GeoArtificial Intelligence (GeoAI), Location Analytics, and GIS in the System Sciences
Permanent URI for this collectionhttps://hdl.handle.net/10125/112431
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Item type: Item , Vision-Language Models (VLMs) in GeoAI Systems: Enhancing Brownfield Change Detection through Semantic Reasoning(2026-01-06) Gollapalli, Sai Kiran Srivatsav; Dürrbeck, Konrad; Lasker, Asifuzzaman; Reich, Daniel; Sk Md, Obaidullah; Fischer, RolandThis paper presents a hybrid GeoAI methodology for semantic change detection in satellite imagery by integrating Vision–Language Models (VLMs) into an unsupervised clustering-based pipeline. Building on earlier work using K–Means clustering to validate pre-selected brownfield regions from SPOT (2021–2023) and aerial imagery, we address the persistent issue of semantic ambiguity, particularly in high-uncertainty zones. We introduce a zero-shot, reasoning-based verification layer that evaluates whether visual differences across time are structurally meaningful. This approach improves interpretability, traceability, and diagnostic robustness. Evaluation across 1,000 human-labeled samples and a temporally unseen test set of size 200 (SPOT23–SPOT24) demonstrates notable improvement in ambiguous zones in reasoning quality and error transparency, especially where prior methods faltered. Our framework maintains the speed and scalability of clustering while injecting semantic precision through natural language decision paths. Designed with the UN’s SDG 11 (Sustainable Cities and Communities) in mind particularly for brownfield redevelopment this work contributes to scalable, interpretable, and operationally viable GeoAI systems.Item type: Item , Structured Pixels: Satellite Imagery as the Cause in Causal Effect Estimation(2026-01-06) Lu, Chien; Chadefaux, ThomasWe present Structured Pixels (SP), a causal inference model that positions satellite imagery as a cause/treatment in a causal graph, rather than merely a proxy for outcomes or confounders. Built on the generalized Robinson decomposition and a two-step, R-learner-inspired algorithm, SP uses learned latent representations to partial out confounding influences and isolate the causal effect. Its modular training pipeline supports integration with diverse machine learning models across domains. We evaluate SP using semi-synthesized datasets on two tasks: the impact of environmental conditions on mosquito populations and the influence of coastal characteristics on dark vessel prevalence. SP consistently outperforms baseline methods, and its learned representations capture meaningful environmental patterns. We further demonstrate SP’s applicability by re-examining the relationship between deforestation and agricultural productivity with real-world data; the results align with prior work. These findings highlight SP’s potential to advance GeoAI for environmental monitoring and resource management.Item type: Item , Rethinking Retail Location Decisions: Industry insights into Decision-making Practice(2026-01-06) Hernandez, Tony; Aversa, JoeRetail location decision-making is facing growing challenges as consumer behavior becomes more complex and dynamic. This paper draws on ten in-depth interviews with location decision-makers at major Canadian retail and service firms. While traditional decision-making practices continue to dominate, there is an increasing interest in leveraging spatial big data and applying data science and geospatial artificial intelligence (GeoAI). Yet, many organizations remain cautious, often relying on institutional knowledge or rebranding existing tools rather than wholly embracing innovation. Experimentation with data science and GeoAI is taking place. However, its effective integration will require strong leaders and better collaboration between data science teams and decision-makers to align analytical models with experiential judgment. Nevertheless, the shift from legacy decision-making toward more adaptive data science and GeoAI-informed strategies is underway. This transition marks a strategic inflection point, with the success of new approaches depending on how well firms overcome inertia and foster innovative decision cultures.Item type: Item , Mining Customer Journeys to Uncover Empirical Retail Agglomerations(2026-01-06) Wagle , Manil; Aversa, Joe; Hernandez, Tony; Doherty, SeanShopping centers are a cornerstone of the retail system, with their success hinging on offering tenant mixes and layouts that stimulate cross-shopping. Despite the rise of e-commerce, physical retail remains vital as consumers increasingly seek blended digital–in-store experiences. Yet, traditional approaches to analyzing shopper behavior often rely on surveys or simple frequency counts, which fail to capture the complexity of customer journeys. This study addresses this gap by applying spatial big data and unsupervised machine learning to investigate empirical retail agglomerations. The research explores how structured, non-random co-visitation patterns within a shopping center can be systematically identified and leveraged within a tenant-mix strategy. Drawing on 24 million anonymized visits to a major Canadian shopping center, the study employs GeoAI and association rule mining, specifically the Apriori algorithm, to uncover high-frequency and high-lift co-visitation rules. Results reveal structured journeys