|
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 |
|
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-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 |
|
prediction fusion |
3 |
|
prediction uncertainty |
3 |
|
propensity score |
3 |
|
resnet-lstm |
3 |
|
resnet-lstm-sp |
3 |
|
saliency analysis |
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 |
|
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 |
