Section 003, Instructor: Shana Pote

In-Class Exercise 2.2: Finding Sources of Data

Here is the exercise.

As discussed in class today, please comment on this post with the following:

  • What dataset did you find
  • Where did you find it
  • Why did you think it was interesting
  • What did you learn from the data
  • How could you use the data or what decisions could you make based on it

Comments are due by 7:59am Tuesday, January 26, 2016.

57 Responses to In-Class Exercise 2.2: Finding Sources of Data

  • Profile photo of Elena K Cipparone

    I found a dataset regarding Residential Parking Permit Blocks in Philadelphia.
    I found it through opendataphilly.org
    I thought it was interesting because I live in Philadelphia, and during certain events, you are not allowed to park in areas where permit parking is required.
    I learned which blocks require a permit to park for an extended period of time. Most permit parking areas allow cars without permits to remain parked for a maximum of two hours.
    I could use this data when looking to move to another residential area in Philadelphia. This will help determine which areas will be heavily populated with cars and which areas will be easier to find parking in.

    One of my favorite movie quotes is “Get busy living, or get busy dying,” from Shawshank Redemption, because it shows how important it is to live your life.

  • Profile photo of Rahsaam K Ray

    The dataset that I have found is a list of 486 historical streets in Philadelphia and I found this information openphillydata.org. I thought that this dataset was interesting because I didn’t realize how many streets have significant historical context within Philadelphia. I learned that even though south Philadelphia has a profound impact on the city’s history, it doesn’t seem that particular streets in south Philadelphia are particularly famous when it comes to historical context. By having access to this data I would make sure that these streets are clear of physical problems (i.e potholes, pipes, etc) because these streets are some of the oldest in the city. With age comes wear and tear so by analyzing the streets on this dataset I would pay close attention to these parts of the city.

  • Profile photo of Shuyue Ding

    1.Consumer Complaint Database
    2. http://www.data.gov
    3. I have never complaint any company in my life, so I think it is interested to see what and why consumers complain about.
    4. I learned a lot of complaints are about credit, loan… that’s actually surprise me because I thought consumers complain more about service.
    5. I think I will be more careful about it in my future work and school life because there are better chance to make mistake in credit card since more people complain about it. I will be more careful on my own credit card.

  • I found a data set about the locations of Bike Racks on the Streets of Philadelphia. I found it on opendataphilly.com. This is data set is interesting because it can be used to determine the amount of bike racks in Philadelphia. I learned that there were many more bike racks nearby me that I previously thought. Based on the data, I can plan a bike trip around Philadelphia and know exactly where there are bike racks where I can safely put my bike.

  • Profile photo of Craig Jacob Kestecher

    http://www.census.gov/foreign-trade/statistics/historical/gands.pdf

    The dataset I found contains information pertaining to the balance of payments, from 1960’s to 2014 for the U.S. I found it on http://WWW.Data.gov. The data interests me because it compares the net level of exports and imports yearly. By using this data, I can compare imports and exports and visually see the change from an export based economy to an import based one. Also, the United States has been importing more than exporting since 1975. I can use this data to make predictions about the amount of net exports or imports in regards to services and goods. The data’s pattern is especially important for economists and financial advisers in terms of investments and global business decisions.

  • The dataset I found showed voter turnout for the general election of 2014 in Philadelphia, PA based on district, political party, and count of voters by party. I found this dataset on opendataphilly.org (https://www.opendataphilly.org/dataset/voter-turnout/resource/05b88632-ea72-44aa-9506-0f77114c56fd). I love politics and so I found this dataset to be appealing. Voter turnout is a major problem in the United States, as a very small percentage of the voting age population actually votes during elections. This dataset showed which political party voted the most. In every district, democrats had the highest voter turnout by far. In district 11, for example, out of the 332 people who voted, 259 of them were democrats. It can be concluded from this data that Philadelphia is largely democratic. This information can be useful when deciding where to move. Democrats would find this city appealing as it is largely democratic and has a large amount of people who share similar political views.
    Another dataset I found showed expenditures on children compared from 1960 and 2013. I found this data on data.gov (http://www.cnpp.usda.gov/sites/default/files / expenditures_on_children_by_families/crc2013.pdf). I found this dataset interesting because it’s true that parents spend extreme amounts of money on their children, and I thought it would be interesting to see the change over time. This dataset taught me that in 1960, the U.S. average for a child in middle-income, husband-wife families was $198,560 compared to $245,340 in 2013. The cost nearly doubled, which is not so shocking as expenses always rise as technology advances and social norms become more expensive (i.e. college.) This data could be used for couples who plan to have children when averaging out if they are financially ready to have a family.
    My favorite movie quote is, “Life moves pretty fast. If you don’t stop and look around once in a while, you could miss it,” from Ferris Bueller’s Day Off. I like this quote because it reminds me to enjoy every minute of life and take nothing for granted.

