Switch branches/tags
Nothing to show
Find file History
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
..
Failed to load latest commit information.
data
resources
visual
website
2016-spr-jlam17_mckay678-report.pdf
README.md
bostonDemographics.py
config.json
demographicsVisualize.py
fixedFood.json
foodLocations.py
foodNames.py
foodVisualize.py
plan.json
provBostonDemographics.json
provFoodLocation.json
provFoodNames.json
pymongo_dm.py
run.sh

README.md

We are using a file called Boston In Context: Neighborhoods, which contains information about the demographics, economic situations, and housing characteristics of the different neighborhoods in Boston; one called Active Food Establishment Licences, which tells us the locations of all the resturants in Boston; one called Summer Farmers Markets, which tells us the locations and availablities of all the farmers markets in the Boston Area; one called Corner Stores, which has the locations of conviences stores and small markets in Boston; and one called Food Pantries, which contains the locations of all the food banks and food pantries in Boston. Our goal with this data is to visualize the food resources of Boston in relation to the demographics of the people. Our hypothesis is that we will see food deserts and lack of certain resources in lower income and more diverse areas. We also believe that there will be a strong correlation between wealthy neighborhoods and higher end resturants, and fastfood/convience stores with lower income neighborhoods. In the future we plan to bring in data about other resources in Boston and visualize it in relation to the demographics, and to detect other resource deserts. Using statistical information such as mean, standard deviation, etc., we are quatifiying and visualizing the resources and demographics in Boston. The data we use contains information about the demographics and food establishments in each neighborhood. By calculating the averages and standard deviations we can see how each neighborhood shapes up in comparision on the local, state, and national levels. Taking these statistics into account we can come up with a grading system to characterize these neighborhoods.