Spatial Analysis Approach Research Articles

A Fuzzy Spatial Multiple Criteria Analysis Methodology for Solid Waste Landfill Siting

The process of siting municipal solid waste landfills in Greece faces significant challenges due to land resource limitations, the country’s mountainous and water-permeable terrain, and strong public opposition. This study introduces a novel methodology for optimizing landfill sites on Lemnos Island in the North Aegean Sea using a Fuzzy Spatial Multiple Criteria Analysis (FSMCA) approach. By combining fuzzy sets theory, Geographic Information Systems (GIS), Analytic Hierarchy Process (AHP), spatial autocorrelation, spatial clustering and sensitivity analysis, this methodology addresses the uncertainties and complexities inherent in landfill siting. The decision problem is structured hierarchically into five levels to manage multiple criteria effectively. Criteria weights are determined using AHP, with discrete criteria graded according to Greek and EU guidelines, and continuous criteria evaluated through fuzzy sets theory. The region’s suitability is assessed using multiple criteria analysis, revealing that 9.7% of Lemnos Island is appropriate for landfill placement. Sensitivity analysis confirms the robustness of the methodology to changes in criteria weights. The case study demonstrates the practical application and benefits of FSMCA in a real-world scenario, underscoring its potential to improve sustainable waste management practices and inform policy making.

Distribution and abundance of Aedes caspius (Pallas, 1771) and Aedes vexans (Meigen, 1830) in the Po Plain (northern Italy).

Knowledge of the distribution and abundance of disease-causing mosquito vectors is fundamental for assessing the risk of disease circulation and introduction. Aedes caspius (Pallas, 1771) and Aedes vexans (Meigen, 1830) have been implicated, to different extents, in the circulation of several arthropod-borne viruses (arboviruses). These two mosquitoes are vectors of Tahyna virus in Europe and are considered potential vectors of Rift Valley fever virus, a virus not present but at risk of introduction on the continent. In this work, we analysed abundance data collected during West Nile virus (WNV) surveillance in northern Italy (Po Plain) via 292 CO2-baited traps to evaluate the distribution and density of these two non-target mosquitoes. We modelled the distribution and abundance of these two mosquito species in the surveyed area using two distinct spatial analysis approaches (geostatistical and machine learning), which yielded congruent results. Both species are more abundant close to the Po River than elsewhere, but Ae. caspius is present in the eastern and western parts of the plain, linked with the occurrence of rice fields and wetlands, while Ae. vexans is observed in the middle area of the plain. Presence and abundance data at the municipality level were obtained and made available through this work. This work demonstrates the importance of maintaining and improving entomological surveillance programs with an adequate sampling effort.

Multi-Approaches for Flash Flooding Hazard Assessment of Rabigh Area, Makkah Province, Saudi Arabia: Insights from Geospatial Analysis

Flash flood hazard assessment is a critical component of disaster risk management, particularly in regions vulnerable to extreme rainfall and climatic events. This study focuses on evaluating the flash flood susceptibility of the Rabigh area, located along the Red Sea coast in Makkah province, Saudi Arabia. Using advanced GIS tools and a spatial multi-criteria analysis approach, the research integrates a variety of datasets, including remotely sensed satellite data, the SRTM Digital Elevation Model (DEM), and topographic indices. The main goal was to produce detailed flood susceptibility maps based on the morphometric characteristics of the region’s drainage basins. These basins were delineated and assessed for their flood vulnerability using three distinct modeling techniques, each highlighting different aspects of flood behavior. The results show that the northern basin (Dulaidila) and the central basins (Rabigh, Algud, and Al Nuaibeaa) exhibit the highest flood risk, with significant susceptibility also observed in the southern basins (Ofoq and Saabar). Other basins in the region display moderate susceptibility levels. A key aspect of this analysis was the overlay of the integrated flood susceptibility map with the Topographic Position Index (TPI), a crucial topographic indicator, which helped refine the understanding of flood-prone areas by linking basin morphometry with in-situ topographic features. This study’s comprehensive approach offers valuable insights that can be applied to other coastal regions where hydrological and climatic data are scarce, contributing to more effective flood risk mitigation and strategic planning.

