[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82112-en":3,"doc-seo-82112-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82112,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Accelerating Point-in-Polygon Predicates via Algebraic Hash-Joins and Discrete Global Grids at Scale","Traditional point-in-polygon queries rely on costly geometric predicates that scale poorly on massive datasets, even with spatial indices. Discrete Global Grid Systems (DGGS) discretize Earth geometries into hierarchical cells, converting spatial relations into constant-time algebraic hash-joins. DGGS adoption shifts cost to ETL encoding and exposes a tooling gap across implementations. This demo delivers an interactive DuckDB-based dashboard evaluating four DGGS (H3, S2, A5, ISEA4H) and shows that pre-indexing yields sub-second join latencies across complex grids.","Accelerating Point-in-Polygon Predicates via Algebraic Hash-Joins and Discrete Global Grids at Scale  \nLeventeJuhász✉  \n[levente.juhasz@ufl.edu](levente.juhasz@ufl.edu)  \nUniversity of Florida  \nFort Lauderdale, Florida, USA  \narXiv :2607 .08956v 1 [ cs .DB] 9 Jul 2026  \nAbstract  \nTraditional vector-based point-in-polygon queries rely on computationally expensive geometric predicates that scale poorly for massive datasets, even when accelerated by spatial indices. Discrete Global Grid Systems (DGGS) offer a scalable alternative by discretizing geometries into hierarchical cells, transforming complex spatial relations into constant-time relational hash-joins. However, adopting a DGGS introduces an overhead to encode data, and current grid implementations exhibit a significant performance “tooling gap.” In this demonstration, we present an interactive dashboard that empirically evaluates these computational tradeoffs across four DGGS implementations (H3, S2, A5, and ISEA4H) using DuckDB. Through progressive scenarios, the platform visualizes the overhead of on-the-fly encoding and demonstrates how pre-indexing spatial datasets eliminates this overhead. Ultimately, the demo proves that when data is pre-indexed, all DGGS regardless of their mathematical complexity or tooling converge to sub-second join latencies, unlocking the throughput of modern vectorized execution engines.  \nCCS Concepts  \n• Information systems → Geographic information systems; Query optimization; Spatial-temporal systems.  \n1 Introduction  \nThe exponential growth of available spatial data has exposed computational bottlenecks in traditional vector-based geoprocessing architectures. In spatial relational databases, operations such as point-in-polygon queries rely on continuous coordinate geometry. To manage large datasets, these systems typically utilize spatial indices (e.g., R-Trees [4]) to cull non-intersecting geometries via bounding-box overlap. However, spatial indices only serve as a preliminary filter and they do not accelerate the core geometric predicate evaluation. For every point that falls within a candidate polygon’s bounding box, the system must execute computationally expensive ray-casting [12] or winding-number algorithms [1] to determine true containment. Against complex vector boundaries at scale, these geometric predicates introduce significant latency.  \nDiscrete Global Grid Systems (DGGS) offer an alternative spatial data model by discretizing the continuous surface of the Earth into cells [11] . By encoding continuous coordinate geometries (latitude and longitude) into discrete identifiers, DGGS transforms the geometric problem of spatial intersection into an algebraic hashjoin. This shift replaces variable-time geometric calculations with constant-time 􀀤(1) database operations, which have been shown to drastically improve computational scalability for large-scale geospatial operations [8] . However, adopting DGGS introduces a computational tradeoff: the Extract, Transform, Load (ETL) overhead to  \nencode points and discretize (polyfill) polygons prior to execution. Furthermore, practical performance is heavily influenced by a tooling gap between highly optimized, industry-backed implementations (e.g., H3, S2) and scientific, equal-area systems (e.g., ISEA4H) that may lack native database integration [5, 7], despite area-equivalence being a prerequisite for geometrically rigorous, unbiased spatial modeling [6] .  \nIn this demonstration, we present an interactive dashboard that visualizes the relational throughput and computational tradeoffs of replacing spatial predicates with discrete algebraic hash-joins. Utilizing DuckDB [10], a vectorized database engine, the dashboard executes real-time spatial joins across multiple DGGS implementations and standard vector baselines. Through interactive global and urban scenarios, the platform allows users to empirically evaluate the upfront encoding overhead against query execution speed, demonstr","cbCaijFUNDHxysTB","https://ap.wps.com/l/cbCaijFUNDHxysTB","pdf",1570052,1,4,"English","en",105,"# Abstract\n# 1 Introduction\n# 2 System Architecture\n## 2.1 Unified DGGS Translation Framework","[{\"question\":\"Why do traditional vector point-in-polygon queries scale poorly at large dataset sizes?\",\"answer\":\"They use continuous coordinate geometry and rely on expensive ray-casting or winding-number checks after a spatial index’s bounding-box filtering, creating high latency for complex polygon boundaries.\"},{\"question\":\"How do Discrete Global Grid Systems (DGGS) accelerate point-in-polygon predicates?\",\"answer\":\"DGGS discretize the Earth into hierarchical grid cells, encoding coordinates into discrete identifiers and transforming spatial intersection into constant-time relational hash-join operations.\"},{\"question\":\"What role does pre-indexing play in the demo’s performance results across different DGGS implementations?\",\"answer\":\"The dashboard shows that when spatial datasets are pre-indexed (encoding overhead paid upfront), all DGGS implementations converge to sub-second join latencies, despite differences in mathematical complexity and tooling integration.\"}]",1784178266,10,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"accelerating-point-in-polygon-predicates-via-algebraic-hash-joins-and-discrete-global-grids-at-scale","",{"@graph":35,"@context":84},[36,52,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":21},"https://docshare.wps.com/document/accelerating-point-in-polygon-predicates-via-algebraic-hash-joins-and-discrete-global-grids-at-scale/82112/",{"url":51,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":23,"description":14,"dateModified":60,"datePublished":61,"encodingFormat":59,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":40,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why do traditional vector point-in-polygon queries scale poorly at large dataset sizes?","Question",{"text":74,"@type":75},"They use continuous coordinate geometry and rely on expensive ray-casting or winding-number checks after a spatial index’s bounding-box filtering, creating high latency for complex polygon boundaries.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How do Discrete Global Grid Systems (DGGS) accelerate point-in-polygon predicates?",{"text":79,"@type":75},"DGGS discretize the Earth into hierarchical grid cells, encoding coordinates into discrete identifiers and transforming spatial intersection into constant-time relational hash-join operations.",{"name":81,"@type":72,"acceptedAnswer":82},"What role does pre-indexing play in the demo’s performance results across different DGGS implementations?",{"text":83,"@type":75},"The dashboard shows that when spatial datasets are pre-indexed (encoding overhead paid upfront), all DGGS implementations converge to sub-second 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