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Regular version of the site

Geospatial Data Science

2023/2024
Academic Year
ENG
Instruction in English
6
ECTS credits
Course type:
Elective course
When:
1 year, 1, 2 module

Instructor

Course Syllabus

Abstract

Video-presentation: https://youtu.be/CEU8hLDVJjI Abstract: About 80% of data has a location component [1]. Geospatial data are diverse and ubiquitous: GPS, maps, satellite imagery, to name a few. Apple [2], Uber [3], MasterCard [4], Google [5] and many other companies heavily utilize geospatial data. However, geospatial data are different from other data types and it is impossible to analyze geospatial data correctly without special knowledge. The “Geospatial Data Science” course gives core theory and algorithms to successfully work with geospatial data: Coordinates and Projections, Spatial Data Formats, Spatial Data Processing (including map algebra and topology), Spatial Data Algorithms, Spatial Data Mining, and other important topics. The course is beneficial for a Data Scientist as it gives expertise to solve practical tasks in public, business, private sectors as well as to do research in Geospatial Data Science. [1] https://www.forbes.com/sites/truebridge/2016/05/06/how-imaging-technologies-are-changing-the-world-part-2/ [2] https://www.cnbc.com/2017/08/02/apple-has-over-70-map-tech-job-openings.html [3] https://eng.uber.com/keplergl/ [4] https://carto.com/blog/carto-mastercard-partnering-location-intelligence-solution/ [5] https://www.google.com/maps
Learning Objectives

Learning Objectives

  • Have a strong understanding of key geospatial data types and formats
  • Have a strong understanding of cartographic projections
  • Know the structure of popular spatial data sources and formats used to store geospatial data
  • Acquire experience in spatial data processing
  • Acquire deep understanding of selected spatial data mining topics
  • Be able to use geospatial data visualization techniques
  • Form a deep understanding of selected spatial data structures and algorithms
  • Form a deep understanding of selected spatial network processing techniques
  • Know GPS, WiFi, cellular, indoor positioning techniques
  • Be aware of current R&D trends on the Geospatial Data Science
Expected Learning Outcomes

Expected Learning Outcomes

  • Acquire a deep understanding of spatial data mining foundations
  • Acquire experience in spatial raster data processing
  • Acquire experience in spatial vector data processing
  • Be able to graphically perform exploratory geospatial data analysis
  • Be able to visualize geospatial data on the Web using a Web mapping service
  • Be aware of current R&D trends in geospatial data science, geoinformatics, big spatial data and systems, novel spatial data applications
  • Be aware of key Spatial Data Structures and Algorithms
  • Form a deep understanding of selected spatial data mining techniques
  • Form a deep understanding of selected spatial data structures and algorithms
  • Form a deep understanding of selected spatial network processing techniques
  • Have a strong understanding of cartographic projections
  • Have a strong understanding of geospatial concepts and properties unique to geospatial data
  • Have a strong understanding of geospatial coordinate properties
  • Have a thorough understanding of key design challenges of spatial data structures and algorithms
  • Know concepts, foundations, and applications of spatial networks
  • Know differences between main spatial coordinate systems and their peculiarities
  • Know GPS, WiFi, cellular, indoor positioning techniques
  • Know key spatial data mining techniques and their applications
  • Know the structure of popular spatial raster data formats used to store raster geospatial data
  • Know the structure of popular spatial vector data formats used to store vector geospatial data
  • Thoroughly understand topological relationship types of spatial data objects
  • Describe the structure of popular spatial raster data forms and their applications
Course Contents

Course Contents

  • Introduction to Geospatial Data Science
  • Geospatial Coordinate Systems
  • Cartographic Projections
  • Geospatial Vector Data
  • Algebraic Topology
  • Spatial Raster Data Forms
  • Spatial Raster Data Processing
  • Spatial Data Mining
  • Spatial Data Visualization
  • Spatial Data Structures and Algorithms
  • Spatial Networks
  • Positioning Systems and Algorithms
  • Geospatial Data Science: The Path Forward
Assessment Elements

Assessment Elements

  • non-blocking EX (exam)
  • non-blocking CW (control work)
  • non-blocking HA1 (Home assignment 1)
  • non-blocking HA2 (Home assignment 2)
Interim Assessment

Interim Assessment

  • 2023/2024 2nd module
    0.25 * CW (control work) + 0.25 * EX (exam) + 0.25 * HA1 (Home assignment 1) + 0.25 * HA2 (Home assignment 2)
Bibliography

Bibliography

Recommended Core Bibliography

  • An introduction to R for spatial analysis & mapping, Brunsdon, C., 2015
  • Applied spatial data analysis with R, Bivand, R., 2008
  • Benjamin S. Baumer, Daniel T. Kaplan, & Nicholas J. Horton. (2017). Modern Data Science with R. Chapman and Hall/CRC.
  • Bin Jiang, & Xiaobai Yao. (2010). Geospatial Analysis and Modelling of Urban Structure and Dynamics. Springer.
  • Cady, F. (2017). The Data Science Handbook. Hoboken, NJ: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1456617
  • Cartography : visualization of spatial data : Chinese edition. (2014). Pearson Education Asia.
  • Cartography: Visualization of spatial data. (2010). Guilford Publications.
  • De Miguel Gonzalez, Rafael, Donert, Karl, Koutsopoulos, Kostis. Geospatial Technologies in Geography Education. 2019. Springer International Publishing
  • Grus, J. (2019). Data Science From Scratch : First Principles with Python (Vol. Second edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2102311
  • He, X., Tao, Y., Wang, Q., & Lin, H. (2019). Multivariate Spatial Data Visualization: A Survey. https://doi.org/10.1007/s12650-019-00584-3
  • Hierarchical modeling and analysis for spatial data, Banerjee, S., 2015
  • Lansley, G., de Smith, M., Goodchild, M., & Longley, P. (2019). Big Data and Geospatial Analysis.
  • Matloff, N. S. (2020). Probability and Statistics for Data Science : Math + R + Data. Chapman and Hall/CRC.
  • Menno-Jan Kraak, & Ferjan Ormeling. (2020). Cartography : Visualization of Geospatial Data, Fourth Edition. CRC Press.
  • Qurban A Memon, & Shakeel Ahmed Khoja. (2019). Data Science : Theory, Analysis and Applications. [N.p.]: CRC Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2198593

Recommended Additional Bibliography

  • Shashi Shekhar and Hui Xiong. 2017. Encyclopedia of GIS (Springer Reference)
  • Data science foundations : geometry and topology of complex hierarchic systems and big data analytics, Murtagh, F., 2018
  • Data science from scratch, Cooper, S., 2018
  • High-dimensional probability : an introduction with applications in data science, Vershynin, R., 2018
  • Morphism for quantitative spatial analysis, Griffith, D. A., 2018
  • R for data science : import, tidy, transform, visualize, and model data, Wickham, H., 2017
  • The data science handbook, Cady, F., 2017