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Cláudia Viana
Centre for Geographical Studies, Institute of Geography and Spatial Planning, Universidade de Lisboa, Rua Branca Edmée Marques, 1600-276 Lisboa, Portugal

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Journal article
Published: 23 July 2021 in Land
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Agricultural statistical data enable the detection and interpretation of the development of agriculture and the food supply situation over time, which is essential for food security evaluation in any country. Based on the historical agricultural statistics, this study produces a long spatial time-series with annual production values of three cereals relevant to global food security—wheat, maize, and rice, aiming to provide geographical and historical perspectives. Therefore, we reconstructed past and current production patterns and trends at the district level over 169 years, which supported a space–time cross-reading of the general characteristics of the regional agricultural production value distributions and relative densities in Portugal. Particularly, the production trends of wheat, maize, and rice showed three different situations: growth (maize), stability (rice), and decline (wheat). For decades, maize and wheat production alternated, depending on agricultural years and political aspects, such as the Wheat Campaign (1929–1938). The changes over time presented a pattern that, in the case of these three cereals, enabled a clear division of the country into major regions according to cereal production. Overall, maize and rice, both grown on irrigated croplands, presented a similar pattern in some regions of Portugal, mainly the central region. In this study, a preliminary analysis was presented and related to successive public policies; however, notably, there are more lessons to be learned from this long spatial time-series.

ACS Style

Cláudia Viana; Dulce Freire; Patrícia Abrantes; Jorge Rocha. Evolution of Agricultural Production in Portugal during 1850–2018: A Geographical and Historical Perspective. Land 2021, 10, 776 .

AMA Style

Cláudia Viana, Dulce Freire, Patrícia Abrantes, Jorge Rocha. Evolution of Agricultural Production in Portugal during 1850–2018: A Geographical and Historical Perspective. Land. 2021; 10 (8):776.

Chicago/Turabian Style

Cláudia Viana; Dulce Freire; Patrícia Abrantes; Jorge Rocha. 2021. "Evolution of Agricultural Production in Portugal during 1850–2018: A Geographical and Historical Perspective." Land 10, no. 8: 776.

Journal article
Published: 25 May 2020 in Sustainability
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The present study used the official Portuguese land use/land cover (LULC) maps (Carta de Uso e Ocupação do Solo, COS) from 1995, 2007, 2010, 2015, and 2018 to quantify, visualize, and predict the spatiotemporal LULC transitions in the Beja district, a rural region in the southeast of Portugal, which is experiencing marked landscape changes. Here, we computed the conventional transition matrices for in-depth statistical analysis of the LULC changes that have occurred from 1995 to 2018, providing supplementary statistics regarding the vulnerability of inter-class transitions by focusing on the dominant signals of change. We also investigated how the LULC is going to move in the future (2040) based on matrices of current states using the Discrete-Time Markov Chain (DTMC) model. The results revealed that, between 1995 and 2018, about 28% of the Beja district landscape changed. Particularly, croplands remain the predominant LULC class in more than half of the Beja district (in 2018 about 64%). However, the behavior of the inter-class transitions was significantly different between periods, and explicitly revealed that arable land, pastures, and forest were the most dynamic LULC classes. Few dominant (systematic) signals of change during the 1995–2018 period were observed, highlighting the transition of arable land to permanent crops (5%) and to pastures (2.9%), and the transition of pastures to forest (3.5%) and to arable land (2.7%). Simulation results showed that about 25% of the territory is predicted to experience major LULC changes from arable land (−3.81%), permanent crops (+2.93%), and forests (+2.60%) by 2040.

ACS Style

Cláudia M. Viana; Jorge Rocha. Evaluating Dominant Land Use/Land Cover Changes and Predicting Future Scenario in a Rural Region Using a Memoryless Stochastic Method. Sustainability 2020, 12, 4332 .

