Luis Encalada
University of Lisbon
5 Papers
2 Citations
Luis Encalada is an academic researcher from University of Lisbon. The author has contributed to research in topics: Land cover & Tourism. The author has an hindex of 3, co-authored 5 publications.
Chat about Author
Papers
Identifying Tourist Places of Interest Based on Digital Imprints: Towards a Sustainable Smart City
TL;DR: From the analysis of the spatial distribution of tourists in the city of Lisbon based on data collected from the ‘Panoramio’ social network, this new data largely contributes to understanding the consumption of space within urban tourist destinations and therefore enables to differentiate the overcrowded places from the ones with potential to grow.
Multitemporal Analysis of Land Use and Land Cover within an Oil Block in the Ecuadorian Amazon
Sergio Llerena-Montoya,Andrés Velastegui-Montoya,Bryan Zhirzhan-Azanza,Viviana Herrera-Matamoros,Marcos Adami,Aline de Lima,Francisco Moscoso-Silva,Luis Encalada +7 more
TL;DR: In this article, the authors analyzed the land use and land cover change and relates spatial patterns within an oil block located in the province of Orellana, Ecuador and found that the low predominance of forest cover within the study region is not directly associated with the beginning of the Block 47 concession.
34
The value of openstreetmap historical contributions as a source of sampling data for multi-temporal land use/cover maps.
TL;DR: 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.
Geographical Patterns in the Tourist City: GIS for Spatiotemporal Analysis
Luis Encalada,Carlos Ferreira,Jorge Rocha,Inês Boavida-Portugal +3 more
- 01 Jan 2018
TL;DR: In this paper, the authors analyzed the spatial distribution of tourists and its changes across time considering a period of 8 years, and a regression analysis method was carried out to find the spatial relations between the observed pattern (geographical agglomeration of tourist photos) and a set of 24 selected independent variables.
2
Mining Big Data for Tourist Hot Spots: Geographical Patterns of Online Footprints
Luis Encalada,Carlos Ferreira,Inês Boavida-Portugal,Jorge Rocha +3 more
- 17 Jan 2019
TL;DR: This chapter addresses the potential for discovering geographical insights regarding tourists’ spatial patterns within a destination, based on the analysis of geotagged data available from two social networks.