Spatial pattern matching is the process of matching query entities and constraints with database entities and relations. It has many applications, including similar region search, housing market search, landmark search, and road network matching. To our knowledge, all existing spatial pattern matching approaches frame the problem in a 2 dimensional space, where entities lie in a cartesian plane and relationships defined between them are contained in 2 dimensions. However, this problem framing has significant limitations when searching for real world entities that have height in addition to position. To address this limitation, we extend spatial pattern matching to 3 dimensions and provide a generalized definition of the problem. We describe a subgraph matching algorithm capable of resolving 3D spatial patterns over distance relations and release two 3D spatial pattern matching datasets, one synthetic and one containing real 3D building data from the city of Hamburg, Germany. We test our subgraph matching algorithm on both datasets and present results as a baseline for future methods to build upon.

Large Language Models (LLMs) can be applied to various real-world tasks, many of which require spatial reasoning, like pattern-based spatial search, Point of Interest (POI) recommendation, and itinerary or travel planning. However, on their own LLMs have been shown to lack reliable spatial reasoning capabilities, especially about distances. Further, success at these tasks requires the LLM to have been exposed to the relevant places and to successfully disambiguate their spatial interpretations, which is affected by the popularity of the place name. To address these problems, we develop a novel approach using spatial Retrieval Augmented Generation (RAG) to enable an LLM to use relevant distance information not explicitly learned during training when answering spatial questions that involve distance reasoning. Our method encodes a spatial graph of geodesic and road network distances between cities and towns, as well as a mapping to their alternative names, and compares a variety of techniques to provide the LLM with the appropriate spatial context from those knowledge stores. By incorporating relevant spatial information, our method significantly outperforms the baseline LLM on several tasks requiring distance-based spatial reasoning, including nearest-neighbor questions and distance analogy questions. We further show through experiments which graph storage and retrieval methods perform best, and which are brittle when applied to more challenging questions. Given the vast array of possible places an LLM could be asked to reason about, our work offers a first step towards providing a rudimentary distance-oriented spatial ‘world model’ to complement the vast linguistic knowledge held in LLMs.

Recent developments in Artificial Intelligence (AI) have led to flexible ways for users to search through vast information. However, users may have questions that are grounded in the real world which require spatial inference, for which language models are not well suited. Conversely, traditional spatial search methods, like spatial pattern matching, can answer spatial reasoning questions correctly but are noise-intolerant, slow, and brittle. Given the current state, there are opportunities to integrate AI and spatial pattern matching to enable robust and flexible spatial search. To bridge this gap, we survey existing spatial pattern matching methods, including the few that apply AI to the problem, discussing their efficiency and limitations, and describing opportunities to further enable spatial search via AI.

Large Language Models (LLMs) are often used for tasks that involve reasoning about the physical world, like recommending travel itineraries. However, success at these tasks requires the LLM to have been exposed to the relevant places, which is not true for lesser-known or alternatively named places, like Indigenous place names. Our prompting technique handles this issue using Retrieval Augmented Generation, encoding a spatial graph of common places and a mapping to their Indigenous alternatives. Our method improves LLM performance on spatial tasks involving lesser-known place names, thus advancing AI fairness.

Large language models (LLMs) like ChatGPT and Claude are increasingly used for location-based tasks such as answering geographic questions and generating travel recommendations. However, these models are typically trained on internet-scale data that under-represents rare place names, potentially leading to unequal performance on questions that involve resolving spatial synonyms. This work investigates whether LLMs respond differently to factual queries about dual-named places when given equivalent Indigenous and Anglicized toponyms, using population estimates for Australian locations as a case study. In addition to measuring response accuracy, we examine the internal embedding representation of the toponyms, revealing that Indigenous names are more diffusely represented, which correlates to a nearly 20-fold increase in mean squared error in estimating population when places are referred to by their Indigenous names compared to their Anglicized counterparts. Our results highlight a representational gap in current LLMs and underscore the need to improve embeddings to enable more culturally equitable LLM performance.

