Spatial Mapping Techniques Research Articles

A novel method for calculating broadband electrical performance of high-speed aircraft radome under thermo-mechanical-electrical coupling

The electrical performance of radomes on high-speed aircraft can be influenced by the thermal and mechanical loads produced during high-speed flight, which can affect the detection distance and accuracy of the guidance system. This paper presents a new method that uses the Finite Difference Time Domain (FDTD) method to calculate the electrical performance of radomes under Thermo-Mechanical-Electrical (TME) coupling. This method can accurately characterize the effects of material dielectric temperature drift and structural deformation on the electrical performance of the radome under flight conditions, enabling high-precision full-wave calculations of the broadband electrical performance of the radome. The method initiates by utilizing a Finite Element Grid Model (FE-GM) of the radome to sequentially acquire the radome’s response temperature field and structural deformation field through thermal and mechanical simulations. Subsequently, spatial mapping techniques are developed to accurately incorporate the dielectric temperature drift and structural deformation of the radome into its Yee grid Electromagnetic (EM) simulation model. A verification case was designed to test the proposed method, and the results confirmed its high computational accuracy. Additionally, the effectiveness and necessity of the method were further demonstrated by analyzing the electrical performance of a fused silica ceramic radome used on a high-speed aircraft.

Unveiling the Spatial Dimensions of Light Pollution in Pakistan: An Emerging Environmental Challenge

The study aims to investigates about the growing and serious concern of light pollution as an emerging environmental challenge in Pakistan. The study also attempts to analyze the spatial dimensions of this issue, including its implications for biodiversity and public health. The study also attempts to analyze the spatial dimensions of this issue, including its implications for biodiversity and public health. In Pakistan, a notable research gap exists regarding the environmental impact of light pollution. Despite the absence of policies regulating artificial lighting, and a lack of public awareness on this issue, there is a pressing need for comprehensive research to address and mitigate the adverse effects of excessive artificial light in the country. This descriptive study employs spatial mapping techniques to investigate and highlight the pervasive issue of light pollution in Pakistan. Utilizing geographic information, the research analyzes and visually represents the spatial distribution of artificial lighting, providing a comprehensive understanding of the extent and intensity of light pollution across the country. It is found that the most light-polluted regions in Pakistan encompass the northern, eastern, and central sectors of Punjab, the southwestern and central areas of Sindh, the central part of Khyber Pakhtunkhwa, and the western region of Baluchistan. The study indicate that artificial sources of nightlight primarily cause light pollution in urban areas. This heightened light pollution coincides with major urban centers such as Rawalpindi, Lahore, Gujranwala, Faisalabad, Multan, Karachi, Hyderabad, Peshawar, and Quetta situated in these areas. Due to strong spatial effects, the light pollution extends from urban regions to their neighboring rural areas and depleting their dark spaces. Similarly, it is observed that, this strong spatial effect also causing light pollution alongside Indian border, substantiate the fact that light pollution from the urban regions of India creating light pollution in Pakistan. Furthermore, the study recommends comprehensive policy measures, including zoning regulations, public awareness campaigns, and technological solutions, to mitigate the adverse effects and spatial spread of light pollution.

