Frontiers in Information Systems

The spatial dynamics of landscapes are the consequence of a multiplicity of relations among physical, biological and social forces. So, it is essential the assessment of the driving forces related to Land Use and Land Cover changes (LULC) to understand the change process. The stochastic modeling technique in Geographical Information System (GIS) - Markov Chain (MC) analysis and Cellular Automata (CA) allows the predictions of future changes based on changes that have occurred in the past. This chapter aims to present a dynamic simulation model for LULC changes in ‘Sado Estuary’ and ‘Comporta-Galé’ Natura 2000 Sites (Portugal) for the beginning of the second half of the XXI century by using MC and CA. Regarding the quantification of the fragmentation processes and LULC changes in ‘Sado Estuary’ and ‘Comporta- Galé’ Natura 2000 Sites, these models are able to reveal non-obvious trends in the data and to describe ecological patterns. From an applied research point of view, this approach is useful to identifying adequate planning and management strategies for coastal ecosystems, for monitoring and planning natural and protected environments.

Agent-Based Models (ABM) are becoming more relevant in computational social science (CSS) due to the potential to model complex phenomena that emerge from individual-based interactions. Most tourism theoretical models recognize the complex nature of the tourism system, and complexity is a subject of growing interest among researchers. Geosimulation models (GM) are presented as potential tools to address tourism in a complex systems lens. Particularly ABM, has a GM tool, as captured growing interest by tourism researchers, however there is little empirical application as a tool to explore and predict tourism patterns. The purpose of the chapter is to frame ABM in GM following a complex systems theoretical approach, in order to increase knowledge by (i) considering the complex nature of tourism, (ii) providing tools to explore the interactions between system components, (iii) discussing the potential for coupling ABM and Geographical Information Systems (GIS) in tourism research, and (iv) giving insights on the functioning of the tourist behaviours and decision-making process through an ABM approach. Also a theoretical ABM is developed to improve knowledge on tourist decision-making in the selection of a destination to vacation. Tourists’ behaviour, such as individual motivation and social network influence in the vacation decision-making process are presented. On-going work on loose coupling of ABM and GIS is discussed.

The geological science has been in recent years an excellent playground for GIS applied studies, especially regarding the mineral deposits prospectivity. Other fields of study in the geological science (e.g. soil risk management, mining exploitation, geothermal resources…) also took advantages of this geocomputing methodology to extract spatial information. The geoscientist community fairly agrees that interrelations between mineral deposits and certain geological features are observed in the terrain, presenting also a non-random spatial regional distribution pattern in a vast majority of cases. This is where the spatial analysis using geocomputational techniques, in this particular case for rare-elements pegmatites, can be used as a great analytical tool to produce a mapping of mineral potential, or unveil the regional zonation patterns for this type of mineralization. In this study, statistical spatial analyses were performed for the pegmatites to highlight any possible relationship, or lack of it, between them and the surrounding granitic plutons, shear zones or schistose foliations. To accomplish our proposed objectives, the geocomputational method of Distance to Nearest Neighbours (DNN), Ripley’s L’- function and pegmatites orientations families were employed to study the spatial distribution pattern of the pegmatites, whereas Euclidean distance and Kernel density distributions aimed the spatial association between these same pegmatites to the various geological features within the study area. The obtained results show: i) Pegmatites spatial distribution following a clustering pattern, presenting the Lienriched pegmatites a higher rate and extent compared to the total pegmatites, as well as a spatial association with moderate to high pegmatites density; ii) Three distinct families of pegmatites orientation; iii) No statistically significant spatial relationship for the total pegmatites or Li-enriched relatively to the granitic pluton; iv) A regime of deformation within the study area, suggesting the presence of corridors of deformation with NW to NNW orientations; and v) Pegmatites spatial emplacement suggesting shear-zones control.

