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Flora Vale, Alberto Nieto
Data Visualization for Spatial Analysis
esriurl.com/spatialstats
Data visualization as part of the spatial analysis workflow
Explore data
Interpret analysis results
Communicate findings
What is data visualization?
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
Why visualize data?
Convert slow reasoning tasks into fast perception tasks
1399683
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5731663
1628760
635200
5401748
3922722
1336265
3111844
6656872
1877879
19631599
12817894
3601157
1050228
8875318
6581982
2815039
2917750
38120066
11575704
12914651
629120
927030
1885932
5903286
5235100
4419036
2908933
8275961
6074504
6636256
3880520
9913774
6539407
26538203
2106392
4885854
3018484
9978939
4796559
2995330
4681639
19383475
9853722
742404
Position
Size
Shape
Value
Hue
Orientation
Texture
1399683
6962578
1019462
1345609
724027
844322
587106
5731663
1628760
635200
5401748
3922722
1336265
3111844
6656872
1877879
19631599
12817894
3601157
1050228
8875318
6581982
2815039
2917750
38120066
11575704
12914651
629120
927030
1885932
5903286
5235100
4419036
2908933
8275961
6074504
6636256
3880520
9913774
6539407
26538203
2106392
4885854
3018484
9978939
4796559
2995330
4681639
19383475
9853722
742404
1399683
6962578
1019462
1345609
724027
844322
587106
5731663
1628760
635200
5401748
3922722
1336265
3111844
6656872
1877879
19631599
12817894
3601157
1050228
8875318
6581982
2815039
2917750
38120066
11575704
12914651
629120
927030
1885932
5903286
5235100
4419036
2908933
8275961
6074504
6636256
3880520
9913774
6539407
26538203
2106392
4885854
3018484
9978939
4796559
2995330
4681639
19383475
9853722
742404
1399683
6962578
1019462
1345609
724027
844322
587106
5731663
1628760
635200
5401748
3922722
1336265
3111844
6656872
1877879
19631599
12817894
3601157
1050228
8875318
6581982
2815039
2917750
11575704
12914651
629120
927030
1885932
5903286
5235100
4419036
2908933
8275961
6074504
6636256
3880520
9913774
6539407
26538203
2106392
4885854
3018484
9978939
4796559
2995330
4681639
19383475
9853722
742404
Good Viz vs Bad Viz
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?
?!
Visualizations to support spatial analysis
Distributions and frequency
Category comparisons
Relationships and correlations
Change over time or distance
Distributions
Understanding the shape of numerical data
Categories
Summarizing and comparing amounts across categorical data
Relationships
Explore correlations and trends
Change
Visualizing trends and cycles over time or distance
When a map (alone) isn’t the best option…
When a map (alone) isn’t the best option…
When a map (alone) isn’t the best option…
When a map (alone) isn’t the best option…
When a map (alone) isn’t the best option…
When a map (alone) isn’t the best option…
When a map (alone) isn’t the best option…
When a map (alone) isn’t the best option…
Demodata exploration
Spatial analysisseeing is believing
“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.
interpreting analysis Demo
Want to learn more???
esriurl.com/spatialstats
TUESDAY_________________________________________
1:45p Data Visualization for Spatial Analysis 146C
3:00p Machine Learning in ArcGIS 146C
4:15p From Means and Medians to Machine Learning: Spatial Statistics Basics and Innovations 146C
WEDNESDAY______________________________________
8:30a Machine Learning in ArcGIS 146C
11a Data Visualization for Spatial Analysis 146C
1:30p From Means and Medians to Machine Learning: Spatial Statistics Basics and Innovations 146C
2:45p Spatial Data Mining: Cluster Analysis and Space Time Analysis 146C
4:00p Beyond Where: Modeling Spatial Relationships and Making Predictions 146C
5:15p The Forest for the Trees: Making Predictions Using Forest-Based Classification and Regression 146C
Please fill out a course survey!!!
Want to learn more???
esriurl.com/spatialstats
TUESDAY_________________________________________
1:45p Data Visualization for Spatial Analysis 146C
3:00p Machine Learning in ArcGIS 146C
4:15p From Means and Medians to Machine Learning: Spatial Statistics Basics and Innovations 146C
WEDNESDAY______________________________________
8:30a Machine Learning in ArcGIS 146C
11a Data Visualization for Spatial Analysis 146C
1:30p From Means and Medians to Machine Learning: Spatial Statistics Basics and Innovations 146C
2:45p Spatial Data Mining: Cluster Analysis and Space Time Analysis 146C
4:00p Beyond Where: Modeling Spatial Relationships and Making Predictions 146C
5:15p The Forest for the Trees: Making Predictions Using Forest-Based Classification and Regression 146C
Please fill out a course survey!!!
