Spatio-Temporal Analysis for Urban Crime Prediction (Record no. 16569)

MARC details
000 -LEADER
fixed length control field 01901nam a22001697a 4500
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.3
Item number P985S
100 ## - MAIN ENTRY--AUTHOR NAME
Personal name Purohit, Naresh
245 ## - TITLE STATEMENT
Title Spatio-Temporal Analysis for Urban Crime Prediction
Statement of responsibility, etc by Naresh Purohit
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication IIT Jodhpur
Name of publisher Department of Computer Science and Technology
Year of publication 2023
300 ## - PHYSICAL DESCRIPTION
Number of Pages vii, 19p.
Other physical details HB
500 ## - GENERAL NOTE
General note In the current security system, police always try to find the criminals after the crime has happened. For the elimination of these crimes, the government is always looking to adopt some decent and reliable strategy and a sustainable e-governance Information System. The prediction of crime would definitely be helpful in this case, especially with the details of time and space. For this purpose, research is done based on the spatial and temporal data for urban crime. The reason and requirement for choosing these spatio-temporal data is that crime mostly happens in a pattern, just like there are more theft cases at night rather than during the day. Similarly, residential areas on the outer part of the city are more likely to report theft cases.<br/><br/>So, the research idea is to observe the spatial and temporal data and come up with the hotspot areas and also to train ML models on these data to get the prediction of crime type with respect to location and time. As part of the experiments, I have followed the basic approach of training the weak learners like Random Forest, k-Nearest Neighbour, and MultiLayer Perceptron and refined them with Adaboost and GradientBoost boosting algorithms. Also, as part of a different approach, I have implemented a 3D CNN model on different crime datasets. The outcome of these experiments can be seen in this report.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Department of Computer Science and Technology
Topical Term Crime Prediction
Topical Term MTech Theses
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Das, Debasis
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Thesis
Holdings
Withdrawn status Lost status Damaged status Not for loan Permanent Location Current Location Shelving location Date acquired Source of acquisition Full call number Accession Number Price effective from Koha item type
        S. R. Ranganathan Learning Hub S. R. Ranganathan Learning Hub Reference 2024-04-01 Office of Academics 006.3 P985S TM00504 2024-06-27 Thesis