New Release of the Geospatial Analytics for Knime

China Data Institute and Future Data Lab

this webinar will introduce the Geospatial Analytics for Knime.

Sponsored by the Spatial Data Lab*, this webinar will introduce the Geospatial Analytics, a new extension of KNIME free for the public. This webinar will demonstrate those functions for spatial data analysis as well as some case studies on business, population and environment, including a comparative analysis of the newly released US and China county Census data with Geospatial Analytics. 
The new Geospatial Extension allows easy, efficient and replicable geospatial analysis without GIS and programming skills. In its first version, it will support the most common vector data types such as points, lines, polygons, and collections of those. Major functionality includes:
•	Spatial IO for reading and writing different geospatial files such as shapefiles
•	Spatial Calculation for computing the area of a polygon, computing distances, performing spatial joins, and other spatial manipulations
•	Spatial Transformation and Conversion for data conversion or projection of different datasets with different formats
•	Spatial Visualization for interactive views and richly configurable static maps for presentations or reports
•	Exploratory Spatial Data Analysis for Moran’s I, Geary ‘s C, Getis-Ord G, and LISA
•	Spatial Modeling for spatial lag/error models, panel models, and GWR/MGWR models
•	 Location Analysis for site selection
•	Open Datasets for easy access to publicly available data such as Open Street Map and the US Census data
This webinar is free to the public. For those who registered but cannot attend the live webinar, we’ll send you the webinar recording after the event. Please contact spatialdatalab@lists.fas.harvard.edu if there is any question.
*The Spatial Data Lab is a joint project supported by the Center for Geographical Analysis at Harvard University, Future Data Lab, KNIME, and the NSF Spatiotemporal Innovation Center, which is designed to promote repeatable, replicable, and scalable spatiotemporal innovation research and global collaboration in related fields.

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New Release of the Geospatial Analytics for Knime

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New Release of the Geospatial Analytics for Knime

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