Rockjunior/kenya-homicide-forecast

Modelling Homicide in Kenya Using Time Series. # **kenya-homicide-forecast.** # Modelling Homicide in Kenya Using Time Series. **"Modelling Homicide in Kenya Using Time Series"**, is a research endeavour submitted in partial fulfilment of the requirements for a Bachelor's degree in Mathematics and Economics with IT at Masinde Muliro University of Science and Technology. The study's primary aim is to understand and predict homicide trends in Kenya to assist law enforcement agencies, public safety initiatives, and policymakers. The research leverages historical data on homicides in Kenya spanning 20 years and employs advanced statistical methods to identify trends, patterns, and factors influencing homicide rates. ## Background. Kenya has faced a significant increase in homicide rates, which poses a challenge to public safety and societal stability. Traditional methods of crime analysis often overlook the complex temporal patterns in homicide data. This study uses time series analysis, specifically the **Autoregressive Integrated Moving Average** **(_ARIMA_)** model, to capture the seasonal, trend, and irregular components within crime data. The rationale for using \*\*ARIMA\*\* is its ability to uncover hidden patterns, detect outliers, and generate accurate forecasts, providing valuable insights for various stakeholders. The project was motivated by the need to provide evidence-based insights into the dynamics of homicide in Kenya to help policymakers develop targeted interventions and allocate resources more efficiently. ## Objectives. 1. General Objective. To model and predict homicide crimes using ARIMA models. 2. Specific Objectives * To identify trends in homicide crime. * To forecast future homicide rates in Kenya. * To analyse and forecast the impact of socio-economic factors, government, or other external factors on homicide rates. ## Methodology. The study utilized a synthetic time series dataset representing homicide trends …