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Modern urbanism is called to face current challenges ranging from intensive demographic growth, economic and social stagnation to resources salvation and climate changes. Under the broader scope of sustainability, we argue that the transition to a holistic perspective of smart and regenerative planning and design is the way to face and yet to prevent these urban challenges. In doing so, we adopt systematic thinking to study the complexity of urban metabolisms at an urban place scale, emphasizing the ongoing coevolution of social-cultural-technological and ecological processes. Focusing on urban places, we give a city or region the sense of a place of stability, security, cultural and social interactions, and a sense of uniqueness. We plan and design innovative urban places that improve the environment and the quality of urban life, able to adapt and mitigate climate changes and natural hazards, leverage community spirit, and power a green-based economy. Designing the conceptual framework of smart and regenerative urban places we contribute to the field of modern urban studies helping practitioners, policymakers, and decision-makers to vision and adopt more environmental-friendly policies and actions using a user-centered approach.
Angeliki Peponi; Paulo Morgado. Transition to Smart and Regenerative Urban Places (SRUP): Contributions to a New Conceptual Framework. Land 2020, 10, 2 .
AMA StyleAngeliki Peponi, Paulo Morgado. Transition to Smart and Regenerative Urban Places (SRUP): Contributions to a New Conceptual Framework. Land. 2020; 10 (1):2.
Chicago/Turabian StyleAngeliki Peponi; Paulo Morgado. 2020. "Transition to Smart and Regenerative Urban Places (SRUP): Contributions to a New Conceptual Framework." Land 10, no. 1: 2.
“Smart city”, “sustainable city”, “ubiquitous city”, “smart sustainable city”, “eco-city”, “regenerative city” are fuzzy concepts; they are established to mitigate the negative impact on urban growth while achieving economic, social, and environmental sustainability. This study presents the result of the literature network analysis exploring the state of the art in the concepts of smart and regenerative urban growth under urban metabolism framework. Heat-maps of impact citations, cutting-edge research on the topic, tip-top ideas, concepts, and theories are highlighted and revealed through VOSviewer bibliometrics based on a selection of 1686 documents acquired from Web of Science, for a timespan between 2010 and 2019. This study discloses that urban growth is a complex phenomenon that covers social, economic, and environmental aspects, and the overlaps between them, leading to a diverse range of concepts on urban development. In regards to our concepts of interest, smart, and regenerative urban growth, we see that there is an absence of conceptual contiguity since both concepts have been approached on an individual basis. This fact unveils the need to adopt a more holistic and interdisciplinary approach to urban planning and design, integrating these concepts to improve the quality of life and public health in urban areas.
Angeliki Peponi; Paulo Morgado. Smart and Regenerative Urban Growth: A Literature Network Analysis. International Journal of Environmental Research and Public Health 2020, 17, 2463 .
AMA StyleAngeliki Peponi, Paulo Morgado. Smart and Regenerative Urban Growth: A Literature Network Analysis. International Journal of Environmental Research and Public Health. 2020; 17 (7):2463.
Chicago/Turabian StyleAngeliki Peponi; Paulo Morgado. 2020. "Smart and Regenerative Urban Growth: A Literature Network Analysis." International Journal of Environmental Research and Public Health 17, no. 7: 2463.
The complexities of coupled environmental and human systems across the space and time of fragile systems challenge new data-driven methodologies. Combining geographic information systems (GIS) and artificial neural networks (ANN) allows us to design a model that forecasts the erosion changes in Costa da Caparica, Lisbon, Portugal, for 2021, with a high accuracy level. The GIS–ANN model proves to be a powerful tool, as it analyzes and provides the “where” and the “why” dynamics that have happened or will happen in the future. According to the literature, ANNs present noteworthy advantages compared to the other methods that are used for prediction and decision making in urban coastal areas. In order to conduct a sensitivity analysis on natural and social forces, as well as dynamic relations in the dune–beach system of the study area, two types of ANNs were tested on a GIS environment: radial basis function (RBF) and multilayer perceptron (MLP). The GIS–ANN model helps to understand the factors that impact coastal erosion changes, and the importance of having an intelligent environmental decision support system to address these risks. This quantitative knowledge of the erosion changes and the analytical map-based frame are essential for an integrated management of the area and the establishment of pro-sustainability policies.
Angeliki Peponi; Paulo Morgado; Jorge Trindade. Combining Artificial Neural Networks and GIS Fundamentals for Coastal Erosion Prediction Modeling. Sustainability 2019, 11, 975 .
AMA StyleAngeliki Peponi, Paulo Morgado, Jorge Trindade. Combining Artificial Neural Networks and GIS Fundamentals for Coastal Erosion Prediction Modeling. Sustainability. 2019; 11 (4):975.
Chicago/Turabian StyleAngeliki Peponi; Paulo Morgado; Jorge Trindade. 2019. "Combining Artificial Neural Networks and GIS Fundamentals for Coastal Erosion Prediction Modeling." Sustainability 11, no. 4: 975.