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Environmental contamination due to poor solid waste management represents a global ecological issue. The real need for quality spatial data in the processes of solid waste management plays a key role in improving work performance and facilitating work processes. Through the implementation of modern platforms for the acquisition of spatial data, an efficient and quality method for conducting measurements without direct contact with the terrain is achieved. This paper presents the monitoring of waste quantities generated at the non-standardized "Mavrovica" landfill in Sveti Nikole using the results obtained from LiDAR scanning of the territory of the Republic of North Macedonia as well as the conducted UAV-based imaging. The presented results represent the progress and refinement of modern platforms for the acquisition of spatial data, thereby serving as a reference point for analyzing the value states of waste generated from a certain temporal distance. [ABSTRACT FROM AUTHOR]

Several studies have explored the use of hologram technology in architecture and urban design, demonstrating its feasibility. Holograms can represent 3D spatial data and offer an immersive experience, potentially replacing traditional methods such as physical 3D and offering a promising alternative to mixed-reality display technologies. Holograms can visualize realistic scenes such as buildings, cityscapes, and landscapes using the novel view synthesis technique. This study examines the suitability of spatial data collected through the Gaussian splatting method for tabletop hologram visualization. Recent advancements in Gaussian splatting algorithms allow for real-time spatial data collection of a higher quality compared to photogrammetry and neural radiance fields. Both hologram visualization and Gaussian splatting share similarities in that they recreate 3D scenes without the need for mesh reconstruction. In this research, unmanned aerial vehicle-acquired primary image data were processed for 3D reconstruction using Gaussian splatting techniques and subsequently visualized through holographic displays. Two experimental environments were used, namely, a building and a university campus. As a result, 3D Gaussian data have proven to be an ideal spatial data source for hologram visualization, offering new possibilities for real-time motion holograms of real environments and digital twins. [ABSTRACT FROM AUTHOR]

Earthquakes are seen to be complex phenomena when their occurrence reasons are investigated. Factors such as the presence of a fault line and between earthquake relationships like foreshocks, mainshocks, and aftershocks may be considered as some of the sources for this complexity. Estimating the earthquake risk and modeling the earthquake intensities over a region is vital in minimizing future tangible and intangible losses. The principle aim of this study is to examine the clusters and seismicity of moderate to major earthquake occurrences in the North Anatolian Fault Zone (NAFZ). For this purpose, a rectangular region including the NAFZ is selected as a study region to analyze the earthquake patterns. Attributes of moderate to major earthquakes with a magnitude higher than 5, which are listed regarding time interval and space domain in the earthquake catalog, are visualized owing to exploratory data analysis techniques. Spatial patterns for the earthquakes are revealed, and simulations of the earthquakes are realized using spatial processes within the specified time and space domain. Intensity changes and related earthquake risks are disclosed. The western part of the region is classified as having a higher risk for future earthquakes because of having higher previous earthquake and magnitude intensities. [ABSTRACT FROM AUTHOR]

Spatial autocorrelation is a fundamental property of spatial data, which violates the assumption of independence between training and test datasets in general cross-validation (CV). Previous studies have reported strong positive spatial autocorrelation generally leads to optimistic biases in general CV results. Spatial CV methods have been developed to address this bias, but their effectiveness remains controversial owing to their potential for excessively pessimistic estimations. This study examines the impact of spatial autocorrelation on general CV results and validates the effectiveness of spatial CV. The first simulation explores the impact of varying spatial autocorrelation levels on the general CV results. Specifically, strong and moderate positive spatial autocorrelation introduces optimistic biases, whereas weak positive or negative spatial autocorrelations have no significant impact. The second simulation shows spatial CV methods can mitigate the optimistic biases in general CV results when dealing with spatial data having strong and moderate positive spatial autocorrelations. However, the hyperparameters of spatial CV should be adjusted based on the level of spatial autocorrelation to avoid excessively pessimistic estimations. [ABSTRACT FROM AUTHOR]

Scientific data concerning climate change are critical for designing mitigation and adaptation strategies. Equally important is how stakeholders perceive climate change because perceptions influence decision-making. In this paper, we employ spatially-delineated primary surveys to evaluate weather perception biases among corn and soybean farmers located on western frontier of the U.S. Corn Belt where substantial loss of grassland has been documented. We characterize farmers' perception biases by measuring the gap between survey-based perception reports for three distinct weather indicators (i.e., temperature, precipitation and drought) and corresponding meteorological evidence. About 70% farmers in our sample misperceive past weather changes. Three-fourths of these misperceiving farmers over-estimate local temperatures and drought frequency and 40% of them under-estimate precipitation trends relative to past records. We further find evidence that farmers' weather change perceptions are systematically biased in a manner that would justify past land use decisions. Particularly, higher cropping incidence on previously protected grasslands effected more farmers to under-perceive drier conditions and over-perceive wetter conditions. Our investigation of perception biases across distinct weather indicators with a reference to past economic decisions enriches the understanding of climate change perceptions and related policies. [ABSTRACT FROM AUTHOR]