Geographical information systems (GIS) incorporate robust tools that allow for the incorporation, manipulation and analysis of different types of data, such as geologic, hydrogeological, meteorological and environmental, and to display large amounts of geocoded data, useful to map the spatial distribution of natural phenomena. Groundwater pollution and soil erosion are some of the environmental concerns at global scale, that require efficient mapping tools. An assessment of the groundwater vulnerability and soil loss through open source applications, developed for this purpose, is a valuable contribution to several communities. The applications presented in this work were developed within the QGIS software, using several open source libraries. The first application was developed to produce maps to evaluate the groundwater vulnerability to pollution. The tool integrates the procedures required to implement the DRASTIC index under a single plugin. The application is easy to use and provides the possibility of importing the attribute table, and allows for the possibility of modifying weights, indexes and attributes, in an interactive manner. Maps can be generated according to the user perception, regarding each aquifer system. The second application is intended to estimate the expected soil loss by water-caused erosion, using the Revised Universal Soil Loss Equation (RUSLE) through a web browser. This application provides the tools to manipulate the input data of the RUSLE model and to create categorical maps needed to assess the risk of soil loss. This web application was implemented in order to be used by users without GIS software skills. In order to test the two applications developed, two study cases were performed: in River Zêzere Basin Upstream of Manteigas (Serra da Estrela) for DRASTIC index and Montalegre municipality for RUSLE. The resulting maps met the expectation of soil scientists for these study areas.

Regarding activities related to sustainable forests management, the spatial location of information is a very important factor, which requires tools capable of acquiring this data and handling them in a georeferenced format. For this reason, forest management has rapidly incorporated geospatial tools offered by new information technologies. Two important technologies used are Geographical Information Systems (GIS) and the remote sensing technology known as LIDAR (Light Detection and Ranging).Forestry applications of these technologies can be grouped into two broad categories: (i) Inventory and monitoring of natural resources; and (ii) Analysis and modeling of resources to facilitate sustainable planning and management. The first category is designed to measure the surface area, quantity, composition and condition of forest and natural resources of a management area. Thus, foresters use the LIDAR technology for acquiring digital information on the structure of the forest and the terrain; this information, properly processed with a GIS, helps analysts in assessing the health of the forest, calculating and classifying forest biomass, classifying land, or identifying soil drainage patterns, among other things. In the second category, once the above mentioned information has been mapped in a GIS environment, it is accessible to managers and researchers who can analyze and create models that optimize the decision-making on the resources under management, facilitating and optimizing forest planning. Therefore, wood felling can be scheduled in a sustainable way, as well as the design of firefighting infrastructures or the optimization of any other decisions related to use of resources or the protection of wildlife. This chapter aims to make the reader familiar with some variables of sustainable forest management, and with their integration into a GIS environment, as well as to introduce the basics of LIDAR technology and its powerful capabilities to acquire useful information for forest managers and planners.

Spatial Biology analyses how space influences species, communities, individuals, and any other ecological processes. Geographical Information Systems (GIS) are essential in this discipline, together with remote sensing, spatial statistics, and ecological niche modelling. In this chapter, a detailed review is presented about the importance of GIS in spatial biology. Several case studies are organised at three levels, depending on the sampling unit: species, populations, and individuals. Examples are offered on species' distributions atlases; determination of chorotypes, biogeographical areas, and protected areas; modelling of species distributions, range shifts, species' dispersions, species' invasions, and hybrid zones; phylogeography and systematics; landscape connectivity; home ranges, and modelling road-kills.

In the recent years, GPS and GIS have been increasingly used in health research as they can be used to measure individuals’ movement and environmental exposure. Community mobility is an important aspect of the function and the environmental interaction of an individual. Indicators of community mobility, including important places visited and the number of trips made, can be extracted from raw GPS trajectory data using a trip detection algorithm with GIS. Those indicators of community mobility were used to monitor how stroke patients return to community and how they respond to rehabilitation treatments. GPS-based spatial footprints integrated with geospatial data such as air pollution, food access, and land use in a Geographical Information System (GIS) environment can help understand the environmental contexts of health behavior.