Summer is here, and you're probably already packing your calendar with vacation escapes, backyard BBQs, and weekend road trips. Of course, the fan-favorite destination for this hot season is the beach, where the breeze is cool and the water is refreshing. But what if we told you that you didn't have to book an Airbnb or waterfront hotel in a beach town the next time you wanted to take a dip in one of nature's pools? Turns out, a beach house may be more in reach than you thought! Zillow recently pulled some data to identify the 20 most affordable seaside cities where you can make your vacation home dreams a reality.While we're not saying these options will get you a beach house on the cheap, the locations typically offer a range of properties with lower price tags that still give you access to the ocean, as well as all the charm that comes with a seaside locale (think Mom and Pop ice cream shops, quaint shopping, and more). Of the top 20, you'll find that Florida dominates the list, with a few other states sprinkled in. Keep reading to see which beach towns have the lowest typical home values, but still all of the sandy perks.For more real estate stories:1Atlantic City, NJFederico ScottoAtlantic City may be best known for its casinos, but the iconic boardwalk along the Atlantic Ocean is a close second. There's plenty to do in this shore town, from visiting the amusement park and eating fresh seafood to spreading out on the sand. Since you're so close to New York City, day trips from either location are extremely easy as well.Typical home value: $215,336Learn More2Daytona Beach, FLFlavio Vallenari//Getty ImagesAny NASCAR fan is familiar with Daytona Beach, but did you know that this Northeastern Florida city is also a festival hub? Every year, the city hosts over 60 different art, music, and other cultural festivals, giving residents and tourists alike opportunities to experience new things. Though you could easily spend every day on the beach, there are plenty of other museums, adventures, and opportunities to try out.Typical home value: $251,750Learn MoreAdvertisement - Continue Reading Below3Deerfield Beach, FLWiniker:Getty ImagesThe small city of Deerfield Beach is ideal if you want to experience South Florida's beaches without the crowds. Located between Boca Raton and Pompano Beach, the town is known for its fishing pier and abundance of outdoor water activities, like paddle-boarding, surfing, and water skiing. Typical home value: Learn More4Myrtle Beach, SCDale Fornoff:Getty ImagesMyrtle Beach is a seaside locale with 60 miles of sandy beach and 14 unique communities meshed together. It provides plenty of classic beach town activities, such as a fun boardwalk and theme park, and is generally a family-friendly location. There are plenty of things to do and places to explore, from the Waccamaw River to 90 different golf courses. Typical home value: $300,720Learn MoreAdvertisement - Continue Reading Below5Hallandale Beach, FLTHEPALMER:Getty ImagesSouth of Fort Lauderdale and north of Miami, Hallandale Beach is home to Gulfstream Park Racing and a handful of public beaches. It's a smaller community that offers a classic beach day if you want to escape the crowds. Typical home value: $301,130Learn More6Pinellas Park, FLMatthew Lindahl : 500px:Getty ImagesPart of the St. Petersburg metropolitan area, Pinellas Park has a population of about 53,000 and provides access to a string of beaches along the northwestern coast of Florida. Though small, there is an arts and culture scene in the town that highlights the community's creative DNA. Typical home value: $314,991Learn MoreAdvertisement - Continue Reading Below7West Haven, CTRedtea:Getty ImagesLocated on the Long Island Sound, West Haven is an affordable option not far from New York City. This town has the longest stretch of public beaches in the state, where you can swim, sunbathe, fish, and explore. Typical home value: $326,043Learn More8Galveston, TXWirestock//Getty ImagesWith over 30 miles of beaches, Galveston is the only Texas seaside city on this list. It's located on the balmy Gulf of Mexico, where there are plenty of museums and art galleries you can visit, along with beaches. The area also has a well-known restaurant scene.Typical home value: $333,127Learn MoreAdvertisement - Continue Reading Below9Palm Coast, FLMichael Warren:Getty ImagesParks, museums, beaches—oh, my! Palm Coast is on the Northeast side of Florida and offers plenty of fun. Relax or fish at one of the beaches, then head over to Washington Oaks Gardens State Park for some biking amid the lush gardens before ending your day at the Florida Agricultural Museum. Did we mention that there's also plenty of delicious seafood to be had?Typical home value: $351,404Learn More10Largo, FLalex grichenko:Getty ImagesSouth of Clearwater, Largo offers access to beaches and two larger metropolitan areas, perfect for the homeowner who wants to be near the action but not caught up in it. There are multiple parks to visit in the town, and art lovers will appreciate all the shows and performances. Typical home value: $353,576Learn MoreAdvertisement - Continue Reading Below11Pompano Beach, FLLagunaticPhoto:Getty ImagesPompano