-Python3
-Django
-Camelot (Python implementation of Tabula)
-BeautifulSoup
-GeoJSON Area
Navigate to the folder where you wish to start your project. Run 'django-admin startproject your_project_name' (plastic is recommended). Navigate to the folder the command created, which should have the same name as the project. In that folder, run python3 manage.py startapp core. This command should make a folder called "core." Please copy the admin, views, models, and url files from the core folder in this repository into the folder you created. Run python3 manage.py migrate to set up the database.
If using the settings module provided, set the "SECRET_KEY" environmental variable to something secure. Django uses the secret key
Run python3 manage.py createsuperuser to make an administrator account on the server, which will allow the user to edit any data.
A list of Zip Codes in Montgomery County can be downloaded here. Some Zip Codes do not have geographical areas. A list of Zip Codes used is also located in the geojson folder.
A data table with Median Household Income and population for Zip Code Tabulation Areas (Census-designated boundaries which roughly mimic Zip Codes) can be found here.
Run extracthigh.py and extractmiddle.py to download all of the recycling data, which will be saved to outputmiddle.csv and outputhigh.csv. For each file, there will be columns with the recycling pounds per person rate for five school years. Make new columns to store the rankings of each school using the Excel rank function. Save this output as a csv.
To obtain the latitude and longitude coordinates of middle schools, run python3 manage.py shell and then exec(open("locatem.py").read()) to load in middle school addresses. Some schools like Silver Creek MS must be manually searched on Google Maps. High schools were manually searched before the automatic scraping program was written. The program uses data from the Census Bureau API. There is no charge or API Key required.
The data above, apart from the coordinates, were manually inputted into the database in the command line using python3 manage.py shell. Depending on how the CSVs are set up with the ranking, import the data by reading each line in each CSV file and writting to the appropriate fields. Import data into the mcpsSchool and zipcode models.
When the data importing is complete one may run python3 manage.py runserver to run the server on 127.0.0.1:8000 by default.
Simply clone the repo and run python3 manage.py runserver using the default admin credentials (username plastic password plastic).
schooltype - the type of school ("middle" or "high")
name - the name of the school
ppp_20XX_20XY - the pounds per person rate for the 20XX-20XY school year
ppp_20XX_20XY_rank - the pounds per person ranking for the 20XX-20XY school year relative to other schools
lat/long - geographical coordinates
desc - a description of any trends noted in each school
link - not used
code - 5 digit name of the Zip Code
geodata - geoJSON geometry of the Zip Code
area - Area of the Zip Code in square meters obtained with the area function
population - Total population of the Zip Code
average_income - Median household income (the mean household income was originally used, hence the field name)
density - Population density in persons per square meter (must be calculated manually)