Infopaket

Urban Research Methods

ВБ9.7.

Вибіркова дисципліна для ОП

First

2023/2024

6 Semester

4

1.1 Knowledge of basic statistical methods of analysis of globalization and urbanization. 1.2 Knowledge of the main R libraries for spatial analysis. 2.1 Be able to use the software environment R for extraction and processing of geospatial data. 2.2 Be able to use R software to visualize spatial data. 2.3 Be able to use R software to identify promising locations of social infrastructure in the city. 3.1 To develop projects of geospatial researches of processes of urbanization and globalization according to the purposes and tasks of the customer.

Full-time form

1. Successfully master the disciplines "Methods of collecting sociological data", "Methods of analysis of sociological data", "Social statistics and demography", "Sociological theories of the city and urbanization" and "Sociology of globalization". 2. Orient in the basic theories of classical and modern sociology. 3. Be able to use the software environment R.

I. Creation and extraction of geospatial data. 1 The concept of geodata, their sources. Geocoding. 2 Spatial objects in R: Spatial and sf-objects. ІІ. Correlation and autocorrelation analysis of geospatial data. 3 The concept of autocorrelation. Moran and Geary indices. 4 Cross-correlation analysis of geospatial data. III. Geomarketing software tools. 5 Geomarketing and its main tasks. 6 Localization of promising locations in the city. IV. Methods of analysis of space-time series. 7 The concept of time series. Features of time and space series. 8Software of time-space series analysis.

1. Cryer Jonathan D., Kung-Sik Chan, Time Series Analysis With Applications in R. – New York: Springer, 2010. – 491 pages. 2. Lansley Guy, Cheshire James. An Introduction to Spatial Data Analysis and Visualization in R, 2016. Режим доступу: http://www.spatialanalysisonline.com/An%20Introduction%20to%20Spatial%20Data%20Analysis%20in%20R.pdf 3. Shumway Robert H., Stoffer David S. Time Series Analysis and Its Applications With R Examples. – New York: Springer, 2011. – 596 pages. 4. Bivand S. Roger, Pebesma Edzer, Gomez-Rubio Virgilio. Applied Spatial Data Analysis with R. – New York: Springer, 2013. – 405 pages. 5. Yaffee Robert Alan. An Introduction to Time Series Analysis and Forecasting: With Applications of SAS and SPSS. – San Diego: Academic Press, 2000. – 528 pages. 6. Logan John R. Making a Place for Space: Spatial Thinking in Social Science // Annual Review of Sociology. – 2012. – Vol. 38. – Pp. 507–524.

Lecture, practical classes, independent work

1. CW 1 on topic 1 - 5 points / 3 points. 2. CW 2 on topic 2 - 5 points / 3 points. 3. CW 3 on topic 3 - 10 points / 6 points. 4. CW 4 on the topic 4 - 10 points / 6 points. 5. CW 5 with topics 5-6 - 10 points / 6 points. 6. CW 6 with topics 7-8 - 10 points / 6 points. 7. Participation in the discussion in practical classes on topics 1-8 - 10 points / 6 points. Final assessment: written exam: The ticket contains test tasks in the form of closed questions of single and multiple choice) - 20 points (correct answer is estimated at 0.5 points) and two practical tasks, each of which is evaluated at 10 points. The minimum score for passing the exam (receiving an overall positive grade) is 24 points

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