  • Profile photo of Akshat A Shah

    1.) I found a dataset regarding federal Student Loan Data.
    2.) I found the data through http://www.data.gov
    3.) It was interesting to me because it shows how many student get financial aid and on average how much it covers of the total tuition the student has to pay.
    4.) I learned that even though the government gives us money in Student loans, it is nothing compared to the amount that we still have to pay in the end.
    5.) I could use that data to make better decisions and make it so that it helps everyone in a better way. In the end, everyone still has to pay a lot of money. By making better decisions, the students could go to college for less in the end.

  • Profile photo of Jose X Villanueva

    What dataset did you find?
    I found data on how much a Hospitals charge for common services

    Where did you find it?

    I found it on http://www.data.gov/health/

    Why did you think it was interesting?

    I found it interesting because one can see the large variation of prices when it comes to hospitals and how much they charge for common services

    What did you learn from the data?
    That the average total payments can vary widely with some hospitals charging around $2,000 and others charging $18,000 for the same exact procedure

    How could you use the data or what decisions could you make based on it?

    If I was working in the healthcare field it could be used to help decide to set the price on procedures and if used by the insurance field it could be used to help set coverage prices and to decided what can be covered and what cant be covered.

  • Profile photo of Sakeena A McLain-Cook

    1) I found a data set of all neighborhoods in Philadelphia.
    2) I found this data set on opendataphilly.org.
    3) I find this interesting because I was never aware of all the neighborhoods in Philadelphia. I used to just split it up into about 10 neighborhoods but I was way off.
    4) I earned that I have been living in the Paschall neighborhood for a majority of my life and have been calling it Woodland. I also learned that I now live in North Central, which I never knew existed. I just thought it was all considered North Philadelphia.
    5) I could use this data set to find out the size of these neighborhoods and determine if I would be willing to travel through or live in these areas.

  • Profile photo of Alice Nguyen

    The dataset I found was MyPyramid Food from http://catalog.data.gov/dataset/mypyramid-food-raw-data-f9ed6, which I found interesting because it catalogs information not only on amount of calories and fat in commonly eaten food but also provides insight on ‘extras’. From this dataset, I can learn how many calories most types of food have and therefore make adjustments based on those numbers and comparisons between similar types of food.

  • Profile photo of Emily A Jacobson

    I found a U.S. hourly precipitation data set at http://www.data.gov. I found it interesting because I was looking for weather data sets because of the recent snow storm. I look at the charts for Pennsylvania and was amazed that so much data was available and that is for 1 of 50 states. People could use this data to determine where they might want to live based on their preferences as well as ideal vacation spots. My favorite movie quote is “life finds a way” from Jurassic Park. It is one of my favorite movies and I think this quote is a reminder for man to see how little control they really have.

  • Profile photo of John W Forsythe

    1.) I found a data set regarding price indexes for single-family houses sold in the United States.
    2.) I found the data through http://catalog.data.gov/dataset/construction-price-indexes/resource/3d7be0b9-25ac-464a-b763-2ba6e911d7db
    3.) I found this data interesting because it had the average prices of single-family houses sold since 1963 in the U.S.
    4.) From this data, I learned that for the past 50 years the prices of single houses has significantly increased every year. In 1963, a single house cost about $19,300 and today the average single house costs around $345,800.
    5.) One could use this data to decide what region in the U.S. they would like to live in. They could analyze the data and decide which regions of the country have the best prices for single houses, and ultimately where they would like to live.
    One of my favorite movie quotes is, “Aim small, miss small,” from The Patriot

  • What dataset did you find?
    • Solar installation projects in Philly.
    Where did you find it?
    http://metadata.phila.gov/#home/datasetdetails/5543866420583086178c4ef5/representationdetails/5670f7309e9a61fd4b25e6ed/
    Why did you think it was interesting?
    • I think solar power is a vastly under used source of renewable energy and to see the places Philly is installing solar panels is exciting to me.
    What did you learn from the data?
    • I learned Philly is more involved in solar energy than I had previously thought. Also I learned the places I can go to find solar energy installations in Philly.
    How could you use the data or what decisions could you make based on it?
    • The most beneficial way I could use this data is to view locations of solar energy installations and where I could install new ones for the most beneficial output.

  • Profile photo of Thomas Alexander Stenberg

    I found a measurement of ocean events, like tides, possible tsunami warnings, temperatures, and atmospheric pressure. I found it by going to data.gov, which led me to the national data buoy center at http://www.ndbc.noaa.gov/. I find it interesting due to the recent storm we had in the Mid-Atlantic which created a lot of flooding in the coastal regions, as well as my history, experiences and love for the ocean. The data can tell us mostly anything we want to know with ocean climate and possible disturbances, during the summer (when we are likely to go to the beach) we can learn how warm the water is and what the tide is. I enjoy surfing and I have used websites similar to this before to dress accordingly for the day, and wether or not the waves are worth the drive; this could be used to make a great surfer friendly phone App.

