The following is a listing of demos of student projects done in the Fall 2022 Principles of Computing course. The students found their choice of data source from resources online and decided how they want to examine, analyze and visualize the data.
| Project Number | Section | Project title |
|---|---|---|
| 1 | 6 | Success of protests |
| 2 | 6 | COVID Intensity and Test Scores |
| 3 | 6 | Global Education |
| 4 | 6 | Types of Squirrels in Central Park |
| 5 | 6 | Aerospace Tracker |
| 6 | 6 | TV Show and Film Data Analysis |
| 7 | 6 | Homelessness in the US |
| 8 | 6 | Availability of Affordable Housing |
| 9 | 6 | Air quality impacts on quality of life |
| 10 | 6 | Observing the Effects of Cattle Production |
| 11 | 6 | Suicide Rates in the US |
| 12 | 6 | Income and Voting |
| 13 | 6 | Visualizing international merchandise trade |
| 14 | 6 | Time Usage in European Countries |
| 15 | 6 | Tracking Corruption in Capitol Hill |
| Project Number | Section | Project title |
|---|---|---|
| 17 | 8 | Air Quality around the world |
| 18 | 8 | Location vs Education |
| 19 | 8 | Mental Health Care |
| 20 | 8 | Education, Wealth, and Development: The Relations Between the Three |
| 21 | 8 | Obesity and Health |
| 22 | 8 | Plan(et) your vacation: Earth or Mars? |
| 23 | 8 | Border Crossings into the United States |
| 24 | 8 | Space Information |
| 25 | 8 | Housing & Race |
| 26 | 8 | World Tourism Data vs World Development Indicators |
| 27 | 8 | Poverty over Covid |
| 28 | 8 | Crime development per state in the USA |
| 29 | 8 | Congress Members' Stock Trades |
| 30 | 8 | Crime analysis over the year by states |
| 31 | 8 | NASA Fireball Data |
| 32 | 8 | Transportation Comparison from South Bend Airport |
| 33 | 8 | Crime Analysis By State |
| 34 | 8 | Chicago Crime Statistics |
| 35 | 8 | Prescription Drugs Safety Information |
| 36 | 8 | Walkability and Welfare |
| 37 | 8 | Greenhouse Gas Emissions |
This project analyzes the effect of locations, like police stations, parks, and technological resources on factors of education. We use the Haversine formula to calculate the distance between two points of longitude and latitude, counting the total number of sites within a given radius of any given school. We then average together these counts to create meaningful statistics and measures to quantify location’s impact on education within Chicago. This project is important because it spreads awareness that everyone has the same resources in their area, or educational opportunity. It displays where resources may need to be allocated to remedy this gap. It provides a starting point for future data analysis and reform implementation. Those that would be interest include people who want to see how their own education in Chicago might be affect by where they live. Parents may want to find the best area for their child to go to school in. City regulators might want to use our analysis to support bills and legislation. The data sources are from the Chicago data portal, and consist of information on crime, schools, police, parks, and technological resources centered around the years 2011-2012. We found a strong negative correlation between crime and safety score, and a strong correlation between location in Chicago and the number of misconducts per 100 students.
The motivation for the project was to call attention to the average amount of time spent performing various activities throughout the day. In our busy lives, it is important to identify the implications of spending time on certain activities. For example, does more leisure time result in greater life expectancy? Does more time spent in school result in higher national IQs? Does more sleep result in lower obesity rates? Our main data source was from the Organisation for Economic Co-operation and Development. Their data sources provided a breakdown of daily time use for different countries as well as statistics on life expectancy and life satisfaction. We also pulled data on wealth, IQ, obesity to look for correlations between national statistics and national time use. Based on our data and visualizations, we identified no relationship between time spent at school and IQ and a slight positive relationship between time spent at work and IQ. We have determined that increased sleep corresponds to higher obesity rates, increased exercise corresponds to decreased obesity rates, and increased leisure corresponds to slightly lower obesity rates. There is a slightly positive relationship between leisure time and national wealth while there is a slight negative relationship between time spent at work and national wealth. There seems to be a weak relationship between leisure time and time spent socializing to life expectancy. There is also a pattern of women having higher life expectancy and also slightly higher life satisfaction rates across the world. People can use this data to determine how to spend their time based on a desired outcome. This data can be used for time management and determining what can feasibly be done in one day. However, this data analysis is not perfect and should not be used as scientific proof.
