报告人简介：
Dr. Paul Harris the Eco-Informatics Scientist in Rothamsted Research(North Wyke site). His research themes includeStatistics (Advances in non-stationary spatial statistics; Visualisation of uncertainty; Anomaly detection; Time series analysis &hybridisation with agricultural process-based models), Statistical methods (for analysis of Remote Sensing, Crowdsourcing & Population (livestock) movement at International scales), Informatics(Environmental database design, set-up & promotion; data mining for quality control and detection of ‘unusualness’; Big Data; Integration of data collection design with sensor technology; smart farms and Internet of Things). He is one of core developers of geographically weighted (GW) models, especially makes original contributions in GW Regression, GW components analysis and krigingmethods with GWvariograms. He has published more than 25 journal papers in the relative fields.
报告摘要：
Spatial statistics provides important analytical techniques in a wide range of disciplines in the natural and social sciences. Geographically weighted (GW) models form a particular branch of spatial statistics, that suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localised calibration provides a better description. The approach uses a moving window weighting technique, where localised models are found at target locations. Outputs are mapped to provide a useful exploratory tool into the nature of the data spatial heterogeneity. Commonly applied GW models include: GW summary statistics, GW principal components analysis and GW regression. In this seminar, the GW model approach is introduced and reviewed, together with a discussion on key methodological issues and recent advances.
时间：9月2日上午10:30— 11：30
地点：遥感信息工程学院附3-202