Territorial patterns of COVID-19 in Iran

Motives: Spatial analysis has become an essential tool in understanding the underlying factors that contribute to the distribution of viral pandemics, diseases, injuries, and mortality patterns. By visualizing geographical data in spatial maps, researchers can identify local distribution patterns and potential drivers behind these patterns. In health and medical sciences, there has been a growing recognition that spatial analysis and mapping techniques are helpful in addressing various challenges related to the allocation of healthcare resource in both urban and rural areas. Aim: The objective of this study was to analyze the spatial distribution pattern of the COVID-19 pandemic and the Index of Proximity Distribution (IPD) across 31 provinces of Iran between February 2019 and February 2023. A two-stage sampling method combining convenience and cluster sampling was used to examine COVID-19 distribution patterns in 31 provinces of Iran between 22 February 2020 and 22 February 2023. COVID-19 and IPD data were collected as part of this panel study. Data were analyzed using t-tests, chi-square tests, and analysis of variance (ANOVA) in SPSS version 28 (α = 0.05). Subsequently, daily COVID-19 infection data for each province in the analyzed period were processed in ArcGIS software, and the spatial distribution pattern of the pandemic in Iran were visualized by point density analysis. Standard distance and standard deviation ellipse techniques were employed to assess the density or dispersion of infected individuals and to determine the spatial distribution pattern of COVID-19 in Iran. A spatial autocorrelation (Moran’s I) analysis was conducted to identify the spatial distribution pattern of COVID-19 in Iran. Additionally, distance-based spatial autocorrelation was used to examine the prevalence of COVID-19 infection across Iranian provinces. In a grouping analysis, 31 Iranian provinces were classified into five groups based on the number of COVID-19 cases, and spatial statistics were used to examine the prevalence of COVID-19 within each group. A hot spot analysis and a standard distance (SD) analysis were conducted to explore spatial correlations in the number of individuals affected by COVID-19 in each province. Results: Based on the Moran index, a random spatial pattern with a Z-Score of 1.485 was identified in March 2019, whereas a clustered distribution of COVID-19 with a Z-Score of 3.039 was determined in February 2023. The distance-based spatial autocorrelation analysis revealed a positive value of the Moran index (0.136627) at a distance of 383.3 kilometers from Tehran, which points to positive spatial autocorrelation and a higher number of COVID-19 cases in nearby regions. Conversely, the Moran index assumed a negative value of 0.040246 at a distance of 726.6 kilometers from Tehran, which suggests that the number of pandemic cases decreased over distance from Tehran. Moreover, based on the results of the hot spot analysis, Tehran province was identified as a hot cluster with a higher prevalence of COVID-19 cases in that region. In contrast, Bushehr province was classified as a cold cluster with a lower prevalence of COVID-19 cases in comparison with the surrounding regions. These findings provide valuable insights into the spatial distribution and clustering of COVID-19 cases in Iran. The shift from a random spatial pattern in 2019 to clustered distribution in 2023 indicates that the pandemic spread rate increased over time. The positive spatial autocorrelation near Tehran highlights the role of proximity and population movement in the transmission of the virus. Furthermore, the identification of hot spots and cold spots in a country can inform targeted interventions and resource allocation to effectively manage and control the pandemic. Overall, this study demonstrates the value of spatial analysis in identifying the spatial distribution patterns and the dynamics of the COVID-19 pandemic in Iran. The integration of spatial analysis techniques with epidemiological data contributes to a better understanding of spatial-temporal patterns, facilitates effective public health responses and resource allocation strategies. These findings contribute to the growing body of knowledge on the spatial epidemiology of COVID-19 and can aid in informing future preparedness and response efforts in Iran and other regions that face similar challenges.

Spatial Regression Modeling of Child Survival on the Distribution of Births and Deaths in Kenya Based on the Kenya Demographic and Health Survey (KDHS) 2022

This study used spatial mapping techniques to examine the distribution of births and deaths in Kenya and their relationship with various factors related to child survival, such as maternal age, education, wealth, and access to health services. Data were obtained from the 2022 Kenya Demographic and Health Survey (KDHS). Spatial autocorrelation analyses were conducted to identify clusters of high or low child mortality rates. The results showed significant spatial autocorrelation in child mortality rates, indicating that neighboring areas had similar mortality rates. Factors such as maternal education, wealth, and access to health services were found to be significantly associated with child mortality rates. These findings can inform targeted interventions and policies to reduce child mortality rates in Kenya, particularly in areas with the highest risk of mortality.

An Impact of Migration on Urbanization Trends in Navi Mumbai: A Socio-Economic Perspective

The incessant flow of migration into urban centres has profoundly shaped the landscape of Navi Mumbai, heralding dynamic changes in its socio-economic fabric. This paper endeavours to explore the multifaceted impact of migration on the urbanization trends within Navi Mumbai, adopting a comprehensive socio-economic perspective. Through a nuanced analysis of demographic shifts, employment patterns, and settlement dynamics, this study aims to elucidate the intricate relationship between migration and the burgeoning urban landscape. The research delves into the transformative effects of migration on the socio-economic structure of Navi Mumbai, examining its implications on infrastructure, housing, and socio-cultural dimensions. By employing a mixed-method approach integrating qualitative interviews, quantitative data analysis, and spatial mapping techniques, the study seeks to unravel the intricate interplay between migration flows and urbanization dynamics. Furthermore, it aims to highlight the challenges and opportunities that arise from this interconnection, shedding light on potential avenues for sustainable urban development and inclusive growth. This investigation aspires to contribute to the scholarly discourse on migration and urbanization, offering valuable insights for policymakers, urban planners, and stakeholders invested in fostering resilient and equitable urban environments amidst the evolving landscape of Navi Mumbai.

