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R A S H T R I Y A U C H C H A T A R S H I K S H A B H I Y A N (R U S A)
COURSE CONTENT
FOR UNIVERSITIES AND ALL AFFILIATED COLLEGES
R A S H T R I YA U C H C H A T A R S H I K S HA AB H I YAN (R U S A)
State Project Directorate, RUSA, Office AddressUnit no.2, 18th Floor, Centre One, World Trade
Centre, Colaba Mumbai 400005
1
Syllabus for Core Course of Geo-Spatial Technologies
Programmes: UndergraduateSemester : IVCredits: 3 creditsTotal Hours: 60Mode: Theory: 3 hrs, Lab:2 hrsCourse Prerequisites: None
Course Outcome: 1. Describe the common terms and definitions of GIS
2. Discuss different GIS data and Data acquisition Technologies
3. Get knowledge about different GIS trends and technologies.
4. Discuss the relevant spatial computing systems and techniques for working with geospatial
data. Apply GIS concepts to solve real world problems.
5. Critically evaluate spatial computing software and systems and determine whether they have
been applied in appropriate ways
Methodology: Class Room Teaching + Lab Hands on, online resources
Recommended:
Set up a learning environment like moodle (for learning resources, uploading assignments, on-
line collaborations and online evaluations
Peer evaluation, presentations, Collaborative learning
Exams and make-up exams policy : flexible
Attendance standards : flexible
2
Course Outline: Geo-Spatial Technologies
1Introduction to GIS : Definitions, Evolution, Components and Objectives
2Geo-Spatial data : Types of geographic data (spatial data with attributes and non-spatial
data), levels of measurements, Concepts of space and time, layers coverage, Spatial data
models, Representation of geographic features in vector, raster data models, Concept of
vertices , point, lines, polygons, and topology, Computer representation for storing spatial
data
3Data acquisition : GPS , History, types, navigation systems and applications
4GPS Trends & Technology: Brief introduction to,Web Based GIS, Enterprise GIS, Mobile
GIS,3-D Visualization and Fly through, Open GIS.
5Application of GIS (Examples) : Watershed Studies, Flood Studies, Ground water Studies,
Health and nutrition geography , Utility Studies, Security and Defense Studies, Urban ( solid
waste management, livability) and infrastructure Studies.
Modalities of assessment
Continuous assessment: Exams, Quizzes, Assignments and Mini projects
Mid semester examination: Quizzes, Assignments and Mini projects
End semester assessment : Project and examination
Reading List:
GIS&T Body of Knowledge https://gistbok.ucgis.org/
Introduction to GIS : http://www.gise.cse.iitb.ac.in/wiki/images/3/3a/
Introduction_to_map_GIS.pdf
Python Geospatial Development - Third Edition by Erik Westra ,May 2016
http://link.springer.com/book/10.1007/978-3-319-25691-7
Google's Earth Engine
Text Books:
1. Longley, P. A., Goodchild, M. F., Maguire, D. J., Rhind, D. W. (2002): Geographical Informa-
tion Systems and Science, John Wiley & Sons, Chichester
2. Lo, C. P., Yeung, A. W. (2002): Concepts Techniques of Geographical Information Systems,
Prentice-Hall of India, New Delhi
3. Chang, K. T. (2008): Introduction to Geographic Information Systems, Avenue of the Americas,
McGraw-Hill, New York
Reference books:
3
1. Korte, G. B. (2001): The GIS Book, Onward Press, Bangalore
2. Demers, M. N. (2000): Fundamentals of Geographic Information Systems, John Wiley and
Sons, New Delhi
3. Burrough, P. A. and McDonnell, R. A. (2000): Principles of Geographical Information Systems,
Oxford University Press, New York
4. Heywood, I., Cornelisus, S., Carver, S. (2011): An Introduction to Geographical Information
Systems, Pearson Education, New Delhi
5. Ahmed, E. L. Rabbany (2002): Introduction to Global Positioning Systems, Artech House, Bo-
ston
6. Sarda, N.L., Acharya, P.S and Sen, S (2019) Geospatial Infrastructure, Applications and Tech-
nologies: India Case Studies, Springer Singapore
4
Recommended Laboratory Assignments on Geo-Spatial Technologies
Objectives:
1. To understand the technology tools used for Geospatial Technologies
2. Provide the students with fundamental concepts and introduce some current technologies of
Geographic Information Systems
3. To offer learning situations and problem solving opportunities from real life including mobile
apps( use of and tweaking/customizing, not full-fledged development) , desktop GIS and Web
GIS.
