Spatial Inference Research Articles

An initial game-theoretic assessment of enhanced tissue preparation and imaging protocols for improved deep learning inference of spatial transcriptomics from tissue morphology.

The application of deep learning to spatial transcriptomics (ST) can reveal relationships between gene expression and tissue architecture. Prior work has demonstrated that inferring gene expression from tissue histomorphology can discern these spatial molecular markers to enable population scale studies, reducing the fiscal barriers associated with large-scale spatial profiling. However, while most improvements in algorithmic performance have focused on improving model architectures, little is known about how the quality of tissue preparation and imaging can affect deep learning model training for spatial inference from morphology and its potential for widespread clinical adoption. Prior studies for ST inference from histology typically utilize manually stained frozen sections with imaging on non-clinical grade scanners. Training such models on ST cohorts is also costly. We hypothesize that adopting tissue processing and imaging practices that mirror standards for clinical implementation (permanent sections, automated tissue staining, and clinical grade scanning) can significantly improve model performance. An enhanced specimen processing and imaging protocol was developed for deep learning-based ST inference from morphology. This protocol featured the Visium CytAssist assay to permit automated hematoxylin and eosin staining (e.g. Leica Bond), 40×-resolution imaging, and joining of multiple patients' tissue sections per capture area prior to ST profiling. Using a cohort of 13 pathologic T Stage-III stage colorectal cancer patients, we compared the performance of models trained on slide prepared using enhanced versus traditional (i.e. manual staining and low-resolution imaging) protocols. Leveraging Inceptionv3 neural networks, we predicted gene expression across serial, histologically-matched tissue sections using whole slide images (WSI) from both protocols. The data Shapley was used to quantify and compare marginal performance gains on a patient-by-patient basis attributed to using the enhanced protocol versus the actual costs of spatial profiling. Findings indicate that training and validating on WSI acquired through the enhanced protocol as opposed to the traditional method resulted in improved performance at lower fiscal cost. In the realm of ST, the enhancement of deep learning architectures frequently captures the spotlight; however, the significance of specimen processing and imaging is often understated. This research, informed through a game-theoretic lens, underscores the substantial impact that specimen preparation/imaging can have on spatial transcriptomic inference from morphology. It is essential to integrate such optimized processing protocols to facilitate the identification of prognostic markers at a larger scale.

A GIS-based multi-criteria approach for identifying areas vulnerable to subsidence in the world's largest ongoing urban socio-environmental mining disaster

This study proposes a GIS-based multi-criteria approach to identify areas vulnerable to subsidence in the world's largest ongoing urban socio-environmental disaster, caused by salt-gem urban mining in Maceió, Alagoas State, Brazil. The analysis revealed critical areas concentrated in regions with salt-gem mines and soil composed of sedimentary rocks near water bodies, where the local population faces significant socio-economic challenges. In addition to addressing environmental vulnerabilities, the developed methodology considers social and economic risks, aiming for integrated actions between the population and the environment. The FITradeoff multi-criteria method used in the geospatial model showed benefits, including a significant reduction in the number of inconsistencies required for geospatial decision-making, ensuring efficiency in the development of prevention and mitigation strategies. The Spatial Inference model showed a high level of accuracy of 88.89 % in classifying the vulnerability of Maceió’s neighborhoods to subsidence, although it faced challenges when assessing neighborhoods with distinctive characteristics. The proposed preference-driven multi-criteria model incorporates decision-maker preferences, opinions, and knowledge of this disaster. Under specific conditions, this model can offer tailored solutions that align with stakeholders’ objectives. The results provide valuable insights for the development of public policies and risk management strategies in affected areas and areas vulnerable to subsidence due to urban mining in Maceió, and may be applicable to other regions of Brazil and the world facing similar challenges.

SIRV: spatial inference of RNA velocity at the single-cell resolution.

