New data sources for social indicators: the case study of contacting politicians by Twitter

ABSTRACT

Data availability is a persistent constraint in social policy analysis. Web 2.0 technologies could provide valuable new data sources, but first, their potentials and limitations need to be investigated. This paper reports on a method using Twitter data for deriving indications of active citizenship, taken as an example of social indicators. Active citizenship is a dimension of social capital, empowering communities and reducing possibilities of social exclusion. However, classical measurements of active citizenship are generally costly and time-consuming. This paper looks at one of such classic indicators, namely, responses to the survey question ‘contacts to politicians’. It compares official survey results in Spain with findings from an analysis of Twitter data. Each method presents its own strengths and weakness, thus best results may be achieved by the combination of both. Official surveys have the clear advantage of being statistically robust and representative of a total population. Instead, Twitter data offer more timely and less costly information, with higher spatial and temporal resolution. This paper presents our full methodological workflow for analysing and comparing these two data sources. The research results advance the debate on how social media data could be mined for policy analysis.     

A European perspective on Digital Earth

Abstract

The purpose of this paper is to contribute to the definition of a European perspective on Digital Earth (DE), identify some actions that can contribute to raise the awareness of DE in the European context and thus strengthen the European contribution to the International Society for Digital Earth (ISDE). The paper identifies opportunities and synergies with the current policy priorities in Europe (Europe 2020, Innovation Union and Digital Agenda) and highlights a number of key areas to advance the development of DE from a European perspective: (1) integrating scientific research into DE; (2) exploiting the Observation Web with human-centred sensing; and (3) governance, including the establishment of stronger linkages across the European landscape of funding streams and initiatives. The paper is offered also as a contribution to the development of this new vision of DE to be presented at the next International DE Conference in Perth, Australia, in August 2011. The global recognition of this new vision will then reinforce the European component and build a positive feedback loop for the further implementation of DE across the globe.     

Free and open source software for geospatial applications (FOSS4G) to support Future Earth

ABSTRACT

The development, integration, and distribution of the information and spatial data infrastructure (i.e. Digital Earth; DE) necessary to support the vision and goals of Future Earth (FE) will occur in a distributed fashion, in very diverse technological, institutional, socio-cultural, and economic contexts around the world. This complex context and ambitious goals require bringing to bear not only the best minds, but also the best science and technologies available. Free and Open Source Software for Geospatial Applications (FOSS4G) offers mature, capable and reliable software to contribute to the creation of this infrastructure. In this paper we point to a selected set of some of the most mature and reliable FOSS4G solutions that can be used to develop the functionality required as part of DE and FE. We provide examples of large-scale, sophisticated, mission-critical applications of each software to illustrate their power and capabilities in systems where they perform roles or functionality similar to the ones they could perform as part of DE and FE. We provide information and resources to assist the readers in carrying out their own assessments to select the best FOSS4G solutions for their particular contexts and system development needs.     

Distributed geospatial information processing: sharing distributed geospatial resources to support Digital Earth

Abstract

This paper introduces a new concept, distributed geospatial information processing (DGIP), which refers to the process of geospatial information residing on computers geographically dispersed and connected through computer networks, and the contribution of DGIP to Digital Earth (DE). The DGIP plays a critical role in integrating the widely distributed geospatial resources to support the DE envisioned to utilise a wide variety of information. This paper addresses this role from three different aspects: 1) sharing Earth data, information, and services through geospatial interoperability supported by standardisation of contents and interfaces; 2) sharing computing and software resources through a GeoCyberinfrastructure supported by DGIP middleware; and 3) sharing knowledge within and across domains through ontology and semantic searches. Observing the long-term process for the research and development of an operational DE, we discuss and expect some practical contributions of the DGIP to the DE.     

The framework of a geospatial semantic web-based spatial decision support system for Digital Earth

Abstract

While significant progress has been made to implement the Digital Earth vision, current implementation only makes it easy to integrate and share spatial data from distributed sources and has limited capabilities to integrate data and models for simulating social and physical processes. To achieve effectiveness of decision-making using Digital Earth for understanding the Earth and its systems, new infrastructures that provide capabilities of computational simulation are needed. This paper proposed a framework of geospatial semantic web-based interoperable spatial decision support systems (SDSSs) to expand capabilities of the currently implemented infrastructure of Digital Earth. Main technologies applied in the framework such as heterogeneous ontology integration, ontology-based catalog service, and web service composition were introduced. We proposed a partition-refinement algorithm for ontology matching and integration, and an algorithm for web service discovery and composition. The proposed interoperable SDSS enables decision-makers to reuse and integrate geospatial data and geoprocessing resources from heterogeneous sources across the Internet. Based on the proposed framework, a prototype to assist in protective boundary delimitation for Lunan Stone Forest conservation was implemented to demonstrate how ontology-based web services and the services-oriented architecture can contribute to the development of interoperable SDSSs in support of Digital Earth for decision-making.     

