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Spatial analysis in social research

General data

Course ID:	1600-SZD-WM-APBS
Erasmus code / ISCED:	07.1 Kod klasyfikacyjny przedmiotu składa się z trzech do pięciu cyfr, przy czym trzy pierwsze oznaczają klasyfikację dziedziny wg. Listy kodów dziedzin obowiązującej w programie Socrates/Erasmus, czwarta (dotąd na ogół 0) – ewentualne uszczegółowienie informacji o dyscyplinie, piąta – stopień zaawansowania przedmiotu ustalony na podstawie roku studiów, dla którego przedmiot jest przeznaczony. / (0532) Earth science The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
Course title:	Spatial analysis in social research
Name in Polish:	Analiza przestrzenna w badaniach społecznych
Organizational unit:	Faculty of Geography and Regional Studies
Course groups:	(in Polish) Przedmioty Szkoły Doktorskiej Nauk Społecznych Methodological classes - Doctoral School of Social Sciences
ECTS credit allocation (and other scores):	(not available) Basic information on ECTS credits allocation principles: the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS; the student’s weekly hourly workload is 45 h; 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes; weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS; work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load.
Language:	Polish
Type of course:	elective courses
Short description:	METHOLOGICAL CLASSES 20 h exercises in the computer lab. Mini lecture, practical exercises, project method. The main purpose of the course is to learn the theoretical and practical skills of using Geographic Information Systems (GIS) in spatial analysis of social phenomena and research.
Full description:	Participants will learn about the functionality of GIS as a tool for analysis and modeling of complex spatial relationships of the studied issues. The use of various spatial data models (vector and raster) and their integration as well as for the production, acquisition, editing and visualization of data will be considered. Spatial data infrastructure, spatial and attribute data, acquisition and ordering. Spatial analysis. Analysis of socio-economic data. Visualization of socio-economic data. Mode of Teaching: - exercises in the computer lab. Teaching Methods: - mini lecture, practical exercises, project method Language of Instruction: English
Bibliography:	• ArcGIS online help (http://resources.arcgis.com/content/web-based-help). • Ciepły M., Głowacz A., Piechota A., Pokojski W., Szkurłat E., Wołoszyńska -Wiśniewska E., Wyka E., Zarychta R., 2019. Geoinformacja w szkolnej edukacji geograficznej. Boguski Wydawnictwo Naukowe. • Gotlib D., Iwaniak A., Olszewski R., 2007, GIS. Obszary zastosowań. Wyd. Nauk. PWN, Warszawa. • Informacja przestrzenna dla samorządów terytorialnych, Białousz St. (red.), Oficyna wydawnicza Politechniki Warszawskiej, Warszawa 2013. • Kraak M., Ormeling F., 2010, Cartography: Visualization of Spatial Data (3 ed.), Pearson Education (dostępne tłumaczenie na język polski wydania pierwszego z 1996 roku). • Longley, P.A., Goodchild M.F., Maguire D.J., Rhind, D.W. 2006, GIS. Teoria i praktyka. PWN, Warszawa. • Peterson G.N., 2009, GIS Cartography. A Guide to Effective Map Design. CRC Press, Boca Raton. • Rola baz danych obiektów topograficznych w tworzeniu infrastruktury informacji przestrzennej w Polsce. GUGiK, Warszawa 2013. • Rola baz danych obiektów topograficznych w tworzeniu infrastruktury informacji przestrzennej w Polsce. GUGiK, Warszawa 2013. • Tomlinson, R., 2008, Rozważania o GIS. Planowanie Systemów Informacji Geograficznej dla Menedżerów. ESRI Polska, Warszawa. • Strony internetowe: GUS, GUGiK, NASA, USGS, OSM, Copernicus.
Learning outcomes:	Knows and understands: - basic principles of knowledge transfer to the economic and social sphere as well as commercialization of results of scientific activities and know-how related to these results. Skills: Student can: - use knowledge from various fields of science to identify, formulate and innovatively solve complex problems or perform research tasks, in particular: - define the aim subject of scientific research, - formulate a research hypothesis - develop research methods, techniques and tools and apply them creatively - infer on the basis of scientific research results.
Assessment methods and assessment criteria:	Assessment Tasks: - attendance, passing a test, positive grade for the project. Learning Outcomes Assessment: - colloquium, project grade. Assessment criteria: - attendance at 80% of classes, passing test, positive evaluation of the project.

Classes in period "Winter semester 2023/24" (past)

Time span:	2023-10-01 - 2024-01-28	Selected timetable range: weekly course term Navigate to timetable MO TU W TH CW FR
Type of class:	Classes, 20 hours, 15 places more information
Coordinators:	Wojciech Pokojski
Group instructors:	Wojciech Pokojski
Students list:	(inaccessible to you)
Examination:	Course - Pass/fail Classes - Pass/fail

Course descriptions are protected by copyright.
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