RNA Velocity allows the inference of cellular differentiation trajectories from single-cell RNA sequencing (scRNA-seq) data. It would be highly interesting to study these differentiation dynamics in the spatial context of tissues. Estimating spatial RNA velocities is, however, limited by the inability to spatially capture spliced and unspliced mRNA molecules in high-resolution spatial transcriptomics. We present SIRV, a method to spatially infer RNA velocities at the single-cell resolution by enriching spatial transcriptomics data with the expression of spliced and unspliced mRNA from reference scRNA-seq data. We used SIRV to infer spatial differentiation trajectories in the developing mouse brain, including the differentiation of midbrain-hindbrain boundary cells and marking the forebrain origin of the cortical hem and diencephalon cells. Our results show that SIRV reveals spatial differentiation patterns not identifiable with scRNA-seq data alone. Additionally, we applied SIRV to mouse organogenesis data and obtained robust spatial differentiation trajectories. Finally, we verified the spatial RNA velocities obtained by SIRV using 10x Visium data of the developing chicken heart and MERFISH data from human osteosarcoma cells. Altogether, SIRV allows the inference of spatial RNA velocities at the single-cell resolution to facilitate studying tissue development.

Dissecting the Spatially Restricted Effects of Microenvironment-Mediated Resistance on Targeted Therapy Responses.

The response of tumors to anti-cancer therapies is defined not only by cell-intrinsic therapy sensitivities but also by local interactions with the tumor microenvironment. Fibroblasts that make tumor stroma have been shown to produce paracrine factors that can strongly reduce the sensitivity of tumor cells to many types of targeted therapies. Moreover, a high stroma/tumor ratio is generally associated with poor survival and reduced therapy responses. However, in contrast to advanced knowledge of the molecular mechanisms responsible for stroma-mediated resistance, its effect on the ability of tumors to escape therapeutic eradication remains poorly understood. To a large extent, this gap of knowledge reflects the challenge of accounting for the spatial aspects of microenvironmental resistance, especially over longer time frames. To address this problem, we integrated spatial inferences of proliferation-death dynamics from an experimental animal model of targeted therapy responses with spatial mathematical modeling. With this approach, we dissected the impact of tumor/stroma distribution, magnitude and distance of stromal effects. While all of the tested parameters affected the ability of tumor cells to resist elimination, spatial patterns of stroma distribution within tumor tissue had a particularly strong impact.

Isodose mapping of terrestrial gamma radiation in Sarawak-Borneo: Insights from surface-weathered soil analysis

Presentation of baseline data on terrestrial gamma radiation (TGR) levels is crucial for assessing the annual effective dose received by the public due to natural radiation exposure. Cumulative doses from various sources can become significant, warranting a spatial understanding of TGR distribution. Few countries have comprehensively mapped TGR on a national scale, often facing challenges due to remote or inaccessible regions. This study investigated the influence of weathered soil groups on TGR dose rates in Sarawak-Borneo, Malaysia, to facilitate insights for TGR projection and isodose mapping. A total of 1044 TGR dose rate measurements were collected using NaI (Tl) scintillation detector survey meters, with a mean of 100 nGy h−1 and a range of 8–375 nGy h−1. Non-parametric statistical analyses of variance using Welch's ANOVA, Brown-Forsythe, and Kruskal-Wallis validated (P-sig.=.000) notable dissimilarities among six categories of superficial-weathered soil. Graphical analysis using Sinclair's cumulative plot revealed deviations at intervals of 50, 80, 100, 120, 175, and 205 nGy h−1. These deviations indicate distinct lithological influences. Skeletal soil (entisols) and podzolic soils had high mean dose rates (148 and 113 nGy h−1, respectively) due to limited development, thus preserving abundant uranium (U) and thorium (Th). Meanwhile, gleysols and thionic soils exhibited compatible means (90 and 82 nGy h−1, respectively), while alluvial (or transported soils) and organic soils displayed lower dose rate ranges (mean of 76 and 47 nGy h−1, respectively), reflecting rapid hydrolysis weathering processes. Simple linear regression analysis revealed a strong relationship between TGR dose rate and mean value of weathered soil groups (y = 0.851x + 0.141 nGy h−1), signifying the significance and magnitude of weathered soil groups' impact on TGR dose rates. The obtained R-value is 0.704, indicating a strong linear correlation among soil group variables, and a Durbin-Watson statistic of 1.41, suggesting positive autocorrelation among residuals, thus positive relationships. An isodose map was successfully developed using the Kriging technique, aligning with lithological features of the study area. Semivariogram analysis reveals spatial dependence within a range of 1.47°, supporting the Kriging technique's suitability for spatial inference. In conclusion, this study has successfully revealed the relationship between TGR dose rates and superficial-weathered soil in Sarawak-Borneo. While the linear relationship is applicable to the Sundaland-Borneo tectonic block, it has potential to be used as a valuable tool for spatial inference of TGR dose rates in isodose development with similar lithologial characteristics, aiding in radiation exposure assessment and environmental monitoring.