Traditional search engines use text-based queries to perform top-k keyword search. However, this approach does not always capture every user intention. For example, pattern-based spatial search can better answer queries involving spatial constraints (i.e. X North of Y). However, pattern-based search usually requires a pictorial query pattern as input, constructed by a user dragging and dropping objects on a canvas in a specialized interface. To bridge the gap between pattern-based spatial search and traditional search engines that require text input, we devise a Natural Language to Pictorial Query (NL2PQ) module that converts natural language queries into pictorial queries that can be refined then resolved using spatial pattern matching algorithms, thus enabling pattern based spatial search via natural language input.

There have been many recent improvements in the ability of Large Language Models (LLMs) to perform complex tasks and answer domain-specific questions through techniques like Retrieval Augmented Generation (RAG). However, reasoning abilities of LLMs, including spatial reasoning abilities, are still lacking. Spatial reasoning is a key component required to answer questions in a variety of domains that are grounded in the physical world, including urban planning, civil engineering, travel, and many others. To advance the development of LLMs and facilitate an impact in these domains, new research techniques must be developed to enable LLMs to reason over spatial data, which is commonly stored in the form of a graph. In this paper we outline the challenges associated with spatial reasoning through LLMs and envision a future in which search engines integrate with LLMs to answer complex spatial questions through graph-enhanced reasoning.

Many real world tasks where Large Language Models (LLMs) can be used require spatial reasoning, like Point of Interest (POI) recommendation and itinerary planning. However, on their own LLMs lack reliable spatial reasoning capabilities, especially about distances. To address this problem, we develop a novel approach, DistRAG, that enables an LLM to retrieve relevant spatial information not explicitly learned during training. Our method encodes the geodesic distances between cities and towns in a graph and retrieves a context subgraph relevant to the question. Using this technique, our method enables an LLM to answer distance-based reasoning questions that it otherwise cannot answer. Given the vast array of possible places an LLM could be asked about, DistRAG offers a flexible first step towards providing a rudimentary `world model' to complement the linguistic knowledge held in LLMs.

In a world where digital results influence real-world decisions, understanding national search trends can be invaluable for marketing strategies, event preparation, and overall safety efforts. This paper presents a geopatial machine learning approach to predict search interest in specific terms across the United States with Google Trends data. Using two different models, Sentence Transformers and LightGBM, we predict search popularity for location-based terms such as sports teams, events, and popular foods. The experimental results of this study achieve a 76% accuracy in identifying the most popular state for the search terms tested but show reduced accuracy for lower ranked states which underscores that this remains an open problem. This work lays the groundwork for forecasting interest in a variety of events, locations, and general items, which provides a solid foundation for smarter and more data-driven decision making for search optimization, advertising, emergency response, and event planning. We suggest future work that focuses on expanding the scale of the data considered to include city-level and smaller granularity, as well as developing additional models for prediction-making.

Visualizations such as bar charts, scatter plots, and objects on geographical maps often convey critical information, including exact and relative numeric values, using shapes. The choice of shape and method of encoding information is often selected arbitrarily, or decided based on convention or common practice. However, past studies have shown that the human eye can be fooled by visual representations. The Ebbinghaus illusion demonstrates that the perceived relative sizes of shapes depends on their configuration, which in turn can affect judgements, especially in visualizations like proportional symbol maps. In this study we evaluate the effects of varying the type of shapes and metrics for encoding geospatial data in visual representations on a spatio-temporal map interface. We find that some combinations of shape and metric are more conducive to accurate human judgements than others, and we provide recommendations for applying these findings in future spatial visualization designs.

Spatial Pattern Matching is an important problem in information retrieval that involves reasoning about the relative position, distance, and orientation of objects with respect to each other. Most spatial pattern matching approaches use large, complex graphs or multigraphs to explicitly encode rich spatial information. The downside of this complexity is that search over spatial patterns remains badly constrained by computationally intensive classes of algorithms, like subgraph matching and constraint satisfaction. This paper highlights the recent approaches to graph-based spatial pattern matching, and presents a vision of the way forward, using graph-based Artificial Intelligence as a flexible, approximate approach to the otherwise intractable problem.

Objective The COVID-19 pandemic emphasized the value of geospatial visual analytics for both epidemiologists and the general public. However, systems struggled to encode temporal and geospatial trends of multiple, potentially interacting variables, such as active cases, deaths, and vaccinations. We sought to ask (1) how epidemiologists interact with visual analytics tools, (2) how multiple, time-varying, geospatial variables can be conveyed in a unified view, and (3) how complex spatiotemporal encodings affect utility for both experts and non-experts.Materials and Methods We propose encoding variables with animated, concentric, hollow circles, allowing multiple variables via color encoding and avoiding occlusion problems, and we implement this method in a browser-based tool called CoronaViz. We conduct task-based evaluations with non-experts, as well as in-depth interviews and observational sessions with epidemiologists, covering a range of tools and encodings.Results Sessions with epidemiologists confirmed the importance of multivariate, spatiotemporal queries and the utility of CoronaViz for answering them, while providing direction for future development. Non-experts tasked with performing spatiotemporal queries unanimously preferred animation to multi-view dashboards.Discussion We find that conveying complex, multivariate data necessarily involves trade-offs. Yet, our studies suggest the importance of complementary visualization strategies, with our animated multivariate spatiotemporal encoding filling important needs for exploration and presentation.Conclusion CoronaViz's unique ability to convey multiple, time-varying, geospatial variables makes it both a valuable addition to interactive COVID-19 dashboards and a platform for empowering experts and the public during future disease outbreaks. CoronaViz is open-source and a live instance is freely hosted at http://coronaviz.umiacs.io.

Electric vehicles (EVs) have the potential to serve as energy storage solutions through bidirectional charging technology, which allows them to both draw power from and feed power back into the grid, homes, or other vehicles. This capability enables EVs to reduce emissions, optimize costs, and support the grid by storing energy during periods of high production and supplying it when demand is high. In this vision paper, we focus on unlocking the potential of EVs as energy storage solutions while ensuring they remain readily available for transportation, their primary purpose. A significant research gap exists in that most current studies prioritize energy management, often using simplistic approaches that inadequately address the travel needs of EV owners. We believe the database community can be instrumental in maximizing the dual role of EVs as transportation and energy storage. We present a non-exhaustive list of research directions for various EV stakeholders, including individual EV owners, groups of independent yet cooperative EVs, commercial EV fleets, and autonomous EVs, and hope to inspire the database community for further exploration.

Spatial reasoning is a particularly challenging task that requires inferring implicit information about objects based on their relative positions in space. In an effort to develop general purpose geo-foundation models that can perform a variety of spatial reasoning tasks, preliminary work has explored what kinds of world knowledge and spatial reasoning capabilities Large Language Models (LLMs) naturally inherit from their training data. Recent work suggests that LLMs contain geospatial knowledge in the form of understanding geo-coordinates and associating spatial meaning to the key terms "near" and "far." In this paper, we show that LLMs lack the ability to adapt the meaning of the words "near" and "far" to the appropriate scale when provided contextual reference points. By uncovering biases in how LLMs answer distance-related spatial questions, we set the groundwork for developing new techniques that may enable LLMs to perform accurate spatial reasoning.

With the advance of the geo-positioning technology, the terrain surface data has become increasingly popular and has drawn much research attention from both academia and industry. Answering a shortest-path query for a given source and a given destination on a terrain surface is a fundamental problem and has many applications including Geographical Information System and 3D virtual games. We observe that all existing exact algorithms are only aware of the position of the source point and is unaware of the information of the destination point. Motivated by this, in this paper, we propose an efficient algorithm, namely direction-oriented algorithm (DIO Algorithm), for answering shortest-path queries on a terrain surface. The algorithm properly guides the search along a direction towards the destination instead of blindly searching all possible directions from the source point. To this end, we convert the geodesic shortest path problem to a shortest obstacle-free Euclidean path problem in the 2D planar unfolding of the terrain surface. Based on this conversion, we derive for each part of the terrain surface a lower bound on the length of the shortest path from the source to the destination passing through the part with a novel method. The lower bounds provide useful information that can be used to decide the visiting order of the parts on the terrain surface and guides the search of finding the destination quickly. Our experiments verified that our algorithm runs faster than the state-of-the-art by more than one order of magnitude.