Ionospheric Total Electron Content (TEC) Anomalies as Earthquake Precursors: Unveiling the Geophysical Connection Leading to the 2023 Moroccan 6.8 Mw Earthquake

The study delves into the relationship between ionospheric total electron content (TEC) anomalies and seismic activity, with a focus on Morocco’s 6.8 Mw earthquake on 8 September 2023, lying within a tectonically active region at the convergence of the African and Eurasian Plates. To enhance the reliability of our findings, we incorporate space weather conditions, utilizing indices (Dst, Kp, and F10.7) to pinpoint periods of stable space weather. This minimizes the possibility of erroneously attributing natural ionospheric fluctuations to seismic events. Notably, our TEC analysis unveils positive and negative anomalies, with some occurring up to a week before the earthquake. These anomalies, exceeding predefined thresholds, provide compelling evidence of significant deviations from typical ionospheric conditions. Spatial mapping techniques employing both station-specific vTEC data and pseudorandom noise codes (PRNs) from multiple global navigation satellite system (GNSS) stations highlight a strong correlation between ionospheric anomalies and the earthquake’s epicenter. The integration of PRNs enhances coverage and sensitivity to subtle anomalies. Additionally, the analysis of satellite imagery and ground displacement data using Sentinel-1 confirms significant ground uplift of approximately 15 cm following the earthquake, shedding light on surface responses to seismic events. These findings underscore the potential of ionospheric science in advancing earthquake early warning systems and deepening our understanding of earthquake precursors, thus contributing to the mitigation of seismic event impacts and the protection of lives and infrastructure.

Evaluating the contribution of climate change and urbanization to the reversal in maximum surface wind speed decline: Case study in the Yangtze River Economic Belt, China

The drivers of variations in wind extremes, such as maximum surface wind speed (MSWS), are important considerations in long-term wind speed studies in urbanized regions, and the contribution of changed surface roughness has been well-examined. However, the contribution of other drivers of the variation in MSWS, including atmospheric circulation and increasing surface air temperature, remains largely unknown. This study integrated statistical methods, spatial mapping techniques, meteorological station data, and land use products to investigate the spatiotemporal influence and integrated contribution rates of surface air temperature, surface roughness, and atmospheric circulation on MSWS in the Yangtze River Economic Belt, China. The annual mean MSWS decreased (−0.662 m·s−1·10a−1) during 1973–2011 and increased (0.115 m·s−1·10a−1) during 2011–2020, whereas the seasonal changes differed significantly, with changes in the mean MSWS in winter being greater than in the other seasons. Moreover, increases in surface air temperature owing to surface roughness change, as well as atmospheric circulation (annual total explanatory power: 91.2%) contributed to MSWS changes from 1973 to 2020. The explanatory power of the integrated contribution was clearly influenced by urbanization, especially at the high urbanization level in the warm season (>86%), and the influence of atmospheric circulation was higher than that of urbanization in the cold seasons at the moderate level (>83%), implying that atmospheric circulation partially affected the key role of urbanization in the reversal of MSWS. The analysis framework of this study, which is based on the contribution rates of surface air temperature, surface roughness, and atmospheric circulation, is transferable to other cities and can be utilized to assist in both decreasing the risk of urban wind extremes and improving wind resource utilization.

Near-Field Coupling with a Nanoimprinted Probe for Dark Exciton Nanoimaging in Monolayer WSe2.

Tip-enhanced photoluminescence (TRPL) is a powerful technique for spatially and spectrally probing local optical properties of 2-dimensional (2D) materials that are modulated by the local heterogeneities, revealing inaccessible dark states due to bright state overlap in conventional far-field microscopy at room temperature. While scattering-type near-field probes have shown the potential to selectively enhance and reveal dark exciton emission, their technical complexity and sensitivity can pose challenges under certain experimental conditions. Here, we present a highly reproducible and easy-to-fabricate near-field probe based on nanoimprint lithography and fiber-optic excitation and collection. The novel near-field measurement configuration provides an ∼3 orders of magnitude out-of-plane Purcell enhancement, diffraction-limited excitation spot, and subdiffraction hyperspectral imaging resolution (below 50 nm) of dark exciton emission. The effectiveness of this high spatial XD mapping technique was then demonstrated through reproducible hyperspectral mapping of oxidized sites and bubble areas.