We chose to analyze state crime data in the US from 1960-2019. We knew this data set would be a great option for our project as it provided an abundance of opportunities to create interesting visualizations and reach meaningful conclusions with the trends it illustrated. Focusing on different categories of crime (such as property crime, violent crime, total crime, etc), we were able to compare and contrast the totals and rates of these crimes for each US state over a wide time period, considering changes in trends, populations, and years. We believe that analyzing this data visually is important in promoting public good, as it allows people to understand our country’s crime history as well as choose safe places to travel and live. We hope to bring awareness to and spark conversations about crime patterns in our country and how that affects people. Our project may interest people who are seeking to study how crime rates have changed in different states throughout history, those looking to promote social justice and advocate for crime prevention, or people looking to travel and live in safe environments. Our main data source features a very large JSON file containing data related to crime rates in different categories, across different states and years from about 1960-2019. The state, year, population, crime rates, and crime categories are the main keys in the JSON file. In terms of visualizations, we had 10 total, divided into two main categories. First we made 6 choropleth maps featuring the rates and totals for property, violent, and all crimes. The map can be interacted with using a slider bar to see how the color in each state, representing the varying crime rate/total from high-low, changed throughout the course of US history from 1960-2019. It is interesting to visually see how the colors of each state change and draw conclusions from that. Additionally we made four line graphs focusing on specific states (California, Illinois, Indiana, and the overall USA), to see the trendlines for how property, violent, and all crime rates varied from 1960-2019. Using different colors for each of the types of crimes, it is easy to compare how crime rates can vary so much in different parts of the country. The visualizations we created from the data source share an abundance of very interesting conclusions and patterns, all mentioned below each visualization in our website. First, the choropleth maps show that crime rates increased significantly from about 1960-1990 in all states across the US, and then decreased again going into the 2000s. This was consistent for property crime rates, violent crime rates, and all crime rates, however there are significantly less violent crimes than property and total crimes, making the colors generally lighter in that graph. When looking at the choropleth maps for crime totals, we see very different results than with the rates. When sliding through the years, it is very evident that certain states stand out with larger totals than others, namely California, Texas, Florida, and New York. This makes sense as there tends to be more crime in more densely-populated states with larger cities. We also see totals increase in the 60s-90s and go back down heading into the 21st century. When comparing the line graphs for California, Indiana, Illinois, and the US overall, we see that California generally has a higher crime rate than the USA as a whole, the graph of crime in Illinois appears almost identical to the USA (containing the same general trends), and the lines for property and total crime in Indiana are noticeably closer than others, indicating that their violent crime rate is considerably lower than the other data pictured.
We believe you should take your next vacation to Mars! Using NASA's Curiosity Rover, we interpreted weather data on Mars. We compared it to the weather in South Bend, Indiana to prove Mars is where you should go on your vacation.
With an alarming increase in political polarization throughout the US in the past few decades, voting in elections has never been more important. Analysts have been studying trends in political voting in order to understand our nation’s increasing polarization. One important trend analysts have been studying is voter turnout. To understand these trends, analysts focus on looking at voter demographics to determine how certain characteristics influence voter turnout. Our project aims to also explore how different aspects about elections influence voter turnout. This project’s findings could be of interest to data analysts, politicians (specifically for campaigning), PACS, or any other political organization. Our data was synthesized together through bar charts, choropleths, and pie charts. One interesting finding we encountered was that (according to the past three elections), voter turnout has slowly decreased in all states except the few swing states. Another interesting finding is that older generations in recent elections have higher voter turnout than younger generations.
