NIFK16009U Managing Big and Spatial Data in Social Science

The amount of data publically available from online sources is increasing dramatically. It is crucial that aspiring researchers and natural resource managers know how to handle these large quantities of data and how to extract information from them. Recognizing these challenges and the inadequacy of spreadsheet approaches to data analysis this course aspires to equip students to the challenge ahead.

The course aims to provide students insight into appropriate data management procedures and critical analysis of empirical quantitative socioeconomic and spatial data such as required in relation to conducting an MSc thesis or doing research. Students will be introduced to concepts, terminology and methods related to handling of time series, cross-sectional and panel data as well as geo-referenced spatial information originating from individual, household and institutional level quantitative socioeconomic data. This includes using log files for writing and debugging code for data handling procedures in relation to merging datasets, overlaying GIS map layers, assessing the quality of the data, data cleaning and coding of different types of variables (outlining the coding principles), identifying and handling outliers, selecting appropriate transformation methods and generating new variables intended for testing specific hypotheses. Students will then be introduced to concepts, terminology and approaches in relation to basic statistical data analysis. This includes developing a data analysis strategy, developing testable hypothesis based on research questions and selecting basic statistical methods to test specific research hypothesis about intergroup and spatial patterns in the data. Examples that will be used are tabulating basic statistics, specification of linear regression models including interactions and interpreting and visualizing model results. Throughout the course, focus will be on making the data handling process transparent and reflecting on the implications of data management choices and choice of statistical approach in relation to validity and reliability of the results of the analysis and good scientific practice. 

The course will develop students’ skills to conduct own data management and analysis through a series of lectures, hands-on group exercises and student presentations of assignments based on provided empirical research datasets. The last part will be independent (supervised) project work. The course will take departure in the free statistical software package R and the geographical information software Q-GIS.