Spatial Knowledge Research Articles

Spatial-Logic-Aware Weakly Supervised Learning for Flood Mapping on Earth Imagery

Flood mapping on Earth imagery is crucial for disaster management, but its efficacy is hampered by the lack of high-quality training labels. Given high-resolution Earth imagery with coarse and noisy training labels, a base deep neural network model, and a spatial knowledge base with label constraints, our problem is to infer the true high-resolution labels while training neural network parameters. Traditional methods are largely based on specific physical properties and thus fall short of capturing the rich domain constraints expressed by symbolic logic. Neural-symbolic models can capture rich domain knowledge, but existing methods do not address the unique spatial challenges inherent in flood mapping on high-resolution imagery. To fill this gap, we propose a spatial-logic-aware weakly supervised learning framework. Our framework integrates symbolic spatial logic inference into probabilistic learning in a weakly supervised setting. To reduce the time costs of logic inference on vast high-resolution pixels, we propose a multi-resolution spatial reasoning algorithm to infer true labels while training neural network parameters. Evaluations of real-world flood datasets show that our model outperforms several baselines in prediction accuracy. The code is available at https://github.com/spatialdatasciencegroup/SLWSL.

Abstract 3798: Advancements in multiplexed spatial phenotyping

Abstract Spatial omics is an expanding research area focused on integrating spatial knowledge of tissue with transcriptomics (RNA) and proteomics (protein). Understanding the complexity of the biological structure(s) is an important biological process in cancer immunotherapy research which requires accurate target classification. Translational profiling of 4+ targets on a single sample at one time can be difficult in many ways, such as, panel design, staining protocols, and data analysis. Furthermore, to design a reliable multi-target biomarker panel, there are many parts to overcome, such as protein abundance and localization, fluorophore compatibility, varying tissue types, and data characterization, to name a few. To address the needs of high target multiplex ability, labeling methods have advanced in cyclic detection enabling iterative labeling and detection using automated fluidics, however these approaches suffer from low simultaneous target throughput. Thermo Fisher Scientific can now provide a streamline process for tissue labelling; a process that enables sufficient labelling of high multiplexed panels completed within a couple of hours. We show successful detection of a wide range of various protein markers across numerous tissue organs used in various biological research applications with our uniform technique of labelling. Citation Format: Leticia Montoya, Scott Clarke, Austin Harvey, Jen Serafin, Mae Voeun. Advancements in multiplexed spatial phenotyping [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3798.

Integrated knowledge graph construction framework for places-of-interest retrieval using a property graph database

ABSTRACT With recent technological advances, the efficient extraction and utilization of valuable information from large-scale data sources have become increasingly important. The development of knowledge graphs (KGs) based on logical relationships between data has garnered attention from various location-related services. To provide results that satisfy the diverse preferences of individuals, explicit attributes and implicit semantic context must be considered during the retrieval of places of interest (POIs). Most POI retrievals often involve not just examining detailed information about places but also specifying places for intended visits. Therefore, spatial knowledge regarding the surroundings of POIs, such as proximity and accessible routes, should be incorporated to support decision-making. In this study, we propose a comprehensive framework for constructing a KG for POI retrieval (PKG), which adeptly integrates the place attributes, semantic features, and spatial context of locations. The core objective of this framework is to acquire suitable data for facilitating POI retrieval that effectively considers diverse user preferences for places. After constructing a PKG of Orlando (FL, USA), we verified the practical applicability of the proposed framework by conducting 10 types of distinct POI retrieval queries catering to a range of user preferences. The graph queries returned a list of POIs that precisely aligned with the requirements of users on not only the explicit attributes of places but also the spatial and semantic features while providing detailed travel route information to these destinations. In conclusion, the PKG enabled POI retrieval that satisfied user preferences and diversified the retrieved places and the information provided. As the PKG offers flexibility in data integration without physical constraints, it can be expanded by incorporating information from various sources.

