ISPRS Int. J. Geo-Inf.2015, 4(3), 1549-1568; doi:10.3390/ijgi4031549 - published 24 August 2015 Show/Hide Abstract
Abstract: Social media data have emerged as a new source for detecting and monitoring disaster events. A number of recent studies have suggested that social media data streams can be used to mine actionable data for emergency response and relief operation. However, no effort has been made to classify social media data into stages of disaster management (mitigation, preparedness, emergency response, and recovery), which has been used as a common reference for disaster researchers and emergency managers for decades to organize information and streamline priorities and activities during the course of a disaster. This paper makes an initial effort in coding social media messages into different themes within different disaster phases during a time-critical crisis by manually examining more than 10,000 tweets generated during a natural disaster and referencing the findings from the relevant literature and official government procedures involving different disaster stages. Moreover, a classifier based on logistic regression is trained and used for automatically mining and classifying the social media messages into various topic categories during various disaster phases. The classification results are necessary and useful for emergency managers to identify the transition between phases of disaster management, the timing of which is usually unknown and varies across disaster events, so that they can take action quickly and efficiently in the impacted communities. Information generated from the classification can also be used by the social science research communities to study various aspects of preparedness, response, impact and recovery.
ISPRS Int. J. Geo-Inf.2015, 4(3), 1530-1548; doi:10.3390/ijgi4031530 - published 21 August 2015 Show/Hide Abstract
Abstract: Borderlands modeling and understanding depend on both spatial and non-spatial data, which were difficult to obtain in the past. This has limited the progress of borderland-related research. In recent years, data collection technologies have developed greatly, especially geospatial Web 2.0 technologies including blogs, publish/subscribe, mashups, and GeoRSS, which provide opportunities for data acquisition in borderland areas. This paper introduces the design and development of a Geoweb-based tagging system that enables users to tag and edit geographical information. We first establish the GeoBlog model, which consists of a set of geospatial components, posts, indicators, and comments, as the foundation of the tagging system. GeoBlog is implemented such that blogs are mashed up with OpenStreetMap. Moreover, we present an improvement to existing publish/subscribe systems with support for spatio-temporal events and subscriptions, called Spatial Publish/Subscribe, as well as the event agency network for routing messages from the publishers to the subscribers. A prototype system based on this approach is implemented in experiments. The results of this study provide an approach for asynchronous interaction and message-ordered transfer in the tagging system.
ISPRS Int. J. Geo-Inf.2015, 4(3), 1512-1529; doi:10.3390/ijgi4031512 - published 20 August 2015 Show/Hide Abstract
Abstract: Due to its relatively high availability and low cost, location-based social network (LBSN) (e.g., Foursquare) data (a popular type of volunteered geographic information) seem to be an alternative or complement to survey data in the study of travel behavior and activity analysis. Illustrating this situation, recently, a number of studies attempted to use LBSN data (e.g., Foursquare check-ins) to investigate patterns of human travel and activity. Of particular note is that compared to other individual-level characteristics of users, such as age, profession, education, income and so forth, gender is relatively highly available in the profiles of Foursquare users. Moreover, considering gender differences in travel and activity analysis is a popular research topic and is helpful in better understanding the changes in women’s roles in family, labor force participation, society and so forth. Therefore, this paper empirically investigates how gender influences the travel and activity patterns of active local Foursquare users in New York City. Empirical investigations of gender differences in travel and activity patterns are conducted at both the individual and aggregate level. The empirical results reveal that there are gender differences in the travel and activity patterns of active local users in New York City at both the individual and aggregate level. Finally, the results of the empirical study and the extent to which LBSN data can be exploited to produce travel diary data are discussed.
ISPRS Int. J. Geo-Inf.2015, 4(3), 1500-1511; doi:10.3390/ijgi4031500 - published 19 August 2015 Show/Hide Abstract
Abstract: As countries become increasingly urbanized, understanding how urban areas are changing within the landscape becomes increasingly important. Urbanized areas are often the strongest indicators of human interaction with the environment, and understanding how urban areas develop through remotely sensed data allows for more sustainable practices. A Landsat satellite sensor which is a remote sensing platform, with its ability to analyze global data, rapidly present itself as being an invaluable tool for studying the growth of urban areas. In this study, we present the virtual geo-library as the geovisualization tools to provide the analytical studies of the urbanization process in Malang City, East Java, Indonesia, using images derived from Landsat sensor family (1989 to 2014). We provide a dynamic geovisualization through virtual geo-library, where users could understand and get valuable scientific information (e.g., urban area changes and land use transformation in higher land). This system is also equipped with the tools to enable users to create automatic cartographic maps and print the results out as a digital pdf format file.
ISPRS Int. J. Geo-Inf.2015, 4(3), 1480-1499; doi:10.3390/ijgi4031480 - published 18 August 2015 Show/Hide Abstract
Abstract: Three-dimensional (3D) point analysis and visualization is one of the most effective methods of point cluster detection and segmentation in geospatial datasets. However, serious scattering and clotting characteristics interfere with the visual detection of 3D point clusters. To overcome this problem, this study proposes the use of 3D Voronoi diagrams to analyze and visualize 3D points instead of the original data item. The proposed algorithm computes the cluster of 3D points by applying a set of 3D Voronoi cells to describe and quantify 3D points. The decompositions of point cloud of 3D models are guided by the 3D Voronoi cell parameters. The parameter values are mapped from the Voronoi cells to 3D points to show the spatial pattern and relationships; thus, a 3D point cluster pattern can be highlighted and easily recognized. To capture different cluster patterns, continuous progressive clusters and segmentations are tested. The 3D spatial relationship is shown to facilitate cluster detection. Furthermore, the generated segmentations of real 3D data cases are exploited to demonstrate the feasibility of our approach in detecting different spatial clusters for continuous point cloud segmentation.
ISPRS Int. J. Geo-Inf.2015, 4(3), 1442-1479; doi:10.3390/ijgi4031442 - published 17 August 2015 Show/Hide Abstract
Abstract: Building Information Models (e.g., IFC) and virtual 3D city models (e.g., CityGML) are revolutionising the way we manage information about our cities. However, the main focus of these models is on the physical and functional characteristics of urban properties and facilities, which neglects the legal and ownership aspects. In contrast, cadastral data models, such as the Land Administration Domain Model (LADM), have been developed for legal information management purposes and model legal objects such as ownership boundaries without providing correspondence to the object’s physical attributes. Integration of legal and physical objects in the virtual 3D city and cadastral models would maximise their utility and flexibility to support different applications that require an integrated resource of both legal and physical information, such as urban space management and land development processes. The aim of this paper is to propose a data model that supports both legal and physical information of urban environments. The methodology to develop this data model is to extend the core cadastral data model and integrate urban features into the data model. The outcome of the research can be utilised to extend the current data models to increases their usability for different applications that require both legal and physical information.