290095 VU Geographical Descriptive Analytics (2023W)
Continuous assessment of course work
Labels
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Mo 04.09.2023 09:00 to Mo 18.09.2023 09:00
- Deregistration possible until Tu 31.10.2023 23:59
Details
max. 20 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
- Tuesday 03.10. 11:30 - 13:00 Multimedia Mapping-Labor, NIG 1.Stock C0110
- Tuesday 10.10. 11:30 - 13:00 Multimedia Mapping-Labor, NIG 1.Stock C0110
- Tuesday 17.10. 11:30 - 13:00 Multimedia Mapping-Labor, NIG 1.Stock C0110
- Tuesday 24.10. 11:30 - 13:00 Multimedia Mapping-Labor, NIG 1.Stock C0110
- Tuesday 31.10. 11:30 - 13:00 Multimedia Mapping-Labor, NIG 1.Stock C0110
- Tuesday 07.11. 11:30 - 13:00 Multimedia Mapping-Labor, NIG 1.Stock C0110
- Tuesday 14.11. 11:30 - 13:00 Multimedia Mapping-Labor, NIG 1.Stock C0110
- Tuesday 21.11. 11:30 - 13:00 Multimedia Mapping-Labor, NIG 1.Stock C0110
- Tuesday 28.11. 11:30 - 13:00 Multimedia Mapping-Labor, NIG 1.Stock C0110
- Tuesday 05.12. 11:30 - 13:00 Multimedia Mapping-Labor, NIG 1.Stock C0110
- Tuesday 12.12. 11:30 - 13:00 Multimedia Mapping-Labor, NIG 1.Stock C0110
- Tuesday 09.01. 11:30 - 13:00 Multimedia Mapping-Labor, NIG 1.Stock C0110
- Tuesday 16.01. 11:30 - 13:00 Multimedia Mapping-Labor, NIG 1.Stock C0110
- Tuesday 23.01. 11:30 - 13:00 Multimedia Mapping-Labor, NIG 1.Stock C0110
- Tuesday 30.01. 11:30 - 13:00 Multimedia Mapping-Labor, NIG 1.Stock C0110
Information
Aims, contents and method of the course
Assessment and permitted materials
The assessment is conducted via three testing elements (T1-3). The first, a written test, is conducted at the very last session of the course. The test assesses the technical skills and the methodological concepts learned throughout the lessons. The second, is the submission of selected exercises done in class. The last one assesses the participation and contribution during in-class activities.The weights of each element are:
- Test 50% (T1)
- Exercises 39% (T2)
- Contribution 11% (T3)
- Test 50% (T1)
- Exercises 39% (T2)
- Contribution 11% (T3)
Minimum requirements and assessment criteria
- A student shall attend at least 75% of the sessions.
- T1 is an obligatory test to pass the course.
- T1 contains a multiple set of questions. The correct answers to the questions sum up to 50 scoring points.
- The structure/style of T1 test is practiced within in-class game activities.- T2 is not an obligatory test to pass the course.
- The maximum number of scoring points that can be accumulated is 39.- T3 is not an obligatory test to pass the course.
- For T3 a conditional mark is given (or not) of 11 points.
- T1 is an obligatory test to pass the course.
- T1 contains a multiple set of questions. The correct answers to the questions sum up to 50 scoring points.
- The structure/style of T1 test is practiced within in-class game activities.- T2 is not an obligatory test to pass the course.
- The maximum number of scoring points that can be accumulated is 39.- T3 is not an obligatory test to pass the course.
- For T3 a conditional mark is given (or not) of 11 points.
Examination topics
Data processing, exploratory (spatial) data analysis, descriptive spatial statistics, python (geo)libraries.
Reading list
- Eldawy, A., & Mokbel, M. F. (2015). The era of big spatial data. 2015 31st IEEE International Conference on Data Engineering Workshops, 42–49.
- Graham, M., & Shelton, T. (2013). Geography and the future of big data, big data and the future of geography. Dialogues in Human Geography, 3(3), 255–261.