In our project we looked at the effect Covid-19 had on student education and more specifically how it affected their standardized test scores. Using the Covid Act Now API and Nation’s Report Card, we extracted statewide Covid data as well as student test scores on the National Assessment of Educational Progress exam from the years 2019 and 2022. The purpose was to compare test scores from 2019 as a pre-Covid era and then 2022 as a post-Covid era. We tracked this data on a state by state basis as well as using some of the many variables included in the Nation’s Report Card to separate students by different groups based on categories we believed could have an impact on their response to Covid-19. We were surprised to find that every single state had a drop in average test scores from 2019 to 2022, indicating the strong effect that Covid-19 had on education regardless of state or policy. Our initial assumptions were that the worse Covid-19 was in a specific state, the more test scores would drop as a response. Overall we found out that this was not necessarily the case. Instead, states where Covid had a stronger presence, their test scores did not drop as much as states with weaker Covid presence. Upon further analysis we believe that this is the case because states with fewer Covid restrictions likely had many more cases but were also less likely to restrict in person schooling or impose other restrictions involving schools, resulting in a lower drop in test score. This is also suggested by our visualization graphing test score difference and vaccine distribution, as states with higher vaccine distributions also had a higher drop in test scores, indicating their stronger Covid policy response.
Our project looks at how Netflix occupies this foreign market of Korean dramas and how its other content compares to it. Furthermore, we aim to answer several questions, seeking to see how Netflix can increase their market share and the success of their future releases. In doing so, we hope others will gain a better understanding of the streaming landscape. We believe our project to be important because it outlines the trends of cultural change and awareness, seeking to explore and visualize how Netflix can aim to diversify its content and better target its target demographic. Our project caters to anyone who wants to learn more about Netflix and their content will learn a lot from our project. However, specifically, Netflix or those wanting to improve a streaming-related service would benefit from our analysis as they better understand the demand and preferences of its users, improving content recommendation. Another audience that would benefit are K-drama fans as they could learn more about the characteristics of the networks that they watch their dramas from. We wanted to pursue this project in hopes to learn more about Netflix and how it operates. Furthermore, we wanted to see if there were any trends that Netflix could monopolize on to catalyze its growth and solidify its place as the leading streaming platform. We're also both South Koreans, so we wanted to find a project that would intersect with our identities and on something that we both enjoy (both avid watchers!). This was a fun, challenging project that allowed us to apply our data analytic skills to provide insights on this topic! We webscraped the links, data, and information about the kdramas from mydramalist—a lengthy progress. Afterwards, we used a dataset form kaggle for netflix and used both as dataframes to create visualizations for our project. We made many different visualizations from heat maps, to chloropeth graphs, to pie charts. Some interesting findings we found were that although Japan and South Korea and tiny in comparison to larger countries, they were surprisingly one of the main producers of TV shows for Netflix. We were also surprised at how dominant the US is in producing tv shows, and how it's astounding that Netflix producers some of the best and most popular kdramas—even doing better than the native companies in Korea.
Our project focuses on United Nations data about World Tourism and various indicators that may affect tourism industries. We decided on this idea because we both enjoy traveling and wanted to investigate how the tourism industry functions worldwide. Learning about the diverse demographics and statistics of the world was exciting to us, and this project provided many interesting insights into the global tourism industry. This project was important because it visualizes how affluence, health, gender equality, and other statistics affect tourism, an economically and culturally important industry. The indicators we investigated gave insight into why certain countries have larger tourism industries than others, and why certain countries export more tourists than they import. Many different types of people could be interested in our project's insights, from world travelers to anthropologists investigating how and why humans travel the world. Our data source was UNdata, the United Nations' online collection of data on many different international statistics. We specifically chose to analyze data from a few select categories such as World Tourism, Human Development Indices, and UNAIDS Data. All of these categories provided data in CSV format available for download. We visualized the data from the CSV files primarily in two formats, scatter plots, and choropleth maps. The scatter plots visualize trends and correlations between the datasets, and the choropleth maps visualize the global distribution of the data. The data we analyzed produced many interesting findings. For instance, we discovered a strong positive correlation between high gender development (primarily a rating of gender equality) and inbound tourism. We also found some more surprising insights, such as that there appears to be no correlation between inbound tourism and AIDS-related deaths based on the available UN data.
