☕ Opening a Coffee Shop in Denver 🌄
  • AI Chat
  • Code
  • Report
    • Beta
    Spinner

    ☕ Opening a Coffee Shop in Denver 🌄

    📖 Background

    You are helping a client who owns coffee shops in Colorado. The company's coffee shops serve high-quality and responsibly sourced coffee, pastries, and sandwiches. They operate three locations in Fort Collins and want to expand into Denver.

    Your client believes that the ideal location for a new store is close to affluent households, and the store appeals to the 20-35 year old demographic.

    Your team collected geographical and demographic information about Denver's neighborhoods to assist the search. They also collected data for Starbucks stores in Denver. Starbucks and the new coffee shops do not compete for the same clients; the team included their location as a reference.

    Image by Nathan Dumlao, unplash.com

    💾 The data

    You have assembled information from three different sources (locations, neighborhoods, demographics):

    Starbucks locations in Denver, Colorado
    • "StoreNumber" - Store Number as assigned by Starbucks
    • "Name" - Name identifier for the store
    • "PhoneNumber" - Phone number for the store
    • "Street 1, 2, and 3" - Address for the store
    • "PostalCode" - Zip code of the store
    • "Longitude, Latitude" - Coordinates of the store
    Neighborhoods' geographical information
    • "NBHD_ID" - Neighborhood ID (matches the census information)
    • "NBHD_NAME" - Name of the statistical neighborhood
    • "Geometry" - Polygon that defines the neighborhood
    Demographic information
    • "NBHD_ID" - Neighborhood ID (matches the geographical information)
    • "NBHD_NAME' - Nieghborhood name
    • "POPULATION_2010' - Population in 2010
    • "AGE_ " - Number of people in each age bracket (< 18, 18-34, 35-65, and > 65)
    • "NUM_HOUSEHOLDS" - Number of households in the neighborhood
    • "FAMILIES" - Number of families in the neighborhood
    • "NUM_HHLD_100K+" - Number of households with income above 100 thousand USD per year

    Starbucks locations were scrapped from the Starbucks store locator webpage by Chris Meller.
    Statistical Neighborhood information from the City of Denver Open Data Catalog, CC BY 3.0 license.
    Census information from the United States Census Bureau. Publicly available information.

    💪 Challenge

    Provide your client a list of neighborhoods in Denver where they should consider expanding. Include:

    • A visualization of Denver's neighborhoods and the Starbucks store locations.
    • Find the neighborhoods with the highest proportion of people in the target demographic.
    • Select the top three neighborhoods where your client should focus their search.

    Setup

    %%capture
pip install geopandas
    # Import modules
import pandas as pd
import numpy as np
import seaborn as sns
import geopandas as gpd
import matplotlib.pyplot as plt

from sklearn.preprocessing import MinMaxScaler

    Data Prep & EDA

    ☕Starbucks Data

    # Read data - Staburcks in Denver
sbux = pd.read_csv('./data/denver.csv')

# Glimpse
sbux

    This dataset looks good overall, it mainly constains information about the 78 Starbucks stores in the region. We will be using the Longitude and Latidude columns later.

    🏘️ Neighborhoods Data

    # Read data of neighborhood in Denver Metropolitan Statistical Area (MSA)
neighborhoods = gpd.read_file('./data/neighborhoods.shp')

# Glimpse
neighborhoods.head()

    The neighborhoods dataset looks good. It also contains a geometry column with polygons, these are useful to plot a shape or area on a map.

    ‌
    ‌
    ‌