Concepts, Cartographic and Statistical Analysis Techniques, and Big Geodata
Start: 22 January 2018
End: 2 February 2018
Location: Max Planck Institute for Demographic Research (MPIDR), Rostock, Germany
Dr. Sebastian Klüsener
Researchers often work with geographically referenced data. In comparative social demographic research it is very common to contrast populations across countries or regions. As a result of the big data revolution, we are also witnessing a massive increase in individual-level data with spatial location information. Biodemographers, as well, often have information on the geographic habitat of animals or plants they study. One can represent these data in non-spatial tabular form and analyze them with standard statistical techniques that do not make use of the spatial information. However, by ignoring spatial information contained in the data, spatial relationships, spatial trends and spatial contextual effects remain unexplored. Analyzing spatial data, geographical maps can help to get a first understanding of the data. But patterns in geographical maps may often not be significant because they are simply the outcome of the intrinsic variability of a phenomenon. Significance tests are hence crucial before jumping to conclusions. Modeling geographical data, the exclusion of spatial information can even lead to biases in the statistical models as important modeling assumptions may be violated. Therefore, understanding the spatial processes underlying the relationships of interest can improve overall knowledge of demographic events as well as enhance the usefulness and applicability of statistical models.
The course will give an introduction to techniques and programs used in the field of Spatial Demography. It covers methods that are useful for social demographers as well as techniques that can be applied in biodemography. The course will start with an overview of concepts and theories used in Spatial Demography. This is followed by a brief introduction to Geographical Information Systems (GIS), spatial data files and the spatial libraries in the software package R. Course participants will then be getting an overview over tools of descriptive analysis and cartographic presentation as well as basic and advanced spatial modeling techniques. Thereby, methods to analyze vector data (e.g. countries, regions), point data (e.g. count data of human individuals, centroids of regions) and raster data (e.g. satellite image data) are covered. These methods include Spatial Econometric Models, Geostatistical Models, (Bayesian) Spatial Multi-Level Models (INLA), and Spatial Agent-Based Simulations. Participants will also learn how to fetch big geodata from online data sources with R-libraries, and to prepare and include these data in their spatial analyses.
This course will be a practical, intensive course composed of one lecture, nine lab sessions, one optional class with an introduction to R, and one class in which the participants present findings of mini research projects that they carried out during the course. The course will be offered between the 22nd of January 2018 and the 2nd of February 2018. We will use the following open-source software packages: R, QGIS, Geoda. Attendance is limited to a maximum of 15 people.
Participants should be familiar with basic multivariate analysis techniques (linear and logistic regression, test of significance, confidence intervals). Prior knowledge of Geographic Information Systems, spatial statistics and cartographic techniques is not required. Some basic knowledge of R is desirable, but not a prerequisite.
Students are expected to submit a mini-project upon completion of the course. For the project students can either analyze data related to their own research or they can submit a project based on datasets that are provided by the course instructor.
Lecture notes, material and datasets will be made available during the course.
Anselin, L. (1988): Spatial Econometrics: Methods and Models. Dordrecht.
Anselin, L. (2005): Exploring Spatial Data with GeoDa : A Workbook. Urbana-Champaign.
Bivand, R.S., E. Pebesma, and V. Gómez-Rubio (2013): Applied Spatial Data Analysis with R. 2nd edition. New York.
Cressie, N. and C.K. Wikle (2011): Statistics for Spatio-Temporal Data. New York.
Tobler, W. (1970) A computer movie simulating urban growth in the Detroit region. Economic Geography, 46: 234-240.
Voss, P.V. (2007): Demography as a Spatial Social Science. Population Research and Policy Review 26: 457-476.
There is no tuition fee for this course. Students are expected to pay their own transportation and living costs.
Recruitment of students
Applicants should either be enrolled in a PhD program (those well on their way to completion will be favored) or have received their PhD. Applications from advanced masters students will also be considered.
A maximum of 15 students will be admitted.
The selection will be made by the MPIDR based on the applicants’ scientific qualifications.
How to apply
Applications should be sent by email to the MPIDR. Please begin your email message with a statement saying that you apply for course IDEM 156 – Spatial Demography. You also need to attach the following items integrated in *a single pdf file*: (1) A two-page curriculum vitae, including a list of your scholarly publications. (2) A one-page letter from your supervisor at your home institution supporting your application. (3) A two-page statement of your research and how it relates to the course. Please include a short description of your knowledge of multivariate analysis techniques and R.
Send your email to Heiner Maier (email@example.com).
Application deadline is 12 November 2017.
Applicants will be informed of their acceptance by 30 November 2017.
Applications submitted after the deadline will be considered only if space is available.