As global warming and climate change continue to be a rising issue, people are becoming increasingly aware of their carbon footprint and the impact their actions have on the environment and the ozone layer. One major contributor to carbon emissions is transportation, with vehicles releasing significant amounts of greenhouse gases into the atmosphere. To address this issue, this website allows users to compare the carbon footprint of different modes of transportation when traveling to a specific destination from South Bend. It provides information on the emissions, time taken, and distance traveled for various transportation options, such as driving, taking public transit, biking, or walking. By providing users with information about the carbon footprint of their transportation choices, individuals can make more informed decisions that are better for the environment. The users that use this website would be people that are planning on travelling from South Bend/Notre Dame and care about their carbon footprint. The data we extracted and used were the time, distance, emissions of all modes of transports as well as the frequency of average daily flights. We had several visualizations that showed various comparisons as our website is supposed to have user interaction to select their destination and all the comparisons for that destination are shown. Other than the specific ones we have 3 viz that show general data for all destinations. From this we found some interesting findings like Georgia having the highest frequency of flights from south bend as well as the largest daily emissions.
Our project examines several datasets associated with travel across the land borders of the United States. These datasets provided information about border crossings into the United States, encounters between law enforcement and immigrants, and seizures of illegal drugs (each sorted by location/region and by month/year). Analyzing this data could be of particular interest to law enforcement officials or policymakers who focus on issues at the border, both in terms of enforcement and in terms of human rights issues. We visualized this data in several ways (heatmaps, bar graphs, line graphs, pie charts) in order to find connections between different aspects of the information. Some interesting conclusions include the fact that the sheer number of violent encounters between law enforcement and border crossers is surprisingly low relative to what we had expected, as well as the (not-so-surprising) effect of the COVID-19 pandemic on border crossings into the United States.
The goal of this project is to analyze global education by evaluating various factors and how they affect literacy rates, completion rates, attendance rates, out-of-school rates, foundational learning, and digital connectivity. The project will consider several key factors that have been shown to impact educational outcomes in different parts of the world, including age, gender, country's wealth, area of residence, country's development, and country's region. To achieve our goal, we searched for data from various sources and countries around the world, cleaned that data, and analyzed it to identify patterns and trends and to examine how different factors may be affecting educational outcomes in different parts of the world. Our analysis will focus on understanding the complex interplay between these factors and how they contribute to educational outcomes, and to identify patterns and trends, such as which factors appear to be most strongly associated with positive educational outcomes. Ultimately, our project aims to contribute to a better understanding of global education and the factors that impact it, with the hope of making progress towards more equitable and effective education systems worldwide.
Our project strives to illuminate elements of an alarming health issue and trend in the United States, namely obesity. We wanted this project to shed light on the issue and explore potential societal implications that connect and/or contribute to obesity. Through our research and analysis, we identified and corroborated our hypothesis that poverty (or income level) and obesity have a directly proportional relationship. We hope our project exposes this unfortunate connection and encourages other students to be forces for good in relation to national health. The importance of this project lies in the severity of obesity. Obesity is an underlying cause for many significant health issues such as diabetes, certain cancers, and heart disease. This issue puts a lot of Americans at risk and also generates a lot of stress on the healthcare system. Furthermore, obesity and poor mental health have some correlation, and mental health is another rising issue in our society. Anyone who is interested in fitness, personal health, lifestyle choices, nutrition, and societal issues such as socioeconomic disparities would likely find this website and project intriguing, eye opening, and hopefully motivating. We relied on the surveys conducted by the Centers for Disease Control and Prevention under the U.S. Department of Health and Human Surveys for our data collected. They conducted a Behavioral Risk Surveillance System on youth and adults which gave copious and crucial data on obesity broken down by selected characteristics. We used additional survey data from the CDC to get a comprehensive representation of the trends and issue at hand. The data visualizations demonstrate the trends in obesity in male and female youth since 1988, and showed a steep increase in the percentage of obesity. We also had a choropleth that showed the spread and increase of obesity across the US states. We then have a few bar graphs that show the correlation between poverty and obesity. Lastly, we have a visualization showing the decrease in a certain healthy behavior, eating vegetables, the parallels the increase in obesity we saw. The most troubling and striking finding was the correlation between poverty and obesity. We read further on this and attributed this trend to the rise in cost of clean and fresh produce and the low-cost and availability of highly processed foods, coupled with the increase in technology usage and sedentary lifestyles. We hope you find this project informative and engaging!
