Predicting disease spread based on climate change

Predicting disease spread based on climate change

Meghan Bongartz Code available at: https://github.com/mbongartz/final-project The conversation about disease in the United States tends to revolve only around those diseases which pose a current threat or problem. This means that we spend far more time on average talking about measles than Ebola – but it makes it far more terrifying when Ebola is being talked about because it means that it is suddenly posing a threat and we do not have the infrastructure to deal with an outbreak. There are some diseases that we don’t currently consider threats in the United States for which it would be difficult to predict when they could become problems due to the way they are spread. However, there are other diseases that may spread or move with climate change, and we should be able to plan for these. My goal was to investigate the risk for spread of tropical diseases in the United States as climate changes over time. There are a plethora of diseases that could be impacted by climate change for various reasons, but I narrowed my area of interest down to vector-borne diseases and, more specifically, mosquito-borne diseases because they show a stronger climate preference than some other vectors such as ticks. I looked at two different vectors: Tiger Mosquitos and Southern House Mosquitos. Before addressing the vectors, though, I needed data on the rate at which climate is changing in the United States. This was available from the National Oceanic and Atmospheric Administration here: http://www.ncdc.noaa.gov/cag/time-series/us. NOAA has information about temperature and precipitation since 1895 that can be downloaded in a nicely formatted CSV file; however, the type of information,...

Teacher Diversity

Katie Worth Schools in the United States, despite decades of nominally trying to diversify their workforce, still mostly employ white teachers. In a 2011 report titled “Teacher Diversity Matters” by the Center for American Progress, author Ulrich Boser noted that at some point in the next several years, the number of non-Hispanic white children in America’s public schools will be outnumbered by the number of children of color – and in fact, that’s already the case in some states, like California, where 72 percent of students are of color. This diversity is not reflected among the teachers who provide these children with an education: Only 17 percent of the country’s teaching force is non-Hispanic white. In California, just 29 percent of teachers are non-Hispanic whites. In fact, more than 20 states have a disparity of more than 25 percentage points between the diversity of students and teachers. There is evidence that this yawning gap between the ethnic racial heritage of the student body and the people who teach them has an impact on the quality of education. A follow-up to the 2011 study, “Teacher Diversity Revisited,” published in 2014, notes that teachers of color can serve as role models for students of color, and help them feel more at home in schools.  Further, non-white students have better educational outcomes if they are taught by teachers of color. The benefits aren’t just for students of color, either: White students with a diverse teachers profit educationally from interacting with people and authority figures who look differently than they do. But this phenomenon hasn’t been explored on a data level before, so...
Is there a housing bubble in Switzerland?

Is there a housing bubble in Switzerland?

Michael and Arthur 1. Financial crisis 2008 and it’s impact on Switzerland Financial crisis was caused by a bubble on the us housing market, it made banks tremble The impact was strong and worldwide Many studies in the US, our reference for this project is the «Bay Area Blues: The Effect of the Housing Crisis». They say expensive houses are less affected than cheap houses Ongoing surge of average house prices in Switzerland in general, but lack of local data. 2. What are the criteria for a bubble? You see strong growth in loan volume and the real estate prices If both grow over years faster than GDP, you can talk about a bubble If we find data that confirm the two criteria, it is worth to investigate further, if not, we have to give up. 3. What did we do? We did look at the Public Data you find on the Webside of Swiss National Bank and Office for Statistics for the time from 2004 to 2014 Inflation: 5.5 % GDP: 32.4 % House prices 54.5 % Housing credits: 86.6 % regional range from 36.8 % to 60.4 %, 8 regions published Conclusion: There must be regions where you can talk about a bubble, let’s go on and find local data. Officially you don’t get statistic on local sales. Finally we found a private company specialized in the housing business that gave as some data. 4. The...

Weibo Text Mining

The objective of our project is to evaluate and predict the public attitudes on a specific social issue through an online social platform called Weibo (a Chinese Twitter). See code for this project here Topic Man’s brutal beating of female driver divides Chinese public after different car videos emerge. The different public opinion on this topic: – The woman deserved it – The man lost his mind Data 7,000 tweets from May 03 to June 03, including usernames, ids, publish date and time, counts of reposts, counts of like, content, and etc. Data Collection – Access to API of Weibo To apply natural language processing techniques on weibo content analysis, we tried to use API of Weibo, and later to do the web scraping try to get the content people posted on this topic.  But we failed to get the dataset because they provide very little data. – Then we found a dataset already made by a person and posted online, in contains over 7000 tweets on this topic. – We use TFIDF to extract the key words in Chinese from over 7000 tweets on this topic Method -Supervised Learning Randomly select 1/10 tweet from the database and analyze the attitude of the content. 1: The woman deserved it; -1: The man lost his mind Read the tweets, decide the attitude of the content, and skip the ones with murky attitude. (Eg: “I think both A and B were wrong, I can’t decide who is at more fault.”) Processing Data -clean data we need to get rid of the reposted content and also pay attention to the punctuation in special...
Apartment Hunting in Reverse

Apartment Hunting in Reverse

Spe Chen We all know apartment hunting is hard in New York City. This is especially true for international students like me. I came to New York this May and needed to find a place settling down in a week before my course started. (That was the most miserable days. The only thing I knew about New York was this was the home of best bagels on the earth.) Even I googled as much as I could, saw apartments in person and met landlords, it was never possible to get the whole picture of that neighborhood before I lived in. Surely landlords and agents want to rent out their apartment as soon as possible, it is unlikely they will tell you the dark side of the area. So should people accept this information inequality as it is? Is there any way to know the drawbacks of that neighborhood before you sign the contract and pay the first month rent? First, let’s change the mindset of typical apartment hunting. Most of time we want to find a place with some nice features: convenience, safety and large windows etc., maybe because it is normally how agents promote their objects. However, as smart apartment hunters, we can not be confined by this thinking. So in this project I propose a new way of apartment hunting – not finding a best place, but finding a less worse neighborhood, that is, the area with fewest type of 311 complaints that you care about. Here is the map of sampled complaints from NYC’s 311 service portal since 2010. I categorized those 200+ complaints into four, which...