Emissions from grassland burning in Kaziranga National Park,India – analysis from IRS-P6 AWiFS satellite remote sensing datasets

Biomass burning from vegetation fires is an important source of greenhouse gas emissions. In this study, we quantify biomass burning emissions from grasslands from the highly sensitive Kaziranga National Park, Assam, Northeast India. Most of the fires in the park are ‘controlled burning fires’ set by the park officials for management purposes. We evaluated the short-term impacts of fires and the resulting air pollution through integrating biomass burnt information from satellite remote sensing datasets. IRS-P6 Advanced Wide Field Sensor (AWiFS) data during March and April corresponding to dry season were evaluated to delineate the burnt areas. These burnt area estimates were then integrated with biomass data and emission factors for quantifying the greenhouse gas emissions. Results suggested that of the total study area of 37,822 ha, nearly 3163.282 ha has been burnt during March, 2005. Within one month, the burnt area increased to 7443.92 ha by April, i.e., from 8.36% to 19.68%. In total, biomass burning from the grasslands contributed to 29.65 Tg CO₂, 1.19 Tg CO, 0.071 Tg NOx, 0.042 Tg CH₄, 0.0625 Tg total non-methane hydrocarbons, 0.152 Tg of particulate matter, and 0.062 Tg of organic carbon and 0.008 Tg of black carbon during April. The importance of ‘fire’ as a management tool for maintaining the wildlife habitat has been highlighted in addition to some of the adverse affects of air pollution resulting from such management practices. The results from this study will be useful to forest officials as well as policy makers to undertake some sustainable forest management practices to maintain an ideal habitat for Kaziranga's wildlife.     

A RESTful proxy and data model for linked sensor data

Abstract

The vision of a Digital Earth calls for more dynamic information systems, new sources of information, and stronger capabilities for their integration. Sensor networks have been identified as a major information source for the Digital Earth, while Semantic Web technologies have been proposed to facilitate integration. So far, sensor data are stored and published using the Observations & Measurements standard of the Open Geospatial Consortium (OGC) as data model. With the advent of Volunteered Geographic Information and the Semantic Sensor Web, work on an ontological model gained importance within Sensor Web Enablement (SWE). In contrast to data models, an ontological approach abstracts from implementation details by focusing on modeling the physical world from the perspective of a particular domain. Ontologies restrict the interpretation of vocabularies toward their intended meaning. The ongoing paradigm shift to Linked Sensor Data complements this attempt. Two questions have to be addressed: (1) how to refer to changing and frequently updated data sets using Uniform Resource Identifiers, and (2) how to establish meaningful links between those data sets, that is, observations, sensors, features of interest, and observed properties? In this paper, we present a Linked Data model and a RESTful proxy for OGC's Sensor Observation Service to improve integration and inter-linkage of observation data for the Digital Earth.     

Building geospatial infrastructure

ABSTRACT

Many visions for geospatial technology have been advanced over the past half century. Initially researchers saw the handling of geospatial data as the major problem to be overcome. The vision of geographic information systems arose as an early international consensus. Later visions included spatial data infrastructure, Digital Earth, and a nervous system for the planet. With accelerating advances in information technology, a new vision is needed that reflects today’s focus on open and multimodal access, sharing, engagement, the Web, Big Data, artificial intelligence, and data science. We elaborate on the concept of geospatial infrastructure, and argue that it is essential if geospatial technology is to contribute to the solution of problems facing humanity.     

Remote sensing-based assessment of veld fire trends in multiple interwoven land tenure systems in Zimbabwe

The study used Landsat imagery, MODIS fire data and in situ meteorological data to determine emerging fire trends in interwoven multiple tenure systems in Zimbabwe. Remote sensing enabled fire trends to be determined across terrain and official records barriers. The number of fires and area burnt increased from 2001 up to 2009 then fluctuated across tenure systems. Fire events rose from 9 to 80 per year in some of the tenure systems. Complex relationships among number of fires, area burnt and weather variables within and across tenure systems were identified. The fire situation was responsive to intervention; the positive fire trends were reversed from 2009 onwards. Projected trends show that fire events could be reduced to negative values in three systems, while in two they could double by 2026. The veld fire problem could be eliminated if a holistic approach is adopted to tackle it across sectoral and land tenure divides.     