The rapid development of Automated Teller Machines (ATMs) necessitates strategic placement to address customer needs, bank business goals, and evolving transaction trends. This study integrates geospatial methods and consumer transaction behavior to identify optimal ATM locations. Geographic Information System (GIS) technology, with overlay techniques, is employed to analyze spatial data, segment transactions, and evaluate geocentricity. Data collection involves tabulation (population density, city infrastructure) and spatial sources (BPS, OJK, Open Street Map), which are digitized and analyzed using methods such as Analytical Hierarchy Process (AHP), Voronoi, and hexagonal tessellation. The results indicate that city centers with high accessibility and comprehensive facilities are optimal locations for ATM placement. Consumer behavior analysis shows preferences for ATMs that are easy to access, open 24 hours, and equipped with complete facilities. By combining GIS and customer behavior data, the study provides actionable insights for efficient ATM placement. This approach enhances banking services, increases profitability, and ensures the sustainability of ATMs amidst digital wallet competition. The findings offer practical guidelines for banks to optimize ATM deployment while addressing customer needs and reducing operational costs. These insights also highlight GIS technology's pivotal role in modern banking asset management. [ABSTRACT FROM AUTHOR]

This article attempts to offer some perspectives on Bayesian inference for finite population quantities when the units in the population are assumed to exhibit complex dependencies. Beginning with an overview of Bayesian hierarchical models, including some that yield design-based Horvitz-Thompson estimators, the article proceeds to introduce dependence in finite populations and sets out inferential frameworks for ignorable and nonignorable responses. Multivariate dependencies using graphical models and spatial processes are discussed and some salient features of two recent analyses for spatial finite populations are presented. [ABSTRACT FROM AUTHOR]

Background. Land cover information is routinely used to represent fuel conditions in wildfire hazard, risk and exposure assessments. Readily available land cover data options that vary in resolution, extent, cost and purpose of collection have become increasingly accessible in recent years. Aim. This study investigates the sensitivity of community-scale wildfire exposure assessments to different land cover information products used to identify hazardous fuel. Methods. Ten versions of a community wildfire exposure assessment were conducted for each of five case study locations in Alberta, Canada, by varying the input land cover data. Proportional and spatial distribution of hazardous fuels and classified exposure are compared across datasets and communities. Key results. We found proportional and spatial variation of exposure values between datasets within each community, but the nature of this variation differed between communities. Land cover classification definitions and scale were important factors that led to inconsistencies in assessment results. Conclusions. Readily available land cover information products may not be suitable for exposure assessments at a localised scale without consideration of unique context and local knowledge of the assessment area. Implications. Results may inform fuel data selection considerations for improved results in various wildfire applications at localised scales. [ABSTRACT FROM AUTHOR]

The geospatial domain increasingly relies on data-driven methodologies to extract actionable insights from the growing volume of available data. Despite the effectiveness of tree-based models in capturing complex relationships between features and targets, they fall short when it comes to considering spatial factors. This limitation arises from their reliance on univariate, axis-parallel splits that result in rectangular areas on a map. To address this issue and enhance both performance and interpretability, we propose a solution that introduces two novel bivariate splits: an oblique and Gaussian split designed specifically for geographic coordinates. Our innovation, called Geospatial Random Forest (geoRF), builds upon Geospatial Regression Trees (GeoTrees) to effectively incorporate geographic features and extract maximum spatial insights. Through an extensive benchmark, we show that our geoRF model outperforms traditional spatial statistical models, other spatial RF variations, machine learning and deep learning methods across a range of geospatial tasks. Furthermore, we contextualize our method's computational time complexity relative to baseline approaches. Our prediction maps illustrate that geoRF produces more robust and intuitive decision boundaries compared to conventional tree-based models. Utilizing impurity-based feature importance measures, we validate geoRF's effectiveness in highlighting the significance of geographic coordinates, especially in data sets exhibiting pronounced spatial patterns. [ABSTRACT FROM AUTHOR]

Abstract Scientific data concerning climate change are critical for designing mitigation and adaptation strategies. Equally important is how stakeholders perceive climate change because perceptions influence decision-making. In this paper, we employ spatially-delineated primary surveys to evaluate weather perception biases among corn and soybean farmers located on western frontier of the U.S. Corn Belt where substantial loss of grassland has been documented. We characterize farmers’ perception biases by measuring the gap between survey-based perception reports for three distinct weather indicators (i.e., temperature, precipitation and drought) and corresponding meteorological evidence. About 70% farmers in our sample misperceive past weather changes. Three-fourths of these misperceiving farmers over-estimate local temperatures and drought frequency and 40% of them under-estimate precipitation trends relative to past records. We further find evidence that farmers’ weather change perceptions are systematically biased in a manner that would justify past land use decisions. Particularly, higher cropping incidence on previously protected grasslands effected more farmers to under-perceive drier conditions and over-perceive wetter conditions. Our investigation of perception biases across distinct weather indicators with a reference to past economic decisions enriches the understanding of climate change perceptions and related policies.

