Spatial Components Research Articles

Estimation of horizontal spatial specifications for ideal head-mounted displays in practical conditions

This study presents the horizontal spatial specifications required for developing ideal head-mounted displays (HMDs) that provide visual experiences indistinguishable from those obtained without wearing HMDs. We investigated the minimum specifications for pixel density and field of view (FoV) such that users cannot perceive any degradation in these aspects. Conventional studies have measured visual acuity in the periphery and FoV size without eye movement. However, these results are not sufficient to determine the spatial specifications because users may notice a degradation in the quality of images displayed in the periphery when shifting their gaze away from the front. In this study, we measured visual characteristics under practical conditions, wherein participants moved their eyes naturally in coordination with their head movements, as observed when viewing natural scenes. Using a cylindrical display that covered the participant’s entire horizontal visual field with high resolution images, we asked participants to identify degraded spatial resolution or narrowed FoV. Results showed that ideal HMDs do not have to provide spatial frequency components above 0.5 and 2 cycles per degree outside the visual fields of approximately 210° and 120° horizontally, respectively. These results suggest that a resolution equivalent to one-fifteenth of the frontal area is sufficient for the lateral areas of the head. In addition, we found that ideal HMDs require a FoV of at least 240°. This is considerably smaller than a naive estimate of 310° based on previous studies, obtained by integrating the FoV size during fixation and the movable range of the eyes. We believe that these findings will be helpful in developing ideal HMDs.

Single-cell morphometrics reveals T-box gene-dependent patterns of epithelial tension in the Second Heart field

The vertebrate heart tube extends by progressive addition of epithelial second heart field (SHF) progenitor cells from the dorsal pericardial wall. The interplay between epithelial mechanics and genetic mechanisms during SHF deployment is unknown. Here, we present a quantitative single-cell morphometric analysis of SHF cells during heart tube extension, including force inference analysis of epithelial stress. Joint spatial Principal Component Analysis reveals that cell orientation and stress direction are the main parameters defining apical cell morphology and distinguishes cells adjacent to the arterial and venous poles. Cell shape and mechanical forces display a dynamic relationship during heart tube formation. Moreover, while the T-box transcription factor Tbx1 is necessary for cell orientation towards the arterial pole, activation of Tbx5 in the posterior SHF correlates with the establishment of epithelial stress and SHF deletion of Tbx5 relaxes the progenitor epithelium. Integrating findings from cell-scale feature patterning and mechanical stress provides new insights into cardiac morphogenesis.

Global shift symmetry on an ADM hypersurface: Toward emergent gravity

Generalized symmetries and their spontaneous breakdown serve as the fundamental concept to constrain the many-body entanglement structure, which allows us to characterize quantum phases of matter and emergent collective excitations. For example, emergent photons may be understood by spontaneous 1-form symmetry breaking, which results from a long-ranged entanglement structure between UV microscopic degrees of freedom. In this study, we show that emergent “gravity” may also arise in a similar fashion, where quotes have been used to emphasize that the symmetry-constrained gravitons show unconventional properties compared to usual gravitons. As the electric 1-form symmetry in Maxwell theory is realized as a global shift symmetry of the spatial component of the U(1) gauge field, generated by the electric field, we demonstrate that a constant shift of the Arnowitt-Deser-Misner (ADM) metric on the spatial hypersurface can be viewed as a global symmetry, generated by the ADM canonical momentum. Deriving a vector-type conserved charge from the variation of action, we construct a shift symmetry operator. Considering a Wick rotation, we demonstrate that a gravitational Wilson loop is charged under the action of this shift symmetry operator, which thus confirms the existence of a generalized global symmetry on the ADM hypersurface. Based on the Ward identity, we show that the spontaneous breaking of this global shift symmetry may give rise to a nonpropagating massless symmetric gauge field at the boundary of the hypersurface. Published by the American Physical Society 2024

EEG Data Augmentation Method for Identity Recognition Based on Spatial–Temporal Generating Adversarial Network