  • Profile photo of Ashley Charlton

    The dataset I found was College Scorecard, which was from data.gov. This dataset was interesting because it claims to help students find the right college for them. The dataset shows the affordability, value, programs/degrees, location, and size of any school you choose. This was really interesting to me because I had difficulties with trying to decide what college to go to at first, so this dataset would have been very useful to me. With the data that is given to you, you could choose the college you want to go to with more ease because you have the ability to use the data to compare all of your college choices.

    One of m favorite movie quotes is from the Lion King. “Oh yes, the past can hurt. But the way I see it, you can either run from it or learn from it.” – Rafiki
    You can really relate to this quote. Learn from your mistakes, do not repeat them and keep moving on do not let what happened in the past affect your present.

  • Profile photo of Junaid K Farnum

    • The dataset that I came across was all the Farmers Market Locations in Philadelphia.
    • I found this dataset on opendataphilly.org specifically, https://www.opendataphilly.org/dataset/farmers-markets-locations/resource/d23195c1-b7ce-4ae0-a81c-51753d096c6f
    • I thought this data set was interesting because I wasn’t aware there were so many different Farmers markets locations in Philadelphia and that many of them are also ran by the same operation.
    • Surprisingly, what I learned from the data had exceeded my expectations on what I was hoping to find which I was expecting just to be the name and location of the establishment but I found much more. The dataset included, the name, address, zip code and operator of the specific farmer market. In addition to this there was also the days and times the markets were open in addition to EBT availability and even map location based on longitude and latitude.
    • I believe this data can be used for decisions you would want to make if considering one’s availability when choosing to shop at a farmer’s market. This data can also be helpful for people who want to use a different method of payment such as EBT.

  • Profile photo of Brandon K Shaffer

    1. I found a data set that shows the Powerball winning numbers since the beginning of 2010.
    2. I found this data set on http://www.data.gov
    3. I Thought it was interesting because we recently had the biggest Powerball jackpot in history and I love money.
    4. I learned that it seems like some numbers appear more often on winning tickets than others.
    5. If you really studied this data, you could possibly use it to increase your chances of winning. If you collect all of the winning number series and record each number, you would be able to see which numbers are drawn most often. The increase in chances of winning would be extremely small, despite studying the data the lottery is mostly just luck.

  • Profile photo of Colin Kelly

    The dataset that I came across has most interesting was the dataset about the Federal Student Loan Program. I found this data on http://www.data.gov (http://catalog.data.gov/dataset/federal-student-loan-program-data). I thought this data was interesting, because now being a college student, it’s important to see all the different types of financial aid that can be received as well as the amounts that can be earned in order to help cover the cost of tuition. What I learned is that while you can earn a a decent amount of money, you’re still going to have to pay for a large portion out of pocket and any loans are going to take a long time to pay back. This data is able to help me think about how much money I should really borrow when needing to cover costs as in the end I’ll still be paying a large amount of the money back.

  • The Philly Fun Guide is an open dataset that I took particular interest in. I found the dataset online at http://www.openphillydata.org. It interested me because it shows events and activities that people can attend in the city of Philadelphia, based on interest and the amount of money you’re willing to spend. The data i received taught me that there are more things for me and my friends to do in Center City for ten dollars, than there is in nearby North Philly. This dataset is extremely beneficial to me because now I can make better decisions when looking for fun and inexpensive things to do in the city.

  • Profile photo of Julianne Johnson

    Given the blizzard we just had, I decided to look up “snow” on opendataphilly.org. I found a dataset titled “Snow Emergency Routes” and I downloaded the CSV file. I thought it was interesting because I am from Philadelphia and didn’t know what snow emergency routes were or what they meant. I learned that a lot of the streets near me were snow emergency routes, including Cottman Avenue, Academy Road and Grant Avenue. I now know that these are streets I could not park on if Philadelphia were under another snow emergency like we were this weekend.

  • Profile photo of Katherine Braccio

    I found an International Data Base through data.gov. This particular database provides “demographic data, including estimates and projections of: birth, death, and growth rates, migration rates, infant mortality, life expectancy, fertility rates, total population, and population by age and sex.” After following the link, the webpage prompted you to chose the desired report, number of years, aggregation, country or countries of interest, and region. From what I can tell, the data offered is pretty extensive given that various options are presented in order to minimize the data and select the data desired. By using this data, the poorest demographic areas can be compared to richer or more developed areas, and the degree of difference can really be noted. I found it interesting that all of this information is available, and it was so applicable to a variety of fields. Social scientists, like anthropologists, could use the data to find demographic patterns by region, continent, or country. Businesses could use this data to find the most developed areas to trade with, or to find poorer demographic areas in need of goods and services.

  • The data set that I found pertained to the different art that can be found throughout Philadelphia. I found this data set on opendataphilly.org. This data set interests me because I love art, and, seeing as how much art there is in Philadelphia, it can become kind of difficult to find some of the best art. A way I would use this data is make an app that takes the location of the different art pieces and places them in the correct geographic location on a map of Philadelphia. Along with the location, the app would provide a picture of the piece, and also a description of it. This would be good for anyone from tourists to people who live in Philly just looking for something new to see.

