Information Systems and Statistics for the Territory
A.Y. 2022/2023
Learning objectives
Module 1: Territorial Information System
The course aims at providing students with advanced knowledge on spatial analysis and the production of thematic cartography in a GIS environment
Module 2: Statistics
The course aims to give students the necessary tools to understand phenomena through statistical analysis of data. Specifically, the student should achieve the following objectives: Knowledge of descriptive and inferential statistics. Evaluation of uncertainty using statistical tests. Correlation analysis between variables and use of linear regression models. Statistical comparison between data sets.
The course aims at providing students with advanced knowledge on spatial analysis and the production of thematic cartography in a GIS environment
Module 2: Statistics
The course aims to give students the necessary tools to understand phenomena through statistical analysis of data. Specifically, the student should achieve the following objectives: Knowledge of descriptive and inferential statistics. Evaluation of uncertainty using statistical tests. Correlation analysis between variables and use of linear regression models. Statistical comparison between data sets.
Expected learning outcomes
Module 1: Territorial Information System
The course aims at providing an advanced understanding of GIS by showing their utility in practical environmental and territorial applications; it also aims at providing students with the necessary ability to solve spatial problems.
At the end of the course, students must demonstrate to have acquired the following competences:
- understand characteristics of spatial data, including Digital Elevation Models and orthophotos;
- to understand and discriminate between the different reference systems in which these data are expressed
- to be able to associate raster and vector data to the appropriate reference system and/or cartographic representation;
- to be able to import, export and convert data in raster and vector format between different reference systems and cartographic projections;
- to understand the characteristics of vector and raster structures and the information necessary to georeference them
- to understand spatial interpolation techniques for the production of raster data (DEM and orthophotos); -
- to be able to carry out queries and spatial selection on vector data and attributes;
- to be able to use the raster calculator to select and analyse raster data
- to understand and successfully apply georeferencing techniques on raster data;
- to be able to break down problems into the steps required to solve it, identifying the most appropriate GIS tools for each case.
Module 2: statistics
Describe the phenomena by the main statistical indicators; Plan a sample surveys; Use the methodology of the analysis of variance at 1 factor; understand the results of statistical surveys.
The course aims at providing an advanced understanding of GIS by showing their utility in practical environmental and territorial applications; it also aims at providing students with the necessary ability to solve spatial problems.
At the end of the course, students must demonstrate to have acquired the following competences:
- understand characteristics of spatial data, including Digital Elevation Models and orthophotos;
- to understand and discriminate between the different reference systems in which these data are expressed
- to be able to associate raster and vector data to the appropriate reference system and/or cartographic representation;
- to be able to import, export and convert data in raster and vector format between different reference systems and cartographic projections;
- to understand the characteristics of vector and raster structures and the information necessary to georeference them
- to understand spatial interpolation techniques for the production of raster data (DEM and orthophotos); -
- to be able to carry out queries and spatial selection on vector data and attributes;
- to be able to use the raster calculator to select and analyse raster data
- to understand and successfully apply georeferencing techniques on raster data;
- to be able to break down problems into the steps required to solve it, identifying the most appropriate GIS tools for each case.
Module 2: statistics
Describe the phenomena by the main statistical indicators; Plan a sample surveys; Use the methodology of the analysis of variance at 1 factor; understand the results of statistical surveys.
Lesson period: year
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
year
Prerequisites for admission
STATISTICS:
The students should be familiar with basic concepts of Calculus I (crash courses available).
TERRITORIAL INFORMATION SYSTEMS:
It is useful although not required to have followed a basic GIS course; basic knowledge of cartography and reference systems are also useful; it is necessary to possess basic IT skills.
The students should be familiar with basic concepts of Calculus I (crash courses available).
TERRITORIAL INFORMATION SYSTEMS:
It is useful although not required to have followed a basic GIS course; basic knowledge of cartography and reference systems are also useful; it is necessary to possess basic IT skills.
Assessment methods and Criteria
STATISTICS.
Written test with open and closed questions, exercises (pocket calculator required) and explanation of the output produced by statistical software.