Traditional identity recognition methods are facing significant security challenges due to their vulnerability to leakage and forgery. Brainprint recognition, a novel biometric identification technology leveraging EEG signals, has emerged as a promising alternative owing to its advantages such as resistance to coercion, non-forgeability, and revocability. Nevertheless, the scarcity of high-quality electroencephalogram (EEG) data limits the performance of brainprint recognition systems, necessitating the use of shallow models that may not perform optimally in real-world scenarios. Data augmentation has been demonstrated as an effective solution to address this issue. However, EEG data encompass diverse features, including temporal, frequency, and spatial components, posing a crucial challenge in preserving these features during augmentation. This paper proposes an end-to-end EEG data augmentation method based on a spatial–temporal generative adversarial network (STGAN) framework. Within the discriminator, a temporal feature encoder and a spatial feature encoder were parallelly devised. These encoders effectively captured global dependencies across channels and time of EEG data, respectively, leveraging a self-attention mechanism. This approach enhances the data generation capabilities of the GAN, thereby improving the quality and diversity of the augmented EEG data. The identity recognition experiments were conducted on the BCI-IV2A dataset, and Fréchet inception distance (FID) was employed to evaluate data quality. The proposed method was validated across three deep learning models: EEGNET, ShallowConvNet, and DeepConvNet. Experimental results indicated that data generated by STGAN outperform DCGAN and RGAN in terms of data quality, and the identity recognition accuracies on the three networks were improved by 2.49%, 2.59% and 1.14%, respectively.

Geographical variability in cancer incidence explained by the socioeconomic environment: an example of lung cancer in northwestern France.

The incidence of lung cancer is unequally distributed in France. Although several studies have shown a link between the socioeconomic environment of populations and the incidence of cancer, the contribution has not been quantified. We aimed to analyse the geographical variability of lung cancer incidence in Normandy and calculate the proportion explained by the socioeconomic environment. We included 7665 lung cancer cases recorded in the General Tumor Registry of Calvados and the Cancer Registry of Manche. A Bayesian model was used to map the spatial variation in the incidence of lung cancer in the territory, and an innovative approach was used to evaluate the influence of geographical variability in the socioeconomic environment on the spatial heterogeneity of lung cancer incidence. The maps of the spatial components showed high contrasts for both genders, and the socioeconomic environment integration in the model made the maps less contrasting. The socioeconomic environment of the population explained one-third of the spatial variation in the incidence of lung cancer in women and one-fifth in men. The results showed that a non-negligible part of the spatial variation in the incidence of lung cancer could be explained by the geographical distribution of the socioeconomic environment.

Cortical Alterations Associated with Executive Function Deficits in Youth with a Congenital Heart Defect

Abstract Adolescents and young adults born with a complex congenital heart defect (CHD) are at risk for executive function (ExF) impairments, which contribute to the psychological and everyday burden of CHD. Cortical dysmaturation has been well described in fetuses and neonates with CHD and early evidence suggests that cortical alterations in thickness, surface area, and gyrification index are non-transient and can be observed in adolescents with CHD. However, cortical alterations have yet to be correlated with ExF deficits in youth with CHD. This study aims to use a data-driven approach to identify the most important cortical features associated with ExF deficits in adolescents and young adults with CHD. To do so, we combined two comparable datasets acquired at the Research Institute of the McGill University Health Centre and the University Children’s Hospital Zurich each including both youth with CHD and healthy controls. For each participant, a high-resolution T1-weighted magnetic resonance image, a self-reported ExF assessment (the Behaviour Rating Inventory of Executive Function— Adult Scale), and their clinical and demographic characteristics were available. Corticometric Iterative Vertex-Based Estimation of Thickness (CIVET) was used to extract cortical thickness, cortical surface area, and local gyrification index measures. Using orthogonal projective non-negative matrix factorization (OPNMF), we identified non-overlapping spatial components that integrate cortical thickness, cortical surface area, and local gyrification index and capture structural covariance across these features. Behavioural partial least squares correlation (bPLS) analysis was then used to compute correlations between the individual variability in the OPNMF covariance patterns and ExF outcomes for each subject. A total of 56 youth with CHD who underwent cardiopulmonary bypass surgery before three years of age and 56 age- and sex-matched healthy controls were included in our analyses. Cortical grey matter volume, cortical thickness, and cortical surface area were found to be significantly reduced in CHD patients compared to controls. OPNMF identified 12 stable cortex-wide components summarizing the inter-subject variability in cortical thickness, cortical surface area, and local gyrification index. bPLS revealed two significant latent variables (LV) accounting for a total of 82.8% of the variance in the sample, each describing distinct patterns between the brain and cognitive data. LV1 summarized a pattern of belonging to the CHD group, worse scores on most BRIEF-A scales, younger age at MRI, and female sex. This pattern was associated with increased cortical thickness, local gyrification index, and decreased cortical surface area in several OPNMF components. Finally, we identified a positive relationship between the LV1 brain-behaviour pattern and total aortic cross-clamp time in the CHD group, indicating that longer aortic cross-clamp time was associated with worse neurodevelopmental outcomes. In this study, we uncover novel multivariate relationships between ExF and alterations in cortical thickness, surface area, and local gyrification index in adolescents and young adults with CHD using a data-driven approach. Although our findings highlight the important role played by the cortex in higher order cognitive processes, future studies are needed to elucidate the individual contribution of individual and clinical attributes into the deficits observed in this population.

