Spatial data analysis 1600-SZD-SPEC-PAD-EF
The class is devoted to the application of statistical and econometric methods for the analysis of spatial data (regional, point, grid data). The aim of the course is to learn how to quantitatively account for location, co-location and distance in modelling the phenomena of diffusion, agglomeration and location density, specialisation, geographical rent, absolute and relative location, spatial interaction, spatial autocorrelation, spatial sorting and heterogeneity. State-of-the-art methods for spatial analysis will be presented, with a strong emphasis on applied approaches. All modelling problems will be presented and solved in R. Examples of problems will concern socio-economic phenomena such as economic policy, social processes in geographical space, location of companies. Teaching methods used: lectures on the issues discussed, solving research problems, planning the details of quantitative analysis, programming in R (loading data, writing codes for the study), interpreting the results and confronting them with the literature of the topic (from a quantitative and thematic perspective). Topics: 1) Visualisation of spatial data in R (regional and point data), 2) Spatial weighting matrices and spatial statistics for regional (area) data, exploration of spatial distributions (Moran I, Getis-Ord, LOSH, LISA, join-count, DBSCAN), statistics for gridded data), 3) econometric modelling of spatial relationships (using spatial weighting matrices) - model and variable selection, estimation, testing, forecasting, data gaps, spatial interaction studies, models on area and point data (including GNS, SDM, SDEM, SAC, SEM, SAR, SLX, GWR), spatial interaction models, 4) Geographically weighted regression (GWR) - spatial drift models (univariate) and spatial-temporal cluster stability (multivariate models), 5) entropy and Vonoia tessellation in agglomeration measurement, challenges of big data and machine learning on spatial data, clustering of point
Type of course
Course coordinators
Learning outcomes
Knowledge | The graduate knows and understands:
WG_01 - to the extent necessary for existing paradigms to be revised - a worldwide body of work, covering theoretical foundations as well as general and selected specific issues - relevant to a particular discipline
within the social sciences
WG_02 - the main development trends in the disciplines of the social sciences in which the education is provided
WG_03 - scientific research methodology in the field of the social sciences
WK_01 - fundamental dilemmas of modern civilisation from the perspective of the social sciences
Skills | The graduate is able to:
UK_05 - speaking a foreign language at B2 level of the Common European Framework of Reference for Languages using the professional terminology specific to the discipline within the social sciences, to the extent enabling participation in an international scientific and professional environment
Social competences | The graduate is ready to
KO_01 - fulfilling the social obligations of researchers and creators
KO_02 - fulfilling social obligations and taking actions in the public interest, in particular in initiating actions in the public interest
KO_03 - think and acting in an entrepreneurial manner
Assessment criteria
Description of requirements related to participation in classes, including the
permitted number of explained absences: Attendance is compulsory, one absence allowed
Principles for passing the classes and the subject (including resit session): Credit at each term includes two components: a) A written review of an article assigned by the instructor; b) A credit paper on a topic agreed with the instructor (own research, replication/extension of existing research, application of existing approaches to new issues). A credit component submitted in the primary session may be recognised in the revision session.
Methods for the verification of learning outcomes: The implementation of the credit elements - review and self-study - makes it possible to examine the degree of assimilation of the assumed learning outcomes
Evaluation criteria: The completed project and review is intended to demonstrate that the doctoral student knows and understands the subject matter of spatial analysis and can carry out the study independently. The final grade reflects the degree of knowledge and understanding of the course topics and the degree of independence in conducting spatial analyses.
Practical placement
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Bibliography
1] Kopczewska K. (red). (2020), Przestrzenne metody ilościowe w R: statystyka, ekonometria, uczenie maszynowe, analiza danych, CeDeWu
[2] Kopczewska K (eds.) (2020), Applied Spatial Statistics and Econometrics: Data Analysis in R, Routledge
[3] Artykuły naukowe (głównie w języku angielskim) dobrane wg zainteresowań doktorantów
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: