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?    Some more episodes you might enjoy   ESRI, GIS careers, Geospatial Data Science  QGIS, Geospatial Python, ArcGIS Pro Google Maps, Geomatics, Cartography Location Intelligence, Mapping  

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   Some more episodes you might enjoy   ESRI, GIS careers, Geospatial Data Science  QGIS, Geospatial Python, ArcGIS Pro Google Maps, Geomatics, Cartography Location Intelligence, Mapping   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/   Some more episodes you might enjoy   ESRI, GIS careers, Geospatial Data Science  QGIS, Geospatial Python, ArcGIS Pro Google Maps, Geomatics, Cartography Location Intelligence, Mapping

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   Some more episodes you might enjoy   ESRI, GIS careers, Geospatial Data Science  QGIS, Geospatial Python, ArcGIS Pro Google Maps, Geomatics, Cartography Location Intelligence, Mapping        

Data is what data does  - more about that later on ;)  This episode focuses on designing for privacy, how do we create value from location data without sacrificing personal privacy?  Well, you might start by adhering to the Enhanced Standards For Precise Location Information which means that information about sensitive places like churches, hospitals, military bases, and LGBTQ+ spaces isn't misused or sold. Plus, they protect our exact location from being shared with law enforcement or bounty hunters!  Yes, that's right bounty hunters!  You might also think about adding noise to the data, maybe you want to blur the time stamp and look at everything in aggregate? It turns out the location data is not just classified as personal data but is actually classified as sensitive personal data in privacy law so if you are going to work with it you really need to understand the risks involved. One of the really interesting ideas mentioned by Elizabeth Hein VP of Compliance & Data Protection was the idea of regulating Use, Harm, and Risk instead of sensitive data  Data Is What Data Does: Regulating Use, Harm, and Risk Instead of Sensitive Data If you want to learn more about POI data and why points of interest data are so hard  check out the episode called All of the Places in the World: https://mapscaping.com/podcast/all-of-the-places-in-the-world/ On a side note, I am working on a side project, it's a job board for geospatial people, and you can find it here https://mapscaping.com/jobs/ it's still in the development phase but feel free to check it out!

When comparing multispectral and hyperspectral data it is not simply a case of “more data more better”!  With hyperspectral you have “The curse of Dimensionality” but you also get more flexibility to pick exactly what bands you want to use! With multispectral you have less noise but you also have less data! This episode is designed to be a beginner's guide to the differences between hyperspectral and multispectral satellite data.   You can reach out to Gordon Logie here: https://sparkgeo.com/blog/team/gordon/   Podcast episode with the CEO of Sentinel-Hub https://mapscaping.com/podcast/sentinel-hub/   Here are some courses that focused on hyperspectral and offer further training   https://eo-college.org/courses/beyond-the-visible/ https://eo-college.org/courses/beyond-the-visible-imaging-spectroscopy-for-agricultural-applications/ https://www.enmap.org/events_education/hyperedu/

This week we are going to learn how Foursquare is trying to identify and map all of the places in the world!  Foursquare uses a mixture of crowd source and data conflation to maintain a database of 205 million places ... and it's not easy! Each phone might see the world slightly differently in terms of location accuracies and crowdsourcing data means that people "check-in" at different locations.    Kyle Fowler - Senior Director, Engineering at Foursquare Is going to give a behind-the-scenes look at how the "Orginal location-based social network" is trying to map all of the places in the world.  This episode is the first in a series of episodes I am going to publish in partnership with Foursquare and the idea is to use it as a reference for later episodes about Privacy and location data, Knowledge Graphs, AI, Location Based Marketing and Big geospatial Data in the Browser.       

Protomaps is a serverless system for planet-scale maps, it's an umbrella project consisting of a few different components one of which is PMtiles. PMtiles is “Cloud Optimise Geotiff” for web mapping, what this means is that you can build a base map and host it without the need for a server!  PMtiles is a single file that you can access via HTTP range requests in the same way that you can access data within a Cloud Optimised Geotiff with the important difference that PMtiles can also contain vector data! What this means is that you can create your own base map, and host it on something like Amazon S3 object storage at a fraction of the cost of other base map solutions!    During this episode, you will hear Brandon, the founder, and creator of Protomaps, talk about scarcity, and well I have never really thought about base maps as being a scarce resource I can definitely see how a product like PMtiles could remove some of the barriers to entry for a lot of creativity in terms of base maps.    More information on Protomaps is here:  https://protomaps.com/   Tippecanoe https://github.com/felt/tippecanoe.git https://bertt.wordpress.com/2023/01/06/creating-vector-pmtiles-with-tippecanoe/ Relevant podcast episodes  Cloud Optimized Point Clouds https://mapscaping.com/podcast/cloud-optimized-point-clouds/ Cloud Native Geospatial https://mapscaping.com/podcast/cloud-native-geospatial/ Microsoft’s Planetary computer https://mapscaping.com/podcast/the-planetary-computer/ Stamen Design - Full Stack Cartography https://mapscaping.com/podcast/full-stack-cartography/   If you have any questions or comments, let me know, I would love to hear from you!

Storytelling with point clouds   This is not your typical point clouds episode! Today we are talking about how to use point clouds to tell a story. During this episode, you will hear Benjamin Muller talk about using a point cloud to make a film about the city of St Gallen in Switzerland and you might be tempted to think … what a waste of time! Why not use the data to make better measurements that lead to better decisions?  How many IT projects have failed, not because they were based on bad decisions but because they failed to get people to adopt the changes?  The best decisions are meaningless unless they are adopted.  So, how do we get people to change or adopt the change we are trying to make?  I think the first thing to understand is that packaging matters!  This episode is a case study into wrapping our ideas in a story and visualizing them using geospatial data.   Here is a link to the visualizations that were created using the point cloud data https://www.gruenesgallustal.ch/resume   You can take a look HxDR platform here https://hxdr.com/   If you are interested in more technical episodes about point clouds you might enjoy these!   The Point Data Abstraction Library https://mapscaping.com/podcast/pdal-point-data-abstraction-library/   Cloud Optimized Point Clouds https://mapscaping.com/podcast/cloud-optimized-point-clouds/   Bathymetric Lidar https://mapscaping.com/podcast/bathymetric-lidar-and-blue-carbon/   Lidar from Drones https://mapscaping.com/podcast/lidar-from-drones/   Lidar from Space https://mapscaping.com/podcast/gedi-space-lasers/