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Existing regionalization methods have largely overlooked the temporal dimension, leading to outcomes that predominantly reflect spatial differentiation of regional variables only at a singular temporal instance, rather than across their entire evolutionary process. In response, this research proposes a novel model for geographic process regionalization based on spatial time-series (STS) data. This model is designed to amalgamate adjacent entities exhibiting analogous trends in regional variable fluctuations, while segregating those with disparate trajectories. The study starts by establishing methodologies for the assessment of three distinct types of STS data similarities: value-based, shape-oriented, and periodic patterns. Following this, a tailored regionalization methodology specific to STS data is delineated. This methodology facilitates a quantitative analysis of time-series variables within each geographical region, leading to the development of a process-oriented geographic boundary model (GBM). The efficacy of this model is validated through empirical testing with synthetic data sets and further elucidated via case studies focusing on the dynamics of population and pollution levels, underscoring its substantial applicability in both human and physical geography. The proposed methodology is adaptable for any time-series data that incorporate locational information for regionalization. [ABSTRACT FROM AUTHOR]

Hyperspectral unmixing, which decomposes mixed pixels into the endmembers and corresponding abundances, is an important image process for the further application of hyperspectral images (HSIs). Lately, the unmixing problem has been solved using deep learning techniques, particularly autoencoders (AEs). However, the majority of them are based on the simple linear mixing model (LMM), which disregards the spectral variability of endmembers in different pixels. In this article, we present a multi-attention AE network (MAAENet) based on the extended LMM to address the issue of the spectral variability problem in real scenes. Moreover, the majority of AE networks ignore the global spatial information in HSIs and operate pixel- or patch-wise. We employ attention mechanisms to design a spatial–spectral attention (SSA) module that can deal with the band redundancy in HSIs and extract global spatial features through spectral correlation. Moreover, noticing that the mixed pixels are always present in the intersection of different materials, a novel sparse constraint based on spatial homogeneity is designed to constrain the abundance and abstract local spatial features. Ablation experiments are conducted to verify the effectiveness of the proposed AE structure, SSA module, and sparse constraint. The proposed method is compared with several state-of-the-art unmixing methods and exhibits competitiveness on both synthetic and real datasets. [ABSTRACT FROM AUTHOR]

A photometric method for analyzing the structure of a two-component medium from the image of the material is presented. At the same time, the peculiarities of the material structure are studied with the help of estimation of the spatial homogeneity of the materials by the geometric characteristics of phases (components of the material). The following image characteristics are used for analysis of the structural characteristics of the material: phase separation, subsequent measurement of grains (their sizes), the spatial distribution over the image of the material, the static moment of inertia of the section, fractal dimension, and the multifractal spectrum. The method is presented for the example of obtaining a composite material by mixing. [ABSTRACT FROM AUTHOR]

There is high confidence that climate change has increased the probability of concurrent temperature‐precipitation extremes, changed their spatial‐temporal variations and affected the relationships between drivers of such natural hazards. However, the extent of such changes has been less investigated in Australia. Daily data spanning the period 1889‐2019 (131 years) were extracted from SILO gridded dataset at 700 grid cells (1◦ × 1◦) across Australia to calculate annual and seasonal mean daily maximum temperature (MMT) and total precipitation (TPR). A nonparametric multivariate copula framework was adopted to estimate the return period of compound hot‐dry (CHD) events based on an 'And' hazard scenario (hotter than a threshold 'And' drier than a threshold). CHD extremes were defined as years with joint return periods of longer than 25 years calculated over the period 1889‐2019. Mann‐Kendall nonparametric tests were used to analyse trends in MMT and TPR as well as in the frequency of univariate and CHD extremes. Results showed a general cooling‐wetting trend over 1889‐1989. Significant increasing trends were detected over 1990‐2019 in the frequency and severity of hot extremes across the country while trends in dry extremes were mostly insignificant (and decreasing). A significant increase in the association between temperature and precipitation was identified at various temporal scales. While the frequency of CHD extremes was mostly stable over 1889‐1989, it significantly increased between 1990 and 2019 at 44% of studied grid cells, mostly located in the north, south‐east and southwest. [ABSTRACT FROM AUTHOR]