Gas-phase metallicity gradients in galaxies at z ∼ 6–8

The study of gas-phase metallicity and its spatial distribution at high redshift is crucial to understand the processes that shaped the growth and evolution of galaxies in the early Universe. Here we study the spatially resolved metallicity in three systems at z ∼ 6 − 8, namely A2744-YD4, BDF-3299, and COSMOS24108, with JWST NIRSpec IFU low-resolution (R ∼ 100) spectroscopic observations. These are among the highest-z sources in which metallicity gradients have been probed so far. Each of these systems hosts several spatial components in the process of merging within a few kiloparsecs, identified from the rest-frame UV and optical stellar continuum and ionised gas emission line maps. The sources have heterogeneous properties, with stellar masses log(M*/M⊙) ∼7.6–9.3, star formation rates (SFRs) ∼1–15 M⊙ yr−1, and gas-phase metallicities 12+log(O/H) ∼7.7–8.3, which exhibit a large scatter within each system. Their properties are generally consistent with those of the highest-redshift samples to date (z ∼ 3 − 10), though the sources in A2744-YD4 and COSMOS24108 are at the high end of the mass-metallicity relation (MZR) defined by the z ∼ 3 − 10 sources. Moreover, the targets in this work follow the predicted slope of the MZR at z ∼ 6 − 8 from most cosmological simulations. The gas-phase metallicity gradients are consistent with being flat in the main sources of each system. Flat metallicity gradients are thought to arise from gas mixing processes on galaxy scales, such as mergers or galactic outflows and supernova winds driven by intense stellar feedback, which wash out any gradient formed in the galaxy. The existence of flat gradients at z ∼ 6 − 8 sets also important constraints on future cosmological simulations and chemical evolution models, whose predictions on the cosmic evolution of metallicity gradients often differ significantly, especially at high redshift, but are mostly limited to z ≲ 3 so far.

A multi-objective approach for timber harvest scheduling to include management of at-risk species and spatial configuration objectives.

Sustainable forestry typically involves integration of several economic and ecological objectives which, at times, may not be compatible with one another. Multi-objective prioritization via harvest scheduling programs can be used to elucidate these relationships and explore solutions. One such program is a spatially explicit harvest scheduler that adopts the Metropolis-Hastings algorithm to iteratively find management solutions to achieve multiple objectives (Habplan). Although this program has been used to address forest management scheduling and simulation-based tasks, its utility is constrained by time-intensive data preparation and challenges with incorporating spatial configuration objectives. To address these shortcomings, we introduce an open-source software package, HabplanR, streamlines data preparation, sets parameters, visualizes results, and assesses spatial components of ecological objectives. We developed four example objectives to incorporate into a multi-objective management problem: habitat quality indices for three species "types" (open, closed, and intermediate-canopy-associated species), and harvested pine pulpwood (revenue). We demonstrate the utility of this package to find management schedules that can accommodate potentially conflicting habitat needs of species, while achieving economic targets. We produced 100 software runs and prioritized individual objectives to select four management schedules for further comparisons. We compared outcome differences of the four schedules, including a spatial comparison of two high performing schedules. The software package makes costs and benefits of different schedules explicit and allows for consideration of the spatial configuration of management outcomes in decision-making.

