| School of Engineering and Technology, (SET) | ||||
| AT76.19 : Advance Spatial Analysis Methods 3(2-3) | ||||
| Course objectives: | ||||
This course aims at providing advance knowledge in discrete and continuous spatial data understanding and analysis. Students will also be exposed to advance modeling techniques, exploratory spatial data analysis, interpolation techniques, terrain modeling, and geostatistical analysis. |
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| Learning Outcomes: | ||||
The students on the completion of this course would be able to:
1. Analyze the discrete and continuous databases
2. Identify and communicate the mathematical, statistical, logical and cartographic modeling methods
3. Rank and compute weights for decision making for planning and site suitability
4. Analyze patterns, trends and hotspots, and find spatial auto-correlations in different multi-criteria phenomenon.
5. Use the concept of geospatial analysis and modeling tools in GIS software such as ArcGIS, Quantum GIS
6. Develop applications in various areas for Urban planning and management, Disaster Risk Management, Agriculture, Health
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| Pre-requisite(s): | ||||
AT76.01 Geographic Information Systems |
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| Course Outline: | ||||
I. Advance Spatial Analysis Methods for Decision Support
1. Introduction
2. Importance of Spatial Data Characterisation
3. Importance of Advance Spatial Data Analysis for Decision Support
II. The Analysis of Discrete Entities in Space
1. Operations on Attributes of Geographic Objects
2. Attribute Descriptors
3. Point Descriptors: Central Tendency, Dispersion and Distribution
4. Operations on Attributes of Multiple Overlapping Entities in Space
5. Cartographic Operations
6. Pattern Detectors: Nearest Neighbor Analysis, Application
7. Characteristics of Linear Features: Directional Statistics and Network Analysis
III. The Spatial Analysis using Continuous Fields
1. Map Algebra and Cartographic Modeling
2. Point Operations
3. Spatial Analysis Using Convolution
4. Deriving Surface Topology and Drainage Networks
5. Spatial Regression Analysis and Modelling
IV. Analytical Hierarchical Processing (AHP)
1. Introduction to AHP
2. Ranking matrix, Random Index and Evaluation of consistency ratio
3. Multi-Criteria decision making
4. Application of AHP for land suitability
V. Information Value Approach (IV)
1. Introduction
2. Bayesian Algorithm for Information Value Index
3. Application of IV for Dengue Incidence Analysis
VI. The Spatial Pattern Descriptors
1. Spatial Relationship
2. Spatial Autocorrelation
3. Scatter Plots: Moran-I
4. Spatial Indices
VII. ArcGIS Structure and Geostatistical analysis
1. Variogram analysis
2. Histogram analysis
3. Modeling of spatial trends
4. Semivariogram creation, linking, selection
5. Advance tools for spatial analysis
VIII. Spatial Analyst – Interpolation Techniques
1. Interpolation techniques
2. Implementation and comparison
3. Spatial interpolation comparison
IX. 3D- GIS
1. Topographic Descriptors
2. TIN Modeling
3. Creating 3 D building model and embedding in online databases
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| Laboratory Sessions: | ||||
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| Learning Resources: | ||||
| Textbook: | ||||
P.A. Longley and Michael Batty:
Advanced Spatial Analysis, ESRI Press, Redlands, California, 2003
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| Reference Books: | ||||
D. O’Sulllivan and D. J. Unwin:
Geographic Information Analysis. John Wiley and Sons, Inc., New York, 2003.
Shashi Shekar and Hui Xiong (Eds.):
Encyclopedia of GIS, Springer, Verlag, 2007.
Roger Tomilson:
Thinking about GIS, ESRI Press. Redlands, California, 2007.
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| Journals and Magazines: | ||||
• International Journal of GIS, Taylor & Francis
• International Journal of Geoinformatics, Association of Geoinformation Technology
• International Journal of Health Geography, BMC, Springer Nature
Others:
Lecture Notes and other ancillary learning resource books
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| Time Distribution and Study Load: | ||||
Lecture: 30 Hrs
Laboratory: 45 Hrs
Laboratory Assignments and Term Paper: 40 Hrs
Self-studies: 80 Hrs
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| Teaching and Learning Methods: | ||||
This course is blend of learning concepts and hands on exercises for developing the skills to apply various software related to GIS and also implement advance modeling through programming in GIS. Each class will comprise of conceptual and knowledge development lectures and discussions which will be followed by hands on exercises and assignments.
Laboratory Sessions: Laboratory exercises will expose students to different advance tools in GIS, other modeling software and analysis techniques. Students have to submit all assignments individually. There will be a lab test to evaluate their skill on GIS tools.
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| Evaluation Scheme: | ||||
The final grade will be based on the following weight distribution: Laboratory assignments (20%), midsem exam (20%), final exam (45%) and term paper (15%). Closed book examinations are used for both midsem and final exams.
An “A” would be awarded if a student can show the ability of having extensive
knowledge on in all learning outcomes and ability to display excellent performance in practical exercises and term paper. A “B” would be awarded if a student shows an overall understanding of the topics covered, a “C” would be given if a student meets below expectation on both knowledge acquired and analysis. A “D” would be
given if a student does not meet basic expectations of the topics presented in the course.
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| Instructor(s): | ||||
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