Geospatial Data Management
A.Y. 2019/2020
Learning objectives
The course aims at providing key concepts for the understanding of geospatial and mobility data, as well as the basic competences for the management of such information through spatial and spatio-temporal databases.
Expected learning outcomes
Students are expected to be able to utilize and integrate georeferenced data from heterogeneous sources. Moreover, students will be able to organize and query geospatial and mobility data using both a spatial DBMS and a GIS platform, and, in addition, to use a few analytical functions.
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
First semester
Course syllabus
The program of the course consists of two main parts: the first part introduces key concepts at the basis of spatial computing; the second part is about advanced topics, in part still of research, principally related to the representation of moving objects and mobility data analysis.
Main topics are:
- Geodetic reference systems, geographic and projected coordinate systems, basic cartographic concepts
- The OGC Simple Feature model for the vector-based representation of spatial objects.
- Architecture for spatial data handling, the PostGIS and QGIS systems
- Introduction to the modeling of spatial trajectories
- Introduction to spatial data analysis, with emphasis on spatial and spatio-temporal data clustering.
Main topics are:
- Geodetic reference systems, geographic and projected coordinate systems, basic cartographic concepts
- The OGC Simple Feature model for the vector-based representation of spatial objects.
- Architecture for spatial data handling, the PostGIS and QGIS systems
- Introduction to the modeling of spatial trajectories
- Introduction to spatial data analysis, with emphasis on spatial and spatio-temporal data clustering.
Prerequisites for admission
Basic knowledge of database concepts and tools
Teaching methods
Traditional lectures alternated with exercises on computer
Teaching Resources
Web site: https://mdamianiae.ariel.ctu.unimi.it/v5/Home/default.aspx
PostGis in Action, Manning editor, 2011
PostGis in Action, Manning editor, 2011
Assessment methods and Criteria
The examination consists of two parts: a) an oral test on the topics of the course; b) presentation of a project. The project is assigned at the end of the course.
It is also planned a written test "in itinere" , to allow students passing the test to only present the project during the examination (no oral test). The project entails the solution of a problem and the documentation of the solution.
La final score, expressed in thirtieth, takes into account the level of knowledge, the originality and accuracy of the project , e the level of participation in the course.
It is also planned a written test "in itinere" , to allow students passing the test to only present the project during the examination (no oral test). The project entails the solution of a problem and the documentation of the solution.
La final score, expressed in thirtieth, takes into account the level of knowledge, the originality and accuracy of the project , e the level of participation in the course.
ING-INF/05 - INFORMATION PROCESSING SYSTEMS - University credits: 6
Lessons: 48 hours
Professor:
Damiani Maria Luisa
Shifts:
-
Professor:
Damiani Maria LuisaProfessor(s)