Carto(graphie)s of disquiet: A geometric dis/assemble and re-assemble of mental space

In his incomplete masterpiece, The Arcades Project, Walter Benjamin advanced a spatial mapping technique that disassembled metaphors of narration to reassemble them as metaphors of the gaze. The work, which is a composition of unfinished and changeable fragments, highlights two aspects that captivate me. Firstly, the importance of a tangible medium to support ideas, such as a book, an image or an atlas. Secondly, the physical spaces that inspire and manifest these ideas, such as the library, the passage and the city. This article visualizes a dynamic structure for the organization of thought and imagination through assembled visual constructs. A spatial configuration, a scaffold observed at different scales forming a porous machinic arrangement altogether. Geometry works as a skeleton for our mental space, readying to be fleshed by imagination, it lays foundations, and sets up the grid. It charts a design trajectory at an a-scalar dimension. Zooming in across said scales, which can be read in no particular order, one can inhabit five moments in the construction of the design process. It visits the depths of our mental space, surveys its structure for imagination; enters musée imaginaires; dissects concepts; materializes, frame-by-frame, in an in situ diagram, ending at the threshold of a line. It proposes a conceptual charter to articulate a taxonomy of possibilities and modes of design immersion. One scaffold, multiple views. This article is accompanied by drawings from my Speculative Assemblies folio. Work that addresses issues of construction, translation and the transformation of meaning through assembled visual constructs. Such translation aims not at a reproduction but a reincarnation of the original, taking on a life of its own in a new language, a mode of expression, a new construct. The action of drawing an architecture that is materialized rather than represented within the drawing, where it can be pursued, found and experienced.

Measuring crime: a new paradigm

PurposeThe purpose of this exploratory study is to expand on a previously developed crime harm index – the California Crime Harm Index (CA-CHI) – by discussing the development of the CA-CHI and presenting a comparison of the distribution of crime count and harm in a large Western city in the United States.Design/methodology/approachThis study used descriptive analyses, spatial univariate mapping and bivariate choropleth maps to analyze the distribution of Part I crime counts and harm.FindingsResults of the analysis show that while there was some variation in the distribution of crime count and harm city wide, spatial mapping and statistics reveal that the geographical distribution of crime count and harm across census tracts are largely consistent.Research limitations/implicationsMinor discrepancies between the distribution of crime count and harm indicate the potential for the CA-CHI to inform law enforcement practices. However, the distributions remain largely similar at the census tract level. There is room for further development of the CA-CHI to better distinguish between the distribution of crime harm and volume.Originality/valueNo other study has used spatial mapping techniques like bivariate choropleth mapping to examine the distribution of crime volume and crime harm based on the CA-CHI in any location in California.

The Effect of Proximity to Urban Rail on Housing Prices in Ottawa

Increasingly, urban rail transit (URT) is seen as a desirable solution for transportation challenges faced by both urban planners and residents of suburban areas alike. The availability and ease of access to URT, in turn, may result in distortions in local real estate markets. The conventional wisdom, in fact, suggests that construction of urban rail lines serves as a magnet for new housing development and, in turn, can lead to increases in property values in proximity to URT stations. Existing studies have, in good measure, confirmed this belief, but largely on the basis of global area studies that can often mask locally differentiating factors affecting housing prices. Using data from the City of Ottawa, this study seeks to move beyond such analyses by using spatial regression and mapping techniques that reveal that the relationship between URT stations and housing prices is far more complex than is commonly believed. The study demonstrates that while at the macro-level housing prices do vary positively with proximity to URT stations, the relationship is spatially dependent and may be affected by factors unique to specific locales.

