Getting Started with KNIME and Its Geospatial Analytics Extension

• Published in: Computational Methods and GIS Applications in Social Science - Lab Manual pp. 1 - 41
• Main Authors: Liu, Lingbo, Wang, Fahui
• Format: Book Chapter
• Language: English
• Published: United Kingdom CRC Press 2024; Taylor & Francis Group
• Edition: 1
• Subjects: Spatial Join; Tcp; Spatial Query; Multiple Ring Buffer; Crs; Choose Column; Editor Canvas; Workflow Editor; Spatial Overlay; Ring Buffer; Census Block Data; Local Workspace; Census Tract Data; Geometry Column; Geospatial Analytics; Population Density Pattern; CSV File; Aggregation Method; Census Tract; Geospatial Data; Computer Operation System; Census Block; EBRP; View Node; File Path
• ISBN: 9781032302430; 1032302437
• DOI: 10.1201/9781003304357-1

This chapter introduces KNIME and its application in a case study. Section 1.1 introduces the installation of KNIME Analytics Platform and its user interface. Section 1.2 illustrates the installation and functionality of Geographic Analytics Extension for KNIME and the KNIME Python integration. Section 1.3 uses the KNIME Analytics Platform to implement case study 1 on analyzing the urban population density pattern in Baton Rouge, Louisiana. Several popular GIS tasks are practiced, such as attribute query, spatial query, attribute join, spatial join, projection, geometric attribute calculation, buffer, spatial overlay, dissolve, intersect, etc. This chapter introduces KNIME and its application in a case study. It also introduces the installation of KNIME Analytics Platform and its user interface. The chapter illustrates the installation and functionality of Geographic Analytics Extension for KNIME and the KNIME Python integration. It utilizes the KNIME Analytics Platform to implement case study on analyzing the urban population density pattern in Baton Rouge, Louisiana. The workflows and data stored in our workspace are subsequently available through the KNIME Explorer in the upper left corner of KNIME AP. Users can run several KNIME AP programs simultaneously with different workspaces. Spatial Visualization nodes like Geospatial View visualize geospatial data. The Geospatial Analytics Extension nodes provide additional functionality for working with geospatial data and performing more complex spatial analysis tasks.