Fused

???Running on the fly Machine Learning Inference with Fused Jeff Faudi ???????? shows us how he uses Fused to streamline computer vision inference on satellite imagery using Fused ???Read: https://lnkd.in/gv7tt3Bm?Watch: https://lnkd.in/g85tauPh

Compound Coffee Shop Density in the North East - FSQ Places More H3 experiments with Four Sqare data: coffee shops again today. Instead just a count, I created a disk of influence around each shop and aggregated overlaps. The result? Areas with high concentrations are amplified, showing the compounding value of availability. This method goes beyond density—highlighting accessibility and impact. What do you think? Fused

Driving Distances from In-N-Out Burger Locations in the Los Angeles Area - FSQ Places The store locations are from the Fused Four Sqaure Places UDF and I utilizied the Get Isochrone UDF to generate the map. The map is saturated but I can make out the city’s highways clearly. Never that far from a Double-Double in LA! ??

Driving Distances from In-N-Out Burger Locations in the Los Angeles Area - FSQ Places The store locations are from the Fused Four Sqaure Places UDF and I utilizied the Get Isochrone UDF to generate the map. The map is saturated but I can make out the city’s highways clearly. Never that far from a Double-Double in LA! ??

?? Modelling wireless networks at high resolution with Ibis, DuckDB & Fused! ??Sameer Lalwani of DigitalTwinSim shares how he can quickly generate network coverage models for his clients. Using Fused UDFs combined with H3 grids allows DigitalTwinSim to evaluate optimal locations for network towers. ?? Read: https://lnkd.in/ehy5ketS

The best way to predict the future is to invent it… the potential for this technology is mind boggling ????

Agricultural field polygon data (https://lnkd.in/g9V322wx) from the Japanese government is now available. I have compiled all available field polygon data and made them accessible through #SourceCooperative (https://lnkd.in/gpCdB_Hd). We ingested one GeoParquet file and visualized it using Fused. The 2024 dataset size is 31GB??

Starbucks vs. Competing Coffee Shops - FSQ Places The Starbucks franchises get their color and height by nearby coffee shops. I've been trying to visualize this, I posted a version with both the Starbucks and the competitors which may have been hard to read. This way you can get the scale of the neighborhoods. I used the Fused Four Square Places UDF to create two H3 indices, one for Starbucks and one for any other coffee shop. I generated a disk of cells around each Starbucks and counted the sum of the competition. The tall, dark orange cells are probably competitive for a reason - high foot traffic. Maps like these can help companies evalulate how they stand up in any market. #duckdb #h3

??? Maps Make Money ?$ Big news! Foursquare just released data on 100 million locations worldwide. But what does this mean for your business? Let me break it down: ?? What are Points of Interest (POIs)? Think of POIs as every business location you see on Google Maps - restaurants, shops, gyms, schools, etc. It's like having a bird's-eye view of where everything is! ?? Why Should You Care? Imagine opening a coffee shop. Wouldn't you want to know: - Where are the office buildings with potential customers? - Are there complementary businesses nearby (like bookstores or co-working spaces)? - Which areas have the right mix of foot traffic? ?? How Can You Use This Data? I've built a tool that lets you: 1. Pick what matters for your business (e.g., offices, retail, entertainment) 2. Rate how important each factor is 3. See a color-coded map showing your perfect locations! ??- Green = Ideal spots ??- Yellow = Good potential ??- Red = Might want to look elsewhere Try it out here! https://lnkd.in/g3yhHz6y ?? Who Is This For? - Business owners looking for their next location - Real estate investors spotting the next hot area - Entrepreneurs planning their first venture - Property developers seeking growth opportunities ?? Starting Today: #POItoprofit Series (feel free to contribute and make this a thing) I'll be sharing insights on: - How to leverage open source data to find the best location for your business - Ways to spot up-and-coming neighborhoods - Turn Maps Insights into ?? Data made accessible by one and only Fused . I nominate the best map artists here on linkedin to build maps that help businesses! Sina Kashuk Isaac Brodsky Maxime Lenormand Stephen Kent Kristin Scholten Kavyajeet Bora Are you a business owner sitting on this goldmine of data, wondering how to use it? Let's connect! Drop a comment if you'd like to learn more about turning POI data into profit! #foursquare #poi #maps #gis #POItoprofit

Query Foursquare's 100M OS places in a one-liner with DuckDB without downloading! If you'd like to see for yourself what kind of entries you can find in Foursquare's dataset, check out this Hugging Face dataset. Link: https://lnkd.in/dk84BKzz Based on Fused's preprocessed geoparquet files (thanks a lot!) I created a single 10Gb file that you can query with DuckDB from your system without downloading it thanks to the magic of httpfs (http range requests). It's as simple as installing DuckDB and running this one-liner: ????????????.??????(??"?????????????*??????????'????://????????????????/????-????/????????????????????_????????????_????????/????????????????????_????????????.??????????????'????????????????????????????????'%????????????%'?") On my system it takes 70 seconds for a query. If you download the file instead and query it locally, it's much faster. On my system (M3 Max), it takes only 5 seconds to query the file! ????????????.??????("?????????????*??????????'????????????????????_????????????.??????????????'????????????????????????????????'%????????????%'?") fyi: As many people have pointed out, there's indeed a whole lot of geospatial defamation in there which - in my opinion - should absolutely have been filtered out.