Computers Environment and Urban Systems

Papers
(The H4-Index of Computers Environment and Urban Systems is 33. 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 2020-07-01 to 2024-07-01.)
ArticleCitations
Understanding the drivers of sustainable land expansion using a patch-generating land use simulation (PLUS) model: A case study in Wuhan, China581
Extracting spatial effects from machine learning model using local interpretation method: An example of SHAP and XGBoost129
Urban function classification at road segment level using taxi trajectory data: A graph convolutional neural network approach81
How did micro-mobility change in response to COVID-19 pandemic? A case study based on spatial-temporal-semantic analytics78
Classification of urban morphology with deep learning: Application on urban vitality64
Impacts of tree and building shades on the urban heat island: Combining remote sensing, 3D digital city and spatial regression approaches63
Using graph structural information about flows to enhance short-term demand prediction in bike-sharing systems58
The potential of nighttime light remote sensing data to evaluate the development of digital economy: A case study of China at the city level56
Analytics of location-based big data for smart cities: Opportunities, challenges, and future directions49
Desirable streets: Using deviations in pedestrian trajectories to measure the value of the built environment49
Global Building Morphology Indicators47
Scale effects in remotely sensed greenspace metrics and how to mitigate them for environmental health exposure assessment46
Decoding urban landscapes: Google street view and measurement sensitivity46
Access to urban parks: Comparing spatial accessibility measures using three GIS-based approaches45
Integrating a Forward Feature Selection algorithm, Random Forest, and Cellular Automata to extrapolate urban growth in the Tehran-Karaj Region of Iran45
Delineating urban functional use from points of interest data with neural network embedding: A case study in Greater London44
Spatial biases in crowdsourced data: Social media content attention concentrates on populous areas in disasters40
Urban morphology and traffic congestion: Longitudinal evidence from US cities40
Using Google Street View imagery to capture micro built environment characteristics in drug places, compared with street robbery40
VictimFinder: Harvesting rescue requests in disaster response from social media with BERT39
Flood depth mapping in street photos with image processing and deep neural networks38
Places for play: Understanding human perception of playability in cities using street view images and deep learning37
Associations between mobility and socio-economic indicators vary across the timeline of the Covid-19 pandemic37
Estimating congestion zones and travel time indexes based on the floating car data37
Cultivating historical heritage area vitality using urban morphology approach based on big data and machine learning37
Interpretable machine learning models for crime prediction36
Estimating quality of life dimensions from urban spatial pattern metrics36
Domain-specific sentiment analysis for tweets during hurricanes (DSSA-H): A domain-adversarial neural-network-based approach35
Mobile phone location data for disasters: A review from natural hazards and epidemics34
Assessing multiscale visual appearance characteristics of neighbourhoods using geographically weighted principal component analysis in Shenzhen, China34
A framework for urban land use classification by integrating the spatial context of points of interest and graph convolutional neural network method33
Investigating the spatiotemporal pattern between the built environment and urban vibrancy using big data in Shenzhen, China33
A systematic review of agent-based models for autonomous vehicles in urban mobility and logistics: Possibilities for integrated simulation models33
0.48190093040466