  • I found a dateset on reported crimes in the Philadelphia Area between 2012 and 2014 on OpenDataPhilly. I found it interesting because it had ALL of the reported crimes within the city area (including South, North etc.) and it was in a visualization (personally i learn better from visuals than any other data type.) Having experience in a journalism internship in South Philadelphia and doing a story on Police, I thought for me it was the best fit. The Data tells you a lot, including a bar graph that tells of which police districts had the highest number of crimes, the top 3 were District 15, 24, and 19. For specific type of crime, thefts were one of the highest reported specific ones (#1 was labeled as “All Other Offenses”.) The data can tell the average person a lot – typically if a new family is moving to the Philadelphia area, it can designated which areas are not so high in crimes and which areas to avoid if one is VERY big on safety for their family.

  • Profile photo of Alexandria M Freeman

    The data set I found was the locations of all the Indego Bikeshare Stations in Philadelphia.
    I found this data set on https://www.opendataphilly.org/.
    I found it interesting for these bikes can be used to navigate basically anywhere in the city. Getting from point A to B in a healthy environmentally friendly fashion.
    I learned that these bike stations are available in most parts of the city. I have seen these stations around campus but did not realize the mass amounts there were.
    From this data my friends and I could rent a bike and take them to any part in the city and be able to explore around the city in a new way. This is a better experience than in your car since you have to actually pay attention to your surroundings and not get sucked into a phone or sleep.

  • Profile photo of Kashif Hasan Malik

    1.) I found a data set on the lottery power ball winning numbers beginning from 2010
    2.) I found it on Data.gov
    3.) With all of the attention surrounding the lottery, I thought it would be interesting seeing what the winning numbers were
    4.) I learned numbers that appeared the most often and how often they appeared in the lottery
    5.) You can use this data to see what numbers you should scratch and hope to get lucky

  • Profile photo of Kennedy Frances Price

    I found the data set of the City Operating Budget of Philadelphia
    I found this on opendataphilly.org
    I found it interesting because I did not know where the city was spending the money it had.
    I learned where the city spends a majority of the budget and how much it costs to keep basic functions of the city operating.
    This data could be used to make decisions based on how to spend the money the city has. If there is too much money being spent on a specific area, this will show you that.

  • Profile photo of Erin Elizabeth Kelly

    I found a data set about Philadelphia Public Schools on OpenDataPhilly. I thought it was interesting to read statistics about schools in my city. More specifically, I researched more in depth elementary schools in my neighborhood. I thought it was interesting how some schools had high attendance rates, yet they did not score well on tests. Nor did these schools perform well during critiques. I think we could learn where is the teaching going wrong if the kids are there, but the test grades are not? Are students performing poorly because of the teachers or the lack or desire to learn? You could use this data to see if the schools need better teachers or help motivating the students.

    One of the favorites quotes is “You ought to spend a little more time trying to make something of yourself and a little less time trying to impress people.” from The Breakfast Club. I think this quote is true and useful, especially at this time in my life when I am trying to figure myself out.

  • Profile photo of Jake Montana

    1. I found a data set on the Expenditures on Children by Families.
    2. http://catalog.data.gov/dataset/expenditures-on-children-by-families
    3. I found this set of data interesting because it shows you the cost of having a child in the present and in the future.
    4. I learned that this set of data could be used to help people understand the value of children and the true cost of having a child in the present and in the future.
    5. This set of data could help a couple decide whether they should have a baby or if they should wait to get a higher paying job or something to earn them more income before having children.

  • Profile photo of Matthew Major

    -The dataset that I found was Consumer complaints about financial services (based on their personal experience).
    -I found the data on data.gov under the consumer topic section.
    -I thought that this data was interesting because it somewhat backed up a hypothesis I recently developed about financial services in general. They are obviously going to always be provided one way or another, but I have a feeling that consumers are starting to trust them less and less, especially financial advisors. It seems to be a progressing issue in the industry, and this dataset was directly correlated to the hypothesis I have already recently developed.
    – I learned that people have a lot of complaints, and the one that stuck out the most to me was that they have problems with credit reporting more frequently. Maybe this is a system that needs restructuring.
    -This data can be used to maximize customer satisfaction by minimizing complaints (used by the business). It could also be used by consumers to see where people are unhappy with financial services, before they put more time and effort into using those services.

    A quote from my favorite TV series “The Wire” actually comes from the Writer of the show David Simon regarding what the show was truly about. I used this quote because I love his insight and explanation to it. I also used a TV series rather than a movie because I am personally way more interested in series’ than movies.

    the show is “really about the American city, and about how we live together. It’s about how institutions have an effect on individuals, and how… whether you’re a cop, a longshoreman, a drug dealer, a politician, a judge [or] lawyer, you are ultimately compromised and must contend with whatever institution you’ve committed to.”

