A review of statistical methods used for developing large-scale and long-term PM2. 5 models from satellite data
Research of PM 2.5 chronic health effects requires knowledge of large-scale and long-term
exposure that is not supported by newly established monitoring networks due to their sparse …
exposure that is not supported by newly established monitoring networks due to their sparse …
Advances of four machine learning methods for spatial data handling: A review
Most machine learning tasks can be categorized into classification or regression problems.
Regression and classification models are normally used to extract useful geographic …
Regression and classification models are normally used to extract useful geographic …
Assessing the impact of energy internet and energy misallocation on carbon emissions: new insights from China
With the deterioration of environmental quality caused by fossil energy use, the research on
energy internet and energy misallocation is of critical relevance to achieve low-carbon …
energy internet and energy misallocation is of critical relevance to achieve low-carbon …
Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling
Abstract Machine learning algorithms such as Random Forest (RF) are being increasingly
applied on traditionally geographical topics such as population estimation. Even though RF …
applied on traditionally geographical topics such as population estimation. Even though RF …
[HTML][HTML] Exploring the spatial and temporal driving mechanisms of landscape patterns on habitat quality in a city undergoing rapid urbanization based on GTWR and …
Landscape pattern significantly impacts habitat quality, especially in cities undergoing rapid
urbanization, where landscape patterns are changing dramatically. However, the spatial and …
urbanization, where landscape patterns are changing dramatically. However, the spatial and …
The spatiotemporal effects of environmental regulation on green innovation: Evidence from Chinese cities
Environmental regulation is expected to stimulate green innovation for the promotion of
urban sustainability, while the effectiveness of this stimulus has long been debated under …
urban sustainability, while the effectiveness of this stimulus has long been debated under …
Housing price prediction incorporating spatio-temporal dependency into machine learning algorithms
Conventional housing price prediction methods rarely consider the spatiotemporal non-
stationary problem in a large data volumes. In this study, four machine learning (ML) models …
stationary problem in a large data volumes. In this study, four machine learning (ML) models …
A geographically and temporally weighted regression model to explore the spatiotemporal influence of built environment on transit ridership
Understanding the influence of the built environment on transit ridership can provide transit
authorities with insightful information for operation management and policy making, and …
authorities with insightful information for operation management and policy making, and …
Deforestation and fires in the Brazilian Amazon from 2001 to 2020: Impacts on rainfall variability and land surface temperature
RM da Silva, AG Lopes, CAG Santos - Journal of Environmental …, 2023 - Elsevier
Deforestation and fires in the Amazon are serious problems affecting climate, and land use
and land cover (LULC) changes. In recent decades, the Amazon biome area has suffered …
and land cover (LULC) changes. In recent decades, the Amazon biome area has suffered …
Geographically weighted regression and multicollinearity: dispelling the myth
AS Fotheringham, TM Oshan - Journal of geographical systems, 2016 - Springer
Geographically weighted regression (GWR) extends the familiar regression framework by
estimating a set of parameters for any number of locations within a study area, rather than …
estimating a set of parameters for any number of locations within a study area, rather than …