GIS Fundamentals – (PostGIS-II)

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This workshop will further develop the material covered on spatial databases in the first workshop a few weeks back. PostGIS is the most widely used spatial database and is built on top of PostgreSQL, a powerful open source relational database. The focus of this workshop is to exploit PostGIS to deal with big vector data, and use familiar tools in R to reduce the burden to learn SQL.

This is a hands-on workshop. Please make sure that you have PostgreSQL installed along with pgAdmin.

Participants should be familiar with vector data processing in R.

GIS Fundamentals – VI (Map visualization)

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This is the sixth workshop in a series of workshops we are offering this semester on the fundamentals of GIS. Each workshop covers one or two key elements of GIS and is self-contained. The focus is on conceptual details that can provide sufficient preparation for applications, but we will also touch upon the technical aspects.

This workshop will cover basic concepts and tools available in QGIS and R for visualizing vector spatial data as single and bi-variate static choropleth maps. We will focus on basic cartography principles for map-making and explore the functionalities of R and QGIS for making production-quality maps.

Participants should have some familiarity with R, but exposure to QGIS is not required.

New on-campus data-science and computational research services available

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Researchers across campus now have access to several new services to help them navigate the new tools and methodologies emerging for data-intensive and computational research.

As part of the U-M Data Science Initiative announced in fall 2015, Consulting for Statistics, Computing and Analytics Research (CSCAR) is offering new and expanded services, including guidance on:

  • Research methodology for data science.
  • Large scale data processing using high performance computing systems.
  • Optimization of code and use of Flux and other advanced computing systems.
  • Advanced data management.
  • Geospatial data analyses.
  • Exploratory analysis and data visualization.
  • Obtaining licensed data from commercial sources.
  • Scraping, aggregating and integrating data from public sources.
  • Analysis of restricted data.

“With Big Data and computational simulations playing an ever-larger role in research in a variety of fields, it’s increasingly important to provide researchers with a comprehensive ecosystem of support and services that address those methodologies,” said CSCAR Director Kerby Shedden.

As part of this significant expansion of its scope, the campuswide statistical consulting service CSCAR has been renamed Consulting for Statistics, Computing and Analytics Research. It was formerly known as the Center for Statistical Consultation and Research.

For more information, see the University Record article.