Interdisciplinary Centre of Social Sciences

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Publications

Urban solar potential for vehicle integrated photovoltaics

Publication . Centeno Brito, Miguel; Santos, Teresa; Moura, Filipe; Pera, David; Rocha, Jorge

Integrating solar photovoltaics in electric vehicles can reduce operating costs and extend the driving range. It is particularly appropriate for urban mobility due to the relatively short typical daily travels of urban vehicles. However, shadowing cast by buildings will reduce the solar irradiation falling on the vehicle, reducing its PV generation. This study assesses the solar potential of onboard solar for roads and urban parking using data in a geographical information system for the case study of Lisbon. Results show that annual losses due to shadowing may reach 25% for roads and over 50% for urban parking spaces. Nevertheless, despite these losses, the annual solar extended range for onboard solar vehicles is between 10 and 18 km/day/kWp, thus significantly reducing charging needs.

2021Journal article

Restricted access

Modelling land-use and land-cover changes: a hybrid approach to a coastal area

Publication . Faria de Deus, Raquel; Tenedório, José A.; Rocha, Jorge

In this chapter, a hybrid approach integrating cellular automata (CA), fuzzy logic, logistic regression, and Markov chains for modelling and prediction of land-use and land-cover (LULC) change at the local scale, using geographic information with fine spatial resolution is presented. A spatial logistic regression model was applied to determine the transition rules that were used by a conventional CA model. The overall dimension of LULC change was estimated using a Markov chain model. The proposed CA-based model (termed CAMLucc) in combination with physical variables and land-use planning data was applied to simulate LULC change in Portimão, Portugal between 1947 and 2010 and to predict its future spatial patterns for 2020 and 2025. The main results of this research show that Portimão has been facing massive growth in artificial surfaces, particularly near the main urban settlements and along the coastal area, and reveal an early and intensive urban sprawl over time.

2021Book part

Restricted access

Decoding the spatial dynamics of sales and rental prices in a high-pressure Portuguese housing market: a random forest approach for the Lisbon Metropolitan Area

Publication . Leal, Miguel; Carreiras, Marina; Alves, Sónia

Sales and rental prices were analysed at parish level using random forest regression for the Lisbon Metropolitan Area. Three dependent variables (new sales, new rents, and all rents) and a set of independent variables/associated factors were used, including location, building/dwelling characteristics, socioeconomic features, and tourism. This geographically-based approach aims not to predict housing prices, but to identify relevant factors associated with sales/rents, ranking their importance. The temporal dimension is also explored by comparing new and all existing rents. The results revealed similarities and differences between housing submarkets. New sales and new rents had similar spatial patterns and dynamics but were different from that of all rents, with different regulations over time. Strong associations were found between the dependent variables and the population's social status and urban quality. However, while location was more strongly related to new sales and new rents, revealing a greater dependence on the current dynamics of the housing market, socioeconomic features were more closely related to all rents, expressing the urban and demographic dynamics of recent decades. Different associated factors prevail inside and outside the Lisbon municipality. The results contribute to a better understanding of housing submarkets and the relationships between sales/rents and associated factors.

2025Journal article

Open access

Decoding the spatial dynamics of sales and rental prices in a high-pressure Portuguese housing market: a random forest approach for the Lisbon Metropolitan Area

Publication . Leal, Miguel; Carreiras, Marina; Alves, Sónia

Sales and rental prices were analysed at parish level using random forest regression for the Lisbon Metropolitan Area. Three dependent variables (new sales, new rents, and all rents) and a set of independent variables/associated factors were used, including location, building/dwelling characteristics, socioeconomic features, and tourism. This geographically-based approach aims not to predict housing prices, but to identify relevant factors associated with sales/rents, ranking their importance. The temporal dimension is also explored by comparing new and all existing rents. The results revealed similarities and differences between housing submarkets. New sales and new rents had similar spatial patterns and dynamics but were different from that of all rents, with different regulations over time. Strong associations were found between the dependent variables and the population's social status and urban quality. However, while location was more strongly related to new sales and new rents, revealing a greater dependence on the current dynamics of the housing market, socioeconomic features were more closely related to all rents, expressing the urban and demographic dynamics of recent decades. Different associated factors prevail inside and outside the Lisbon municipality. The results contribute to a better understanding of housing submarkets and the relationships between sales/rents and associated factors

2025Journal article

Open access

Modeling photovoltaic potential for bus shelters on a city-scale: a case study in Lisbon

Publication . Santos, Teresa; Lobato, Killian; Rocha, Jorge; Tenedório, José António

The 2030 Agenda for Sustainable Development set 17 Sustainable Development Goals (SDGs). These include ensuring access to affordable, reliable, sustainable and modern energy for all (SGD7) and making cities and human settlements inclusive, safe, resilient and sustainable (SGD11). Thus, across the globe, major cities are moving in the smart city direction, by, for example, incorporating photovoltaics (PV), electric buses and sensors to improve public transportation. We study the concept of integrated PV bus stop shelters for the city of Lisbon. We identified the suitable locations for these, with respect to solar exposure, by using a Geographic Information System (GIS) solar radiation map. Then, using proxies to describe tourist and commuter demand, we determined that 54% of all current city bus stop shelters have the potential to receive PV-based solutions. Promoting innovative solutions such as this one will support smart mobility and urban sustainability while increasing quality of life, the ultimate goal of the Smart Cities movement

2020Journal article

Open access