This document discusses using heatmaps to analyze large tax income data from the Netherlands. It describes how heatmaps can reveal patterns in the data across different subpopulations and variable pairs. Key steps in generating heatmaps are discussed, including determining variable ranges, binning the data, counting observations in bins, assigning color scales, and issues that can arise like outliers, skewed data distributions, and zero counts. Examples of heatmaps are shown for variables like gross income across socioeconomic status and age. The document emphasizes that visualization is important for exploring data and assumptions and recommends always playing with resolution and color scales when generating heatmaps.
Computer Science - Hexadecimal
You will be able to learn how to calculate hexadecimal conversion calculations along with what hexadecimal is. This presentation will help with your gcse or a level studies or just learning about computer systems. There are also some questions with answers.
Computer Science - Hexadecimal
You will be able to learn how to calculate hexadecimal conversion calculations along with what hexadecimal is. This presentation will help with your gcse or a level studies or just learning about computer systems. There are also some questions with answers.
Presentation given at strata+hadoop 2014: Visualizing dutch income tax data using heatmaps, giving guidelines for heatmap presentation, and using heatmaps as an explorative tool before applying machine learning techniques on your data.
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This presentation will cover all aspects of modeling, from preparing data, training and evaluating the results. There will be descriptions of the mainline ML methods including, neural nets, SVM, boosting, bagging, trees, forests, and deep learning. common problems of overfitting and dimensionality will be covered with discussion of modeling best practices. Other topics will include field standardization, encoding categorical variables, feature creation and selection. It will be a soup-to-nuts overview of all the necessary procedures for building state-of-the art predictive models.
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- The octal and hexadecimal Numeral System
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- Array Addressing with "Cast Contact Lenses"
- The Heap: Segmentation and "Why do Programs crash?"
- How to understand and fix Bugs
- The Teddybear Principle
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M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Presentation given at strata+hadoop 2014: Visualizing dutch income tax data using heatmaps, giving guidelines for heatmap presentation, and using heatmaps as an explorative tool before applying machine learning techniques on your data.
Foundations of Machine Learning - StampedeCon AI Summit 2017StampedeCon
This presentation will cover all aspects of modeling, from preparing data, training and evaluating the results. There will be descriptions of the mainline ML methods including, neural nets, SVM, boosting, bagging, trees, forests, and deep learning. common problems of overfitting and dimensionality will be covered with discussion of modeling best practices. Other topics will include field standardization, encoding categorical variables, feature creation and selection. It will be a soup-to-nuts overview of all the necessary procedures for building state-of-the art predictive models.
(7) cpp memory representation_pointer_arithmeticsNico Ludwig
http://de.slideshare.net/nicolayludwig/7-cpp-memory-representationpointerarithmeticsexercises-38510699
- The octal and hexadecimal Numeral System
- Byte Order
- Memory Representation of Arrays and Pointer Arithmetics
- Array Addressing with "Cast Contact Lenses"
- The Heap: Segmentation and "Why do Programs crash?"
- How to understand and fix Bugs
- The Teddybear Principle
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
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Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
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3. Who am I?
Statistical consultant / Data scientist
working @ R&D department of Statistics
Netherlands
Statistics Netherlands (SN):
- Government agency
- Produces all official statistics of The Netherlands
3
5. ‘Big’ data at CBS (2):
– Muncipal population register
– Income tax
– Payroll tax
– Central Registration of Higher Education
– Health care use (DBC’s; ‘diagnose-behandelcombinatie’)
– Company data
– Etc.
All datasets are anonymized and stripped of identifiable
records.
5
8. Tax Income data
– Contains all income tax records for the Netherlands
– Approx 17M records with 550 variables.
– Used to produce income statistics!
Analysis is not trivial
– Income Tax is complex (at least in the Netherlands)
‐ stages of progressive tax
‐ Complex Tax deductions
‐ Complex Tax benefits
8
9. Tax data (2)
- 550 variables (for each person in NL):
- 15 identificators/unique keys
- Dwelling, person id, etc.
- 70 categorical
- 250 numerical variables from the income tax
form
- >200 derived variables (useful for analysis)
- E.g. expandable income, income of
dwelling/household
9
20. Meta pattern?
Meta patterns constitutes of repeating pattern in:
‐ different subpopulations
• E.g. Male/female, Socio economic status, Branch
of Industry
‐ different pairs of variables
• Income x age
• Benefits x age
• Etc.
So patterns that are generic over different heatmaps.
20
21. Looking for patterns
Subpopulations:
– Generate heatmap per category e.g. Age x Gross Income
per socio economic status
– Cluster/compare heatmaps
Pairs of variables:
- Generate heatmaps for all pairs
- Prune: remove heatmaps with low support
1. Use image classification to cluster them
2. Or Cluster on extracted mode/line (wip)
You will still need to visualize!
