Urban indicators are efficient tools designed to simplify, quantify and communicate relevant information for land planners. Since urban data has a strong spatial representation, one can use geographical data as the basis for constructing information regarding urban environments. One important source of information about the land status is imagery collected through remote sensing. Afterwards, using digital image processing techniques, thematic detail can be extracted from those images and used to build urban indicators. Most common metrics are based on area (2D) measurements. These include indicators like impervious area per capita or surface occupied by green areas, having usually as primary source a spectral image obtained through a satellite or airborne camera. More recently, laser scanning data has become available for large-scale applications. Such sensors acquire altimetric information and are used to produce Digital Surface Models (DSM). In this context, LiDAR data available for the city is explored along with demographic information, and a framework to produce volumetric (3D) urban indexes is proposed, and measures like Built Volume per capita, Volumetric Density and Volumetric Homogeneity are computed.

Multi-temporal analysis of census small-area microdata is hampered by the fact that census tract shapes do not often coincide between census exercises. Dasymetric mapping techniques provide a workaround that is nonetheless highly dependent on the quality of ancillary data. The objectives of this work are to: (1) Compare the use of three spatial techniques for the estimation of population according to census tracts: Areal interpolation and dasymetric mapping using control data—building block area (2D) and volume (3D); (2) demonstrate the potential of unmanned aerial vehicle (UAV) technology for the acquisition of control data; (3) perform a sensitivity analysis using Monte Carlo simulations showing the effect of changes in building block volume (3D information) in population estimates. The control data were extracted by a (semi)-automatic solution—3DEBP (3D extraction building parameters) developed using free open source software (FOSS) tools. The results highlight the relevance of 3D for the dasymetric mapping exercise, especially if the variations in height between building blocks are significant. Using low-cost UAV backed systems with a FOSS-only computing framework also proved to be a competent solution with a large scope of potential applications.

Presently, the use of new technologies for the acquisition of 3D geographical data on time is very important for urban planning. Applications include evaluation and monitoring of urban parameters (ie. volumetric data),indicators of an urban plan, or monitoring built-up areas and illegal buildings. This type of 3D data can be acquired through an Airborne Laser Scanning system, also known as LiDAR (Light Detection And Ranging) or by Unmanned Aerial Vehicles (UAV). The aim of this article is to use and compare these two technologies for extracting building parameters (facade height and volume). Existing literature evaluates each technology separately. This work pioneers benchmarking between LiDAR and UAV point-clouds. The basic function of LiDAR is collecting a georeferenced and dense 3D point-cloud from a laser scanner during flight. Therefore it is possible to obtain a similar 3D point-cloud using processing algorithms for stereo aerial images, obtained by large or small-format digital cameras (the small-format camera implemented in Unmanned Aerial Vehicles). The chosen study area is located in Praia de Faro, an open sandy beach in Algarve (Southern Portugal), limited west by the Ria Formosa barrier island system. The area defined has an extension of 300100m. The methodology is divided in two distinct stages: (1) building parameters extraction, (2) comparative technology analysis. Lidar point-cloud resolution is approximately 6 pts/m2 and UAV point-cloud 60 pts/m2. FOSS technologies have proven to be the most adequate adequate platform for the development and diffusion of advanced analytical tools in the Geographical Information Sciences (GISci). Data management in this paper is supported by a Geographical Database Management System (GDBMS), implemented using PostgreSQL and PostGIS. Statistical analysis is performed using R whilst advanced spatial functions are used in GRASS.

Keywords: Geocaching, Urban environments, Recreational activities, Spatial Analysis, GIS Created in 2000 in the United States of America, Geocaching has become a major phenomenon all around the world, counting actually with millions of Geocaches (or caches) that work as a recreational motivation for millions of users, called Geocachers. During the last 30 days over 5,000,000 new logs have been submitted worldwide, disseminating individual experiences, motivations, emotions and photos through the official Geocaching website (www.geocaching.com), and several official or informal national web forums. The activity itself can be compared with modern treasure hunting that uses handheld GPS, Smartphones or Tablets, WEB 2.0, wiki features and technologies to keep Geocachers engaged with their activity, in a strong social-network. All these characteristics make Geocaching an activity with a strong geographic component that deals closely with the surrounding environment where each cache has been hidden. From previous work, significance correlation has been found regarding hides and natural/rural environments, but metropolitan and urban areas like Lisbon municipality (that holds 3.23% of the total 27534 Portuguese caches), still registers the higher density of Geocaches, and logs numbers. Lacking "natural/rural" environment, Geocaching in cities tend to happen in symbolic areas, like public parks and places, sightseeing spots and historical neighborhoods. The present study looks to Geocaching within the city of Lisbon, in order to understand how it works, and if this activity reflects the city itself, promoting its image and cultural heritage. From a freely available dataset that includes all Geocaches that have been placed in Lisbon since February 2001, spatial analysis has been conducted, showing the informal preferences of this activity. Results show a non-random distribution of caches within the study area, similar to the land use distribution. Preferable locations tend to be in iconic places of the city, usually close to the Tagus River, that concentrates 25% of the total caches. Since most of these places are known to be touristic destinations, the TOP15 logged Caches were also analyzed regarding their description and logs in order to understand if Geocaching reflects tourism and if it works as a tourist promotion tool within urban environments. Final results also reflect the Geocaching performance and major trends within urban environments providing new insights regarding this activity impacts and implications.