hong kong |
7 |
beijing |
6 |
deep learning |
6 |
big data |
5 |
china |
5 |
fine-grained resolution |
5 |
air pollution modelling |
4 |
air quality |
4 |
environmental injustice |
4 |
high spatial resolution |
4 |
pm2.5 pollution |
4 |
social deprivation index |
4 |
air pollution control regulation |
3 |
air pollution control regulations |
3 |
air pollution forecast |
3 |
air pollution monitoring |
3 |
air quality measurements |
3 |
artificial intelligence |
3 |
bayesian deep-learning |
3 |
bayesian lstm |
3 |
blue sky day |
3 |
city-wide |
3 |
citywide domain-specific information |
3 |
clean air act |
3 |
climate policy |
3 |
cnn-lstm |
3 |
coal |
3 |
competitiveness |
3 |
computational social science |
3 |
counterfactual analysis |
3 |
data interpretability |
3 |
data privacy and security |
3 |
domain-specific knowledge |
3 |
ecological modernisation |
3 |
effects of regulatory interventions |
3 |
energy policy |
3 |
environmental inequality |
3 |
environmental innovation |
3 |
fine-grained air pollution estimation and forecast |
3 |
fuel cell vehicles |
3 |
geo-coded tweets |
3 |
health and well-being improvement |
3 |
health management |
3 |
household wealth proxies |
3 |
image-based pollution estimation |
3 |
institutional characteristics |
3 |
inter-disciplinarity |
3 |
international movement |
3 |
knowledge characteristics |
3 |
low-cost sensor |
3 |
low-rank matrix completion |
3 |
machine learning |
3 |
met-resnet-lstm |
3 |
missing data recovery |
3 |
multi-task learning |
3 |
personalization |
3 |
personalized air pollution estimation |
3 |
pm (1.0,2.5) |
3 |
pm2.5 and pm₁₀ estimation |
3 |
pollution |
3 |
portable sensor node |
3 |
prediction fusion |
3 |
prediction uncertainty |
3 |
propensity score |
3 |
resnet-lstm |
3 |
resnet-lstm-sp |
3 |
saliency analysis |
3 |
sensor calibration |
3 |
short-term happiness |
3 |
singular value thresholding |
3 |
smart behavioural intervention |
3 |
smart cities |
3 |
smart environment |
3 |
socio-economic status |
3 |
spatial-temporal data |
3 |
station-wide |
3 |
stationary-camera-taken images |
3 |
street canyon effect |
3 |
subjective well-being prediction |
3 |
sustainability studies |
3 |
sustainable development |
3 |
traffic congestion |
3 |
traffic speed |
3 |
transfer calibration |
3 |
transport |
3 |
twitter users |
3 |
united kingdom |
3 |
air pollution |
2 |
air pollution policy |
2 |
alzheimer’s disease |
2 |
atmospheric measurements |
2 |
autism |
2 |
barriers |
2 |
blood biomarkers |
2 |
california fuel cell partnership |
2 |
cancer |
2 |
carbon dioxide mitigation -- economic aspects |
2 |
cellular reprogramming |
2 |
challenge |
2 |
citizen-centric |
2 |
climatic changes -- government policy |
2 |
climatic changes -- government policy -- united states |
2 |
cognition |
2 |
comprehensive assessment |
2 |
computational text analysis |
2 |
convolutional neural network |
2 |
cross-domain dataset |
2 |
crowd intelligence |
2 |
data quality |
2 |
daytime satellite image |
2 |
deeply-supervised |
2 |
design |
2 |
design methodology |
2 |
developed metropolis |
2 |
discourse analysis |
2 |
district heating network |
2 |
domain adaptation |
2 |
driver |
2 |
drivers |
2 |
dynamic accounting |
2 |
e-learning technologies |
2 |
electric vehicles -- china -- hong kong |
2 |
electricity-saving behaviours |
2 |
emotion recognition |
2 |
emotiw 2018 challenge |
2 |
energy |
2 |
energy policy -- decision making |
2 |
energy policy -- united states |
2 |
environmental law -- california |
2 |
environmental law -- china -- hong kong |
2 |
environmental monitoring |
2 |
environmental regulation |
2 |
expression recognition |
2 |
gaussian process |
2 |
generative adversarial networks |
2 |
genetic algorithm |
2 |
government |
2 |
gp-mixed-siamese-like-double-ridge model |
2 |
grouping and collaborating |
2 |
health cost accounting |
2 |
high potential users |
2 |
house price |
2 |
human factors |
2 |
hydrogen cars -- china -- hong kong |
2 |
hydrogen economy |
2 |
innovation-oriented regulation |
2 |
interactive classrooms |
2 |
interpolation |
2 |
low-carbon |
2 |
lpg taxis |
2 |
mobile crowd sensing (mcs) |
2 |
monitoring |
2 |
mortality |
2 |
multi-aspect characterization |
2 |
multi-region ensemble |
2 |
multi-type sensor placement |
2 |
neural networks |
2 |
nuclear safety |
2 |
object movement prediction |
2 |
object tracking |
2 |
optimization |
2 |
peer reviews |
2 |
photograph taking |
2 |
policy recommendation |
2 |
pollution measurement |
2 |
post-fukushima |
2 |
post-traumatic stress disorder |
2 |
power resources -- united states |
2 |
pre-fukushima |
2 |
price responsiveness |
2 |
public health |
2 |
scenic spot profiling |
2 |
sensor placement |
2 |
sessional-daily variation |
2 |
side-output layers |
2 |
smart demand response |
2 |
smart energy information interventions |
2 |
smart energy management system (sems) |
2 |
smart energy monitors (sems) |
2 |
smart mobile devices |
2 |
social media data fusion |
2 |
sox17 |
2 |
spatial crowdsourcing (sc) |
2 |
stakeholder concerns |
2 |
static accounting |
2 |
sub-regional and city-district-level |
2 |
submodular maximization |
2 |
submodular optimization |
2 |
task allocation |
2 |
technological environmental innovation |
2 |
technological innovations -- environmental aspects |
2 |
technological innovations -- environmental aspects -- china -- hong kong |
2 |
time-based pricing |
2 |
transportation -- environmental aspects -- california |
2 |
transportation -- environmental aspects -- china -- hong kong |
2 |
trends |
2 |
variable selection |
2 |
wind energy development |
2 |
wnt signaling |
2 |
β-catenin |
2 |
barrier |
1 |
china's wind energy development |
1 |
expert opinion survey |
1 |
industry-university collaboration |
1 |
innovation policy |
1 |
logistic regression |
1 |
open innovation |
1 |
policy drivers |
1 |