ISPRS Int. J. Geo-Inf.2015, 4(4), 1982-2003; doi:10.3390/ijgi4041982 - published 6 October 2015 Show/Hide Abstract
Abstract: A large body of recent studies—from both inside and outside of China—are devoted to the understanding of China’s regional inequality. The current study introduces “the spatial field model” to achieve comprehensive evaluation and multi-scale analysis of regional inequality. The model is based on the growth pole theory, regional interaction theory, and energy space theory. The spatial field is an abstract concept that defines the potential energy difference that is formed in the process of a regional growth pole driving the economic development of peripheral areas through transportation and communication corridors. The model is able to provide potentially more precise regional inequality estimates and generates isarithmic maps that will provide highly intuitive and visualized presentations. The model is applied to evaluate the spatiotemporal pattern of economic inequality in China from 2000 to 2012 amongst internal eastern-central-western regions as well as north-south regions at three geographical scales—i.e., inter-province, inter-city, and inter-county. The results indicate that the spatial field model could comprehensively evaluate regional inequality, provide aesthetically pleasing and highly adaptable presentations based on a pixel-based raster, and realise the multi-scale analyses of the regional inequality. The paper also investigates the limitations and extensions of the spatial field model in future application.
ISPRS Int. J. Geo-Inf.2015, 4(4), 1965-1981; doi:10.3390/ijgi4041965 - published 6 October 2015 Show/Hide Abstract
Abstract: Geospatial information of many kinds, from topographic maps to scientific data, is increasingly being made available through web mapping services. These allow georeferenced map images to be served from data stores and displayed in websites and geographic information systems, where they can be integrated with other geographic information. The Open Geospatial Consortium’s Web Map Service (WMS) standard has been widely adopted in diverse communities for sharing data in this way. However, current services typically provide little or no information about the quality or accuracy of the data they serve. In this paper we will describe the design and implementation of a new “quality-enabled” profile of WMS, which we call “WMS-Q”. This describes how information about data quality can be transmitted to the user through WMS. Such information can exist at many levels, from entire datasets to individual measurements, and includes the many different ways in which data uncertainty can be expressed. We also describe proposed extensions to the Symbology Encoding specification, which include provision for visualizing uncertainty in raster data in a number of different ways, including contours, shading and bivariate colour maps. We shall also describe new open-source implementations of the new specifications, which include both clients and servers.
ISPRS Int. J. Geo-Inf.2015, 4(4), 1936-1964; doi:10.3390/ijgi4041936 - published 29 September 2015 Show/Hide Abstract
Abstract: Due to the fact that geospatial information technology is considered necessary for disaster risk management (DRM), the need for more effective collaborations between providers and end users in data delivery is increasing. This paper reviews the following: (i) schemes of disaster risk management and collaborative data operation in DRM; (ii) geospatial information technology in terms of applications to the schemes reviewed; and (iii) ongoing practices of collaborative data delivery with the schemes reviewed. This paper concludes by discussing the future of collaborative data delivery and the progress of the technologies.
ISPRS Int. J. Geo-Inf.2015, 4(4), 1913-1935; doi:10.3390/ijgi4041913 - published 29 September 2015 Show/Hide Abstract
Abstract: The social vulnerability of the Yemeni population to humanitarian emergencies is not evenly distributed between the governorates. Some governorates may be more susceptible to the impacts than others, based on the circumstances of the people residing within them. In this paper, we present a methodology for assessing social vulnerability of governorates of Yemen to humanitarian emergencies using a Geographic Information Systems approach. We develop a spatial index of social vulnerability from an initial list of 80 variables that were reduced to 12 factors through Principal Component Analysis. Our findings show that the differences in social vulnerability between governorates are primarily driven by 12 factors, of which education, lack of basic services in health, water and sanitation, electricity, housing quality, poverty, limited livelihood opportunities, and internal and external displacement are the major determinants. The results show that the factors that contribute to social vulnerability are different for each governorate, underpinning the need for context-specific vulnerability reduction approaches. The most vulnerable governorates are characterized by conflicts, armed clashes and violence. The geographic variability in social vulnerability further underpins the need for different mitigation, humanitarian response and recovery actions. The use of Geographic Information Systems approach has contributed to our understanding of the geographies of vulnerability to humanitarian emergencies in Yemen.
ISPRS Int. J. Geo-Inf.2015, 4(4), 1894-1912; doi:10.3390/ijgi4041894 - published 25 September 2015 Show/Hide Abstract
Abstract: Information flows on social media platforms are able to show trends and user interests as well as connections between users.In this paper, we present a method how to analyze city related networks on the social media platform Twitter based on the user content. Forty million tweets have been downloaded via Twitter’s REST API (application programming interface) and Twitter’s Streaming API. The investigation focuses on two aspects: firstly, trend detection has been done to analyze 31 informational world cities, according the user activity, popularity of shared websites and topics defined by hashtags. Secondly, a hint of how connected informational cities are to each other is given by creating a clustered network based on the number of connections between different city pairs. Tokyo, New York City, London and Paris clearly lead the ranking of the most active cities if compared by the total number of tweets. The investigation shows that Twitter is very frequently used to share content from other services like Instagram or YouTube. The most popular topics in tweets reveal great differences between the cities. In conclusion, the investigation shows that social media services like Twitter also can be a mirror of the society they are used in and bring to light information flows of connected cities in a global network. The presented method can be applied in further research to analyze information flows regarding specific topics and/or geographical locations.
ISPRS Int. J. Geo-Inf.2015, 4(4), 1870-1893; doi:10.3390/ijgi4041870 - published 25 September 2015 Show/Hide Abstract
Abstract: This paper presents an integrated framework for exploratory multi-scale spatio-temporal feature extraction and clustering of spatio-temporal data. The framework combines the multi-scale spatio-temporal decomposition, feature identification, feature enhancing and clustering in a unified process. The original data are firstly reorganized as multi-signal time series, and then decomposed by the multi-signal wavelet. Exploratory data analysis methods, such as histograms, are used for feature identification and enhancing. The spatio-temporal evolution process of the multi-scale features can then be tracked by the feature clusters based on the data adaptive Fuzzy C-Means Cluster. The approach was tested with the global 0.25° satellite altimeter data over a period of 21 years from 1993 to 2013. The tracking of the multi-scale spatio-temporal evolution characteristics of the 1997–98 strong El Niño were used as validation. The results show that our method can clearly reveal and track the spatio-temporal distribution and evolution of complex geographical phenomena. Our approach is efficient for global scale data analysis, and can be used to explore the multi-scale pattern of spatio-temporal processes.