Biomass energy is one of the renewable energy sources used as an alternative to supply the increasing energy consumption in today's conditions. Appropriate determination of the locations of biomass power plant (BPP), which are considered to be of critical importance in energy efficiency, is necessary to obtain maximum benefit from renewable energy. In this study, it is aimed to perform spatial statistical analyzes by taking into account the location of BPPs, established in Turkey, and the attribute values in the location. Basic information was obtained by applying exploratory spatial data analysis and spatial pattern analysis. Spatial autocorrelation analyzes and spatial interpolation were performed by including the installed power values of BPP spatial point data as feature data. It was concluded in the spatial statistical analyzes obtained with ArcGIS Pro, which is used as a GIS software program, that the spatial distribution of BPPs according to their location and installed power values is not random. It has been shown as a result of the Kriging analysis applied for spatial interpolation that the power value predictions can be made according to the location of the newly installed BPPs by creating a forecasting map.

This research emphasizes the visualization of spatial data for statistical modelling and analysis of the relative risk associated with the COVID-19 pandemic in Alabama, USA. We used Bayesian analysis and the Integrated Nested Laplace Approximation (INLA) approach on data ranging from March 11, 2020, to December 31, 2022, which included observed COVID-19 cases, the population for each of the Alabama counties, and a Geographical map of the state. The geographical distribution of COVID-19’s relative risk was determined using various spatial statistical techniques, indicating high-risk locations. The study used Besag-York-Mollié (BYM) models to assess the posterior relative risk of COVID-19, and it found a statistically significant average decrease in COVID-19 case rates across the 67 counties evaluated. These findings have practical implications for evidence-based policymaking in pandemic prevention, mitigation, and preparation.

Spatial auto-regressive (SAR) models are widely used in geosciences for data analysis; their main feature is the presence of weight (W) matrices, which define the neighboring relationships between the spatial units. The statistical properties of parameter and forecast estimates strongly depend on the structure of such matrices. The least squares (LS) method is the most flexible and can estimate systems of large dimensions; however, it is biased in the presence of multilateral (sparse) matrices. Instead, the unilateral specification of SAR models provides triangular weight matrices that allow consistent LS estimates and sequential prediction functions. These two properties are strictly related and depend on the linear and recursive nature of the system. In this paper, we show the better performance in out-of-sample forecasting of unilateral SAR (estimated with LS), compared to multilateral SAR (estimated with maximum likelihood, ML). This conclusion is supported by numerical simulations and applications to real geological data, both on regular lattices and irregularly distributed points.

AbstractThe aim of this contribution is to propose new flexible spatial models based on a class of generalized sinh transformations. Our proposal greatly extends the Gaussian random field mainly in terms of asymmetry, heavy tail behavior, and bimodality. We demonstrate that we obtain a valid random field. We ensure that it is mean square continuous. We derive explicit expressions for the moments, the covariance function, the skewness, and the kurtosis of the random fields. In comparison with the available skew-Gaussian models, the proposed models could capture a greater amount of skewness and also accommodate clustered spatial outliers. The proposed models are computationally very tractable within the Bayesian framework here adopted. They are compared with the Gaussian and skew-Gaussian spatial models through simulation studies and an application to the spatial prediction of weekly rainfall near Darwin, Australia. The result suggests that the predictive performance in terms of different criteria under our method tends to be smaller than those obtained from the competing ones. [ABSTRACT FROM AUTHOR]

While, many of the machine learning (ML) and artificial intelligence (AI) methods that are now commonly being used to answer questions across scientific disciplines have been around for some time, their widespread application to spatial data and spatially-explicit research questions is much more recent. The large number of excellent review papers and special issues in leading journals published in the last few years—which this issue of the Journal of Geographical Systems takes its place among—attest to the growing interest in the application and development of cutting-edge methodologies for spatial data. This editorial begins by proposing a new inclusive definition for spatial ML, then provides a brief overview of each of the six papers in this special issue, and ends with a suggestion of several possible directions for future research in spatial ML. [ABSTRACT FROM AUTHOR]

Copyright of Turkish Journal of Geographical Sciences / Coğrafi Bilimler Dergisi is the property of Cografi Bilimler Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We describe a Python-based software tool called RADIAN (RAnDom spatIal dAta geNerator) developed with the purpose of generating simple synthetic spatial datasets. These datasets can be used in many contexts such as teaching and learning of GIS, testing of spatial algorithms, testing and visualization approaches. This paper provides a motivation for the need for a tool such as RADIAN along with a survey of other similar approaches. The methodological component of how RADIAN generates synthetic spatial datasets is described. We describe experimental results from the comparison of RADIAN with QGIS, which is the most closely comparable tool available at the time of writing. Finally, we provide some conclusions on the impact and potential of RADIAN with some interesting avenues for future work and development. RADIAN is available as open-source software. [ABSTRACT FROM AUTHOR]

