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Řádek 3: Řádek 3:
* Code: 155ISDP
* Code: 155ISDP
* Lectures: {{lide|Ing. Martin Landa, Ph.D.}}
* Lectures: {{lide|Ing. Martin Landa, Ph.D.}}
* Practical classes: {{lide|Ing. Martin Landa, Ph.D.}} (head of the classes), {{lide|Ing. Ondřej Pešek, PhD.}}
* Practical classes: {{lide|Ing. Martin Landa, Ph.D.}} (head of the classes), {{lide|Ing. Ondřej Pešek, Ph.D.}}
* lectures per week: 2 hours  
* lectures per week: 2 hours  
* exercises per week: 2 hours
* exercises per week: 2 hours
Řádek 16: Řádek 16:
==Lectures==
==Lectures==


'''Course tutors:''' {{lide|Ing. Martin Landa, Ph.D.}} (ML), {{lide|Ing. Ondřej Pešek, PhD.}} (OP)
'''Course tutors:''' {{lide|Ing. Martin Landa, Ph.D.}} (ML), {{lide|Ing. Ondřej Pešek, Ph.D.}} (OP)


* Wednesday 4pm-5:40pm room: B-870
* Wednesday 4-5:40pm room: B-870
* Thursday 9am-10:40am room: B-s111
* Thursday 9-10:40am room: B-s111


# (19+20.2.) [ML] Introduction into GIS, open geospatial datasets  
# (19+20.2.) [ML] Introduction into GIS, open geospatial datasets  
Řádek 26: Řádek 26:
# (12+13.3.) [ML] Introduction into Python data processing (Esri)
# (12+13.3.) [ML] Introduction into Python data processing (Esri)
# (19+20.3.) [OP] Introduction into Python data processing (open source)
# (19+20.3.) [OP] Introduction into Python data processing (open source)
# (26+27.3.) [OP] Geospatial Python packages
# (26+27.3.) [OP] <strike>Geospatial Python packages</strike>
# (02+03.4.) [ML] Space-time geospatial data processing
# (02+03.4.) [ML] Geospatial Python packages<strike>Space-time geospatial data processing</strike>
# (09+10.4.) [ML] Introduction into databases, SQL
# (09+10.4.) [ML] Introduction into databases, SQL
# <strike>(16+17.4.)</strike> Class cancelled
# (16+<strike>17.4.</strike>) [ML] Geospatial data in SQL (part 1) (''change: 17.4. class cancelled'')
# (23+24.4.) [ML] Geospatial data in SQL
# (23+<strike>24.4.</strike>) [ML] Geospatial data in SQL (part 2) (''change: 24.4. class cancelled'')
# (29.4+7.5.) [ML] Geospatial data processing in SQL (change: 29.4. 4-5:40pm B-870)
# (29.4+7.5.) [ML] Geospatial data processing in SQL (''change: 29.4. 9-10:40am B-s111'')
# (14+15.5.) [TB?] Cloud-based geospatial data processing (change: 4-7:40pm B-870)
# (14+15.5.) [TB] Geospatial data processing in SQL <strike>Cloud-based geospatial data processing</strike>
<!--# (17.5.) Network analysis in SQL-->


==Exercises==
==Exercises==
Řádek 42: Řádek 41:


JupyterHub: http://gislab.fsv.cvut.cz:8000/hub
JupyterHub: http://gislab.fsv.cvut.cz:8000/hub
== Assignments ==
Each student will work on two mini-projects, one using Python and the other using SQL. Student's evaluation is based on successful fulfillment of these two assignments.
Project assignment/requirements:
* use open geospatial data as input
* demonstrate knowledge of automated geospatial data processing and its analysis
* provide results interpretation (discussion, graphs, tables, ...)
=== Python-based mini-project assignment  ===
* Assignment 27.3.
* '''Presentation 30.4.'''
Expected submission form: Jupyter Notebook or Python script (input data included)
=== SQL-based mini-project assignment  ===
* Assignment 8.5.
* '''Presentation 29.5.'''
Expected submission form: SQL file (input data included)


== Install instructions for MS Windows ==
== Install instructions for MS Windows ==


* [https://github.com/jupyterlab/jupyterlab-desktop JupyterLab]
* [https://qgis.org/download/ QGIS]
* [https://grass.osgeo.org/download/ GRASS GIS] (on MS Windows GRASS GIS is installed together with QGIS)
 
=== JupyterLab ===
 
* [https://www.python.org/downloads/ Python] + [https://github.com/jupyterlab/jupyterlab-desktop JupyterLab]
 
Or use online platforms, eg. [https://colab.google/ Google Colab]


In Jupyter notebook run
In Jupyter notebook run
Řádek 52: Řádek 82:
!pip install pandas geopandas
!pip install pandas geopandas
</syntaxhighlight>
</syntaxhighlight>
to install required packages.