21
24. Anscombe’s quartet
Property Value
Mean of x1, x2, x3, x4 All equal: 9
Variance of x1, x2, x3, x4 All equal: 11
Mean of y1, y2, y3, y4 All equal: 7.50
Variance of y1, y2, y3, y4 All equal: 4.1
Correlation for ds1, ds2, ds3, ds4 All equal 0.816
Linear regression for ds1, ds2, ds3,
ds4
All equal: y = 3.00 + 0.500x
Looks the same, right?
28. Visualization helps to …
– Test your (hidden model) assumptions!
– To find structure in data, e.g.
“How is my data distributed?”
– Explore patterns:
‐ Are there clusters?
‐ Are there outliers?
28
31. 31
1. Take two numerical variables x and y
2. Determine range 𝑟𝑥 = [min 𝑥 , max(𝑥)]
3. Chop 𝑟𝑥 in 𝑛 𝑥 equal pieces
4. Repeat for y
5. We now have 𝑛 𝑥 . 𝑛 𝑦 bins
6. Count # records in each bin
7. Assign colors to counts
8. Plot matrix
9. Enjoy!
32. Easy as pie?
Best practices and problems with heatmaps:
- Resolution
- Rescaling
- Zooming
- Outliers
- Color scales
32
34. 34
1. Take two numerical variables x and y
2. Determine range 𝐫𝐱 = [𝐦𝐢𝐧 𝐱 , 𝐦𝐚𝐱(𝐱)]
3. Chop 𝑟𝑥 in 𝑛 𝑥 equal pieces
4. Repeat for y
5. We now have 𝑛 𝑥 . 𝑛 𝑦 bins
6. Count # records in each bin
7. Assign colors to counts
8. Plot matrix
9. Enjoy!
37. Range outliers…
Does your data contain outliers?
- If so: most pixels are empty
- Most cases: outliers have low mass and are
barely visible
Truncate range: in x or y direction: e.g. 99%
quantile
- Interactively: allow for zoom and pan.
37
38. Range: data skewed?
38
–Many variables are not normal
distributed:
‐ Power law: 𝒙 𝛼
‐ Exponential: 𝑒 𝑎𝒙+𝑏
So rescale x or y or both
39. 39
1. Take two numerical variables x and y
2. Determine range rx = [min x , max(x)]
3. Chop 𝒓 𝒙 in 𝒏 𝒙 equal pieces
4. Repeat for y
5. We now have 𝑛 𝑥 . 𝑛 𝑦 bins
6. Count # records in each bin
7. Assign colors to counts
8. Plot matrix
9. Enjoy!
47. Chop: Too small / Too big
If #bins too small:
- patterns are hidden
If #bins too large:
- heatmap is noisy (signal vs noise)
Optimal nr bins depends on data.
(kernel based approx), but always play with
resolution!
47
50. 50
1. Take two numerical variables x and y
2. Determine range rx = [min x , max(x)]
3. Chop 𝑟𝑥 in 𝑛 𝑥 equal pieces
4. Repeat for y
5. We now have 𝑛 𝑥 . 𝑛 𝑦 bins
6. Count # records in each bin
7. Assign colors to counts
8. Plot matrix
9. Enjoy!
54. 54
1. Take two numerical variables x and y
2. Determine range rx = [min x , max(x)]
3. Chop 𝑟𝑥 in 𝑛 𝑥 equal pieces
4. Repeat for y
5. We now have 𝑛 𝑥 . 𝑛 𝑦 bins
6. Count # records in each bin
7. Assign colors to counts
8. Plot matrix
9. Enjoy!
56. Colors: scales
– Color ‘intensity’ implies value
– Percieved response depends on ‘color’ and ‘color
lightness’ (compare #00ff00 with #0000ff)
– Different models for color response:
‐ RGB (models computer monitor)
‐ HSV
‐ HCL
‐ CIELAB (models human eye)
– Gradient generator:
http://davidjohnstone.net/pages/lch-lab-colour-gradient-picker
56
57. Colors
– Color has two functions in heatmap:
‐ Show ‘counts’ in your data
‐ Show ‘patterns’
At least, use a perceptually uniform gradient
- Libs: chroma.js, colorbrewer (R)
…but patterns need distinct colors
57
58. Color scales
– Range of color scale depends on distribution of data.
– Often have multiple populations/distributions in data
– Severe spikes/stripes drown the smaller distributions:
‐ We suggest log scale
‐ Sometimes log scale is not enough
– In practice, linear scale with low maximum cut-off works
well
– Effect is best understood in 3D (!).
58
65. Meta pattern
Meta patterns constitutes of repeating pattern in:
‐ different subpopulations
‐ different pairs of variables
So patterns that are generic over different
heatmaps.
65
70. Patterns
– Stripes are real, not outliers:
– Corresponds with benefits, tax breaks
– Needs paradigm shift: data is not normally
distributed (but we knew that).
70
88. Questions?
Thank you for your attention!
More info?
ah.priem@cbs.nl
e.dejonge@cbs.nl
Heatmapping code available at
https://github.com/alexpriem/heatmapr
88
