The Good Country Index is developed by Mr Simon Anholt, a policy advisor and strategist. It measures the cumulative contribution of 125 countries towards the benefit of the world in the technological, cultural, peace-related, world orderliness, environmental, prosperous, and health wellbeing aspects, thus making up the seven categories of the Good Country Index (Figure 1). Countries included in the index do not necessarily have to be good for their own citizens, because the index does not look at the existing infrastructure that make up the development and dynamics of countries, but rather, it only focuses on countries’ external outputs and resultant motives. On the contrary, there are plenty of other global indices (for example, Human Development Index, or Social Progress Index) that measure and rank countries in terms of their existing infrastructure. If we can substitute the category components of the Good Country Index with other global indices that correspond to the respective category component, we may be able to use the global indices as drivers to evaluate a country’s ‘goodness’. This project entails a number of statistical methods for analytical purposes. Two types of analysis are carried out, the first type is non-spatial analysis which employs bivariate correlation to test for the existence of relationship between the Good Country Index components, and other global indices. Regression curves and decision tree rules are also obtained to employ predictive analysis on the Good Country Index in relation to other global indices as explanatory variables, with the main focus being made on the ‘Most Good’, and ‘Least Good’ countries. The second type is spatial analysis which includes spatial autocorrelation to test for the presence of underlying spatial processes by the Good Country Index, and global indices. If clustering is present, the degree of clustering is displayed. The cluster and outlier analysis, as well as hot spot analysis geovisualise the clusters, outliers, and hot and cold spots on various maps. Finally, grouping analysis is carried out to group similar countries into geographically compact clusters, and mapped. This project focuses on exploratory analysis to find out if the use of global indices may be suitable as drivers to account for countries’ ‘goodness’ in the Good Country Index, as well as geovisualisation through mapping of the Good Country Index, in relation to its corresponding global indices. It must be greatly emphasized that this project will attempt to investigate levels of association (or correlation) between variables, as the use of explanatory variables does not necessarily result in causation, hence no strong judgement is made when attempting to relate the Index with other global indices.

1. GEOVISUALISATION
AND ANALYSIS OF
THE GOOD COUNTRY
INDEX
MAJOR RESEARCH PROJECT IN GIS FOR BUSINESS
PREPARED BY: CORNELIA TAN
29 MAY, 2015

2. PRESENTATION OVERVIEW
• What is the Good Country Index?
• Project overview
• Data sources and preparation
• Beautiful Soup Python geoprocessing
• Global indices
• Statistical analysis
• Predictive analytics
• Spatial statistics – analyzing patterns, mapping clusters &
grouping analysis
• Return on investment
• Conclusions
• Limitations
• Recommendations
Introduction
Analysis
Wrap-Up

3. THE GOOD COUNTRY INDEX
• Developed by Simon Anholt and Robert Govers
• Measures contributions of 125 countries in terms of
international benefit based on the 7 categories below:
o Science & Technology  “Science”
o Culture  “Culture”
o International Peace & Security  “Peace”
o World Order  “World”
o Planet & Climate  “Planet”
o Prosperity & Equality  “Prosperity”
o Health & Wellbeing  “Health”

4. THE GOOD COUNTRY INDEX
OVERALL RANKINGS
URL: http://www.goodcountry.org/overall

5. THE STRUCTURE
OF THE GOOD COUNTRY INDEX

6. THE MAP OF THE GOOD COUNTRY INDEX

7. PROJECT OVERVIEW
• Based on how the Good Country Index operates, what factors or
drivers influence a country’s performance?
• What if we use other global indices to explain their
performance in the Good Country Index?
• Apply exploratory analysis to investigate the suitable use of
other global indices as drivers to evaluate countries’ goodness
in the Good Country Index
• Geovisualise the findings on maps

8. DATA SOURCES & PREPARATION
• Good Country Index dataset was provided by Robert Govers,
most of the data is taken from 2010
• Datasets of global indices are taken from their respective
websites, care is taken to ensure that 2010 data is used
• Good Country Index dataset and global indices’ datasets are
merged into a single wide spreadsheet
• End product is a .dbf file for use in ArcMap, to be combined
with the country polygon shapefile
• It must be noted that not all 125 countries will have complete
data

9. GLOBAL INDICES
• Global indices were selected based on general relevancy to the
respective Good Country Index components based on theme
 For example, a global index measuring the level of innovation in
countries could be used to correspond with the Science &
Technology category of the Good Country Index
• Global indices must correlate with the Good Country Index
components
• Global indices are merely used for casual analysis to generate
observations and hypotheses

