24,213 Results Found

Air pollution is a widespread issue. One approach to predicting air pollution levels in specific locations is through the development of mathematical models. Spatial models are one such category, and they can be optimized using calculation methods li...

This paper proposes a methodology for numerical modeling of terraforming Mars’ atmosphere using high-energy asteroid impact and greenhouse gas production processes. The developed simulation model uses a spatial data science approach to analyze the Gl...

During dangerous circumstances, knowledge about population distribution is essential for urban infrastructure architecture, policy-making, and urban planning with the best Spatial-temporal resolution. The spatial-temporal modeling of the population d...

The potential for the development of digital data and their infrastructure creates new opportunities for economic growth. The purpose of this study was to develop an approach to identify a set of indicators to quantify the data economy and model its...

This paper examines the possibility of the spatial modelling of the Earth’s natural pulsed-electromagnetic-field measured values, which form a closed profile without the data inside. This geophysical method allows us to map active tectonic move...

Swordfish, Xiphias gladius (Linnaeus, 1758), is a commercially important species that is widely distributed throughout three oceans. This species inhabits oceanic waters with preferred environmental ranges and migrates vertically to the surface layer...

Online food delivery services are rapidly growing in popularity, making customer satisfaction critical for company success in a competitive market. Accurate delivery time predictions are key to ensuring high customer satisfaction. While various metho...

With the acceleration of global urbanization, airport noise pollution has emerged as a significant environmental concern that demands attention. Traditional airport noise monitoring systems are fraught with limitations, including restricted spatial c...

Marine spatial “open” data infrastructures (MSDI) have a significant economic and societal potential for coastal nations and their realization is driven by the evolution of the International Hydrographic Organization’s (IHO) S-100 d...

Building fire is a complex geographic process related to the indoor spatial environment, a smart spatial data model can accurately describe the spatial-temporal information of a building fire scene, which is important for modeling a fire process. Wit...

Spatial models are essential in the prediction of climate phenomena because they can model the complex relationships between different locations. In this study, we discuss an expanded spatial Durbin model with ordinary kriging on unobserved locations...

Small-scale spatial variability in NO₂ concentrations is analysed with the help of pollution maps. Maps of NO₂ estimated by the Airviro dispersion model and land use regression (LUR) model are fused with measured NO₂ concentrations from low-cost sens...

As urban built-up areas are the main generators of carbon emissions, scientific and accurate estimation of carbon emission levels in urban built-up areas is an important method to help implement the carbon neutrality target. Nowadays, developing a sp...

This article is focused on the creation of a data model of an information system to support the processes of population sheltering (PoS). PoS processes have undergone significant changes in the Czech Republic. The PoS system was transformed for the u...

We apply the Ising model with nearest-neighbor correlations (INNC) in the problem of interpolation of spatially correlated data on regular grids. The correlations are captured by short-range interactions between “Ising spins”. The INNC algorithm can...

Soil and land use information are important inputs for physically-based hydrological modeling such as SWAT. Although fine resolution local or regional data are often preferred for modeling, it is not always reliable that these data can lead to better...

This study presents a system model developed for collecting and analyzing spatial data on the project environment of transport infrastructure development in the post-war context, with a focus on supporting sustainable management and recovery planning...

In this paper, the spatial dynamic panel data (SDPD) model is extended to the single-index spatial dynamic panel data (Si-SDPD) model by introducing a nonlinear connection function to reflect the interaction between explanatory variables. The Si-SDPD...

The safe operation of dams is related to the lifeline of the national economy, the safety of the people, and social stability, and dam safety monitoring plays an essential role in scientifically controlling the safety of dams. Since the effects of en...

A common approach to estimating the spatial–temporal distribution of atmospheric species properties is data assimilation. Data assimilation methods provide the best estimate of the required parameter by combining observations with appropriate p...

Classification is a significant subject in hyperspectral remote sensing image processing. This study proposes a spectral-spatial feature fusion algorithm for the classification of hyperspectral images (HSI). Unlike existing spectral-spatial classific...

This paper studies the estimation and inference of a partially linear varying coefficient spatial autoregressive panel data model with fixed effects. By means of the basis function approximations and the instrumental variable methods, we propose a tw...

The spatio-temporal random effect (STRE) model, a type of spatio-temporal Kalman filter model, can be used for the fusion of the Global Navigation Satellite System (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) data to generate high spat...

Multiscale 3D geological characterization and joint analysis are increasingly important topics in spatial information science. However, the non-uniform spatial distribution of objects and scale heterogeneity in geological surveys lead to dispersed st...

This paper studies quantile regression for spatial panel data models with varying coefficients, taking the time and location effects of the impacts of the covariates into account, i.e., the implications of covariates may change over time and location...

