# Location-Based Published articles for Location-Based. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Inside the Geofence: Faster Pickups Start Here DevFeed: [Inside the Geofence: Faster Pickups Start Here]() Original publisher: [Read original article]() Author: Global Technology Published: 2026-05-12T13:14:04Z Content type: article Language: en Sources: [McDonald's Technical Blog - Medium]() Topics: [Testing](), [Mobile](), [real-time](), [Wi-Fi](), [data](), [App]() Tags: [app](), [automation](), [geofencing](), [gps](), [location](), [location-based](), [mobile](), [notifications](), [push-notifications](), [quality-assurance](), [real-time](), [technology](), [testing](), [wi-fi](), [workflows]() ### AI overview The article explains how McDonald's integrates geofencing into mobile field testing to evaluate digital features under real-world conditions such as drive-thru lanes, parking areas, and restaurant environments. It reports that this approach improved accuracy, timing, and reliability for experiences including Ready on Arrival, curbside pickup, loyalty, notifications, and order-preparation workflows. ### Source excerpt Geofencing field testing turns noisy GPS signals into reliable arrivals for drive-thru, curbside, and beyond. By Chandrani Gupta Chowdhury, Sr. Manager, Quality Assurance Engineering Quick Bytes: Lab testing alone can't fully capture the real-world conditions that shape mobile experiences inside McDonald's restaurants As customers drive toward a restaurant, pull into a parking spot, or move through a drive-thru lane, geofencing-enabled field testing shows how digital features respond in real time This approach improved accuracy, timing, and reliability -- strengthening customer experiences like Ready on Arrival (ROA), curbside, and loyalty Mobile experiences don't happen in perfect conditions -- they happen in cars, parking lots, drive-thru lanes, and busy restaurants where signals fluctuate and customer behavior is unpredictable. For McDonald's, ensuring digital features work reliably in these real-world environments is critical to delivering fast, seamless experiences at scale. To meet that challenge, teams began integrating geofencing into field testing, allowing them to observe how mobile features perform as customers approach, arrive, and move through restaurant spaces. Inside the digital boundary of the restaurant Geofencing is a location-based technology that leverages GPS, Wi-Fi, and cellular data to create virtual boundaries around physical locations, enabling applications to trigger predefined actions when users enter or exit those zones. The benefits of incorporating geofencing into field testing are significant. From an operational standpoint, it helps ensure restaurants are prepared to receive and fulfill digital orders efficiently, reducing customer wait times and improving order accuracy. From a customer experience perspective, geofencing enables timely, relevant, and seamless interactions, reinforcing trust in the digital journey. Within the McDonald's ecosystem, geofencing can be applied across restaurant footprints -- including parking areas and drive ## How to Use User Location in Applications and Assess Its Limitations DevFeed: [How to Use User Location in Applications and Assess Its Limitations]() Original publisher: [Read original article]() Author: Austin Published: 2024-07-25T14:10:28Z Content type: article Language: en Sources: [Back End - Austin Gil]() Topics: [Programming](), [App]() Tags: [app](), [back-end](), [business-logic](), [coding](), [compliance](), [development](), [edge-compute](), [front-end](), [geolocation](), [html](), [ip](), [javascript](), [js](), [location](), [location-based](), [privacy](), [programming](), [software](), [web-development]() ### AI overview This article examines location-based programming, including use cases such as regional language, currency, store locators, weather data, and geofencing. It distinguishes user-experience, application-logic, and policy or compliance concerns, then discusses four ways to obtain location: user reporting, device heuristics, IP address, and edge compute. ### Source excerpt A post discussing the nuances around location-based programming. Various ways to access user location, how they can fail, and what to do about it. ## Location-Based Fashion Recommendations at Myntra DevFeed: [Location-Based Fashion Recommendations at Myntra]() Original publisher: [Read original article]() Author: Siddhartha Devapujula Published: 2024-04-22T11:25:37Z Content type: tutorial Language: en Sources: [Myntra]() Topics: [recommendation systems](), [Machine learning](), [recommendations]() Tags: [artificial-intelligence](), [ecommerce](), [fashion](), [location-based](), [machine-learning](), [personalisation](), [recommendation-system](), [recommendation-systems](), [search]() ### AI overview Myntra explains how machine-learning recommendation systems can use location as user side information to improve personalized fashion recommendations, including for cold-start users. ### Source excerpt Authored By Rohit Gupta & Siddhartha Devapujula Introduction Millions of users visit Myntra daily to upgrade their wardrobes and millions of items are listed on the platform at any given time. Users neither have the time nor the capability to scroll through this vast list of items. Even after applying category and attribute filters, usually the number of items is still in thousands. Hence it becomes critical that the top search results for any user are both relevant and personalized. Just like search, many other recommendation widgets across the platform face the same challenges. Fashion Diversity -- Every Region has its own Fashion Showing each user the best styles for them from a catalog of million plus products is where machine learning based recommendation systems come into play. From search results on google to your netflix home screen, recommendation systems