Beach is a hidden gem on the Gold Coast, neighboring Boca Raton, Fort Lauderdale, and Hollywood. The city offers miles of beach with temperate waters from the Gulf Stream, as well as plenty of things to do, like snorkeling, shopping, festivals, and golfing. Typical home value: $356,795Learn More12Delray Beach, FLThomas Green:Getty ImagesFor a mix of water activities and a thriving art scene, consider Delray Beach. The arts district is part of what makes this South Florida city so special, and the municipal beach is just the cherry on top. It can definitely get busy on a nice day.Typical home value: $359,963Learn MoreAdvertisement - Continue Reading Below13Clearwater, FLJohn Murphy Photography:Getty ImagesIf Clearwater's three miles of white sand beaches aren't enough to entice you, maybe the plethora of activities and events will. Clearwater is part of the Tampa-St. Petersburg metropolitan area, and it has plenty to offer, from the nightly festival at Pier 60 to the Clearwater Marine Aquarium. Typical home value: $362,300Learn More14Bradenton, FLDawn Damico:Getty ImagesExplore your love of the beach and historical sites in Bradenton along the Manatee River. For a small city, there's plenty to do, including the Bishop Museum of Science and Nature, the riverwalk, the Manatee Village Historical Park, and multiple beaches.Typical home value: $370,091Learn MoreAdvertisement - Continue Reading Below15St. Petersburg, FLJohn Coletti:Getty ImagesOne of the largest cities on this list in terms of population, St. Petersburg is known as the "Sunshine City" and is home to great shops, top-ranked beaches, and a thriving arts district. For those who want both beach and city life, this should be a top contender on your list. You can find multiple museums, like the Dali Museum and a living museum of botanicals and tropical plants at the Sunken Gardens.Typical home value: $372,035Learn More16Ormond Beach, FLArt Wager:Getty ImagesGet that small-town feel in Ormond Beach, which is at the northern end of the Daytona Beach area. It's a quieter refuge, though it's not lacking in culture. There are multiple state parks located in this town, along with museums and cultural centers that are good to visit when you're not taking a dip in the Atlantic. Typical home value: $379,800Learn MoreAdvertisement - Continue Reading Below17Oakland Park, FLShobeir Ansari:Getty ImagesOakland Park is just north of Fort Lauderdale and has excellent access to the metropolitan area's beaches. Think of this town of around 44,000 people as any other small American town, just with closer access to the Atlantic Ocean. Typical home value: $381,610Learn More18Riviera Beach, FLCrystal Bolin Photography:Getty ImagesRiviera Beach is just off the coast of Singer Island, and it's a wonderful location for those who love to bask in the sun and take in all types of water activities. There are multiple parks to explore and plenty of opportunities to see and learn about the marine life that lives in Florida.Typical home value: $397,829Learn MoreAdvertisement - Continue Reading Below19West Palm Beach, FLMasao Taira:Getty ImagesThis bustling city might not be the most affordable destination on this list, but it offers a lot for its elevated prices. From exciting nightlife and exceptional culinary options to an exciting art scene, West Palm Beach is a vibrant destination with plenty of beach access. Typical home value: $403,731Learn More20Navarre, FLArt Wager:Getty ImagesThis small city in Western Florida, on the Gulf Coast, just an hour and a half from Mobile, Alabama, boasts white sand beaches, clear blue water, and proximity to Santa Rosa Island. It's a tranquil destination with opportunities to learn about marine life at the multiple refuges and conservation centers.Typical home value: $415,063Learn More

We may earn a commission from links on this page.Garmin has launched a new feature in its Connect app (the companion app you use when you sync your watch to your phone). It’s called Garmin Trails, and it purports to help you find hiking trails near you. With a Connect+ subscription, you can even sync courses for those trails to your map-enabled watch. It sounds pretty great. Unfortunately, its selection of trails is pretty disappointing at launch—it doesn't even suggest I try out my own favorite trails.How to access Garmin TrailsYou don’t need a Connect+ subscription to search for trails, filter for features, and look at the routes and reviews of trail—all of that comes free with the regular Connect app, whether on your phone or through the Connect Web interface. If you have a Garmin device, you already have a login for these services. To find it Garmin Trails from within Connect, tap the More menu in the app, find and tap Training and Planning, and then scroll to Garmin Trails. You'll be given a map of suggested trails. Zoom the map and tap on the little hiker icons to see each individual trail, or search by name in the search bar above. Unfortunately, the search results aren’t sorted in any logical way, such as by distance. If I search for the name of a trail near me in Pennsylvania, I see results from Maine and Maryland before the Pennsylvania ones.There’s also a filter icon, which