Teaching

Undergraduate Teaching, Deparment of Geography, Harokopio University of Athens, Greece

Module Title: Introduction to Cartography (2nd Semester)

Module Leader: Stamatis Kalogirou, Assistant Professor

Module Objective: The aim of the course is the introduction to the science of cartography and maps. Generally, cartography about the art, the science and the ethical issues of creating and using maps. This course presents issues concerning the creation and use of maps such as map projections, cartographic composition, creating thematic and choropleth maps.

Teaching: The teaching of the course includes theory and practice. The teaching methods consist of three-hour lectures (theory) and exercises or workshops on a weekly basis. Within the time framework of the course, visits to agencies relating to the teaching of the course will take place.

Teaching material: The teaching material includes lectures, notes and practical exercises.


Module Title: Spatial Analysis (5th Semester)

Module Leader: Stamatis Kalogirou, Assistant Professor

Module Objectives: Spatial Analysis is a module in the scientific fields of quantitative geography and geoinformatics. The module aims to advance the student knowledge on quantitative spatial data analysis methods and their application to real data. Special attention is paid to Tobler’s first law of geography, the study of spatial autocorrelation and linear regression. At the same time this module provides the necessary technical tools and skills to study the spatial dimension of various phenomena from a geographic perspective. These include the teaching of open source software: the statistical programming language R and OpenGeoDa. This module also aims to inform students about current trends in spatial analysis and to give them the theoretical foundations to be able to address contemporary research issues in the science of geography.

At the end of the module students should:
– have understood what spatial analysis is, what methods can be used to perform spatial analysis and how some of these methods should be applied
– be able to select the appropriate data and appropriate methods of analysis in order to study a simple phenomenon with a spatial dimension
– have practical experience in applying spatial analysis methods in addressing geographical issues in the real world using special software
– have a clear understanding of the theoretical and practical problems concerning the application of spatial analysis methods
– have learned Tobler’s first law of Geography and the concept of spatial autocorrelation
– have skills in the use of R and OpenGeoDa software
– have an overview of modern methods of spatial analysis used in the industry and in research projects in which the science of geography plays an important role

Models Contents: Spatial analysis is a broad field. This module focuses on teaching quantitative methods of exploratory and explanatory spatial data analysis. The process of analysis that adds value to spatial data in order to extract information that leads to knowledge is being taught. In this way, it is possible to understand the spatial dimension of a phenomenon. The examples / applications discussed refer primarily to human activity in space and to a lesser extent to the natural processes in space. For example, the spatial distribution, spatial inequality and spatial variation in factors affecting issues on the labour market (unemployment and income), population (aging, internal migration), public services (health, education), retail (socioeconomic profile areas) and the environment (recycling, climate conditions) are examined.

Indicative Lectures:
– Introduction to spatial analysis
– Historical trends and schools of thought
– The role of GIS and Remote Sensing
– The visualization of spatial data as an analysis method
– Spatial Inequalities
– Classification, Clustering and Geodemographics
– Factor and Principal Components Analysis
– Spatial Correlation and Multicollinearity
– Spatial Autocorrelation
– Linear Regression
– Generalised Linear Models

Teaching: The teaching of this module includes lectures on theory and computer lab practice in using Statistical Analysis (R) and open source GIS software (QGIS, OpenGeoDa). Typically, teaching consists of 3-hour lectures on theory on a weekly basis while for two weeks teaching consists of one hour theory and 2 hours practical training in the computer lab. In addition, a day or two fieldtrip is organized in order to visit an organization that uses spatial analysis methods and to do fieldwork, such as data collection. The basic evaluation method is a written exam paper at the end of the semester (3 hours).

Teaching material: The teaching material includes lecture presentations, notes and detailed notes for the practical exercises. Optional tutorials are offered for the students to familiarize with the software (out of the teaching hours). There is a key text available for this modules: Spatial Analysis: Methodology and Applications with R.


Module Title: Geoinformatics Applications Development (6th Semester)

Module Leader: Stamatis Kalogirou, Assistant Professor

Module Objectives: This module aims to introduce students to the development of computer applications (software development) in the areas of spatial analysis and geoinformatics and deepen their knowledge in statistical programming. This module also aims to inform students on spatial data visualization capabilities with writing code. The technical knowledge acquired by the student refers to software development in the programming language R for implementing Statistical algorithms, Spatial Analysis and data management with GIS principles. This is done in order to support calculations and function that are not necessarily provided by the relevant commercial software.

Indicative Lectures:
– The principles of software design
– The importance of the user friendliness of software
– Principles of Human – Computer Interaction
– Object-oriented programming
– Introduction to the statistical programming language R
– Variables, data types and tables
– Cases and Loops
– Functions in R
– Visualization of spatial data with rgdal
– Data visualization with ggplot

Teaching: 3-hour lectures that include either theory or practical exercises; they take place in the computer lab.

Teaching material: The teaching material includes lectures and practical exercises to familiarize students with software development. Students are informed the R package lctools (http://gisc.gr/software/lctools-r-package). Sample code from this software is teaching material. Teaching material includes two e-books authored by the module leader: Chapter 9 in Spatial Analysis and Examples of Spatial Analysis with R.