Hyperspectral unmixing, which decomposes mixed pixels into the endmembers and corresponding abundances, is an important image process for the further application of hyperspectral images (HSIs). Lately, the unmixing problem has been solved using deep learning techniques, particularly autoencoders (AEs). However, the majority of them are based on the simple linear mixing model (LMM), which disregards the spectral variability of endmembers in different pixels. In this article, we present a multi-attention AE network (MAAENet) based on the extended LMM to address the issue of the spectral variability problem in real scenes. Moreover, the majority of AE networks ignore the global spatial information in HSIs and operate pixel- or patch-wise. We employ attention mechanisms to design a spatial–spectral attention (SSA) module that can deal with the band redundancy in HSIs and extract global spatial features through spectral correlation. Moreover, noticing that the mixed pixels are always present in the intersection of different materials, a novel sparse constraint based on spatial homogeneity is designed to constrain the abundance and abstract local spatial features. Ablation experiments are conducted to verify the effectiveness of the proposed AE structure, SSA module, and sparse constraint. The proposed method is compared with several state-of-the-art unmixing methods and exhibits competitiveness on both synthetic and real datasets.

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The article raises the issue of comprehensive human-geographical research of administrative-territorial units formed as a result of reforming the administrative-territorial structure of Ukraine. The human-geographical meaning of the concept of "administrative district capacity" as its ability to function effectively in accordance with natural, demographic and economic potentials is introduced and revealed. Using a cartographic method, a series of analytical maps elaborated, which revealed significant spatial heterogeneity in terms of territorial communities of Stryi Raion (district) in terms of providing by land resources, population and settlement density, level of urbanization, general budget revenues per unit area and per capita, taфability index. Based on the Methodology for the Formation of Capable Territorial Communities (2020), groups of territorial communities with high (3.9 - 4.6) and medium (3.2 - 3.8) levels of capacity identified at the studied area. Elaboration of the approved strategies for the development of territorial communities made it possible to summarize the priority areas of economic development of the district: tourism and recreational, transport and logistical, agrarian, industrial. Clustering is proposed as an effective process of territorial organization of the economy of Stryi Raion. The need to transform the city-forming functions of the former district centers (Zhydachiv, Mykolaiv, Skole) into new economic realities is emphasized. [ABSTRACT FROM AUTHOR]

• Resonance photoplasma in a Na−Ar gas cell was investigated for different Na and Ar pressures. • Self-consistent photoplasma modeling was made. • Collision broadening and Voigt profile of resonance lines were considered. • The dependencies of electron density and temperature on the Na pressure were obtained. • Quenching of resonance levels and spatial homogeneity of photoplasma were investigated. A self-consistent modeling of the resonance photoplasma in pure Na and Na−Ar mixtures with spatial homogeneity of photoexcitation of the resonance levels of Na in the volume of a cylindrical gas cell has been carried out. The Voigt line shape and the collision broadening of the D1 and D2 lines of Na were considered. The main photoplasma parameters were calculated for the Na pressure P Na =0.0045−0.165 torr, the P Ar / P Na =0 and 10k (k = 1, 2 and 3) values and the gas temperature of 680 K at the pumping resonance radiation flux density F = 40 W/(m2nm). It has been established that the electron density N e and temperature T e increase non-linearly with the growth of the P Na and reach, respectively, 6.4·1018 m−3 at P Na =0.165 torr and P Ar / P Na =100 and 0.28 eV for the pure Na photoplasma. Herewith, the dependence of T e on the Ar pressure P Ar is non-monotonic. It is a consequence of the cooperative action of electron inelastic collisions of the first and second kinds with Na atoms and elastic collisions with Na and Ar atoms. The comparison of the obtained results for the L = 4 R = 0.02 m case with those calculated for the infinite cylinder case with the same R value showed the good validity of the infinite cylinder approximation. As a result of our analysis of the quenching of the resonance levels and theoretical results from the literature, it has been established that the spatial non-homogeneity of the investigated photoplasma caused by this quenching does not exceed 20 % for the P Na ≤0.1 torr and P Ar / P Na =0 − 103. The obtained data can be used in studies of alkali-containing photoplasmas and to create photoelectric converters based on them. [ABSTRACT FROM AUTHOR]

