ISPRS Int. J. Geo-Inf.2014, 3(3), 1039-1057; doi:10.3390/ijgi3031039 - published online 29 July 2014 Show/Hide Abstract
Abstract: We aimed to study the geographic variation in the incidence of COPD. We used health survey data (weighted to the population level) to identify 56,944 cases of COPD in Manitoba, Canada from 2001 to 2010. We used five cluster detection procedures, circular spatial scan statistic (CSS), flexible spatial scan statistic (FSS), Bayesian disease mapping (BYM), maximum likelihood estimation (MLE), and local indicator of spatial association (LISA). Our results showed that there are some regions in southern Manitoba that are potential clusters of COPD cases. The FSS method identified more regions than the CSS and LISA methods and the BYM and MLE methods identified similar regions as potential clusters. Most of the regions identified by the MLE and BYM methods were also identified by the FSS method and most of the regions identified by the CSS method were also identified by most of the other methods. The CSS, FSS and LISA methods identify potential clusters but are not able to control for confounders at the same time. However, the BYM and MLE methods can simultaneously identify potential clusters and control for possible confounders. Overall, we recommend using the BYM and MLE methods for cluster detection in areas with similar population and structure of regions as those in Manitoba.
ISPRS Int. J. Geo-Inf.2014, 3(3), 1023-1038; doi:10.3390/ijgi3031023 - published online 24 July 2014 Show/Hide Abstract
Abstract: Human health is part of an interdependent multifaceted system. More than ever, we have increasingly large amounts of data on the body, both spatial and non-spatial, its systems, disease and our social and physical environment. These data have a geospatial component. An exciting new era is dawning where we are simultaneously collecting multiple datasets to describe many aspects of health, wellness, human activity, environment and disease. Valuable insights from these datasets can be extracted using massively multivariate computational techniques, such as machine learning, coupled with geospatial techniques. These computational tools help us to understand the topology of the data and provide insights for scientific discovery, decision support and policy formulation. This paper outlines a holistic paradigm called Holistics 3.0 for analyzing health data with a set of examples. Holistics 3.0 combines multiple big datasets set in their geospatial context describing as many areas of a problem as possible with machine learning and causality, to both learn from the data and to construct tools for data-driven decisions.
ISPRS Int. J. Geo-Inf.2014, 3(3), 1003-1022; doi:10.3390/ijgi3031003 - published online 21 July 2014 Show/Hide Abstract
ISPRS Int. J. Geo-Inf.2014, 3(3), 980-1002; doi:10.3390/ijgi3030980 - published online 21 July 2014 Show/Hide Abstract
Abstract: Sustainable development is a key component in urban studies. Earth Observation (EO) can play a valuable role in sustainable urban development and planning, since it represents a powerful data source with the potential to provide a number of relevant urban sustainability indicators. To this end, in this paper we propose a conceptual list of EO-based indicators capable of supporting urban planning and management. Three cities with different typologies, namely Basel, Switzerland; Tel Aviv, Israel; and Tyumen, Russia were selected as case studies. The EO-based indicators are defined to effectively record the physical properties of the urban environment in a diverse range of environmental sectors such as energy efficiency, air pollution and public health, water, transportation and vulnerability to hazards. The results assess the potential of EO to support the development of a set of urban environmental indicators towards sustainable urban planning and management.
ISPRS Int. J. Geo-Inf.2014, 3(3), 968-979; doi:10.3390/ijgi3030968 - published online 17 July 2014 Show/Hide Abstract
Abstract: Being cleaner and climate friendly, wind energy has been increasingly utilized to meet the ever-growing global energy demands. In the State of Nebraska, USA, a wide gap exists between wind resource and actual energy production, and it is imperative to expand the wind energy development. Because of the formidable costs associated with wind energy development, the locations for new wind turbines need to be carefully selected to provide the greatest benefit for a given investment. Geographic Information Systems (GIS) have been widely used to identify the suitable wind farm locations. In this study, a GIS-based multi-criteria approach was developed to identify the areas that are best suited to wind energy development in Northeast Nebraska, USA. Seven criteria were adopted in this method, including distance to roads, closeness to transmission lines, population density, wind potential, land use, distance to cities, slope and exclusionary areas. The suitability of wind farm development was modeled by a weighted overlay of geospatial layers corresponding to these criteria. The results indicate that the model is capable of identifying locations highly suited for wind farm development. The approach could help identify suitable wind farm locations in other areas with a similar geographic background.
ISPRS Int. J. Geo-Inf.2014, 3(3), 942-967; doi:10.3390/ijgi3030942 - published online 16 July 2014 Show/Hide Abstract
Abstract: Shortly after the Fukushima Daiichi nuclear disaster in 2011, the Federal Government of Germany decided to change the structure of the country’s energy supply system by ending nuclear energy conversion and strongly promoting the development of renewable energies. In order to politically set the course for sustainable energy supply in this time of transition, it is important to analyze the factors influencing the future development of renewable energies. This work contributes to this purpose in the field of onshore wind electricity generation by displaying the temporal development of areas suitable for wind energy use. The availability of such areas is crucial to the extension of sites for wind energy plants. In our approach, the current potential area is determined by excluding areas unsuitable for this kind of electricity generation. For the determination of potential areas of the future, assumptions are made based on the expansion of settlement and traffic areas, and the occupation of protection areas. According to various scenarios, a decline of potential areas between 3% and 8% between 2011 and 2030 is indicated.