ISPRS Int. J. Geo-Inf.2015, 4(2), 900-927; doi:10.3390/ijgi4020900 - published 26 May 2015 Show/Hide Abstract
Abstract: Shallow coral reefs threatened by climate change must be spatio-temporally analyzed in terms of their protection of coastal human populations. This study combines Japanese spatio-temporal gradients of population/asset and coral buffering exposure to stress-inducing and stress-mitigating factors so that the socio-economic and ecological (SEE) resilience tied to coral reefscapes can be regionally mapped (1200 km) at a fine resolution (1 arcsec) over a decade (11 years). Fuzzy logic was employed to associated environmental factors based on the related population/asset/coral buffering responses, as found in the literature. Once the factors were weighted according to their resilience contributions, temporally static patterns were evident: (1) a negative correlation occurs between coral buffering resilience and latitude; (2) the least resilient islands are low-lying, deprived of wide reef barriers, and located on the eastern and southern boundaries of the Nansei archipelago; (3) the southwestern-most, middle and northeastern-most islands have the same SEE resilience; and (4) Sekisei Lagoon islands have a very high coral buffering resilience. To overcome uncertainty, future studies should focus on the socio-ecological adaptive capacity, fine-scale ecological processes (such as coral and fish functional groups) and the prediction of the flood risks in the coming decades.
ISPRS Int. J. Geo-Inf.2015, 4(2), 883-899; doi:10.3390/ijgi4020883 - published 21 May 2015 Show/Hide Abstract
Abstract: To elucidate a realistic traffic assignment scenario, a multi-criterion decision system is essential. A traffic assignment model designed to simulate real-life situation may therefore utilize absolute and/or relative impedance. Ideally, the decision-making process should identify a set of traffic impedances (factors working against the smooth flow of traffic) along with pertinent parameters in order for the decision system to select the most optimal or the least-impeded route. In this study, we developed geospatial algorithms that consider multiple impedances. The impedances utilized in this study included, traffic patterns, capacity and congestion. The attributes of the decision-making process also prioritize multi-traffic scenarios by adopting first-in-first-out prioritization method. We also further subdivided classical impedance into either relative impedance or absolute impedance. The main advantage of this innovative multi-attribute, impedance-based trip assignment model is that it can be implemented in a manner of algebraic approach to utilize shortest path algorithm embedded in a Geographic Information Systems (GIS)—Graphical User Interface tool. Thus, the GIS package can therefore handle the multi-attribute impedance effectively. Furthermore, the method utilized in this paper displays flexibility and better adaptation to a multi-modal transportation system. Transportation, logistics, and random events, such as terrorism, can be easily analyzed with pertinent impedance.
ISPRS Int. J. Geo-Inf.2015, 4(2), 858-882; doi:10.3390/ijgi4020858 - published 20 May 2015 Show/Hide Abstract
Abstract: Non-motorized transportation modes, especially cycling and walking, offer numerous benefits, including improvements in the livability of cities, healthy physical activity, efficient urban transportation systems, less traffic congestion, less noise pollution, clean air, less impact on climate change and decreases in the incidence of diseases related to vehicular emissions. Considering the substantial number of short-distance trips, the time consumed in traffic jams, the higher costs for parking vehicles and restrictions in central business districts, many commuters have found that non-motorized modes of transportation serve as viable and economical transport alternatives. Thus, local governments should encourage and stimulate non-motorized modes of transportation. In return, governments must provide safe conditions for these forms of transportation, and motorized vehicle users must respect and coexist with pedestrians and cyclists, which are the most vulnerable users of the transportation system. Although current trends in sustainable transport aim to encourage and stimulate non-motorized modes of transportation that are socially more efficient than motorized transportation, few to no safety policies have been implemented regarding vulnerable road users (VRU), mainly in large urban centers. Due to the spatial nature of the data used in transport-related studies, geospatial technologies provide a powerful analytical method for studying VRU safety frameworks through the use of spatial analysis. In this article, spatial analysis is used to determine the locations of regions that are characterized by a concentration of traffic accidents (black zones) involving VRU (injuries and casualties) in São Paulo, Brazil (developing country), and Rome, Italy (developed country). The black zones are investigated to obtain spatial patterns that can cause multiple accidents. A method based on kernel density estimation (KDE) is used to compare the two cities and show economic, social, cultural, demographic and geographic differences and/or similarities and how these factors are linked to the locations of VRU traffic accidents. Multivariate regression analyses (ordinary least squares (OLS) models and spatial regression models) are performed to investigate spatial correlations, to understand the dynamics of VRU road accidents in São Paulo and Rome and to detect factors (variables) that contribute to the occurrences of these events, such as the presence of trip generator hubs (TGH), the number of generated urban trips and demographic data. The adopted methodology presents satisfactory results for identifying and delimiting black spots and establishing a link between VRU traffic accident rates and TGH (hospitals, universities and retail shopping centers) and demographic and transport-related data.
ISPRS Int. J. Geo-Inf.2015, 4(2), 837-857; doi:10.3390/ijgi4020837 - published 18 May 2015 Show/Hide Abstract
Abstract: Three-dimensional (3D) maps have many potential applications, such as navigation and urban planning. In this article, we present the use of a 3D virtual world platform Meshmoon to create intelligent open data 3D maps. A processing method is developed to enable the generation of 3D virtual environments from the open data of the National Land Survey of Finland. The article combines the elements needed in contemporary smart city concepts, such as the connection between attribute information and 3D objects, and the creation of collaborative virtual worlds from open data. By using our 3D virtual world platform, it is possible to create up-to-date, collaborative 3D virtual models, which are automatically updated on all viewers. In the scenes, all users are able to interact with the model, and with each other. With the developed processing methods, the creation of virtual world scenes was partially automated for collaboration activities.
ISPRS Int. J. Geo-Inf.2015, 4(2), 815-836; doi:10.3390/ijgi4020815 - published 13 May 2015 Show/Hide Abstract
Abstract: This article reviews the range of delivery platforms that have been developed for the PySAL open source Python library for spatial analysis. This includes traditional desktop software (with a graphical user interface, command line or embedded in a computational notebook), open spatial analytics middleware, and web, cloud and distributed open geospatial analytics for decision support. A common thread throughout the discussion is the emphasis on openness, interoperability, and provenance management in a scientific workflow. The code base of the PySAL library provides the common computing framework underlying all delivery mechanisms.
ISPRS Int. J. Geo-Inf.2015, 4(2), 799-814; doi:10.3390/ijgi4020799 - published 5 May 2015 Show/Hide Abstract
Abstract: The relevance of geographic information to mobile users must be evaluated by taking into account the usage context. This paper assumes that emerging Location-based Social Networks (LBSNs) contain contextual information rich enough to be used in order to contextualize such an evaluation process. This assumption is demonstrated through an exploratory analysis of a Foursquare check-in dataset, which reveals the impacts of two contextual factors—temporal and spatial—on mobility patterns. This paper then proposes an approach that may be used to contextualize the evaluation of geographic information’s relevance. The proposed algorithm links apriori relevance to the contextualized relevance using the hidden impacts of contextual factors. Improved performance from the experiments carried out confirms the validity of the proposed approach, as well as the benefits of utilizing contextual information within the relevance evaluation process.