Difference between revisions of "Course information"
| Line 91: | Line 91: | ||
** Research paper 20% | ** Research paper 20% | ||
** Artifact 15% | ** Artifact 15% | ||
| + | |||
| + | There will be no mid-term test or final examination for this course. | ||
Latest revision as of 09:11, 6 January 2019
IS415 GeoSpatial Analytics and Applications
|
|
|
|
|
|
Contents
Synopsis
In this globalising and competitive business environment, the value of location as a business measure is fast becoming an important consideration for organisation. GIS with its capability to capture, manage, display, and analyse business information spatially is emerging as a location intelligence tool.
This course provides students with an introduction to the concepts, principles and methods of geospatial analytics and their practical applications of geospatial analytics in real world operations. Emphasis will be placed on (i) locating, acquiring and integrating business data into geospatial data repository, (ii) understand the principles and methodologies of the geocoding process, (iii) become familiar with geovisualisation, spatial analysis and location modelling techniques, and (iv) explore the technologies and possibilities of server-based and/or web-based spatially enabled decision support systems.
Course Objectives
Upon completion of the course, students will be able to:
- Understand the basic concepts and theories of GIScience and geospatial analytics,
- Create and manage spatially-enabled real world data,
- Use appropriate geovisualisation and mapping techniques to analyse and visualise geographical data,
- Understand the basic concepts and methods of geocomputation and geospatial analytics,
- Use appropriate geospatial analysis methods in detecting, analysing and modelling geospatial patterns and relationships, and
- Design and implement spatially enabled geospatial analytics applications.
Competencies
- Explaining the concepts of and principles of Geospatial Analytics.
- Describing the differences between Geospatial Analytics and Geographic Information Systems (GIS).
- Importing, wrangling and transforming geographical data.
- Geocoding and georeferencing geographical data.
- Describing the basic principles and concepts of geographical data visualisation and thematic mapping design.
- Performing geoprocessing and spatial analysis for solving real world problems.
- Applying raster-based cartographic modelling for solving real world problems.
- Explaining the principles of spatial point patterns and providing accurate interpretation of spatial point patterns analysis results.
- Explaining the methods of area-based analysis and providing accurate interpretation of area-based analysis results.
- Explaining the methods of geographically weighted regression and providing accurate interpretation of the analysis results.
- Explaining the methods of spatial data mining and providing accurate interpretation of the analysis results.
- Designing geospatial application programmatically by using free and open source software and packages (i.e. R, R packages and R shinny).
Course Assessment Details
Class Participation
A strict requirement for each class meeting is to complete the assigned readings and to try out the hands-on exercises before coming to class. Readings will be provided from the textbook on technical information and from provided documents and articles on business applications of geospatial analytics. Students are required to review the recommended readings and class exercises before coming to class. Without preparation, the learning and discussions would not be as meaningful. Student sharing of insights from readings and hands-on exercises of assigned materials in class participation will form a large part of the learning in this course. Students may also be quizzed orally in class and thereby contribute to class participation.
Take-Home Exercise
There are four take home exercises that are due throughout the term. They aim to provide students the opportunities to apply the methods learned in class by working through mini real world cases.
Students may work together to help one another with computer or geospatial issues and discuss the materials that constitute the take home exercise. However, each student is required to prepare and submit the take home exercise (including any computer work) on their own. Cheating is strictly forbidden. Cheating includes but not limited to: plagiarism and submission of work that is not the student’s
Geospatial Analytics Project
The purpose of the Geospatial Analytics Project is to provide students first-hand experience on collecting, processing and analysing spatial data using real world data. A project may involve creating geospatially enabled business data and subsequently analysing these data for business strategic or market analysis. Alternatively, a project may be in the form of application development by integrating analytical tools or models within a web framework. Students are encouraged to focus on research topics that are relevant to their field of study. Additional information are available at the Geospatial Analytics Project page of this wiki..
Grading Summary
The grading distribution of this course are as follows:
- Class Participation 10%
- Take-home Exercises 40%
- Geospatial Analytics Application Project 50%
- Project wiki 5%
- Project poster 10%
- Research paper 20%
- Artifact 15%
There will be no mid-term test or final examination for this course.
