So why would anyone want to put alot of data into a browser? Well, for a lot of the same reasons that edge computing and distributed computing have become so popular. You get the data a lot closer to the user and you don’t have to pay for the compute ;)  … this sounds great but as I found out during this conversation it's not as easy as it might seem!  There are a lot of trade-offs that need to be evaluated when moving data and analytics to the client.   Nick Rabinowitz  Senior Staff Software Engineer at Foursquare has a ton of experience with this so he volunteered his time to help us understand more about it. https://location.foursquare.com/ https://studio.foursquare.com/home If you are not familiar with the Arrow data format it might be worth checking out   Apache Arrow defines a language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware like CPUs and GPUs. The Arrow memory format also supports zero-copy reads for lightning-fast data access without serialization overhead   Related podcast episodes that you might find interesting include H3 grid system https://mapscaping.com/podcast/h3-geospatial-indexing-system/ The H3 geospatial indexing system is a discrete global grid system consisting of a multi-precision hexagonal tiling of the sphere with hierarchical indexes. H3 is a really interesting approach to tiling data that was developed by UBER and has been open-sourced.  Hex Tiles https://mapscaping.com/podcast/hex-tiles/ If you have not heard of the H3 grid system before listen to that episode first before listening to this one it will add a lot of useful context! Spatial Knowledge Graphs https://mapscaping.com/podcast/spatial-knowledge-graphs/ Foursquare is moving away from spatial joins and focusing on building a knowledge graph. If you are not familiar with graphs this might be a good place to start, also its interesting to hear the reasons for the move from spatial joins to another data structure.   Distribution Geospatial Data https://mapscaping.com/podcast/distributing-geospatial-data/ This is interesting if you want to understand more about distributed databases and some of the strategies for doing this. It sounds complicated but this episode is a really good introduction!    Cloud Native Geospatial https://mapscaping.com/podcast/cloud-native-geospatial/ This episode give a solid overview of what cloud-native means and some of the current geospatial cloud native formats out there today   I am constantly thinking about how I can make this podcast better for you so if you have any ideas or suggestions please let me know!  Also, I am thinking of recording a behind-the-scenes episode, is that something you might be interested in? if so what questions do you have?   

Sounds like a great idea right?   In this episode, Paul Ramsey explains why you shouldn't ... unless you want to ... and how you can ... if you have to.   You can find Paul's blog here: http://blog.cleverelephant.ca/about   Previous episodes with Paul  Spatial SQL https://mapscaping.com/podcast/spatial-sql-gis-without-the-gis/   GDAL https://mapscaping.com/podcast/gdal-geospatial-data-abstraction-library/   Dynamic Vector Tiles https://mapscaping.com/podcast/dynamic-vector-tiles-straight-from-the-database/   Blog posts by Paul about Rasters in the Database https://www.crunchydata.com/blog/postgres-raster-query-basics https://www.crunchydata.com/blog/waiting-for-postgis-3.2-secure-cloud-raster-access   Check Out Our Geospatial Job Board! https://mapscaping.com/jobs/      

A knowledge graph is a network of relationships between real work entities and in this episode, you will learn how and why knowledge graphs might be a better choice than spatial joins!    Further listening! The H3 Indexing System https://mapscaping.com/podcast/h3-geospatial-indexing-system/   Hex Tiles https://mapscaping.com/podcast/hex-tiles/   Points of Interest data https://mapscaping.com/podcast/all-of-the-places-in-the-world/   Dark Data https://mapscaping.com/podcast/unstructured-data-is-dark-data/

I am sure you have heard of ChatGPT by now so the hope of this episode is to give you some more context about what is it built on and how it works.   To do that I invited Daniel Whitneck back on the podcast  You can connect with Daniel here https://datadan.io/   and listen to his previous episode here: https://mapscaping.com/podcast/an-introduction-to-artificial-intelligence/   This is perhaps the quote for the episode that I have spent the most time thinking about "We always thought AI would be logical and lack creativity - but it is almost the exact opposite" This reframes the idea of being wrong to being creative which I think you could argue really depends on the context!    If you have not already played around with ChatGPT it's well worth spending the time to experiment with it ... while its still free ;)  https://chat.openai.com/auth/login   Further listening    If you have not already listened to this episode about computer vision and GeoAI you might find it interesting. Listen out for the discussion around plausible / realistic data and real measurements - I think this gives more context to the use cases for generative AI  https://mapscaping.com/podcast/computer-vision-and-geoai/   You might also enjoy this episode about fake satellite imagery  https://mapscaping.com/podcast/fake-satellite-imagery/   BTW  I have started a job board for geospatial people feel free to check it out!        

Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images.    You might think that this is exactly what we are doing in earth observation but there are a few important differences between computer vision and what some people refer to as GeoAI.   This week Jordi inglada is going to help you understand what those differences are and why it's not always possible to use Computer vision techniques in the field of Remote Sensing.   Listen out for these key points during the conversation! Why plausible or realistic data is not always a substitute for actual measurements, except when it is ;)  In computer vision we can learn from the data, in earth observation we know the physics To do interesting work in data science you need to - Computer science, applied math, and domain expertise. You don’t need to be an expert in all three but you need to be interested in all three Vectors in the machine learning world don’t necessarily have anything to do with points lines and polygons ;)   Sponsored by Sinergise, as part of Copernicus Data Space Ecosystem knowledge sharing. dataspace.copernicus.eu/ http://dataspace.copernicus.eu/   Related Podcast Episodes   Super Resolution https://mapscaping.com/podcast/super-resolution-smarter-upsampling/ Fake Satellite Imagery https://mapscaping.com/podcast/fake-satellite-imagery/   Sentinal Hub https://mapscaping.com/podcast/sentinel-hub/ Google Earth Engine  https://mapscaping.com/podcast/introducing-google-earth-engine/   Microsofts Planetary Computer  https://mapscaping.com/podcast/the-planetary-computer/   BTW MapScaping has started a Job Board!  it's in the early stages but it's live Jobs - Mapscaping.com