OOIR: Observatory of International Research

Papers

(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 2020-03-01 to 2024-03-01.)

Article	Citations
Understanding the drivers of sustainable land expansion using a patch-generating land use simulation (PLUS) model: A case study in Wuhan, China	470
Understanding spatio-temporal heterogeneity of bike-sharing and scooter-sharing mobility	112
Portraying the spatial dynamics of urban vibrancy using multisource urban big data	107
An integrated physical-social analysis of disrupted access to critical facilities and community service-loss tolerance in urban flooding	81
Extracting spatial effects from machine learning model using local interpretation method: An example of SHAP and XGBoost	81
How did micro-mobility change in response to COVID-19 pandemic? A case study based on spatial-temporal-semantic analytics	70
Urban function classification at road segment level using taxi trajectory data: A graph convolutional neural network approach	67
Uncovering inconspicuous places using social media check-ins and street view images	63
Modeling urban growth using spatially heterogeneous cellular automata models: Comparison of spatial lag, spatial error and GWR	53
Estimating pedestrian volume using Street View images: A large-scale validation test	52
Using graph structural information about flows to enhance short-term demand prediction in bike-sharing systems	51
The potential of nighttime light remote sensing data to evaluate the development of digital economy: A case study of China at the city level	51
Impacts of tree and building shades on the urban heat island: Combining remote sensing, 3D digital city and spatial regression approaches	51
Classification of urban morphology with deep learning: Application on urban vitality	51
Morphological tessellation as a way of partitioning space: Improving consistency in urban morphology at the plot scale	47
Towards concentration and decentralization: The evolution of urban spatial structure of Chinese cities, 2001–2016	45
Desirable streets: Using deviations in pedestrian trajectories to measure the value of the built environment	42
Automatic geo-referencing of BIM in GIS environments using building footprints	42
Scale effects in remotely sensed greenspace metrics and how to mitigate them for environmental health exposure assessment	42
A framework for extracting urban functional regions based on multiprototype word embeddings using points-of-interest data	41
Land suitability and urban growth modeling: Development of SLEUTH-Suitability	40
Decoding urban landscapes: Google street view and measurement sensitivity	39
Global Building Morphology Indicators	38
Access to urban parks: Comparing spatial accessibility measures using three GIS-based approaches	38
Integrating a Forward Feature Selection algorithm, Random Forest, and Cellular Automata to extrapolate urban growth in the Tehran-Karaj Region of Iran	38

Analytics of location-based big data for smart cities: Opportunities, challenges, and future directions	37
Spatial biases in crowdsourced data: Social media content attention concentrates on populous areas in disasters	36
Delineating urban functional use from points of interest data with neural network embedding: A case study in Greater London	36
Using Google Street View imagery to capture micro built environment characteristics in drug places, compared with street robbery	35
Delineating urban park catchment areas using mobile phone data: A case study of Tokyo	34
Associations between mobility and socio-economic indicators vary across the timeline of the Covid-19 pandemic	33
Estimating congestion zones and travel time indexes based on the floating car data	33
Cultivating historical heritage area vitality using urban morphology approach based on big data and machine learning	32
Assessing multiscale visual appearance characteristics of neighbourhoods using geographically weighted principal component analysis in Shenzhen, China	32