The purpose of our project is to explore the quality of education as it varies by wealth and gender across the globe.Through analyzing UNICEF datasets, we hope to bring attention toward the discrepancies in the quality of education across the globe. We hope to uncover any differences or similarities in the quality of education based on gender and wealth as it varies across different regions. Every person is deserving of an education, and the first step toward improving education for all is to recognize places where access to a quality education is lacking. Our project attempts to take this first step, locating where education can be improved. The four datasets analyzed in the project are all Excel spreadsheets compiled by UNICEF. They cover Out-of-School rates, School Completion Rates, School Net Attendance Rates, and Foundational Learning Skills. Each Excel sheet includes tabs for different grade levels and in each tab there is a list of countries, some identifying information for each, and then percentage rates for different categories, including Female, Male, and five separate wealth quintiles. The visualizations compare the rates of the two genders and the different wealth levels to hopefully display disparities in education quality to the viewer. These comparisons include attendance rates versus completion rates and numeracy mastery rates versus literacy mastery rates on scatter plots and out of school rates on choropleths. Our final conclusion decided that since data for both genders seemed similar when comparing visualizations, we did not find a significant difference between the quality of education based on gender. However, we did find a significant difference between the quality of education based on wealth. Across all visualizations, members of the wealthier quintiles tended to be more educated than members of poorer quintiles. Through our visualizations, we discovered that the Sub-Saharan Africa (SSA) region tended to have the lowest quality of education, and the Europe and Central Asia (ECA) region tended to have the highest quality of education. Analyzing data based on grade-level, we furthermore discovered that as people grow older, they are less likely to attend/complete school. Recognizing this fact, to improve education overall, further effort is necessary to ensure children continue to attend/complete school at all grade-levels.
The United States Government releases the stock trading information of those in Congress on a regular basis. In this project, this data was examined and analyzed using various visualizations. The data that was used was obtained from a JSON file containing dictionaries of information regarding each trade. The data was cleaned, separated and transformed into more readable data. Going into this analysis, the primary objective was to observe how those in the House of Representatives traded in the stock market. There was no definite conclusion that was expected to be taken away from this other than an insight into how those in some of the most powerful positions in the United States handled their own personal finances and to see any differences between the representatives along state and party lines. The data that follows shows these differences and may subvert some expectations that one might have going into this. In particular, the spending differences between states as well as between parties was information that was not expected. This analysis and ability to display this data is important for the transparency between those who hold power and their governed that is so crucial to democracy. It is the goal of this website to better inform anyone who browses it on how those in congressional positions handle their money.
This website assesses the interactivity between squirrels and humans specifically, in Central Park in 2018. We thought this would be interesting because Mary is from New York and Caz is from New Jersey, so we are both familiar with central park squirrels. Our data sources give us two main types of data. The physical description of the squirrels and the actions of the squirrel. Examples of physical characteristics are the age, color, time of day, and location of the squirrel. Examples of actions of the squirrel include what it is currently doing (running, climbing, eating) and what it does when it sees the human (tail twitches, moans, quas, runs away). We use the physical data within the data set to find correlations and trends with the squirrels behavior. Data such as location and color and age can be used to analyze different behaviors and activities of the squirrels. We hope to do so in order to make educated conclusions about squirrel behavior.
With recent spikes in gun violence, school shootings, hate crimes and more, it seems that there has never been more crime. This project aims to place today's experience of crime within the greater context of history. Using US Census data for crime rates and population density from 1960-2019, we bring you a variety of visualizations and insights to broaden your understanding.