‘Nowhere and no one is safe’: spatial analysis of damage to critical civilian infrastructure in the Gaza Strip during the first phase of the Israeli military campaign, 7 October to 22 November 2023

BackgroundSince the Hamas attacks in Israel on 7 October 2023, the Israeli military has launched an assault in the Gaza Strip, which included over 12,000 targets struck and over 25,000 tons of incendiary munitions used by 2 November 2023. The objectives of this study include: (1) the descriptive and inferential spatial analysis of damage to critical civilian infrastructure (health, education, and water facilities) across the Gaza Strip during the first phase of the military campaign, defined as 7 October to 22 November 2023 and (2) the analysis of damage clustering around critical civilian infrastructure to explore broader questions about Israel’s adherence to International Humanitarian Law (IHL).MethodsWe applied multi-temporal coherent change detection on Copernicus Sentinel 1-A Synthetic Aperture Radar (SAR) imagery to detect signals indicative of damage to the built environment through 22 November 2023. Specific locations of health, education, and water facilities were delineated using open-source building footprint and cross-checked with geocoded data from OCHA, OpenStreetMap, and Humanitarian OpenStreetMap Team. We then assessed the retrieval of damage at and with close proximity to sites of health, education, and water infrastructure in addition to designated evacuation corridors and civilian protection zones. The Global Moran’s I autocorrelation inference statistic was used to determine whether health, education, and water facility infrastructure damage was spatially random or clustered.ResultsDuring the period under investigation, in the entire Gaza Strip, 60.8% (n = 59) of health, 68.2% (n = 324) of education, and 42.1% (n = 64) of water facilities sustained infrastructure damage. Furthermore, 35.1% (n = 34) of health, 40.2% (n = 191) of education, and 36.8% (n = 56) of water facilities were functionally destroyed. Applying the Global Moran’s I spatial inference statistic to facilities demonstrated a high degree of damage clustering for all three types of critical civilian infrastructure, with Z-scores indicating < 1% likelihood of cluster damage occurring by random chance.ConclusionSpatial statistical analysis suggests widespread damage to critical civilian infrastructure that should have been provided protection under IHL. These findings raise serious allegations about the violation of IHL, especially in light of Israeli officials’ statements explicitly inciting violence and displacement and multiple widely reported acts of collective punishment.

The covariance environment defines cellular niches for spatial inference.

A key challenge of analyzing data from high-resolution spatial profiling technologies is to suitably represent the features of cellular neighborhoods or niches. Here we introduce the covariance environment (COVET), a representation that leverages the gene-gene covariate structure across cells in the niche to capture the multivariate nature of cellular interactions within it. We define a principled optimal transport-based distance metric between COVET niches that scales to millions of cells. Using COVET to encode spatial context, we developed environmental variational inference (ENVI), a conditional variational autoencoder that jointly embeds spatial and single-cell RNA sequencing data into a latent space. ENVI includes two decoders: one to impute gene expression across the spatial modality and a second to project spatial information onto single-cell data. ENVI can confer spatial context to genomics data from single dissociated cells and outperforms alternatives for imputing gene expression on diverse spatial datasets.

Explicit3D: Graph network with spatial inference for single image 3D object detection

Indoor 3D object detection is an essential task in single image scene understanding, impacting spatial cognition fundamentally in visual reasoning. Existing works on 3D object detection from a single image either pursue this goal through independent predictions of each object or implicitly reason over all possible objects, failing to harness relational geometric information between objects. To address this problem, we propose a sparse graph-based pipeline named Explicit3D based on object geometry and semantics features. Taking the efficiency into consideration, we further define a relatedness score and design a novel dynamic pruning method via group sampling for sparse scene graph generation and updating. Furthermore, our Explicit3D introduces homogeneous matrices and defines new relative loss and corner loss to model the spatial difference between target pairs explicitly. Instead of using ground-truth labels as direct supervision, our relative and corner loss are derived from homogeneous transforms, which renders the model to learn the geometric consistency between objects. The experimental results on the SUN RGB-D dataset demonstrate that our Explicit3D achieves better performance balance than the-state-of-the-art.

