Search

Geographically weighted regression model with a spatially autoregressive term of the response variable (GWR‐Lag model for short) is a useful tool to simultaneously model spatial autocorrelation in the response variable and spatial heterogeneity in the regression relationship. The profile maximum likelihood estimation has been developed to calibrate the model, where a single bandwidth is used for all of the spatially varying coefficients. However, due to different spatial scales at which the explanatory variables might operate, the coefficients may have different levels of spatial heterogeneity and cannot be effectively estimated by a single bandwidth. To deal with such multiscale problem, a backfitting method is proposed to calibrate the GWR‐Lag model, where the optimal bandwidth size is separately selected for each coefficient and the autoregressive parameter is estimated by maximizing the log‐likelihood function. A simulation study with a comparison of the GWR‐based profile maximum likelihood (GWR‐ML) estimation is conducted to assess the performance of the backfitting approach. The empirical results show that the backfitting method not only provides useful scale information for each explanatory variable, but also yields more accurate estimators of the coefficients. Furthermore, a real‐life data set is analyzed to demonstrate the effectiveness of the backfitting approach. [ABSTRACT FROM AUTHOR]

Geographically and temporally weighted regression (GTWR) has been demonstrated as an effective tool for exploring spatiotemporal data under spatial and temporal heterogeneity. Exploiting the advantages of the two most popular GTWR methods, we propose an alternative GTWR with a good balance between complexity and interpretability via a unilateral temporal weighting scheme called unilateral GTWR (UGTWR). When compared to the other two popular GTWR methods, the simulation experiment shows that UGTWR has comparable estimation accuracy and model fit, but it is more efficient. Furthermore, we propose its multiscale extension, coined multiscale UGTWR (MUGTWR), to characterize the spatiotemporal dynamic regression relationships at multiple scales. The proposed MUGTWR was applied to the analysis of house prices in the period of 2014–2018 in Beijing as a case study. Our analysis reveals that MUGTWR can effectively capture different levels of spatiotemporal heterogeneity in selected factors affecting house prices at different scales. Therefore, this study is useful for the formulation of housing policy in which the spatiotemporal dynamics of house prices with respect to specific factors can be considered. [ABSTRACT FROM AUTHOR]

Identification and mapping of various habitats with sufficient spatial details are essential to support environmental planning and management. Considering the complexity of diverse habitat types in a heterogeneous landscape, a context-dependent mapping framework is expected to be superior to traditional classification techniques. With the aim to produce a territory-wide habitat map in Hong Kong, a three-stage mapping procedure was developed to identify 21 habitats by combining very-high-resolution satellite images, geographic information system (GIS) layers and knowledge-based modification rules. In stage 1, several classification methods were tested to produce initial results with 11 classes from a WorldView-2/3 image mosaic using a combination of spectral, textural, topographic and geometric variables. In stage 2, modification rules were applied to refine the classification results based on contextual properties and ancillary data layers. Evaluation of the classified maps showed that the highest overall accuracy was obtained from pixel-based random forest classification (84.0%) and the implementation of modification rules led to an average 8.8% increase in the accuracy. In stage 3, the classification scheme was expanded to all 21 habitats through the adoption of additional rules. The resulting habitat map achieved >80% accuracy for most of the evaluated classes and >70% accuracy for the mixed habitats when validated using field-collected points. The proposed mapping framework was able to utilize different information sources in a systematic and controllable workflow. While transitional mixed habitats were mapped using class membership probabilities and a soft classification method, the identification of other habitats benefited from the hybrid use of remote-sensing classification and ancillary data. Adaptive implementation of classification procedures, development of appropriate rules and combination with spatial data are recommended when producing an integrated and accurate map. [ABSTRACT FROM AUTHOR]

Ambient fine particulate matter (PM 2.5 ) has severe adverse health impacts, making it crucial to reduce PM 2.5 exposure for public health. Meteorological and emissions factors, which considerably affect the PM 2.5 concentrations in the atmosphere, vary substantially under different climate change scenarios. In this work, global PM 2.5 concentrations from 2021 to 2100 were generated by combining the deep learning technique, reanalysis data, emission data, and bias-corrected CMIP6 future climate scenario data. Based on the estimated PM 2.5 concentrations, the future premature mortality burden was assessed using the Global Exposure Mortality Model. Our results reveal that SSP3-7.0 scenario is associated with the highest PM 2.5 exposure, with a global concentration of 34.5 μg/m 3 in 2100, while SSP1-2.6 scenario has the lowest exposure, with an estimated of 15.7 μg/m 3 in 2100. PM 2.5 -related deaths for individuals under 75 years will decrease by 16.3 and 10.5% under SSP1-2.6 and SSP5-8.5, respectively, from 2030s to 2090s. However, premature mortality for elderly individuals (>75 years) will increase, causing the contrary trends of improved air quality and increased total PM 2.5 -related deaths in the four SSPs. Our results emphasize the need for stronger air pollution mitigation measures to offset the future burden posed by population age.