We have included the following datasets:
(Snow Emergencies) http://bostonopendata-boston.opendata.arcgis.com/datasets/4f3e4492e36f4907bcd307b131afe4a5_0
(Traffic Signals) http://bostonopendata-boston.opendata.arcgis.com/datasets/de08c6fe69c942509089e6db98c716a3_0
(Buildings) https://data.boston.gov/dataset/buildings
(Census (Block Groups) 2010) https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/5AF9LK
Census (Blocks) 2010) https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/FI1YED
(Roads) https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/OV5PXF
(Boston Neighborhoods) http://bostonopendata-boston.opendata.arcgis.com/datasets/3525b0ee6e6b427f9aab5d0a1d0a1a28_0
We believe that these datasets will allow us to answer the following question: "How can we create snow emergency routes that will pollute traffic in the city of Boston the least?" Since we have all the building blocks (no pun intended) for displaying city infrastructure with our datasets (buildings, roads), we are able to roughly map out how the roads are interconnected and shaped. Next, we have the census that will roughly (because they are slightly outdated due to data being formed at 2010) know how many people live in which neighborhoods - it is possible to discern high traffic areas and potential bottlenecking routes. Then, using the snow emergency route dataset, we can construct more optimally better routes for different situations based on some algorithm that is to-be-described. We would combine relevant information which would aggregate things based on intersection/street/landmark names, and from that we could use our data points to plot relevant information with all the information provided.
Two of our datasets are in the form of CSVs, specifically our census and roads data. We uploaded these to datamechanics.io and pulled them in their respective get modules. These are pulled as strings, we parse them line by line to turn into dictionaries and insert into Mongo.