Promoting rural regions is crucial for societies all over the world. Prosperous and vital rural regions can contribute to solving many pressing problems that threaten humanity, such as climate change, poverty, hunger, health or clean energy. The attractiveness of rural regions can be improved through targeted measures and support. For the design of such targeted interventions, high-quality assessments of rural attractiveness can provide a solid information basis. However, the attractiveness of rural regions is a complex construct and therefore difficult to assess. Thus, in this paper, we present tools for the assessment of rural attractiveness that address these complexities and support use and interpretation of the results of rural attractiveness assessments by stakeholders: First, we develop a Rural Attractiveness Index (RAI) which provides a general blueprint for assessing rural attractiveness, yet still is flexible and adaptable to each specific context. As integrated measure of rural attractiveness it also facilitates interpretation by stakeholders. Second, to further enhance interpretation and communication, we propose to visualize the RAI in map-based form. We demonstrate the application of these tools through an illustrative showcase in a European context. We discuss strengths, limitations and challenges of the presented tools and highlight directions for future research. [ABSTRACT FROM AUTHOR]

The analysis of the subsidence cone at Salina Ocna Dej involved modern measurement techniques, including drones, to evaluate terrain changes and generate a detailed 3D model. Data collection occurred in two stages, in 2021 and 2022, utilizing drones to capture a large number of high-resolution images (5472x3648 pixels) [1], resulting in a significant volume of data. These images were processed using specialized software to create a 3D model, employing advanced alignment, data fusion, and interpolation techniques. The results demonstrated that using drones offers considerable benefits over traditional methods, including increased accuracy and reduced time and resource consumption [2], with minimal errors recorded at just 0.7 mm. The project emphasized the importance of adapting initial plans to field conditions and considering weather forecasts to prevent accidents. Post-processing the data enabled clear delineation of the subsidence contour and slope angles, facilitating their integration into the analysis of other mining activities in the region. The generated 3D model serves as a reference for monitoring subsidence evolution and assessing risks to nearby residences. Continuous measurement and constant monitoring of the subsidence cone are essential to prevent potential future damage caused by slope instability and erosion. [ABSTRACT FROM AUTHOR]

The growth of the Internet of Things (IoT) has become a crucial area of modern research. While the increasing number of IoT devices has driven significant advancements, it has also introduced several challenges, such as data storage, data privacy, communication protocols, complex network topologies, and IoT device management. In essence, the management of IoT devices is becoming more and more challenging, especially with the limited capacity and power of the IoT devices. The devices, having limited capacities, cannot store the information of the entire environment at once. In addition, device power consumption can affect network performance and stability. The devices' sensing areas with device grouping and management can simplify further networking tasks and improve response quality with data aggregation and correction techniques. In fact, most research papers are looking forward to expanding network lifetimes by relying on devices with high power capabilities. This paper proposes a device spatial clustering technique that covers crucial challenges in IoT. Our approach groups the dispersed devices to create clusters of connected devices while considering their coverage, their storage capacities, and their power. A new clustering protocol alongside a new clustering algorithm is introduced, resolving the aforementioned challenges. Moreover, a technique for non-sensed area extraction is presented. The efficiency of the proposed approach has been evaluated with extensive experiments that gave notable results. Our technique was also compared with other clustering algorithms, showing the different results of these algorithms. [ABSTRACT FROM AUTHOR]

Consumption is a daily social and moral activity that represents lifestyle, desires, thoughts and ideals with its own symbols and signs. Consumption has evolved into a fundamental component of “identification” and has surpassed the capacity to satisfy requirements. The aim of the current research is to identify and analyze the spatial patterns of consumption (selfprovision, collective, and mixed) in Tehran. The data and the study area of Tehran metropolis were gathered from the plan of cultural, social, and identity typology of Tehran city neighborhoods and the designation of neighborhood patterns and local communities in 2016. The study included 12,000 samples and 105 neighborhoods, which were divided into 22 regions. GIS and GeoDa Software were employed to analyze the necessary data in the form of a map. The results indicated that the consumption pattern in Tehran's health, healthcare, and treatment sectors is primarily characterized by collective consumption. Polarity is the orientation of the spatial pattern of health and treatment service consumption in approximately 50% of Tehran's neighborhoods. The field of education and empowerment exhibits a consumption pattern that is approximately 49% collective consumption and 35% self-sufficiency consumption. A cluster is the spatial distribution of the quantity of consumption of self-provision of education and empowerment. The spatial distribution and analysis demonstrated the difference and inequality between the southern and northern regions of the city. The results also suggest that the localities are grouped together with similar groupings based on their economic and social characteristics. [ABSTRACT FROM AUTHOR]

