The Large-scale Reference Database (LRD) is a geographical base map and object-oriented database for Flanders that contains information on buildings, roads, parcels, and other infrastructure. It is maintained through a public-private partnership and aims to be the single authoritative source of geospatial information. The LRD undergoes regular updates through various processes to integrate new or changed objects based on data from public works, surveys, and other sources. Its data is freely available through download and web services and is used widely in both public and private applications. Officials are working to develop the LRD into interconnected base registries that can serve as foundational geospatial references.
This document discusses using historical public transportation data to model commuter profiles. It describes Switzerland's open public transportation API and an app that uses it to show station connections. The document then shows examples of commuter profiles modeled from historical schedule data, including details like time, day of week, and intermediate stops. Finally, it discusses future directions like aggregating data, connecting to other open data types, privacy considerations, and how transportation companies could use the data for traffic predictions.
The document summarizes a presentation about an Open Sensor Network (OSN) Platform that stores and provides access to dynamic open data sets. The OSN Platform allows users to browse a catalog of static and dynamic datasets, select datasets of interest to develop apps, and download data in XML or JSON format. Example datasets discussed include transportation, tourism, and economic data from various cities. The presentation discusses how the OSN Platform has been used to create apps and envisions expanding the platform to include more private and public live data sources from cities to power more smart city applications.
CITYDB is a new dashboard and database that will provide a state of the art view into five UK cities - London, Cardiff, Edinburgh, Belfast, and Leeds - over a six month period. It will have four sections: CityDB Live for real-time data feeds; CityDB Store for linking to open government data; CityDB Surveyor for collecting new data; and CityDB Future for simulated outputs based on research. The goal is to map data automatically and provide information to the public, policymakers, and academics.
The document discusses OpenStreetMap in Spain and outlines several topics:
1. Statistics showing Spain's OSM data has grown from 20% to 40% of road kilometers mapped and from 0.6% to 3.1% the size of 1:25k map files between August 2008 and August 2009.
2. Spatial data infrastructures and the proliferation of web map services and open data portals across different levels of government with little consistency or licensing.
3. Current OSM import projects in Spain including imports from the national road database and public transit data.
4. Reasons for establishing an OSM Foundation chapter in Spain related to legal representation, streamlining imports, and clarifying the
A spatial approach to the energy generating potential of real estateJene van der Heide
This document discusses a project by the Netherlands' Cadastre to map state and city buildings in The Hague to analyze their energy characteristics and potential for renewable energy generation. The Cadastre is collecting data on building attributes, underground infrastructure, and land ownership to visualize on an ArcGIS Online platform. Stakeholders like the city and utility companies are sharing data on energy usage, employees, and solar potential. The ultimate goals are to define opportunities to reduce and stabilize energy costs and facilitate private sector involvement through an open tender. Visualization of spatial energy and heat exchange potentials between buildings is seen as key to developing viable business cases for renewable solutions.
When deciding on how to describe cultural heritage resources in common exchange formats (e. g. MARC 21, RDF or XML), publishing organisations need to align their content standards with wide-spread, broadly adopted data standards in order to make information exchange as effective as possible.
This presentation from the IFLA Committee on Standards session in Cape Town on August 19, 2015 (2015-08-19) makes that case. There is also an accompanying paper in the IFLA library at http://library.ifla.org/id/eprint/1194
Team 1: Easy-to-Use Satellite Images Discovery in a Map Viewer plan4all
This document summarizes an application that unifies satellite image data and metadata. It retrieves images and metadata from the Copernicus Open Access API and NASA API (in progress) and parses the JSON metadata into GeoJSON. The application is built using HSLayers NG and allows users to search for and filter images by location and attributes in a map viewer. Future work will add notification and download capabilities for new images. The goal is to provide an easy-to-use tool for discovering satellite images.
The Large-scale Reference Database (LRD) is a geographical base map and object-oriented database for Flanders that contains information on buildings, roads, parcels, and other infrastructure. It is maintained through a public-private partnership and aims to be the single authoritative source of geospatial information. The LRD undergoes regular updates through various processes to integrate new or changed objects based on data from public works, surveys, and other sources. Its data is freely available through download and web services and is used widely in both public and private applications. Officials are working to develop the LRD into interconnected base registries that can serve as foundational geospatial references.
