Education zach harmer-open data examples

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Open data can be used in geographic information system (GIS) projects by accessing freely available data sources like Landsatlook Viewer, Natural Resources Canada, and geospatial databases from the NWT Centre for Geomatics. Open data is important because it allows GIS to be applied to various projects. Some examples of open data sources that can be used include maps, data and GIS libraries.

Open Data Examples
Zachary Harmer
Bachelor of Science (Honours) – Physical Geography
Brock University

How do I use Open Data?
• Use GIS in any project that it can be applied
• As a result Open Data is very important
• Example of Data Sources
• Map, Data and GIS Library
• Landsatlook Viewer
• Natural Resources Canada
• Geospatial Databases
(e.g. NWT Centre for Geomatics)

Education zach harmer-open data examples

Department of Geography and Geoinformation Science Seminar, George Mason University, Falls Church, VA, September 2015. Increasingly, GIS is part of the collaboration between computer scientists, information scientists, and domain scientists to solve complex scientific questions. Successfully addressing scientific problems, such as informing regional decision- and policy-making for coastal zone management and marine spatial planning, requires integrative and innovative approaches to analyzing, modeling, and developing extensive and diverse data sets. The current chaotic distribution of available data sets, lack of documentation about them, and lack of easy-to-use access tools and computer modeling and analysis codes are still major obstacles for scientists and educators alike. Contributing solutions to these problems is part of an emerging science agenda at Esri for a range of environmental, conservation, climate and ocean sciences that will be discussed. The talk will highlight some recent projects in progress, including a new global map of ecological land units, new tools to support multidimensional scientific data, continued work on an ocean basemap, and more.

Lec_1_Shapefile

Atiqa khan

Feature Geo Analytics and Big Data Processing: Hybrid Approaches for Earth Sc...

Dawn Wright

Invited talk for 2016 AGU Fall Meeting Session IN12A Big Data Analytics I Introduced is a new approach for processing spatiotemporal big data by leveraging distributed analytics and storage. A suite of temporally-aware analysis tools summarizes data nearby or within variable windows, aggregates points (e.g., for various sensor observations or vessel positions), reconstructs time-enabled points into tracks (e.g., for mapping and visualizing storm tracks), joins features (e.g., to find associations between features based on attributes, spatial relationships, temporal relationships or all three simultaneously), calculates point densities, finds hot spots (e.g., in species distributions), and creates space-time slices and cubes (e.g., in microweather applications with temperature, humidity, and pressure, or within human mobility studies). These “feature geo analytics” tools run in both batch and streaming spatial analysis mode as distributed computations across a cluster of servers on typical “big” data sets, where static data exist in traditional geospatial formats (e.g., shapefile) locally on a disk or file share, attached as static spatiotemporal big data stores, or streamed in near-real-time. In other words, the approach registers large datasets or data stores with ArcGIS Server, then distributes analysis across a cluster of machines for parallel processing. Several brief use cases will be highlighted based on a 16-node server cluster at 14 Gb RAM per node, allowing, for example, the buffering of over 8 million points or thousands of polygons in ~1 minute. The approach is “hybrid” in that ArcGIS Server integrates open-source big data frameworks such as Apache Hadoop and Apache Spark on the cluster in order to run the analytics. In addition, the user may devise and connect custom open-source interfaces and tools developed in Python or Python Notebooks; the common denominator being the familiar REST API.

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