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Data Fusion Mapping Estimation

技術優勢

Compared to current methods, it can give more geographically accurate probability density estimates.

技術應用

This method can be used for such applications as:Mapping threat level probabilities for crimeSolving geographic profiling problemsAscertaining geographic location using wireless technology

詳細技術說明

UCLA researchers have developed a method that gives more geographically accurate probability density estimates. It uses a novel set of models that restrict the support of the density estimate to the valid region and ensure realistic behavior. This approach embodies new fast computational methods for density estimation using maximum penalized likelihood estimations.

*Abstract

UCLA researchers in the Department of Mathematics have developed a novel probability density estimation method that incorporates geographical information.

*Applications

This method can be used for such applications as:

*IP Issue Date

Jan 20, 2015

*Principal Investigation

Name: Andrea Bertozzi

Department:

Name: Matthew Keegan

Department:

Name: George Mohler

Department:

Name: Laura Michelle Smith

Department:

Name: Todd Wittman

Department:

附加資料

Patent Number: US20120257818A1
Application Number: US13306919A
Inventor: Bertozzi, Andrea L. | Smith, Laura M. | Keegan, Matthew S. | Wittman, Todd | Mohler, George O.
Priority Date: 29 Nov 2010
Priority Number: US20120257818A1
Application Date: 29 Nov 2011
Publication Date: 11 Oct 2012
IPC Current: G06K000962
US Class: 382155
Assignee Applicant: The Regents of the University of California
Title: SYSTEMS AND METHODS FOR DATA FUSION MAPPING ESTIMATION
Usefulness: SYSTEMS AND METHODS FOR DATA FUSION MAPPING ESTIMATION
Novelty: System for generating probability density to estimate probability that an event will occur in a region of interest, includes programming for inputting spatial event data comprising events occurring in region of interest

主要類別

信息和通信技術/電信

細分類別

軟件/應用

申請號碼

8938115

其他

State Of Development

Researchers have finished and published the computer model simulation results on a residential burglary dataset.

Background

High resolution and hyperspectral satellite images, city and county boundary maps, census data, and other types of geographical data provide much information about a given region. It is desirable to integrate this knowledge into models defining geographically dependent data. However, common methods of density estimation, such as Kernel Density Estimation, do not incorporate geographical information. Using such methods could result in predicting events in unrealistic or unreasonable geographic locations, such as residential burglary in the ocean.

Related Materials

Smith, Laura M., et al. "Improving density estimation by incorporating spatial information." EURASIP Journal on Advances in Signal Processing 2010 (2010): 7.

Additional Technologies by these Inventors

• Rapid Computational Technique for Inpainting of High Contrast Images
• Automated Activity Classification Of Video From Body Worn Cameras

Tech ID/UC Case

22308/2011-324-0

Related Cases

2011-324-0

國家/地區

美國