Devi2k20
I am a CSIR- Senior Research Fellow, currently working in spatiotemporal data mining.
Pondicherry UniversityPuducherry
Pinned Repositories
covid19
SIR code in python to model the spread of COVID-19
Covid_Spatial_Analysis
Crime-Predictor-Chicago
One of the most important questions people ask before moving to a new place or traveling is: Is this locality safe? We answer this question using co-location pattern mining. Our crime predictor gives the possibility of all crimes that can happen given the presence of certain geographical entities and certain crimes in the locality. Given a set of boolean spatial features, the co-location pattern discovery process finds the subsets of features frequently located together. We have used a transaction free approach for this using Haversine distance as a metric for finding proximity neighborhood and measuring all spatial autocorrelations. We also propose an improvement to distance measure approach while calculating R-proximity neighborhood using boundary window. We used distance and participation thresholds for determining the correlation between features and crimes. Our algorithm exploits the anti-monotone property and optimizes the co-location pattern discovery algorithm by prevalence based pruning.
datasciencecoursera
data science course
datasharing
The Leek group guide to data sharing
Delhi_india-air-pollution
hello-world
exploring github
it-cert-automation-practice
Google IT Automation with Python Professional Certificate - Practice files
ProgrammingAssignment2
Repository for Programming Assignment 2 for R Programming on Coursera
Wordcloud
Devi2k20's Repositories
Devi2k20/covid19
SIR code in python to model the spread of COVID-19
Devi2k20/Covid_Spatial_Analysis
Devi2k20/Crime-Predictor-Chicago
One of the most important questions people ask before moving to a new place or traveling is: Is this locality safe? We answer this question using co-location pattern mining. Our crime predictor gives the possibility of all crimes that can happen given the presence of certain geographical entities and certain crimes in the locality. Given a set of boolean spatial features, the co-location pattern discovery process finds the subsets of features frequently located together. We have used a transaction free approach for this using Haversine distance as a metric for finding proximity neighborhood and measuring all spatial autocorrelations. We also propose an improvement to distance measure approach while calculating R-proximity neighborhood using boundary window. We used distance and participation thresholds for determining the correlation between features and crimes. Our algorithm exploits the anti-monotone property and optimizes the co-location pattern discovery algorithm by prevalence based pruning.
Devi2k20/datasciencecoursera
data science course
Devi2k20/datasharing
The Leek group guide to data sharing
Devi2k20/Delhi_india-air-pollution
Devi2k20/hello-world
exploring github
Devi2k20/it-cert-automation-practice
Google IT Automation with Python Professional Certificate - Practice files
Devi2k20/ProgrammingAssignment2
Repository for Programming Assignment 2 for R Programming on Coursera
Devi2k20/Wordcloud
Devi2k20/Rdatamining
This will be linked with my R studio