Discovering Sericulture Clusters Through the Use of Local Indicators of Spatial Association (LISA)

The aim of this study is to analyze the spatial pattern of sericulture in Türkiye with spatial analysis approaches, taking into account the data of 81 provinces, by creating a database based on Geographic Information Systems (GIS). This study employed data from the TURKSTAT, which included the amount of fresh cocoon produced and the silkworm egg boxes opened, between 2004-2022. In order to evaluate the relationship between the cocoon yield in each province and the cocoon yield in neighboring provinces, Moran's I measurements were made for the relevant time periods, and each period's Moran's I scatter plot was created. The Local Spatial Association Indicator (LISA) was used to assess the spatial structure of fresh cocoon yield. There was determined spatial clustering and a positive spatial association for cocoon yield, and all of the Moran's I values were found to be more than 0. The LISA maps were demonstrated the distinct local clustering patterns in the cocoon yield. More local clusters were identified (High-High or Low-Low) compared to local spatial outliers (High-Low or Low-High). High-High clusters were generally identified in the Marmara Region. Important implications have been obtained in terms of sustainability, such as how to take advantage of hotspot spillover effects and prioritize hotspot locations when allocating resources. As a result, spatial relations should be taken into account in the policies to be developed for sericulture and more activities should be carried out on sericulture.

Advances in Remote Sensing and Machine Learning Methods for Invasive Plants Study: A Comprehensive Review

This paper provides a comprehensive review of advancements in the detection; evaluation; and management of invasive plant species (IPS) using diverse remote sensing (RS) techniques and machine learning (ML) methods. Analyzing the high-resolution datasets received from drones, satellites, and aerial photography enables the perfect cartography technique and analysis of the spread and various impacts of ecology on IPS. The majority of current research on hyperspectral imaging with unmanned aerial vehicle (UAV) enhanced by ML has significantly improved the accuracy and efficiency of identifying mapping IPS, and it also serves as a powerful instrument for ecological management. The integrative association is essential to manage the alien species better, as researchers from multiple other fields participate in modeling innovative methods and structures. Incorporating advanced technologies like light detection and ranging (LiDAR) and hyperspectral imaging shows potential for improving spatial and spectral analysis approaches and utilizing ML approaches such as a support vector machine (SVM), random forest (RF), artificial neural network (ANN), convolutional neural network (CNN), and deep convolutional neural network (DCNN) analysis for detecting complex IPS. The significant results indicate that ML methods, most importantly SVM and RF, are victorious in recognizing the alien species via analyzing RS data. This report emphasizes the importance of continuous research efforts to improve predictive models, fill gaps in our understanding of the connections between climate, urbanization and invasion dynamics, and expands conservation initiatives via utilizing RS techniques. This study also highlights the potential for RS data to refine management plans, enabling the implementation of more efficient strategies for controlling IPS and preserving ecosystems.

Optimizing Higher Education for Entrepreneurship Digital economy and Innovation: A Novel Spatial Analysis Approach

The paper extensively examined the intricate components underpinning innovation ability, culminating in the construction of a linear spatial model delineating innovation and entrepreneurship prowess. This paper analyzed the components of the connotation of innovation ability, then constructs a linear spatial model of innovation and entrepreneurship ability, proposes a multi-objective function model of the utilization efficiency and allocation efficiency of education resources, and uses the grey correlation algorithm The experimental simulation and model solution are carried out. The simulation results show that, through the optimization, the utilization efficiency and allocation efficiency of the educational resources for innovation and entrepreneurship for all are increased by 18.72\% and 20.98\% respectively, and tend to be in equilibrium, which can achieve the optimization of educational resources allocation. Among all people, the correlation value with ideal entrepreneurship is 0.3177, achieving the most excellent innovation and entrepreneurship education.

Optimizing Pedestrian Safety: A Spatial Analysis Approach to Grade-Separated Pedestrian Crossings in Haripur City, Pakistan

Optimizing Pedestrian Safety: A Spatial Analysis Approach to Grade-Separated Pedestrian Crossings in Haripur City, Pakistan

Characterization of tumor heterogeneity through segmentation-free representation learning.

The interaction between tumors and their microenvironment is complex and heterogeneous. Recent developments in high-dimensional multiplexed imaging have revealed the spatial organization of tumor tissues at the molecular level. However, the discovery and thorough characterization of the tumor microenvironment (TME) remains challenging due to the scale and complexity of the images. Here, we propose a self-supervised representation learning framework, CANVAS, that enables discovery of novel types of TMEs. CANVAS is a vision transformer that directly takes high-dimensional multiplexed images and is trained using self-supervised masked image modeling. In contrast to traditional spatial analysis approaches which rely on cell segmentations, CANVAS is segmentation-free, utilizes pixel-level information, and retains local morphology and biomarker distribution information. This approach allows the model to distinguish subtle morphological differences, leading to precise separation and characterization of distinct TME signatures. We applied CANVAS to a lung tumor dataset and identified and validated a monocytic signature that is associated with poor prognosis.