Methodology: Teaching + Experimentation in Lab Sessions
Recommended:
Domain focus should be incorporated. For example, Insurance domain, Water planning,
Town-planning.
Peer evaluation, presentations, Collaborative learning
Incentive for uploading projects online
Experiments/ Practical List
1 Installation of QGIS
2 lab includes to perform following tasks:
Task 1 – Learn to work with QGIS Browser.
Task 2 – Become familiar with geospatial data models.
Task 3 – Viewing geospatial data in QGIS Desktop.
3 lab includes to perform following tasks:
Task 1 – Add data, organize map layers and set map projections.
Task 2 – Style data layers.
Task 3 – Compose map deliverable.
4 Creating Dynamic Maps in QGIS Using Python
5 Accessing the Map Canvas, Changing Map Units, and Iterating over Layers
6 lab to perform the following tasks:
Task 1 – Data Preparation
Task 2 – Querying and Extracting Subsets of Data
Task 3 – Buffering and Clipping Data
Task 4 – Preparing a Map
7 Understanding Remote Sensing and Analysis using QGIS
5
Lab Reference books/Software manual:
1. Kurt Menke, Richard Smith Jr., Luigi Pirelli and John van Hoesen (2015) Mastering QGIS
Pakt Publishing.
6
Syllabus for Core Course of Spatial and Graph Databases Undergraduate Programmes: Under-
graduate
Semester : V
Credits: 3 credits
Total Hours: 60
Mode: Theory: 3 hrs, Lab:2 hrs
Course Prerequisites : Geo Spatial technologies
Course Outcome:1. Describe the common terms and definitions of spatial data & databases 2. Discuss different data models3. Get knowledge about spatial query languages.
4. Apply spatial databases concepts to solve real world problems.
Methodology: Class Room Teaching + Lab Hands on
Recommended:
Set up a learning environment like moodle (for learning resources, uploading assignments, on-
line collaborations and online evaluations
Peer evaluation, presentations, Collaborative learning
Exams and make-up exams policy : flexible
Attendance standards : flexible
7
Course Outline: Spatial Databases
1Introduction to spatial databases : Requirement of spatial databases , DBMS
Support for Geospatial Data, Users in SDBMS, Example of SDBMS
2Spatial Concepts and Data Models : Geographic Space Modelling, Spatial Data
Formats and Exchange Standards,Three step database design, Extending ER model
with spatial constraints
3Spatial Query Language : Standard database query languages, Extending SQL for
spatial data, Object relational SQL
4Spatial Storage and Indexing : Brief introduction to spatial data storage,
Definition of spatial indexing, Different techniques for indexing spatial data
5Query Processing and Optimization : Introduction to spatial query processing,
Evaluation of spatial operations, Query Optimization techniques
6Trends in Spatial Databases : Database support for field entities, Content based
retrieval, Introduction to spatial data-warehouses, System Integration, Big
geospatial data platforms
Modalities of assessment
Continuous assessment: Exams, Quizzes, Assignments and Mini projects
Mid semester examination: Quizzes, Assignments and Mini projects
End semester assessment : Project and examination
Reading List:
A Gentle Introduction to GIS https://docs.qgis.org/2.8/en/docs/gentle_gis_introduction/
data_capture.html
https://www.e-education.psu.edu/geog585/syllabus#top
https://www.youtube.com/watch?v=EUUWUUDjU4o postgis
Books:
1. Spatial Databases: A Tour 1st Edition, Shashi Shekhar, Sanjay Chawla
2. Spatial Databases: with Application to GIS, Rigaux et al, Morgan Kaufmanm (or the other
popular book in the field: Spatial Databases: A Tour, Shekhar and Chawla, Prentice-Hall);
Reference books:
8
Recommended Laboratory Assignments on Spatial and Graph Databases
Objectives:
1. To understand the database concepts and tools used for Geospatial Technologies
2. To understand design and modelling the Spatial problems
3. To understand and execute complex queries on spatial databases.
Methodology: Teaching + Experimentation in Lab Sessions
Recommended:
Domain focus should be incorporated. For example, Insurance domain, Water planning,
Town-planning.