How is GPS used? Understanding navigation system use and its relation to spatial ability

Given how commonly GPS is now used in everyday navigation, it is surprising how little research has been dedicated to investigating variations in its use and how such variations may relate to navigation ability. The present study investigated general GPS dependence, how people report using GPS in various navigational scenarios, and the relationship between these measures and spatial abilities (assessed by self-report measures and the ability to learn the layout of a novel environment). GPS dependence is an individual’s perceived need to use GPS in navigation, and GPS usage is the frequency with which they report using different functions of GPS. The study also assessed whether people modulate reported use of GPS as a function of their familiarity with the location in which they are navigating. In 249 participants over two preregistered studies, reported GPS dependence was negatively correlated with objective navigation performance and self-reported sense of direction, and positively correlated with spatial anxiety. Greater reported use of GPS for turn-by-turn directions was associated with a poorer sense of direction and higher spatial anxiety. People reported using GPS most frequently for time and traffic estimation, regardless of ability. Finally, people reported using GPS less, regardless of ability, when they were more familiar with an environment. Collectively these findings suggest that people moderate their use of GPS, depending on their knowledge, ability, and confidence in their own abilities, and often report using GPS to augment rather than replace spatial environmental knowledge.

Architectural Typology and Its Influence on Authentic Living

In this study, the transformative effects of architectural typologies on the community’s sense of belonging and relationship with their environment are examined. Through a range of investigative methodologies, the research highlights the shift from traditional architectural forms to contemporary designs, focusing on the role of political decisions, and globalized construction materials. The research examines a notable conflict: the modern spaces built with little spatial knowledge and modern material do not resonate with the community’s historical experiences and customary living patterns. Furthermore, the rapid pace of these architectural shifts has led to a growing sense of disconnection among community members. The findings highlight a central aspect: the new architectural forms fail to reflect the historical sentiments embedded in the community’s fabric and its connection to the surrounding environment. Consequently, there emerges a subtle yet significant loss of the community’s identity and heritage. The study argues for the importance of making design decisions that are sustainable, utilizing local construction knowledge in a modern way, thereby preserving the intricate and enduring connections between architectural, historical, social, and environmental factors. By doing so, designers can create spaces that preserve socio-cultural dynamics, be environmentally sustainable, yet also progress with the contemporary construction demands.

Mapping 3-D classroom seats based on partial object point cloud completion

ABSTRACT 3-D classroom seating information is of great significance in a host of planning contexts including indoor navigation and facility management. Mapping classroom 3-D seats based on a single-view LiDAR scan is challenging because of occlusions and viewpoint limitations. In essence, one is left with incomplete point clouds, making object recognition difficult. The aim of this paper is to acquire complete point clouds for objects in a room based on a single-view LiDAR scan, focusing in particular on chairs in indoor spaces such as classrooms, lecture halls, theaters, concert venues, and sports arenas. A spatial optimization enabled framework is proposed for 3-D classroom seat mapping from a single-view LiDAR scan. A two-step clustering method is proposed to segment each individual chair object in the LiDAR scan. A chair model is extracted and used to complete other chair point clouds via registration. Notably, a parameter estimation method is devised based on spatial priors and integrated in an iterative closest point optimization approach to refine registration. Applications to multiple classroom scans demonstrate the effectiveness and accuracy of the proposed methodology. The developed segmentation-registration framework harnesses spatial knowledge to complete a single-view LiDAR scan with many identical objects. The resultant object point cloud is critical for navigation, route guidance, facility management, pandemic response and planning, indoor mapping and venue utilization, among others, which require precise and complete spatial information.

Fiscal expenditure efficiency and green economic efficiency: evidence from Chinese cities

This study develops indicators for evaluating Green Economy Efficiency (GEE) and Fiscal Expenditure Efficiency (FEE) at the prefecture level and above within Chinese cities. Utilizing the super-efficiency SBM model and a three-stage DEA model, we empirically examine the influence of FEE on GEE. To address the issue of endogeneity arising from reverse causality between FEE and GEE, this research employs fiscal autonomy and officials’ tenure as instrumental variables for FEE. The key findings are summarized as follows: (1) GEE exhibits a fluctuating upward trend from 2006 to 2020, while FEE remains relatively stable overall. Notably, the GEE and FEE in the eastern region surpass those in the central and western regions. (2) A one percentage point increase in FEE corresponds to a substantial 7.597% rise in GEE, indicating a significant promotional effect, a result consistently upheld in robustness tests. Furthermore, factors such as urbanization level, economic development, education level, and industrial structure also contribute to urban green economic growth. (3) FEE primarily contributes to GEE through local economic growth, urban infrastructure development, and regional innovation levels. (4) The impact of FEE on GEE is more pronounced in developed cities, larger cities, those with high fiscal freedom, and highly urbanized cities. Based on these conclusions, policy recommendations are proposed: (1) contingent upon local circumstances, each municipal government should optimize its fiscal expenditure structure and enhance FEE based on its unique endowments; (2) make full use of spatial correlation and knowledge spillover effect to promote the coordinated growth of FEE and GEE; (3) improve urban infrastructure construction and encourage regional technological innovation.