you can use to limit your results by distance from your current location, the amount of ascent in the trail, as well as rating, difficulty, trail type, and features. The features you can filter for include: Dog friendlyNo dogsWaterfallsLakesRiversOceansMountaineeringSteep sectionsForestFlowersKid friendlyPermit requiredWater sourcesReaches peaksRough roadOnly the U.S. and a few European countries are supported right nowAt launch, trails are only available in the U.S. and in four European countries: Austria, Germany, Lichtenstein, and Switzerland. “New trails in different regions will be added periodically,” says a Garmin support page.Garmin’s original announcement didn’t mention this restriction, so much of the chatter I'm seeing about this online involves disappointment from users outside this handful of countries. This writer in the U.K., for example, calls the feature “NoTrails” (referencing competitor AllTrails, get it?) In the U.S., I’m definitely seeing some trails, but not enough to make the feature particularly useful. I’m just not seeing a lot of trailsI like to run and hike on trails, and I live near a county park that is absolutely riddled with hikeable, runnable, and mountain bike-able trails. There’s a local trail running group that meets six times a week, seemingly always at a different spot (I’m sure they repeat their favorites, but there is plenty of variety on offer). So what does Garmin Trails have in its database for my area? Here’s Garmin on the left, and the county trails website on the right:  Garmin, left; Allegheny County website, right. Credit: Beth Skwarecki/Garmin/Allegheny County Kudos to Garmin for knowing about the Rachel Carson trail, that long one that appears as a black line. It’s 46 miles long and quite well-known in the area. Garmin has it listed as a series of short segments. They are correctly labeled as “hard” difficulty. But the entire North Park area has two trails? I’m not even sure how a person would find out about the Orange and Green trails without learning about all the others in the park. Where is the Red trail, with its red-and-blue branch with all the switchbacks? Where is the flat half-mile trail by the nature center that I always took my kids on when they were little? Where is the White trail that circles the baseball fields? As for those Orange and Green trails, both are labeled with their correct distance, but they’re also both marked as “easy,” which they are definitely not. I did a trail run last week that included the whole Orange trail and part of the Green. It was about five miles and had 800 feet of elevation gain. That’s not an easy trail at all. I asked a Garmin rep where the trail data comes from, and was told Garmin used, “a variety of sources and our in-house cartography team.” As far as I can tell, they aren’t copied from AllTrails or any other source, or at least not directly. (Might be better if they were; AllTrails has the Green trail more reasonably labeled as moderate rather than easy.)Browsing through other areas I’m familiar with, it seems that the most iconic trails in any spot are in the database, so this tool isn’t useless, just incomplete (hopefully temporarily). A few of the gorge trails I remember most fondly from Ithaca, NY are in there. Yellowstone National Park has plenty of labeled trails. I asked fellow Lifehacker writer Daniel Oropeza to look for trails in areas he was familiar with, and he compared the Three Sisters Waterfall area in Garmin and AllTrails. Garmin has one trail to the waterfall, while there are actually four; the wider area has 19 trails in the Garmin database, but 143 in AllTrails.  Garmin, left; AllTrails, right. Credit: Beth Skwarecki/Garmin, Daniel Oropeza/AllTrails It’s free to browse trails, but you must pay to send them to your watchGarmin Trails seems to be a pot-sweetener for the new Connect+ subscription tier. The Trails feature itself is free, but only subscribers to Connect+ (or Maps+, another Garmin offering) can send trail courses from this database to their devices. Compatible devices include anything that supports courses, like most recent Fenix, Forerunner, and Instinct watches (and Edge cycling computers). A watch that supports maps, like the new Forerunner 970, would be a natural pairing with this new feature.Trails also seems to be a slightly different version of the Courses feature that Garmin has had for a while. (You can find Courses at this link, if you have a Garmin login). Courses can be private or user-created; there’s currently no way for users to create a trail. That means there can be multiple versions of the same trail, and courses that don’t correspond to a specific trail—somebody who ran part of the Orange and part of the Green, let’s say, because that happened to be convenient for them on the day they created that course. If more trails get added to Garmin Trails, it could eventually be a useful feature. The trail ratings, comments, and filters are handy to have. But AllTrails already has those features, and you can load AllTrails routes to your Garmin watch if you subscribe to AllTrails Plus, which is $35.99/year—cheaper than Garmin Connect+, which will cost you $6.99/month or $69.99/year.

