Open AccessExploratory data analysis of crime report
Author(s) -
Irwan Setiawan,
Suprihanto Suprihanto
Publication year - 2021
Publication title -
matrix : jurnal manajemen teknologi dan informatika/matrix: jurnal manajemen teknologi dan informatika
Language(s) - English
Resource type - Journals
eISSN - 2580-5630
pISSN - 2088-284X
DOI - 10.31940/matrix.v11i2.2449
Subject(s) - computer science , bivariate analysis , visualization , exploratory data analysis , data science , data analysis , univariate , data visualization , data mining , identification (biology) , crime analysis , visual analytics , data type , range (aeronautics) , bivariate data , multivariate statistics , machine learning , engineering , psychology , botany , criminology , biology , programming language , aerospace engineering
Visualization of data is the appearance of data in a pictographic or graphical form. This form facilitates top management to understand the data visually and get the messages of difficult concepts or identify new patterns. The approach of the personal understanding to handle data; applying diagrams or graphs to reflect vast volumes of complex data is more comfortable than presenting over tables or statements. In this study, we conduct data processing and data visualization for crime report data that occurred in the city of Los Angeles in the range of 2010 to 2017 using R language. The research methodology follows five steps, namely: variables identification, data pre-processing, univariate analysis, bivariate analysis, and multivariate analysis. This paper analyses data related to crime variables, time of occurrence, victims, type of crime, weapons used, distribution, and trends of crime, and the relationship between these variables. As the result shows, by using those methods, we can gain insights, understandings, new patterns, and do visual analytics from the existing data. The variations of crime variables presented in this paper are only a few of the many variations that can be made. Other variations can be performed to get more insights, understandings, and new patterns from the existing data. The methods can be performed on other types of data as well.