors more distinct and provide a more precise scale so that the differences in letter frequency can be more clearly surveyed. I would also be highly interested in a chart that visualized this for four and six letter words, and then another chart that accounts for all English words. One problem with this visualization and any language focused visualization is that language is an ever-evolving subject, and this visualization is unclear about the source used for “all five letter words”, as dictionaries and colloquialisms vary this could affect the data. I also found the frequency of the letter “n” to be surprisingly low, as a child I was taught that most words contain at least one vowel the letters n, t or s. While the use of S is frequent as expected, I found it surprising that n and t are less frequently used. Interestingly, the data seems to show that e and s are the most frequently used letters in five letter words. This data is most useful during your next hangman or wheel of fortune game!

week4.md

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+--Sophia Stranothat  Week 3 Reflection--
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+![image](https://external-preview.redd.it/WLrFGeAw9Fq9Jy1ScLB1wVG3h831cIlVMKPAgePL_eU.png?auto=webp&s=a4ae31c66753a5ca9efca3c379a463e732293f0b)
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+source: https://www.reddit.com/r/dataisbeautiful/comments/sl7mvv/percent_of_birth_via_cesarean_delivery_csection/hvp5bwv/
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+
+The visualization I've selected this week is a color coded chart representing the rate of birth via Cesarean section across the European Union and the United States. The author created this visualization in MS office, which I found interesting as I've never used MS office to create a visualization and wouldn't think to use those tools to create this type of visualization. It's worth noting the Author also removed "Ex EU or Partially EU" Countries, as well as U.S. territories that are not states. It's very difficult to read the information included about these in faint grey, so I'd improve this visualization by making it more readable. It's important to note that high rates of ceserean section don't actually correlate to birth complications- Healthy expectant mothers are commonly reccommend to schedule a C-section to 'reduce the stress of uncertainty of when the baby will come'. In reality, scheduling delivery months ahead of time allows doctors to schedule timely due dates so that they may maximize the number of babies delivered per month, thereby maximizing income. C-sections can be a life-saving procedure for women experiencing any complications during delivery, however extensive research shows that if possible natural birth leads to a smoother recovery for both the mother and baby. C-section scheduling has also led to a higher number of premature births, although not by a large margin, on average women are scheduling delivery dates a few weeks earlier than the full 40-week term, which can create potential critical health risks for the newborn and reduce the positive affects associated with interacting with the mother's microbiome in the final weeks of development and during birth. Particularly high C-section rates are recorded in Greece, where this practice is especially common. Widely available C-sections have saved countless lives internationally, and are undoubtedly an incredible tool, comprabale to how the appendectomy has saved countless lives- but you'd still be better off keeping your appendix if you didn't have a reason to surgically remove it. You could also call into question if this removes the selective pressure for babies with large heads and mothers who are prone to delivery complications, which could contribute to the higher rate of C-sections, sources included below the author's visualization include more detail about increased encouragement for C-sections in the medical field before you even begin to think logically about the improbability of over 50% of greek women experiencing complicated deliveries. It's important to note that in general, researching procedures, medications, surguries, or any treatments for a given afflication is expressly important for the patient before and after consulting a doctor. Practioners should always have your best interest in mind, and they often do, but it's good to keep in mind that this can be very lucrative field, dominated by competitive, busy people who may not view you as an individual when evaluating your needs. 
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week5.md

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+![image](https://i.redd.it/nj8tm3f2tmh81.png)
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+The visualization I've chosen for this week's reflection is a representation of relative wealth inequality of a given country compared to income inequality, meaning the disparity between the earnings of the wealthiest and poorest in a given country. Income inequality was measured using the Gini (2019) coefficient from the world bank, while wealth inequality is represented using the Wealth Databook 2018 by Credit Suisse. This visualization was created using ggplot2, which I thought was unique as most of the subreddit consists of excel graphs and other tools to create animated graphs. This was attached to some extremely informative statistical information about the signiigance and history Gini index. 
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+This visualization does an excellent job of consolidating an overwhelming amount of information that's bogged down with tons of mathematical jargon. Wealth inequality from nation to nation is a concept that most are globally aware of, but the visualization helps to give context to the inflated numbers reported for income and GDP across different countries. Mostly, this visualization simplifies the implication of the gini index without having to view a multitude of line plots. For context, the Gini index is calculated as the ratio of the area between the 'perfect equality line' and the Lorenz curve (A) divided by the total area under the perfect equality line (A + B). The perfect eqaulity line is a calculated ideal based on the value of labor, while a Lorenz curve is a graphical representation of the distribution of income or wealth within a population. Lorenz curves show percentiles of the population against cumulative income of all those falling below that percentile. The level of unequal distribution increases when the Lorenz curve drifts away from the baseline, leading to a higher Gini value. While these can be understood by viewing the line plots generated using the Gini index, the bubble chart shown makes the information much more digestable
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+I also found the various different colors used to represent continents to be helpful in identifying countries I'm less familiar with. I would improve this visualization by adding more degrees of accuracy to the X axis, 'enlarging it', to more easily view the size difference between different countries' datapoints.  