  • Profile photo of Jake Montana

    1. I found a data set on the Expenditures on Children by Families.
    2. http://catalog.data.gov/dataset/expenditures-on-children-by-families
    3. I found this set of data interesting because it shows you the cost of having a child in the present and in the future.
    4. I learned that this set of data could be used to help people understand the value of children and the true cost of having a child in the present and in the future.
    5. This set of data could help a couple decide whether they should have a baby or if they should wait to get a higher paying job or something to earn them more income before having children.

    1. I found a data set on Bike Racks – Streets of Philadelphia.
    2. https://www.opendataphilly.org/dataset/philadelphia-bike-racks
    3. I found this set of data interesting because it shows you information about the adopt-a-rack program on the streets of Philadelphia.
    4. I learned that this set of data could be helpful to those who rides bikes around the city.
    5. This set of data could help those who use bikes for transportation and could decide where they would want to store their bike while they don’t need it.

    My favorite movie quote comes from the Wolf of Wall Street: “Let me tell you something. There’s no nobility in poverty. I’ve been a poor man, and I’ve been a rich man. And I choose rich every single time.” – Jordan Belfort. As a business student, I aspire to be wealthy one day and this quote captures how I feel.

  • Profile photo of Mark Anthony Negro

    1. The dataset I found was a map of the crimes in Philadelphia.

    2. I found this data on operndataphilly.org

    3. I found this interesting because I live in Philadelphia. It is nice to know about what is going on in the surrounding areas.

    4. I learned about all the different crimes and rates of those going on. That information could be very beneficial.

    5. This information can teach what streets and areas to stay away from or where you can go to keep safe.

  • Profile photo of Sunny W. S. Tam

    1. I found a data-set regarding Indego bike share trips.
    2. I found the data-set on open data philly (https://www.opendataphilly.org/dataset/indego-bike-share-trips/resource/dbf81a97-d40a-430a-98db-b0f15d1bb460).
    3. What I found interesting was the number of variables included in the data-set. Not only did it include the identification number of each bike, checkout and check in times at which kiosks but also data regarding type of trip and the pass-holder type.
    4. Many hypothesis could be drawn from the data-set. But these conclusion would be based on the variables of checkout time and durations of trips primarily. I learned that the majority of checkout times occurred after 4 PM for varied periods of times.
    5. Therefore using the data, I can conclude that there might be a strong correlation between the checkout time for Indego trips depending on the time of day. In this case, the reason why there is a sudden increase of of checkouts after 4PM could be due to the end of the workday and Indego passholder decide to bike home instead of taking another form of transportation.

  • Profile photo of William G Roman

    One dataset I found was the United States’ Trade Overview for 2013. I found this on data.gov. I found this interesting because it showed how much the U.S. trades, who we trade with, and what that trade does for the United States. I learned that the total imports and exports in the U.S. have increased in almost every year since 1990. I also learned that trade increased the amount of jobs in the U.S. Using this data, I could predict that trade in the U.S. will continue to increase over time and that more jobs will result from the increase in trade. Another dataset that I found was the Pennsylvania ACT scores for public schools in 2011. I found this on opendataphilly.org. I thought it was interesting because it showed the number of students who took the test in each school and their average scores for each subject. I learned how many of the schools compared for ACT scores, which includes my old high school. I could use this data to determine what schools are teaching their students what they need to in order to succeed on the ACT. My favorite movie quote is from Forrest Gump. “Mama said life was like a box of chocolates. You never know what you’re gonna get.” I like this quote because that’s how life really is. You can predict that something will happen or what you want to happen, but you never really know what will happen.

  • 1. The dataset that I found was obesity in California of adults, adolescents and children.
    2. This dataset was found on http://catalog.data.gov/dataset/obesity-in-california-2012-and-2013
    3. I thought this dataset was interesting because it seemed to give accurate body mass index calculation that would consider people of
    different ages obese. By doing so, people may become aware of their unhealthy acts and balance out their nutrition to avoid being overweight.
    4. From this data, I learned that adults tend to have higher vulnerability to becoming obese due to the BMI calculations of being 30 or above, while adolescents and children are considered obese at the 95th percentile or above.
    5. I would be able to utilize the data by observing the factors that make adults, adolescents, and children obese. For example, the speed of their metabolism.

  • Profile photo of David J D'Angelo

    -I found a dataset regarding SEPTA and average train usage during severe weather
    -this dataset came from opendataphilly.org
    -i thought this dataset was interesting because i used SEPTA on a regular basis and was curious about how the recent weather effected revenue and work/school schedules
    -i learned from the dataset that during severe weather, trains tend to be delayed or totally out of use, which effects everyone who depends on SEPTA for daily transportation
    -i could start to create a back up schedule or another train line based off of the predicted weather

  • Profile photo of Joshua J Affainie

    What dataset did you find
    National student loans data system
    Where did you find it
    http://catalog.data.gov/dataset/national-student-loan-data-system
    Why did you think it was interesting
    It was interesting to because it explains how many students get grants from the government .
    What did you learn from the data
    I learned that even though the government gives us grants,at the end of the college year students still pays a lot of money.
    How could you use the data or what decisions could you make based on it
    I could use data to make better decisions and allows students to go to school for less.