A deep learning approach to recognizing fine-grained expressway location reference from unstructured texts in Chinese

Complex nested and discontinuous location references are common in unstructured text. Extracting them is essential for accurate location information retrieval and spatial inference. However, traditional methods struggle with these references due to annotation system and model architecture limitations. In this study, we introduce a deep learning approach to uniformly recognize flat, nested, and discontinuous location references, motivated by recognizing fine-grained expressway location references. The approach uses a pre-trained language model to generate semantic sentence representations and a distance and direction-aware Transformer for contextual encoding. Then, it recognizes location references by modeling the adjacency and boundary relations between word pairs. We evaluated the approach on seven benchmark datasets and compared it with state-of-the-art methods. The results show that the approach achieves higher accuracy with faster inference, validating our modeling paradigm and architecture. The ablation study further confirms the effectiveness of submodules in architecture. These findings can provide valuable insights for developing advanced unified location reference recognition methods. Moreover, the detailed labeled dataset for location references can facilitate the evaluation and comparison of unified recognition methods and systems.

Enhancing spatial inference of air pollution using machine learning techniques with low-cost monitors in data-limited scenarios.

Ensuring environmental justice necessitates equitable access to air quality data, particularly for vulnerable communities. However, traditional air quality data from reference monitors can be costly and challenging to interpret without in-depth knowledge of local meteorology. Low-cost monitors present an opportunity to enhance data availability in developing countries and enable the establishment of local monitoring networks. While machine learning models have shown promise in atmospheric dispersion modelling, many existing approaches rely on complementary data sources that are inaccessible in low-income areas, such as smartphone tracking and real-time traffic monitoring. This study addresses these limitations by introducing deep learning-based models for particulate matter dispersion at the neighbourhood scale. The models utilize data from low-cost monitors and widely available free datasets, delivering root mean square errors (RMSE) below 2.9 μg m-3 for PM1, PM2.5, and PM10. The sensitivity analysis shows that the most important inputs to the models were the nearby monitors' PM concentrations, boundary layer dissipation and height, and precipitation variables. The models presented different sensitivities to each road type, and an RMSE below the regional differences, evidencing the learning of the spatial dependencies. This breakthrough paves the way for applications in various vulnerable localities, significantly improving air pollution data accessibility and contributing to environmental justice. Moreover, this work sets the stage for future research endeavours in refining the models and expanding data accessibility using alternative sources.

SSRL: Self-Supervised Spatial-Temporal Representation Learning for 3D Action Recognition

For 3D action recognition, the main challenge is to extract long-range semantic information in both temporal and spatial dimensions. In this paper, in order to better excavate long-range semantic information from large number of unlabelled skeleton sequences, we propose Self-supervised Spatial-temporal Representation Learning (SSRL), a contrastive learning framework to learn skeleton representation. SSRL consists of two novel inference tasks that enable the network to learn global semantic information in the temporal and spatial dimensions, respectively. The temporal inference task learns the temporal persistence of human actions through temporally incomplete skeleton sequences. And the spatial inference task learns the spatially coordinated nature of human action through spatially partially skeleton sequence. We design two transformation modules to efficiently realize these two tasks while fitting the encoder network. To avoid the difficulty of constructing and maintaining high-quality negative samples, our proposed framework learns by maintaining consistency among positive samples without the need of any negative sample. Experiments demonstrate that our proposed method can achieve better results in comparison with state-of-the-art methods under a variety of evaluation protocols on NTU RGB+D 60, PKU-MMD and NTU RGB+D 120 datasets.

Soil Salinity Mapping of Croplands in Arid Areas Based on the Soil–Land Inference Model

Soil salinization can decrease soil productivity and is a significant factor in causing land degradation. Precision mapping of salinization in agricultural fields would improve farmland management. This study focuses on the cropland in the Manas River Basin, located in the arid region of northwest China. It explores the potential of a soil mapping method, the Soil–Land Inference Model (SoLIM), which only requires a small number of soil samples to infer soil salinization of farmlands in arid areas. The model was utilized to create spatial distribution maps of soil salinity for the years 2009 and 2017, and changes in the distribution were analyzed. The research results indicate: (1) Through the analysis of sample point data, it was observed that soil salinity in the study area tends to accumulate in the surface layer (0–30 cm) in spring and in the subsoil layer (60–90 cm) during the crop growing season, with significant spatial variability. Therefore, it is necessary to conduct detailed salinity mapping. (2) Using field measurements as validation data, the simulation results of the SoLIM were compared with spatial interpolation methods and regression models. The SoLIM showed higher inference accuracy, with R2 values for the simulation results of the three soil layers all exceeding 0.5. (3) The SoLIM spatial inference showed salt accumulation in the northern part and desalination in the southern part. The findings of this study suggest that the SoLIM has the potential to effectively map soil salinization of croplands in arid areas, offering an efficient solution for monitoring soil salinity in arid oasis croplands.

