Geospatial Data Mining


Geospatial Data Mining (GSDM) has distinct characteristics from general data mining (DM) conducted based on company data. Although a large number of coincidences exist between them, there are some differences, which are very important and must not be neglected. This course aims to present the methodology of data mining, as well as its main tools and further emphasize the specifics that exist in geospatial data exploration. Thus, by the end of this course, students should have a good understanding of the main tools of data mining, as well as critical thinking regarding its application in the context of geographic information science (GISc)

General characterization





Responsible teacher

Roberto André Pereira Henriques


Weekly - Available soon

Total - Available soon

Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English




Papers will be supplied for each module of the course; 0; 0; 0; 0

Teaching method

The curricular unit is based on theoretical and practical lessons. A variety of instructional strategies will be applied, including lectures, slide show demonstrations, step-by-step applications (with and without software), questions and answers. The sessions include presentation of concepts and methodologies, solving examples, discussion and interpretation of results. The practical component includes exercises to consolidate the theoretical concepts covered in the theoretical classes.

Evaluation method

Grades are between 0 and 20, to pass you need to have at least 10;

Attend and participate in the face to face sessions;

Read the proposed texts

Complete the proposed projects;

  • Project 1 deadline (23:59 September 13, 2021)
  • Project 2 deadline (23:59 September 20, 2021)
  • Project 3 deadline (23:59 October 4, 2021)
  • Project 4 deadline (23:59 October 20, 2021)
  • The final grade will be calculated as follow:

FG=exam×0.3 + proj1×0.1 + proj2×0.1 + proj3×0.25 + proj4×0.25


Subject matter

LU1. Introduction to Data Mining

LU2. The role of Data in Data Mining

LU3. Data Preprocessing

LU4. Unsupervised Classification (clustering)

LU5. Supervised Classification (predictive modelling)