Correlative Imaging for Comprehensive Molecular Mapping of Individual Cell Types in Biological Tissues.

Mass spectrometry imaging (MSI) is a powerful technique for label-free spatial mapping of multiple classes of biomolecules in tissue sections. However, differences in desorption and ionization efficiency of different classes of molecules make it challenging to simultaneously map biomolecules at each omics layer in the same tissue sample. Herein, we present a correlative imaging method using nanospray desorption electrospray ionization (nano-DESI) MSI, which enables the spatial mapping of lipids, metabolites, peptides, and proteins with cellular-level spatial resolution in a single tissue section. We demonstrate the molecular profiling of specific cell types and identify truncated peptides in mouse pancreatic tissue. Distinct chemical gradients of peptides and lipids extending from endocrine cells to exocrine cells indicate their different roles in endocrine-exocrine crosstalk and intracellular signaling. The results underscore the power of the developed imaging approach for spatial multi-omics analysis that provides deep insights into cellular diversity and the intricate molecular interactions that occur within heterogenous biological tissues.

A cochlear progenitor pool influences patterning of the mammalian sensory epithelium via MYBL2.

During embryonic development, Wnt signaling influences both proliferation and sensory formation in the cochlea. How this dual nature of Wnt signaling is coordinated is unknown. In this study, we define a novel role for a Wnt-regulated gene, Mybl2, which was already known to be important for proliferation, in determining the size and patterning of the sensory epithelium in the murine cochlea. Using a quantitative spatial analysis approach and analyzing Mybl2 loss-of-function, we show that Mybl2 promoted proliferation in the inner sulcus domain but limited the size of the sensory domain by influencing their adjoining boundary position via Jag1 regulation during development. Mybl2 loss-of-function simultaneously decreased proliferation in the inner sulcus and increased the size of the sensory domain, resulting in a wider sensory epithelium with ectopic inner hair cell formation during late embryonic stages. These data suggest that progenitor cells in the inner sulcus determine boundary formation and pattern the sensory epithelium via MYBL2.

Combination of macroscopic and microscopic crash prediction models with multiple modeling approaches: A highway case study

Road traffic crashes are a global problem and represent a major cause of mortality among children and young people worldwide. Crash prediction models (CPM) play a crucial role in identifying crash-prone areas by predicting the expected number of crashes, thus facilitating the implementation of proactive measures. Previous studies have explored various CPM based on spatial analysis or modeling approaches. Nonetheless, a substantial gap still exists in exploring CPM that simultaneously address the combination of spatial units and modeling approaches, using of heterogeneous data at both the macroscopic and microscopic levels to develop models using Multi-Criteria Decision-Making and Statistical methods. Thus, the objective of this study is to develop multiple CPM using various spatial units of analysis and employing different modeling approaches, combine them, and conduct comparisons to determine the final model that best fits the data. A total of 7 models have been developed: three at the macroscopic level, utilizing Analytic Hierarchy Process (AHP), Negative Binomial (NB), and a combination of both; one at the microscopic level using Geographic Weighted Poisson Regression (GWPR). Additionally, three combined models bring together the previous from both levels, resulting in AHP+GWPR, NB+GWPR, and AHP+NB+GWPR models. These combined and individual models have been compared, and the best-performing has been selected through a validation process. The models were calibrated using data from a case study of a highway located in the northwest of Spain for the period 2016 to 2020. Subsequently, predictions for the year 2021 were made and compared to the actual crash occurrences during that year. The results indicate that the best model is the one that combines the macroscopic and microscopic levels using the approaches AHP+NB+GWPR, which presents a Mean Absolute Deviation (MAD) of 1.55, a Mean Square Error (MSE) of 2.89, does not underestimate any segments and shows a recall of 0.88. The results obtained confirm that the combination of models at the macroscopic and microscopic levels yields the best crashes predictions. This approach offers the advantage that our model, in addition to learning from historical data using statistical methods, also fits to the importance weights assigned by knowledgeable experts on the crashes propensity of the highway in the case study area using the Analytical Hierarchical Process.

