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UCLA researchers have found that space-mapping neurons -- the GPS system in the brain -- have a strong dependence on what is being looked at when triangulating location, a finding that resolves a neurological mystery that has vexed scientists for more than four decades.
This also expands on an earlier finding that neurons responsible for creating spatial maps react differently in virtual reality than they do in the real-world environments. Researchers again used rats in a virtual reality environment to test the long-debated theory of whether landmarks are necessary or whether that region of the brain is also counting steps or directional movement to determine location, said Mayank Mehta, a UCLA professor of neurology, physics and astronomy, and neurobiology in the UCLA College and the study's senior author.
The study, which appears today in the peer-reviewed journal Cell, showed that many neurons were firing selectively only when rats were looking at certain landmarks on screens, either in the real or in the virtual reality environment. continued…
R has a full library of tools for working with spatial data. This includes tools for both vector and raster data, as well as interfacing with data from other sources (like ArcGIS) and making maps.
These tutorials — which build off Claudia Engel’s excellent GIS in R tutorials — are designed for users with some familiarity with R, but require no knowledge of spatial analysis. If you aren’t used to working with R, you will probably want to spend some little time familiarizing yourself with the language before starting this series. (Here’s a good set of R tutorials if you need them)
Each tutorial is divided into several parts (to be done in sequence), and include a zipped folder of data for exercises. In addition, cheatsheets are provided which may be of help in remembering the various commands you will frequently use. Each tutorial is meant to take ~1.5 hours, not including software installation.
These tutorials have a share-and-share-alike Creative Commons license, so please feel free to use and modify as you see fit. You can find rmarkdown source files on github here.
Command Cheatsheets:
Learning GIS in R involves learning both concepts and vocabulary. Here are some cheatsheets to help with the later.
continued…Over the last 10 years, businesses, scientists and hobbyists from all over the world have been using Google Earth Pro for everything from planning hikes to placing solar panels on rooftops. Google Earth Pro has all the easy-to-use features and detailed imagery of Google Earth, along with advanced tools that help you measure 3D buildings, print high-resolution images for presentations or reports, and record HD movies of your virtual flights around the world. <
Starting today, even more people will be able to access Google Earth Pro: we're making it available for free. To see what Earth Pro can do for you—or to just have fun flying around the world— and download Earth Pro today. If you're an existing user, your key will continue to work with no changes required. continued…
Google Maps has proved to be a highly successful Google service, incorporating a range of invaluable tools such as Street View, Route Planning and Google Traffic. Many companies and organizations rely on Google Maps to provide their services – and it’s thanks to the Google Maps API that they’re able to do so.
Google introduced the Google Maps API in 2005. This allowed developers to create custom applications with Maps. Google subsequently launched APIs for Android and iOS application development.
As useful as the Maps APIs are, they take a bit of knowhow to utilize. That’s where GMaps.js comes in. GMaps.js is an open-source, MIT-licence JavaScript library. Written by Gustavo Leon, GMaps aims to simplify the original Google Maps JavaScript API. It takes care of the extensive API code and provides appropriate methods to deal with Maps. It’s cleaner, more concise and hence easier to use.
In this article, we’ll look at map components like Markers, Polygons and Overlays. We’ll also discuss Static Maps, and the use of Geolocation and Geocoding to operate on a user’s location. All of this will be in reference to GMaps. It helps you create map applications with easy-to-use methods. I’ve used it to create a sample application (Mapit), which I’ll discuss further at the end of the article. continued…
The membership of the Open Geospatial Consortium (OGC®) has approved the OGC CF-netCDF 3.0 encoding using GML Coverage Application Schema, an extension to the OGC CF-netCDF 3.0 encoding standard.
The OGC CF-netCDF 3.0 encoding standard has emerged as a widely used and well supported data model and encoding for domains such as atmospheric science, oceanography, climatology, meteorology, and hydrology. It supports multi-dimensional data representing space and time-varying phenomena.
The new extension to the OGC CF-netCDF standards suite specifies how CF-netCDF datasets are encoded to conform to “OGC Implementation Schema for Coverages." Coverages are data such as the output of weather and climate forecast models, weather station and ocean buoy observations, balloon soundings, ground-base radar, satellite imagery, digital elevation models, and LIDAR point clouds. This extension specifies how these complex multi-dimensional CF-netCDF data are encoded as OGC coverages for use in geographic information systems (GIS) or other geospatial systems.
The documents for the OGC netCDF-GMLCOV Standard are available at www.opengeospatial.org/standards/netcdf.
The OGC is an international consortium of more than 515 companies, government agencies, research organizations, and universities participating in a consensus process to develop publicly available geospatial standards. OGC Standards support interoperable solutions that "geo-enable" the Web, wireless and location-based services, and mainstream IT. OGC Standards empower technology developers to make geospatial information and services accessible and useful with any application that needs to be geospatially enabled. Visit the OGC website at www.opengeospatial.org.
International GIS supplier Esri has committed to commercially support SAP HANA as a single enterprise geodatabase for its flagship product ArcGIS, resulting in significantly faster and easier spatial query performance of big data.
The move is part of a recently announced global technology partnership between SAP and Esri.
Planned for 2016, the next release of Esri’s ArcGIS mapping and geospatial intelligence technology will expand the existing native integration with SAP HANA to allow organizations to run all their SAP Business Suite and ArcGIS applications within a SAP HANA architecture.
The collaboration between SAP and Esri is expected to enable organizations to run both GIS workloads and advanced spatial analytics on the single SAP HANA geodatabase.
“We’re very excited about the enterprise geodatabase support for SAP HANA,” said Jack Dangermond, president, Esri. “It will bring our customers speed, simplicity and better integration with their enterprise information. For SAP customers, it delivers a complete platform for mapping and geospatial intelligence. By synchronising our platforms, our respective customers will benefit across the enterprise. We’re doing the work to make sure that GIS and mapping is available to all — not just traditional mapping experts.” continued…