Characterising a genetic stronghold amidst pervasive admixture: Morelet’s crocodiles (Crocodylus moreletii) in central Yucatan

When backcrosses are fertile, interbreeding between endangered taxa can lead to the admixture of gene pools under threat. One such case pertains to the Mesoamerican crocodile Crocodylus moreletii, a species which shows strong signatures of both recent hybridisation and historic intogression with the American crocodile C. acutus across large parts of its range. In the present paper, we use RAD-seq derived SNPs (4980 nuclear and seven mtDNA loci) to demonstrate that C. moreletii populations inhabiting the region of Calakmul in central Yucatan (Mexico) are rather unaffected by hybridization, despite being surrounded by coastal areas where pervasive admixture has previously been documented. All (based on fastSTRUCTURE) and 96% (based on NGSadmix) of 84 genotyped individuals from 18 sampled waterbodies (locally termed aguadas) were free from nuclear introgression of C. acutus DNA at at threshold of 0.95. Seven individuals (8%) possessed a C. acutus mtDNA haplotype, five of which were derived from two adjacent, rather peripheral aguadas. Spatial inferences based on a DAPC and fineRADstructure further showed that the region of Calakmul is inhabited by three genetic clusters spanning across a set of distinct aguadas each. Taken together, our findings reveal that central Yucatan contains the currently largest documented stronghold of C. moreletii populations only marginally affected by introgression, which has major implications for the conservation management of this important flagship species.

A Spatio-Temporal Inference System for Abnormality Detection and Localization of Battery Systems

In this research, a spatio-temporal inference system is proposed to detect and locate thermal abnormalities of battery systems. The proposed spatio-temporal inference system consists of three modules: spatio-temporal processing module, abnormality inference module, and spatial inference module. Based on the distributed temperatures on the battery system, the monitoring statistic can be developed in the spatio-temporal processing module. The abnormality inference module is constructed to detect the abnormality based on the derived statistic index. Then, the spatial Bayes model is designed to estimate the abnormality location. The Bayes risk analysis indicates that the proposed method has a bounded error. Experiments on a lithium-ion (Li-ion) battery cell and a battery pack demonstrate that the proposed spatio-temporal inference system can detect and locate the internal short circuit (ISC) fault before it develops into a thermal runaway.

A novel hierarchical aggregation algorithm for optimal repartitioning of statistical regions

Data regionalisation allows spatial inference over a population. The statistical regions must be updated to account for population changes, but this update process is more restrictive and iterative than ab initio regionalisation. This creates a need for an algorithmic solution that minimises human-in-the-loop involvement in population-driven regionalisation. The new method must address the basic regionalisation criteria – contiguity, compactness, homogeneity, equinumeriosity, and temporal consistency. We present a novel validation metric to assess the quality of partition based on these criteria. We have developed a novel hybrid aggregation algorithm (HeLP), combining elements of hierarchical and graph-theoretic approaches, for the primary purpose of repartitioning. This algorithm operates in average computational time complexity. HeLP was tested on simulated data and the Australian Statistical Geography Standard. The method can emulate the human operator successfully, providing statistically significant results in repartitioning parcel-based systems, such as the Cadastre.

Environmental consequences of ISO 14001 in European economies amidst structural change and technology innovation: Insights from green governance dynamism

In this last decade, the green growth paradigm has attracted a lot of attention. In addition to protecting the environment and people, green growth also instigates other stringent responsibilities on organizations and governments. Thus, to maintain and protect the environment, organizations must provide support for complex environmental concerns under a governance-enabling regulatory framework. Voluntary environmental initiatives have shown to be an effective strategy for reducing pollution. Through a variety of means, including modifications to procedures, adjustments to raw materials, redesign of products and packaging, and innovations in pollution control technology, these voluntary projects seek to reduce pollution. In order to access this channel within the European countries, we investigate how environmental safeguards are affected by ISO 14001, Structural change, technological innovation, and green Governance. We also took cognizant of spatial inference of environmental pollution using spatial econometric models. According to our econometric findings, (i) obtaining ISO 14001 certification leads to a sizable decrease in carbon emissions in European nations. (ii) The findings indicate spatial agglomeration, and neighbouring nations' pollution patterns are comparable. Because of the physical agglomeration between nations, nations must work together to combat air pollution and ensure progressive, sustainable development.

