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# Step 1: Define criminal activities
criminal_activities = ["robbery", "assault", "vandalism", "accessory", "fraud"]

# Step 2: Gather descriptions (fake example)
descriptions = {
    "robbery": "A robbery occurred at a convenience store last night. The perpetrator demanded money at gunpoint.",
    "assault": "There was a brutal assault reported in the downtown area. The victim suffered severe injuries after being attacked with a blunt object.",
    "vandalism": "Several cars were vandalized in the parking lot. The vandals spray-painted graffiti on the vehicles and smashed their windows.",
    "accessory": "An individual was arrested for being an accessory to the robbery. They aided the perpetrator in escaping the scene.",
    "fraud": "A case of fraud was uncovered involving forged documents and financial deception."
}

# Step 3: Collect quantized longitude and latitude for locations and associate descriptions
location_coordinates = {
    "New York City": {"latitude": 40.7128, "longitude": -74.0060},
    "Los Angeles": {"latitude": 34.0522, "longitude": -118.2437},
    "London": {"latitude": 51.5074, "longitude": -0.1278},
    "Tokyo": {"latitude": 35.6895, "longitude": 139.6917},
    "Sydney": {"latitude": -33.8688, "longitude": 151.2093}
}

# Generate correlated data
correlated_data = []
for location, coordinates in location_coordinates.items():
    for activity in criminal_activities:
        correlated_data.append((location, coordinates["latitude"], coordinates["longitude"], descriptions[activity], activity))

# Print correlated data
for location, latitude, longitude, description, activity in correlated_data:
    print(f"Location: {location}, Latitude: {latitude}, Longitude: {longitude}, Description: {description}, Activity: {activity}")

# Additional functionality: correlate words with locations
word_library = {
    "robbery": ["theft", "crime", "money", "masked", "gunpoint"],
    "assault": ["violence", "attack", "injury", "victim", "brutal"],
    "vandalism": ["damage", "destruction", "graffiti", "vandals", "smashed"],
    "accessory": ["aiding", "abetting", "accomplice", "arrested", "suspect"],
    "fraud": ["forgery", "deception", "financial", "scam", "counterfeit"]
}

correlated_data_word = []
for location, coordinates in location_coordinates.items():
    for activity, words in word_library.items():
        for word in words:
            correlated_data_word.append((location, coordinates["latitude"], coordinates["longitude"], f"An image related to {activity} depicting {word}.", activity))

# Print correlated data based on words
for location, latitude, longitude, description, activity in correlated_data_word:
    print(f"Word Location: {location}, Latitude: {latitude}, Longitude: {longitude}, Description: {description}, Activity: {activity}")

# Balance points on a sequence for association
def balance_sequence(sequence):
    n = len(sequence)
    balanced_sequence = []
    for i in range(n):
        if i % 2 == 0:
            balanced_sequence.append(sequence[i // 2])
        else:
            balanced_sequence.append(sequence[n - 1 - i // 2])
    return balanced_sequence

# Define associated sequence
associated_sequence = ["point1", "point2", "point3", "point4", "point5"]

# Balance the sequence
balanced_sequence = balance_sequence(associated_sequence)
print("Balanced Sequence:", balanced_sequence)

# Check equations and declare "no" when reaching negative reference
# E≠imc
E = 2.718  # Euler's number
i = 10  # Some imaginary unit
m = 5  # Mass
c = 3 * 10**8  # Speed of light
r = 1  # radius
pi = 3.14159  # pi

equations = [
    ("E ≠ i * m * c", E != i * m * c),
    ("-E = (imc)^2", -E == (i * m * c) ** 2),
    ("-E = imc^2", -E == i * m * c ** 2),
    ("-E = ((i)(mc))^2", -E == ((i) * (m * c)) ** 2),
    ("-E = mic^2", -E == m * i * c ** 2),
    ("E = (mc / (2πr))^2", E == (m * c / (2 * pi * r)) ** 2),
    ("E = mc^2", E == m * c ** 2),
    ("E = i", E == i)
]

positive_outcomes = []
neutral_outcomes = []
negative_outcomes = []

for equation, result in equations:
    print(equation + ":", result)
    if result:
        print("No")
        break

# If the condition is not met, reassociate the point endlessly
while E < 0:
    balanced_sequence = balance_sequence(associated_sequence)
    print("Balanced Sequence:", balanced_sequence)
    for equation, result in equations:
        if result:
            print("No")
            break
    else:
        if E > 0:
            positive_outcomes = correlated_data
        elif E < 0:
            negative_outcomes = correlated_data
        else:
            neutral_outcomes = correlated_data

print("Positive Outcomes:")
for outcome in positive_outcomes:
    print(outcome)

print("Negative Outcomes:")
for outcome in negative_outcomes:
    print(outcome)