Principal Software Engineer, ServicesRiot GamesShanghai, China2 hours agoApplyRiot Games was established in 2006 by entrepreneurial gamers who believe that player-focused game development can result in great games. In 2009, Riot released its debut title League of Legends to critical and player acclaim. As the most played video game in the world, over 100 million play every month. Players form the foundation of our community and it’s for them that we continue to evolve and improve the League of Legends experience.We’re looking for humble but ambitious, razor-sharp professionals who can teach us a thing or two. We promise to return the favor. Like us, you take play seriously; you’re passionate about games. We embrace those who see things differently, aren’t afraid to experiment, and who have a healthy disregard for constraints.That's where you come in.Riot的工程师不仅在特定技术领域拥有深厚的知识，同时也珍视在多元领域工作的机会。作为一名资深软件工程师，你将深入参与以跨团队目标为重点的项目，推动全局一致和标准化。你将主导多人游戏玩法功能开发、执行引擎修改，并为其他工程师提供卓越工程解决方案的清晰案例。你汇报对象是游戏制作人，所在团队目前处于项目早期阶段，正快速迭代并验证核心玩法。这个阶段决定了你会有足够的空间去定义技术路线、设定标准并构建技术中台能力。你的工作：构建高并发全球服务框架：设计并实现支持上万并发、全球在线的游戏后端服务框架，为多人实时玩法提供底层支撑。搭建实时玩法基础设施：打造并优化玩法逻辑同步、房间服务、匹配、会话管理等核心基础设施，保障玩家实时互动体验。提升架构的可扩展性与安全性：推进服务器架构的可扩展性、安全性与稳定性，提升系统整体韧性。建设卓越工程文化：指导团队成员，营造高效协同的工程文化，助力团队共同成长。构建现代云原生服务栈：应用云原生工具（如 AWS、K8s、Docker），构建现代化的后端服务技术栈。推动创意落地体验：与策划、美术等团队密切配合，拆解创意点子，将其真正转化为玩家可体验的内容。我们希望你具备：后端开发经验：10 年以上大型在线游戏或实时服务产品开发经验。项目主程经历：5年以上主程经验，至少一次完整从早期研发到上线的项目主程经验，能独立承担技术框架设计、需求讨论、系统设计与上线运维。分布式系统能力：具备扎实的分布式系统设计功底，能够设计高可用、高扩展性的后台架构。编程技能：精通至少一种主流后端编程语言（C++ / Java / Go）。云技术与自动化：熟悉 AWS/GCP 等云计算平台，了解 CI/CD 流水线及自动化运维工具。沟通与协作：能够以清晰、易懂的方式解释复杂技术方案，有效推动跨团队协作。额外加分项：具备10年以上手游服务端开发经验，特别是高并发、高并行玩法方向。有“万人同服”移动端服务端架构主导经验。熟悉 LOL 世界观或参与过 Riot IP 项目。熟悉客户端/服务端双端联调与性能调优。熟练应用 Docker、K8s、Jenkins、Prometheus 等现代运维工具。对玩法设计有热情，善于与设计同学探讨功能如何实现 live ops 运营。英文听说读写能力优秀，能够参与全球化或跨区沟通。 Create Your Profile — Game companies can contact you with their relevant job openings. Apply

At DICE and GDC this year I heard talk of a trend in game development that sent a chill down my spine: "deprofessionalization." As A16z marketing partner Ryan K. Rigney defines it, deprofessionalization is a phenomenon driven by the overperformance of older titles (particularly free-to-play live service games), large studios struggling to drive sales, and the outsized success of some solo developers and small teams.These three forces, he argues, will combine to "drive career professionals from the traditional, professionalized side of the games industry.""Some of these people will decide to go indie," he continues. "Others will leave gaming altogether. And in between there’s a vast spectrum of irregular working arrangements available."Is this trend real? It sure felt so at PAX East 2025. It's no secret that the COVID-19 pandemic led to many game companies decamping from expo floors, retreating to either all-online promotion or in-person community meetups structured around intermittent panels. Gone are the days where a chunk of the development team can get one-on-one facetime with players—shifts in supply and demand have simply moved where marketing takes place.But something else lurked under the surface. Some notable studios like Behaviour Interactive and Funcom had classic booths up on the show floor. Devolver Digital had maybe the tallest booth on display—but it was only using it to showcase three games: Mycopunk, Monster Train 2, and Botsu. The bulk of the remaining space was taken up by small publishers and game studios.Related:Wandering through these booths, I found a mix of truly excellent and inspiring games. But also found myself bubbling with frustration. Few of the developers on display were working on teams larger than three people. They talked about publishers wanting ever-more-expensive offerings as part of their pitch deck. Short-term contractors seemed to be the best way to plug gaps. Why did it feel like so few proper businesses were fighting to get their games in front of players at PAX?Speaking with Rigney and other developers, I sensed that "deprofessionalization" isn't just a catchy phrase to describe demand-side economics in game industry hiring. It's a frustrating reality that may undervalue games from big and small teams alike.Deprofessionalization is built on the back of devaluing laborRigney offered some extra nuance on his "deprofessionalization" theory in an email exchange we had before PAX. He predicted that marketing roles at studios would be "the first" on the chopping block, followed by "roles that seem replaceable to management (even if they're not)."Related:"The winners will be the creative renegades. I'm talking about the people making work that would have never gotten greenlit at one of these bigger publishers in the first place. Some of these creatives will start their own studio, or dabble in side projects...This is the only creative industry on the planet where one person can make $100 million making something by themselves."That held up in my survey of the games boothing at PAX. The developers of Mycopunk and Cat Secretary had some of the larger teams on the floor of about 5-6 people. Indie publisher Playism was showing off a number of excellent-looking games like Mind Diver and Break Arts III. Executive producer Shunji Mizutani told me the average team size the company is looking to back is around 1-3 developers (though he said it's not a hard and fast rule).My favorite game I saw, We Harvest Shadows is being developed by The First Tree solo developer David Wehle. Wehle explained that he's hiring a contract coder to help with the dense system design fueling the "farming" part of his "horror farming simulator." The story was the same everywhere I went. Solo devs, two-person teams, and publishers fishing for low-budget indie hits were the talk of the show.Related:I want to be clear here—no one I spoke with at PAX East should feel "obligated" to give anyone a job. They're small teams making the most of limited resources, and it's the acceleration in game development technology that's made it possible. What feels wrong is how few people seem to benefit from this status quo.Image via ReedPop.To go back to Rigney for a