ISPRS Int. J. Geo-Inf.2014, 3(3), 1118-1121; doi:10.3390/ijgi3031118 (registering DOI) - published 16 September 2014 Show/Hide Abstract
Abstract: This special issue of the ISPRS International Journal of Geographic Information about “Coastal GIS” is motivated by many circumstances. More than one-half of the world’s human population lives in coastal areas (within 200 kilometers of coast) as of 2000 . The trend toward coastal habitation is expected to continue in the US with the total being 75 percent by 2025, meaning that coastal human–environment interactions will likely increase and intensify . Geographic information systems (GIS) are being developed and used by technical specialists, stakeholder publics, and executive/policy decision makers for improving our understanding and management of coastal areas, separately and together as more organizations focus on improving the sustainability and resilience of coastal systems. Coastal systems—defined as the area of land closely connected to the sea, including barrier islands, wetlands, mudflats, beaches, estuaries, cities, towns, recreational areas, and maritime facilities, the continental seas and shelves, and the overlying atmosphere—are subject to complex and dynamic interactions among natural and human-driven processes. Coastal systems are crucial to regional and national economies, hosting valued human-built infrastructure and providing ecosystem services that sustain human well-being. This special issue of IJGI about coastal GIS presents a collection of nine papers that address many of the issues mentioned above. [...]
ISPRS Int. J. Geo-Inf.2014, 3(3), 1101-1117; doi:10.3390/ijgi3031101 - published 10 September 2014 Show/Hide Abstract
Abstract: Growing food in urban areas could solve a multitude of social and environmental problems. These potential benefits have resulted in an increased demand for urban agriculture (UA), though quantitative data is lacking on the feasibility of conversion to large-scale practices. This study uses multiple land use scenarios to determine different spaces that could be allocated to vegetable production in Montréal, including residential gardens, industrial rooftops and vacant space. Considering a range of both soil-bound and hydroponic yields, the ability of these scenarios to render Montréal self-sufficient in terms of vegetable production is assessed. The results show that the island could easily satisfy its vegetable demand if hydroponics are implemented on industrial rooftops, though these operations are generally costly. Using only vacant space, however, also has the potential to meet the city’s demand and requires lower operating costs. A performance index was developed to evaluate the potential of each borough to meet its own vegetable demand while still maintaining an elevated population density. Most boroughs outside of the downtown core are able to satisfy their vegetable demand efficiently due to their land use composition, though results vary greatly depending on the farming methods used, indicating the importance of farm management.
ISPRS Int. J. Geo-Inf.2014, 3(3), 1077-1100; doi:10.3390/ijgi3031077 - published 26 August 2014 Show/Hide Abstract
Abstract: Spatial information for coastal risk assessment is inherently uncertain. This uncertainty may be due to different spatial and temporal components of geospatial data and to their semantics. The spatial uncertainty can be expressed either quantitatively or qualitatively. Spatial uncertainty in coastal risk assessment itself arises from poor spatial representation of risk zones. Indeed, coastal risk is inherently a dynamic, complex, scale-dependent, and vague, phenomenon in concept. In addition, representing the associated zones with polygons having well-defined boundaries does not provide a realistic method for efficient and accurate representing of the risk. This paper proposes a conceptual framework, based on fuzzy set theory, to deal with the problems of ill-defined risk zone boundaries and the inherent uncertainty issues. To do so, the nature and level of uncertainty, as well as the way to model it are characterized. Then, a fuzzy representation method is developed where the membership functions are derived based on expert-knowledge. The proposed approach is then applied in the Perce region (Eastern Quebec, Canada) and results are presented and discussed.
ISPRS Int. J. Geo-Inf.2014, 3(3), 1058-1076; doi:10.3390/ijgi3031058 - published 14 August 2014 Show/Hide Abstract
Abstract: Crowdsourcing, volunteered geographic information (VGI) and citizens acting as sensors are currently being used in Australia via GeoWeb 2.0 applications for environmental sustainability purposes. This paper situates the origins of these practices, phenomena and concepts within the intersection of Web 2.0 and emerging online and mobile spatial technologies, herein called the GeoWeb 2.0. The significance of these origins is akin to a revolution in the way information is created, curated and distributed, attributed with transformative social impacts. Applications for environmental sustainability have the potential to be similarly transformative or disruptive. However, Web 2.0 is not described or conceptualised consistently within the literature. Australian examples implementing the GeoWeb 2.0 for environmental sustainability are diverse, but the reasons for this are difficult to ascertain. There is little published by the creators of such applications on their decisions, and Australian research is nascent, occurring across a variety of disciplinary approaches. While a substantial research literature emanates from North America and Europe, its transferability to Australia requires careful assessment. This paper contributes to this assessment by providing a review of relevant literature in the context of Australian examples for environmental sustainability.
ISPRS Int. J. Geo-Inf.2014, 3(3), 1039-1057; doi:10.3390/ijgi3031039 - published 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 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.