Abstract Background Camera-based photoplethysmography (cbPPG) is a measurement technique which enables remote vital sign monitoring by using cameras. To obtain valid plethysmograms, proper regions of interest (ROIs) have to be selected in the video data. Most automated selection methods rely on specific spatial or temporal features limiting a broader application. In this work, we present a new method which overcomes those drawbacks and, therefore, allows cbPPG to be applied in an intraoperative environment. Methods We recorded 41 patients during surgery using an RGB and a near-infrared (NIR) camera. A Bayesian skin classifier was employed to detect suitable regions, and a level set segmentation approach to define and track ROIs based on spatial homogeneity. Results The results show stable and homogeneously illuminated ROIs. We further evaluated their quality with regards to extracted cbPPG signals. The green channel provided the best results where heart rates could be correctly estimated in 95.6% of cases. The NIR channel yielded the highest contribution in compensating false estimations. Conclusions The proposed method proved that cbPPG is applicable in intraoperative environments. It can be easily transferred to other settings regardless of which body site is considered.

High-Efficiency Video Coding (HEVC) is the new emerging video coding standard of the ITU-T Video Coding Experts Group (VCEG) and the ISO/IEC Moving Picture Experts Group (MPEG). The HEVC standard provides a significant improvement in compression efficiency in comparison with existing standards such as H264/AVC by means of greater complexity. In this paper we will examine several HEVC optimizations based on image analysis to reduce its huge CPU, resource and memory expensive encoding process. The proposed algorithms optimize the HEVC quad-tree partitioning procedure, intra/inter prediction and mode decision by means of H264-based methods and spatial and temporal homogeneity analysis which is directly applied to the original video. The validation process of these approaches was conducted by taking into account the human visual system (HVS). The adopted solution makes it possible to perform HEVC real time encoding for HD sequences on a low cost processor with negligible quality loss. Moreover, the frames pre-processing leverages the logic units and embedded hardware available on an Intel GPU, so the execution time of these stages are negligible for the encoding processor. [ABSTRACT FROM AUTHOR]

The principle of transparency not only is changing but its importance is increasing. International organizations, including the OECD, are seeking to develop harmonized standards of transparency in public administration. This fact is of particular importance in the search for common solutions for the OECD countries in the implementation of transparency standards, but also this organisation has sought adequate methods for measuring implementation of these standards. The main purpose of the study is to analyze the level of spatial homogeneity in the area of application of the standards of "integrity" of the OECD, which are an expression of the principle of transparency in the member states of this organization, on the European continent. In the study vector calculus was used. [ABSTRACT FROM AUTHOR]

High-Efficiency Video Coding (HEVC) is the latest video coding standard of the Joint Collaborative Team on Video Coding (JCT-VC). HEVC noticeably improves compression performance when compared with previous standards such as H264, and represents a major step forward in video compression technology. However, this improvement is achieved by increasing the complexity of the encoding process. HEVC employs a novel flexible quad-tree coding block partitioning structure that enables the use of large and multi-sized coding, prediction, and transform blocks. This system is more efficient but also more computationally demanding. In this article an optimized CU size decision algorithm is proposed to reduce the computational cost of quad-tree partitioning by means of spatial and temporal homogeneity analysis and classification, which are directly applied to the input image. If a CU is classified as spatially or temporally homogeneous the quad-tree recursive process is stopped. Furthermore, this image pre-analysis is performed using logic units and embedded hardware on a GPU, thus avoiding unnecessary waiting states, so the computational cost associated with this process is zero for the processor in charge of the encoding process. In comparison with the reference HM16.2 test model, the encoding time is reduced by up to 32.69%, with negligible quality loss and a maximum BD-Rate increase of 1.2% for low-delay P configuration. [ABSTRACT FROM AUTHOR]