  • Profile photo of Tae Shin

    1.) Drink Philly Happy Hours & Specials
    2.) http://www.opendataphilly.org
    3.) I thought it was interesting to see all the different restaurants around me and the specials they convey to the customers. The dataset is categorized by days and under the day tab, are the stores that have specials that day.
    4.) There is a gray label next to the restaurants and it states the area where that restaurant lies in. Some examples are Center City, Old City, Rittenhouse, University City, and Fish town. What I learned from these datasets of metadata is where the best deals are.
    5.) With analyses of these datasets, I would be able to determine where the best restaurants are with the best deals. I would be able to answer the question of “where is the cheapest place to drink on a Monday evening”. Also if I was determined to open up a happy hour restaurant, I would use this data to determine where the least competition is but at the same time use the datasets to determine at what price I should set my prices to in order to compete. Also from what hours should I keep my happy hours open.

  • Profile photo of Brittney Michelle Pescatore

    I found the dataset called “SEPTA Next to Arrive”
    I found this dataset on opendataphilly.org
    This dataset was interesting to me because I live in Philadelphia and I sometimes rely on SEPTA to get me places around the city.
    From this dataset I learned that SEPTA has an application that can notify and give users updates on the arrival times and other various information, generally concerning Regional Rail.
    The data from this dataset could help me figure out the status of the train I was planning to take. For example if the line I usually take to work was on a delay or running late, I could decide to find an alternate way to get to work or plan to leave earlier.

  • Profile photo of Xiaoxu Liu

    The dataset I found was the 2015-16 Private School Universe Survey.
    I found it on http://catalog.data.gov/dataset/201516-private-school-universe-survey.
    I thought it was interesting because I went to public elementary and secondary schools, and I wanted to know the difference between public schools and private schools.
    I learned religious orientation, level of school, length of school year, length of school day, race/ethnicity of students, number of high school graduates, number of teachers employed and program emphasis of private schools.
    I could use this data to compare private schools with public schools. This would give parents more information about private schools and the difference between private schools and public schools.

  • 1.) I found a data set for crime incidents in Philadelphia from 2012 to 2014.
    2.) I found the data on OpenDataPhilly at https://www.opendataphilly.org/dataset/crime-incidents/resource/d8e013b7-748a-4592-9688-897b4ea2290e?inner_span=True
    3.) I thought it was interesting because I spend a lot of my time in one of the most dangerous parts of the city. Crime reports can give me an idea of how safe/unsafe I am when I’m at Temple.
    4.) I was unable to find any real trends in the data, but I did learn that there is a whole lot of crime in the city. Given more time to analyze, I think I could find out what crimes occur more often than others,
    5.) Using this data, I could be more aware of what crimes occur more often and therefore, be more careful to keep myself safe from those crimes. Someone in the Philadelphia Police Department could use this data to help prevent these crimes as well.
    6.) One of my favorite quotes from a movie is, “I never had any friends later on like the ones I had when I was twelve. Jesus, does anyone?” The quote is from the movie Stand By Me, and I like it because I think it accurately represents the innocence of childhood and how when we get older, we often let the wrong things get in the way of our friendships.

  • Profile photo of Rehan M. Chowdhury

    1)I found the dataset is ” Housing Affordability Data System”
    2) I found it through http://www.data.gov
    3)I found it interesting because I saw that in my neighborhood the price of the houses are going up day by day. Infact it went up almost 85% within past couple of years but still people badly wanted to buy house no matter what the price are.
    4) I learned the system how it categorizes the price of the houses based on the affordability and household of income, Fair market rent and etc.
    5) Now in future, If I decide to buy a house or anyone who I know, wants to buy house, I could able to figure out the actual price of the particular house or can make a guess what should be the price of the house by using this data.

  • Profile photo of Alexander Somers Greene

    I found a data set with information about how SEPTA runs during harsh weather conditions like heavy snow.
    opendataphilly.org
    This data set is of interest to me because Temple does not hold classes if SEPTA is not running that day, and I use SEPTA on a semi-regualer basis when traveling to and from Temple.
    The information provided by this set shows that during harsh weather conditions SEPTA trains tend to be delayed or even completely canceled depending in how severe the weather is.
    This information can be used to determine how late the average train runs during harsh conditions and can allow commuters and other passengers to plan accordingly.

