## 6 Conclusions  
The combination of descriptive and machine-learning techniques has been applied to the Spanish STP case study, revealing the feasibility of the data analysis and their use for a better understanding of STPs and informing decision making. The proposed methodology provides an overview of the general situation of STPs in Spain, validates three main types of STPs, and provides valuable quantitative information about the core features. The methodology benefits from being highly applicable in guiding the management of STPs. Therefore, the study has successfully fulfilled the outlined research objectives. The results obtained for the Spanish case are relevant because the maturity of the STP ecosystem in Spain is significant and includes STPs with a great variety in terms of the degree of university involvement. Therefore, the results can be useful for other regions with similar characteristics. Additionally, both the selected core features and the analysis methodology are common reference points for similar research that can be developed in other regions. Moreover, the following concluding remarks can be made based on the data analysis conducted for the Spanish case study:  
Figure 9: Qualitative representation of the core features for Spanish SPs and TPs. * The exploratory and a critical analysis presented in this study deepens the understanding of STPs. The methodology used is effective for achieving this goal. The previously developed STP dataset and the dashboard tool enable the data analysis and the visualisation of the techniques applied as an integrated tool to better analyse STPs. * It is possible to establish a clear taxonomy of the two main groups, science parks and technology parks, based on their intrinsic features. * A third expanded group of hybrid parks should be considered to include projects that not only have university and the governmental sector involvement, but also other outliers that cannot be strictly grouped as an SP or TP. * The typical core features of the SPs and TPs of the Spanish use case have been obtained. SPs are more oriented toward R&D, innovation, and incubation, while TPs present higher values in size, employment, and turnover, for example. * Relevant differences are clearly detectable in TPs, where a relevant subgroup of \"big TPs\" can be observed with very remarkable values in terms of turnover and number of employees. * The descriptive and data analysis presented in this work enable greater accuracy in the classification of STP types. * It is possible to predict the STP type and the distance from the \"prototype STP\" for each group, which could assist in STP management and decision making. The study presents some limitations, as only essential features describing endogenous factors of STPs are initially considered. External factors, such as the regional economic situation or the university ecosystem, can strongly influence the performance of an STP, as described by [PERSON] et al. [2]. These exogenous factors have not been considered in this study and can be incorporated to develop a more comprehensive analysis of the characteristics. In this sense, the availability of information sources in the field of STPs is crucial for achieving better results. Another limitation is that this extended analysis has only been performed for specific use cases, with Spanish STPs in this study, as well as a similar analysis for Argentinian STPs [24], where the sample of STPs and the number of features is more restricted. A wider universe of STPs from different countries and more features could be included. In this sense, the methodology enables the easy incorporation of information from other sources and facilitates the updating of the dataset and the replication of the data analysis. Future work will deepen both prediction and prescription in the field of STPs using machine-learning tools. It is also possible to incorporate other features and extend the analysis to other geographical areas, making it possible to develop a holistic STP observatory.