Atmospheric pollutant levels have a large impact on quality of life. Unfortunately, the countries that experience the worst air pollution levels often have the fewest economic means to improve those levels, and often cannot afford to implement environmental regulations that would limit emissions and improve air quality. We hope to explore the connection between a country's economic power (as approximated by GDP) and the levels of various pollutants present in the air above that country. We expect to find that countries with lower GDP will generally have higher levels of air pollution, and we hope to confirm/reject that hypothesis and explore which types of pollutants fit this correlation best. People who would be interested with be environmentalists or people who perhaps wants to understand why their country has worse air quality than another country and understand what they could do about it depending on which sector emits the most. The data sources comes from the World Health Organization and the World Bank, as well as Climate Watch. These let us relate the GDP to AQI of each country as well as look deeper into the different sectors and their emissions as well. The visualizations that we have are choropleths, scatter plots, and bubble plots that look at the relationships between these factors. Some findings that we have found are that the higher the GDP, the higher the levels of AQI, which was opposite to what we thought the trend was. This relationship is especially prominent in the US, India, China, and other European countries.
Abstract: When you take a bite into a juicy burger from your favorite late-night fast food chain or set your steak ablaze on your cast iron pan, do you ever consider what it took to get that meat on your plate? In this project, we analyzed the social, economic, and environmental factors within the United State’s cattle industry, specifically with wholesale/retail beef, to forecast where the country’s beef industry is heading and what effects it contributes to our lives and the environment. By exploring the subsections on this website, you can learn about different aspects of the U.S. beef industry and read our analysis and forecasts on the datasets we analyzed. Whether or not the visualizations and interpretations we introduce to you will change your diet, our ultimate goal is to spread awareness of the effects of the meat we eat. While anyone could easily understand the data visualizations we present, viewers who are interested in learning about or have a background relating to the beef industry from an economic, social, or environmental perspective would be best suited for this--this includes scientists, farmers, policy-workers, or even high school students. They could also be people who wish to seek change in the beef industry for a more sustainable or affordable future, as it depends on which sector in the beef industry they value. The data we processed were mostly from government or public data sources (either sourced from a study or annually-updated data provisions from credible agencies) that mostly focused on data from the United States. Most of the year-to-year data started from around the 1970s to the early 2020s. Our visualizations are of the following types: 1. Bar Graphs: Resource use and emissions of different food entities compared to beef, beef consumptions and prices (wholesale and retail), and beef production. 2. Line Graphs: Total supply vs total disappearance of beef, CO2 emissions in the US vs the World's average 3. Choropleth (of the US by state): Number of vegan restaurants, GDP per capita, and beef exports Overall, we found it interesting to see how beef consumption is influenced by supply and demand, and how, despite the negative environmental impacts of beef production or the rising in prices and number of vegan restaurants, there is still a upward trend of beef consumption. However, we forecasted ways that, if certain legislative action or cultural interest takes place to decrease beef production, there may be a way to decrease environmental damages as well as promote more sustainable alternatives.
The idea of the project is to analyze Chicago crime and connect it to aspects of daily life. For instance, we divided the Chicago crime by ward to find the ward with the highest number of crimes and the lowest/highest arrest rates. For someone looking to move into Chicago, information on the safest wards will prove to be very valuable. Another interesting component of the project is the connection between crime and school performance. We gathered data from a government website that gives statistics regarding high school performance such as ACT scores, suspension rates, and attendance rates. The project stresses the importance of education when it comes to minimizing crime. If kids have distractions outside of school, doing well in the classroom becomes much harder. The project visualizations make it easy for the users to identify connections between the different data points. Most of the visualizations are on a map of Chicago to make it simple to identify unsafe areas.
