Geographical Data Analysis
a) Recognize the specificity of spatial data;
b) Analyse data and data sources;
c) Master concepts, methods and techniques of descriptive statistics in the treatment of geographic information;
d) Apply spatial data treatment methods and techniques;
e) Identify relationships between variables;
f) Use proper graphical representation techniques;
g) Interpret graphical representations;
h) Use the spreadsheet in data exploration and analysis.
Fernando Ribeiro Martins
Weekly - 4
Total - 168
Barroso, M. (2003). Exercícios de estatística descritiva para as ciências sociais. Lisboa:Sílabo, p.1 1-248 e p. 325-376.
D’Hainaut, L. (1990). Conceitos e métodos da estatística, I – uma variável a uma dimensão. Lisboa: Fundação Calouste Gulbenkian, p.1-158.
Pinto, M. (2011). Microsoft Excel 2010. Famalicão: Centro Atlântico, p. 1-139.
Reis, E. (2002). Estatística Descritiva. Lisboa: Sílabo, p. 4-134.
Silva, A. (2006). Gráficos e mapas. Representação de informação estatística. Lisboa / Porto / Coimbra: Lidel.
Walford, N .(2002). Geographical Data: Characteristics and sources. Chichester: Wiley.
Each student is required to read the mandatory literature and the resolution of practical exercises of application of knowledge.
a) Lectures and participatory based on practical problem solving using scientific calculator.
b) Practical sessions dedicated to computer problem solving using Microsoft Excel 2010 program.
a) In the theoretical part (T): midterm test (40%); final test (60%);
b) In the practical part (P): midterm test (20%); final test (60%); exercises in the classroom (20%).
c) Participation and attendance (K).
The Final Evaluation (AF) will be calculated based on the following formula: AF = (T x 0.6) + (D x 0.35) + (0.05 x K).(100%)
1) Geographical objects, statistical and spatial units.
2) Statistic and geographical populations: i) Heterogeneity of geographical populations (defining geographic population; spatial units differential importance; variables in different scales of analysis; ii) Space completeness; iii) Dependence and independence of observations.
3) Distributions and degree of spatial differentiation: i) The distribution and spatial structure indicator; ii) distribution parameters; iii) The central tendency of skewness and kurtosis and spatial regularities; iv) The measures of heterogeneity and concentration.
4) Measures of spatial relationships: i) Statistical models and geographical reality; ii) Simple regression; iii) Simple correlation.
5) Treatment of geographic data: i) Exploration and analysis of data in the worksheet; ii) Graphical representation of variables.
Programs where the course is taught: