Spatial Data Modeling

NA

Considering the previously stated objectives, the following teaching methods will be used. The expository method will be used to promote the mastery of fundamental concepts and principles related to spatial data modeling.

Alongside the expository method, the interrogative method will also be used in order to increase the spirit of reflection. In this context, it is desirable that the answers can be found by the class.

In addition, the demonstrative method will play an important role in motivating students to perform tasks. Students will have at least to assignments for the resolution of practical problems.

In the phases following the application of the demonstrative method, the active method will be applied through the formulation of practical problems that can be solved by all students.

Approval in this curricular unit requires a grade equal to or greater than 7.50 in all elements of evaluation and final classification (CF) of at least 9.5 values, considering the scale from 0 to 20 values

At the end of the course unit the student should be able to:

a) Know the main sources of geographic data, collect and organize data in spatial databases;

b) Work and transform data according to the needs of the problem in question;

c) Apply the acquired knowledge in solving territorial planning problems.

Introduction to spatial data modeling: definition of the concept and its relevance in spatial planning;

    Data collection and analysis:

        Main sources of geographic data available;

        Data structuring procedures considering different scopes of analysis;

        Data query operations: arithmetic and relational;

        Geoprocessing operations.

    Development of a spatial model (most likely in a Geographic Information Systems environment):

        Formulation and resolution of a spatial problem;

        Analysis and interpretation of the results obtained.

• - Individual work - 50.0%
• - individual written test - 50.0%

• - Individual work - 50.0%
• - individual written test - 50.0%

NAO

Anselin, L., Syabri, I., & Kho, Y. (2006). An introduction to spatial data analysis. Geographical analysis, 38(1), 5-22.

Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2000). Quantitative geography: perspectives on spatial data analysis. Sage.

Haining, R. P., & Haining, R. (2003). Spatial data analysis: theory and practice. Cambridge university