Spatial Data Analysis on On-Demand Cab Services Using Spark

Spatial Data Analysis on On-Demand Cab Services Using Spark | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatial Data Analysis on On-Demand Cab Services Using Spark Likhitha Poritigadda, Sk. Fathimabi, Manohar Raj Kokkiligadda This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4220741/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Spatial data pattern analysis is a technique used to analyze large amounts of spatial-temporal data generated by on-demand cab services such as Uber, Lyft, and Grab. This type of data includes information on the pickup and drop-off locations of riders, the trajectories of drivers, and the times of day when demand for cabs is highest. By analyzing this data using spatial temporal pattern analysis techniques, on-demand cab services can gain insights into rider demand patterns, optimize driver allocation, improve pricing strategies, and enhance overall system efficiency. Apache Spark is a versatile and high-speed open-source distributed computing framework for big data processing. With in-memory capabilities and a unified API, it supports batch processing, interactive queries, machine learning, and real-time streaming, making it an ideal choice for data analytics. In the realm of spatial data analysis with Spark, data preprocessing and establishing a Spark session are crucial initial steps. This includes cleaning, transforming datetime data, and handling missing values. Once preprocessed, the data is loaded into Spark, enabling diverse spatial analysis techniques. These techniques include spatial clustering and trajectory analysis, helping uncover valuable insights from geospatial data. Large-scale spatial data analysis benefits from advanced indexing techniques like R-tree, Quad Tree, and KD Tree. These methods streamline query handling and visualization through interactive maps, enhancing data interpretation. In our analysis, we've measured retrieval times for these indexing strategies, with R-Tree at 2.18 seconds, Quad Tree at 2.23 seconds, and KD Tree as the fastest at 1.94 seconds. These findings offer insights into the efficiency and performance of each approach, aiding in informed decision-making. Spark Spark Session Indexing Spark Dataframe Full Text Additional Declarations Competing interest reported. we are interested in cluster computing Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4220741","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":289102403,"identity":"e28c65ce-e28c-411a-bced-1d4af9c7b0f5","order_by":0,"name":"Likhitha Poritigadda","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYBACPmY2NoYEhgMM8vIPGx8ABXj4CGlhA2tJOMBg2JDcbADSwkZQCwNQCwNQC8OB9DYJiAghLexsaQ8e/rgjz9hwsK3ya46dDBsD88NHN/A77LhBQsIzw3bGxrbbstuSgQ5jMzbOwauFvU0iIeEwY2MzY9ttyW3MQC08bNLEaLFvOMbYViy5rZ4YLWzHQFoSG84wtjF+3HaYKC1pEglph5M3zmBslmbcdpwHKILfL/z8x8wkf9gctp0vwf7w489t1fb87M0PH+PTggKYecAkscpBgPEHKapHwSgYBaNgxAAAMutD79QTASAAAAAASUVORK5CYII=","orcid":"","institution":"velagapudi ramakrishna siddhartha engineering college","correspondingAuthor":true,"prefix":"","firstName":"Likhitha","middleName":"","lastName":"Poritigadda","suffix":""},{"id":289102404,"identity":"f0b203fc-e878-4392-a206-1c41fff839af","order_by":1,"name":"Sk. 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