This study aims to propose a method for constructing basic digital twin data in South Korea by adhering to international standards and by utilizing publicly available data. Specifically, the study focuses on designing and proposing a digital twin data model for buildings, as building-related digital twin data are the most applicable among the basic digital twin data. To achieve this, the first section provides essential background information, introduces concepts and requirements related to basic digital twin data, and offers a brief overview of City Geography Markup Language (CityGML). The second section explains the methodology and the data used in this study. The third section presents the main findings: the selection of public data (building data) for constructing basic digital twin data, the mapping process using CityGML, and the creation of Unified Modeling Language (UML) diagrams. The fourth section discusses these findings. Finally, the conclusion and recommendations for future research are provided. This approach enhances the accuracy of building-related digital twin data and supports the use of digital twin services in both public and private sectors by enabling various spatial analyses. [ABSTRACT FROM AUTHOR]

Spatial auto-regressive (SAR) models are widely used in geosciences for data analysis; their main feature is the presence of weight (W) matrices, which define the neighboring relationships between the spatial units. The statistical properties of parameter and forecast estimates strongly depend on the structure of such matrices. The least squares (LS) method is the most flexible and can estimate systems of large dimensions; however, it is biased in the presence of multilateral (sparse) matrices. Instead, the unilateral specification of SAR models provides triangular weight matrices that allow consistent LS estimates and sequential prediction functions. These two properties are strictly related and depend on the linear and recursive nature of the system. In this paper, we show the better performance in out-of-sample forecasting of unilateral SAR (estimated with LS), compared to multilateral SAR (estimated with maximum likelihood, ML). This conclusion is supported by numerical simulations and applications to real geological data, both on regular lattices and irregularly distributed points. [ABSTRACT FROM AUTHOR]

In this paper, we tackle estimation and prediction at non-visted sites in a spatial semi-functional linear regression model with derivatives that combines a functional linear model with a nonparametric regression one. The parametric part is estimated by a method of moments and the other one by a local linear estimator. We establish the convergence rate of the resulting estimators and predictor. A simulation study and an application to ozone pollution prediction at non-visited sites are proposed to illustrate our results. [ABSTRACT FROM AUTHOR]

The Yellow River Basin of China is a key region that contains myriad interactions between human activities and natural environment. Industrialization and urbanization promote social-economic development, but they also have generated a series of environmental and ecological issues in this basin. Previous researches have evaluated urban resilience at the national, regional, urban agglomeration, city, and prefecture levels, but not at the watershed level. To address this research gap and elevate the Yellow River Basin's urban resilience level, we constructed an urban resilience evaluation index system from five dimensions: industrial resilience, social resilience, environmental resilience, technological resilience, and organizational resilience. The entropy weight method was used to comprehensively evaluate urban resilience in the Yellow River Basin. The exploratory spatial data analysis method was employed to study the spatiotemporal differences in urban resilience in the Yellow River Basin in 2010, 2015, and 2020. Furthermore, the grey correlation analysis method was utilized to explore the influencing factors of these differences. The results of this study are as follows: (1) the overall level of urban resilience in the Yellow River Basin was relatively low but showed an increasing trend during 2010-2015, and significant spatial distribution differences were observed, with a higher resilience level in the eastern region and a low-medium resilience level in the western region; (2) the differences in urban resilience were noticeable, with industrial resilience and social resilience being relatively highly developed, whereas organizational resilience and environmental resilience were relatively weak; and (3) the correlation ranking of resilience influencing factors was as follows: science and technology level>administrative power>openness>market forces. This research can provide a basis for improving the resilience level of cities in the Yellow River Basin and contribute to the high-quality development of the region. [ABSTRACT FROM AUTHOR]

In this study, we describe a frailty model for spatially correlated survival data based on the class of Archimedean copulas in the spatial regression, assuming that counties are allowed to have varying sizes of spatial structure. In the spatial frailty model, a Bayesian nonparametric prior for a baseline survival function is provided to improve model flexibility. The proposed approach was evaluated by analyzing survival data for acute myeloid leukemia in the North West of England. [ABSTRACT FROM AUTHOR]

Copyright of Landscape Architecture Frontiers is the property of Higher Education Press Limited Company and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Spatial data management is crucial for applications like urban planning and environmental monitoring. While traditional relational databases are commonly used, they struggle with large and complex spatial data. NoSQL databases provide support for unstructured data and scalability. This article compares the performance and disk space usage of SQL Server (a relational database) and MongoDB (NoSQL database) using an open-source library. Experiments conducted with the OpenStreetMap dataset from Central America show that the MongoDB database outperformed the relational SQL Server database in most cases, offering practical advantages for spatial data management in Geographic Information System applications. [ABSTRACT FROM AUTHOR]

