Concepts, Spatial Statistics, GIS and Cartographic Techniques
Start: 5 January 2015
End: 16 January 2015
Location: MPI Rostock
• 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. Biodemographers, on the other hand, often have information on the geographic habitat of animals or plants they study. One can represent this information in non-spatial tabular form and analyze it 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 where some of the basic 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 improve 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 images of land use) are covered. This includes Spatial Lag/Spatial Error Models, Spatial Multi-Level Models, and Geostatistical Models.
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 5th of January 2015 and the 16th of January 2015. We will use the following software packages: R, Geoda, Quantum GIS. 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(2): 234-240.
Voss, P.V. (2007): Demography as a Spatial Social Science. In: 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. However, a limited number of scholarships are available on a competitive basis for outstanding candidates and for those applicants who might otherwise not be able to come.
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 include the following three documents, either in the text of the email or as attached documents. (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. Please indicate (a) whether you would like to be considered for financial support and (b) if you would be able to come without financial aid from our side.
Send your email to Heiner Maier (firstname.lastname@example.org).
Application deadline is 10 October 2014.
Applicants will be informed of their acceptance by 31 October 2014.
Applications submitted after the deadline will be considered only if space is available.