10. DATA SOURCES - GLOBAL INDICES
Good Country
Index Component
Global Index (& Dataset Source)
Overall
Social Progress Index (“SPI”) – by the Social Progress Imperative
(URL: http://www.socialprogressimperative.org/data/spi) [2014 dataset]
Science
Global Innovation Index (“GII”) – by INSEAD & World Intellectual Property Organization
(URL: https://www.globalinnovationindex.org) [2012 dataset]
Culture
Global Creativity Index (“GCI”) – Martin Prosperity Institute
(URL: http://martinprosperity.org/content/creativity-and-prosperity-the-global-
creativity-index) [2010 dataset]
Peace Social Progress Index (Personal Safety category) (“SPI-PS”) – refer to Overall component
World
Human Development Index (“HDI”) – United Nations Development Programme
(URL: http://hdr.undp.org/en/content/human-development-index-hdi) [2010 dataset]
Planet
Environmental Performance Index (“EPI”) – Yale University & Columbia University
(URL: http://epi.yale.edu/epi) [2010 dataset]
Prosperity
Global Opportunity Index (“GOI”) – Milken Institute
(URL: http://www.globalopportunityindex.org/) [2011 dataset]
Health
Health Expenditure as % of Total GDP (“HealthExp”) – World Health Organisation
(URL: http://www.who.int/gho/health_financing/total_expenditure/en/) [2010 dataset]

11. BEAUTIFUL SOUP PYTHON
GEOPROCESSING
• Python script tool was used up prior to obtaining Good Country
Index dataset from Robert Govers
• Beautiful Soup is a Web-scraping method to extract data from
online sources, and present it in tabular format
• Box plots without any numeric figures are problematic
• Webpage’s HTML source code were screened to make use of box
widths instead, and convert them into percentages
• The alternative ways of data extraction were demonstrated,
although raw dataset is eventually obtained after that

12. PYTHON SCRIPT USED AS ARCMAP TOOL
• The final .dbf table is output below

13. STATISTICAL ANALYSIS –
MEASURING CORRELATION
• Bivariate correlations were calculated
• The relationships between a Good Country Index component and a global index are found
through correlation (Spearman’s rho)
• Null hypothesis: Two correlated variables are independent of each other, i.e. no
relationship exists between them
Good Country
Index
Global Indices
No. of
Countries
Correlation
Coefficient (r)
Significance
(2-tailed) (p)
Overall Social Progress Index 124 -0.773 ** 0.0001
Science Global Innovation Index 121 -0.757 ** 0.0001
Culture Global Creativity Index 79 -0.796 ** 0.0001
Peace Global Peace Index 123 -0.012 0.896
(replaced with)
Social Progress Index
(Personal Safety category)
114 0.422 ** 0.0001
World
Human Development
Index
122 -0.572 ** 0.0001
Planet
Environmental
Performance Index
124 -0.416 ** 0.0001
Prosperity Global Opportunity Index 91 -0.457 ** 0.0001
Health
Health Expenditure as %
of Total GDP
124 -0.361 ** 0.0001
Double asterisks (**)
indicate alpha cut-off
at 0.01 (or 1% chance)
level

14. STATISTICAL ANALYSIS –
MEASURING CORRELATION
• Good Country Index Overall component is then correlated against all global
indices chosen
• This project is mainly focused on overall component
Double asterisks (**) indicate alpha cut-off at 0.01 (or 1% chance) level
Good Country
Index
Global Indices No. of Countries
Correlation
Coefficient (r)
Significance (2-
tailed) (p)
Overall
Social Progress Index 124 -0.773 ** 0.0001
Global Innovation Index 121 -0.732 ** 0.0001
Global Creativity Index 79 -0.820 ** 0.0001
Social Progress Index
(Personal Safety category)
114 -0.608 ** 0.0001
Human Development Index 122 -0.678 ** 0.0001
Environmental Performance Index 124 -0.642 ** 0.0001
Global Opportunity Index 91 -0.694 ** 0.0001
Health Expenditure as % of Total GDP 124 -0.661 ** 0.0001
Global Peace Index 123 0.636 ** 0.0001

15. PREDICTIVE ANALYTICS –
CURVE ESTIMATION
• Curve estimation was conducted with IBM SPSS Statistics
• The best regression model obtained is based on highest R2
statistic value

