This paper presents a big data analytics-based model developed for electric distribution utilities aiming to forecast the demand of service orders (SOs) on a spatio-temporal basis. Being fed by robust history and location data from a database provide...
Enormous heterogeneous sensory data are generated in the Internet of Things (IoT) for various applications. These big data are characterized by additional features related to IoT, including trustworthiness, timing and spatial features. This reveals m...
Urban mobility systems generate large volumes of heterogeneous geospatial data that differ in temporal resolution, spatial coverage, update frequency, and semantic structure, making integrated near-real-time analysis difficult. This paper presents a...
Regarded as one of the most dangerous types of natural disaster, tropical cyclones threaten the life and health of human beings and often cause enormous economic loss. However, intensity forecasting of tropical cyclones, especially rapid intensificat...
High-fidelity characterization and effective monitoring of spatial and spatiotemporal processes are crucial for high-performance quality control of many manufacturing processes and systems in the era of smart manufacturing. Although the recent develo...
During the past decades, multiple remote sensing data sources, including nighttime light images, high spatial resolution multispectral satellite images, unmanned drone images, and hyperspectral images, among many others, have provided fresh opportuni...
The growing complexity of natural disasters, intensified by climate change, has amplified the challenges of managing emergency shelter demand. Accurate shelter demand forecasting is crucial to optimize resource allocation, prevent overcrowding, and e...
The world evolves continuously across space and time. Massive volumes of data are generated through sensing, simulation, remote observation, and human activities, capturing dynamic processes in environmental, social, economic, and engineered systems....
In this paper we present a visual analytics approach for deriving spatio-temporal patterns of collective human mobility from a vast mobile network traffic data set. More than 88 million movements between pairs of radio cells—so-called handovers—serve...
One longstanding complication with Earth data discovery involves understanding a user’s search intent from the input query. Most of the geospatial data portals use keyword-based match to search data. Little attention has focused on the spatial and te...
Climate studies involve petabytes of spatiotemporal datasets that are produced and archived at distributed computing resources. Scientists need an intuitive and convenient tool to explore the distributed spatiotemporal data. Geovisual analytical tool...
Trajectory data allow the study of the behavior of moving objects, from humans to animals. Wireless communication, mobile devices, and technologies such as Global Positioning System (GPS) have contributed to the growth of the trajectory research fiel...
As we move into the big data era, data grows not just in size, but also in complexity, containing a rich set of attributes, including location and time information, such as data from mobile devices (e.g., smart phones), natural disasters (e.g., earth...
The primary goal of collecting Earth observation (EO) imagery is to map, analyze, and contribute to an understanding of the status and dynamics of geographic phenomena. In geographic information science (GIScience), the term object-based image analys...
Mobile phone data is a typical type of big data with great potential to explore human mobility and individual portrait identification. Previous studies in population classifications with mobile phone data only focused on spatiotemporal mobility patte...
Traffic flow prediction is a significant component for the new generation intelligent transportation. In the field of air transportation, accurate prediction of airport flight flow can help airlines schedule flights and provide a decision-making basi...
Risk zone delineation and mobility behavior control constitute critical measures in pandemic containment. Numerous studies utilize static demographic data or dynamic mobility data to calculate the high–risk zones present in cities; however, the...
The vitality of a city is shaped by its social structure, environmental quality, and spatial form, with child-friendliness being an essential component of urban vitality. While there are numerous qualitative studies on the relationship between child-...
Digital city planning increasingly relies on data-driven approaches to address complex urban sustainability challenges through innovative network analysis methodologies. This study introduces a comprehensive spatiotemporal network framework to examin...
With the deployment of multi-modality and large-scale sensor networks for monitoring air quality, we are now able to collect large and multi-dimensional spatio-temporal datasets. For these sensed data, we present a comprehensive visual analysis appro...
Urban residents’ transportation modes play a pivotal role in shaping transportation planning and policies for sustainable cities. Mining refined transportation modes from mobile phone location (MPL) data is a key spatiotemporal big data applica...
This narrative review examines how big data analytics supports geospatial decision-making in smart cities through the combined roles of spatial data foundations, GeoAI methods, and urban digital twins. Methodologically, the article follows a structur...
Fire management in protected areas faces mounting obstacles as climate change alters disturbance regimes, resources are diverted to fighting wildfires, and more people live along the boundaries of parks. Evidence-based prescribed fire management and...
Sea ice acts as both an indicator and an amplifier of climate change. High spatial resolution (HSR) imagery is an important data source in Arctic sea ice research for extracting sea ice physical parameters, and calibrating/validating climate models....
Treatment of air pollution and health impacts are both crucial components of long-term sustainability. Measuring individual exposure to air pollution is significant to evaluating public health risks. In this paper, we introduce a big data analytics f...
Urban systems involve a multitude of closely intertwined components, which are more measurable than before due to new sensors, data collection, and spatio-temporal analysis methods. Turning these data into knowledge to facilitate planning efforts in...
Prolonged exposure to public health crises in the post-pandemic era poses significant threats to global mental health. To address this, we developed a conceptual model to analyse the spatiotemporal distribution of public emotions, using Weibo data fr...
Traffic accidents significantly impact societies and economies. The risk of collision is highest in urban areas, leading to devastating loss of life and escalating socioeconomic costs. In this context, numerous studies have focused on accurately pred...
This study investigated the spatiotemporal dynamics and driving mechanisms of urban expansion in the Chongqing Metropolitan Area by integrating multi-source big data and employing a suite of quantitative analytical methods. Drawing upon high-resoluti...
Digital societies could be characterized by their increasing desire to express themselves and interact with others. This is being realized through digital platforms such as social media that have increasingly become convenient and inexpensive sensors...
28 February 2022
Urban noise causes a variety of health problems, and its prevention and control have thus become an important research topic in urban governance. Although existing literature is fairly comprehensive in revealing the physical noise patterns, it lacks...
Rapid transit systems or metros are a popular choice for high-capacity public transport in urban areas due to several advantages including safety, dependability, speed, cost, and lower risk of accidents. Existing studies on metros have not considered...
This paper analyses the spatio-temporal trends and variability in annual, seasonal, and monthly rainfall with corresponding rainy days in Bhilangana river basin, Uttarakhand Himalaya, based on stations and two gridded products. Station-based monthly...
With the acceleration of global urbanization and the large-scale development of underground spaces, underground engineering faces extremely complex and variable geological environments and high-risk construction disturbances. The widespread applicati...
Under intensifying climate change impacts, accurate quantification of population exposure to urban flooding has become an imperative component of risk mitigation strategies, particularly when considering the dynamic nature of human mobility patterns....
Research on watershed ecological protection and restoration within the framework of territorial spatial planning serves as a critical approach to ensuring national ecological security and plays a vital role in enhancing ecosystem stability. In recent...
As a pivotal ecological–economic nexus in China, the Yangtze River Basin (YRB)’s spatiotemporal evolution of habitat quality (HQ) profoundly influences regional sustainable development. This study establishes a tripartite analytical frame...
The emergence of cloud computing, big data analytics, and machine learning has catalysed the use of remote sensing technologies to enable more timely management of sustainability indicators, given the uncertainty of future climate conditions. Here, w...