Spatial data is data that contains the influence of location with non-homogeneous variance at each location, or spatial heterogeneity. To address spatial heterogeneity, the Geographically Weighted Regression (GWR) model is used. However, in the GWR model, there is a phenomenon of multicollinearity, which is a strong relationship between independent variables that will reduce the accuracy of parameter estimation. To overcome multicollinearity in the GWR model, the Least Absolute Shrinkage and Selection Operator (LASSO) method is used. The LASSO method estimates the parameters of the GWR model by minimizing the sum of squared errors subject to a constraint function, which is solved using the Least Angle Regression (LARS) algorithm. This results in the Least Absolute Shrinkage and Selection Operator (LASSO) regression model to address the problem of multicollinearity in spatial data. Based on the research results, the LASSO method can overcome multicollinearity by shrinking the coefficients of parameters that contribute less and have a strong correlation with other independent variables in the GWR model, resulting in 33 final models. One of the models is for Nias Regency, where the factors influencing the poverty rate are the open unemployment rate, life expectancy, average length of schooling, gross participation rate, and per capita income. In Nias Regency, the value of s is 0.288 with an R-squared value of 0.9403. In Nias Regency, 94.03% of the variation in the poverty rate is explained by the independent variables in the model, while the remaining 5.97% is attributed to external factors not covered by the model. Coefficient of the Human Development Index variable shrinks to exactly zero, indicating that it has no effect on the poverty rate in Nias Regency. [ABSTRACT FROM AUTHOR]

This study aimed to assess the environmental variables affecting the Body Mass Index of older adults at neighborhood levels (1 ha) while mapping probability distributions of normal, overweight-obese, and underweight older adults. We applied a data-driven method that integrates open-access remote sensing products and geospatial data, along with the first nutritional survey in the Philippines with geo-locations conducted in 2021. We used ensemble machine learning of different presence-only and presence-absence models, all subjected to hyperparameter tuning and variable decorrelation. The cross-validated ensemble model was found to have AUC=0.76-0.93 and TSS =0.45-0.81, which indicates that the models are performing better than random chance. We found that neighborhoods with (a) short distances to the main city, (b) short distances to roads, and (c) with densest road network all drive overweight-obese cases. The latter (c) contrasts the findings in Western developed countries because of delimiting factors in a tropical developing country related to active public transport, crime, weather, the walkability of roads, and even the COVID-19 restrictions during the time of the surveys. The probability distribution maps revealed that the older adults in the Philippine case cities were mostly overweight-obese, especially within and nearby city centers. We finally showed priority neighborhoods for intervention and local policy implementation, providing valuable insights for local government units.

This paper presents a methodology aimed to enhance urban energy analysis through the utilization of geospatial data to collect and integrate not only building data but also data related to the urban context in which buildings are situated. Utilizing datasets like the GeoDirectory Building Energy Ratings (BER) dataset of Ireland, supplemented by data of Digital Landscape Models (DLM) Core Data from Tailte Éireann Surveying (PRIME2 Dataset), landscape map of Dublin, we acquire both geometric and non-geometric data related to buildings in Dublin at both building and neighborhood scales. These datasets enable us to perform effective neighborhood-scale analysis and built environment analysis within a geospatial context. Our methodology employs a diverse array of tools and software, including programming languages such as MATLAB and Python ( in the Jupyter Notebook interface), with libraries such as Geopandas, Pandas, NumPy, Seaborn, and Scikit-learn were used for data processing and analysing. In addition, we conduct geospatial analyses using the toolbox and plugins of the ArcGIS and QGIS software. Our data integration encompasses various parameters including building attributes, neighborhood characteristics, and urban-scale built environment metrics at both building and neighborhood scales. This comprehensive dataset provides valuable insights into building energy performance and urban energy dynamics. Researchers can leverage this data to develop data-driven approaches and predictive models for analyzing environmental factors, thereby formulating effective urban planning strategies for sustainability and energy analysis of buildings, neighborhoods, and residential zones in Dublin.

