Social media analytics has become prominent in natural disaster management. In spite of a large variety of metadata fields in social media data, four dimensions i.e. space, time, content and network have been given particular attention for mining useful information to gain situational awareness and improve disaster response. In this article, we review how existing studies analyze these four dimensions, summarize common techniques for mining these dimensions, and then suggest some methods accordingly. We then propose a schema to categorize the gathered articles into 15 classes and facilitate the generation of data analysis tasks. We find that 1 a large part of studies involve multiple dimensions of social media data in their analyses, 2 there are both separate analyses for each dimension and simultaneous analyses for multiple dimensions and 3 there are fewer simultaneous analyses as dimensions increase. Finally, we suggest research opportunities and challenges in fusing social media data with authoritative datasets, i.e. census data and remote-sensing data.

Social media analytics for natural disaster management

Traditional gazetteers are built and maintained by authoritative mapping agencies. In the age of Big Data, it is possible to construct gazetteers in a data-driven approach by mining rich volunteered geographic information (VGI) from the Web. In this research, we build a scalable distributed platform and a high-performance geoprocessing workflow based on the Hadoop ecosystem to harvest crowd-sourced gazetteer entries. Using experiments based on geotagged datasets in Flickr, we find that the MapReduce-based workflow running on the spatially enabled Hadoop cluster can reduce the processing time compared with traditional desktop-based operations by an order of magnitude. We demonstrate how to use such a novel spatial-computing infrastructure to facilitate gazetteer research. In addition, we introduce a provenance-based trust model for quality assurance. This work offers new insights on enriching future gazetteers with the use of Hadoop clusters, and makes contributions in connecting GIS to the cloud computing environment for the next frontier of Big Geo-Data analytics.

Constructing Gazetteers from Volunteered Big Geo-Data Based on Hadoop

In this report I propose to examine the concept of the 'smart city' from the standpoint of spatial enablement I analyse emerging research on smart cities, particularly those addressing the potential role of GISciences in the development and implementation of the concept of smart cities I develop the idea that the intelligence of a city should be measured by its ability to produce favourable conditions to get urban operators (citizens, organizations, private companies, etc) actively involved into sociospatial innovation dynamics To obtain such a commitment, I believe that operators should be able to develop and mobilize (digital) spatial skills so that they could efficiently manage their spatiality In other words, I argue that a smart city is first of all a spatially enabled city Language: en

Geographic information science I: why does a smart city need to be spatially enabled?

Constructing gazetteers from volunteered Big Geo-Data based on Hadoop

We present XGSN, an open-source system that relies on semantic representations of sensor metadata and observations, to guide the process of annotating and publishing sensor data on the Web. XGSN is able to handle the data acquisition process of a wide number of devices and protocols, and is designed as a highly extensible platform, leveraging on the existing capabilities of the Global Sensor Networks (GSN) middleware. Going beyond traditional sensor management systems, XGSN is capable of enriching virtual sensor descriptions with semantically annotated content using standard vocabularies. In the proposed approach, sensor data and observations are annotated using an ontology network based on the SSN ontology, providing a standardized queryable representation that makes it easier to share, discover, integrate and interpret the data. XGSN manages the annotation process for the incoming sensor observations, producing RDF streams that are sent to the cloud-enabled Linked Sensor Middleware, which can internally store the data or perform continuous query processing. The distributed nature of XGSN allows deploying different remote instances that can interchange observation data, so that virtual sensors can be aggregated and consume data from other remote virtual sensors. In this paper we show how this approach has been implemented in XGSN, and incorporated to the wider OpenIoT platform, providing a highly flexible and scalable system for managing the life-cycle of sensor data, from acquisition to publishing, in the context of the semantic Web of Things.

/pdf/xgsn-an-open-source-semantic-sensing-middleware-for-the-web-2p0yvvtzen.pdf

XGSN: An Open-source Semantic Sensing Middleware for the Web of Things

The Geosciences and Geography are not just yet another application area for semantic technologies. The vast heterogeneity of the involved disciplines ranging from the natural sciences to the social sciences introduces new challenges in terms of interoperability. Moreover, the inherent spatial and temporal information components also require distinct semantic approaches. For these reasons, geospatial semantics, geo-ontologies, and semantic interoperability have been active research areas over the last 20 years. The geospatial semantics community has been among the early adopters of the Semantic Web, contributing methods, ontologies, use cases, and datasets. Today, geographic information is a crucial part of many central hubs on the Linked Data Web. In this editorial, we outline the research field of geospatial semantics, highlight major research directions and trends, and glance at future challenges. We hope that this text will be valuable for geoscientists interested in semantics research as well as knowledge engineers interested in spatiotemporal data.

/pdf/geospatial-semantics-and-linked-spatiotemporal-data-past-nkvurnrhq3.pdf

Todd Pehle

Papers

Geospatial semantics and linked spatiotemporal data --Past, present, and future