Automated spatial omics landscape analysis approach reveals novel tissue architectures in ulcerative colitis

The utility of spatial omics in leveraging cellular interactions in normal and diseased states for precision medicine is hampered by a lack of strategies for matching disease states with spatial heterogeneity-guided cellular annotations. Here we use a spatial context-dependent approach that matches spatial pattern detection to cell annotation. Using this approach in existing datasets from ulcerative colitis patient colonic biopsies, we identified architectural complexities and associated difficult-to-detect rare cell types in ulcerative colitis germinal-center B cell follicles. Our approach deepens our understanding of health and disease pathogenesis, illustrates a strategy for automating nested architecture detection for highly multiplexed spatial biology data, and informs precision diagnosis and therapeutic strategies.

Evaluation of the Harmony of Trade Activities in the Letda Sujono Street Corridor Medan Tembung District Medan City

The development of the population and its supporting facilities has an impact on improving the economy of the people of a city. The Jalan Letda Sujono corridor has experienced significant development of trade and service activities with the location of the area on the border of Medan City with Deli Serdang Regency and the access to the Bandar Selamat Toll Gate. The development of trade and service activities encourages the development of other activities such as housing, public services, commerce, warehousing and industry. This has a big impact on the demands of land provision, the potential for inconsistencies in the use of space, and the disruption of traffic circulation. Inconsistent land use is expected to affect the growth and development of the city which is irregular and directed (urban sprawl). The factor of incompatibility in space utilization in spatial planning is caused by the lack of effective control of space utilization. The Jalan Letda Sujono corridor is planned by the Medan City RDTR as a Trade Sub-Zone zoning which covers 3 (three) planned blocks and there are provisions for activities (zoning regulations) for several activities that are limited by a maximum proportion of 10% of the planned block area. For this reason, it is necessary to conduct a study of the suitability of existing activities with the zoning plan that has been stipulated in the Medan City RDTR as a content of space control in order to realize safe, comfortable and sustainable spatial arrangements. In this study, a qualitative descriptive method with a spatial analysis approach is used to be able to directly compare the existing activity map with the zoning map that has been determined in the Medan City RDTR. The analysis carried out was an analysis of the interpretation of existing activities using satellite images, an analysis of the conformity of the provisions of activities with spatial ovelaping, an analysis of the suitability of activities in excess of 10% proportion, and an analysis of the suitability of activities in the Trade Sub-Zone in the Letda Sujono Street Corridor. The results of this study show that there are 45 types of existing activities consisting of 6,403 persils of activities (polygons). The suitability of activities in the research area was 102.23 Ha (60.84%) and the non-conformity of activities was 65.80 Ha (39.16%). The Plan Block that has been exceeded by activities with a proportion of 10% is Block 17-07-01 (Tembung). In the Trade Sub-Zone in the corridor of Jalan Letda Sujono, it is known that the suitability of its activities covers an area of 19.45 Ha (89.05%) and the non-conformity of its activities covers an area of 2.39 Ha (10.95%). This indicates that the spatial zoning plan has not been fully realized with the existence of Unpermitted Activities (X) in the form of Integrated Schools and the existence of Conditional Activities (B) in the form of Vehicle Pools, Workshops, and Warehouses/Warehouses. The conditional activities are no longer allowed (not allowed) in the Plan Block 17-07-01 (Tembung) because it has exceeded the proportion of activities of 10% of the plan block.

Evaluating the Role of GDP Per Capita in Shaping Internal Migration Patterns in China: A Spatial Econometric and Regression Analysis Approach

Evaluating the Role of GDP Per Capita in Shaping Internal Migration Patterns in China: A Spatial Econometric and Regression Analysis Approach

The Role of Small Industries in Achieving Sustainable Development in Qassim Region (KSA)

Small industries are one of the most important pillars of sustainable development. Accordingly, many governments have supported the establishment and growth of small industries. In this respect, Saudi Arabia has taken great steps ahead to ensure the spread and growth of this important economic sector. This study sought to know the role of small industries in achieving sustainable development in Qassim (KSA). It used the historical, inductive and spatial analysis approaches. The population included all small industries in Qassim, and this study was applied to the entire study population, which resulted in 112 small industries. The findings revealed that small industries in Qassim have achieved sustainable economic and environmental development, yet they have failed to achieve sustainable social development. Thus, this study recommends that small industries in Qassim should increase the rate of national workforce so as to achieve sustainable social development.

Geospatial Analysis of Lumpy Skin Disease Outbreaks among Cattle in Uttar Pradesh, India, 2021-2022.