AMA Style

Cláudia M. Viana, Jorge Rocha. Evaluating Dominant Land Use/Land Cover Changes and Predicting Future Scenario in a Rural Region Using a Memoryless Stochastic Method. Sustainability. 2020; 12 (10):4332.

Chicago/Turabian Style

Cláudia M. Viana; Jorge Rocha. 2020. "Evaluating Dominant Land Use/Land Cover Changes and Predicting Future Scenario in a Rural Region Using a Memoryless Stochastic Method." Sustainability 12, no. 10: 4332.

Book chapter
Published: 13 November 2019 in Geographic Information Systems and Science
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ACS Style

Cláudia M. Viana; Patrícia Abrantes; Jorge Rocha. Introductory Chapter: Geographic Information Systems and Science. Geographic Information Systems and Science 2019, 1 .

AMA Style

Cláudia M. Viana, Patrícia Abrantes, Jorge Rocha. Introductory Chapter: Geographic Information Systems and Science. Geographic Information Systems and Science. 2019; ():1.

Chicago/Turabian Style

Cláudia M. Viana; Patrícia Abrantes; Jorge Rocha. 2019. "Introductory Chapter: Geographic Information Systems and Science." Geographic Information Systems and Science , no. : 1.

Journal article
Published: 09 May 2019 in Remote Sensing
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The increasing availability and volume of remote sensing data, such as Landsat satellite images, have allowed the multidimensional analysis of land use/land cover (LULC) changes. However, the performance of image classification is highly dependent on the quality and quantity of the training set and its temporal continuity, which may affect the accuracy of the classification and bias the analysis of the LULC changes. In this study, we intended to apply a long-term LULC analysis in a rural region based on a Landsat time series of 21 years (1995 to 2015). Here, we investigated the use of open LULC source data to provide training samples and the application of the K-means clustering technique to refine the broad range of spectral signatures for each LULC class. Experiments were conducted on a predominantly rural region characterized by a mixed agro-silvo-pastoral environment. The open source data of the official Portuguese LULC map (Carta de Uso e Ocupação do Solo, COS) from 1995, 2007, 2010, and 2015 were integrated to generate the training samples for the entire period of analysis. The time series was computed from Landsat data based on the normalized difference vegetation index and normalized difference water index, using 221 Landsat images. The Time-Weighted Dynamic Time Warping (TWDTW) classifier was used, since it accounts for LULC-type seasonality and has already achieved promising overall accuracy values for classifications based on time series. The results revealed that the proposed method was efficient in classifying a long-term satellite time-series with an overall accuracy of 76%, providing insights into the main LULC changes that occurred over 21 years.

ACS Style

Cláudia M. Viana; Inês Girão; Jorge Rocha. Long-Term Satellite Image Time-Series for Land Use/Land Cover Change Detection Using Refined Open Source Data in a Rural Region. Remote Sensing 2019, 11, 1104 .

AMA Style

Cláudia M. Viana, Inês Girão, Jorge Rocha. Long-Term Satellite Image Time-Series for Land Use/Land Cover Change Detection Using Refined Open Source Data in a Rural Region. Remote Sensing. 2019; 11 (9):1104.

Chicago/Turabian Style

Cláudia M. Viana; Inês Girão; Jorge Rocha. 2019. "Long-Term Satellite Image Time-Series for Land Use/Land Cover Change Detection Using Refined Open Source Data in a Rural Region." Remote Sensing 11, no. 9: 1104.

