Jaichandran R, S. Jagan, S. Khasim, Logeshwari Dhavamani, V. Mathiazhagan, Dilip Kumar Bagal
{"title":"基于gis的犯罪可视化框架","authors":"Jaichandran R, S. Jagan, S. Khasim, Logeshwari Dhavamani, V. Mathiazhagan, Dilip Kumar Bagal","doi":"10.1109/iccica52458.2021.9697127","DOIUrl":null,"url":null,"abstract":"Past decades have experienced the rage of GIS technology in a never-ending scope. In today’s era, visualization of high-dimensional hyperspectral data is an indispensable task and GIS is simply a platform to practically experience the visualization. Furthermore, crime is an unprecedented event and to analyze crime their exists many technologies but to visualize it we are limited to adopt a few technologies and GIS is one of those few. Since crime data which are location-specific acts as a better data for crime analysis, we prefer GIS technology for mapping and visualization. This paper focuses on visualization of crime data using GIS technology and proposes a framework for futuristic crime analysis with the aid of deep learning acting at the backend. The experimentation performed over crime dataset presents the visualization of crime hotspots over a specified region basing on the dataset. The entire workflow is mentioned as a consequence of GIS technology over crime hotspot detection. Furthermore, the essence of deep learning is proposed as a future research direction for the real-time visualization of crime so that it can be checked before it happens. Finally, this paper provides the crime hotspot mappings as the output for visualization and analysis through proper experimentation.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Crime Visualization using A Novel GIS-Based Framework\",\"authors\":\"Jaichandran R, S. Jagan, S. Khasim, Logeshwari Dhavamani, V. Mathiazhagan, Dilip Kumar Bagal\",\"doi\":\"10.1109/iccica52458.2021.9697127\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Past decades have experienced the rage of GIS technology in a never-ending scope. In today’s era, visualization of high-dimensional hyperspectral data is an indispensable task and GIS is simply a platform to practically experience the visualization. Furthermore, crime is an unprecedented event and to analyze crime their exists many technologies but to visualize it we are limited to adopt a few technologies and GIS is one of those few. Since crime data which are location-specific acts as a better data for crime analysis, we prefer GIS technology for mapping and visualization. This paper focuses on visualization of crime data using GIS technology and proposes a framework for futuristic crime analysis with the aid of deep learning acting at the backend. The experimentation performed over crime dataset presents the visualization of crime hotspots over a specified region basing on the dataset. The entire workflow is mentioned as a consequence of GIS technology over crime hotspot detection. Furthermore, the essence of deep learning is proposed as a future research direction for the real-time visualization of crime so that it can be checked before it happens. Finally, this paper provides the crime hotspot mappings as the output for visualization and analysis through proper experimentation.\",\"PeriodicalId\":327193,\"journal\":{\"name\":\"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)\",\"volume\":\"2015 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/iccica52458.2021.9697127\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/iccica52458.2021.9697127","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Crime Visualization using A Novel GIS-Based Framework
Past decades have experienced the rage of GIS technology in a never-ending scope. In today’s era, visualization of high-dimensional hyperspectral data is an indispensable task and GIS is simply a platform to practically experience the visualization. Furthermore, crime is an unprecedented event and to analyze crime their exists many technologies but to visualize it we are limited to adopt a few technologies and GIS is one of those few. Since crime data which are location-specific acts as a better data for crime analysis, we prefer GIS technology for mapping and visualization. This paper focuses on visualization of crime data using GIS technology and proposes a framework for futuristic crime analysis with the aid of deep learning acting at the backend. The experimentation performed over crime dataset presents the visualization of crime hotspots over a specified region basing on the dataset. The entire workflow is mentioned as a consequence of GIS technology over crime hotspot detection. Furthermore, the essence of deep learning is proposed as a future research direction for the real-time visualization of crime so that it can be checked before it happens. Finally, this paper provides the crime hotspot mappings as the output for visualization and analysis through proper experimentation.