moment, his key example of a post-deprofessionalization game developer is veteran developer Aaron Rutledge, a former lead designer on League of Legends, Call of Duty: Black Ops 4, and Apex Legends. After leaving Respawn Entertainment in 2024 he founded a consultancy firm Area Denial, acting as a "gun for hire" for studios.Rutledge deserves his success, and the life of a traveling creative called on by other studios sounds romantic. But as a foundation for game development, it's a framework that celebrates the few over the many. It narrows which roles are considered "essential" for making great games (often designers or programmers) and treats other positions as somehow less essential. You could see someone like Wehle hiring someone like Rutledge to bring some of that triple-A experience to a small game.But that feels like the polar ends of who can benefit in the deprofessionalized world—developers with the stability to swing big for big-shot ideas, and programmers or designers with deep career experience that can be called in like a group of noble mercenaries. People in between will be left out.Who gets left behind in a world mainly filled with small teams?My PAX trip validated my fear that three professions are especially vulnerable in this deprofessionalized world: artists, writers, and those working in game audio or music. These roles seemed vulnerable because on these small teams, they were the roles developers mentioned doing in some kind of shared or joint fashion.All three risk compartmentalization as "asset creators," their work treated as products you can purchase off the store shelf.Every artist in games knows how hard it is to make a living doing what you love. In-house artist positions have faded away as companies look overseas to produce as many assets as humanly possible at the lowest living wage. Enthusiasm for AI-generated assets (that look like dogshit) are nudging this trend along. In the "gun for hire" mindset, working artists aren't worth anything to game development because they're producing goods to be used, not participants in the process. Art directors are in a slightly more stable position, but only by virtue of knowing "what looks good" and telling someone else what they want to do.As someone who recently shipped his second game as a writer, the cuts to game narrative teams hit close to home. The GDC 2025 State of the Industry survey reported that of the 11 percent of developers laid off in the last year, 19 percent of them worked in game narrative, the highest of any responding demographic. Two diverging trends are hurting this field: the growth of successful games that don't feature much narrative (either focusing on deep game mechanics or story-lite multiplayer) and the spread of story-driven games authored by the creative director and maybe one or two collaborators create conditions that lower the number of available jobs.Image via ReedPop.Game writers have long described frustration with how they're treated by the industry, often brought in later in the process and sometimes treated as if they lie in opposition to the rest of the development team. Some studios leaned on the job title of "narrative designer" for professionals who write and implement narrative events, but that still speaks to a mistrust of the profession, that producing words isn't enough to bring value to a team.Finally, game audio and music professionals both produce work that can be bundled into licensable libraries, with implementation left to designers on a team. Sometimes this work is essential, the number of sounds a game needs can't be produced by an individual human. And composers don't always want to be tied to one studio—working with multiple teams frees them to explore creative projects and keep working when they aren't necessarily needed in a day-to-day game development environment.But again, treating them this way puts them on the rim of the game development wheel, implying their labor could be deprioritized by true talent that deserves to reap the benefits of game design.A decentralized creative community needs to benefit creativesRigney explained to me that the game industry has one ace up its sleeve that other creative fields don't: its "indie" market is a commercially viable market. "People are paying for these games!," he exclaimed. "This is not happening for indie filmmakers. This isn't happening for books. What's happening for indie games and small studios won't replace the jobs lost at the major publishers, but it will create opportunity for the most creative and most determined people."But don't rush off to start your indie dreams—it's still as true as it was for years that most indie games do not succeed. And those that don't succeed can still be financial fodder for the shovel merchants of the worlds—your technology companies, your payment processors, your game platforms, your investors, etc. Plenty of companies are standing ready to profit on the devs gunning to be the next Schedule I.Is there a way deprofessionalization can benefit the developers left behind? Rigney raised one fair point: part of the reason some indies are running circles around large companies is that those companies can mismanage creatives so badly they go for years without shipping a game. If someone smart could crack that problem—improve management at large organizations and make sure games make it out the door—that could be a way to balance the trend."Right now none of the solutions are well equipped to solve all the problems. I work in venture capital, which isn't great for funding individual games, but can work well when funding teams that are pursuing large scale growth via some new distribution or technological edge."Indeed, PAX East showed that we need creative solutions. One shouldn't need to be a social media wunderkind, years of hard-to-earn triple-A experience, or be a jack-of-all-trades to have a career in game development. That path does bring us some wildly inventive games—but leaves us with a community of developers hustling on gig work to keep their dream alive.Update 5/16: This piece has been updated to clarify Rigney's job title at A16z.

