The emblematic French Way of Saint James (Camino de Santiago Francés) crosses towns, cities, and Spanish regions to the Cathedral of Santiago de Compostela (Galicia, Spain), However, where is The French Way of Saint James going with respect to the urban sustainability of its host cities? As each city is unique and urban sustainability favors the revitalization and transition of urban areas, to know where to go, it is first necessary to establish a diagnosis that makes the different urban situations visible. In this article, the behavior of urban sustainability is analyzed in the six host cities of The French Way of Saint James in the Autonomous Community of Castilla y León, a region characterized by its link with the rural environment and its current depopulation problems. The data and indicators used are officially provided by the Spanish Urban Agenda, which, through the normalization of its values, are able to territorialize the SDGs at the local level and reflect the realities of the cities of Burgos, Astorga, Cacabelos, León, Ponferrada, and Valverde de la Virgen. The results make it possible to diagnose and compare these host cities, identifying weaknesses, skills, and opportunities that favor the promotion of action plans, local or joint (favored by The French Way of Saint James), in the multiple aspects of sustainability. In addition, they show that Valverde de la Virgen is the city with the best performance in terms of urban sustainability.

Urban green spaces improve quality of life by mitigating urban temperatures. However, there are challenges in obtaining urban data to analyze and understand their influence. With the aim of developing innovative methodologies for this type of research, Artificial Neural Networks (ANNs) were developed to predict daily and hourly temperatures in urban green spaces from sensors placed in situ for 41 days. The study areas were four urban allotment gardens (with dynamic and productive vegetation) and a forested urban park in the city of Valladolid, Spain. ANNs were built and evaluated from various combinations of inputs (X), hidden neurons (Y), and outputs (Z) under the practical rule of “making networks simple, to obtain better results”. Seven ANNs architectures were tested: 7-Y-5 (Y = 6, 7, …, 14), 6-Y-5 (Y = 6, 7, …, 14), 7-Y-1 (Y = 2, 3, …, 8), 6-Y-1 (Y = 2, 3, …, 8), 4-Y-1 (Y = 1, 2, …, 7), 3-Y-1 (Y = 1, 2, …, 7), and 2-Y-1 (Y = 2, 3, …, 8). The best-performing model was the 6-Y-1 ANN architecture with a Root Mean Square Error (RMSE) of 0.42 °C for the urban garden called Valle de Arán. The results demonstrated that from shorter data points obtained in situ, ANNs predictions achieve acceptable results and reflect the usefulness of the methodology. These predictions were more accurate in urban gardens than in urban parks, where the type of existing vegetation can be a decisive factor. This study can contribute to the development of a sustainable and smart city, and has the potential to be replicated in cities where the influence of urban green spaces on urban temperatures is studied with traditional methodologies.

Cities exemplify the evolving world with changing demographics and climates. Urban green spaces play a crucial role in improving the quality of life of people through their potential to mitigate temperatures. Therefore, comprehending their impact is of para-mount interest. Given the challenges in obtaining temperature data from urban locations, this study develops Artificial Neural Networks (ANNs) to predict daily and hourly temperatures in Valladolid, Spain, with a particular focus on urban allotment gardens and a forested urban park. ANNs were built and evaluated from various combinations of inputs (X), hidden neurons (Y) and outputs (Z) under the practical rule of "making net-works simple, to obtain better results". The best performing model was 6-Y-1 ANN archi-tecture with an impressive result of Root Mean Square Error (RMSE) = 0.42°C in the urban garden called Valle de Arán. However, other five ANN architectures were also tested (7-Y-5; 6-Y-5; 7-Y-1; 3-Y-Z and 2-Y-1). ANNs dedicated to urban temperature analysis hold immense potential for urban planning and research, aiding in under-standing the urban climate, forecasting future temperatures, identifying temperature mitigation strategies and even for managing urban crops