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.

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.

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.