Knowledge‐driven spatial competitive intelligence for tourism

AbstractCompetition among tourism enterprises is an ineluctable component of sustainable tourism growth, requiring comprehensive studies to understand its dynamic and develop appropriate strategies. The literature employs text mining or statistical analyses to identify correlations between tourism areas as competitive relationships. However, this approach may not be fully applicable, due to the sparsity of crucial coexistence phenomena, and may fail to investigate fine‐grained attractions' competition inside destination using large‐scale geospatial data. To overcome the limitations, this study proposes a knowledge‐driven competitive intelligence framework for tourism management, utilizing knowledge graph (KG) construction and inference technologies. First, multi‐mode heterogeneous tourism data are integrated into a unified KG, including tourist check‐in, online text, and basic geographic information. Second, the spatial‐dependent GNN‐based model absorbing abundant spatial semantic knowledge from tourism‐oriented KG can enhance the performance of competition reasoning. Third, with multiple analyses via symbolic queries on KG, a comprehensive panorama of competition situations can be revealed.

Enhancing wayfinding and route learning: a human-centred study of the route-defining locations algorithm

ABSTRACT Navigation services play a crucial role in everyday wayfinding. One of their key features is the turn-by-turn guidance, enabling wayfinders to navigate efficiently between locations. However, over-reliance on turn-by-turn guidance can hinder wayfinders' ability to perceive their surroundings and develop spatial awareness, negatively impacting user experience. Previous research has addressed these issues by modifying instructions, user interaction, or both. However, the applicability of these methods beyond specific studies remains unclear. This paper presents a human-centred study by evaluating an algorithm that automatically generates navigation instructions. The instructions are generated by adding route-defining locations to ‘standard’ turn-by-turn instructions to enhance wayfinder's spatial knowledge acquisition, and improve user experience. Route-defining locations, identified using a previously proposed algorithm, serve as anchors for the wayfinder to form a spatial understanding of the environment. The study involved 36 participants divided into two groups: one received ‘standard’ turn-by-turn (TBT) instructions, while the other received instructions that included route-defining locations (RDL). Participants navigated in an unfamiliar virtual environment, and their wayfinding and route learning were compared between the two groups. Results show that RDL instructions led to better wayfinding and route learning performance compared to TBT instructions, suggesting the potential to improve navigation systems and user experiences.

Anatomical knowledge enhancement through echocardiography and videos, with a spotlight on cognitive load, self-efficacy, and motivation.

In recent years, there has been a growing recognition of the importance of integrating ultrasound into undergraduate medical curricula. However, empirical evidence is lacking as to its effect on anatomy learning and related student cognition. Therefore, the present study compared the impact of echocardiography-based instruction with narrated videos on students' understanding of anatomical relationships, as well as the interaction with students' autonomous motivation, self-efficacy beliefs, mental load, and attitudes. Second-year medical students were given the opportunity to enroll in a supplementary booster course about cardiac anatomy. On the base of a randomized controlled trial with a cross-over design, we studied the effect of taking this course on spatial anatomical knowledge. After completing a pre-test (T0), students were allocated randomly to either the echocardiography-based teaching condition (cohort A) or the narrated anatomy video condition (cohort B). Next, participants were crossed over to the alternative intervention. Immediately after each phase in the intervention, students were asked to rate their mental load. Additionally, a spatial anatomical knowledge test, an autonomous motivation scale, and a self-efficacy scale were administered before (T0) and after the first intervention (T1) and at the end of the study (T2). Finally, each student completed a perception-based survey. The study design allowed a comparative evaluation of both interventions at T1, while the cross-over design facilitated the assessment of the most optimal sequencing in the interventions at T2. A total of 206 students participated (cohort A: n = 99, cohort B: n = 107). At T1, no significant differences in the knowledge test and the autonomous motivation scale were observed between cohorts A and B. However, cohort A showed higher self-efficacy beliefs compared to cohort B (p = 0.043). Moreover, cohort A reported higher levels of perceived mental load (p < 0.001). At T2, the results showed that neither sequence of interventions resulted in significant differences in anatomy scores, autonomous motivation, or self-efficacy. However, a significant difference in mental load was found again, with students in cohort B reporting a higher level of mental load (p < 0.001). Finally, based on the perception-based survey, students reported favorably on the echocardiography experience. In conclusion, the hands-on echocardiography sessions were highly appreciated by the medical students. After participating in the ultrasound sessions, they reported higher levels of self-efficacy beliefs compared to the video-based condition. However, despite embodied cognition principles, students in the echocardiography condition did not outperform students in the narrated anatomy video condition. The reported levels of mental load in the ultrasound condition could explain these findings.