are working in the background to get you the best results. It is impossible to imagine modern age internet experience without these systems. The uber goal of these models is to take the user features and the vast list of items as input ,and generate a small personalized list of items for each user. For these systems to work, we mainly use the user's historical activity on the platform. In this blog we will see how using other kinds of user details can also enhance the quality of recommendations. In the next sections, we dive into the details of recommendation systems and related techniques. We explain the motivation for a location based recommendation system and how we built one at Myntra. Later we discuss a few use cases at Myntra, results and potential future work. Basics of recommendation systems This is a very simple read about recommendation systems by Google -- Recommendations: What and Why? | Machine Learning | Google for Developers. Readers can skip if they are already aware of this. Traditional recommendation models focus on using the user's historical interactions on the platform to learn. This wor ## Dear Postgres DevFeed: [Dear Postgres]() Original publisher: [Read original article]() Author: Map Published: 2017-10-12T20:55:56Z Content type: opinion Language: en Sources: [Craig Kerstiens]() Topics: [Databases](), [Database](), [data](), [Geographic Information System](), [JSON](), [functions](), [XML]() Tags: [b-tree](), [database](), [databases](), [gis](), [indexes](), [json](), [jsonb](), [location-based](), [postgres](), [postgresql](), [window-functions]() ### AI overview An appreciative commentary on Postgres describes how it has expanded beyond traditional relational database capabilities while retaining a focus on data durability, standards, and steady improvement. The article discusses indexes, GIS and PostGIS, analytical features such as window functions and CTEs, and JSON and JSONB support. ### Source excerpt Dear Postgres, I've always felt an affinity for you in my 9 years of working with you. I know others have known you longer, but that doesn't mean they love you more. Years ago when others complained about your rigidness or that you weren't as accommodating as others I found solace in your steadfast values: Don't lose data Adhere to standards Move forward with a balancing act between new fads of the day while still continuously improving You've been there and seen it all. Years ago you were being disrupted by XML databases. As companies made heavy investment into what such a document database would do for their organization you proceeded to "simply" add a datatype that accomplished the same and brought your years of progress along with it. In the early years you had the standard format of index b-tree that most database engines leveraged. Then quietly but confidently you started adding more. Then came K-nearest neighbor, generalized inverted indexes (GIN), and generalized search-tree (GiST), only to be followed by space partitioned GiST and block range indexes (BRIN). Now the only question is which do I use? All the while there was this other camp using for something that felt cool but outside my world: GIS. GIS, geographical information systems, I thought was something only civil engineers used. Then GPS came along, then the iPhone and location based devices came along and suddenly I wanted to find out the nearest path to my Peets, or manage geographical region for my grocery delivery service. PostGIS had been there all along building up this powerful feature set, sadly to this day I still mostly marvel from the sideline at this whole other feature set I long to take advantage of... one day... one day. A little over 5 years ago I fell in love with your fastly improving analytical capabilities. No you weren't an MPP system yet, but here came window functions and CTEs, then I almost understood recursive CTEs (still working on that one). I can iterate over data in a recurs ## Target Joins the W3C DevFeed: [Target Joins the W3C]() Original publisher: [Read original article]() Author: Target Brands, Inc Published: 2014-10-15T05:00:00Z Content type: release Language: en Sources: [Target]() Topics: [Web](), [Internet of things](), [Internet](), [coding-community]() Tags: [internet-of-things](), [location-based](), [open-source](), [standards](), [w3c](), [web](), [web-services]() ### AI overview Target announced its membership in the World Wide Web Consortium (W3C) and described plans to collaborate on web services, location-based content distribution, and the Internet of Things for retail-related technology. ### Source excerpt Tuesday, October 14th 2015, Target affirmed its commitment to technology and leadership in the retail space by joining the World Wide Web Consortium (W3C). As a company that cares about delivering great experiences for customers, we realize those experiences are now steeped in technology. One way Target can have a significant impact on shaping technology is open engagement with other companies and organizations at the heart of emerging development - the W3C enables us to do just that. So you joined - now what? There are so many areas of technology important to retail and associated businesses. Initially, we're interested in engaging with tech leaders, businesses, and organizations on web services, location-based content distribution (iBeacons, smart stores, etc.), and the Internet of Things. Through open dialogue and collaborative work in the W3C, we seek to push these areas of technology forward to enable new and better experiences for our customers. More to come soon! This is just the beginning. As an organization we will be engaging with the developer community in new and different ways, proposing ideas on the technologies that matter to us, and providing feedback of our progress. Now let's get to work! Target Joins the W3C was originally published by Target Brands, Inc at target tech on October 15, 2014.