The dataset collects and harmonizes financial data about public works in Italy, focusing on soil defence investments. The data are sourced from three distinct platforms: the Italian Ministry of Economics and Finance's open data platform OpenBDAP, the OpenCoesione website, concerned with interventions framed in cohesion policies financed by additional resources from the European and national budgets, and the ReNDiS database, provided by the Italian Institute for Environmental Protection and Research (ISPRA), that exclusively gathers information about public works in soil defence. The data records belonging to these three sources are linked by a unique project code (CUP), ensuring that there is no duplication of data. The OpenBDAP and OpenCoesione repositories report financial variables classified into various funds. In contrast, the ReNDiS database only provides the total amount of financial resources allocated to each intervention. Consequently, the merged dataset consolidates these financial variables into one, representing the total investment amount. For the first two databases this aggregate is derived by summing the financial flows from the different funds. Geographical referencing has been added to each intervention and each financial observation is associated with an Italian municipality. The database includes information on the region, province, and municipality for each record. Each database entry has also been equipped with the coordinates of the municipality's centroid and with the polygonal shape of the municipality area. Overall, the merged dataset encompasses 28 variables reporting three descriptive variables, one financial variable representing the total amount of financial resources, six geographic variables representing the codes and names of regions, provinces, and municipalities, sixteen variables referring to key dates of the process of public works, two geographical references variables respectively representing the centroids and the shape polygons of the municipality. This comprehensive dataset allows to analyse the spatial distribution of the resources allocated to soil defence investments. It offers insights to policymakers striving to allocate resources more efficiently, thereby fostering sustainable land management practices and ensuring the long-term health of the Italian ecosystems. The dataset can be complemented with additional information related to various concomitant aspects such as those pertaining to the environmental and socio-economic fields. This integration allows for broad analysis of the relationships between soil defence efforts and surrounding environmental and socio-economic contexts.

ABSTRACTThe availability of spatial data in grids allows to enrich the statistics ranked by census units, being of great interest for regional analysis and territorial planning. The objective of this work is the elaboration of cartography, independent of administrative boundaries, which collects demographic data, accessibility and major land uses in cells of one square kilometre. Applying a methodology that has allowed the assignment of a category related to the predominant territorial model in each grid, the main result is a synthesis map of the rural-urban gradient that can be recognised in the province of Jaén, showing the contrasts and imbalances derived from the physiographic conditions and the recent territorial changes.Key policy highlightsWe propose a method for mapping the spatial differentiation of the rural-urban gradient.We use demographic, accessibility and predominant land use variables integrated into six different categories.The scale, variables, thresholds and combinations of spatial information must be adapted to each particular territory and problem.The method used is suitable for diagnosing plans and measures to face the demographic challenge and territorial polarization.

In this study, we employed a novel approach of combining Gaussian processes (GPs) with boosting techniques to model the spatial variability inherent in End-Stage Kidney Disease (ESKD) data. Our use of the Gaussian processes boosting, or GPBoost, methodology underscores the efficacy of this hybrid method in capturing intricate spatial dynamics and enhancing predictive accuracy. Specifically, our analysis demonstrates a notable improvement in out-of-sample prediction accuracy regarding the percentage of the population remaining on the wait list within geographic regions. Furthermore, our investigation unveils race and gender-based factors that significantly influence patient wait-listing. By leveraging the GPBoost approach, we identify these pertinent factors, shedding light on the complex interplay between demographic variables and access to kidney transplantation services. Our findings underscore the imperative for a multifaceted strategy aimed at reducing spatial disparities in kidney transplant wait-listing. Key components of such an approach include mitigating gender disparities, bolstering access to healthcare services, fostering greater awareness of transplantation options, and dismantling structural barriers to care. By addressing these multifactorial challenges, we can strive towards a more equitable and inclusive landscape in kidney transplantation.

Located 30 km inland from Lebanon’s coast, The Beqaa Valley (or Beqaa Plains) is considered the agricultural backbone of the country. The Beqaa’s built geographies were shaped by the political and economic hierarchies established by the Roman and Ottoman Empires and revised by the French Mandate. Local and regional economic hardships in the last six decades have led the Beqaa to cycle through periods of decline and recovery, with quick introductions of infrastructural technologies, spurts of loosely regulated building development, and hasty innovations in industrial activity. In this vein, ‘reflexive realism’ concepts of risk regime, logic of production, topographical fragmentation, and internal connectivity, are useful to examine how towns and cities in the Beqaa developed, deteriorated, and adjusted. However, spatial evidence that would inform such inquiries in Rayak, Beqaa, is far from similar to evidence observed in Beirut. Urban morphology research techniques combined with the concept of vernacular architecture can help decode the layers and uses of the built environment. This article introduces a mapping workflow that typologizes built fabrics using five morphological criteria (streets, density, open space, architectural character, and land use) to construct a spatial narrative that can begin characterizing the nature of the Beqaa’s cities and towns.

By March of 2020, most cities worldwide had enacted stay-at-home public health orders to slow the spread of COVID-19. Restrictions on nonessential travel had extensive impacts across the transportation sector in the short term. This study explores the effects of COVID-19 on shared e-scooters by analyzing route trajectory data in the pre- and during-pandemic periods in Austin, TX, from a single provider. Although total shared e-scooter trips decreased during the pandemic, partially owing to vendors pulling out of the market, this study found average trip length increased, and temporal patterns of this mode did not meaningfully change. A count model of average daily trips by road segment found more trips on segments with sidewalks and bus stops during the pandemic than beforehand. More trips were observed on roads with lower vehicle miles traveled and fewer lanes, which might suggest more cautious travel behavior since there were fewer trips in residential neighborhoods. Stay-at-home orders and vendor e-scooter rebalancing operations inherently influence and can limit trip demand, but the unique trajectory data set and analysis provide cities with information on the road design preferences of vulnerable road users.