Generating Spatial Knowledge Graphs with 2D Indoor Floorplan Data: A Case Study on the Jeonju Express Bus Terminal

With the boom in online information, knowledge graphs like Freebase, Wikidata, and YAGO have emerged, thanks to the introduction of the RDF (Resource Description Framework). As RDF data grew, more and more spatial data was incorporated into it. While we have a lot of 2D data for outdoor spaces, mapping indoor spaces in 3D is challenging because it is expensive and time-consuming. In our research, we turned 2D blueprints into detailed 3D maps and then translated this into RDF format. We used the Jeonju Express Bus Terminal in South Korea as our test case. We made an automated tool that can turn 2D spatial data into 3D data that fits the IndoorGML standard. We also introduced terms like ‘loc’, ‘indoorgml-lite’, and ‘bloc’ to describe indoor spaces in the RDF format. Once we put our data into a GraphDB database, we could easily search for specific details and routes inside buildings. This work fills a significant gap in knowledge graphs concerning indoor spaces. However, the production of large-scale data across varied areas remains a challenge, pointing towards future research directions for more comprehensive indoor spatial information systems.

Children’s and young people’s cognizance of physical-symbolic reality: The production and acquisition of spatial knowledge

In this article, we address the central topic of the call for papers, childhood educates space, from the perspective of children’s and young people’s spatial knowledge. We argue that both childhood and youth educate as much as are educated by space. To shed light on this dynamic and complex interaction, we focus on the arenas and agencies at play within the production and acquisition of spatial knowledge. The term arena refers to the spatial conditions that delineate an area of activity related to the production and acquisition of spatial knowledge, and agency denotes the crucial means, agents, and tools that are instrumental in and buttress such area of activity. To render operative the analysis, we draw on two driving themes: (i) the gradual development of a comprehensive conception of space and (ii) its accompanying (and traversing) learning processes. Moreover, to substantiate the assertion that education between childhood and youth and space is mutually constitutive, we present a selection of findings from a qualitative meta-analysis conducted to reconstruct the evolution of young people’s spatial knowledge from the 1970s onwards. All in all, we claim that the process of production and acquisition of spatial knowledge forms the basis of, on the one hand, the keenness and depth of the perception that children and young people have of the physical world and, on the other, the ways they subjectively and symbolically characterize it.

Landscape and Architecture: Yawanawá Design Lessons from the Brazilian Amazon Forest

The purpose of this article is to observe the spatial design practices of the Yawanawá indigenous population that inhabits the Rio Gregorio Indigenous Land, located in the Brazilian Amazon Forest. In collaboration with this community, the intention is to reflect on approaches that can enrich the research of their spatial knowledge, thereby contributing to the consolidation of Amerindian knowledge in the academic field of architecture and design. The participant observation methodology allowed an immersion into Yawanawá narratives that rescue their ancestral role in the architectural and landscape configuration of the territory. Through elaborating a photographic visual investigation, it is sought to expand voices and meanings in strata of coexisting times, articulating the images to the Amerindian architectural conceptions that survived colonial abuse. In the conclusions, the lessons that the Yawanawá express and manifest are highlighted when building spaces of resistance in a poetic, symbolic, and daily way where the urban power structures constructed and/or that cross their territory were and are re-signified to test other ways of living in the forest.

