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Palabras clave – provistas por la editorial

the elderly; regression analysis; walking event; green space; missing data; crop residue burning; correlation analysis; imputation; physical environment; crowdedness; Guangzhou; mobile phone data; GPS trace; noise pollution; mental disorders; Beijing; urban leisure; environmental exposure; environmental context cube; subway stations; air pollution exposure; long-distance walking; car ownership; multilevel model; CHAS; ecological momentary assessment; cycling for transportation; cognitive aging; 3SFCA; interannual and seasonal variations; well-being experience; personal projects; spatial spread; E2SFCA; activity space; catchment areas; structural equation modeling; transport modes; greenspace exposure; health; train stations; human mobility; quantile regression; the neighborhood effect averaging problem (NEAP); emissions estimation; taxi GPS trajectories; real-time traffic; primary healthcare; rail travel; spatial accessibility; commuting route; GPS; urban planning; environmental health; Brazil; EMA; geographical accessibility; big data; dynamic assessment; obesity; healthcare accessibility; population demand; the uncertain geographic context problem (UGCoP); geographic impedance; collective leisure activity; multimodal network; GIS; 2009 influenza A(H1N1) pandemic; UGCoP; environmental exposures; spatial data; the uncertain geographic context problem; Singapore; built environment; adults; time-weighted exposure; geographic imputation; Public Participatory GIS (PPGIS); access probability; life-course perspectives; China; walking; active travel; foodscape exposure; car use; food environment; fuel consumption; ageing; Healthcare services; road traffic accidents; space-time kernel density estimation; multilevel Bayesian model; environmental context exposure index; spatial autocorrelation; PM concentrations; physical activity; bike paths