Computers Environment and Urban Systems

(The H4-Index of Computers Environment and Urban Systems is 32. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2020-05-01 to 2024-05-01.)
Understanding the drivers of sustainable land expansion using a patch-generating land use simulation (PLUS) model: A case study in Wuhan, China521
Understanding spatio-temporal heterogeneity of bike-sharing and scooter-sharing mobility119
Extracting spatial effects from machine learning model using local interpretation method: An example of SHAP and XGBoost103
How did micro-mobility change in response to COVID-19 pandemic? A case study based on spatial-temporal-semantic analytics76
Urban function classification at road segment level using taxi trajectory data: A graph convolutional neural network approach73
Uncovering inconspicuous places using social media check-ins and street view images70
Classification of urban morphology with deep learning: Application on urban vitality58
Impacts of tree and building shades on the urban heat island: Combining remote sensing, 3D digital city and spatial regression approaches57
Estimating pedestrian volume using Street View images: A large-scale validation test55
Modeling urban growth using spatially heterogeneous cellular automata models: Comparison of spatial lag, spatial error and GWR55
The potential of nighttime light remote sensing data to evaluate the development of digital economy: A case study of China at the city level54
Using graph structural information about flows to enhance short-term demand prediction in bike-sharing systems53
Desirable streets: Using deviations in pedestrian trajectories to measure the value of the built environment47
Scale effects in remotely sensed greenspace metrics and how to mitigate them for environmental health exposure assessment44
Analytics of location-based big data for smart cities: Opportunities, challenges, and future directions44
Global Building Morphology Indicators42
Access to urban parks: Comparing spatial accessibility measures using three GIS-based approaches42
Land suitability and urban growth modeling: Development of SLEUTH-Suitability41
Decoding urban landscapes: Google street view and measurement sensitivity41
Delineating urban functional use from points of interest data with neural network embedding: A case study in Greater London40
Integrating a Forward Feature Selection algorithm, Random Forest, and Cellular Automata to extrapolate urban growth in the Tehran-Karaj Region of Iran39
Using Google Street View imagery to capture micro built environment characteristics in drug places, compared with street robbery39
Spatial biases in crowdsourced data: Social media content attention concentrates on populous areas in disasters38
Urban morphology and traffic congestion: Longitudinal evidence from US cities37
Delineating urban park catchment areas using mobile phone data: A case study of Tokyo37
Cultivating historical heritage area vitality using urban morphology approach based on big data and machine learning36
Associations between mobility and socio-economic indicators vary across the timeline of the Covid-19 pandemic36
Places for play: Understanding human perception of playability in cities using street view images and deep learning34
Flood depth mapping in street photos with image processing and deep neural networks34
Estimating congestion zones and travel time indexes based on the floating car data34
Assessing multiscale visual appearance characteristics of neighbourhoods using geographically weighted principal component analysis in Shenzhen, China34
Estimating quality of life dimensions from urban spatial pattern metrics33
Domain-specific sentiment analysis for tweets during hurricanes (DSSA-H): A domain-adversarial neural-network-based approach32
Interpretable machine learning models for crime prediction32
VictimFinder: Harvesting rescue requests in disaster response from social media with BERT32