scholarly journals Spatio-Temporal Crime Analysis Using KDE and ARIMA Models in the Indian Context

2020 ◽  
Vol 12 (4) ◽  
pp. 1-19
Author(s):  
Prathap Rudra Boppuru ◽  
Ramesha K.
Keyword(s):  
Data Mining ◽  
Predictive Analytics ◽  
Time Series Prediction ◽  
Arima Model ◽  
Estimation Method ◽  
Time Of Day ◽  
Crime Patterns ◽  
Model Time Series ◽  
Spatio Temporal ◽  
One Year

In developing countries like India, crime plays a detrimental role in economic growth and prosperity. With the increase in delinquencies, law enforcement needs to deploy limited resources optimally to protect citizens. Data mining and predictive analytics provide the best options for the same. This paper examines the news feed data collected from various sources regarding crime in India and Bangalore city. The crimes are then classified on the geographic density and the crime patterns such as time of day to identify and visualize the distribution of national and regional crime such as theft, murder, alcoholism, assault, etc. In total, 68 types of crime-related dictionary keywords are classified into six classes based on the news feed data collected for one year. Kernel density estimation method is used to identify the hotspots of crime. With the help of the ARIMA model, time series prediction is performed on the data. The diversity of crime patterns is visualized in a customizable way with the help of a data mining platform.

scholarly journals Air Pollution Prediction in Smart Cities by using Machine Learning Techniques

2020 ◽  
Vol 9 (5) ◽  
pp. 1272-1279
Keyword(s):  
Air Pollution ◽  
Smart Cities ◽  
Time Series Prediction ◽  
Arima Model ◽  
Urban Air Pollution ◽  
Handling Time ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Air Contamination ◽  
Spatio Temporal

The urban air pollution has an immediate effect on man health specifically in developing and mechanical countries. It can cause health issues such as cancer, cardiovascular diseases and high mortality rates. Continuous checking of contamination empowers the metropolitans to dissect the present traffic circumstance of the city and take their decision accordingly. Existing exploration has utilized diverse AI apparatuses for pollution forecast; notwithstanding, relative examination of these methods is regularly required to have a superior comprehension of their handling time for numerous datasets. In this work, we look at forecasting the air contamination by dealing with parameters of three different gases like SO2 ,NO2 ,O3 .This process involves to pre-processing the times series. However, pre-processing involves a similarity measure, we explore the use of Dynamic Time Warping (DTW),LSTM,ARIMA Model for time series prediction, Kmeans, Support Vector Regression is then used to classify the spatio-temporal pollution data of different areas over a period of 10 years.

Enhancing Wellwork Efficiency With Data Mining and Predictive Analytics

2014 ◽  
Author(s):  
Mohamed Sidahmed ◽  
Eric Ziegel ◽  
Shahryar Shirzadi ◽  
David Stevens ◽  
Maria Marcano
Keyword(s):  
Data Mining ◽  
Predictive Analytics

Exploratory spatio-temporal data mining and visualization

2007 ◽  
Vol 18 (3) ◽  
pp. 255-279 ◽  
Author(s):  
P. Compieta ◽  
S. Di Martino ◽  
M. Bertolotto ◽  
F. Ferrucci ◽  
T. Kechadi
Keyword(s):  
Data Mining ◽  
Temporal Data Mining ◽  
Temporal Data ◽  
Spatio Temporal
Sign in / Sign up

Export Citation Format

Share Document