This document discusses using historical public transportation data to model commuter profiles. It describes Switzerland's open public transportation API and an app that uses it to show station connections. The document then shows examples of commuter profiles modeled from historical schedule data, including details like time, day of week, and intermediate stops. Finally, it discusses future directions like aggregating data, connecting to other open data types, privacy considerations, and how transportation companies could use the data for traffic predictions.
The document summarizes a presentation about an Open Sensor Network (OSN) Platform that stores and provides access to dynamic open data sets. The OSN Platform allows users to browse a catalog of static and dynamic datasets, select datasets of interest to develop apps, and download data in XML or JSON format. Example datasets discussed include transportation, tourism, and economic data from various cities. The presentation discusses how the OSN Platform has been used to create apps and envisions expanding the platform to include more private and public live data sources from cities to power more smart city applications.
CITYDB is a new dashboard and database that will provide a state of the art view into five UK cities - London, Cardiff, Edinburgh, Belfast, and Leeds - over a six month period. It will have four sections: CityDB Live for real-time data feeds; CityDB Store for linking to open government data; CityDB Surveyor for collecting new data; and CityDB Future for simulated outputs based on research. The goal is to map data automatically and provide information to the public, policymakers, and academics.
The document discusses OpenStreetMap in Spain and outlines several topics:
1. Statistics showing Spain's OSM data has grown from 20% to 40% of road kilometers mapped and from 0.6% to 3.1% the size of 1:25k map files between August 2008 and August 2009.
2. Spatial data infrastructures and the proliferation of web map services and open data portals across different levels of government with little consistency or licensing.
3. Current OSM import projects in Spain including imports from the national road database and public transit data.
4. Reasons for establishing an OSM Foundation chapter in Spain related to legal representation, streamlining imports, and clarifying the
A spatial approach to the energy generating potential of real estateJene van der Heide
This document discusses a project by the Netherlands' Cadastre to map state and city buildings in The Hague to analyze their energy characteristics and potential for renewable energy generation. The Cadastre is collecting data on building attributes, underground infrastructure, and land ownership to visualize on an ArcGIS Online platform. Stakeholders like the city and utility companies are sharing data on energy usage, employees, and solar potential. The ultimate goals are to define opportunities to reduce and stabilize energy costs and facilitate private sector involvement through an open tender. Visualization of spatial energy and heat exchange potentials between buildings is seen as key to developing viable business cases for renewable solutions.
When deciding on how to describe cultural heritage resources in common exchange formats (e. g. MARC 21, RDF or XML), publishing organisations need to align their content standards with wide-spread, broadly adopted data standards in order to make information exchange as effective as possible.
This presentation from the IFLA Committee on Standards session in Cape Town on August 19, 2015 (2015-08-19) makes that case. There is also an accompanying paper in the IFLA library at http://library.ifla.org/id/eprint/1194
Team 1: Easy-to-Use Satellite Images Discovery in a Map Viewer plan4all
This document summarizes an application that unifies satellite image data and metadata. It retrieves images and metadata from the Copernicus Open Access API and NASA API (in progress) and parses the JSON metadata into GeoJSON. The application is built using HSLayers NG and allows users to search for and filter images by location and attributes in a map viewer. Future work will add notification and download capabilities for new images. The goal is to provide an easy-to-use tool for discovering satellite images.
Since 2010, geospatial data of Swiss gov agencies can be viewed and accessed over geo.admin.ch in a fast, free and state of the art manner. Over 200 datasets are accessed by up to 50’000 users daily. In an analysis we show which datasets were actually requested by different user groups and our lessons learned to leverage usage itself
The document describes a proposed system to improve search and rescue efforts for victims in mountain environments. The system would integrate geospatial data, allow for multiple competing hypotheses, and support rescuers' reasoning processes. It includes features like a searchable tree of map items, management of clues and uncertainty parameters, and visualization of probable location areas on an interactive map. The goal is to help rescuers more effectively deduce victims' locations based on verbal clues and manage the imperfect nature of information received.