Extreme-value modelling of migratory bird arrival dates: Insights from citizen science data

Abstract Citizen science mobilises many observers and gathers huge datasets but often without strict sampling protocols, resulting in observation biases due to heterogeneous sampling effort, which can lead to biased predictions. We develop a spatiotemporal Bayesian hierarchical model for bias-corrected estimation of arrival dates of the first migratory bird individuals at their breeding sites. Higher sampling effort could be correlated with earlier observed dates. We implement data fusion of two citizen-science datasets with fundamentally different protocols (BBS, eBird) and obtain posterior distributions of the latent process, which contains four spatial components endowed with Gaussian process priors: species niche; sampling effort; position and scale parameters of annual first arrival date. The data layer consists of four response variables: counts of observed eBird locations (Poisson); presence-absence at observed eBird locations (Binomial); BBS occurrence counts (Poisson); first arrival dates (Generalised Extreme-Value). We devise a Markov Chain Monte Carlo scheme and check by simulation that the latent process components are identifiable. We apply our model to several migratory bird species in the northeastern United States for 2001−2021 and find that the sampling effort significantly modulates the observed first arrival dates. We exploit this relationship to effectively bias-correct predictions of the true first arrivals.

Unveiling the lifeblood of cities: Identifying urban ecological networks from the perspective of biodiversity conservation

Urban biodiversity faces threats from habitat loss, landscape fragmentation, and human disturbances. Ecological networks (ENs) can enhance habitat connectivity and bolster population resilience to disruptions. To safeguard the biodiversity of Nanjing's urban area, this study selected understory insectivorous birds as indicator species for biodiversity, employing a comprehensive approach integrating the maximum entropy (MaxEnt) model and the area threshold method to delineate ecological source areas. Key indicators directly linked to the species distribution were extracted based on the habitat suitability assessment results, and an ecological resistance surface was crafted using spatial principal component analysis. Subsequently, the circuit theory model was applied to pinpoint ecological corridors, pinch points, and barrier points. The findings unveiled the following: (1) The normalized difference vegetation index (NDVI), human activities (HA), and canopy height (CH) were critical indicators influencing biodiversity. The response curves of the NDVI and CH were positively correlated with the probability distribution of indicator species, while the response curve of the HA showed an overall negative correlation with the same distribution. (2) The spatial distribution characteristics of the ecological network revealed a ‘one axis, two cores, intersecting’ pattern. Ecological sources were supported by mountainous forests and riverside green spaces, symmetrically distributed on both sides of the Yangtze River. Ecological corridors were forming continuous tree belts along highways and riverways, predominantly concentrated on the eastern side of the Yangtze River. Ecological pinch points and barriers were primarily located at the intersections of fragmented green spaces and developed areas in the northeastern and southeastern regions of the study area. (3) This study finally identified 30 ecological source areas, 65 ecological corridors, 8 pinch points, and 8 barrier points. This study presents a construction paradigm for urban ENs from the vantage point of biodiversity conservation, including both regional specificity and universal applicability, providing vital theoretical underpinnings and pragmatic insights for urban sustainability.

A new class of traversable wormhole metrics

In this work, we have formulated a new class of traversable wormhole metrics. Initially, we have considered a wormhole metric in which the temporal component is an exponential function of r but the spatial components of the metrics are fixed. Following that, we have again constructed a generalized wormhole metric in which the spatial component is an exponential function of r, but the temporal component is fixed. Finally, we have considered the generalized wormhole metric in which both the temporal and spatial components are generalized exponential functions of r. We have also studied some of their properties including throat radius, stability, and energy conditions, examined singularity, the metric in curvature coordinates, effective refractive index, innermost stable circular orbit (ISCO) and photon sphere, Regge–Wheeler potential and their quasinormal modes, gravitational entropy, and determined the curvature tensor. The radius of the throat is found to be consistent with the properties of wormholes and does not contain any types of singularities. Most interestingly, we find that their throat radius is the same for the same spatial component and the same range of values of m. In addition to these, they also violate the Null Energy Condition (NEC) near the throat. These newly constructed metrics form a new class of traversable wormholes.