16. PREDICTIVE ANALYTICS –
REGRESSION EQUATIONS
• The score of the Overall component of the Good Country Index
(Y) is predicted from the known scores of the global indices (X)
Good Country
Index (Y)
Global Indices (X) Model R Square, R2 Regression Equation
Overall
Social Progress Index Cubic 0.738 Y = 62.431 + 0.0205X2 + -0.000290X3
Global Innovation Index Cubic 0.966 Y = 82.892 + 0.450X + -0.0239X2 + 8.384X3
Global Creativity Index Cubic 0.750 Y = 89.150 + -65.960X + 34.585X2 + -48.237X3
Social Progress Index
(Personal Safety category)
Cubic 0.569
Y = 131.615 + -3.548X + 0.0712X2 + -
0.000488X3
Human Development
Index
Quadratic 0.957 Y = 34.370 + 203.383X + -224.667X2
Environmental
Performance Index
Power 0.983 Y = 7452.813X-1.185
Global Opportunity Index S 0.975 Y = exp(2.739 + 6.944/X
Health Expenditure as %
of Total GDP
Cubic 0.918 Y = 80.845 + 3.181X + -1.190X2 + 0.0521X3

17. PREDICTIVE ANALYTICS –
RULE INDUCTION
• Another way of exploring predictive analytics is rule induction and
decision trees in IBM Modeler
• Target variable: Overall component of Good Country Index (categorical)
Predictor variables: Global indices and two nominal variables: Region,
and Income Group
• Auto Classifier tool is used (categorical variable) so Overall component
is binned into quintile groupings of 25 cases each, namely: ‘Most
Good’, ‘More Good’, ‘Moderate’, ‘Less Good’, and ‘Least Good’
• C5.0 model is most optimal with overall accuracy of 85.6%

18. PREDICTIVE ANALYTICS –
THE MOST IMPORTANT PREDICTORS

19. PREDICTIVE ANALYTICS –
RULE INDUCTION
Category Ruleset
No. of
Records
Most
Good
IF 28.6 < GII_OverallScore <= 55.2 and EPI_OverallScore > 72.541 and SPI_PersonalSafetyScore <= 73.83
THEN 75% confidence
4
IF GII_OverallScore > 55.2 and GPI_OverallScore <= 1.942 THEN 95.7% confidence 23
LeastGood
IF GII_OverallScore <= 55.2 THEN 81.2% confidence 16
IF 28.6 < GII_OverallScore <= 55.2 and EPI_OverallScore <= 72.541 and GPI_OverallScore <= 1.637 and
SPI_OverallScore <= 49.46 THEN 66.7% confidence
3
IF 28.6 < GII_OverallScore <= 55.2 and EPI_OverallScore <= 72.541 and GPI_OverallScore > 1.637 and
Region = East Asia & Pacific THEN 71.4% confidence
7
IF 28.6 < GII_OverallScore <= 55.2 and EPI_OverallScore <= 72.541 and GPI_OverallScore > 1.637 and
GCI_OverallScore <= 0.053 and Region = Latin America & Caribbean THEN 50% confidence
2
IF 28.6 < GII_OverallScore <= 55.2 and EPI_OverallScore <= 72.541 and GPI_OverallScore > 1.637 and
Region = Middle East & North Africa THEN 75% confidence
4

20. ANALYZING SPATIAL PATTERNS
• Numerous tools employed in ArcMap are used
• Spatial Weights Matrix file is generated for conceptualisation of
spatial relationships
• Null hypothesis: Complete spatial randomness is present
• Spatial Autocorrelation (Global Morans’ Index) is used to
understand the spatial variation in dataset values across
geographic boundaries
• High/Low Clustering (Getis-Ord General G) is used to measure
the degree of clustering for either high or low values

21. RESULTS OF ANALYSING
SPATIAL PATTERNS
Input Field
Spatial Autocorrelation High-Low Clustering
Moran’s I z-score Pattern General G z-score Pattern
Good Country Index (Overall) 0.332321 10.028708
Clustered
0.008531 6.330571
Clusters of
High Values
Social Progress Index 0.245731 7.542804 0.008191 1.405174
Random
Global Innovation Index 0.349895 10.585667 0.008186 1.485126
Global Creativity Index 0.344705 10.361778 0.010390 6.819060
Clusters of
High Values
Social Progress Index
(Personal Safety category)
0.258125 7.857364 0.008256 1.788520
Human Development Index 0.404714 12.258911 0.008267 2.971190
Environmental Performance
Index
0.362926 11.066855 0.008140 1.574872
Random
Global Opportunity Index 0.142178 4.410854 0.008314 1.342041
Health Expenditure as % of
Total GDP
0.282204 8.573501 0.008338 2.809860
Clusters of
High Values

22. MAPPING CLUSTERS
• Numerous tools employed in ArcMap are used
• Cluster and Outlier Analysis (Local Morans’ Index) is used to
identify the presence of outliers where they are more
pronounced than one would expect in a random distribution
• Hot Spot Analysis (Getis-Ord Gi*) is employed to identify spatial
locations of high (hot spot) and low (cold spot) cluster values
• Only Good Country Index Overall scores (continuous) are used
for the input variable
 The lower the scores, the better.

