Spatial Statistics

Long-Term Dynamic Monitoring and Driving Force Analysis of Eco-Environmental Quality in China

Accurate assessments of the historical and current status of eco-environmental quality (EEQ) are essential for governments to have a comprehensive understanding of regional ecological conditions, formulate scientific policies, and achieve the United …

Health geography studies based on spatial lifecourse epidemiology framework

To introduce some cases of my health geography studies.

Assessing spatiotemporal bikeability using multi-source geospatial big data: A case study of Xiamen, China

we develop a bikeability evaluation framework by combining the collected multi-source spatio-temporal big data.

Seeking your interest and going ahead will contribute to success

We just want to talk about how to become a academic scholar.

A mixed-effect model for estimation of large area subtropical forest biomass

A mixed-effect model for estimation of large area subtropical forest biomass. CN108959705A

Improving the accuracy of forest biomass estimate using source analysis and machine learning

Improving the accuracy of forest biomass estimate using source analysis and machine learning. CN111814397A

A forest inventory biomass estimation model by multisources data fusion

A forest inventory biomass estimation model by multisources data fusion. CN110162872A.

Do neighborhood boundaries matter for examining the associations between environmental factors and obesity

Using Gaode location based service data and travel time mapping produced by multi-sources geospatial data to investigate the effect of neighborhood boundaries on examining the associations between environmental factors and obesity.

Global spreading of Omicron variant of COVID-19

Although two years have passed since the coronavirus disease 2019 (COVID-19) outbreak, various variants are still rampant across the globe. The Omicron variant, in particular, is rapidly gained dominance through its ability to spread. In this study, …

Improving Plot-Level Model of Forest Biomass: A Combined Approach Using Machine Learning with Spatial Statistics

Estimating the aboveground biomass (AGB) at the plot level plays a major role in connecting accurate single-tree AGB measurements to relatively difficult regional AGB estimates. The goal of this study is to determine whether combining machine learning with spatial statistics reduces the uncertainty of plot-level AGB estimates.