Sasha  Roberts

Sasha Roberts

1659574200

RGeo: Geospatial Data Library for Ruby

RGeo 

RGeo is a geospatial data library for Ruby.

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This organization is looking for maintainers, see this issue for more information.

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Summary

RGeo is a key component for writing location-aware applications in the Ruby programming language. At its core is an implementation of the industry standard OGC Simple Features Specification, which provides data representations of geometric objects such as points, lines, and polygons, along with a set of geometric analysis operations. This makes it ideal for modeling geolocation data. It also supports a suite of optional add-on modules that provide various geolocation-related services.

Use the core rgeo gem to:

  • Represent spatial and geolocation data objects such as points, lines, and polygons in your Ruby application.
  • Perform standard spatial analysis operations such as finding intersections, creating buffers, and computing lengths and areas.
  • Correctly handle spherical geometry, and compute geographic projections for map display and data analysis.
  • Read and write location data in the WKT and WKB representations used by spatial databases.

Dependencies

RGeo works with the following Ruby implementations:

  • MRI Ruby 2.3.0 or later.
  • Partial support for JRuby 9.0 or later. The FFI implementation of GEOS is available (ffi-geos gem required) but CAPI is not.
  • See earlier versions for support for older ruby versions.

Some features also require the following:

  • GEOS 3.2 or later is highly recommended. (3.3.3 or later preferred.) Some functions will not be available without it. This C/C++ library may be available via your operating system's package manager (sudo aptitude install libgeos-dev for debian based Linux distributions, yum install geos geos-devel for redhat based Linux distributions), or you can download it from http://trac.osgeo.org/geos
  • On some platforms, you should install the ffi-geos gem (version 1.2.0 or later recommended.) JRuby requires this gem to link properly with Geos, and Windows builds probably do as well.

Installation

Install the RGeo gem:

gem install rgeo

or include it in your Gemfile:

gem "rgeo"

If you are using proj.4 extensions, include rgeo-proj4:

gem "rgeo-proj4"

Extensions

The RGeo organization provides several gems that extend RGeo:

rgeo-proj4

Proj4 extensions

rgeo-geojson

Read and write GeoJSON

rgeo-shapefile

Read ESRI shapefiles

activerecord-postgis-adapter

ActiveRecord connection adapter for PostGIS, based on postgresql (pg gem)

activerecord-mysql2spatial-adapter

ActiveRecord connection adapter for MySQL Spatial Extensions, based on mysql2

activerecord-spatialite-adapter

ActiveRecord connection adapter for SpatiaLite, based on sqlite3 (*not maintained)

Development and support

RDoc Documentation is available at https://www.rubydoc.info/gems/rgeo

Contributions are welcome. Please read the Contributing guidelines.

Support may be available on the rgeo-users google group or on Stack Overflow.

Documentation

You can see more in-depth documentation in the doc folder. Factories and methods are documented inline, you should consider checking https://rubydoc.info/gems/rgeo with the version you are currently using. Or generate documentation locally if you're working on RGeo: yardoc server.

Here's the current list of available topics:

You can see an exhaustive and up to date list at https://rubydoc.info/gems/rgeo/index.

Acknowledgments

Daniel Azuma created RGeo. Tee Parham is a former maintainer. Keith Doggett, Ulysse Buonomo are current maintainers.

Development is supported by:

RGeo calls the GEOS library to handle most Cartesian geometric calculations, and the Proj4 library to handle projections and coordinate transformations. These libraries are maintained by the Open Source Geospatial Foundation; more information is available on OSGeo's web site.

JRuby support is made possible by the ffi-geos (and upcoming ffi-proj4) gems, by J Smith.


Author: rgeo
Source code: https://github.com/rgeo/rgeo
License: View license

#ruby  #ruby-on-rails 

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RGeo: Geospatial Data Library for Ruby
 iOS App Dev

iOS App Dev

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Gerhard  Brink

Gerhard Brink

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Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).


This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Gerhard  Brink

Gerhard Brink

1624699032

Introduction to Data Libraries for Small Data Science Teams

At smaller companies access to and control of data is one of the biggest challenges faced by data analysts and data scientists. The same is true at larger companies when an analytics team is forced to navigate bureaucracy, cybersecurity and over-taxed IT, rather than benefit from a team of data engineers dedicated to collecting and making good data available.

Creative, persistent analysts find ways to get access to at least some of this data. Through a combination of daily processes to save email attachments, run database queries, and copy and paste from internal web pages one might build up a mighty collection of data sets on a personal computer or in a team shared drive or even a database.

But this solution does not scale well, and is rarely documented and understood by others who could take it over if a particular analyst moves on to a different role or company. In addition, it is a nightmare to maintain. One may spend a significant part of each day executing these processes and troubleshooting failures; there may be little time to actually use this data!

I lived this for years at different companies. We found ways to be effective but data management took up way too much of our time and energy. Often, we did not have the data we needed to answer a question. I continued to learn from the ingenuity of others and my own trial and error, which led me to the theoretical framework that I will present in this blog series: building a self-managed data library.

A data library is _not _a data warehousedata lake, or any other formal BI architecture. It does not require any particular technology or skill set (coding will not be required but it will greatly increase the speed at which you can build and the degree of automation possible). So what is a data library and how can a small data analytics team use it to overcome the challenges I’ve described?

#big data #cloud & devops #data libraries #small data science teams #introduction to data libraries for small data science teams #data science

Cyrus  Kreiger

Cyrus Kreiger

1618039260

How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt

Macey  Kling

Macey Kling

1597579680

Applications Of Data Science On 3D Imagery Data

CVDC 2020, the Computer Vision conference of the year, is scheduled for 13th and 14th of August to bring together the leading experts on Computer Vision from around the world. Organised by the Association of Data Scientists (ADaSCi), the premier global professional body of data science and machine learning professionals, it is a first-of-its-kind virtual conference on Computer Vision.

The second day of the conference started with quite an informative talk on the current pandemic situation. Speaking of talks, the second session “Application of Data Science Algorithms on 3D Imagery Data” was presented by Ramana M, who is the Principal Data Scientist in Analytics at Cyient Ltd.

Ramana talked about one of the most important assets of organisations, data and how the digital world is moving from using 2D data to 3D data for highly accurate information along with realistic user experiences.

The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment, 3D data for object detection and two general case studies, which are-

  • Industrial metrology for quality assurance.
  • 3d object detection and its volumetric analysis.

This talk discussed the recent advances in 3D data processing, feature extraction methods, object type detection, object segmentation, and object measurements in different body cross-sections. It also covered the 3D imagery concepts, the various algorithms for faster data processing on the GPU environment, and the application of deep learning techniques for object detection and segmentation.

#developers corner #3d data #3d data alignment #applications of data science on 3d imagery data #computer vision #cvdc 2020 #deep learning techniques for 3d data #mesh data #point cloud data #uav data