Peer evaluation, presentations, Collaborative learning
Incentive for uploading projects online
Sr.
No
Experiments/ Practical List
1 A. Installation of Postgres, PostGIS and QGIS, pgAdmin
B. Ice cream entrepreneurs Jen have opened business and now need a database to track or-
ders. When taking an order they record the customer's name, the details of the order such
as the flavors and quantities of ice cream needed, the date the order is needed and the de-
livery address. Their database needs to help them answer two important questions:
1. Which orders are due to be shipped within the next two days?
2. Which flavors must be produced in greater quantities?
Implement a Database Design for above scenario.
2 Introduction to Postgres's graphical interface: pgAdmin
A. Create a new schema,
B. Load data from a shapefile
C. Create a new table
D. Load data using the COPY command
E. Write queries in pgAdmin
3 Query-Writing Assignment
4 Spatial Select Queries
5 PostGIS Geometry Types Queries.
A. Create a new empty spatial table
9
Sr.
No
Experiments/ Practical List
B. Add rows to the spatial table
C. Create and populate a table of linestrings
D. Create and populate a table of polygons
E. 3- and 4-dimensional geometries
F. Multipart geometries
G. Mixing geometries
6 Add PostGIS data to QGIS
Quantum GIS (QGIS, pronounced kyü'-jis) is a free and open-source desktop GIS package
view the tables we created and populated in the previous Assignments
7 PostgreSQL provides several index types: B-tree, R-tree, Hash, and GiST. Each index type
uses a different algorithm that is best suited to different types of queries. Create a
database execute all index type queries and measure the performance.
8 Write queries on following using following Spatial Relationship Functions : ST_Contains(),
ST_Within(), ST_Covers(), ST_CoveredBy(), ST_Intersects(), ST_Disjoint(),
ST_Overlaps(), ST_Touches(), ST_Dwithin(), ST_DFullyWithin()
9 Write queries on following using following Spatial Measurement Functions : ST_Area(),
ST_Centroid(), ST_Distance(), ST_Distance_Spheroid() and ST_Distance_Sphere(),
ST_Length(), ST_Length_Spheroid(), ST_Length3D(), ST_Length3D_Spheroid(),
ST_Perimeter(), ST_Perimeter3D()
Lab Reference books/Software manual:
1. Regina O. Obe and Leo S. Hsu (2015): PostGIS in Action, Manning Publications
2. Kurt Menke, Richard Smith Jr., Luigi Pirelli and John van Hoesen (2015) Mastering QGIS
Pakt Publishing.
10
Syllabus for Core Course of Introduction to Geospatial Data Analytics
Programmes: Undergraduate
Semester : VI
Credits: 3 credits
Total Hours: 60
Mode: Theory: 3 hrs, Lab:2 hrs
Course Prerequisites : Geospatial Technologies
Course Outcome:
The student should be able to
1. Understand the spatial data types
2. Understand principles of visual and statistical analysis with geospatial data
3. To comprehend the effective reporting of geospatial analysis
Methodology: Class Room Teaching + Lab Hands on
Recommended:
Set up a learning environment like moodle (for learning resources, uploading assignments, on-
line collaborations and online evaluations
Peer evaluation, presentations, Collaborative learning
Exams and make-up exams policy : flexible
Attendance standards : flexible
Course Outline:
1Introduction: Data & Spatial Data Analysis, Types of Spatial Data, The Spatial
Data Matrix, Spatial Autocorrelation, The Tyranny of Spatial Data.
2Map based analytics: Visualizing spatial processes in maps, Developing readable
maps and interactive analysis. Fundamental data analytics principles.
Communicating with maps.
11
3Methods Spatial Interaction Data: The General Spatial Interaction Model,
Maximum Likelihood estimation of the Poisson Spatial Interaction Model,
Generalization of Poisson Model of Spatial Interaction
4Models for Spatial Interaction Data: Visualizing & Exploring Spatial Interaction
data, The General Spatial Interaction Model, Functional Specifications & the
method of Ordinary least Square Regression.