Spatial learning using Google Streetview in an online wayfinding task

The use of navigation applications changed the way people find their way in an unfamiliar environment. A combination of maps, images and textual route instructions shown (or with audio) on one screen guides the user to the destination but may sometimes be overwhelming. This article investigated the spatial knowledge participants acquired after being presented with different types of route instructions, human and computer-generated, in an online wayfinding task using Google Streetview (without the 2D map) of an unfamiliar environment. The results showed a significant difference in the wayfinding performance for deviations from computer-generated instructions, whilst there was no difference in the time spent and the scene recall. Sketch maps revealed both route-like and survey-like characteristics. But most sketch maps are characterised by high route-likeness. Furthermore, this study showed a significant effect of the environmental layout on the participant’s performance based on deviations incurred during wayfinding. The results of this study have implications for improving navigation system instructions and design as well as for learning with geospatial technologies.

Living and post-mortem CT scans in the gross anatomy lab: A study investigating differences in first-year medical students' exam performance and perceptions.

Basic competency in radiological imaging is essential for physicians to identify and manage diseases. An optimal place in which to include imaging in the medical curriculum is during anatomy as students can correlate the 3D anatomy from their body donors with the 2D cross-sectional anatomy. The goal of this project was to enhance first-year medical students' knowledge of cross-sectional imaging in the gross anatomy lab and to investigate whether there are benefits to learning cross sectional imaging via scans from body donors versus living individuals. Student participant performance was evaluated on laboratory practical examinations, CT image questions and spatial anatomical knowledge in the thorax and abdomen sections of gross anatomy. Students learned the cross-sectional imaging during dissections where they accessed the images relevant to their study on Pacsbin, a web-based Digital Imaging and Communication in Medicine viewer, via iPads. Results showed no statistically significant differences in practical examination scores, spatial anatomical knowledge, or identification of anatomical structures on CT image questions between participants who learned from images on body donors versus living individuals. In a questionnaire given at the end of the course, participants cited that the CT images improved their anatomical and imaging knowledge and that they felt better prepared to use imaging software and interpret diagnostic imaging results upon entering clerkships. While there were no differences in academic performance between the groups, positive outcomes regarding student perceptions of anatomical and imaging knowledge and preparedness for use of imaging software were identified in this study.

Improving Coffee Yield Interpolation in the Presence of Outliers Using Multivariate Geostatistics and Satellite Data

Precision agriculture for coffee production requires spatial knowledge of crop yield. However, difficulties in implementation lie in low-sampled areas. In addition, the asynchronicity of this crop adds complexity to the modeling. It results in a diversity of phenological stages within a field and also continuous production of coffee over time. Big Data retrieved from remote sensing can be tested to improve spatial modeling. This research proposes to apply the Sentinel-2 vegetation index (NDVI) and the Sentinel-1 dual-polarization C-band Synthetic Aperture Radar (SAR) dataset as auxiliary variables in the multivariate geostatistical modeling of coffee yield characterized by the presence of outliers and assess improvement. A total of 66 coffee yield points were sampled from a 4 ha area in a quasi-regular grid located in southeastern Brazil. Ordinary kriging (OK) and block cokriging (BCOK) were applied. Overall, coupling coffee yield with the NDVI and/or SAR in BCOK interpolation improved the accuracy of spatial interpolation of coffee yield even in the presence of outliers. Incorporating Big Data for improving the modeling for low-sampled fields requires taking into account the difference in supports between different datasets since this difference can increase uncontrolled uncertainty. In this manner, we will consider, for future research, new tests with other covariates. This research has the potential to support precision agriculture applications as site-specific plant nutrient management.

Parallel cognitive maps for multiple knowledge structures in the hippocampal formation.

The hippocampal-entorhinal system uses cognitive maps to represent spatial knowledge and other types of relational information. However, objects can often be characterized by different types of relations simultaneously. How does the hippocampal formation handle the embedding of stimuli in multiple relational structures that differ vastly in their mode and timescale of acquisition? Does the hippocampal formation integrate different stimulus dimensions into one conjunctive map or is each dimension represented in a parallel map? Here, we reanalyzed human functional magnetic resonance imaging data from Garvert et al. (2017) that had previously revealed a map in the hippocampal formation coding for a newly learnt transition structure. Using functional magnetic resonance imaging adaptation analysis, we found that the degree of representational similarity in the bilateral hippocampus also decreased as a function of the semantic distance between presented objects. Importantly, while both map-like structures localized to the hippocampal formation, the semantic map was located in more posterior regions of the hippocampal formation than the transition structure and thus anatomically distinct. This finding supports the idea that the hippocampal-entorhinal system forms parallel cognitive maps that reflect the embedding of objects in diverse relational structures.

