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Visualizations
2016-spr-jtsliu_kmann-report.pdf
README.md
calculate_crime_liquor_correlation.py
create_crime_near_alcohol.py
create_zipcode_profile.py
create_zipcode_with_liquor_and_property_value.py
fetch_crime_data.py
fetch_hospital_data.py
fetch_liquor_data.py
fetch_property_assessment.py
fetch_public_school_data.py
plan.json
pymongo_dm.py
query_zipcodes.py
reverse_geocode.py
run.sh
solve_optimal_zipcode.py
test.py

README.md

Narrative

Since our first project our goal has changed slightly. Our current goal is to classify a zipcode. More specifically, we wish to allow a user to specify some constraits and have them recieve a zipcode that satisfies those constraints. Something like this would include: "I want the safest neighborhood with the lowest tax rates, lowest crime rates, with 2 schools, and 1 hospital". We could still come up with a way to have a specific metric rating (1 - 10 per say) of a zipcode. That being said, we currently support queries of the above type to an extent (see the solutions sections for more details and desires). In order to try and solve the problem we have done the following. We have selected some data from the city of Boston (see source sets for details); these include, property information, crime rates, liquor license, public schools, and hospitals. We next combined data sets to get a "profile" for a zipcode. We attempted to correlate crimes to alcohol by the street address and proximity (approx. walking distance) between the crime and liquor source. Additionally, we calculate the average tax per square foot in USD. From this we also see how many hospitals and schools are in each zipcode. Using all this, we can create a profile for the neighborhood and begin to do some optimization and satisfaction problems. Additionally, we wanted to investigate if there is any correlation between liquor sales availability and crime - particularly crime involving alcohol.

Source Datasets

  1. Crime data from city of Boston: https://data.cityofboston.gov/Public-Safety/Crime-Incident-Reports/7cdf-6fgx

This dataset was chosen because we believe it represents a pretty strong correlation to the dangerousness of a neighborhood - high violent crime is probably a dangerous area.

  1. Liquor license data from city of Boston: https://data.cityofboston.gov/dataset/Liquor-Licenses/hda6-fnsh

This dataset is a bit of an experiment. Perhaps it is the case that in areas with easily available alcohol, there is high crime rate. In particular, violent crime related to alcohol - this would be a cause of a neighborhood becoming more dangerous potentially.

  1. Property Assessment: https://data.cityofboston.gov/resource/qz7u-kb7x

This dataset was chosen to see if we could learn anything about the value of an area based on the assessment from the census. We calculate the average tax per square foot in properties.

  1. Public Schools: https://data.cityofboston.gov/resource/e29s-ympv

This is used to rate a zipcode - how many schools does it have?

  1. Hospital Locations: https://data.cityofboston.gov/resource/46f7-2snz

Also used to rate a zipcode - how many hospitals does it have?

Currently our transformations classify all of the data by zipcode, which is a fairly large geospacial area. Perhaps we could narrow it to locations in the future, but currently, we can try to classify zipcodes based on some interesting information.

Running data retrieval

Note: please run the fetch_liquor_data file first for fresh runs, as this generates the plan.json file again.

To run files:

$ python3 fetch_liquor_data.py $ python3 fetch_property_assessment.py $ python3 fetch_crime_data.py $ python3 fetch_public_school_data.py $ python3 fetch_hospital_data.py

Dependencies: These files do not have any dependencies outside the expected ones (prov, pymongo, json, etc.)

Transformations

We run the transformations as follows:

$ python3 create_zipcode_with_liquor_and_property_value.py

This file uses the liquor data and property data we collected as resources. From them, we derive a new data set that holds information about a zip code: how much the average tax per square foot is and how many liquor locations there are.

The next transformation does have some problems. Mainly we are trying to work around reaching our request limit for geolocation services. We want to reverse geolocate the zipcode of a crime, so we can use the same metric we did above to evaluate a zipcode. Currently we do not have the entirety of the data processed, but plan on implementing a different method

$ python3 reverse_geocode.py

Dependencies: Same as previous scripts with the addition of the geopy module (pip3 install geopy) We use this library for obtaining zipcodes

IMPORTANT: when running this script for the first time, uncomment lines 34 and 35 to properly add the data to the repo; however, due to the current limitations, we comment this out since subsequent runs result in dropping the database with no way to regenerate the data because we lack the requests in our api key.

Notes: we would like to construct a full data set for the latter script, and hope to do so soon. When generating the provenance for the transformations, any dataset we used for the transformation was added as a resource that the new dataset was derived from

Additional transformations (implemented in project 2)

$ python3 create_crime_near_alcohol.py

This was inspired by Enze's dataset; however, when we went to use it, we noticed a few things we wanted to change, so we drew from his idea and created our own dataset. Using this dataset we are approximating how many crimes occured near a liqour sale. We can then aggregate this for a count

$ python3 create_zipcode_profile.py

This creates the actual profile from the zipcodes. This is mostly aggregations of all the above datasets. We count how many alcohol associated crimes occured, the tax rate, the number of schools, and the number of hospitals. We then try to get useful information from this.

Problem Solutions (project 2)

First we wanted to see if there was a correlation between various data elements in a zipcode. To see our results run:

$ python3 calculate_crime_liquor_correlation.py

We believe this data is slightly skewed because of a lack of judicious filtering of crime.

Now we wanted to solve a problem - the one we originally described. Based on some user input, what is the optimal zipcode. This problem is simple to solve now, as we can just issue queries on the mongo db. Lame. So we came up with a more complicated one - we can min and max in two dimensions for a subset of size k for the zipcodes, all varied on user input. This is done using z3!

NOTE: This script has NOT been tested on python 3. For some reason we could not use z3 for python 3. As a result, this is only tested in python 2. That being said, I don't think there is any python 2 specific syntax or functionality, so it theory it should be okay. This also obviously has an aditional dependency on the z3 library for python.

$ python solve_optimal_zipcode.py

Note: If you want to issue a few simple queries here was the original script we used for this problem, we saved it mostly for legacy purposes, but it is not so bad - so here it is:

$ python3 query_zipcodes.py

Limitations and problems

The zipcode as mentioned above is a pretty large area, maybe we should explore smaller subsets.

We should filter out more crimes that we associate with liquor licenses.

Our optimal zipcode query is only tested in python 2

Visualizations

We present two interactive visualizations. The way we set them up however, requires a webserver running from that directory. If you have done a:

$ sudo npm install http-server -g

Then you can simply run:

Visualizations $ http-server

From within the visualizations directory. These visualizations depend on D3 - this can be seen near the top of the files. They were heavily tested on Safari, and seemed to work on Chrome.

We have included screenshots of the visualizations for convenience

NOTES: The histogram could have been implemented better, but we were running into bugs, where a bunch of crappy if statements ended up being the easiest solution. (We are 90% certain there is a better way - sorry) The data used: For the histograms, we used subset of our zipcode_profile collection For the scatterplot, we did an aggregation on an intermediate data set that had liquor related crimes, and liquor locations. Because both of these sets were just aggregated from data sets, we did not write additional provenance They are the same as the datasets they came from.