23. MAP OF CLUSTERS

24. HOT SPOT ANALYSIS MAP

25. GROUPING ANALYSIS
• Grouping analysis is used to find spatial clusters or groups containing
similarly-occurring features, while ensuring that groups remain
different from each other
• Sturges’ Rule (1 + 3.3 log n) is applied to determine the number of
groups – 8
• Three output map are produced, using the following diagnostic
variables:
o Good Country Index Overall and Category components – without a spatial
constraint
o Good Country Index Overall component and Global Indices – without a
spatial constraint
o Good Country Index Overall component and Global Indices – with a spatial
constraint

26. GROUPING ANALYSIS PROCESSING
• For the map output with a spatial constraint, cluster validation is
based on silhouette coefficient using Dramowicz & Pierre (2014)’s
guide, and Python script tool to automate processes
• Delaunay Triangulation spatial constraint is found to be most
optimal
• Resultant groups (i.e. Group 1, Group 2, etc.) are labelled based
on mean Overall score obtained from the pdf output that
correspond to the quintile categories of the Overall component of
the Good Country Index

27. GROUPING ANALYSIS RESULTS
• As expected of ‘No Spatial Constraint’ map outputs, there is no
clear distinction of group clusters as spatial proximity
relationships are not taken into account
• 1st and 2nd map outputs show the agreement between themselves
based on resultant groups that the outlier countries are found in
Australia Cyprus Kenya Algeria
Good Country Index Overall (Original) Most Good Most Good More Good Least Good
Good Country Index Overall & Categories
(No Spatial Constraint) (1st map output)
Most Good Most Good Moderate Least Good
Good Country Index Overall & Other Global
Indices (No Spatial Constraint) (2nd map
output)
Most Good Most Good Moderate Least Good

28. GROUPING ANALYSIS FOR GOOD COUNTRY
INDEX OVERALL AND CATEGORY COMPONENTS
• 1st map output - No Spatial Constraint

29. GROUPING ANALYSIS FOR GOOD COUNTRY
INDEX OVERALL AND GLOBAL INDICES
• 2nd map output - No Spatial Constraint

30. GROUPING ANALYSIS FOR GOOD COUNTRY
INDEX OVERALL AND GLOBAL INDICES
• 3rd map output - Delaunay Triangulation Spatial Constraint

31. GROUPING ANALYSIS – GROUP PROFILES
• Group 1: European Influence. All global indices have constantly high values, thus agreeing with the low mean overall
score. It is made up of countries in Europe namely, Bosnia and Herzegovina, Czech Republic, Spain, Estonia, France,
Croatia, Hungary, Italy, Lithuania, Luxembourg, Latvia, Poland, Portugal, Serbia, Slovakia, and Slovenia.
• Group 2: Double Twins. It fares better than Group 1, with very high values in most of the global indices, and a lower
mean overall score. It is made up of the Japan-South Korea, and Australia-New Zealand close neighbour pairs.
• Group 3: Latin America. All global indices have values that mostly hover in the intermediate range. The same is true for
the mean overall score. It takes up the whole South American continent, as well as countries in North Africa namely,
Algeria, Morocco, and Tunisia.
• Group 4: The Best. It has the lowest mean overall score and very high values across all global indices, therefore, it
comprises of mainly ‘Most Good’ countries. namely, Austria, Belgium, Canada, Switzerland, Germany, Denmark, Finland,
United Kingdom, Ireland, Iceland, Netherlands, Norway, Sweden, and the United States of America.
• Group 5: ISIS (‘Islamic State of Iraq and Syria’). It has the highest mean overall score, and four out of the eight global
indices have extremely low values. Coincidentally, it belongs to the ISIS, comprising of Iraq and Syria.
• Group 6: Sub-Saharan Africa. It fares slightly better than either Groups 5 or 8, and countries are mostly confined to
within Africa.
• Group 7: Asian Influence. It has a similar mean overall score with Group 6’s, but it fares better than Group 6 for all
global indices. It is the largest group as it takes up the whole of Asia, and Russia, as well as countries in Eastern Europe,
and the Middle East.
• Group 8: The Last. It has a lower mean overall score as compared to Group 5’s, but values across all global indices are
similar. It comprises of countries namely, Democratic Republic of the Congo, Libya, Qatar, Sudan, and Yemen.

