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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | CRIME HOTSPOT DETECTION AND SAFE PATH SCHEMER USING STATISTICAL AND GEO-SPATIAL METHODS |
| Authors Name: | Shraddha Bhat , Mr. Mohammed Aimon , Ms. Rashmitha Hegde , Mr. Shadman , Mrs. Jyothi Prasad |
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IJNRD_193873
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| Published Paper Id: | IJNRD2305047 |
| Published In: | Volume 8 Issue 5, May-2023 |
| DOI: | |
| Abstract: | The safe path schemer and crime hotspot identification methods used in this research study combine statistical and GIS techniques. The suggested methodology makes use of crime data from numerous sources, such as police reports, social media, and citizen reporting systems, to pinpoint urban crime hotspots. The statistical techniques used include regression analysis, hypothesis testing, and clustering based on density. The results of the statistical analysis are visualized using geospatial techniques like geographic information systems (GIS), which are also used to find spatial patterns and connections between crime hotspots and other urban aspects like land use and transportation systems. Additionally, a safe path schemer is created using a network analysis algorithm that makes use of the data from crime hotspots to determine the safest and most practical paths for both vehicles and pedestrians. To determine the safest paths, the algorithm takes into account a number of variables, such as crime rates, road connections, and automobile and pedestrian traffic. The suggested methodology is evaluated using actual crime statistics from a large metropolis, and the findings demonstrate that the system is successful in locating crime hotspots and offering safe route planners for metropolitan areas. Overall, this research study makes a significant contribution to the subject of urban crime prevention and public safety and offers insights into the application of statistical and geospatial methodologies to enhance crime prevention efforts and foster safer urban settings. |
| Keywords: | K-Means Clustering, Crime hotspot detection, Safest path, Crime count |
| Cite Article: | "CRIME HOTSPOT DETECTION AND SAFE PATH SCHEMER USING STATISTICAL AND GEO-SPATIAL METHODS", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 5, page no.a348-a353, May-2023, Available :http://www.ijnrd.org/papers/IJNRD2305047.pdf |
| Downloads: | 000118763 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2305047 Registration ID: 193873 Published In: Volume 8 Issue 5, May-2023 DOI (Digital Object Identifier): Page No: a348-a353 Country: Moodubidri, Karnataka, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2305047 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2305047 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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