5Spatial Interaction Models & Spatial Dependence: The Independence Spatial
Interaction Model in Matrix Notation, Econometric Extension to the Independence
Spatial Interaction Model, Spatial Filtering Versions of Spatial Interaction Models
Text Books:
1. "Geospatial Data and Analysis" by Bill Day, Jon Bruner, Aurelia Moser Publisher: O'Reilly Me-
dia, Inc. ISBN: 9781491984314
2. Menno-Jan Kraak, F.J. Ormeling (2013) Cartography: Visualization of Spatial Data, Routledge
Publishing.
3. Michael J de Smith, Michael F Goodchild, Paul A Longley (2007) Geospatial Analysis: A Com-
prehensive Guide to Principles, Techniques and Software Tools, Troubador Publishing.
4. Michael Dorman (2014) Learning R for Geospatial Analysis, Packt Publishing.
Modalities of assessment
Continuous assessment: Exams, Quizzes, Assignments and Mini projects
Mid semester examination: Quizzes, Assignments and Mini projects
End semester assessment : Project and examination
12
Recommended Laboratory Assignments on Geospatial Data Analytics
Objectives :
To understand the geospatial data tools
To understand the analytical tools
Ability use analytic tools for analysis
Ability to visualise and interpret the analysis
Methodology: Teaching + Experimentation in Lab Sessions
Recommended:
Domain focus should be incorporated. For example, Insurance domain, Water planning,
Town-planning.
Peer evaluation, presentations, Collaborative learning
Incentive for uploading projects online
Experiments/ Practical List
1 Creating and manipulating spatial data using R
2 Logistic Regression implementation in R
3 Multinomial Logistic Regression with Categorical Response Variables at 3 Levels
using R
4 Unsupervised Classification using QGIS and Grass GIS
5 Supervised Classification using QGIS and Grass GIS
6 Interpolating Point Data using QGIS
7 Nearest Neighbor Analysis using QGIS
Lab Reference books/Software manual:
1. Kurt Menke, Richard Smith Jr., Luigi Pirelli and John van Hoesen (2015) Mastering QGIS
Pakt Publishing.
2. Guy Lansley and James Cheshire (2016) An Introduction to Spatial Data Analysis in R avail-
able on http://www.spatialanalysisonline.com
13
Syllabus for Core Course of WebGIS, Geospatial Programming and Visualization
Semester V
Credits: 3 credits
Total Hours: 60
Mode: Theory Class+ Lab
Theory: 3 hrs, Lab:2 hrs
Prerequisites: Geo-Spatial Technologies
Course Outcome:
1. To understand programming tools in geospatial applications
2. To understand spatial databases and representations on web
3. Able to make programming in WebGIS
4. Able to implement geospatial applications
Methodology: Class Room Teachingc + Lab Hands on
Recommended:
Set up a learning environment like moodle (for learning resources, uploading assignments, on-
line collaborations and online evaluations
Peer evaluation, presentations, Collaborative learning
Exams and make-up exams policy : flexible
Attendance standards : flexible
Course Outline:
1Building the web: HTML, CSS, and Javascript : Learn how to build the
structure and content of a web page with HTML, Apply styling rules with CSS, and
automate and script the page with javascript/jquery, Learn javascript basics: strings,
numbers, arrays, objects,, functions, loops, etc.
14
2Mapping with Leaflet.js ( or openlayers.js) : Use the leaflet.js library to add a
map element to a website, Add basemaps, markers, and controls to your map,
Explore advanced techniques and features of leaflet, including choropleth
mapping/dynamic styling, leaflet plugins, events, etc.
3Spatial Data for the Web : Understand how data is stored, moved and organized
for integration into a web map. e.g geoserver , Explore data sources and techniques
for transforming/preparing data for use in web maps., Add vector data to a leaflet.js
map . Tiling of data. Styles of spatial features.
