NDLI: Spatial Prediction Models for Mining Spatial Data

Content Provider	IEEE Xplore Digital Library
Author	Caiping Hu Xiaolin Qin Jun Zhang
Copyright Year	2007
Description	Author affiliation: Nanjing Univ. of Aeronaut. & Astronaut., Nanjing (Caiping Hu; Xiaolin Qin; Jun Zhang)
Abstract	The multivariate linear regression (MLS) model is a very good technique for non-spatial prediction. But spatial prediction needs to account for spatial information, which makes the MLS model inappropriate, for it assume that the learning samples are independently and identically distributed(i.i.d). Due to account for spatial information, the spatial auto-regression (SAR) model can be used for spatial prediction, but it is computationally very expensive. In this paper, we add spatial information into input variables by replacing each input variables with the weighted average of its neighbors and feed the new input variables to a MLS model to estimate model parameters, and then make spatial prediction, where MLS* stands for this model. Experimental results show that the MLS* model and the SAR model have almost identical effects on spatial prediction, while the MLS model is computationally more efficient than the SAR model.
Starting Page	369
Ending Page	372
File Size	196204
Page Count	4
File Format	PDF
ISBN	1424410916
DOI	10.1109/ICITECHNOLOGY.2007.4290498
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-03-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	multivariate linear regression model Parameter estimation Input variables Linear regression Multilevel systems spatial auto-regression model Predictive models spatial autocorrelation Spatial databases Data mining spatial prediction Space technology Autocorrelation Feeds
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A fuzzy projection pursuit model for prediction in high-dimensional space with fuzzily dependent inputs

Modelling multivariate data by neuro-fuzzy systems

An Ensemble Method for Medicine Best Selling Prediction

A data mining method for selecting input variables for forecasting model of global solar radiation

Iterative Multivariate Regression Model for Correlated Responses Prediction

Imputation for the analysis of missing values and prediction of time series data

Global and Local Spatial Data Mining on Literacy Rates of Bangladesh

Urban traffic flow prediction: A MapReduce based parallel multivariate linear regression approach

Mining real estate listings using Oracle data warehousing and predictive regression

Spatial Prediction Models for Mining Spatial Data

Similar Documents

A fuzzy projection pursuit model for prediction in high-dimensional space with fuzzily dependent inputs

Modelling multivariate data by neuro-fuzzy systems

An Ensemble Method for Medicine Best Selling Prediction

A data mining method for selecting input variables for forecasting model of global solar radiation

Iterative Multivariate Regression Model for Correlated Responses Prediction

Imputation for the analysis of missing values and prediction of time series data

Global and Local Spatial Data Mining on Literacy Rates of Bangladesh

Urban traffic flow prediction: A MapReduce based parallel multivariate linear regression approach

Mining real estate listings using Oracle data warehousing and predictive regression

Spatial Prediction Models for Mining Spatial Data