Unlock the full potential of GIS by analyzing practical urban applications.
You know that you are into GIS, but not sure where to specialize?
Do you already have a GIS background but want to grasp its full capabilities?
Do you want to take your first steps away from commercial GIS software to independent scripting?
Are you a smart city enthusiast and you want to understand how to analyze urban problems to build useful applications?
Then this course is for you!
Join me in this 2.5 hour course we will unlock more than enough tools and algorithms to highlight and analyze urban problems. Well packed with their background theory so that you don’t miss anything and that you also become ready to further delve into them and a 10 question quiz to test your knowledge!
Enroll now and let’s get to work!
Learn geospatial analysis with GIS and Python and boost your knowledge and skills.
Course curriculum
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01
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Greetings from your personal virtual instructor
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Lesson 1: About the course, aim and scope, and prerequisites
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Lesson 2: Curriculum and necessary software
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02
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Lesson 3: Vector datasets: Administrative boundaries, street network and Urban atlas
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Lesson 4: Raw satellite data: Sentinel 2 multispectral imagery
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03
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Lesson 5: Creating and organizing a geodatabase
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Lesson 6: Setting up a topology
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Lesson 7: Validating the topology
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04
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Lesson 8: Geocoding addresses with Google MyMaps
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Lesson 9: Importing a dataset into python for geocoding
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Lesson 10: Geocoding with geopy and creating an output shapefile
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Lesson 11: Exporting the shapefile
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Lesson 12: OpenWeatherMap API for meteorological data: Making the call
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Lesson 13: OpenWeatherMap API for meteorological data: Importing and visualizing in GIS
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05
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Lesson 14: Kids in danger: Locating schools closest to most traffic accidents
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Lesson 15: A simple locationing problem
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06
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Lesson 16: Fundamental statistics aspects: Random variables, probability functions, common distributions and the Normal distribution
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Lesson 17: Fundamental statistics aspects: Hypothesis testing, statistical significance, results interpretation and spatial statistics metrics
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Lesson 18: Preparing the data: Attribute filling with Arcpy and generating spatial weights
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Lesson 19: Global spatial autocorrelation: Moran’s I for traffic accidents’ time zones
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Lesson 20: Local autocorrelation and clustering: Getis – Ord’s Gi* and Anselin’s Local Moran’s I
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Lesson 21: Calculating global spatial autocorrelation with PySAL
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Lesson 22: Calculating and visualizing local autocorrelation with PySAL and matplotlib
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07
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Lesson 23: Introduction: Why and what is Remote Sensing?
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Lesson 24: Preparing the data: Auxiliary vector data and automatic clipping of rasters
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Lesson 25: Mapping urban green with NDVI
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Lesson 26: Detecting informal settlements with unsupervised classification
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Lesson 27: Automating procedures with Model Builder
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08
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Lesson 28: What is a network dataset?
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Lesson 29: Preparing the data: Building our network
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Lesson 30: Generating service areas for Super Markets
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Lesson 31: Calculating accessibility to green open spaces
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09
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Lesson 32: Discussion, key takeaways, possible future courses
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Discussion with your Personal Virtual Instructor!
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Test your knowledge
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What will you learn?
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Data analysis in the urban scales to highlight problematic areas and inequalities
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Basic to advanced functionality of most GIS tools that are useful for urban geospatial analysis
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Use of Model Builder and arcpy to unlock the full potential of GIS
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Use of independent python scripts to supplement your GIS work and build the confidence to make your own algorithms
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Satellite image processing using python
Any prerequisites
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An ArcGIS desktop license
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Familiarity with basic GIS tools and features
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Understanding of basic scripting principles, such as variables, loops etc. (optional)
Student Profile?
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Under/post graduate students
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Professionals and Companies
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Master students and PhD candidates
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Researchers and Academics
About your Instructor
Dimitris Sykas
Remote Sensing Engineer, Ph.D. · Co-founder and Director, GOVADI · Founder, GEO University
[email protected]