## 5 Discussion and Validation  
In this section, the results in relation to the general and specific objectives outlined in Section 2 are interpreted and discussed separately:  
* SO1: To have an overview of the general situation of STPs in Spain. As presented in Section 4.1, a description of the current situation of the Spanish STPs is obtained from the resources and descriptive techniques applied. General statistics references with each core feature, grouped by STPs (all), SP type, TP type, and HP type, has been obtained and are available at [[https://github.com/gplsi/data-stp](https://github.com/gplsi/data-stp)]([https://github.com/gplsi/data-stp](https://github.com/gplsi/data-stp)) (accessed on 27 October 2023). Forty-nine STPs were considered in the study, with 61% of them grouped initially as technology parks (TPs), 31% grouped as science parks (SPs), and 8% as hybrid parks (HPs). Figure 7: Clustering analysis for: (**a**) standard economic impact; and (**b**) innovation and entrepreneurship impact. Stars represent the centroids of each cluster. Most TPs were promoted by regional governmental institutions, whereas most SPs were promoted by public universities. A predominance of technology parks promoted between 25 and 10 years ago is observed and the aggregated values of the 21 core features studied in this work are available. * SO2: To validate that there are different types of STPs with distinct characteristics and whether they correspond to the three types proposed in the literature (SPs, TPs, and HPs). As shown in Table 3, 12 of the 20 analysed core features are significantly different between STP types. Decision tree analysis (Section 4.2.1) also reveal with high accuracy rates that SPs and TPs present singular tendencies, and a typical profile for each type can be obtained. Clustering analysis (Section 4.2.2) also confirms this SP-TP difference in terms of innovation and entrepreneurial impact. Regarding the distinct characteristics, the decision tree analysis shows that Fe03. Size and Fe18. Innovative profile 1 are determinants when differentiating SPs from TPs. Clustering analysis also identifies a natural aggrupation tendency for SPs when considering the innovation and entrepreneur impact, with more than 60% of SPs performing highly in this respect. SPs seem to be clearly more R&D- and innovation-oriented in relative terms considering R&D employment, but no significantly different activity is observed in terms of patents. Fe19. Innovative profile 2, which represents the relative influence of R&D investment, was not available neither for decision tree analysis nor for clustering, due to over 16% of the data being missing for this feature. The TP group present higher values with respect to SPs in terms of Fe03. Size, Fe06. Employment, Fe05. Turnover, Fe09. Average company size 1, and Fe10. Average company size 2. The data analyses using machine learning carried out in this study confirms notable differences between the SP type and the TP type. However, the HP group is not clearly defined. In the decision tree and clustering analysis, there is a slight tendency to be closer to the SPs. However, this group of HPs is a very small sample, and it is difficult to draw categorical or clear conclusions. Based on the above, the results suggest that it is possible to establish a clear taxonomy of the two main groups: SPs and TPs. For both types, there are some cases that escape the trend where the behaviour is not typical of their group but tends to behave like the other group. The study suggests that few SPs perform as TPs and vice versa. Concretely, four SPs behave as TPs and five TPs behave as SPs. In addition, there is a third group of hybrid parks (HPs) with a very small initial sample (four) and which also shows intermediate behaviour between TPs and SPs. This study suggests that this initial sample of HPs would be bigger after using the proposed data analysis and intrinsic evaluation.