A new hybrid Artificial Intelligence (AI) approach for hydro energy sites selection and integration

The increase of energy demand in this era leads exploration of new renewable energy sites. Renewable energy offers multiple benefits; hence it is suitable to be harnessed to meet power needs. In Sarawak, exploitation of hydro energy is a very feasible potential due to the abundant river flows and high rainfall volume. Thus, in this paper, 155 potential Hydro Energy Sites (HES) are identified and divided into six districts using a raw and unprocessed data provided by Sarawak Energy Berhad (SEB). Since there are no similar researches previously done for identification and integration of hydro energy sources, in this paper, two stage complex data management was built using 155 HES locations in Sarawak. New spatial mapping technique were used for the first stage. From the new spatial mapping technique, the mapped data were categorized into groups, analysed and created new accurate mapping locations on the Sarawak map in terms of the districts using GIS Spatial tools. Their exact geographical locations were identified, and their coordinate systems have been retrieved as complete final data with geo-referencing technique in QGIS with ID numbers. Moreover, the power capacity of each location of all the 155 HES was quantified. By employing this data, the identified locations have been integrated into the already created 155 HES sites. For the second stage, a new two-part AI hybrid approach has been proposed and applied to improve optimal transmission line routing for each district to locate transmission line paths. The first part of hybrid AI implemented in this paper was TSP-GA and second part implemented in this paper was based on improved fuzzy logic with TSP-GA together. To ensure the optimal results are reliably achieved, both first part of TSP-GA and second part of improved fuzzy TSP-GA are utilized to generate the transmission line routing. These two approaches are required to obtain the minimal values of total distance and total elevation difference of each HES. Based on the benchmarking results, fuzzy TSP-GA successfully improved 12.99% for Song district, 7.52% for Kapit district, 3.71% for Belaga district, 1.54% for Marudi district, 18.01% for Limbang district, 11.00% for Lawas district when comparing against the ordinary TSP-GA approach.

Correction to: Molecular DNA-based spatial mapping technique predicting diversity and distribution of otters (Lutrinae) in Peninsular Malaysia using non-invasive fecal samples

Correction to: Molecular DNA-based spatial mapping technique predicting diversity and distribution of otters (Lutrinae) in Peninsular Malaysia using non-invasive fecal samples

The times, they are a-changin’: tracking shifts in mental health signals from early phase to later phase of the COVID-19 pandemic in Australia

IntroductionWidespread problems of psychological distress have been observed in many countries following the outbreak of COVID-19, including Australia. What is lacking from current scholarship is a national-scale assessment that tracks...

Mise en place for gastronomy geography through food: Flavor regions in Turkey

The spatial variation of foodstuffs derived from a combination of Turkish culinary culture and geographical conditions, as well as the spatial distribution of kitchen richness, are critical for the development of food culture. Therefore, the main purpose of this study was to determine whether there are statistically significant flavor region boundaries based on the spatial distribution of ingredient proportions in certain dishes. Turkey was selected as the study area because of its diversity in terms of physical and human geography due to its geopolitical location. Four dishes were selected as examples in this study, for which a total of 4098 recipes were compiled from 973 districts. Flavor regions were determined using a spatial mapping technique that considered provinces located in the high-high and low-low regions in the LISA (local indicator of spatial association) ingredient maps, and the Moran's I values were found to be significant at the 0.001 level in a spatial autocorrelation analysis. Overall, 15 distinct flavor regions were identified. These findings provide a significant resource for improving existing destinations and developing new destinations for gastronomy tourism by considering the various flavor regions of Turkish cuisine and their general characteristics.

Spatial scan statistics based on empirical likelihood

Spatial cluster is a spatial analysis and mapping technique for cluster identification in spatial phenomena. This work proposes a nonparametric scan method for cluster detection based on the empirical likelihood, and it was compared with the Kulldorff test. The main contribution of this method is that no family of distributions has to be assumed in the analysis. The method is able to deal with the presence of overdispersion, zero inflated and other characteristics that are common in real data. The proposal was evaluated via simulations studies considering data following a zero inflated Poisson distribution. The results show that the proposed method can substantially reduce the probabilities of the error type I for zero inflated data, with low power probabilities for cluster with size less than eight observations. In a case study of measle in São Paulo, Brazil, only the Kulldorff test identified a cluster. It is recommended that a cluster detected by the spatial scan statistic of Kulldorff should be interpreted with caution when it is not confirmed by the empirical likelihood scan statistic.

Linescan Lattice Microscopy: A Technique for the Accurate Measurement and Mapping of Lattice Spacing and Strain with Atomic Force Microscopy.

