Modeling Spatial Variation of Money Laundering Crime in Indonesia Using Geographically Weighted Multinomial Logistic Regression

Abstract

Abstract This document presents the mapping of crime in Indonesia. Crime is a real threat that needs to be optimally handled which increases significantly and it is very detrimental to the country’s economy. The crime that occurred created another more serious crime, namely Money Laundering Crime. The impact of Money Laundering Crime is the flow of illicit fund through the payment system to financial service providers as well as providers of goods and services. The perpetrator of Money Laundering crime is a player who is shrewd and familiar with the state financial system, so that the flow of funds does not involve a region but will expand to other areas in a country. This study analyses the spatial variations in the factors causing the Money Laundering crime in Indonesia by using a Geographically Weighted Multinomial Logistic Regression (GWMLR) technique. In this document, the GWMLR method is used to model the Money Laundering Crime and its explanatory variables by considering the effect of geographic location using the Adaptive Kernel weighting function. The response variables were divided into four categories with nominal scales by combining the general criminal case and money laundering crime.