4GIS data access and manipulation with Python: Data storage and retrieval in
QGIS, Reading vector attribute data, Accessing data fields, Reading through
records, Retrieving records using an attribute query , Retrieving records using a
spatial query, Writing vector attribute data, Updating existing records, Inserting
new records, Working with rasters
5Web Application Development for Geospatial: Building a Web Map, Turning
Your Map into an App, Embedding a Map, Configuring an App Based on a
Template, Creating an App with the Web AppBuilder, use of geonode as a stack
Text Books:
1. Introduction to web programming for GIS applications by Michael Miller
2. Web mapping by Prof. Dr. Martin Breunig
3. Web Mapping and Geospatial Web Services: An Introduction by Emmanuel Stefanakis
References :
1. www.geonode.org
2. www.geoserver.org
Modalities of assessment
Continuous assessment: Exams, Quizzes, Assignments and Mini projects
Mid semester examination: Quizzes, Assignments and Mini projects
End semester assessment : Project and examination
Practical : Recommended Laboratory Assessment
1 Write a Page Using XHTML and CSS
2 UCreate Your First 2D Map of your hometown and Post the map containing
your hometown to your e-portfolio use openlayers or leaflet
15
3Creating An Interactive Map With Leaflet and OpenStreetMap
4 Update the web page which is created in assignment no2 to include choropleth
mapping/dynamic styling
5 Find which cities have at least two park within their boundaries.Mark the
"HasTwoParkAndRides" field as "True" for all cities that have at least two park
and calculate the percentage of cities that have at least two park within their
boundaries and print this for the user using Python
6 You have been provided two things:
A text file StatePoints.txt containing the coordinates of a state
boundary.
An empty polygon shapefile that uses a geographic coordinate system.
write a Python script that reads the text file and creates a state boundary
polygon out of the coordinates.
7 Turn Map into an App using geonode
16
Syllabus for Electives Course of Geo-Informatics and Spatial Computing Programmes: Undergraduate Semester : V/VI, ElectiveCredits: 3 creditsTotal Hours: 60Mode: Theory: 3 hrs, Lab:2 hrsCourse Prerequisites: None Course Outcome:
1. Describe the theoretical foundations of geospatial data and its various representations
2. Select and use the appropriate spatial computing technologies and systems to solve any of a
variety of real-world problems
3. Build integrated applications that combine geographic data and applications for processing
that data
4. Understand, create, and apply semantic descriptions of geographic data which can then be
used for searching, integrating, and sharing geographic knowledge
5. Discuss the relevant spatial computing systems and techniques for working with geospatial
data
6. Apply relevant spatial computing techniques to solve spatial problems
7. Critically evaluate spatial computing software and systems and determine whether they have
been applied in appropriate ways
Methodology: Class Room Teachingc + Lab Hands on
Recommended:
Set up a learning environment like moodle (for learning resources, uploading assignments, on-
line collaborations and online evaluations
Peer evaluation, presentations, Collaborative learning
Exams and make-up exams policy : flexible
Attendance standards : flexible
17
Course Outline: Geo-Informatics and Spatial Computing
1Introduction to basics of spatial data, representations of spatial data, structured spatial data,
unstructured spatial data, streaming data, coordinate systems, datum, projections, etc.
Introduction to real-world spatial computing problems and challenges in using traditional GI
systems
2Structured Spatial Data: Introduction to capabilities of spatial systems that handle large
spatial datasets Online Spatial Data: online GIS software and datasets, with a focus on
Google Maps, Bing Maps, and Google Earth
3Machine-Understandable Spatial Data: Introduction to methods and applications for
representing and reasoning about geospatial data using the infrastructure of the Semantic
Web, Introduction to research and techniques for creating and using geospatial linked data
4Unstructured Spatial Data: Introduction to new methods and applications for linking
addresses to locations, Introduction to methods and applications for linking textual
information to geographic locations
5Advanced Spatial Computing: Introduction to advanced techniques for handling spatial data,
including spatial data mining, reasoning, and streaming
Modalities of assessment
Continuous assessment: Exams, Quizzes, Assignments and Mini projects
Mid semester examination: Quizzes, Assignments and Mini projects
End semester assessment : Project and examination
Reading List:
A Gentle Introduction to GIS https://docs.qgis.org/2.8/en/docs/gentle_gis_introduction/
data_capture.html
Text Books:
1. Clarke K C (2011) Getting Started with Geographic Information Systems (Fifth Edition). Up-
per Saddle Creek, NJ: Prentice Hall
2. Drive your research with Open Source GIS https://www.osgeo.org/initiatives/geo-for-
all/drive-research-open-source-gis/
18
Practical's : Recommended Laboratory Assignment
Objectives :
Given the fact that students of MA (Geography) and MSc (Geography) have rich knowledge
of geography, but
To understand the technology tools used for Geospatial Technologies
Provide the students with fundamental concepts and introduce some current technologies of
Geographic Information Systems (GIS)
To offer learning situations and problem solving opportunities from real life including mobile
apps( use of and tweaking/customizing, not full-fledged development) , desktop GIS and Web
GIS.
