|
ai-generated content (aigc) |
7 |
|
generative ai |
7 |
|
generative method |
7 |
|
generative urban design |
7 |
|
human-machine collaboration |
7 |
|
urban form generation |
7 |
|
architectural design |
4 |
|
built environment |
4 |
|
covid-19 prevalence |
4 |
|
high density city |
4 |
|
china |
2 |
|
computational social science |
2 |
|
factor analysis |
2 |
|
geo⁃social computing |
2 |
|
land use change |
2 |
|
shanghai |
2 |
|
social computing |
2 |
|
spatial big data |
2 |
|
spatial computing |
2 |
|
spatial⁃social computing |
2 |
|
urban air pollution |
2 |
|
urban built environment |
2 |
|
urban climate change |
2 |
|
urban planning |
2 |
|
urban vibrancy |
2 |
|
wrf/noah lsm/slucm |
2 |
|
air pollution monitoring |
1 |
|
big data |
1 |
|
carbon emissions |
1 |
|
citizen science |
1 |
|
coastal wetland |
1 |
|
crowdsourcing |
1 |
|
eddy covariance |
1 |
|
geographically weighted regression |
1 |
|
global warming potential |
1 |
|
greenhouse gas |
1 |
|
indoor air pollution |
1 |
|
lower-cost sensor technology |
1 |
|
mangrove |
1 |
|
methane |
1 |
|
mobile phone data |
1 |
|
multi-objective genetic algorithm |
1 |
|
network-based spatial optimization |
1 |
|
open data |
1 |
|
optimal sensor placement |
1 |
|
pandemic control |
1 |
|
points-of-interest |
1 |
|
random forest |
1 |
|
sensor calibration |
1 |
|
sewage surveillance |
1 |
|
social media |
1 |
|
urban analytics |
1 |