• Proposition of a fast CU (Coding Unit) size decision method to reduce the HEVC calculation cost based on spatial homogeneity. • Realization of a statistical study, using different video sequences, covering the four angles of the Temporal Information/Spatial Information (TI / SI) plane and for several QP values. • Comparison of proposed approach results with the HM16.13 benchmark test model and results from other recent works. • Experiments of the approach proposed on the different test sequences from the common JCT-VC (Joint Collaborative Team on Video Coding) covering the whole TI/SI plan and for different quality parameters (QP). • Evaluation the impact of the proposed approach on the disparity in terms of SI/TI (Spatial Information/Temporal Information) of the reconstructed video sequences compared to the original video sequences. For the same video quality, HEVC gives 25% to 50% bitrate savings, compared to its predecessor the Advanced Video Coding H.264 and thus supports resolutions up to 8 K UHD. However, the reduction in bitrates provided by the HEVC leads to an increase in the computational cost of the encoding operation. This complexity can become a true handicap especially for real-time video streaming and also for VANET (Vehicular Ad-Hoc Network) applications such as traffic safety and Video surveillance. The improvement in the bitrates and also the increase in the calculation cost are due to the use of large and multi-sized coding, prediction and transform blocks. Indeed, the H264 coder is based on structure macroblocks with sizes 4 × 4, 8 × 8 and 16 × 16, while H.265 depends on Coding Tree Units (CTUs), CTUs select sizes 4 × 4, 8 × 8, 16 × 16, 32 × 32 and 64 × 64 blocks. This paper proposes a fast CU (Coding Unit) size decision method to reduce the HEVC calculation cost based on spatial homogeneity. Compared with the HM16.13 benchmark test model, the average coding time is reduced by around 40% for CIF / QCIF video sequences, 35% to 43% for class A, B and C test sequences. These important reductions in coding time are obtained with negligible loss of quality and an average increase in bitrates which does not exceed 0.89% for the three configuration modes (All intra, Random Access and Low Delay). [ABSTRACT FROM AUTHOR]

Remote sensing (RS) and proximal soil sensing (PSS) technologies offer an advanced array of methods for obtaining soil property information and determining soil variability for precision agriculture. A large amount of data collected by these sensors may provide essential information for precision or site-specific management in a production field. Data clustering techniques are crucial for data mining, and high-density data analysis is important for field management. A new clustering technique was introduced and compared with existing clustering tools to determine the relatively homogeneous parts of agricultural fields. A DUALEM-21S sensor, along with high-accuracy topography data, was used to characterize soil variability in three agricultural fields situated in Ontario, Canada. Sentinel-2 data assisted in quantifying bare soil and vegetation indices (VIs). The custom Neighborhood Search Analyst (NSA) data clustering tool was implemented using Python scripts. In this algorithm, part of the variance of each data layer is accounted for by subdividing the field into smaller, relatively homogeneous, areas. The algorithm’s attributes were illustrated using field elevation, shallow and deep apparent electrical conductivity (ECa), and several VIs. The unique feature of this proposed protocol was the successful development of user-friendly and open source options for defining the spatial continuity of each group and for use in the zone delineation process.

Spatial heterogeneity of biodiversity has been extensively researched, but its spatial homogeneity is virtually unstudied. An intertidal seagrass system at Knysna (South Africa) known to display spatially homogeneous macrobenthic species density at scales ≥0.0275 m 2 was re-investigated at four smaller spatial grains (0.0015 m 2 - 0.0095 m 2 ) via a lattice of 8 × 8 stations within a 0.2 ha area. The aim was to investigate the null hypothesis that spatial homogeneity of species density is not a fixed emergent assemblage property but breaks down at small spatial grains within given spatial extents. Although assemblage abundance was significantly heterogeneous at all spatial grains investigated, both species density and functional-group density were significantly homogeneous across those same scales; observed densities not departing from those expected on the basis of independent assortment. Spatial homogeneity is therefore an emergent assemblage property within given spatial extents at Knysna and probably at equivalent sites elsewhere. Equivalent species density in South Africa, Australia and the UK at spatial grains <0.03 m 2 , however, is a scale-related sampling artefact, as may be temporal homogeneity of species density at Knysna over a 3 year period, but close similarity in shape of their species occupancy distributions remains unexplained. [ABSTRACT FROM AUTHOR]

Global environmental change is bringing extreme precipitation, and the combination of natural and artificial impacts are resulting in serious floods on the west coast of Taiwan. Disparity in social, economic and infrastructure resources contributes to spatial variation in the vulnerability to flood disaster. Owing to the high frequency of torrential rain and serious land subsidence in the study area, this paper attempts to categorize vulnerability indicators under varied assumptions of spatial homogeneity and spatial heterogeneity. The results show that the spatial heterogeneity indeed affects the distribution of flood vulnerability indicators. The core value of this article is that it measures the improvement from using geographically weighted statistics rather than traditional statistics. For the flood vulnerability discussion, this paper demonstrates the importance of considering spatial heterogeneity when allocating resources against floods. [ABSTRACT FROM AUTHOR]