Methodology: Teaching + Experimentation in Lab Sessions
Recommended:
Domain focus should be incorporated. For example, Insurance domain, Water planning, Solid
Waste Management, Town-planning ( DP).
Peer evaluation, presentations, Collaborative learning
Incentive for uploading projects online
Exp Lab/Experiment
1 Registering and using Google earth engine resources
2 Introduction to Google earth engine editor
3 Writing a simple program in google earth engine using Javascript
4 Introduction to linking addresses to locations using google earth engine
5 Simple classification of data using google earth engine
19
Syllabus for Electives on Course in Geospatial Big data analytics and cloud computing
Program : Undergraduate
Semester VII
Credits: 3 credits
Total Hours: 60
Mode: Theory Class+ Lab
Theory: 3 hrs, Lab:2 hrs
Prerequisites: Geo-Spatial Technologies
Course Outcome:
1. To understand big data concepts
2. To understand cloud concepts
3. To able to understand geospatial cloud applications deployment
4. To able to perform geospatial analysis methodologies
Course Outcome:
The main objectives of this course are:
1. To train the students with geospatial technologies (preferably open source softwares, like Geonode,
geoserver, Geospark, HIVE, Spatial Hadoop) through hands-on experiences in solving real-life or
nearly real-life problems in above sectors.
2. To analyse geospatial data using open source platforms with large size.
3. To enable the converging global trends in geo-awareness, geo-enablement, geo-technologies,
citizen science, and storytelling towards enabling societal dialogue.
4. To describe the common terms and definitions of GIS, Discuss different GIS data acquisition
Technologies, and Get knowledge about different GIS trends and technologies.
5. Apply GIS concepts to solve real world problems.
Methodology: Class Room Teaching + Lab Hands on
Recommended:
Set up a learning environment like moodle (for learning resources, uploading assignments, on-
line collaborations and online evaluations
20
Peer evaluation, presentations, Collaborative learning
Exams and make-up exams policy : flexible
Attendance standards : flexible
Course Outline:
1 Introduction to Geographic Information and Big Data: Introduction to GIS as
big data ,Characteristics of Geospatial Data as big data, Different types of geospa-
tial big data
2 Parallel Computation of Algorithm on GPU with large volume of spatial Data,
MMDBM Algorithm, Distributed File Systems and Computations
3 Big Data Scientific Workflows in the Cloud Challenges and Future Prospects
4 Survey of geospatial big data platform: Introduction to various big data
platforms, Spatial Hadoop, Geospark, Geomessa, Hive spatial, geonode,
5 Geospatial Big Data, Analytics : Challenges, Applications and Potential
Text Books:
1. Cloud Computing for Geospatial Big Data Analytics, Editors: Das, H., Barik, R.K., Dubey,
H., Roy, D.S. (Eds.), Springer
2. Jiang Zhe and Shashi Shekhar (2017) Spatial Big Data Science Classification Techniques for
Earth Observation Imagery. Springer
Reference books:
1. Chris Eaton, Dirk Deroos, Tom Deutsch et al., “Understanding Big Data”, McGrawHIll, 2012.
2. Eric Siegel, Thomas H. Davenport, “Predictive Analytics: The Power to Predict Who Will
Buy", Bye, Lie, or Die”, Wiley, 2013.
3. http://cran.r-project.org/doc/manuals/R-intro.html
Modalities of assessment
Continuous assessment: Exams, Quizzes, Assignments and Mini projects
Mid semester examination: Quizzes, Assignments and Mini projects
End semester assessment : Project and examination
21
Recommended Laboratory Assignments on Geospatial Big data analytics and cloud computing
Objectives :
1. To be able to do understand cloud services
2. To be able to operate cloud applications
3. To be able to operate geospatial cloud applications
4. To be able to perform analytics on geospatial datasets
Methodology: Teaching + Experimentation in Lab Sessions
Recommended:
Domain focus should be incorporated. For example, Insurance domain, Water planning,
Town-planning.