week6.md

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+# Week 5 Reflection - Monopoly!
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+![image](https://i.redd.it/zzy3xqz7m5i81.png)
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+The visualization I've chosen for this week's reflection is a distribution of the overall probability of ending up on a given monopoly space at the end of a turn. Monopoly is a classic boardgame about the wonders of capitalism and how in a few short hours it can tear your family apart. When the game begins, all players have an equal amount of money, and can move across the board freely by rolling two traditional six-sided dice. The spaces landed on represent property, which can be purchased by the first player who lands on it. Players who land on property owned by other players then have to pay them rent, which can increase if the owning player chooses to purchase houses to place on their property. As the spaces eventually all become owned by a player, the probabilities shown essentially become a player's % chance of having to pay rent on a given space. 
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+It's important to highlight the probabilities given are representative of the probability a player being on a certain space at their end step on their turn, which gives more insight about the 0% probability of going to jail. Although players will land on the go-to-jail space, they will never end their turn while still on the space directing them to jail, meaning there is no chance a player will land there. For similar reasons, the probability of ending your turn on spaces like the chance or treasure square are also considerably lower, as a player will often draw cards that direct them to move to another space. 
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+This visualization could definitely be improved by increasing the variation in color from space to space, as the heavy saturated yellows and browns are incredibly close in color and therefore add little meaning to the Author's visualization. Coupled with the fact that the numbers on each space are within a small range, this visualization leaves a little bit to be desired in terms of readability without additional information. It could be highly improved with the use of context through greater color differences, as well as a potential animation of how this probability changes throughout the game, from turn one until it reaches this randomized point shown in the visualzation. The author decided to update their visualization with a link to about fourty images that show the progression of the changing probability throughout the game, which I included a link to below for consolidation. 
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+![Author's improved visualizations on imgur](https://imgur.com/a/tw6gXEP)
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week7.md

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+# Week 7 Reflection : Ukraine
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+
+![image](https://preview.redd.it/qkl0y9e5jfk81.jpg?width=960&crop=smart&auto=webp&s=116b13e91845870d4b00ca100953362944f3be40)
+![-- Link to live map of the Russian invasion of Ukraine](https://www.google.com/maps/d/viewer?mid=1E8DKPzMqLvWa_nbXZG2vFyo97ijJfAEX&ll=48.528488496304774%2C32.6842201&z=6)
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+Source : https://www.reddit.com/r/dataisbeautiful/comments/t2w3qk/oc_russian_invasion_of_ukraine_as_of_242_pm_est/?utm_source=share&utm_medium=web2x&context=3
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+The visualization I've chosen for this week's reflection is a recently updated map (literally less than a day old as this is being written) of the ongoing conflict which sparked in Europe last week, the most brazen attempt to redraw borders since WWII begun by Russia invading Ukraine on multiple eastern fronts. The author of the visualization shown, a map of the current state of the invasion as of 2/27/22 2:42PM, also works with multiple contributors on a live map that shows the progress of the conflict in real time which is linked above. 
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+The visualization helps give meaning and explanation to the current conflict for those unfamiliar with the geography of ukraine. The visualization uses a combination of color saturation as well as symbol-coded bubbles to represent invaded territory as well as pockets of conflict in their nature. Legends are incldued to provide increased context about the areas subject to heavy bombing, as well as important information designated to special colors, like Moldova's support of Russia in the southwest. This map intentionally highlights the progress of the Russian invasion and not the Ukrainian response, to avoid spreading information that could be potentially harmful to Ukraine. 
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+Despite being incredibly informative about a rapidly changing topic, there are some visual improvement that can be made to the given map. If I could improve this visualization I'd enlarge it so the text was more readable, and I'd provide english alphabetical translations of the locations listed on the map so they may be more easily identified. It's important to note that while the author was able to cite multiple reputable international news sources for the data shown, typically it's difficult to get a truly accurate picture of the number of casualties based on what's reported. No one is immune to propaganda, and data is often manipulated when government is under extreme pressure. Many speculate the death toll to be higher than what's shown. While this historic conflict has ended Switzerland's 207 years of neutrality to support Ukraine as well as Germany and the majority of the EU's pacifiscm, creating unity across Europe, it's undebatable that an immediate ceasefire would be the most benenficial thing for the people of Russia and Ukraine alike. 
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