Mental health awareness is extremely important. Many people either know or know of someone who has struggled with suicidal thoughts, so it is not an issue to take lightly. In this project, we used a data file containing the suicide rates from 1980 to 2017 based on the categories of race, sex, and age and visualized them into graphs to gain more clear insights. If people saw an intense increase in recent years, there might be more movement for change. May is actually Mental Health Awareness Month in the United States. The Mental Health America organization started it, and there are many other organizations out there that would be interested in the data that we are presenting. This is because of the correlation between suicide and poor mental health. The rising suicide rates show that these organizations need to step up now more than ever. This information was found on the data.gov catalog by the U.S. Department of Health & Human Services and dates over many years. Some of the visualizations we created from the data were pie charts, animated bar charts, and line plots. These visualizations were charted over years and were based on the suicide rates of Americans. These visualizations were also separated based on more specific criteria such as age, race, and sex. The most interesting, or perhaps concerning finding is that the national suicide rate is increasing and is higher than it has ever been in the last 40 years. We noticed a sharp increase around 2007 and a minimum around the year 2000. We also observed the fact that the suicide rate in men is almost three times higher than that of women. Another concerning finding is that the rate of suicide in teens and young adults is increasing the most rapidly than in any other age group.
The topic that we are exploring through data visualization is air quality and the effects that it has on different aspects of life in the United States. The idea is that we are using graphs to compare the air quality in different locations with other aspects of that location such as median income, life expectancy, population, and natural disaster risk. The goal is for people to become more aware of how much of an impact air quality has on life so that they can be mindful when engaging in activities that generate excess air pollution. We chose this topic for a couple reasons. To begin with, we're both really interested in the environment and the ouroboros of people-impacting-the-environment-which-then-impacts-people. Air quality is a good entry point to exploring this interplay because there are good data available for it, and it's often a fairly ignored form of pollution because, save for some cases, it often arises from an agglomeration of nonpoint sources: for example, it's much easier to clean some pollutants from a single manufacturing plant's smokestack, but the emissions of thousands of car tailpipes passing by a neighborhood is not so acutely obvious and can thus often be disregarded. We hope that users leave our site newly informed of how impactful air quality can be on one's whole life. Spelling out that impact in a visual, straightforward cause-and-effect manner. We hope that this project will interest all people of the United States, especially those in areas with poor air quality. Our project is broad and encapsulates information about all different parts of the United States, so anyone living in the United States can learn more about where they live. Much of this information is not displayed often, leaving people uninformed. Most of our visualizations are chloropleths since we chose to take a geographical approach to analyzing this topic. We have chloropleths for the percentage of good air quality days in places around the United States and for the life expectancy by county in the United States. From this comparison, we learned that areas with lower life expectancy tended to have fewer good air quality days. This was what we had expected. We also have a scatter plot that displays this same data in a different form, but this comparison adds in population. We did not see as strong of a correlation as we were expecting here. For example, Los Angeles had a high life expectancy, high population, and poor air quality. Visualizing the data in different forms helps to reveal all of the insights that can come from the data. We have another scatter plot that compares median air quality index, population, and natural disaster risk. This one revealed insights that we expected, places with a higher population generally had worse air quality, and were at a greater risk for natural disasters. Finally, we have two bar graphs that compare median income to median air quality by state. We found that lower income does not correlate to worse air quality.
Global warming is a serious issue in the 21st century, with increased industrialization, travel, and technologies contributing to higher levels of gas emissions. Our project highlights this issue in the aerospace field by looking at the various increases in gas emissions and temperature in the previous years. Additionally, we include visualizations on the countries around the world that contribute the most to aerospace gas emissions in the past couple of years. This project is intended for people who are interested in the ramifications of global warming and to bring awareness to the need for pollution regulation. We sourced our data from apis on global warming, which included data on the emissions of green house gasses also from an api on airlines per country, which we used to calculate the approximate contributions to global warming that each country's aerospace sectors had. Our visualization show that over the past decade, there have been significant increases in green house gasses and how temperatures have changed in the last century.