The saying goes that 80% of data collected, stored and maintained by governments can be associated with geographical locations. Although never empirically proven, it illustrates the importance of location within data. Ever growing data volumes put constraints on systems that handle geospatial data. Common Big Data compute engines, originally designed to scale for textual data, need adaptation to work efficiently with geospatial data — think of geographical indexes, partitioning, and operators. Here, I present and illustrate how to utilize the Microsoft Fabric Spark compute engine, with the natively integrated ESRI GeoAnalytics engine# for geospatial big data processing and analytics. The optional GeoAnalytics capabilities within Fabric enable the processing and analytics of vector-type geospatial data, where vector-type geospatial data refers to points, lines, polygons. These capabilities include more than 150 spatial functions to create geometries, test, and select spatial relationships. As it extends Spark, the GeoAnalytics functions can be called when using Python, SQL, or Scala. These spatial operations apply automatically spatial indexing, making the Spark compute engine also efficient for this data. It can handle 10 extra common spatial data formats to load and save data spatial data, on top of the Spark natively supported data source formats. This blog post focuses on the scalable geospatial compute engines as has been introduced in my post about geospatial in the age of AI. Demonstration explained Here, I demonstrate some of these spatial capabilities by showing the data manipulation and analytics steps on a large dataset. By using several tiles covering point cloud data (a bunch of x, y, z values), an enormous dataset starts to form, while it still covers a relatively small area. The open Dutch AHN dataset, which is a national digital elevation and surface model, is currently in its fifth update cycle, and spans a period of nearly 30 years. Here, the data from the second, third, and forth acquisition is used, as these hold full national coverage (the fifth just not yet), while the first version did not include a point cloud release (only the derivative gridded version). Another Dutch open dataset, namely building data, the BAG, is used to illustrate spatial selection. The building dataset contains the footprint of the buildings as polygons. Currently, this dataset holds more than 11 million buildings. To test the spatial functions, I use only 4 AHN tiles per AHN version. Thus in this case, 12 tiles, each of 5 x 6.25 km. Totalling to more than 3.5 billion points within an area of 125 square kilometers. The chosen area covers the municipality of Loppersum, an area prone to land subsidence due to gas extraction. The steps to take include the selection of buildings within the area of Loppersum, selecting the x,y,z-points from the roofs of the buildings. Then, we bring the 3 datasets into one dataframe and do an extra analysis with it. A spatial regression to predict the expected height of a building based on its height history as well as the history of the buildings in its direct surroundings. Not necessarily the best analysis to perform on this data to come to actual predictions* but it suits merely the purpose of demonstrating the spatial processing capabilities of Fabric’s ESRI GeoAnalytics. All the below code snippets are also available as notebooks on github. Step 1: Read data Spatial data can come in many different data formats; we conform to the geoparquet data format for further processing. The BAG building data, both the footprints as well as the accompanied municipality boundaries, come in geoparquet format already. The point cloud AHN data, version 2, 3 and 4, however, comes as LAZ file formats — a compressed industry standard format for point clouds. I have not found a Spark library to read LAZ (please leave a message in case there is one), and created a txt file, separately, with the LAStools+ first. # ESRI - FABRIC reference: https://developers.arcgis.com/geoanalytics-fabric/ # Import the required modules import geoanalytics_fabric from geoanalytics_fabric.sql import functions as ST from geoanalytics_fabric import extensions # Read ahn file from OneLake # AHN lidar data source: https://viewer.ahn.nl/ ahn_csv_path = "Files/AHN lidar/AHN4_csv" lidar_df = spark.read.options(delimiter=" ").csv(ahn_csv_path) lidar_df = lidar_df.selectExpr("_c0 as X", "_c1 as Y", "_c2 Z") lidar_df.printSchema() lidar_df.show(5) lidar_df.count() The above code snippet& provides the below results: Now, with the spatial functions make_point and srid the x,y,z columns are transformed to a point geometry and set it to the specific Dutch coordinate system (SRID = 28992), see the below code snippet&: # Create point geometry from x,y,z columns and set the spatial refrence system lidar_df = lidar_df.select(ST.make_point(x="X", y="Y", z="Z").alias("rd_point")) lidar_df = lidar_df.withColumn("srid", ST.srid("rd_point")) lidar_df = lidar_df.select(ST.srid("rd_point", 28992).alias("rd_point"))\ .withColumn("srid", ST.srid("rd_point")) lidar_df.printSchema() lidar_df.show(5) Building and municipality data