The emergence of lumpy skin disease (LSD) among cattle in India is concerning. District-level data on LSD cases in Uttar Pradesh between 2021 and 2022 were analyzed. A stepwise spatial analytical approach was followed by first constructing yearly and monthly disease maps for LSD incidence rates (IRs), then spatially interpolating the LSD IRs, followed by evaluating the global and local clustering of LSD IRs and finally conducting spatial regression modeling. Overall, 5784 LSD cases from 6 districts and 112,226 cases from 33 districts were detected in 2021 and 2022, respectively. In the incremental spatial autocorrelation analysis, the highest global clustering of LSD IRs for the 2022 outbreak was detected at 196.49 km. For the 2021 LSD outbreak, one district with high-low and nine districts with low-high LSD IRs were identified in the eastern region of the state. For the 2022 LSD outbreak, 13 districts with high-high and 7 districts with low-high LSD IRs were identified in the western part of the state. A geographically weighted regression model identified the impact of climate (temperature and humidity) and land cover (pasture, fallow, and non-agricultural land) on LSD IRs. The study results can aid animal health authorities in developing LSD prevention and control programs.

Examining the Prevalence and Predictors of Stunting in Indian Children: A Spatial and Multilevel Analysis Approach

Examining the Prevalence and Predictors of Stunting in Indian Children: A Spatial and Multilevel Analysis Approach

Pemetaan Daerah Rawan Tanah Longsor di Daerah Desa Sanggang, Kecamatan Bulu, Kabupaten Sukoharjo

<p>Landslides are one of the frequent natural disasters occurring in various regions of Indonesia, including the Village of Sanggang in Bulu District, Sukoharjo Regency, Central Java. This area boasts diverse topography with numerous steep slopes, rendering it susceptible to landslides. The Bulu District is located in a hilly region with a high vulnerability to landslides, where from 2019 to 2020, there were recorded 11 landslide disaster events, with 10 of them occurring in the Bulu District. This study aims to map landslide-prone areas in the Village of Sanggang using a spatial analysis approach and relevant risk factors. The analytical methods include the use of topographical data, soil types, rainfall patterns, and historical landslide occurrences. The mapping results will provide a deeper understanding of locations vulnerable to landslides in this area. The outcomes of this research are expected to serve as a foundation for the development of more effective mitigation strategies, including recommendations for prevention measures and early warnings. A better understanding of these susceptible areas will assist local government authorities and stakeholders in safeguarding their communities and assets from the potential landslide hazards in the Village of Sanggang, Bulu District, Sukoharjo Regency.</p>

Elaborating and performing optimization approaches for web-based 3D spatial analysis of 3D city models

ABSTRACT Three-dimensional (3D) geographic information systems (GIS) are vital for urban planning and architectural design, enabling detailed visualization and analysis of urban landscapes. However, the growing size of 3D city data and the transition to web environments cause performance challenges for real-time applications. This study addresses the need for optimizing 3D spatial analyses for the web using Quadtree data structures and multiprocessing tools. Optimization scenarios based on data size and hardware configurations are evaluated, and the best hosting plans for GIS applications on cloud services are also considered.

Exploring the effective distance of 3D urban compact form correlated with building energy consumption and optimization strategies

ABSTRACT With the acceleration of urbanization, building energy consumption carbon emissions (BECCE) were increasing, weightily influencing global warming and the social-economic developments in cities. The detailed exploration of the most effective distance of urban three-dimensional (3D) compact form on BECCE guides urban space optimization. In this study, 135 People’s Banks of China (PBC) were taken as samples. Based on the banks building features, socioeconomic conditions, macroclimate, and urban 3D compact form at different buffers, the most effective distance on the BECCE was determined. According to Partial least-squares regression (PLSR), the results show that the most effective distance of the urban compact form on the BECCE was 150 m. The study divided the normalized 3D compactness index (NVCI) values from 135 buildings for the distance of 150 m into five categories and used the geographic detector to recognize statistically obvious differences between the subregions. The natural breakpoint method was used to categorize the study area into five classes, specifically: low, medium-low, medium, medium-high, and high compact form. Based on the geographic detector method result, the compact form of medium, medium high, and high compact form was optimized to low, medium low. The optimization can effectively reduce the BECCE due to changing the compact form of the building by 65.2% or 65.7%. The study combined mathematical statistics with spatial analysis approaches to identify the most effective distance for reducing energy consumption. Our results will contribute toward considerable reductions in the BECCE.