== Literature ==
== Literature ==


<bibtex>
* [https://link.springer.com/book/10.1007/978-0-387-68574-8 Open Source GIS: A Grass GIS Approach]
@book{neteler2004open,
* [https://books.google.cz/books/about/Desktop_GIS.html?id=xZ7tHwAACAAJ&redir_esc=y Desktop GIS: Mapping the Planet With Open Source Tools]
  title={Open Source GIS: A Grass GIS Approach},
* [http://books.google.cz/books?id=JZNuu8XODQMC Open Source Approaches in Spatial Data Handling]
  author={Neteler, M. and Mitasova, H.},
* [http://books.google.cz/books?id=AnCNQQAACAAJ OpenStreetMap: Using and Enhancing the Free Map of the World]
  isbn={9781402080647},
* [http://books.google.cz/books?id=SULMdT8qPwEC Geospatial Analysis: A Comprehensive Guide to Principles, Techniques and Software Tools]
  lccn={04051566},
  series={The International Series in Engineering and Computer Science Series},
  url={http://books.google.cz/books?id=Qvp9iFg\_WPEC},
  year={2004},
  publisher={Kluwer Academic Pub}
}
</bibtex>
<bibtex>
@book{sherman2008desktop,
  title={Desktop GIS: Mapping the Planet With Open Source Tools},
  author={Sherman, G.E.},
  isbn={9781934356067},
  lccn={2010280046},
  series={Pragmatic Bookshelf Series},
  url={http://books.google.cz/books?id=xZ7tHwAACAAJ},
  year={2008},
  publisher={Pragmatic Bookshelf}
}
</bibtex>
<bibtex>
@book{hall2008open,
  title={Open Source Approaches in Spatial Data Handling},
  author={Hall, G.B. and Leahy, M.G.},
  isbn={9783540748311},
  lccn={2008932589},
  series={Advances in geographic information science},
  url={http://books.google.cz/books?id=JZNuu8XODQMC},
  year={2008},
  publisher={Springer London, Limited}
}
</bibtex>
<bibtex>
@book{ramm2010openstreetmap,
  title={OpenStreetMap: Using and Enhancing the Free Map of the World},
  author={Ramm, F. and Topf, J. and Chilton, S.},
  isbn={9781906860110},
  url={http://books.google.cz/books?id=AnCNQQAACAAJ},
  year={2010},
  publisher={Uit Cambridge Limited}
}
</bibtex>
<bibtex>
@book{de2007geospatial,
  title={Geospatial Analysis: A Comprehensive Guide to Principles, Techniques and Software Tools},
  author={De Smith, M.J. and Goodchild, M.F. and Longley, P.A.},
  isbn={9781905886609},
  url={http://books.google.cz/books?id=SULMdT8qPwEC},
  year={2007},
  publisher={Matador}
}
</bibtex>

Aktuální verze z 8. 5. 2025, 08:41

Basic Information

  • Up-to-date and complete information are ON THIS PAGE
  • Code: 155ISDP
  • Lectures:

Ing. Martin Landa, Ph.D.

  • Practical classes:

Ing. Martin Landa, Ph.D. (head of the classes), Ing. Ondřej Pešek, Ph.D.

  • lectures per week: 2 hours
  • exercises per week: 2 hours
  • Credits: 6
  • finished with: an exam / project
  • summer semester

Anotation

Introduction into geospatial data processing. Various workflow automation techniques (interactive tools, Python programming language, and Structured Query Language). Demonstration of various processing environments (desktop applications, database environments, cloud-based computing).

Lectures

Course tutors: Ing. Martin Landa, Ph.D. (ML), Ing. Ondřej Pešek, Ph.D. (OP)

  • Wednesday 4-5:40pm room: B-870
  • Thursday 9-10:40am room: B-s111
  1. (19+20.2.) [ML] Introduction into GIS, open geospatial datasets
  2. (26+27.3.) [OP] Geospatial data and web services
  3. (05+06.3.) [OP/ML] Data processing automation
  4. (12+13.3.) [ML] Introduction into Python data processing (Esri)
  5. (19+20.3.) [OP] Introduction into Python data processing (open source)
  6. (26+27.3.) [OP] Geospatial Python packages
  7. (02+03.4.) [ML] Geospatial Python packagesSpace-time geospatial data processing
  8. (09+10.4.) [ML] Introduction into databases, SQL
  9. (16+17.4.) [ML] Geospatial data in SQL (part 1) (change: 17.4. class cancelled)
  10. (23+24.4.) [ML] Geospatial data in SQL (part 2) (change: 24.4. class cancelled)
  11. (29.4+7.5.) [ML] Geospatial data processing in SQL (change: 29.4. 9-10:40am B-s111)
  12. (14+15.5.) [TB] Geospatial data processing in SQL Cloud-based geospatial data processing

Exercises

Materials: https://geo.fsv.cvut.cz/courses/155isdp/

Sample data: https://geo.fsv.cvut.cz/courses/155isdp/data

JupyterHub: http://gislab.fsv.cvut.cz:8000/hub

Assignments

Each student will work on two mini-projects, one using Python and the other using SQL. Student's evaluation is based on successful fulfillment of these two assignments.

Project assignment/requirements:

  • use open geospatial data as input
  • demonstrate knowledge of automated geospatial data processing and its analysis
  • provide results interpretation (discussion, graphs, tables, ...)

Python-based mini-project assignment

  • Assignment 27.3.
  • Presentation 30.4.

Expected submission form: Jupyter Notebook or Python script (input data included)

SQL-based mini-project assignment

  • Assignment 8.5.
  • Presentation 29.5.

Expected submission form: SQL file (input data included)

Install instructions for MS Windows

  • QGIS
  • GRASS GIS (on MS Windows GRASS GIS is installed together with QGIS)

JupyterLab

Or use online platforms, eg. Google Colab

In Jupyter notebook run 

!pip install pandas geopandas

to install required packages.

Literature

Citováno z „https://geo.fsv.cvut.cz/index.php?title=155ISDP_Introduction_to_Spatial_Data_Processing&oldid=43114“