can be read with the extended spark.read function for geoparquet, see the code snippet&: # Read building polygon data path_building = "Files/BAG NL/BAG_pand_202504.parquet" df_buildings = spark.read.format("geoparquet").load(path_building) # Read woonplaats data (=municipality) path_woonplaats = "Files/BAG NL/BAG_woonplaats_202504.parquet" df_woonplaats = spark.read.format("geoparquet").load(path_woonplaats) # Filter the DataFrame where the "woonplaats" column contains the string "Loppersum" df_loppersum = df_woonplaats.filter(col("woonplaats").contains("Loppersum")) Step 2: Make selections In the accompanying notebooks, I read and write to geoparquet. To make sure the right data is read correctly as dataframes, see the following code snippet: # Read building polygon data path_building = "Files/BAG NL/BAG_pand_202504.parquet" df_buildings = spark.read.format("geoparquet").load(path_building) # Read woonplaats data (=municipality) path_woonplaats = "Files/BAG NL/BAG_woonplaats_202504.parquet" df_woonplaats = spark.read.format("geoparquet").load(path_woonplaats) # Filter the DataFrame where the "woonplaats" column contains the string "Loppersum" df_loppersum = df_woonplaats.filter(col("woonplaats").contains("Loppersum")) With all data in dataframes it becomes a simple step to do spatial selections. The following code snippet& shows how to select the buildings within the boundaries of the Loppersum municipality, and separately makes a selection of buildings that existed throughout the period (point cloud AHN-2 data was acquired in 2009 in this region). This resulted in 1196 buildings, out of the 2492 buildings currently. # Clip the BAG buildings to the gemeente Loppersum boundary df_buildings_roi = Clip().run(input_dataframe=df_buildings, clip_dataframe=df_loppersum) # select only buildings older then AHN data (AHN2 (Groningen) = 2009) # and with a status in use (Pand in gebruik) df_buildings_roi_select = df_buildings_roi.where((df_buildings_roi.bouwjaar<2009) & (df_buildings_roi.status=='Pand in gebruik')) The three AHN versions used (2,3 and 4), further named as T1, T2 and T3 respectively, are then clipped based on the selected building data. The AggregatePoints function can be utilized to calculate, in this case from the height (z-values) some statistics, like the mean per roof, the standard deviation and the number of z-values it is based upon; see the code snippet: # Select and aggregrate lidar points from buildings within ROI df_ahn2_result = AggregatePoints() \ .setPolygons(df_buildings_roi_select) \ .addSummaryField(summary_field="T1_z", statistic="Mean", alias="T1_z_mean") \ .addSummaryField(summary_field="T1_z", statistic="stddev", alias="T1_z_stddev") \ .run(df_ahn2) df_ahn3_result = AggregatePoints() \ .setPolygons(df_buildings_roi_select) \ .addSummaryField(summary_field="T2_z", statistic="Mean", alias="T2_z_mean") \ .addSummaryField(summary_field="T2_z", statistic="stddev", alias="T2_z_stddev") \ .run(df_ahn3) df_ahn4_result = AggregatePoints() \ .setPolygons(df_buildings_roi_select) \ .addSummaryField(summary_field="T3_z", statistic="Mean", alias="T3_z_mean") \ .addSummaryField(summary_field="T3_z", statistic="stddev", alias="T3_z_stddev") \ .run(df_ahn4) Step 3: Aggregate and Regress As the GeoAnalytics function Geographically Weighted Regression (GWR) can only work on point data, from the building polygons their centroid is extracted with the centroid function. The 3 dataframes are joined to one, see also the notebook, and it is ready to perform the GWR function. In this instance, it predicts the height for T3 (AHN4) based on local regression functions. # Import the required modules from geoanalytics_fabric.tools import GWR # Run the GWR tool to predict AHN4 (T3) height values for buildings at Loppersum resultGWR = GWR() \ .setExplanatoryVariables("T1_z_mean", "T2_z_mean") \ .setDependentVariable(dependent_variable="T3_z_mean") \ .setLocalWeightingScheme(local_weighting_scheme="Bisquare") \ .setNumNeighbors(number_of_neighbors=10) \ .runIncludeDiagnostics(dataframe=df_buildingsT123_points) The model diagnostics can be consulted for the predicted z value, in this case, the following results were generated. Note, again, that these results cannot be used for real world applications as the data and methodology might not best fit the purpose of subsidence modelling — it merely shows here Fabric GeoAnalytics functionality. R20.994AdjR20.981AICc1509Sigma20.046EDoF378 Step 4: Visualize results With the spatial function plot, results can be visualized as maps within the notebook — to be used only with the Python API in Spark. First, a visualization of all buildings within the municipality of Loppersum. # visualize Loppersum buildings df_buildings.st.plot(basemap="light", geometry="geometry", edgecolor="black", alpha=0.5) Here is a visualization of the height difference between T3 (AHN4) and T3 predicted (T3 predicted minus T3). # Vizualize difference of predicted height and actual measured height Loppersum area and buildings axes = df_loppersum.st.plot(basemap="light", edgecolor="black", figsize=(7, 7), alpha=0) axes.set(xlim=(244800, 246500), ylim=(594000, 