- Kitchin, R. (2013). Big data and human geography: Opportunities, challenges and risks. Dialogues in Human Geography, 3(3), 262–267.
- Lee, J.-G., & Kang, M. (2015). Geospatial Big Data: Challenges and Opportunities. Big Data Research, 2(2), 74–81.
- Leszczynski, A., & Crampton, J. (2016). Introduction: Spatial big data and everyday life. Big Data & Society, 3(2), 2053951716661366.
- Liao, C., Brown, D., Fei, D., Long, X., Chen, D., & Che, S. (2018). Big data‐enabled social sensing in spatial analysis: Potentials and pitfalls. Transactions in GIS, 22(6), 1351–1371.
- Miller, H. J., & Goodchild, M. F. (2015). Data-driven geography. GeoJournal, 80(4), 449–461.
- Robertson, C., & Feick, R. (2018). Inference and analysis across spatial supports in the big data era: Uncertain point observations and geographic contexts. Transactions in GIS, 22(2), 455–476.
- Graham, M., & Shelton, T. (2013). Geography and the future of big data, big data and the future of geography. Dialogues in Human Geography, 3(3), 255–261.
- Kitchin, R. (2013). Big data and human geography: Opportunities, challenges and risks. Dialogues in Human Geography, 3(3), 262–267.
- Lee, J.-G., & Kang, M. (2015). Geospatial Big Data: Challenges and Opportunities. Big Data Research, 2(2), 74–81.
- Leszczynski, A., & Crampton, J. (2016). Introduction: Spatial big data and everyday life. Big Data & Society, 3(2), 2053951716661366.
- Liao, C., Brown, D., Fei, D., Long, X., Chen, D., & Che, S. (2018). Big data‐enabled social sensing in spatial analysis: Potentials and pitfalls. Transactions in GIS, 22(6), 1351–1371.
- Miller, H. J., & Goodchild, M. F. (2015). Data-driven geography. GeoJournal, 80(4), 449–461.
- Robertson, C., & Feick, R. (2018). Inference and analysis across spatial supports in the big data era: Uncertain point observations and geographic contexts. Transactions in GIS, 22(2), 455–476.
Association in the course directory
(MR1-a-PI) (MK2-PI) (MA UF GW 02)
Last modified: Tu 03.10.2023 09:08
GIS, GPS, crowdsourced, and sensor data are complex geodatasets that are stretching the limits of GIS packages, thus a real challenge is to develop innovative ways to deal with all the available data and datasets. Nowadays, geodata analysts should be able to cure and manage both structured and unstructured data while at the same accomplish their tasks timely using computing architectures adequate for datasets of millions of spatiotemporal observations. Furthermore, demands on data exploration go beyond basic statistical measures, charts, and EDA techniques to spatial statistical methods that look at the global or local patterns of univariate-bivariate-multivariate geodata and various forms of cartographic visualization. An additional challenge is that although a significant percentage of data can be characterized as geodata, at times the “spatial information” needs to be processed or even engineered. In this course, the focus is to process, analyse, and then describe geodata with techniques are applicable to many domains such as health, natural disasters, crime analysis, agriculture, human mobility, or geodemographics.Content
Python tools (Jupyter notebooks & python libraries), EDA, Mapping, Processing, Parallel Computing, Global & Local patterns, LISA, Geocoding, Data Scrapping, Hungry Minds extra topics.Methods
The course has a fairly equal amount of both lectures and practical work. The practical work is either tutorials guided by the lecturer or exercises of multiple tasks to be solved/answered given sufficient support material. Most of the times learning of theory and methods precedes their application. There is also a rotational pattern in which topics/concepts of older lessons are re-used and tested within newer lessons. Game-based learning and other interactive learning approaches are regular activities during the lectures.Entry requirements
Knowledge of basic Python scripting is a prerequisite for this course. Knowledge of descriptive statistics or spatial statistics would be advantageous but is not required.