that highlight strong co-visitation between anchors and specialty tenants, confirming that shopping center behavior is far from random. These patterns suggest optimal adjacencies and provide a data-driven framework for leasing and tenant layout. The study contributes theoretically by extending retail agglomeration research using unsupervised methods to examine behavioral clustering on a large-scale dataset empirically. For practitioners, the research approach offers actionable insights for leasing and tenant-mix optimization.Item type: Item , Geospatial Analysis of Wildfire Impact and Predictive Modeling of Susceptibility: A Case Study of Maui, Hawaii and California(2026-01-06) Zhao, Jiyue; Fang, Shiwei; Tan, Sheng; He, Sen; Wang, ZiCatastrophic wildfires pose a growing global threat. This study analyzes their impacts through a comparative analysis, developing a predictive risk model that integrates satellite remote sensing within a cloud-based Geographic Information Systems (GIS) framework. On the Google Earth Engine platform, the differenced Normalized Burn Ratio (dNBR) and Normalized Difference Vegetation Index (NDVI) were applied to assess burn severity and vegetation health for the 2023 Maui wildfire and the early 2025 California wildfire. Despite stronger resilience, California's fires cause a heavier overall economic impact, while Maui's smaller fire delivered a more concentrated, catastrophic blow to its community. The predictive risk model demonstrated high accuracy when validated against historical fire data, successfully identifying low fuel moisture, topography, land cover and human factors as key drivers of susceptibility. This research underscores the need for context-specific management and shows that GIS and cloud-based analysis are powerful tools for enhancing wildfire resilience, response, and planning.Item type: Item , Interactive Spatiotemporal Visualization for Understanding the Spread of Invasive Species(2026-01-06) Zong, Hannah; Jiang, Xingyu; Qian, Cheryl; Kong, Nicole; Fei, Songlin; Chen, YingjieInvasive species pose serious threats to ecosystems and economies, yet common Geographic Information Systems (GIS) struggle to show how invasions unfold through time and space. We present an interactive decision-support system that integrates first-detection chronology, local directional structure, and global linkages with contextual overlays. The design follows geovisual analytics principles and leverages cognitive-fit by matching tasks (chronology, direction, linkage) to encodings (heatmap, rays, arrows), complementing static maps and traditional GIS. The tool enables users to identify invasion centers, explore both local and global spread patterns, and overlay terrain and highway networks for contextual insight. We demonstrate the approach on four forest pests in the eastern United States using county-level first-detection records (1905–2020). A qualitative evaluation with three domain experts found the interface clear and useful for exploratory analysis; feedback informed planned improvements for interaction cues, accessibility, and edit transparency.Item type: Item , The Role of Alcohol Availability and Community-level Factors on Fatal and Non-Fatal Overdoses(2026-01-06) Stepnowski, Kendra; Snowden, Aleksandra; Kostelac, ConstanceThis study explores the dynamic interplay between alcohol and community health outcomes, focusing on the influence of alcohol outlets on fatal and non-fatal overdose risk across urban spaces. Central to this discourse is the recognition of the pivotal impact of opioids on drug overdose incidents within communities. Existing literature has laid the groundwork for understanding the complex web of factors contributing to overdose fatalities. However, gaps persist in our understanding of how the distribution of alcohol outlets across urban spaces interacts with other contextual variables to shape overdose risk at the local level. By employing geospatial techniques, this research seeks to bridge these gaps, shedding light on substance-related harms and informing targeted interventions tailored to specific communities in Milwaukee, Wisconsin.Item type: Item , New Possibilities, New Responsibilities: Ethical Challenges and Guidance for Location Tracking Device-based Research in Supply Chains(2026-01-06) Asdecker, Björn; Felch, VanessaThis study explores the use of location tracking devices (LTDs) as a method for generating independent, geospatially grounded data to enhance transparency in global supply chains. While LTDs offer promising alternatives to self-reported data, their application introduces complex ethical challenges. Through a developmental literature review, this work synthesizes how LTDs have been empirically employed and how ethical concerns – such as consent, fairness, and data protection – have been addressed. Drawing on these insights, it proposes a practical ethics framework tailored to LTD-based studies in supply chain contexts. The findings contribute to geographic information systems (GIS) research by promoting the integration of geospatial data into opaque domains and support the Green IS agenda by advancing responsible, sustainability-oriented research design.Item type: Item , Introduction to the Minitrack on GeoArtificial Intelligence (GeoAI), Location Analytics, and GIS in the System Sciences(2026-01-06) Aversa, Joe; Shin, Namchul; Pick, James; Sarkar, Avijit