Two-Stream spectral-spatial convolutional capsule network for Hyperspectral image classification

Recently, the capsule network and its enhanced version named convolutional capsule network (Conv-CapsNet) were applied to hyperspectral image (HSI) classification and delivered outstanding performance, attributing to the enriched capabilities of capsules for feature expression and complex spatial knowledge excavation. However, restricted by the locality of convolutions in primary feature extraction, the rich spectral-spatial diagnostic information found in HSIs were insufficiently utilized, which degrades classification performance to a certain degree. In addition, most capsule networks involve a complex structure with many parameters, which are haunted by a common bottleneck of limited available training samples. To tackle these challenges, this paper presents a novel two-stream spectral-spatial convolutional capsule network (TSCCN) for HSIs to boost accurate classification with small samples. Based on the advanced convolutional capsule concept, a two-stream Conv-CapsNet architecture is developed that reduces trainable parameters and mitigates overfitting when samples are insufficient. Specifically, a 1D Conv-CapsNet and a 2D Conv-CapsNet sharing similar structures are developed as accompanying streams to fully excavate favorable features from the continuous spectra and raw spectral-spatial cubes, with the separate features fused to generate joint deep features for classification. To overcome the locality of primary convolutions, an efficient structural information mining module (SIM) is designed for each stream, to learn complementary cross-channel and cross-kernel inter-dependencies for subsequent capsule learning and further improve classification performance. Experiments on three well-known HSI datasets demonstrate the superiority of the proposed algorithm over nine representative state-of-the-art deep classifiers and its efficacy under small sample conditions, where all the methods were implemented in the Tensor-Flow framework and repeated for 10 times.

The role of place attachment and spatial anxiety in environmental knowledge

Different factors have been suggested to explain why humans differ in how well and efficiently they acquire and use spatial knowledge about environments. However, the role of emotional variables has been scarcely investigated. Here, we aimed to explore the role of the emotional dimension in terms of the emotional bonds that individuals develop towards places (i.e., the emotional dimension of place attachment that is place identity) and spatial anxiety in explaining individual differences in different kinds of environmental knowledge. A sample of ninety-nine students at the Magna Graecia University of Catanzaro (UMG) were given self-report questionnaires probing place attachment, spatial anxiety, sense of direction, and familiarity with the UMG campus. They were also given three performance tasks probing landmark, route, and survey knowledge of the UMG campus. Correlational analyses showed a significant association between place identity and all the environmental knowledge tasks, while spatial anxiety was significantly associated only with the performance in the survey knowledge task. However, the logistic regression models suggested only a contribution of place identity to landmark knowledge and no effect of spatial anxiety when familiarity and the self-reported sense of direction were considered. We propose that the emotional bond with a place could have a positive effect only in retrieving the visual features of navigational objects (i.e., landmarks), but future studies should expand on the topic with research paradigms involving the actual navigation of a real environment.

Black Spatial Storylines: Connections of Black Space, Sound and Story as Pedagogy

Black education has interrogated race, context, and power questions, yet these practices spanning geographies and learning contexts have not always been valued as spatial knowledge. Further, Black scholars have carved out spaces that honor the communal and spatial sensibilities of Black students, educators, and communities. Black geographies thought can help us reshape how we understand and interrogate issues within education and learning with attention to anti-blackness, futurities, imagining, placemaking, and efforts to create a sense of belonging despite perpetual unbelongingness in dominant educational and learning spaces. Thus, our piece engages with Black Geographies to emphasize the Black radical traditions of space and freedom-making to reorganize our approaches to pedagogy and storytelling. We engage what we call Black Spatial Storylines through our shared and individual stories. We present multiple vignettes and examples to model the ways Black Sound, particularly hip-hop, invites us to engage Black Spatial Storylines as both methodological and pedagogical techniques that start at Blackness. Not only do we highlight and use our own stories as examples, we detail how this process shifts our understanding of Black urban life, and allows us to reorient our educational praxis through Blackness. We conclude with suggested pathways for future applications of a Black geographies framework to education and learning, including the abundance that is the interweaving of Blackness. Thus, we hope to honor and uplift Black communities’ spatial knowledge by formulating our foundational understandings of Black spatial knowledge and the role it plays in education and learning studies.