Most multivariate outlier detection procedures ignore the spatial dependency of observations, which is present in many real datasets from various application areas. This article introduces a new outlier detection method that accounts for a (continuously) varying covariance structure, depending on the spatial neighborhood of the observations. The underlying estimator thus constitutes a compromise between a unified global covariance estimation, and local covariances estimated for individual neighborhoods. Theoretical properties of the estimator are presented, in particular related to robustness properties, and an efficient algorithm for its computation is introduced. The performance of the method is evaluated and compared based on simulated data and for a dataset recorded from Austrian weather stations. to the article are available online. [ABSTRACT FROM AUTHOR]

This article discusses how geographic information technologies (GIS) can be used to monitor and administer urban environments. In modern conditions, when cities face the challenges of rapid population growth, urbanization, climate change and environmental degradation, traditional methods of urban management become insufficiently effective. GIS technologies offer innovative solutions to collect, analyze and visualize spatial data to enable more informed decision making. The article includes a review of previous research in this area, including the work of Michael Goodchild, Rodrigo Garcia and Yang Li, who have made significant contributions to the theory and practice of GIS applications in urban management. The focus is on methods and algorithms used for data monitoring and analysis, such as data collection, data processing, analysis using clustering and spatial regression techniques, and data visualization using cartographic techniques and 3D models. The paper details the flowcharts of urban environment monitoring processes and algorithms used for data analysis. Examples of mathematical models and thematic maps used to present the results of the analysis are given. The conclusion offers perspectives on the further development of GIS and its integration with other advanced technologies, such as the Internet of Things and artificial intelligence, to solve complex urban management problems. The purpose of this article is to substantiate the importance and effectiveness of using geoinformation technologies for monitoring and administration of urban environment, as well as to provide practical recommendations and examples of using GIS technologies in various aspects of urban management. [ABSTRACT FROM AUTHOR]

Urban sustainability assessment is fundamental to ensuring the long-term resilience of social, environmental, and economic development. This study proposed a comprehensive evaluation system to assess the sustainable development of the HYRB during 2000–2020 through an integrated sustainable development index (SDI) based on entropy method with spatial data. The major findings of this study were as follows: (1) The SDI of the HYRB increased from 0.438 to 0.489 from 2000 to 2010, while the SDI slightly decreased by 0.026 during 2010–2020. (2) The highest SDIs of 2000 (0.502), 2010 (0.529) and 2020 (0.480) were found in Anyang. Conversely, the lowest SDIs of 2000 (0.390), 2010 (0.459) and 2020 (0.417) were located in Zhengzhou. (3) The SDI variation across regions became more evident in the later study period. This study offers a comprehensive assessment of sustainability levels in the HYRB, providing useful implications for its future sustainable development. [ABSTRACT FROM AUTHOR]

The classical linear regression model allows for a continuous quantitative variable to be modeled simply from other variables. However, this model assumes independence between observations, which, if ignored, can lead to methodological issues. Additionally, not all data follow a normal distribution, prompting the need for alternative modeling methods. In this context, geographically weighted beta regression (GWBR) incorporates spatial dependence into the modeling process and analyzes rates or proportions using the beta distribution. In this study, GWBR was applied to the traffic injury (fatal and non-fatal) crash proportions in Fortaleza, Ceará, Brazil, from 2009 to 2011. The results demonstrated that the local approach using the beta distribution is a viable model for explaining the traffic injury crash proportions, due to its flexibility in handling both symmetric and skewed distributions. Therefore, when analyzing rates or proportions, the use of the GWBR model is recommended. [ABSTRACT FROM AUTHOR]

In this study, we employed a novel approach of combining Gaussian processes (GPs) with boosting techniques to model the spatial variability inherent in End-Stage Kidney Disease (ESKD) data. Our use of the Gaussian processes boosting, or GPBoost, methodology underscores the efficacy of this hybrid method in capturing intricate spatial dynamics and enhancing predictive accuracy. Specifically, our analysis demonstrates a notable improvement in out-of-sample prediction accuracy regarding the percentage of the population remaining on the wait list within geographic regions. Furthermore, our investigation unveils race and gender-based factors that significantly influence patient wait-listing. By leveraging the GPBoost approach, we identify these pertinent factors, shedding light on the complex interplay between demographic variables and access to kidney transplantation services. Our findings underscore the imperative for a multifaceted strategy aimed at reducing spatial disparities in kidney transplant wait-listing. Key components of such an approach include mitigating gender disparities, bolstering access to healthcare services, fostering greater awareness of transplantation options, and dismantling structural barriers to care. By addressing these multifactorial challenges, we can strive towards a more equitable and inclusive landscape in kidney transplantation. [ABSTRACT FROM AUTHOR]