Prediction of crime rate in urban neighborhoods based on machine learning

2021 ◽  
Vol 106 ◽  
pp. 104460
Author(s):  
Jingyi He ◽  
Hao Zheng
Keyword(s):  
Machine Learning ◽  
Crime Rate ◽  
Urban Neighborhoods

scholarly journals Hate Crime Analysis based on Artificial Intelligence Methods

2021 ◽  
Vol 251 ◽  
pp. 01062
Author(s):  
Shaoxuan Wang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Income Inequality ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Household Income ◽  
Crime Rate ◽  
Hate Crime ◽  
Hate Crimes ◽  
Median Household Income

Hate crimes always take a toll on American citizens, which harms social security. It is essential for researchers to explore the factors, which lead to hate crimes. This research is to find out the relationship between hate crimes and factors including income inequality, median household income, race using Machine Learning methods. Machine Learning, as an important branch in Artificial Intelligence, is a good way for finding relationships between things. The research is based on a dataset of hate crimes rates in the 2016 U.S. presidential election as well as hate crimes rates in every U.S. state from 2010 to 2015. Simply linear regression and multiple linear regression are used to describe the factors that influence the crime rate and their contributions, such as share of white poverty or share of non-white residents, or the median household income. Then, K-means is applied to classify hate crimes into 5 levels according to the crime rate. Furthermore, KNearest Neighbors is used to demonstrate a prediction of hate crime. At last, a histogram is applied to indicate the variance of the hate crimes in different states. From linear regression, four highest correlation coefficients with a hate crime can be found out, which are income inequality, median household income, the share of noncitizen, and race in turn. Income inequality has the highest correlation coefficient with a hate crime. From multiple linear regression, it can be found out that only by implementing income inequality, median household income, and race can we obtain the highest R square values, which are 0.44 for 2010 to 2015 hate crimes and 0.33 for 2016 hate crimes. From the K-Nearest Neighbors method, hate crimes can be predicted with an accuracy of 40% by applying median household income. Adding the race factor, accuracy rises to 50%. In summary, income inequality, median household income, and race have a high impact on the crime rate. The median household income and the race could predict the crime rate with an accuracy of about 50%.

scholarly journals An Effective Mechanism for Detecting Crime Rate in Chennai Location Using Supervised Machine Learning Approach

2020 ◽  
pp. 326-331
Author(s):  
R. Poorvadevi ◽  
G. Sravani ◽  
V. Sathyanarayana
Keyword(s):  
Machine Learning ◽  
Crime Rate ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
User Group ◽  
Effective Mechanism ◽  
The Public ◽  
Digital World ◽  
Machine Learning Approach ◽  
Important Challenge

In the current era of digital world, the crime is the important challenge among the distinct user. People are applying various techniques to prevent and reduce the crime. But there is no specific solution is optimal for crime issues. It is need to be tracking the all sets of crimes which is managed and stored in the crime specific database. The proposed work brings the solution to identify the occurrences of the crime for Chennai region and also tracking the location and type of threats over the criem can be detected in the public user group. This mechanism will be achiened the effective outcomes by applying the supervised meachine learning approach.

scholarly journals Design and Development of Machine Learning Algorithm for Forecasting Crime Rate

2019 ◽  
Vol 8 (11S) ◽  
pp. 1217-1222
Keyword(s):  
Machine Learning ◽  
Crime Rate ◽  
Clustering Algorithm ◽  
Learning Algorithm ◽  
Computational Cost ◽  
Learning Approaches ◽  
Average Value ◽  
Data Mining Approach ◽  
Initial Stage ◽  
Euclidean Distance Function

Controlling crime is one of the necessary things for a peaceful life. Forecasting the crime helps in planning the strategies in this task. Modern data analysis techniques like classification and prediction may be utilized for this purpose. Classification is a data mining approach that allocates items in a group to target categories or classes. It also may be used to label a target item into any one of the classes identified.Among many available classification techniques, clustering is one of the unsupervised machine learning approaches that could be used for creating clusters as features to enhance classification models. There are various clustering algorithm available like K-mean clustering, Kernel K-mean algorithm etc.PCA algorithm is used to reduce the dimension of the huge amount of data used so that the data can be represented in smaller database with reduced noise in the dataset. In general, mode is a set of values which occurs frequently. Hence, instead of k-mean which is an average value, frequent values may produce better result.K-Mean algorithm creates clusters and groups data properly. But randomly assuming centroid for clusters in the initial stage leads to too much of computational cost. So, in this work, K mode Clustering algorithm was used for clustering asit replaces the Euclidean distance function with the simple matching dissimilarity measure. Once the clusters were formed, a new algorithm was used to forecast the crime rate or future values of the data in the cluster.The proposed approach was tested on crime dataset and found efficient in this domain while comparing with some existing approaches

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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