Lateral resolution and accuracy in scanning probe microscopies are limited by the nonideality of piezoelectric scanning elements due to phenomena including nonlinearity, hysteresis, and creep. By taking advantage of the well-established atomic-scale stick-slip phenomenon in contact-mode atomic force microscopy, we have developed a method for simultaneously indexing and measuring the spacing of surface atomic lattices using only Fourier analysis of unidirectional linescan data. The first step of the technique is to calibrate the X-piezo response using the stick-slip behavior itself. This permits lateral calibration to better than 1% error between 2.5 nm and 9 μm, without the use of calibration gratings. Lattice indexing and lattice constant determination are demonstrated in this way on the NaCl(001) crystal surface. After piezo calibration, lattice constant measurement on a natural bulk MoS2(0001) surface is demonstrated with better than 0.2% error. This is used to measure nonuniform thermal mismatch strain for chemical vapor deposition (CVD)-grown monolayer MoS2 as small as 0.5%. A spatial mapping technique for the lattice spacing is developed and demonstrated, with absolute accuracy better than 0.2% and relative accuracy better than 0.1%, within a map of 12.5 × 12.5 nm2 pixels using bulk highly oriented pyrolytic graphite (HOPG) and MoS2 as reference materials.

Critical Approaches to GIS and Spatial Mapping in Indonesia Forest Management and Conservation

The conventional Geographic Information Systems (GIS) and spatial mapping techniques fail to understand a spatially complex forest area. This is because forest landscapes cannot be easily mapped into a two-dimensional map, which is usually used in spatial planning. In addition, planning maps are the fundamental factors of land grabbing issues. To solve this problem, counter-mapping arises at the local level as a tool to overcome the problem of land grabbing in forest areas in Indonesia. Counter mapping is defined here as part of a broader term under participatory mapping or citizen mapping. However, counter-mapping also faces critiques in terms of different epistemological and socio-economic-political conditions at the local level. This article elaborates the use of GIS and spatial mapping methods within a 'critical' social science approach based on literature review and field experiences. This article also aims to analyze counter-mapping as a ‘tool’ to solve the forest land-use problem, which can contribute to the choice of policy instruments in forest management and conservation in Indonesia.

Towards a Non-Destructive Method of Mapping the E-J Relation Using Force Decay Measurements on Superconducting Bulks

In many applications of high-temperature superconductivity, such as superconducting magnetic bearings, precise knowledge of the non-linear voltage-current (E-J) relation is necessary for making accurate quantitative predictions of performance. Magnetic microscopy allows the spatial mapping of coated conductor superconducting properties. There is no equivalent spatial mapping technique for bulk superconductors. A convenient method of mapping the E-J relation for superconductor bulks to be used in superconducting magnetic bearings is desired. The maglev force in a superconducting bearing decays logarithmically over time due to flux creep. The non-linear E-J behaviour describes this flux-creep behaviour and maximum force. It should, therefore, be possible to map the E-J power law of a bulk superconductor using levitation force decay characteristics. Using the H-formulation finite-element method we have simulated the levitation force decay under zero field cooled conditions and identified a relation between the logarithmic force decay parameters and the n-index of the E-J power law. This potentially offers a non-destructive method of determining the E-J relation in large superconducting bulks.

Nonmatching Discontinuous Cartesian Grid Algorithm Based on the Multilevel Octree Architecture for Discrete Ordinates Transport Calculation

Obtaining sufficiently accurate geometric descriptions is a crucial prerequisite for reliable particle transport calculations. Conventional transport algorithms on Cartesian grids use a highly efficient sweep technique and numerous mature discretization methods despite their modeling deficiency for complex geometries. To achieve a more accurate geometric description, a cell-based nonmatching Cartesian grid algorithm is proposed on the basis of the multilevel octree architecture. Transport sweep is performed according to the tree branch relationship of nested mesh distributions. The angular flux transmission between discontinuous grids is handled by the flux spatial moment mapping technique, and multiple zero-order mapping schemes are developed, including finite element interpolation, distance interpolation, and exponential fitting methods for treating upwind flux distributions of different relative shapes. The first-order mapping schemes are modified and improved for linear and exponential short characteristic discretization methods. The mapping accuracy is evaluated for polynomial and exponential functions, and a new spatial shape factor is presented for measuring the degree of nonlinearity. The multilevel octree grid (MLTG) algorithm is tested for neutron transport benchmarks, and good agreement with Monte Carlo and standard SN results is achieved. The number of meshes in the VVER shielding model is reduced from 18 million to 2 million using 3-level octree grids with the same geometric description accuracy. Numerical verification of a one-group fixed-source problem shows that 4-level and 5-level MLTG results with proper spatial discretization schemes can achieve relative deviations of less than 3% and 5% for detector region flux, respectively.