Challenges and strategies for the visual exploration of complex environmental data

ABSTRACT

In this opinion paper, we, a group of scientists from environmental-, geo-, ocean- and information science, argue visual data exploration should become a common analytics approach in Earth system science due to its potential for analysis and interpretation of large and complex spatio-temporal data. We discuss the challenges that appear such as synthesis of heterogeneous data from various sources, reducing the amount of information and facilitating multidisciplinary, collaborative research. We argue that to fully exploit the potential of visual data exploration, several bottlenecks and challenges have to be addressed: providing an efficient data management and an integrated modular workflow, developing and applying suitable visual exploration concepts and methods with the help of effective and tailored tools as well as generating and raising the awareness of visual data exploration and education. We are convinced visual data exploration is worth the effort since it significantly facilitates insight into environmental data and derivation of knowledge from it.     

Geospatial web services pave new ways for server-based on-demand access and processing of Big Earth Data

ABSTRACT

Big Earth Data has experienced a considerable increase in volume in recent years due to improved sensing technologies and improvement of numerical-weather prediction models. The traditional geospatial data analysis workflow hinders the use of large volumes of geospatial data due to limited disc space and computing capacity. Geospatial web service technologies bring new opportunities to access large volumes of Big Earth Data via the Internet and to process them at server-side. Four practical examples are presented from the marine, climate, planetary and earth observation science communities to show how the standard interface Web Coverage Service and its processing extension can be integrated into the traditional geospatial data workflow. Web service technologies offer a time- and cost-effective way to access multi-dimensional data in a user-tailored format and allow for rapid application development or time-series extraction. Data transport is minimised and enhanced processing capabilities are offered. More research is required to investigate web service implementations in an operational mode and large data centres have to become more progressive towards the adoption of geo-data standard interfaces. At the same time, data users have to become aware of the advantages of web services and be trained how to benefit from them most.     

A cross-analysis framework for multi-source volunteered,crowdsourced, and authoritative geographic information: The case study of volunteered personal traces analysis against transport network data

Abstract

The paper discusses the need of a high-level query language to allow analysts, geographers and, in general, non-programmers to easily cross-analyze multi-source VGI created by means of apps, crowd-sourced data from social networks and authoritative geo-referenced data, usually represented as JSON data sets (nowadays, the de facto standard for data exported by social networks). Since an easy to use high-level language for querying and manipulating collections of possibly geo-tagged JSON objects is still unavailable, we propose a truly declarative language, named J-CO-QL, that is based on a well-defined execution model. A plug-in for a GIS permits to visualize geo-tagged data sets stored in a NoSQL database such as MongoDB; furthermore, the same plug-in can be used to write and execute J-CO-QL queries on those databases. The paper introduces the language by exemplifying its operators within a real study case, the aim of which is to understand the mobility of people in the neighborhood of Bergamo city. Cross-analysis of data about transportation networks and VGI from travelers is performed, by means of J-CO-QL language, capable to manipulate and transform, combine and join possibly geo-tagged JSON objects, in order to produce new possibly geo-tagged JSON objects satisfying users’ needs.     

A spatial‐temporal‐semantic approach for detecting local events using geo‐social media data

Social media networks allow users to post what they are involved in with location information in a real‐time manner. It is therefore possible to collect large amounts of information related to local events from existing social networks. Mining this abundant information can feed users and organizations with situational awareness to make responsive plans for ongoing events. Despite the fact that a number of studies have been conducted to detect local events using social media data, the event content is not efficiently summarized and/or the correlation between abnormal neighboring regions is not investigated. This article presents a spatial‐temporal‐semantic approach to local event detection using geo‐social media data. Geographical regularities are first measured to extract spatio‐temporal outliers, of which the corresponding tweet content is automatically summarized using the topic modeling method. The correlation between outliers is subsequently examined by investigating their spatial adjacency and semantic similarity. A case study on the 2014 Toronto International Film Festival (TIFF) is conducted using Twitter data to evaluate our approach. This reveals that up to 87% of the events detected are correctly identified compared with the official TIFF schedule. This work is beneficial for authorities to keep track of urban dynamics and helps build smart cities by providing new ways of detecting what is happening in them.     

Design and evaluation of a geovisual analytics system for uncovering patterns in spatio-temporal event data

ABSTRACT

It remains difficult to develop a clear understanding of geo-located events and their relationships to one another, particularly when it comes to identifying patterns of events in less-structured textual sources, such as news feeds and social media streams. Here we present a geovisualization tool that can leverage computational methods, such as T-pattern analysis, for extracting patterns of interest from event data streams. Our system, STempo, includes coordinated-view geovisualization components designed to support visual exploration and analysis of event data, and patterns extracted from those data, in terms of time, geography, and content. Through a user evaluation, we explore the usability and utility of STempo for understanding patterns of recent political, social, economic, and military events in Syria.     

