/moco_hacks_2021

Primary LanguagePython

moco_hacks_2021

Corona-Safe

PROJECT BREAKDOWN

FILE STRUCTURE BREAKDOWN

  1. Final Product:
    • final_product --> coronasafe.py
      • Contains all the code for the project, including GUI
        • This is because importing other python files and external functions is difficult in Tkinter.
  2. Foot Traffic Backend (not run in final product):
    • backend --> foot_traffic --> foot_traffic.py
      • Contains all code for foot traffic processing & geocoding.
    • backend --> foot_traffic --> geocoding.py
      • Contains all code for geocoding.

APIs USED

Google Geocoding API

Google Places API

Prerequisite: Google API Key

DOCUMENTATION

Main Groups

  1. COVID-19 risk calculator based on an input address

    • Libraries used: datetime, and requests
      • Pip installation commands:
        • datetime: pip install DateTime
        • requests: pip install requests
    • Parameters: google_api_key, address, city, state, zip_code, search_radius
      • state needs to be formatted in their 2-letter, all caps format
      • Sample parameters (for the Empire State Building): g_api_key, "20 W 34th St", "New York", "NY", "10001", radius
        • NOTE: the default value for radius is set to 0.5 (miles)
    • functionality breakdown:
      • Uses address fields from user to generate a formatted address: address_formatter(address, city, state, zip_code)
      • The formatted address is geocoded using the Google Geocoding API: master_geocoding(google_api_key, address, city, state, zip_code)
        • A json request is made to access the Google Geocoding API
        • The latitude and longitude of the address is stored
      • A radius is then traced around the geocoded coordinates, and a list of (up to 20) nearby locations is extracted using the Google Places API: key_buildings_search(google_api_key, address, city, state, zip_code, search_radius)
        • A json request is made to access the Google Geocoding API
        • Each item in the list contains a types metadata
          • This is checked against a list of important types that signify a location that usually has a lot of people, and the total number of important locations is stored
      • The raw number from c.ii. is taken and multiplied by a weight relative to the current hour of day before being divided by the maximum time-weighted and multiplied by 100 to get a scale of risk from 0-100: risk_rating_scaler(google_api_key, address, city, state, zip_code, search_radius)
        • Weightage approximates typical times when people are the busiest on a 0.0-1.0 scale
      • The 100 scale is divided in thirds to output a low, medium, or high risk level that is returned to be turned into a visual representation in the GUI: risk_rating_scaler(google_api_key, address, city, state, zip_code, search_radius) & tkinter GUI code
        • Sample function call (for the Empire State Building): risk_rating_scaler(g_api_key, "20 W 34th St", "New York", "NY", "10001", radius)

  2. Real time COVID-19 map that updates every 24 hours Libraries used: ssl, pandas, csv, and urllib

    • Pip installation commands:

    • ssl: pip install ssl

    • pandas: pip install pandas

  • functionality breakdown:

    • Uses pandas to read the following csv files:

    • https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv

    • https://raw.githubusercontent.com/jasonong/List-of-US-States/master/states.csv

    • https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-counties.csv

    • SSL is imported to connect plotly with the CSV files through this code:

    • ssl._create_default_https_context = ssl._create_unverified_context

    • Two maps are created:

    • A heat map that collects the sum of all COVID-19 cases in each state.

    • A choropleth map that collects the sum of all COVID-19 cases in each county at a particular state that the user inputs.

    • Choropleth map:

    • Pandas iterates through all the columns that contain the keyword for a particular state:

      • A manually coded example (doesn't take into account for user input)': df_Maryland = df[ df['state'] == "Maryland"]

    • Pandas is used to set the date to the current date by using the built-in function max() function and then is used to iterate through all COVID-19 cases for the current date in that particular state:

      • last_date = df['date'].max()
      • df = df_Maryland[ df_Maryland['date'] == last_date] (manually coded example)

    • Pandas is also used calculate the sum of all COVID-19 cases and deaths for a particular state through the built-in function sum():

      • df['cases'].sum()
      • df['deaths'].sum()
      • This iterates through all cases and deaths for each county for that state in the csv file.

    • Plotly is used to make a choropleth map that iterates through the COVID-19 data for all counties in order to create the map:

      • fig = px.choropleth(df, geojson=counties, locations='fips', color='cases', color_continuous_scale="Viridis", range_color=(0, 20000) )

    • Heat map:

    • Pandas is again used set the date to the current date by using the built-in function max() function and then is used to iterate through all COVID-19 cases for the current date in that particular state:

      • last_date = df['date'].max()
      • df = df[ df['date'] == last_date]

    • Pandas is also used to calculate the sum of all COVID-19 cases and deaths in the U.S. through the built-in function sum():

      • df['cases'].sum()
      • df['deaths'].sum()
      • This iterates through all cases and deaths for for each state in the csv file which gets added up to calculate the sum of all COVID-19 cases and deaths in the U.S.

    • Pandas is used to calculate the sum of all COVID-19 cases and deaths in each U.S. state through the built-in function sum() and to_frame:

      • df = df.groupby('state')['cases'].sum().to_frame()

    • Plotly is used to make a heat map that iterates through the COVID-19 data for all states in order to create the map:

      • fig = px.choropleth(df, locations=df['Abbreviation'], color=df['cases'], locationmode="USA-states", color_continuous_scale="hot", range_color=(0, 4500000), scope="usa"))