  • Profile photo of Gabriella C Baldini

    The first data set I found was the “Meat, Poultry, & Egg Inspection Directory by Establishment Number” from the Federal Department of Agriculture on Data.gov. It was sparked my interest because food health and contamination is super important, especially in light of recent contamination issues to hit several large chains recently. I learned the names that these establishments sell their meats/products under. The data also lists the processes each company uses (thus I learned which use more humane practices than others). This data can help determine which companies are passing these inspections consistency (if you look at the report each year), and also can show the numerous names one establishment can sell their products under. The second data set I found was “Philadelphia Dance Class Calendar” from PhiladelphiaDANCE.org on Opendataphilly.org. It was definitely of interest to me because I have been a dancer my whole life and it’s so nice to have one place to look for classes/performances here in the city. I learned which places hold the most frequent classes, which out-of-state companies are here in Philly on tour, and even where to find good deals. This data is a one-stop-shop. Uses can find classes in a calendar-style setup, look up shows, and get the best deals, all in one site.
    As for my favorite movie quote – it comes from My Big Fat Greek Wedding: “Here tonight, we have, apple and orange. We all different, but in the end, we all fruit.” I love that quote because the father is referencing his Greek daughter marrying a non-Greek guy, and it shows that he realizes that everyone is the same inside, regardless of race, religion, background, etc.

  • Profile photo of Prince Patel

    1. The Data set that I found was “Airline On-time Performance and Causes of Flight Delays.
    The data set contains scheduled and actual departure and arrival times, reason of delay and total minutes delayed. It also includes the name of city of departure/arrival with flight detailed of the flights delayed.
    2. I found the data set on http://catalog.data.gov/dataset/airline-on-time-performance-and-causes-of-flight-delays
    3. This Data is interesting to me as an international student I travel a lot. It would be very useful to know common patterns of flight delays at a certain city due to weather or air traffic.
    4. I Learned that many Flight delays are caused by weather on specific cities according to location and time of the year. I also found that at the biggest,busiest airports like JFK flight delays are still caused due to air traffic.
    5. By analyzing patterns of flight delays at certain cities during any time of the year can help decide best transportation option. It can also help airport authority to manage the air traffic if they have data that gives some idea about common flight delay patterns. This data set can also help decide travelers where to depart or where to go according to air traffic at certain major airport.

  • 1. I found the city of Philadelphia Health Center Data
    2. The data was found on Opendataphilly under the health center tabs.
    3. I found this interesting because this heath care situation is getting big. Theres a bunch of new health care organizations booming because health care is a must now in the US.
    4. Ive learned from this data what each company is associated with and if there is a dental option at that location.
    5. Not all health centers have dental, and i love my teeth. i could use this information to see which center i would like to make my primary one so i can get that clean feeling.

  • Profile photo of Jordan Timothy Motter

    I found the DTV reception maps
    I found it on Data.gov
    I found it interesting because you can see the signal strength of Digital TV by simply searching an address
    People in Big cities get more channels than people in smaller cities
    You could decide where you want to live, based on which channels you get in the area.

  • Profile photo of Erica Corinne Rudy

    I found a data set on data.gov about bullying rates dropping in schools. I have friends who were bullied in grade school and high school, so I know how it can affect someone, and I thought this set was interesting because it shows that younger students may be pushing toward a more equal classroom. I learned that the rate of bullied students was 28%, but has dropped to 22% of students between the ages of 12-18. Many decisions could be made with this information, but I think the most prevalent could be from counselors within schools, whether they “experience” bullying or not. There could be workshops and activities to educate students on bullying to continue to drop the rate.

  • The dataset that I found interesting from http://www.data.gov was the Estimated Annual Sales of U.S. Retail and Food Services Firms by Kind of Business. The dataset was really interesting, and showed data of the annual sales for many kinds of businesses dating all the way back to 1992 and spanning up to 2013. The data had bulk facts for things like food and beverage stores, which had metadata about the different types of stores in that category such as Grocery stores, specialty food stores and beer wine and liquor stores. We can use the data to see patterns in the past to use as a base for assumptions for the future. One thing I saw is that gas seems to go up and down, and we have just hit another peak high in gas station sales with 2013 being the year we are starting to go down in again. Retail sales, on the other hand, have pretty much always had a steady increase in their estimated annual sales. This may be why, or would be a good reason for, more and more gas stations are starting to sell retail items like sports blankets and coffee mugs, so they can keep a steadier sales rate. If a gas station is struggling, another idea is including a car shop or selling car parts because of the association between the two kinds of business and the steady revenue and extra customers the new business will bring in. These are decisions that businesses need to make when analyzing data like this, so it’s nice to have metadata that’s organized for the decision makers to make it easier.

    Sorry for the late response.

  • What dataset did you find?
    -Environmental Sensitivity Index (ESI) Threatened and Endangered Species REST Services. The marine and coastal environments and wildlife based on sensitivity to spilled oil.

    Where did you find it?
    -www.data.gov

    Why did you think it was interesting?
    -The metadata tracks the sensitivity of an environment based on what is living there and the vulnerability to human deviation and pollution into such environments.
    .
    What did you learn from the data?
    -Several environments contain creatures considered to be in sensitive life stages. Over production, oil being a common example, could be a determining factor of the stress placed upon creatures’ natural habitats by man’s influence.

