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1. Using Machine Learning Algorithms to Analyze Visualize and Detect Crime Data in Three Dimensional Directions.

Author

Kumari, B. Geetha and Reddy, Kandukuru Sneha

Subjects
MACHINE learning, CRIME, CRIMINAL act, CRIMINAL methods, CRIMINAL behavior, CRIME statistics, THREE-dimensional modeling
Abstract

Crime is a huge problem in our society, and fighting it is crucial. On a daily basis, there are several crimes done on a massive scale. For future reference, a database needs to be kept of all criminal acts and acts of misconduct, as these events are unlikely to be reported later. One of the issues now encountered is keeping up with crime statistics as well as analyzing this data to assist in preventing and solving future crimes. The aim of this project is to gather and analyze datasets containing a multitude of criminal events, and to determine which kinds of criminal activity may occur in the future based on various factors. We will use Machine learning algorithms for crime prediction in Chicago by applying machine learning algorithms to the Chicago criminal data set. Decision Tree, Gaussian Naive Bayes, k-NN, Logistic Regression are all excellent supervised classification tools for this task. With this method, we are attempting to predict crimes by classifying, recognizing patterns, and using effective technologies and tools. We can correlate aspects which can aid our comprehension of future crime patterns by looking at crime data trends that have occurred in the past. Machine learning and visualization approaches are applied to estimating that crimes dispersion over an area in this project. Once the primary records are analyzed and shown based on the need, the procedure shifted to the next stage. A three-dimensional modeling of Cologne's city is constructed using crime statistics. Result visualization in geovirtual environments and real-time situation monitoring in a social protection context result from exploring crimes data analysis in geovirtual environments. Three-dimensional measurements are rapidly becoming the norm in the documenting procedure for crime scenes. By utilizing 3D measuring tools, a richer investigation is provided, revealing the presence of each piece of evidence in relation to the rest of the crime scene. [ABSTRACT FROM AUTHOR]

Published
2021

2. TAGCN: Station-level demand prediction for bike-sharing system via a temporal attention graph convolution network.

Author

Zi, Wenjie, Xiong, Wei, Chen, Hao, and Chen, Luo

Subjects
*PROBLEM solving, *FORECASTING, *MACHINE learning
Abstract

Nowadays, bike-sharing is available in many cities, solving the problem of the last mile, and it is an environmental-friendly way to commute. However, there is a tidal phenomenon in the bike-sharing system, and the rents/returns of bikes at different stations are unbalanced. Thus, bikes at different stations need to be rebalanced regularly and station-level demand prediction plays an essential role in bike-sharing rebalancing. In this paper, a novel deep graph convolutional network (GCN) model with temporal attention (TAGCN) is proposed for bike check-out/in number prediction of each station. TAGCN can not only model the spatial and temporal dependency between varying stations, but also reflect the influence of different time granularity, which are hour-level, day-level and week-level time periodicity. With the help of well-designed temporal attention mechanism, our model can capture the dynamical temporal correlations and comprehensive spatial patterns in bike check-out/in flow effectively. The proposed model consistently outperforms state-of-the-art methods on four real-world bike-sharing datasets that are four seasons data of Divvy Bike System in Chicago. [ABSTRACT FROM AUTHOR]

Published
2021
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