# Territorial Development as an Innovation Driver: A Complex Network Approach
## 2 Materials and Methods
### Socio-Economic Factors Influencing Development of Countries: World Development Indicators  
Network construction was based on the World Development Indicators (WDIs) database, \"a compilation of relevant, high quality, and internationally comparable statistics about global development and the fight against poverty\" [52], which collects yearly indicators  
Figure 1: Pipeline representation. First, data related to the StartupBlink countries were collected from the WDI database and preprocessed (**top left**); then, a similarity network was built, in which links between two countries were weighted by the Pearson correlation between their sets of WDIs, provided it was statistically significant (**top right**); finally, community detection was performed on the similarity network to detect homogeneous groups of countries (**bottom right**), which were used to reinterpret their achievements in the StartupBlink rankings (**bottom left**).  
starting, in the best case, from 1960, for 217 country's economies (mostly belonging to the United Nations) and more than 40 economic or geographical country groups. Here, we will focus only on the 100 StartupBlink countries. The choice of basing our network on WPIs satisfies the need for a development representation that is as multidimensional as possible. The WDI database actually includes a wide variety of data: the indicators that will be used in this work are taken from the Environment, Economic Policy and Debt, Education, Financial Sector, Gender, Health, Infrastructure, Private Sector and Trade, Social Protection and Labor categories, which cover essentially all the aspects of the development of a country.  
The bulk file that we used for this study was updated to 15 th September 2021. The dataset records 1443 WPIs, but missing entries are present in a number that undergoes wide variations from one country to another. Data availability also changes with time, increasing, due to collection process improvements, in an overall monotonous way from 1960 to the 2005-2016 period (a maximum is reached in 2010), and dropping in the following years, due to the fact that some recent results are still unrecorded.  
The choice to focus on 2019 indicators, motivated by the need to avoid pandemic biases, was also dictated by a tradeoff between recentness and data availability. Missing entries in 2019 were borrowed from 2018 data or, in case even the latter data are unavailable, from the 2017 dataset.  
Then, 426 indicators, whose complete list is reported in the Supplementary Material (Table S2), were used to perform our analysis. Selection followed the criteria of data availability, consistency and information non-redundancy, comprising the following sequence of actions:  
1. Indicators with more than 10% missing values were excluded.
2. To mitigate the effect of outliers, indicator values exceeding the 99 th percentile and below the 1 st percentile were replaced by the reference percentiles.
3. Each indicator was scaled in the interval \([0,1]\) in such a way that 0 corresponds to the minimum value and 1 to the maximum.
4. To avoid redundancy, we calculated the Pearson correlation coefficient between all couples of indicators and identified the ones having a correlation value larger than 0.98. Then, for each of these couples, we selected the indicator having the smaller number of missing entries and excluded the other.