    How could you use the data or what decisions could you make based on it?
    -Decisions to live a healthier and more conscientious life in terms of consumption are directly supported by data such as this. There is a growing need to re-think the effect humanity has on earth’s natural processes.

  • -https://services.arcgis.com/fLeGjb7u4uXqeF9q/arcgis/rest/services/Walkable_Access_Heathy_Food/FeatureServer/0/query?outFields=*&where=1%3D1
    -My dataset is about the walkable access of healthy food in the city of Philadelphia.
    -The dataset from opendataphilly.org
    -What I found most interesting is while a few people in the sample stated that being 21 unit (I am unsure of what unit of distance they are using) to be consider ‘No Access”, another labeled ‘Low Access” when their distance is 31.
    -From this sample I learn that subjectivity can be easily transferred and discredit a dataset if one is not careful. The relative meaning of what “low access” to “no access” makes the meter of measurement not a solid one. There should be a standard meter that defines the meaning of each aspect of the study.
    -Although I don’t think this dataset carries a lot of weight, a business owner in the food industry may look at this and see future investments in healthy food marks in inner-city neighborhood where the market is relatively low since most of the sample answered they were did not have walkable access to healthy stores.

  • Profile photo of Devon D Harris

    I found a dataset that contained all the arrival times of every single SEPTA regional rail from 2009-present. I found this dataset on opendataphilly. This was quite interesting to me, because as a commuter to a big university I rely on trains and public transportation a lot to get to school everyday so knowing which trains come late frequently is eye opening as I know the difficulty of having the trains being your only source to get to and from school. I learned most trains arrive in mostly on time except for in the winter due to weather conditions. I could possible use this data to see which train lines improved or fell behind in their arrival times over the years and see if it holds true today.

  • https://inventory.data.gov/dataset/032e19b4-5a90-41dc-83ff-6e4cd234f565/resource/38625c3d-5388-4c16-a30f-d105432553a4

    Here is what I found on the website. It is the dataset which illustrate the certain information of universities such as names, zip codes, addresses and states. I am interested in this dataset because it can be easily seen that the list of information in a table, even if it seems that there is a little complex for some certain simplified information. And I also found that this dataset was end by 2013, in terms of this situation, I believe that some of the information were not upgraded and it may cause inconvenience for people who is using this dataset.

  • Profile photo of Kiranya Chappell Chumtong

    What dataset did you find
    The dataset I found is Demographic Statistics by Zip Code in New York

    Where did you find it
    I found it on data.gov

    Why did you think it was interesting
    I thought it was interesting because it demonstrated how diverse New York is, and how much the demographics can change simply based on zip code; whether it be certain zip codes that contain a lot of diversity, or certain zip codes that gravitate towards one race or income level. This is most likely because, say you are Asian and move to the New York City, it might be easier for them to make the transition if they reside in an area with other Asians that they can relate to.

    What did you learn from the data
    I learned that there are neighborhoods (zip codes) that contain a lot of diversity, but there are also a lot of neighborhoods that gravitate towards one race or income level; (such as Chinatown, or section 8 housing, respectively.)

    How could you use the data or what decisions could you make based on it
    You could use the data if you were looking to move to New York, and say you are Asian and want to reside in an area that many other Asians live, you could use this data in order to determine which zip codes, or neighborhoods, you search for a home.

  • Profile photo of Isaiah J Carroll

    I found a dataset on small business lending on open data. I found this dataset on http://www.data.gov/finance/ . I found it interesting because it goes t show that the government and the financial industry are trying to make it possible for small start ups to get loans for their ventures. They are also implementing a new analytics for businesses to see such things as how many people are partaking in the retirement plan, how many employees their are, etc. By looking at the data an employer can determine rather or not his employees are happy and if they are accepting of the retirement plan set in place.

  • Profile photo of Isaiah J Carroll

    I found a dataset on “Small Business Lending on Open Data”. I found this dataset on http://www.data.gov/finance/ . I found it interesting because it goes t show that the government and the financial industry are trying to make it possible for small start ups to get loans for their ventures. They are also implementing a new analytics for businesses to see such things as how many people are partaking in the retirement plan, how many employees their are, etc. By looking at the data an employer can determine rather or not his employees are happy and if they are accepting of the retirement plan set in place.

  • What dataset did you find?
    • Solar installation projects in Philly.
    Where did you find it?
    http://metadata.phila.gov/#home/datasetdetails/5543866420583086178c4ef5/representationdetails/5670f7309e9a61fd4b25e6ed/
    Why did you think it was interesting?
    • I think solar power is a vastly under used source of renewable energy and to see the places Philly is installing solar panels is exciting to me.
    What did you learn from the data?
    • I learned Philly is more involved in solar energy than I had previously thought. Also I learned the places I can go to find solar energy installations in Philly.
    How could you use the data or what decisions could you make based on it?
    • The most beneficial way I could use this data is to view locations of solar energy installations and where I could install new ones for the most beneficial output.

    “One time I wrestled a giraffe to the ground with my bare hands.” — Dale :from step brothers

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