big-data-2019-e-poster-abstracts

2 / 3

Big Data 2019 American Journal of Computer Science and Information Technology
ISSN: 2349-3917 Page 24
March 04-05, 2019
Barcelona, Spain
8th Edition of International Conference on
Big Data &
Data Science
It is indisputable that machine learning techniques and big
data analysis have become the main topics in almost all
discipline of science and industry during the past decade.
Concurrently, numerous governments in the world are collecting
enough amounts of administrative data that can be analyzed
by machine learning techniques to investigate the causes
of social phenomena and to improve the efficiency of public
administration. Despite the data analytic techniques and the
capability of data storage have been remarkably improved, a large
number of scholars in the field of social science hold conservative
perspective on applying machine learning and big data analysis
to explaining social phenomena. The goal of this study is to fill
the void by providing empirical evidence. The present study will
attempt to examine the validity of using administrative big data to
predict crime incidents. Records of calls for service through 311
mayor’s hotline system in Houston, Texas and the official crime
reports of Houston Police Department were examined to assess
whether signs of physical decay and the presence of social
nuisance predict the crime incidents at neighborhood level. The
results of this study will corroborate the Broken Windows Theory
and present new windows to explore the causes of crime. Several
policy implications for government and police administrators will
be developed and discussed.
Recent Publications
1. OhGandConnollyEJ(2019)Angerasamediatorbetween
peer victimization and deviant behavior in South Korea: A
cross-cultural application of general strain theory. Crime
and Delinquency DOI: 10.1177/0011128718806699.
2. Kim J, Oh G and Siennick E (2018) Unraveling the
effect of cell phone reliance on adolescent self-control.
Children and Youth Services Review 87:78-85.
3. Ha T, Oh G and Park H H (2015) Comparative analysis
of defensible space in CPTED housing and non-CPTED
housing. International Journal of Law, Crime, and
Justice, 43(4):496-511.
4. Park HH, Oh G and Paek S Y (2012) Measuring the crime
displacement and diffusion of benefit effects of open-
street CCTV in South Korea. International Journal of
Law, Crime, and Justice, 40(3):179-191.
Biography
Gyeongseok Oh is pursuing his PhD at SHSU Criminal Justice. He has com-
pleted his MS in Criminology and Criminal Justice at Florida State University
in 2016 andMA in Criminal Justice at Yong In University. Before pursuing his
graduate studies, he worked as a Detective in the Korean Police Agency for
six years after obtaining his Bachelor’s degree at Korean National Police Uni-
versity. He is currently working on the research project entitled, “Social me-
dia analysis of neighborhood sentiment and its impact on crime patterns”
with Dr. Yan Zhang. His primary research interests include crime analysis
using Big Data and machine learning, policing, and biosocial criminology.
Crime prediction using administrative big data
and machine learning
Gyeongseok Oh and Zhang Yan
Sam Houston State University, USA
Gyeongseok Oh et al., Am J Compt Sci Inform Technol 2019, Volume 7
DOI: 10.21767/2349-3917-C1-009