Assessing Territorial Units’ Contribution to Convergence Beyond Aggregate Measures: The Role of Neighbourhood

AbstractThis paper provides an analytical framework for the study of spatial income dynamics using the classical measures of $$\sigma$$ σ -convergence (reduction in dispersion) and $$\beta$$ β -convergence (poor areas grow more quickly than rich ones) linked by a re-ranking metric and reinterpreted based on the ‘leave no one behind’ principle. Our approach allows identifying the contribution of each territorial unit to each of the three facets of distributional change ($$\sigma$$ σ -convergence, $$\beta$$ β -convergence, and re-ranking), as well as gauging the part of each component of distributional change that corresponds to geographically neighbouring and non-neighbouring units. We illustrate our proposal by examining convergence across the census tracts of Malaga – the sixth most populated city of Spain – before and after the COVID-19 pandemic. We find that while income convergence across Malaga’s census tracts tended to improve over the period 2015–2019, this process was interrupted during the first year of the pandemic, affecting some specific census tracts. We examine our outcomes by grouping the results into deciles and districts made up of specific census tracts. Finally, we analyse the impact of neighbours on regional convergence and each of its components. This spatial decomposition highlights the crucial role of the spatial component in the convergence process.

Bayesian spatial-temporal analysis and determinants of cardiovascular diseases in Tanzania mainland

BackgroundCardiovascular Diseases (CVDs) are health-threatening conditions that account for high mortality in the world. Approximately 23.6 million deaths due to CVD is expected in the year 2030 worldwide. The CVD burden is more severe in developing countries, including Tanzania.ObjectivesThis study analyzed the spatial-temporal trends and determinants of cardiovascular diseases in Tanzania from 2010 to 2019.MethodsIndividual data were extracted from Jakaya Kikwete Cardiac Institute (JKCI), Mbeya Zonal Referral Hospital (MZRH), Kilimanjaro Christian Medical Centre (KCMC) and Bugando hospitals and the geographical data from TMA. The model containing spatial and temporal components was analyzed using the Bayesian hierarchical method implemented using Integrated Nested Laplace Approximation (INLA).ResultsThe results found that the incidence of CVD increased from 2010 to 2014 and decreased from 2015 to 2019. The southern highlands, lake, central and coastal zones were more likely to have CVD problems than others. It was also revealed that people aged 60–64 years OR = 1.49, females OR = 1.51, smokers OR = 1.76, alcohol drinkers OR = 1.48, and overweight OR = 1.89 were more likely to have CVD problems. Additionally, a 1oC increase in the average annual air maximum temperature was related to a 14% risk of developing CVD problems. The study revealed that the model, which included spatial and temporal random effects, was the best-predicting model.ConclusionThe study shows a decreased CVD incidence rate from 2015 to 2019. The CVD incidences occurred more in Tanzania’s coastal and lake areas between 2010 and 2019. The demographic, lifestyle and geographical risk factors were significantly associated with the CVD.

Hawkes Models and Their Applications

The Hawkes process is a model for counting the number of arrivals to a system that exhibits the self-exciting property—that one arrival creates a heightened chance of further arrivals in the near future. The model and its generalizations have been applied in a plethora of disparate domains, though two particularly developed applications are in seismology and in finance. As the original model is elegantly simple, generalizations have been proposed that track marks for each arrival, are multivariate, have a spatial component, are driven by renewal processes, treat time as discrete, and so on. This article creates a cohesive review of the traditional Hawkes model and the modern generalizations, providing details on their construction and simulation algorithms, and giving key references to the appropriate literature for a detailed treatment.