Module Title: Spatial Analysis Applications in Real Estate Management (7th Semester)

Module Leader: Stamatis Kalogirou, Assistant Professor

Module Objectives: The main objective of this module is to introduce students to the use of new technologies and geoinformatics (such as Geographic Information Systems and geospatial data on the web) in property management in the private sector (such as Property Management Companies) and public sector (such as National Cadastre & Mapping Agency S.A.). An additional aim is the introduction to property valuation based on the European Valuation Standards as they are used in the real estate market in Greece. Moreover, a thorough presentation of the Geographically Weighted Regression (GWR) method to estimate property values based Hedonic Price Modelling theory takes place. It is a requirement for this module that students have a good knowledge of statistics, spatial analysis and GIS.

By the end of this module the student should:
– understand how new technologies and geoinformatics can help in better property management
– know what it is and what the National Cadastre & Mapping Agency S.A. does
– know what are the values of a property in the Greek real estate market
– know which property evaluation methods are used in the Greek real estate market
– be able to collect the appropriate data and apply methods of spatial analysis to estimate property values based on hedonic price modelling theory .
– has knowledge about contemporary spatial analysis methods, such as the Geographically Weighted Regression, and be able to apply them to real estate data and to draw conclusions from the results of the analysis

Indicative lectures:
– Introduction to Property Management
– Geoinformatics and Real Estate
– Geographic Information Systems in Property Management
– Introduction to property valuation
– Property Valuation Standards and Methods
– Hedonic Price Models for Property Valuation
– Geographically Weighted Regression (GWR)
– The Greek real estate market and Property Management Companies

Teaching: The teaching of this module includes real estate theory and a computer practical in Geographically Weighted Regression. Usually the course consists of three hours of theory on a weekly basis and in one or two weeks teaching in the computer lab. Students are encouraged to participate in conferences or workshops relating to real estate (property valuation, real estate investments and management).


Module Title: Special Topics in Spatial Analysis (8th Semester)

Module leader: Stamatis Kalogirou, Assistant Professor in Applied Spatial Analysis

Module Objectives: The main objective of this module is to introduce students to concepts of human spatial behavior. For the purpose of this objective this module is concerned with two theories in the science of geography that can be applied to real data with spatial analysis methods. The first theory refers to the Spatial Interaction Models and the second to Spatial Cognition. Examples are given mainly for the application of the first theory, such as those in internal migration, trade and home-to-workplace commuting.


Postgraduate Teaching, Deparment of Geography, Harokopio University of Athens, Greece

MSc in Geoinformatics

Module title: Programming for spatial data analysis

Module Leader: Stamatis Kalogirou, Assistant Professor in Applied Spatial Analysis

Module Description: The module aims to provide more detailed knowledge in information technology and more specifically in the statistical programming for analyzing spatial data in the degree these are required in the science of geography and geoinformatics. In the theoretical part the concepts of object-oriented programming, a modern open source statistical programming language, R and its environment RStudio, as well as libraries and tools for GIS and spatial analysis are introduced. The emphasis of software development is on libraries with classes and methods that can manage and analyse geographic data in order for the student to be able to develop the ability to create applications in Geoinformatics. The computer labs aim to allow students to familiarize with the programming language R and developed several applications that analyze and visualize spatial data. Students also develop geographical data mapping code and texts using the documentation language R Markdown.

Teaching: The course includes two-hour lectures and a number of laboratory exercises. Considerable working time outside teaching hours is required by the students. The selection, preparation and delivery of projects which includes software development is submitted at the end of the semester.


Module title: Applied Spatial Analysis

Module Leaders: Stamatis Kalogirou, Assistant Professor in Applied Spatial Analysis & Alexandra Tragaki, Associate Professor

Module Description: The course aims at familiarizing with quantitative methods of spatial data analysis and practical application of these methods. Emphasis is placed on strengthening the skills of the students in using open source statistical software such as R and GeoDa. This course is a presentation of specific topics of descriptive statistics (exploratory analysis) – Assessment of concentration – dispersion of activities in space, methods of deviation / participation (and statistical variations of) convergence model. Methods of multinomial regression analysis, Non-parametric tests and Special regression issues. It also presents spatial autocorrelation, spatial inequalities and spatial regression methods.

Teaching: The course includes two-hour lectures and a number of laboratory exercises. The selection, preparation and delivery of projects is submitted at the end of the semester.


Postgraduate Teaching, Department of Statistics and Actuarial Science, University of Piraeus, Greece

MSc in Applied Statistics

Module title: Statistical methods in social sciences

Module Leaders: Stamatis Kalogirou, Assistant Professor in Applied Spatial Analysis & Verropoulou Georgia, Associate Professor

Module Description: The main purpose of the course is to familiarise students with socio-economic research and data analysis methods such survey as multinomial logistic regression, ordinal regression, quantile regression and hierarchically models (multilevel models). In addition students are introduced to the spatial analysis, cartography and Geographic Information Systems and familiarise themselves through examples and laboratory exercises. Finally Geographically Weighted Regression is being taught.

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