#mapassignment

One of my goals with my community, African-Americans suffering from depression, is to not only build a safe forum for discussing mental health, but to also provide resources for seeking treatment. After a couple of hours (and yes, I do mean hours) I finally found some useful, relevant, mappable data.

While on the Substance Abuse & Mental Health Services Administration’s  (SAMHSA’s) website, probing for a useful dataset file, I discovered a treatment facility locator. The locator is updated weekly and lists, with several exceptions, most public, private for-profit, and non-profit State-licensed or nationally accredited mental health facilities around the nation. Another plus? The locator allows you to download a real-time .csv file of the records. And even better, you’re able to filter the facility locator data, which is perfect when searching for specific types of facilities (e.g. a Public/government sponsored facility that accepts Medicaid) or services for a specific demographic (e.g. treatment for adolescents or LGBTQ patients). A useful resource for my community? You bet.

I will use the data to generate a map of shapes and will then merge it with the US State shapefile in CardoBD, listing facilities per state. I'll most likely be using the common field “state.” If time permits, I'd like to also try generating a NY-based map using a NYS zip code shapefile.

Similar to what Luis is doing I will be mapping the commute for our high school soccer athletes, butinstead I will focus on those who attend The DaltonSchool. Since this is our private school it will be interesting to comparecommute distance here versus Martin Luther King Jr.

This map will also reveal howmany students at Dalton live in some of the most affluent neighborhoods in theU.S. According to Forbesmagazine 6 of the 10 richest zip codes are in NYC, and several of the studentslive in these zip codes. The results will be important later when evaluating how certain demographics (neighborhood, income, private coaching, etc) contribute to the success of an athlete.

I will be mapping data points using player zip codes. The information I have is from the students emergency care form, which is currently a pdf, but will be converted into an excel document for use in CartoDB. An example of student zip code information can be found in my gist. The information will be sorted per color in each zip code, and I’d like to explore bubble size whereas the largest bubbles indicate wealthiest zip codes and decrease in size according to areas average income levels.

The data I'm going to use to make a map is from the Vision Zero data page, and the data is about the location of fatal crashes in New York. The crash data comes from a DOT database that uses NYPD information, according to Vision Zero's metadata file that was last updated this month. 

Because the data is available as a shapefile (woo-hoo!), I don't expect I'll need to combine it with other geographic information in order to get CartoDB to map it. I expect the data to be a collection of points on a map. 

Other notes, while I'm thinking of them:

The most interesting thing I learned in the metadata file was: "All crashes are mapped to the nearest intersection." That's useful in case we're trying to pinpoint a specific accident that happened in front of a school or someplace not right at an intersection.

I might try to work with the data and condense it into groups in Excel and then have it shade shapes for different areas based on the number of crashes, in which case I might need to combine that dataset with a neighborhood or school boundary map. Unless analyzing the data like that is something I can do all within CartoDB? I'll try to figure that out.

© OpenStreetMap contributors ©CartoDB CartoDB attribution

This is my map of the car accidents that have involved pedestrian deaths. I limited the time range to 2013 to now. The SQL formula I used for that was:

SELECT * FROM fatality_yearly WHERE yr IN ('2013','2014','2015') AND pedfatalit > 0

At first I just limited the time range, but then after clicking on the points to try to figure out what information to show in the infowindow, it was saying "0" for a lot of the points. Then I realized it was still mapping the fatal car-only and car-bike crashes, so I added the "pedfatalit > 0" and it changed how the map looked a lot.