Confined seepage analysis of saturated soils using fuzzy fields

Seepage refers to the flow of water through porous materials. This phenomenon has a crucial role in dam, slope, excavation, tunnel, and well design. Performing seepage analysis usually is a challenging task, as one must cope with the uncertainty associated with the parameters such as the hydraulic conductivity in the horizontal and vertical directions that drive this phenomenon. However, at the same time, the data on horizontal and vertical hydraulic conductivities are typically scarce in spatial resolution. In this context, so-called non-traditional approaches for uncertainty quantification (such as intervals and fuzzy variables) offer an interesting alternative to classical probabilistic methods, since they have been shown to be quite effective when limited information on the governing parameters of a phenomenon is available. Therefore, the main contribution of this study is the development of a framework for conducting seepage analysis in saturated soils, where uncertainty associated with hydraulic conductivity is characterized using fuzzy fields. This method to characterize uncertainty extends interval fields towards the domain of fuzzy numbers. In fact, it is illustrated that fuzzy fields are an effective tool for capturing uncertainties with a spatial component, since they allow one to account for available physical measurements. A case study in confined saturated soil shows that with the proposed framework, it is possible to quantify the uncertainty associated with seepage flow, exit gradient, and uplift force effectively.

Investigating Spatial Effects through Machine Learning and Leveraging Explainable AI for Child Malnutrition in Pakistan

While socioeconomic gradients in regional health inequalities are firmly established, the synergistic interactions between socioeconomic deprivation and climate vulnerability within convenient proximity and neighbourhood locations with health disparities remain poorly explored and thus require deep understanding within a regional context. Furthermore, disregarding the importance of spatial spillover effects and nonlinear effects of covariates on childhood stunting are inevitable in dealing with an enduring issue of regional health inequalities. The present study aims to investigate the spatial inequalities in childhood stunting at the district level in Pakistan and validate the importance of spatial lag in predicting childhood stunting. Furthermore, it examines the presence of any nonlinear relationships among the selected independent features with childhood stunting. The study utilized data related to socioeconomic features from MICS 2017–2018 and climatic data from Integrated Contextual Analysis. A multi-model approach was employed to address the research questions, which included Ordinary Least Squares Regression (OLS), various Spatial Models, Machine Learning Algorithms and Explainable Artificial Intelligence methods. Firstly, OLS was used to analyse and test the linear relationships among selected variables. Secondly, Spatial Durbin Error Model (SDEM) was used to detect and capture the impact of spatial spillover on childhood stunting. Third, XGBoost and Random Forest machine learning algorithms were employed to examine and validate the importance of the spatial lag component. Finally, EXAI methods such as SHapley were utilized to identify potential nonlinear relationships. The study found a clear pattern of spatial clustering and geographical disparities in childhood stunting, with multidimensional poverty, high climate vulnerability and early marriage worsening childhood stunting. In contrast, low climate vulnerability, high exposure to mass media and high women’s literacy were found to reduce childhood stunting. The use of machine learning algorithms, specifically XGBoost and Random Forest, highlighted the significant role played by the average value in the neighbourhood in predicting childhood stunting in nearby districts, confirming that the spatial spillover effect is not bounded by geographical boundaries. Furthermore, EXAI methods such as partial dependency plot reveal the existence of a nonlinear relationship between multidimensional poverty and childhood stunting. The study’s findings provide valuable insights into the spatial distribution of childhood stunting in Pakistan, emphasizing the importance of considering spatial effects in predicting childhood stunting. Individual and household-level factors such as exposure to mass media and women’s literacy have shown positive implications for childhood stunting. It further provides a justification for the usage of EXAI methods to draw better insights and propose customised intervention policies accordingly.