32. TOP AND BOTTOM TEN COUNTRIES
Good Country
Index
Top Ten Countries Bottom Ten Countries
Overall
Ireland, Finland, Switzerland, Netherlands,
New Zealand, Sweden, United Kingdom,
Norway, Denmark, Belgium
Yemen, Venezuela, Benin, Indonesia, Zimbabwe,
Angola, Azerbaijan, Iraq, Vietnam, Libya
Science
United Kingdom, Austria, Cyprus, Czech
Republic, Israel, Switzerland, Finland,
Sweden, Hungary, New Zealand
Bolivia, Venezuela, Sudan, Cambodia, Paraguay,
Republic of Congo, Indonesia, Libya, Angola, Iraq
Culture
Belgium, Netherlands, Malta, Austria,
Germany, Estonia, Ireland, Czech Republic,
Denmark, Luxembourg
Iraq, Venezuela, Laos, Iran, Cameroon, Rwanda,
Yemen, Democratic Republic of the Congo, Libya,
Sudan
Peace
Egypt, Jordan, Tanzania, Lesotho, Uruguay,
Togo, Benin, Paraguay, Nigeria, Ecuador
Portugal, Ukraine, Sudan, South Korea, Spain,
Czech Republic, Azerbaijan, Hungary, Latvia,
Lithuania
World
Germany, Austria, Netherlands, Ireland,
Denmark, Malta, Norway, Sweden, United
Kingdom, Switzerland
Angola, Cambodia, Qatar, Saudi Arabia, Rwanda,
Singapore, United Arab Emirates, Vietnam, Iraq,
Oman
Planet
Iceland, Canada, Sweden, Norway, Brazil,
Australia, New Zealand, Congo, Uganda,
France
Libya, Bangladesh, Belarus, Macedonia, Ukraine,
Benin, Mauritius, Vietnam, Serbia, Zimbabwe
Prosperity
Ireland, Switzerland, Finland, Sweden,
Belgium, Ghana, Singapore, Netherlands,
United Kingdom, Malaysia
Madagascar, India, Laos, Libya, Iraq, South Africa,
Algeria, Brazil, Venezuela, Paraguay
Health
Spain, Netherlands, Belgium, Canada,
Denmark, United Kingdom, United States of
America, Sweden, Ireland, Switzerland
Lesotho, Republic of Congo, Venezuela,
Zimbabwe, Namibia, Mongolia, Mozambique,
Libya, Zambia, Cameroon

33. RETURN ON INVESTMENT
• No monetary return, but the use of GIS, and statistical analysis
instils value in this project by offering an avenue for spatial
analysis
• The Good Country Index is a forum for stakeholders interested
in countries’ ‘goodness’ for them to tap on, for example, to be
part of an international effort that promotes global wellbeing in
a specific category
• Pooling Good Country Index with other global indices allows for
identification of areas (or drivers)
• Prediction rules are useful for initiation of future
developmental strategies

34. CONCLUSIONS
• Exploratory analysis helps to understand relationships between
the Good Country Index and other global indices
• Generally, the Good Country Index agrees with the use of other
global indices as proxies for its category components
• To achieve an efficient and equitable world economy, countries
and states need to be interdependent in order to allow for
sustainable development and foster solidarity through
international cooperation

35. LIMITATIONS
• If not for ability to obtain raw dataset from Robert Govers, how
to extract non-numerical data from online sources for the
layman?
• Some countries in the Good Country Index are not present in
global indices, hence they are given zero values in the compiled
dataset
• Ordinal scale of measurement in almost all of the data, limits
the use of some analytical techniques
• Dataset restricted to cumulative overall scores of indices, may
be helpful to dive deeper into indicator scores

36. RECOMMENDATIONS
• Use individual indicator scores for a more robust analytical
approach
• There is a room for comparative analysis in future expansion of
project

37. QUESTIONS?
“The trouble is, most countries carry on behaving as if they were
islands, focusing on developing domestic solutions to domestic
problems. We will never get anywhere unless we start to change
this habit.” ~ Simon Anholt, Good Country Index creator ~