TERRITORIAL INFORMATION SYSTEMS:
Learning will be assessed through an oral examination with questions on the theoretical part of the course, followed by a project, which will demonstrate that the student has acquired the required skills in the elaboration, visualization and query of data in QGIS.
To successfully complete the course, the student must demonstrate, both in the oral examination and in the practical part, to have acquired the skills listed as the learning outcomes.
In the final assessment, the oral examination accounts for 60% of the vote and aims to verify the acquisition of theoretical and methodological knowledge. The project accounts for the remaining 40%, and aims to verify the ability of the student to apply the theoretical knowledge and his/her self-learning abilities. Students with LSD should contact the teacher to plan compensatory and dispensatory measures, following the guidelines provided by the University.
Written test with open and closed questions, exercises (pocket calculator required) and explanation of the output produced by statistical software.
TERRITORIAL INFORMATION SYSTEMS:
Learning will be assessed through an oral examination with questions on the theoretical part of the course, followed by a project, which will demonstrate that the student has acquired the required skills in the elaboration, visualization and query of data in QGIS.
To successfully complete the course, the student must demonstrate, both in the oral examination and in the practical part, to have acquired the skills listed as the learning outcomes.
In the final assessment, the oral examination accounts for 60% of the vote and aims to verify the acquisition of theoretical and methodological knowledge. The project accounts for the remaining 40%, and aims to verify the ability of the student to apply the theoretical knowledge and his/her self-learning abilities. Students with LSD should contact the teacher to plan compensatory and dispensatory measures, following the guidelines provided by the University.
Module 1: Territorial Information System
Course syllabus
Recalls of basic cartographic concepts: reference systems, projections, contents of maps; Italian official cartography.
Geographic information systems: definition of conceptual and logical models.
Recalls of basic GIS concepts:
Raster data: Spatial and radiometric resolution. Georeferencing raster data. Analysis of raster data. Most common raster formats;
Vector data: primitives, entities; topological primitives. Most common vector formats.
Vectorization and cartographic editing.
Visualization, selection and query of raster and vector attributes.
Techniques and technologies to acquire data for the generation of Digital Elevation Models, orthophotos, and GIS at large.
Digital Elevation models (DEM). DEM components. Interpolation models and data structures.
DEM operations.
Digital orthophotos. Advantages and disadvantages compared to numerical cartography. Generation of orthophotos.
Databases at municipality, regional and national level, techniques for data distribution and sharing (webGIS, geoservices).
Lab workshops.
Use of DEM generation software, multi-temporal comparisons, volume calculations.
Presentation and use of QGIS: data import, definition of the reference system, data export, operations on the geometry and attributes of vector data.
Queries, join and spatial selection. Zonal statistics.
Georeferencing raster data. Using the raster calculator.
DEM operations.
Applications of vector and raster data for spatial analysis.
Geographic information systems: definition of conceptual and logical models.
Recalls of basic GIS concepts:
Raster data: Spatial and radiometric resolution. Georeferencing raster data. Analysis of raster data. Most common raster formats;
Vector data: primitives, entities; topological primitives. Most common vector formats.
Vectorization and cartographic editing.
Visualization, selection and query of raster and vector attributes.
Techniques and technologies to acquire data for the generation of Digital Elevation Models, orthophotos, and GIS at large.
Digital Elevation models (DEM). DEM components. Interpolation models and data structures.
DEM operations.
Digital orthophotos. Advantages and disadvantages compared to numerical cartography. Generation of orthophotos.
Databases at municipality, regional and national level, techniques for data distribution and sharing (webGIS, geoservices).
Lab workshops.
Use of DEM generation software, multi-temporal comparisons, volume calculations.
Presentation and use of QGIS: data import, definition of the reference system, data export, operations on the geometry and attributes of vector data.
Queries, join and spatial selection. Zonal statistics.
Georeferencing raster data. Using the raster calculator.
DEM operations.
Applications of vector and raster data for spatial analysis.
Teaching methods
The course includes frontal lectures on theoretical subjects and laboratory exercises using QGIS (for data processing in a GIS environment) and other tools for DEM analysis. The proportion of frontal lectures and laboratory exercises is 2:3.
Teaching Resources
The reference text for the course is: F. Migliaccio, D. Carrion. Sistemi informativi territoriali. UTET, 2016.
