Comprehensive Comparison of Five U.S. Cities

This detailed comparison examines five major U.S. cities: New York City, Los Angeles, Chicago, Houston, and Phoenix. We will explore various aspects including population, area, income, notable features, airport codes, time zones, and more. This information is presented in a structured format for easy understanding and comparison.

City Overview

Here's a table summarizing key data points for each city:

City	State	Population (2023 est.)	Area (sq mi)	Median Household Income	Time Zone	Notable Feature	Major Airport Code(s)	Area Code(s)
New York City	NY	8,336,817	302.6	67,000	Eastern (EST/EDT)	Statue of Liberty	JFK, LGA, EWR	212, 646, 917
Los Angeles	CA	3,898,747	503	62,000	Pacific (PST/PDT)	Hollywood Sign	LAX	213, 310, 323
Chicago	IL	2,746,388	234	58,000	Central (CST/CDT)	Willis Tower	ORD, MDW	312, 773
Houston	TX	2,304,580	637	59,000	Central (CST/CDT)	Space Center	IAH, HOU	713, 281, 832
Phoenix	AZ	1,608,139	517	60,000	Mountain (MST)	Desert Landscape	PHX	602, 480, 623

Detailed City Information

Below, we delve deeper into specific aspects of each city:

New York City, New York

• Population: Approximately 8,336,817 (2023 est.)
• Area: 302.6 square miles
• Median Household Income: $67,000
• Time Zone: Eastern Time Zone (EST/EDT)
• Notable Feature: The Statue of Liberty, a symbol of freedom and democracy.
• Major Airports:
  • John F. Kennedy International Airport (JFK)
  • LaGuardia Airport (LGA)
  • Newark Liberty International Airport (EWR)
• Area Codes: 212, 646, 917

Los Angeles, California

• Population: Approximately 3,898,747 (2023 est.)
• Area: 503 square miles
• Median Household Income: $62,000
• Time Zone: Pacific Time Zone (PST/PDT)
• Notable Feature: The Hollywood Sign, an iconic landmark of the entertainment industry.
• Major Airport: Los Angeles International Airport (LAX), one of the busiest airports globally.
• Area Codes: 213, 310, 323

Chicago, Illinois

• Population: Approximately 2,746,388 (2023 est.)
• Area: 234 square miles
• Median Household Income: $58,000
• Time Zone: Central Time Zone (CST/CDT)
• Notable Feature: The Willis Tower (formerly Sears Tower), a prominent skyscraper.
• Major Airports:
  • O'Hare International Airport (ORD)
  • Midway International Airport (MDW)
• Area Codes: 312, 773

Houston, Texas

• Population: Approximately 2,304,580 (2023 est.)
• Area: 637 square miles
• Median Household Income: $59,000
• Time Zone: Central Time Zone (CST/CDT)
• Notable Feature: The Space Center, a hub for space exploration and research.
• Major Airports:
  • George Bush Intercontinental Airport (IAH)
  • William P. Hobby Airport (HOU)
• Area Codes: 713, 281, 832

Phoenix, Arizona

• Population: Approximately 1,608,139 (2023 est.)
• Area: 517 square miles
• Median Household Income: $60,000
• Time Zone: Mountain Standard Time (MST), does not observe Daylight Saving Time.
• Notable Feature: The unique desert landscape and climate.
• Major Airport: Phoenix Sky Harbor International Airport (PHX)
• Area Codes: 602, 480, 623

Additional Notes

• Population Figures: Population estimates are based on the latest available data as of 2023. These figures can fluctuate due to various factors such as migration, birth rates, and economic changes.
• Airport Codes: Airport codes are standardized three-letter codes assigned by the International Air Transport Association (IATA). They are commonly used for ticketing, baggage handling, and flight planning.
• Time Zones: Note that Phoenix does not observe Daylight Saving Time (DST), remaining on Mountain Standard Time (MST) year-round.
• Area Codes: Cities often have multiple area codes due to population growth and the increasing demand for phone numbers.

Python Code for Data Handling

Here's an example of how you can store and manipulate this data using Python:

        
cities = {
    'New York City': {
        'state': 'NY',
        'area_codes': ['212', '646', '917'],
        'population': 8336817,
        'time_zone': 'Eastern',
        'area': 302.6,
        'median_income': 67000,
        'notable_feature': 'Statue of Liberty',
        'airports': ['JFK', 'LGA', 'EWR']
    },
    'Los Angeles': {
        'state': 'CA',
        'area_codes': ['213', '310', '323'],
        'population': 3898747,
        'time_zone': 'Pacific',
        'area': 503,
        'median_income': 62000,
        'notable_feature': 'Hollywood Sign',
        'airports': ['LAX']
    },
    'Chicago': {
        'state': 'IL',
        'area_codes': ['312', '773'],
        'population': 2746388,
        'time_zone': 'Central',
        'area': 234,
        'median_income': 58000,
        'notable_feature': 'Willis Tower',
        'airports': ['ORD', 'MDW']
    },
    'Houston': {
        'state': 'TX',
        'area_codes': ['713', '281', '832'],
        'population': 2304580,
        'time_zone': 'Central',
        'area': 637,
        'median_income': 59000,
        'notable_feature': 'Space Center',
        'airports': ['IAH', 'HOU']
    },
    'Phoenix': {
        'state': 'AZ',
        'area_codes': ['602', '480', '623'],
        'population': 1608139,
        'time_zone': 'Mountain',
        'area': 517,
        'median_income': 60000,
        'notable_feature': 'Desert Landscape',
        'airports': ['PHX']
    }
}

def get_population(city):
    return cities[city]['population']

def get_area_codes(city):
    return cities[city]['area_codes']

def get_cities_in_timezone(timezone):
    return [city for city, data in cities.items() 
            if data['time_zone'] == timezone]

def get_median_income(city):
    return cities[city]['median_income']

def get_notable_feature(city):
    return cities[city]['notable_feature']

def get_area(city):
    return cities[city]['area']

def get_airports(city):
    return cities[city]['airports']

# Example usage
print(f"Population of Chicago: {get_population('Chicago')}")
print(f"Area codes in Los Angeles: {get_area_codes('Los Angeles')}")
print(f"Cities in Central time zone: {get_cities_in_timezone('Central')}")
print(f"Median income in Houston: {get_median_income('Houston')}")
print(f"Notable feature of Phoenix: {get_notable_feature('Phoenix')}")
print(f"Area of New York City: {get_area('New York City')}")
print(f"Airports in Phoenix: {get_airports('Phoenix')}")
        
    

This code creates a dictionary of dictionaries to store the city data and includes example functions to access and filter the data. The example usage demonstrates how to retrieve specific information about each city.