In the field of mobility, the focus in the past few years has been on the proverbial last mile connectivity. However this paper, narrows the scope from "miles" to the "last 100 yards" presenting a unique sets of issues that are not seen at other levels. The last 100 yards encompass routing and connectivity issues within confined spaces such as houses, apartment building, office spaces and many others. Some of the challenges in this context include coordinating between traditional delivery services (e.g., Fedex, DHL or Amazon Prime) and specialized pilots authorized to operate within the human dominated spaces of the last 100 yards. Ensuring timely delivery of perishable items, addressing the risks of delivery theft and recipient accuracy, and managing the storage and redelivery of packages when recipient are not present further complicates the process. Despite the challenges, the last 100 yards also present opportunities for novel solutions based on automation, robotic routing, social modelling, and industrial planning. In these confined spaces fully automated robotic solutions become feasible as navigation speed and routes are limited and the area can be easily geofenced.

Spatial Pattern Matching is an important search problem that involves reasoning about the relative position, distance, and orientation of objects with respect to one another. Spatial relationships between objects contain a lot of information about the world, which makes them useful in applications like Point of Interest (POI) retrieval and location-based services. However, spatial pattern matching is an NP-hard problem in the worst case. This paper presents a theoretical comparison of spatial pattern matching approaches, showing how the prominent methods compare for each type of spatial relation they support. We further highlight the common techniques used to gain performance improvements and provide suggestions towards developing approximate solutions to this form of spatial search.

Sun glare during driving poses a significant threat to driver and pedestrian safety. Navigation and route planning typically seeks to minimize the distance or time between the desired origin and destination, accounting for traffic patterns and other heuristics like minimizing the number of stoplights or left turns encountered on a route. However, current navigation methods do not support avoidance of complicated, temporally-dependent safety factors, like adverse road and environmental conditions. We take avoiding incident sun glare to the driver as an example of dynamic safety-aware navigation and lay out potential strategies for addressing this previously unexplored problem. We present a reinforcement learning-based method for computing sun glare-low routes through an elastic function that accounts for the direct angle between the sun and the driving direction. Our preliminary work shows that in some cases it is possible to reduce the sun glare exposure on a route by trading off additional travel distance. We envision future safety-aware navigation approaches that can automatically balance this trade-off and account for additional dynamic spatially and temporally-dependent safety-related environmental factors, like road and weather conditions, to determine the safest and most efficient route between any two given points.

Text clustering methods traditionally rely on a shared vocabulary and script, which poses a challenge for cross-lingual text clustering problems that arise in a variety of domains including social media, news, finance, and more. Recent approaches to cross-lingual clustering have found success by leveraging latent embedding space representations of neural models and more recently by directly using Large Language Models (LLMs) to do text clustering in zero-shot or few-shot settings. However, much of the recent work focuses on short text, like social media posts. In this paper, we use cross-lingual clustering in the news domain as a case study to test whether LLMs can effectively cluster long documents by extracting and maintaining keyphrases associated with each cluster of documents. We compare the clustering several LLMs produce in a zero-shot setting to a more traditional online clustering method that uses TF-IDF to cluster documents based on their content and time of publication. We find that LLMs tend to cluster the articles based on the text, in particular based on the language of the text more than the content, and ignore the time and location of publication, indicating further work is needed before LLMs can reliably be used in clustering news articles across multiple languages.

The Spatial Pattern Matching paradigm offers a promising direction for searching with incomplete or imperfect information, but it is badly constrained by dependence on graph-based representations and computationally intensive search algorithms like subgraph matching and constraint satisfaction. To address these limitations, we present COMPASS, a suite of data structures and algorithms that enable pattern-based search by encoding the directional relationships between objects in abstracted matrix representations rather than graph structures. We provide a series of recursive search algorithms that leverage our matrix representations to enable spatial search queries with directional constraints, which are typically too dense for previous graph-based approaches. Our search methods find matches even when the query pattern is not aligned to the global coordinate system, resulting in perfect recall in our evaluation. Computationally, our search methods scale with the number of query objects times the number of database objects squared in the worst case, which is significantly better than previous methods. Our empirical measurements show that the performance is typically even better, approaching logarithmic in the number of query terms.