Journal article
Published: 28 February 2019 in ISPRS International Journal of Geo-Information
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OpenStreetMap (OSM) is a free, open-access Volunteered geographic information (VGI) platform that has been widely used over the last decade as a source for Land Use Land Cover (LULC) mapping and visualization. However, it is known that the spatial coverage and accuracy of OSM data are not evenly distributed across all regions, with urban areas being likelier to have promising contributions (in both quantity and quality) than rural areas. The present study used OSM data history to generate LULC datasets with one-year timeframes as a way to support regional and rural multi-temporal LULC mapping. We evaluated the degree to which the different OSM datasets agreed with two existing reference datasets (CORINE Land Cover and the official Portuguese Land Cover Map). We also evaluated whether our OSM dataset was of sufficiently high quality (in terms of both completeness accuracy and thematic accuracy) to be used as a sampling data source for multi-temporal LULC maps. In addition, we used the near boundary tag accuracy criterion to assesses the fitness of the OSM data for producing training samples, with promising results. For each annual dataset, the completeness ratio of the coverage area for the selected study area was low. Nevertheless, we found high thematic accuracy values (ranged from 77.3% to 91.9%). Additionally, the training samples thematic accuracy improved as they moved away from the features’ boundaries. Features with larger areas (> 10 ha), e.g., Agriculture and Forest, had a steadily positive correlation between training samples accuracy and distance to feature boundaries

ACS Style

Cláudia M. Viana; Luis Encalada; Jorge Rocha. The value of OpenStreetMap Historical Contributions as a Source of Sampling Data for Multi‑temporal Land Use/Cover Maps. ISPRS International Journal of Geo-Information 2019, 8, 116 .

AMA Style

Cláudia M. Viana, Luis Encalada, Jorge Rocha. The value of OpenStreetMap Historical Contributions as a Source of Sampling Data for Multi‑temporal Land Use/Cover Maps. ISPRS International Journal of Geo-Information. 2019; 8 (3):116.

Chicago/Turabian Style

Cláudia M. Viana; Luis Encalada; Jorge Rocha. 2019. "The value of OpenStreetMap Historical Contributions as a Source of Sampling Data for Multi‑temporal Land Use/Cover Maps." ISPRS International Journal of Geo-Information 8, no. 3: 116.

Article
Published: 28 July 2018 in Networks and Spatial Economics
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As a result of mounting concerns over the adverse ecological and socio-economic effects of mobility systems dominated by individual motorized transport, metropolitan areas worldwide have expressed a renewed interest in the role of public transport. Like many other metropolitan areas, the Brussels Capital Region (BCR) faces the problem of an increasingly congested transport system. Against this backdrop, the Regional Express Railway (RER) network is intended as a rapid-transit railway system serving an area of 30 km around the region, with the objective of improving the capacity and frequency of the railway services between the BCR and its periphery. In order to inform policy prescription, this paper reports on a systematic empirical assessment of all RER railway stations in terms of transport and land use characteristics, by drawing on the node-place modeling and transit oriented development literature. The proposed accessibility instrument considers different catchment area sizes in order to increase its empirical basis. Based on this systematic railway station inventory, cluster analysis was conducted revealing seven comparative accessibility profiles, of which some prove highly robust over the different precinct sizes. When combining the quantitative analyses reported in this paper with the more intuitive expertise of practitioners and stakeholders involved in the planning process, the accessibility instrument may effectively assist the identification of differentiated development opportunities for the RER stations.

ACS Style

Freke Caset; David Vale; Cláudia M. Viana. Measuring the Accessibility of Railway Stations in the Brussels Regional Express Network: a Node-Place Modeling Approach. Networks and Spatial Economics 2018, 18, 495 -530.

AMA Style

Freke Caset, David Vale, Cláudia M. Viana. Measuring the Accessibility of Railway Stations in the Brussels Regional Express Network: a Node-Place Modeling Approach. Networks and Spatial Economics. 2018; 18 (3):495-530.

Chicago/Turabian Style

Freke Caset; David Vale; Cláudia M. Viana. 2018. "Measuring the Accessibility of Railway Stations in the Brussels Regional Express Network: a Node-Place Modeling Approach." Networks and Spatial Economics 18, no. 3: 495-530.