@healthmap shared detailed anonymised data. Filter England data to match Local Authority boundaries. Pivot-table them into time-series attributes per Local Authority. Use @kennethfield coxcombs in ArcGIS. Parse the class_ field into dates. Post the newly time-enabled coxcombs to fan out and show infectuous arrivals. Step-by-step starts slide 11.
This risk assessment form summarizes a proposed activity to record sound effects in a location in the UK. It involves a crew of two people, Jerdon and Thomas, recording sounds around Hackney Court Street in London. The hazards identified include electrocution and physical injury. The responsible manager and authorizer are also listed, along with contact details and distribution of the risk assessment.
ODSC Europe: Weather and Climate Data: Not Just for MeteorologistsMargriet Groenendijk
Weather is part of our everyday lives. Who doesn’t check the rain radar before heading out, or the weather forecast when planning a weekend away? But where does this data come from, and what is it made of? The answer is a mix of measurements, models and statistics, meaning that the use of weather and climate data can get complex very quickly.
These slides provide a brief overview of the science behind weather and climate forecasts and provides you with the tools to get started with weather data - even if you aren't a meteorologist. Learn how to connect weather data to other data sources, how to visualize weather and climate data in an interactive weather dashboard embedded in a Python notebook, and other ways you can use weather data for yourself, from examples using weather APIs, maps, PixieDust and Machine Learning.
The document appears to be a collection of copyright notices from 2013-2018 for Kosugi no University. There are several repetitions of the copyright notice but no other substantive information that could be summarized.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
Since 2010, geospatial data of Swiss gov agencies can be viewed and accessed over geo.admin.ch in a fast, free and state of the art manner. Over 200 datasets are accessed by up to 50’000 users daily. In an analysis we show which datasets were actually requested by different user groups and our lessons learned to leverage usage itself
The document describes a proposed system to improve search and rescue efforts for victims in mountain environments. The system would integrate geospatial data, allow for multiple competing hypotheses, and support rescuers' reasoning processes. It includes features like a searchable tree of map items, management of clues and uncertainty parameters, and visualization of probable location areas on an interactive map. The goal is to help rescuers more effectively deduce victims' locations based on verbal clues and manage the imperfect nature of information received.
@healthmap shared detailed anonymised data. Filter England data to match Local Authority boundaries. Pivot-table them into time-series attributes per Local Authority. Use @kennethfield coxcombs in ArcGIS. Parse the class_ field into dates. Post the newly time-enabled coxcombs to fan out and show infectuous arrivals. Step-by-step starts slide 11.
This risk assessment form summarizes a proposed activity to record sound effects in a location in the UK. It involves a crew of two people, Jerdon and Thomas, recording sounds around Hackney Court Street in London. The hazards identified include electrocution and physical injury. The responsible manager and authorizer are also listed, along with contact details and distribution of the risk assessment.
ODSC Europe: Weather and Climate Data: Not Just for MeteorologistsMargriet Groenendijk
Weather is part of our everyday lives. Who doesn’t check the rain radar before heading out, or the weather forecast when planning a weekend away? But where does this data come from, and what is it made of? The answer is a mix of measurements, models and statistics, meaning that the use of weather and climate data can get complex very quickly.
These slides provide a brief overview of the science behind weather and climate forecasts and provides you with the tools to get started with weather data - even if you aren't a meteorologist. Learn how to connect weather data to other data sources, how to visualize weather and climate data in an interactive weather dashboard embedded in a Python notebook, and other ways you can use weather data for yourself, from examples using weather APIs, maps, PixieDust and Machine Learning.
The document appears to be a collection of copyright notices from 2013-2018 for Kosugi no University. There are several repetitions of the copyright notice but no other substantive information that could be summarized.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
9. Open data retrieved from Transport for London webpage on May 15, 2020, see https://cycling.data.tfl.gov.uk. A small fraction of low use
stations discarded from analysis. Analysis done with Python library Pandas. Data visualizations done with Python library Bokeh. Green (#008837)
and Purple (#7B3294) are used. Descriptive analysis of London bike rental data available at https://medium.com/@AJOhrn/data-footprint-of-
bike-sharing-in-london-be9e11425248 . The analysis has no political agenda — numbers rule.
Info as infographic
Footnotes