595500)) df_buildings.st.plot(ax=axes, basemap="light", alpha=0.5, edgecolor="black") #, color='xkcd:sea blue' df_with_difference.st.plot(ax=axes, basemap="light", cmap_values="subsidence_mm_per_yr", cmap="coolwarm_r", vmin=-10, vmax=10, geometry="geometry") Summary This blog post discusses the significance of geographical data. It highlights the challenges posed by increasing data volumes on Geospatial data systems and suggests that traditional big data engines must adapt to handle geospatial data efficiently. Here, an example is presented on how to use the Microsoft Fabric Spark compute engine and its integration with the ESRI GeoAnalytics engine for effective geospatial big data processing and analytics. Opinions here are mine. Footnotes # in preview * for modelling the land subsidence with much higher accuracy and temporal frequency other approaches and data can be utilized, such as with satellite InSAR methodology (see also Bodemdalingskaart) + Lastools is used here separately, it would be fun to test the usage of Fabric User data functions (preview), or to utilize an Azure Function for this purpose. & code snippets here are set up for readability, not necessarily for efficiency. Multiple data processing steps could be chained. References GitHub repo with notebooks: delange/Fabric_GeoAnalytics Microsoft Fabric: Microsoft Fabric documentation – Microsoft Fabric | Microsoft Learn ESRI GeoAnalytics for Fabric: Overview | ArcGIS GeoAnalytics for Microsoft Fabric | ArcGIS Developers AHN: Home | AHN BAG: Over BAG – Basisregistratie Adressen en Gebouwen – Kadaster.nl zakelijk Lastools: LAStools: converting, filtering, viewing, processing, and compressing LIDAR data in LAS and LAZ format Surface and Object Motion Map: Bodemdalingskaart – The post The Geospatial Capabilities of Microsoft Fabric and ESRI GeoAnalytics, Demonstrated appeared first on Towards Data Science.

Suit up! The first trailer for Ironheart is here. Set after Black Panther: Wakanda Forever, the new Marvel series brings high-tech genius to the streets of Chicago—where science meets sorcery! The VFX are made by:Base FXCantina CreativeFrame By FrameILM (VFX Supervisor: Vincent Papaix)Luma PicturesRISE (VFX Supervisor: Sebastian Lauer)SDFX StudiosTippett Studio (VFX Supervisor: Chris Morley) The Production VFX Supervisor is Greg Steele.The Additional VFX Supervisor is Fernando Zorrilla.The Production VFX Producer is David Masure-Bosco. Directors: Samantha Bailey, Angela BarnesRelease Date: June 24, 2025 (Disney+) © Vincent Frei – The Art of VFX – 2025 The post Ironheart appeared first on The Art of VFX.

Krysten Ritter is officially returning as Jessica Jones for Daredevil: Born Again Season 2.As reported by Variety, news of Ritter’s return as Marvel’s crime-fighting private investigator was revealed during the Disney Upfront presentation in New York today. The confirmation follows months of rumors that the Disney+ show could reunite more than a few of the Netflix Defenders as fans hope to see them make their Marvel Cinematic Universe (MCU) debut sooner rather than later.Ritter took the stage alongside Daredevil and The Defenders co-star Charlie Cox to express her excitement about the return of Jessica Jones.“It’s so great to be back, returning to Jessica after three seasons and The Defenders and now joining the MCU,” Ritter said. “I’m so excited to bring back this iconic character, and without giving too much away, there is much more in store for Jessica Jones. This is going to be an incredible season!”The 25 Best MCU HeroesRitter first portrayed the New York-based Marvel hero when Jessica Jones Season 1 premiered on Netflix in 2015. Two more seasons eventually followed, as did The Defenders team-up series, but as Netflix’s time with original Marvel programming began to wind down, so too did hopes that Ritter would ever play the character again.That was until 2021, when Disney regained ownership of the rights to certain characters, including Jessica Jones. As Cox was eventually brought back for a brief Spider-Man: No Way Home cameo as well as his own Disney+ show with Daredevil: Born Again, fans began to speculate about how the revival project could serve as a vehicle to reintroduce other Netflix heroes. With Punisher serving a pivotal role in Season 1, it makes sense to see Ritter returning for Season 2.Ritter last played the character in Jessica Jones Season 3, which premiered in 2019. Her tease at the upfront presentation suggests the MCU may see more Jessica Jones in the future, but for now, we can at least expect to see her in Daredevil: Born Again Season 2. Disney has yet to announce a release date, but showrunner Dario Scardapane has told fans to expect more episodes in March 2026.For more, you can read our 8/10 review of the Daredevil revival show’s first season as well as our reviews for Jessica Jones seasons 1, 2, and 3. You can also check out every street-level hero we could potentially see in Daredevil: Born Again Season 2 here.Michael Cripe is a freelance contributor with IGN. He's best known for his work at sites like The Pitch, The Escapist, and OnlySP. Be sure to give him a follow on Bluesky (@mikecripe.bsky.social) and Twitter (@MikeCripe).