A framework for focal and connectomic mapping of transiently disrupted brain function

The distributed nature of the neural substrate, and the difficulty of establishing necessity from correlative data, combine to render the mapping of brain function a far harder task than it seems. Methods capable of combining connective anatomical information with focal disruption of function are needed to disambiguate local from global neural dependence, and critical from merely coincidental activity. Here we present a comprehensive framework for focal and connective spatial inference based on sparse disruptive data, and demonstrate its application in the context of transient direct electrical stimulation of the human medial frontal wall during the pre-surgical evaluation of patients with focal epilepsy. Our framework formalizes voxel-wise mass-univariate inference on sparsely sampled data within the statistical parametric mapping framework, encompassing the analysis of distributed maps defined by any criterion of connectivity. Applied to the medial frontal wall, this transient dysconnectome approach reveals marked discrepancies between local and distributed associations of major categories of motor and sensory behaviour, revealing differentiation by remote connectivity to which purely local analysis is blind. Our framework enables disruptive mapping of the human brain based on sparsely sampled data with minimal spatial assumptions, good statistical efficiency, flexible model formulation, and explicit comparison of local and distributed effects.

Impact of genome and epigenome on intratumor heterogeneity in colorectal cancer

Impact of genome and epigenome on intratumor heterogeneity in colorectal cancer

Improving Bayesian radiological profiling of waste drums using Dirichlet priors, Gaussian process priors, and hierarchical modeling

We present three methodological improvements of our recently proposed approach for Bayesian inference of the radionuclide inventory in radioactive waste drums, from radiological measurements. First we resort to the Dirichlet distribution for the prior distribution of the isotopic vector. The Dirichlet distribution possesses the attractive property that the elements of its vector samples sum up to 1. Second, we demonstrate that such Dirichlet priors can be incorporated within an hierarchical modeling of the prior uncertainty in the isotopic vector, when prior information about isotopic composition is available. Our used Bayesian hierarchical modeling framework makes use of this available information but also acknowledges its uncertainty by letting to a controlled extent the information content of the indirect measurement data (i.e., gamma and neutron counts) shape the actual prior distribution of the isotopic vector. Third, we propose to regularize the Bayesian inversion by using Gaussian process (GP) prior modeling when inferring 1D spatially-distributed mass or, equivalently, activity distributions. As of uncertainty in the efficiencies, we keep using the same stylized drum modeling approach as proposed in our previous work to account for the source distribution uncertainty across the vertical direction of the drum. A series of synthetic tests followed by application to a real waste drum show that combining hierarchical modeling of the prior isotopic composition uncertainty together with GP prior modeling of the vertical Pu profile across the drum works well. We also find that our GP prior can handles both cases with and without spatial correlation. Of course, our GP prior modeling framework only makes sense in the context of spatial inference. Furthermore, the computational times involved by our approach are on the order of a few hours, say about 2, to provide uncertainty estimates for all variables of interest in the considered inverse problem. This warrants further investigations to speed up the inference.

Gender Specific Study of Students' Spatial Inference in Solving Geometry Problem

Pemikiran spasial sangat penting untuk akuisisi dan konstruksi. Pemikiran spasial dapat membantu siswa memahami konsep geometris. Siswa mulai memahami konsep geometri pada tahap prilaku. Pemikiran spasial siswa pada tahap perilaku konkret tidak sedikit mampu melakukan eksplorasi. Penelitian ini mengkaji kemampuan siswa menggunakan penalaran spasial untuk menyelesaikan masalah geometri berbasis gender. Jenis penelitian ini adalah deskriptif kualitatif. Melalui lembar kerja, siswa mampu melakukan penalaran spasial. Selain lembar kerja, pengumpulan data pendukung diperoleh melalui wawancara mendalam. Tes dan wawancara digunakan untuk mengumpulkan data. Sampel yang menjadi target pada penelitian ini adalah siswa sekolah dasar kelas lima, responden pria dan wanita. Hasil penelitian menunjukkan bahwa siswa laki-laki memiliki keunggulan penalaran dibandingkan siswa perempuan, khususnya dalam visualisasi spasial.