155ISDP Introduction to Spatial Data Processing: Porovnání verzí

← Porovnání se starší verzí

VizuálníWikitext

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

(19+20.2.) [ML] Introduction into GIS, open geospatial datasets
(26+27.3.) [OP] Geospatial data and web services
(05+06.3.) [OP/ML] Data processing automation
(12+13.3.) [ML] Introduction into Python data processing (Esri)
(19+20.3.) [OP] Introduction into Python data processing (open source)
(26+27.3.) [OP] ~~Geospatial Python packages~~
(02+03.4.) [ML] Geospatial Python packages~~Space-time geospatial data processing~~
(09+10.4.) [ML] Introduction into databases, SQL
(16+~~17.4.~~) [ML] Geospatial data in SQL (part 1) (change: 17.4. class cancelled)
(23+~~24.4.~~) [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. 9-10:40am B-s111)
(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

Python + JupyterLab

Or use online platforms, eg. Google Colab

In Jupyter notebook run

!pip install pandas geopandas

to install required packages.

@@ Řádek 1: / Řádek 1: @@
 ==Basic Information==
-* 2 hours lectures per week
+* Up-to-date and complete information are '''ON THIS PAGE'''
-* 2 hours exercises per week
+* Code: 155ISDP
-* 6 credits
+* Lectures: {{lide|Ing. Martin Landa, Ph.D.}}
-* finished with an exam / project
+* Practical classes: {{lide|Ing. Martin Landa, Ph.D.}} (head of the classes), {{lide|Ing. Ondřej Pešek, Ph.D.}}
+* lectures per week: 2 hours
+* exercises per week: 2 hours
+* Credits: 6
+* finished with: an exam / project
 * summer semester
@@ Řádek 10: / Řádek 14: @@
 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).
-=== Literature ===
+==Lectures==
+'''Course tutors:''' {{lide|Ing. Martin Landa, Ph.D.}} (ML), {{lide|Ing. Ondřej Pešek, Ph.D.}} (OP)
+* Wednesday 4-5:40pm room: B-870
+* Thursday 9-10:40am room: B-s111
+# (19+20.2.) [ML] Introduction into GIS, open geospatial datasets
+# (26+27.3.) [OP] Geospatial data and web services
+# (05+06.3.) [OP/ML] Data processing automation
+# (12+13.3.) [ML] Introduction into Python data processing (Esri)
+# (19+20.3.) [OP] Introduction into Python data processing (open source)
+# (26+27.3.) [OP] <strike>Geospatial Python packages</strike>
+# (02+03.4.) [ML] Geospatial Python packages<strike>Space-time geospatial data processing</strike>
+# (09+10.4.) [ML] Introduction into databases, SQL
+# (16+<strike>17.4.</strike>) [ML] Geospatial data in SQL (part 1) (''change: 17.4. class cancelled'')
+# (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. 9-10:40am B-s111'')
+# (14+15.5.) [TB] Geospatial data processing in SQL <strike>Cloud-based geospatial data processing</strike>
+==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)
-<bibtex>
+=== SQL-based mini-project assignment  ===
-@book{neteler2004open,
-  title={Open Source GIS: A Grass GIS Approach},
-  author={Neteler, M. and Mitasova, H.},
-  isbn={9781402080647},
-  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>
-==Lectures==
+* Assignment 8.5.
+* '''Presentation 29.5.'''
+Expected submission form: SQL file (input data included)
+== Install instructions for MS Windows ==
+* [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
+<syntaxhighlight lang=python>
+!pip install pandas geopandas
+</syntaxhighlight>
-'''Lecturers:''' [[Ing. Martin Landa, Ph.D.]], Ing. Ondřej Pešek, Ing. Linda Kladivová
+to install required packages.
-# (22.2.) Introduction into GIS, open geospatial datasets
+== Literature ==
-# (01.3.) Geospatial data and web services
-# (08.3.) Data processing automation
-# (15.3.) Introduction into Python data processing
-# (22.3.) Geospatial Python libraries (open source)
-# (29.3.) Geospatial Python libraries (Esri)
-# <strike>(05.4.) No tuition</strike>
-# (12.4.) Space-time geospatial data processing
-# (19.4.) Cloud-based geospatial data processing
-# (26.4.) Introduction into databases, SQL
-# (03.5.) Geospatial data in SQL
-# <strike>(10.5.) No tuition</strike>
-# Geospatial data processing in SQL
-# (17.5.) Network analysis in SQL
-==Exercises==
+* [https://link.springer.com/book/10.1007/978-0-387-68574-8 Open Source GIS: A Grass GIS Approach]
+* [https://books.google.cz/books/about/Desktop_GIS.html?id=xZ7tHwAACAAJ&redir_esc=y Desktop GIS: Mapping the Planet With Open Source Tools]
+* [http://books.google.cz/books?id=JZNuu8XODQMC Open Source Approaches in Spatial Data Handling]
+* [http://books.google.cz/books?id=AnCNQQAACAAJ OpenStreetMap: Using and Enhancing the Free Map of the World]
+* [http://books.google.cz/books?id=SULMdT8qPwEC Geospatial Analysis: A Comprehensive Guide to Principles, Techniques and Software Tools]