Data_Visualization_and_Engineering_UC_2022.pdf
- 1. Chris Allen (he/him) Flora Vale (she/her) Lynne Buie (she/her) Data Engineering & Visualization for Spatial Data Science esriurl.com/spatialstats
- 2. Spatial Analysis & Data Science
- 4. What is data engineering?
- 6. Descriptive statistics • Nulls • Chart Preview • Minimum • Maximum • Mean • Standard Deviation • Median • Count • Number of Unique Values • Mode • Least Common • Outliers • Sum • Range • Interquartile Range • First Quartile • Third Quartile • Coefficient of Variation • Skewness • Kurtosis
- 7. Descriptive statistics • Nulls • Chart Preview • Minimum • Maximum • Mean • Standard Deviation • Median • Count • Number of Unique Values • Mode • Least Common • Outliers • Sum • Range • Interquartile Range • First Quartile • Third Quartile • Coefficient of Variation • Skewness • Kurtosis
- 9. Clean Construct Integrate Format Data engineering
- 12. What is data visualization?
- 13. What is data visualization?
- 14. Charles Minard’s Flow Map of Napoleon’s Russian Campaign of 1812 Dr. John Snow’s Map of London Cholera Outbreak of 1854 Florence Nightingale’s Rose Diagram of the Causes of Mortality in the Army of the East of 1859 Hans Rosling’s Animated Visualization of Global Life Expectancy Over Time from his 2006 TED Talk
- 15. Visualizations to support spatial analysis Distributions and frequency Category comparisons Relationships and correlations Change over time or distance
- 16. Distributions Understanding the shape of numerical data
- 17. When a map (alone) isn’t the best option…
- 18. Categories Summarizing and comparing amounts across categorical data
- 19. When a map (alone) isn’t the best option…
- 20. When a map (alone) isn’t the best option…
- 21. When a map (alone) isn’t the best option…
- 22. Relationships Explore correlations and trends
- 23. Relationships when variables are related, information can be learned about one variable by observing the values of the related variable(s)
- 24. When a map (alone) isn’t the best option…
- 25. When a map (alone) isn’t the best option…
- 26. When a map (alone) isn’t the best option…
- 27. Anscombe's quartet When a map (alone) isn’t the best option…
- 29. Visualizations to support spatial analysis Distributions and frequency Category comparisons Relationships and correlations Change over time or distance
- 30. Change Visualizing trends and cycles over time or distance
- 31. When a map (alone) isn’t the best option…
- 32. When a map (alone) isn’t the best option…
- 33. When a map (alone) isn’t the best option…
- 36. Spatial analysis seeing is believing
- 37. “Through collaboration with artists and designers, we can work toward the demystification of climate science because when science becomes understandable to the public, people become interested in not only the results but the scientific process, discussions, and, most importantly, solutions.” Tosca, M. (2019), Transcending science: Can artists help scientists save the world?, Eos, 100, https://doi.org/10.1029/2019EO127493. Published on 02 July 2019.
- 39. esriurl.com/spatialstats esriurl.com/spatialdatasciencemooc esriurl.com/dataengineering esriurl.com/charts TUESDAY_________________________________________ 10am Spatial Data Science in ArcGIS: An Overview Ballroom 06 C 4p Spatial Statistics: Making Predictions Room 05 A WEDNESDAY______________________________________ 8:30a Geostatistical Analyst: Concepts and Applications of Kriging Room 05 A 10a Spatial Statistics: Analyzing Space-Time Data Room 05 A 1p Applying Spatial Data Science: A Complete Workflow Room 09 2:30p Data Engineering and Visualization for Spatial Data Science Room 31 ABC THURSDAY________________________________________ 8:30a Spatial Statistics: Statistical Cluster Analysis Room 16 A 8:30a Spatial Analysis and Data Science: The Road Ahead Room 33 ABC 10a Spatial Statistics: Analyzing Space-Time Data Room 10 12:15p A Tour of the R-ArcGIS Bridge Expo Demo Theater 01 1p Spatial Statistics: Machine Learning-Based Cluster Analysis Room 16 A 2:30p Geostatistical Analyst: Concepts and Applications of Kriging Room 16 A 4p Spatial Statistics: Making Predictions Room 15 B 4p Data Engineering and Visualization for Spatial Data Science Room 16 A 4p Using Spatial Analysis to Reveal and Address Inequities Room 02 FRIDAY__________________________________________ 9a Spatial Statistics: Statistical Cluster Analysis Room 03 Please fill out a course survey!!!
- 40. esriurl.com/spatialstats esriurl.com/spatialdatasciencemooc esriurl.com/dataengineering esriurl.com/charts TUESDAY_________________________________________ 10am Spatial Data Science in ArcGIS: An Overview Ballroom 06 C 4p Spatial Statistics: Making Predictions Room 05 A WEDNESDAY______________________________________ 8:30a Geostatistical Analyst: Concepts and Applications of Kriging Room 05 A 10a Spatial Statistics: Analyzing Space-Time Data Room 05 A 1p Applying Spatial Data Science: A Complete Workflow Room 09 2:30p Data Engineering and Visualization for Spatial Data Science Room 31 ABC THURSDAY________________________________________ 8:30a Spatial Statistics: Statistical Cluster Analysis Room 16 A 8:30a Spatial Analysis and Data Science: The Road Ahead Room 33 ABC 10a Spatial Statistics: Analyzing Space-Time Data Room 10 12:15p A Tour of the R-ArcGIS Bridge Expo Demo Theater 01 1p Spatial Statistics: Machine Learning-Based Cluster Analysis Room 16 A 2:30p Geostatistical Analyst: Concepts and Applications of Kriging Room 16 A 4p Spatial Statistics: Making Predictions Room 15 B 4p Data Engineering and Visualization for Spatial Data Science Room 16 A 4p Using Spatial Analysis to Reveal and Address Inequities Room 02 FRIDAY__________________________________________ 9a Spatial Statistics: Statistical Cluster Analysis Room 03 Please fill out a course survey!!!