Crime is one of the biggest issues around the world. Bringing chaos, pain, and suffering throughout the world we decided to study how crime rates have changed throughout the United States. Knowing that crime continues to be a pressing issue, despite efforts from the police, we decided to see if we could find common patterns in how crime changes in the different states throughout the year, connecting them to factors that may affect their rates such as GDP per state, population, and unemployment. Crime is an essential factor that should be studied to understand its patterns in an attempt to keep the indices down. A criminal act does not only imply/affect the victim but it often has repercussions in their family as well as in the society as a whole. We want to be able to understand the causes of an increase in crime to determine if there's any type of prevention measurement that would decrease the rates. As international students, we decided that analyzing crime would give us a broader perspective on life in the United States. It would tell us what to be more aware of, which places we would rather live in to be safe, and which factors we should always consider. We believe not only will international people benefit from understanding crime but also the general public and citizens of the United States might be able to visualize patterns and trends between factors and crimes that can ultimately help form and create some type of prevention against these. For this project, we decided to utilize 4 different data sources, most of which were CSV files and one JSON file. The JSON file is our main data source as it contains the total amount of crimes per state. The CSV files were just additional information that we would like to compare to the total crimes to see if we could determine some sort of pattern within the information. Throughout our experiment, we were able to visualize important information like which were the states with the most or least number of total crimes per 100,000 population. We realized that Florida, Nevada, and the District of Columbia all make up the higher crime rate states. We were able to confirm that the highest type of crime throughout the years in the United States is larceny, which is almost 60% of the total crimes. Having this information we could start trying to find a way to decrease the levels of larceny, which would have an overall effect of lowering crime rates. Many other factors were compared such as GDP and unemployment factors that we would have initially considered had an effect on the total crimes, but yet did not show a direct correlation.
Crime is one of the biggest issues around the world. Bringing chaos, pain, and suffering throughout the world we decided to study how crime rates have changed throughout the United States. Knowing that crime continues to be a pressing issue, despite efforts from the police, we decided to see if we could find common patterns in how crime changes in the different states throughout the year, connecting them to factors that may affect their rates such as GDP per state, population, and unemployment. Crime is an essential factor that should be studied to understand its patterns in an attempt to keep the indices down. A criminal act does not only imply/affect the victim but it often has repercussions in their family as well as in the society as a whole. We want to be able to understand the causes of an increase in crime to determine if there's any type of prevention measurement that would decrease the rates. As international students, we decided that analyzing crime would give us a broader perspective on life in the United States. It would tell us what to be more aware of, which places we would rather live in to be safe, and which factors we should always consider. We believe not only will international people benefit from understanding crime but also the general public and citizens of the United States might be able to visualize patterns and trends between factors and crimes that can ultimately help form and create some type of prevention against these. For this project, we decided to utilize 4 different data sources, most of which were CSV files and one JSON file. The JSON file is our main data source as it contains the total amount of crimes per state. The CSV files were just additional information that we would like to compare to the total crimes to see if we could determine some sort of pattern within the information. Throughout our experiment, we were able to visualize important information like which were the states with the most or least number of total crimes per 100,000 population. We realized that Florida, Nevada, and the District of Columbia all make up the higher crime rate states. We were able to confirm that the highest type of crime throughout the years in the United States is larceny, which is almost 60% of the total crimes. Having this information we could start trying to find a way to decrease the levels of larceny, which would have an overall effect of lowering crime rates. Many other factors were compared such as GDP and unemployment factors that we would have initially considered had an effect on the total crimes, but yet did not show a direct correlation.
In our project, we analyzed walkability scores provided by the EPA’s Smart Location Database. Our idea for the project was to see how walkability differs between different neighborhoods and how factors like average income and population density affect such rankings. We think this project is important because the ability to use walking as a primary mode of transportation is essential to low-income individuals in many cases. Our data source is the EPA Smart Location Database, which we combined with shapefiles to be able to plot the EPA data on a map. The Database contains information on employment levels, population, income, land-area, walkability, and other valuable metrics. Using this database, we were able to determine multiple locations within the cities which we graphed spatially where walkability didn’t increase with increasing population density and other factors.
The idea is for a user to learn more about mental health and how it affects different demographics to understand more about mental health and treatments. This can also be used for mental health professionals and health educators to understand how they should look at increasing mental health treatment towards certain demographics. The data sources are from the CDC and explain suicide rates, depression/anxiety indicators, and mental health care across the U.S. according to demographics. Some interesting findings come from two choropleths that show estimates of depression/anxiety and mental health care. There was a gap shown in mental health and treatments. For example, southern states seemed to have higher indicators of depression and lower mental health treatments. These gaps seem to be across many demographics ultimately showing the need to increased mental health treatments towards high risk demographics.