Epidemiological data often include excess zeros. This is particularly the case for data on rare conditions, diseases that are not common in specific areas or specific time periods, and conditions and diseases that are hard to detect or on the rise. I...

This paper presents a novel Earth-System Stratified Grid (ISEA4H-ESSG) model, designed to address the challenges in multi-layer geoscience data management and analysis. In the realm of geosciences, which encompasses the solid earth, atmosphere, hydro...

Geographical constraints limit the number and placement of gauges, especially in mountainous regions, so that rainfall values over the ungauged regions are generally estimated through spatial interpolation. However, spatial interpolation easily misse...

Environmental productivity comprehensively measures economic growth and environmental quality. Environmental innovation is considered to be the key to solving economic and environmental problems. Therefore, discussing the impact of environmental inno...

Worsening environmental effects caused by the rapid large-scale urban expansion in most Chinese cities is a worrying trend. In response, China is advocating an economic transition from rapid (raw growth) to a high-quality development model that incor...

Road traffic congestion is a common problem in most large cities, and exploring the root causes is essential to alleviate traffic congestion. Travel behavior is closely related to the built environment, and affects road travel speed. This paper inves...

Spatial data not only enables smart cities to visualize, analyze, and interpret data related to location and space, but also helps departments make more informed decisions. We apply a Bayesian quantile regression (BQR) of the partially linear varying...

Consider a fixed number of clustered areas identified by their geographical coordinates that are monitored for the occurrences of an event such as a pandemic, epidemic, or migration. Data collected on units at all areas include covariates and environ...

Chronic overfishing has depleted numerous elasmobranch stocks in the North East Atlantic, but addressing this issue has been hampered by management complications and lacking data. Spatial management approaches have thus been advocated. This work pres...

In the rapid development of urban construction, underground pipelines play a crucial role. However, the current underground pipelines have poor association with relevant management departments, and there are deficiencies in data completeness, accurac...

Visualization and interpretation of user-generated spatial content such as Volunteered Geographic Information (VGI) is challenging because it combines enormous data volume and heterogeneity with a spatial bias. When dealing with point data on a map,...

To respond to the adverse impact of climate change on agricultural total factor productivity, the question of how to adopt actively appropriate strategies is particularly critical for the stakeholders. However, the previous researchers have paid more...

The level of unemployment in a region can be a good illustration of its socio-economic development. The choice of the data modeling method, both in terms of spatial and time-spatial approaches depends on the results of exploratory data analysis. The...

Fuzzy set theory has shown potential for reducing uncertainty as a result of data sparsity and also provides advantages for quantifying gradational changes like those of pollutant concentrations through fuzzy clustering based approaches. The ability...

This paper proposes a regression model using the Eigenvector Spatial Filtering (ESF) method to estimate ground PM_2.5 concentrations. Covariates are derived from remotely sensed data including aerosol optical depth, normal differential vegetation inde...

Industrial heritage—particularly large modern cement plants—serves as a crucial witness to the architectural and technological evolution of modern urbanization. In Europe, North America, and East Asia, many decommissioned cement factories...

Epidemiologists and health geographers routinely use small-area survey estimates as covariates to model areal and even individual health outcomes. American Community Survey (ACS) estimates are accompanied by standard errors (SEs), but it is not yet s...

Cultural heritage is a very important element affecting the sustainable development. To analyze the various forms of spatial management inscribed into sustainable development, information on the location of objects and their concentration at specific...

The objective of this research is to analyze regional economic difference and explore the influencing factors, which would eventually provide an effective foundation to narrow the regional economic differences. In this paper, a new regional economic...

Due to the emergence of new big data technology, mobility data such as flows between origin and destination areas have increasingly become more available, cheaper, and faster. These improvements to data infrastructure have boosted spatial and tempora...

Significant computation challenges are emerging as agent-based modeling becomes more complicated and dynamically data-driven. In this context, parallel simulation is an attractive solution when dealing with massive data and computation requirements....

Weather-related disasters represent a major threat to the sustainable development of society. This study focuses directly on the assessment of the state of spatial information quality for the needs of hydrodynamic modeling. Based on the selected proc...

The urban agglomeration on the north slope of the Tianshan Mountains is a pivotal place in Western China; it is essential for the economic growth of Xinjiang and acts as a critical bridge between China’s interior and the Asia–Europe conti...

This paper describes the use of citizen science-derived data for the creation of a land-use regression (LUR) model for particulate matter (PM_2.5 and PM_coarse) for a vulnerable community in Imperial County, California (CA), near the United States (US)...

Yield gridded datasets are essential for agricultural land management, food security and harmonious human–land relationships. Many studies have developed yield spatialization models that are based on cropland areas. However, crop planting areas...