Università degli Studi di Napoli "Parthenope"

  1. Teaching
  2. APPLIED COMPUTER SCIENCE (MACHINE LEARNING AND BIG DATA)
  3. Big Geo-Data Management

Stampa

Big Geo-Data Management

Teaching schedule

Academic year: 
2018/2019
Belonging course: 
Course of Master's Degree Programme on APPLIED COMPUTER SCIENCE (MACHINE LEARNING AND BIG DATA)
Location: 
Napoli
Disciplinary sector: 
PHYSICAL GEOGRAPHY AND GEOMORPHOLOGY (GEO/04)
Language: 
Italian
Credits: 
6
Year of study: 
2
Teachers: 
AUCELLI Pietro Patrizio Ciro
Cycle: 
First Semester
Hours of front activity: 
48

Language

Italian (Slides in English)

Course description

The main aim of the course is to provide to the students the basic knowledge on the type and origin of Territorial data and the methods of management of large databases (Big GeoData) through a practical theoretical path that aims to illustrate the main functions implemented in most common GIS software both commercial and open source. In addition, the issues of big data archiving and geodatabase generation is analyzed, as well as the most frequently adopted procedures for data management and solving real problems.
At the end of the course the student will be able to use the acquired concepts and the tools necessary to proceed with the management of geodata, spatial analysis and the overlay of spatial data, the creation of geodatabases, geodatabase interrogation, geovisualization procedures, implementation of spatial data in Web-GIS platform and the editing of thematic mapping in the GIS environment.
The student will be able to deal with the possible situations and to adopt the most efficient solution and, at the end of the class, he will be capable to process territorial data and linked geodatabase and to write technical report using correctly the specific scientific language (In English too).

Prerequisites

Fundamentals of DBMS and numerical computing related to data interpolation.

Syllabus

The main spatial data and their origin, Introduction to the management criteria of the territorial databases (Big GeoData). Main types of software and hardware adopted for the management and analysis of BigGeoData.
Fundamentals of cartography: the main types of cartographic representation and projective systems; the official Italian IGMI cartography and the Regional Technical Cartography. Reading the Topographic maps.
The numerical cartography and GIS. The coordinate systems mainly used in Italy and the georeferencing of cartographic data. Coordinate conversion criteria.
Input, output and storage systems of spatial data. The multilayer structuring of spatial data.
Raster data and vector data. Structure of raster data, geometric and radiometric resolution. Structure of vector data, geometric primitives, attributes and topological relationships. Raster-vector and vector-raster transformations.
The territorial attributes and organization by relational model. The geodatabase.
Querying of geospatial data using SQL language. Spatial and a-spatial queries. Reclassification.
Main data interpolation criteria: global and local methods (IDW, Trend Surface, Spline etc.)
Vector and raster digital terrain models: DSM, TIN and DEM. Main interpolation methods for the realization of digital terrain models starting from point clouds and / or contour lines. Main applications of digital terrain models and their use for the realization of derived maps (slopes, exposure, etc.).
Integrated analysis criteria between raster and vector data. Spatial analysis: the neighborhood filter, the cell statistics, the proximity analysis (buffer and distance matrix, etc.). The thematic overlay. The map algebra, the topological overlay and the integration between spatial data and attribute data (spatial joining).
Overview of geostatistical analysis with particular attention to the multivariate approach. Geo-visualization procedures, the implementation of spatial data in Web-GIS environments and the editing of thematic map in the GIS environment.
Analysis of the most commonly adopted procedures for solving real problems, practical applications and implementation of a GIS project.

Introduction to Big Geo Data (4 h):
The main spatial data and their origin, Introduction to the management criteria of the territorial databases (Big GeoData). Main types of software and hardware adopted for the management and analysis of BigGeoData.

Fundamentals of cartography (8 h):
The numerical cartography and GIS. The georeferencing of cartographic data.

The spatial data (10 h):
Input, output and storage systems of spatial data. The multilayer structuring of spatial data.
Raster data and vector data. The territorial attributes and organization by relational model. The geodatabase.

The analysis of territorial data (22 h):
Main data interpolation criteria. The digital terrain models and their applications.
The querying of GIS data. Spatial analysis and map overlay of big data. Some aspects of multivariate geostatistcal analysis.

The Geo-visualization (4 h)
Geo-visualization procedures, the implementation of spatial data in Web-GIS environments and the editing of thematic map in the GIS environment.
Analysis of the most commonly adopted procedures for solving real problems.

Teaching Methods

Lectures and laboratory activities. Practical applications and implementation of a GIS project.

Textbooks

Kang-Tsung Chang – Introduction to Geographic Information Systems – Mc Graw Hill.
P. A. Longley & M. F. Goodchild - Geographic Information Science and Systems - Wiley
D. O'Sullivan $ D. J. Unwin - Geographic Information Analysis - Wiley
Emanuela Caiaffa – ECDL GIS – La rappresentazione cartografica e I fondamenti dei GIS – MC-GRAW Hill
Elvio Lavagna e Guido Lucarino - Geocartografia. Guida alla lettura delle carte geotopografiche. Zanichelli.

Learning assessment

The objective of the exam is to quantify the level of achievement of the pre-established training objectives. The procedure consists of an oral exam on the theoretical aspects of the course (70% of the vote) and the presentation of a project work agreed with the teacher that the student undertakes during the course and completes autonomously (30% of the vote). The oral test is intended to establish the acquired knowledge of the student about theoretical aspects of the numerical cartography, data organization and main criteria for spatial analysis. Otherwise, the aim of the GIS project is to test the level of competence and independence of the student facing recurring problems about Big GeoData management, geodatabase implementation, spatial analysis, thematic overlay and cartographic production with related reports. The exam is passed if the score obtained is equal to or higher than 18/30.

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