The socio-environmental data explorer (SEDE): a social media–enhanced decision support system to explore risk perception to hazard events

ABSTRACT

Social media are increasingly recognized as a useful data source for understanding social response to hazard events in real time and in post-event analysis. This article establishes social media–enhanced decision support systems (SME-DSS) as a synergistic integration of social media and decision support systems (DSSs) to provide structured access to native, near real-time data from a large and diverse population to assess social response to social, environmental, and technological risk and hazard events. We introduce a prototype SME-DSS entitled socio-environmental data explorer (SEDE) to explore the opportunities and challenges of leveraging social media for decision support. We use a winter storm during 25–28 January 2015 that accumulated record amounts of snow along the East Coast of the United States as a case study to evaluate SEDE in helping assess social response to environmental risk and hazard events as well as evaluate social media as a theoretical component within the social amplification of risk framework (SARF) that serves as a theoretical foundation for SME-DSS.     

Forest structural diversity characterization in Mediterranean landscapes affected by fires using Airborne Laser Scanning data

ABSTRACT

Forest fires can change forest structure and composition, and low-density Airborne Laser Scanning (ALS) can be a valuable tool for evaluating post-fire vegetation response. The aim of this study is to analyze the structural diversity differences in Mediterranean Pinus halepensis Mill. forests affected by wildfires on different dates from 1986 to 2009. Several types of ALS metrics, such as the Light Detection and Ranging (LiDAR) Height Diversity Index (LHDI), the LiDAR Height Evenness Index (LHEI), and vertical and horizontal continuity of vegetation, as well as topographic metrics, were obtained in raster format from low point density data. In order to map burned and unburned areas, differentiate fire occurrence dates, and distinguish between old and more recent fires, a sample of pixels was previously selected to assess the existence of differences in forest structure using the Kruskal–Wallis test. Then, k-nearest neighbors algorithm (k-NN), support vector machine (SVM) and random forest (RF) classifiers were compared to select the most accurate technique. The results showed that, in more recent fires, around 70% of the laser returns came from grass and shrub layers, yielding low LHDI and LHEI values (0.37–0.65 and 0.28–0.46, respectively). In contrast, the areas burned more than 20 years ago had higher LHDI and LHEI values due to the growth of the shrub and tree strata. The classification of burned and unburned areas yielded an overall accuracy of 89.64% using the RF method. SVM was the best classifier for identifying the structural differences between fires occurring on different dates, with an overall accuracy of 68.79%. Furthermore, SVM yielded an overall accuracy of 75.49% for the classification between old and more recent fires.     

Crowdsourcing functions of the living city from Twitter and Foursquare data

ABSTRACT

Urban functions are closely related to people’s spatiotemporal activity patterns, transportation needs, and a city’s business distribution and development trends. Studies investigating urban functions have used different data sources, such as remotely sensed imageries, observation, photography, and cognitive maps. However, these data sources usually suffer from low spatial, temporal, and thematic resolution. This article attempts to investigate human activities to understand urban functions through crowdsourcing social media data. In this study, we mined Twitter and Foursquare data to extract and analyze six types of human activities. The spatiotemporal analysis revealed hotspots for different activity intensities at different temporal resolution. We also applied the classified model in a real-time system to extract information of various urban functions. This study demonstrates the significance and usefulness of social sensing in analyzing urban functions. By combining different platforms of social media data and analyzing people’s geo-tagged city experience, this article contributes to leverage voluntary local knowledge to better depict human dynamics, discover spatiotemporal city characteristics, and convey information about cities.     

An approach of surface coal fire detection from ASTER and Landsat-8 thermal data: Jharia coal field,India

Radiant temperature images from thermal remote sensing sensors are used to delineate surface coal fires, by deriving a cut-off temperature to separate coal-fire from non-fire pixels. Temperature contrast of coal fire and background elements (rocks and vegetation etc.) controls this cut-off temperature. This contrast varies across the coal field, as it is influenced by variability of associated rock types, proportion of vegetation cover and intensity of coal fires etc. We have delineated coal fires from background, based on separation in data clusters in maximum v/s mean radiant temperature (13th band of ASTER and 10th band of Landsat-8) scatter-plot, derived using randomly distributed homogeneous pixel-blocks (9 × 9 pixels for ASTER and 27 × 27 pixels for Landsat-8), covering the entire coal bearing geological formation. It is seen that, for both the datasets, overall temperature variability of background and fires can be addressed using this regional cut-off. However, the summer time ASTER data could not delineate fire pixels for one specific mine (Bhulanbararee) as opposed to the winter time Landsat-8 data. The contrast of radiant temperature of fire and background terrain elements, specific to this mine, is different from the regional contrast of fire and background, during summer. This is due to the higher solar heating of background rocky outcrops, thus, reducing their temperature contrast with fire. The specific cut-off temperature determined for this mine, to extract this fire, differs from the regional cut-off. This is derived by reducing the pixel-block size of the temperature data. It is seen that, summer-time ASTER image is useful for fire detection but required additional processing to determine a local threshold, along with the regional threshold to capture all the fires. However, the winter Landsat-8 data was better for fire detection with a regional threshold.