Suggested readings will be provided during classes.
Lecture presentations will be made available through the Ariel website.
Suggested readings will be provided during classes.
Lecture presentations will be made available through the Ariel website.
Module 2: Statistics
Course syllabus
Descriptive Statistica:
1) Sampling from populations and statistical variables.
2) Frequency distributions, histograms, barplots.
3) Location (average, mode, median) and dispersion (range,standard deviation, variance) indices, quantiles. Outliers.
Probability and random variables:
4) Sampling space , events, probability.
5) Union, intersection, complement of events. Incompatible and independent events. Conditional probability, Byes Rule.
6) Expected value, Variance and standard deviation of discrete random variables.Statistical families of distributions: Bernoulli, Binomial, Poisson.
7) Continuous random variables: the Uniform. The Normal family: standardization and main properties.
Inference:
8) Target population, random sample, parameter, estimator. The sampling distribution of sample means. The law of large numbers and the Central Limit Theorem. Point estimation.
9) Confidence intervals: estimate of a population proportion.
10) Confidence interval for the mean (variance known and unknown). The Student t distribution.
Testing Statistical hypotheses.
11) Null and alternative hypotheses: type I and type II errors, test statistics, significance, power function, pvalue, rejection region.
12)Test on a proportion. Test about the population mean (knwon and unknown variance).
13) Two sample tests: difference of proportions, difference of means (independent samples).
14) One-way ANOVA.
15) Simple linear regression.
16) Chisquare test of independence and for goodness of fit.
Case studies and exercises
17) Descriptive statistics, inference and tests applied to case studies using simple statistical software.
1) Sampling from populations and statistical variables.
2) Frequency distributions, histograms, barplots.
3) Location (average, mode, median) and dispersion (range,standard deviation, variance) indices, quantiles. Outliers.
Probability and random variables:
4) Sampling space , events, probability.
5) Union, intersection, complement of events. Incompatible and independent events. Conditional probability, Byes Rule.
6) Expected value, Variance and standard deviation of discrete random variables.Statistical families of distributions: Bernoulli, Binomial, Poisson.
7) Continuous random variables: the Uniform. The Normal family: standardization and main properties.
Inference:
8) Target population, random sample, parameter, estimator. The sampling distribution of sample means. The law of large numbers and the Central Limit Theorem. Point estimation.
9) Confidence intervals: estimate of a population proportion.
10) Confidence interval for the mean (variance known and unknown). The Student t distribution.
Testing Statistical hypotheses.
11) Null and alternative hypotheses: type I and type II errors, test statistics, significance, power function, pvalue, rejection region.
12)Test on a proportion. Test about the population mean (knwon and unknown variance).
13) Two sample tests: difference of proportions, difference of means (independent samples).
14) One-way ANOVA.
15) Simple linear regression.
16) Chisquare test of independence and for goodness of fit.
Case studies and exercises
17) Descriptive statistics, inference and tests applied to case studies using simple statistical software.
Teaching methods
Lectures and classroom practice.
Teaching Resources
1) "Introduzione alla Statistica Applicata", di F.M.Stefanini, ed. Pearson, 2007.
2) "Introduzione alla Statistica", di M. K. Pelosi e T. M. Sandifer, ed. McGraw-Hill, 2009.
3) Notes/slides downloadable from Ariel - UNIMI site.
2) "Introduzione alla Statistica", di M. K. Pelosi e T. M. Sandifer, ed. McGraw-Hill, 2009.
3) Notes/slides downloadable from Ariel - UNIMI site.
Module 1: Territorial Information System
INF/01 - INFORMATICS - University credits: 6
Lessons: 48 hours
Professor:
Fugazza Davide
Module 2: Statistics
SECS-S/01 - STATISTICS - University credits: 6
Lessons: 48 hours
Professors:
Baldi Lucia, Stefanini Federico Mattia
Educational website(s)
Professor(s)
Reception:
on appointment
Via Celoria 2, Milan, Italy, 3rd floor (or by Skype/Teams/Zoom)
Reception:
by appointment on Tuesday and Wednesday (email)
Via Celoria 10