Journal article
Published: 01 May 2018 in Journal of Transport Geography
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Car dependency and associated car modal share is increasing in the vast majority of metropolitan areas throughout the world, and an important contributory factor lies in the lack of clear and effective integration of land use with transportation. Transit-oriented development (TOD) has been adopted as a major urban policy to achieve such integration. TOD explicitly promotes a balance between public transportation-driven supply and land use-driven demand, while simultaneously improving the pedestrian friendliness of the station areas. The objective of balancing transport with land use is the founding principle of the node-place model. Three principle dimensions can be evaluated under the extended version of this model: i) the node-index, reflecting the accessibility of the station area by several transportation modes; ii) the place-index, reflecting the land use features of the station areas; and iii) the design-index, reflecting the urban design conditions that influence pedestrian accessibility of the station areas. In this paper, we apply the extended node-place model at a local scale, using Lisbon subway stations as the focus points of our analysis, applying the same principles and methodology as for the metropolitan scale, but adjusting the parameters to reflect the subway network. Our results suggest that the introduction of a third index better distinguishes between balanced situations identified in the original node-place model. In Lisbon, the average node index is higher than the place index, and the design index varies substantially across the subway network. In general terms, city center subway stations exhibit the highest index values, whereas peripheral stations tend to be more unbalanced. Transfer stations constitute special cases in the network, having high node and design indexes but average place indexes. The typology of Lisbon subway stations based on the extended node-place model might be used to support urban planning, specifically with regard to establishing regulations for locating activities and parking supply, guiding location-sensitive or place-based fiscal policies, and also identifying the types of intervention needed to achieve the desired integration between transportation accessibility, land use intensity and diversity, and urban design.

ACS Style

David S. Vale; Cláudia M. Viana; Mauro Pereira. The extended node-place model at the local scale: Evaluating the integration of land use and transport for Lisbon's subway network. Journal of Transport Geography 2018, 69, 282 -293.

AMA Style

David S. Vale, Cláudia M. Viana, Mauro Pereira. The extended node-place model at the local scale: Evaluating the integration of land use and transport for Lisbon's subway network. Journal of Transport Geography. 2018; 69 ():282-293.

Chicago/Turabian Style

David S. Vale; Cláudia M. Viana; Mauro Pereira. 2018. "The extended node-place model at the local scale: Evaluating the integration of land use and transport for Lisbon's subway network." Journal of Transport Geography 69, no. : 282-293.

Journal article
Published: 05 January 2018 in Journal of Transport and Land Use
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There is a vast literature on the relationship between built environment and travel, emphasizing the importance of built environment as a determinant of travel. However, the majority of studies focuses on the characteristics of origins and neglects the influence that the destination might have on travel, despite the already demonstrated importance of destinations to explain travel. In this paper, we test the relationship between residential and workplace built environment and the commuting pattern of staff and students of the University of Lisbon, a multi-campus university. Data was obtained through a dedicated travel survey, containing 1474 georeferenced individuals. Chi-square analyses were developed to analyze differences between staff and students and between different campuses. A logistic regression model was developed to explain car commuting, controlling for socio-demographic data. Two different models were developed for students and staff. Our results show the built environment and associated multimodal accessibility of the campuses are important explanatory variables of commuting. Free parking at the campus is crucial for car commuting, especially for students. These results emphasize the importance of measuring destinations as explanatory variables and promoting good urban integration of the campus in the city, increasing its multimodal accessibility.

ACS Style

David Sousa Vale; Mauro Pereira; Claudia Morais Viana. Different destination, different commuting pattern? Analyzing the influence of the campus location on commuting. Journal of Transport and Land Use 2018, 11, 1 .

AMA Style

David Sousa Vale, Mauro Pereira, Claudia Morais Viana. Different destination, different commuting pattern? Analyzing the influence of the campus location on commuting. Journal of Transport and Land Use. 2018; 11 (1):1.

Chicago/Turabian Style

David Sousa Vale; Mauro Pereira; Claudia Morais Viana. 2018. "Different destination, different commuting pattern? Analyzing the influence of the campus location on commuting." Journal of Transport and Land Use 11, no. 1: 1.