PhD in Computer Science

Spatial Analysis and Modeling

Spatial Analysis and Modeling

Academic year 2018/2019

Sommario del corso

• Course objectives
• Results of learning outcomes
• Learning assessment methods
• Program
• Teaching tools

Course objectives

With the continuing advances of technological development, spatial data have been easily and increasingly available in the past decades and becoming important information sources in daily decision makings. This class is intended for students who are interested in working with spatial data analysis and have an overview of practical and research domains where it is applied with a special focus on modeling the dynamics of modern cities. The course will introduce fundamental concepts and commonly used methods in quantitative analysis of spatial data.

Results of learning outcomes

By the end of the course, students will be able to:

• Demonstrate advanced Geographic Information System (GIS)/Geographic Data Science (GDS) concepts and be able to use the tools programmatically to import, manipulate and analyse spatial data in different formats.
• Understand the motivation and inner workings of the main methodological approcahes of GDS, both analytical and visual.
• Critically evaluate the suitability of a specific technique, what it can offer and how it can help answer questions of interest.
• Apply a number of spatial analysis techniques and explain how to interpret the results, in a process of turning data into information.
• When faced with a new data-set, work independently using GIS/GDS tools programmatically to extract valuable insight.

Learning assessment methods

To integrate your learning of all the material covered in the course, the final exam consists in a project you will design, undertake and report on an individually chosen spatial analysis project that will be the context of discussion during the classes. The final project will consist in four main components:

• Proposal (10%)
• A brief description of the spatial question(s) you would like to ask or the spatial problem you want to solve and briefly how you plan to solve it.
• Data Report (20%)
• A draft of the section of your final report that discusses the data you will use and the exploration of that data that you have already completed.
• Presentation (20%)
• A oral presentation open to all students in the course
• Project Report (50%)
• A written report on your project methodology and outcomes.

Program

The course will be organized in the following (tentative) macro blocks: 

• Introduction to Spatial Analysis 
• Spatial Data 
• Manipulating spatial data in Python
• Visualizing Spatial Data
• The examples of Python and Web technologies
• Choropleth mapping
• Spatial Weights
• Exploratory Spatial Data Analysis (ESDA)
• Spatial clustering
• Spatial causal inference
• Examples of spatial analysis in action 
• The urban planing use case
• The social media use case
• The human mobility use case

• Teaching materials
• Exams and finals
• Go to Moodle
• Visit the forums