A geographically and temporally weighted regression model for assessing intra-urban variability of volatile organic compounds (VOCs) in Yangpu district, Shanghai
Sign Up to like & getrecommendations! Published in 2019 at "Atmospheric Environment"
DOI: 10.1016/j.atmosenv.2019.06.052
Abstract: Abstract The adverse health effects of volatile organic compounds (VOCs) highlight the need of spatiotemporal models to assess exposure for epidemiological studies. Herein, a geographically and temporally weighted regression (GTWR) model was developed to estimate… read more here.
Keywords: volatile organic; temporally weighted; model; district ... See more keywords
A spatio-temporally weighted hybrid model to improve estimates of personal PM2.5 exposure: Incorporating big data from multiple data sources.
Sign Up to like & getrecommendations! Published in 2019 at "Environmental pollution"
DOI: 10.1016/j.envpol.2019.07.034
Abstract: An accurate estimation of population exposure to particulate matter with an aerodynamic diameter read more here.
Keywords: pm2; model improve; spatio temporally; exposure ... See more keywords
Estimating ground-level PM2.5 concentrations in Beijing using a satellite-based geographically and temporally weighted regression model
Sign Up to like & getrecommendations! Published in 2017 at "Remote Sensing of Environment"
DOI: 10.1016/j.rse.2017.06.001
Abstract: Abstract Most time-sequenced ambient air pollution data in China is published through daily Air Quality Index (AQI). However, few studies have used the AQI data to calibrate satellite-based estimates of fine particulate matter (PM 2.5… read more here.
Keywords: weighted regression; regression; gtwr model; model ... See more keywords
Multiscale geographically and temporally weighted regression with a unilateral temporal weighting scheme and its application in the analysis of spatiotemporal characteristics of house prices in Beijing
Sign Up to like & getrecommendations! Published in 2021 at "International Journal of Geographical Information Science"
DOI: 10.1080/13658816.2021.1912348
Abstract: ABSTRACT 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… read more here.
Keywords: regression; analysis; house prices; geographically temporally ... See more keywords
Geographically and temporally weighted co-location quotient: an analysis of spatiotemporal crime patterns in greater Manchester
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Geographical Information Science"
DOI: 10.1080/13658816.2022.2029454
Abstract: Abstract Incident data, a form of big data frequently used in urban studies, are characterized by point features with high spatial and temporal resolution and categorical values. In contrast to panel data, such spatial data… read more here.
Keywords: temporally weighted; crime patterns; weighted location; geographically temporally ... See more keywords
Spatio-Temporally Weighted Two-Step Method for Retrieving Seismic MBT Anomaly: May 2008 Wenchuan Earthquake Sequence Being a Case
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"
DOI: 10.1109/jstars.2019.2962719
Abstract: Referring to the first law of geography and its extension in time domain, this research makes improvements on the two-step method (TSM) presented to retrieve seismic microwave brightness temperature (MBT) anomaly, and a new method… read more here.
Keywords: two step; mbt; earthquake; method ... See more keywords
Communication-Efficient Federated Deep Learning With Layerwise Asynchronous Model Update and Temporally Weighted Aggregation
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Neural Networks and Learning Systems"
DOI: 10.1109/tnnls.2019.2953131
Abstract: Federated learning obtains a central model on the server by aggregating models trained locally on clients. As a result, federated learning does not require clients to upload their data to the server, thereby preserving the… read more here.
Keywords: communication; temporally weighted; model; learning ... See more keywords