Spatial and temporal effects of heat waves on the diversity of European stream invertebrate communities

The frequency and magnitude of extreme events, such as heat waves, are predicted to increase with climate change. However, assessments of the response of biological communities to heat waves are often inconclusive. We aimed to assess the responses in abundance, taxonomic and functional diversity indices of stream invertebrate communities to heat waves using long-term monitoring data collected across Europe. We quantified the heat waves' magnitude, analyzed the spatial (i.e., long-term mean) and temporal (anomaly around the long-term mean) components of variation in the magnitude of heat waves, and their interaction with anthropogenic stressors (ecological quality and land cover). For the spatial component of variation, we found a negative association of the community indices to the increasing magnitude of heat waves. Sites undergoing heat waves of higher magnitude showed fewer species and lower trait diversity compared with sites experiencing lower magnitude heat waves. However, we could not detect an immediate temporal response of the communities to heat waves (i.e., the temporal component). Furthermore, we found that the effects of heat waves interacted with the ecological quality of the streams and their surrounding land cover. Diversity declined with increasing heat waves' magnitude in streams with higher ecological quality or surrounded by forest, which may be due to a higher proportion of sensitive species in the community. Heat waves' impacts on diversity were also exacerbated by increasing urban cover. The interaction between heat waves' magnitude and anthropogenic stressors suggests that the effects of extreme events can compromise the recovery of communities. Further, the predicted increase in heat waves will likely have long-term effects on stream invertebrate communities that are currently undetected.

PERMAINAN TRADISIONAL MADURA: DARI PERMAINAN TANJAN KE IDE AKTIVITAS SPATIAL SKILLS DALAM PEMBELAJARAN MATEMATIKA

The tanjan game is believed to be one of the cultural games in Madura. In addition to being an enjoyable game, this game also requires spatial skills, as players are able to engage with spatial images directly. This research employs a qualitative approach, with the researcher as a participant. Data were collected through observation, documentation, and a literature review on spatial skills components. The data analysis techniques used are data reduction, data display, and verification. This study has identified eleven spatial skill activities from the traditional tanjan game. These activities are in line with the spatial skill components, starting from before the game starts or when drawing tanjan to when the tanjan game takes place. In addition, this study determines the mathematical elements contained in each spatial skill activity.

Imaged Experiences: Mapping the Trabzon City Center

This research sets out from the cycle of impaired belonging between city dwellers and the cities they live in and the weakened perceptibility of urban identity in spatial experiences as a result of the chaos and rapid flow of daily life in today's cities that evolve around consumption culture. This cycle is addressed with experiential mapping applications, which prompt citizens to take a step back and slow down their rhythm of life as a way to connect with their city. Based on this, it is aimed to reveal the deepening of the relationship between city dwellers and the city through experiential mapping, which provides a more meaningful and conscious interaction with the urban environment. In this research, the urban identity formed between urban space and the user was analyzed by image analysis, taking the Theory of Imageability as a basis. The application process consisted of three main phases and was conducted with ten students studying at Karadeniz Technical University, Department of Architecture. The research findings show that experiential mapping and semantic rating, applied with a view to increasing the visibility of urban images peculiar to Trabzon city center, enabled the participants to direct their focus on and explore the spatial components of Trabzon city center, suggesting that experiential mapping can be an effective tool in exploring the potentials and problematic aspects of cities.

Study on the influencing factors of the evolution of space pattern based on principal component analysis in Duolun County

In order to explore the influencing factors of spatial and temporal evolution of production-living-ecological space in Beijing Tianjin sandstorm source area, the remote sensing images, natural environment and socio-economic data of Duolun County in Inner Mongolia from 2000 to 2020 were selected, and the spatial auto-correlation model and principal component analysis model were used to analyze the spatial pattern evolution and influencing factors of production-living-ecological space. The results show that: (1) the function of production space decreases slightly, and the degree of spatial agglomeration decreases; (2) The function of living space rose slightly, and its spatial agglomeration degree showed an upward trend; (3) The ecological spatial function showed a slow upward trend, and its spatial agglomeration degree increased; (4) The spatial pattern of production-living-ecological space is characterized by “high in the southwest and low in the northeast”; (5) Precipitation has the greatest impact on the spatial evolution of the production-living-ecological space. The distance from the main residential areas, per capita GDP, the distance from the main roads and the distance from the main waters have strong explanatory power on the spatial evolution of the production-living-ecological space.