File size: 3,956 Bytes
e4c4709 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 |
import streamlit as st
import pandas as pd
import numpy as np
from geopy.distance import geodesic
from sklearn.cluster import KMeans
# Load the dataset
@st.cache_data
def load_data():
file_path = r"C:\Users\hp\Desktop\project\predictive crime\preprocessed_df1.csv" # Update with your file path
return pd.read_csv(file_path, low_memory=False)
# Function to calculate distance between two coordinates
def calculate_distance(coord1, coord2):
return geodesic(coord1, coord2).km
# Function to generate deployment plan
def generate_deployment_plan(df, centroids):
deployment_plan = {}
for cluster in centroids.keys():
cluster_data = df[df['Cluster'] == cluster]
number_of_incidents = len(cluster_data)
suggestions = ""
if number_of_incidents == 0:
suggestions = "Increase police presence and conduct thorough investigations to prevent future incidents."
elif number_of_incidents <= 10:
suggestions = "Continue monitoring the area and conduct periodic patrols to maintain security."
else:
suggestions = "<span style='color:red;font-weight:bold'>Intensify surveillance efforts</span> and consider implementing community policing strategies."
deployment_plan[cluster] = {
'cluster_centroid': centroids[cluster],
'number_of_incidents': number_of_incidents,
'suggestions': suggestions
}
return deployment_plan
# Function to predict the number of incidents based on input latitude and longitude
def predict_incidents(input_latitude, input_longitude, df, centroids):
offense_location = (input_latitude, input_longitude)
closest_centroid = min(centroids.keys(), key=lambda x: calculate_distance(offense_location, (centroids[x]['Latitude'], centroids[x]['Longitude'])))
return closest_centroid
# Streamlit app
def main():
st.title("Crime Deployment Plan")
# Load the data
df = load_data()
# Preprocessing
df = df[(df['Latitude'] != 0.0) & (df['Longitude'] != 0.0)]
# Clustering
features = ['Latitude', 'Longitude']
X = df[features]
X = (X - X.mean()) / X.std()
kmeans = KMeans(n_clusters=5, random_state=42)
df['Cluster'] = kmeans.fit_predict(X)
# Define the latitude and longitude of the cluster centroids
centroids = {
1: {'Latitude': -0.26350117329990513, 'Longitude': 0.20905869782346226},
2: {'Latitude': 5.807340212426832, 'Longitude': -5.187200322116295},
3: {'Latitude': -1.2370956602478118, 'Longitude': -5.921660553559399},
4: {'Latitude': -0.2676250405697857, 'Longitude': 59.52073073890124},
5: {'Latitude': 0.023547431083855475, 'Longitude': 0.09870306054587759}
}
# User input for latitude and longitude
st.header("Input Latitude and Longitude")
input_latitude = st.number_input("Latitude", value=0.0)
input_longitude = st.number_input("Longitude", value=0.0)
# Button to trigger the calculation
if st.button("Predict and Generate Suggestions"):
# Predict the cluster and get suggestions
cluster_prediction = predict_incidents(input_latitude, input_longitude, df, centroids)
deployment_plan = generate_deployment_plan(df, centroids)
suggestions = deployment_plan[cluster_prediction]['suggestions']
# Calculate the number of incidents based on input value
offense_location = (input_latitude, input_longitude)
closest_centroid_data = df[df['Cluster'] == cluster_prediction]
number_of_incidents = len(closest_centroid_data)
# Display suggestions and number of incidents
col1, col2 = st.columns(2)
with col1:
st.subheader("Suggestions")
st.markdown(suggestions, unsafe_allow_html=True)
with col2:
st.subheader("Number of Incidents")
st.write(number_of_incidents)
if __name__ == "__main__":
main()
|