Peer evaluation, presentations, Collaborative learning
Incentive for uploading projects online
Recommended Laboratory Assessment
1 To install and configure spatial Hadoop on Linux
2 To execute Gzip compression algorithms on spatial Hadoop
3 To implement R index algorithms on spatial hadoop on geospatial data
4 To implement KNN on spatial Hadoop
5 To implement range queries on spatial Hadoop
6 To install and configure Geospark on Linux
7 To execute Gzip compression algorithms on Geospark
8 To implement R index algorithms on Geospark on geospatial data
9 To implement KNN on Geospark
10 To implement range queries on Geospark
Lab Reference books/Software manual:
1. Jiang Zhe and Shashi Shekhar (2017) Spatial Big Data Science Classification Techniques for
Earth Observation Imagery. Springer
22
Syllabus for Electives Course in Advance course on geospatial technologies.
Program : Postgraduate
Semester II
Credits: 4 credits
Total Hours: 80
Mode: Theory Class+ Lab
Theory: 4 hrs, Lab:2 hrs
Prerequisites: Geo-Spatial Technologies
Course Objectives
The main objectives of this course are:
● To create human resource with proven expertise in geospatial technologies who are able to act
as enablers of the technology as well as design solutions employing spatial thinking
● Using case based studies and hands on experience, create the capacity to design and
implement systems that harness the evolving potential of geospatial data and applications.
● Through on the job learning with industry and public service organisations, provide students
to real-world experience, thereby building confidence and ensuring necessary exposure for
students to transition into practitioners
Methodology: Class Room Teaching + Lab Hands on
Unit Topics & Sub Topics Hrs
1 Geospatial Applications and components of GIS
Location centric workflows, Applications of geospatial technologies, components of
GIS, modern GIS, GIS as a part of mainstream IT.
10
2 Spatial Data, Databases and Programming
Introduction to spatial data, need for spatial databases, creating and using spatial
data, introduction to scripting. Automating geospatial processes.
SQL code, Python Scripting
14
23
3 Geo-visualization, thematic mapping and scripting
Creating spatial displays. Maps as extended graphs, visual analytics through maps,
introduction to digital cartography.
R, R packages/Spatial Analyst
14
4 Spatial statistics, analytics for spatial data science
Introductory spatial statistics and its applications, GIS and other spatial data for data
science. Spatial autocorrelation, variation and interpolation. Spatial inference
Cartographic reports, mapmaking (digitizing, raster-vec), neogeogrphy
12
5 Web -GIS and Mobile GIS
(spatial) Data sharing on the web, standard map interfaces on the web, crowd
mobile-gis and its applications, trade-offs in relation to mobile and web mapping.
Geonode, R
14
Text books;
1. Introduction to web programming for GIS applications by Michael Miller
2. Web mapping by Prof. Dr. Martin Breunig
24
Lab Work/ Experiments
Sr. No Lab/Experiment Hrs
1Geospatial Applications and components of GIS, OGC standards, Open Source
GIS
20
2*Data Acquisition
20
4 Spatial Data, Databases and Programming 30
5 *Geovisualization, thematic mapping and scripting 20
6 Spatial statistics, analytics for spatial data science 20
7 *System integration, geospatial project lifecycle, project planning 10
Web -GIS and Mobile GIS 15
Spatial Mini Project 45
25
References / Further Reading List :
A Gentle Introduction to GIS https://docs.qgis.org/2.8/en/docs/gentle_gis_introduction/
data_capture.html
UCGIS Body of Knowledge
o Old:http://www.aag.org/galleries/publications-files/GIST_Body_of_knowledge.pdf
o New : http://gistbok.ucgis.org/
Tutorials online : https://goo.gl/gPrB9T ( some documents and some links)
Introduction to GIS : http://www.gise.cse.iitb.ac.in/wiki/images/3/3a/
Introduction_to_map_GIS.pdf
Python Geospatial Development - Third Edition by Erik Westra ,May 2016
http://link.springer.com/book/10.1007/978-3-319-25691-7
https://www.e-education.psu.edu/geog585/syllabus#top
https://www.youtube.com/watch?v=EUUWUUDjU4o postgis
https://www.geos.ed.ac.uk/~gisteac/gis_book_abridged/
https://www.youtube.com/watch?v=08LS7XZWzPE web mapping on Windows
Industry, Software :
Next-Generation Google Maps : https://tinyurl.com/bbhvdk o
https://viewer.nationalmap.gov/launch/
ISRO’s GIS : http://bhuvan.nrsc.gov.in/map/bhuvan/bhuvan2d.php
Opensource framework for GIS : www.geoshape.org
26