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About this book

This handbook covers a wide range of topics related to the collection, processing, analysis, and use of geospatial data in their various forms. This handbook provides an overview of how spatial computing technologies for big data can be organized and implemented to solve real-world problems. Diverse subdomains ranging from indoor mapping and navigation over trajectory computing to earth observation from space, are also present in this handbook. It combines fundamental contributions focusing on spatio-textual analysis, uncertain databases, and spatial statistics with application examples such as road network detection or colocation detection using GPUs. In summary, this handbook gives an essential introduction and overview of the rich field of spatial information science and big geospatial data.

It introduces three different perspectives, which together define the field of big geospatial data: a societal, governmental, and governance perspective. It discusses questions of how the acquisition, distribution and exploitation of big geospatial data must be organized both on the scale of companies and countries. A second perspective is a theory-oriented set of contributions on arbitrary spatial data with contributions introducing into the exciting field of spatial statistics or into uncertain databases. A third perspective is taking a very practical perspective to big geospatial data, ranging from chapters that describe how big geospatial data infrastructures can be implemented and how specific applications can be implemented on top of big geospatial data. This would include for example, research in historic map data, road network extraction, damage estimation from remote sensing imagery, or the analysis of spatio-textual collections and social media. This multi-disciplinary approach makes the book unique.

This handbook can be used as a reference for undergraduate students, graduate students and researchers focused on big geospatial data. Professionals can use this book, as well as practitioners facing big collections of geospatial data.

Table of Contents


Spatial Computing Systems andApplications


Chapter 1. IBM PAIRS: Scalable Big Geospatial-Temporal Data and Analytics As-a-Service

The rapid growth of geospatial-temporal data from sources like satellites, drones, weather modeling, IoT sensors etc., accumulating at a pace of PetaBytes to ExaBytes annually, opens unprecedented opportunities for both scientific and industrial applications. However, the sheer size and complexity of such data presents significant challenges for conventional geospatial information systems (GIS) which are supported by relational geospatial databases and cloud-based geospatial services based on file systems (mostly manifested as object stores or “cold” tape storages).
To fully exploit the value of geospatial-temporal data, particularly by leveraging the latest advances in machine-learning (ML) and artificial intelligence (AI), a new paradigm for platforms and services is required. Some of the necessary salient features include: (i) scalable cloud-based deployment capable of handling hundreds of PetaBytes of data, (ii) harmonization of data in order to mask the complexity of data (schema, map projection etc.) from end users, (iii) advanced search capabilities of data at a “pixel level” (in contrast to “file level”), and (iv) “in-data” analytics and computation to avoid downloading the mammoth amount of data through the internet.
In this chapter, we review the current trend of the design, implementation, and functionalities of such geospatial-temporal platforms and associated services, focusing on those based upon scalable key-value datastores. IBM PAIRS (Physical Analytics Integrated Data and Repository Services) Geoscope will be used as an example through which we illustrate how the architecture and key-value datastore design supports the aforementioned features and high-performance data ingestion, query, and analytics. The specific implementation of a publicly available PAIRS instance will be presented along with its performance benchmarking.
Furthermore, we review the RESTful API interface of IBM PAIRS. The APIs are minimalistic and designed to provide the end users from different perspectives – data providers, industrial analysts, software developers, data scientists – a smooth experience to seamlessly exploit and use geospatial-temporal data. The API interaction with PAIRS will be illustrated through a few query examples and use cases in extended range weather forecasting and electric utilities. The use cases also highlight how contextual insights can be rapidly gained through a variety of “cross-layer” queries and analytics to reveal relationships/patterns and to predict trends.
Siyuan Lu, Hendrik F. Hamann

Chapter 2. Big Geospatial Data Processing Made Easy: A Working Guide to GeoSpark

In the past decade, the volume of available geospatial data increased tremendously. Such data includes but not limited to: weather maps, socio-economic data, and geo-tagged social media. Moreover, the unprecedented popularity of GPS-equipped mobile devices and Internet of Things (IoT) sensors has led to continuously generating large-scale location information combined with the status of surrounding environments. For example, several cities have started installing sensors across the road intersections to monitor the environment, traffic and air quality. Making sense of the rich geospatial properties hidden in the data may greatly transform our society. This includes many subjects undergoing intense study: (1) Climate analysis: that includes climate change analysis (N. R. C. Committee on the Science of Climate Change 2001), study of deforestation (Zeng et al. 1996), population migration (Chen et al. 1999), and variation in sea levels (Woodworth et al. 2011), (2) Urban planning: assisting government in city/regional planning, road network design, and transportation/traffic engineering, (3) Commerce and advertisement (Dhar and Varshney 2011): e.g., point-of-interest (POI) recommendation services. These data-intensive spatial analytics applications highly rely on the underlying database management systems (DBMSs) to efficiently manipulate, retrieve and manage data.
Jia Yu, Mohamed Sarwat

Chapter 3. Indoor 3D: Overview on Scanning and Reconstruction Methods

This chapter covers the essentials regarding indoor 3D data, from scanning to reconstruction. It is aimed for education and professionals. The order of presentation is background, history in measurement method development, sensors, sensor systems, positioning algorithms, reconstruction, and applications. The authors’ backgrounds are in indoor 3D, mobile laser scanning, indoor reconstruction, and robotics. In order to maintain a coherence in the text and provide some useful tools for the reader, we have selected to focus solely on the ICP version of simultaneous localization and mapping (SLAM). Regardless, this should give a solid base for the reader to understand other (e.g. probabilistic) indoor SLAM methods as well. Reconstruction algorithms (starting from room segmentation and opening detection) are discussed with the help of abundant figures. At the very end, we discuss future trends with a connection to the current applications and propose some exercise questions for students.
Ville V. Lehtola, Shayan Nikoohemat, Andreas Nüchter

Chapter 4. Big Earth Observation Data Processing for Disaster Damage Mapping

Ever-growing earth observation data enable rapid recognition of damaged areas caused by large-scale disasters. Automation of data processing is the key to obtain adequate knowledge quickly from big earth observation data. In this chapter, we provide an overview of big earth observation data processing for disaster damage mapping. First, we review current earth observation systems used for disaster damage mapping. Next, we summarize recent studies of global land-cover mapping, which is essential information for disaster risk management. After that, we showcase state-of-the-art techniques for damage recognition from three different types of disaster, namely, flood mapping, landslide mapping, and building damage mapping. Finally, we summarize the remaining challenges and future directions.
Bruno Adriano, Naoto Yokoya, Junshi Xia, Gerald Baier

Chapter 5. Spatial Data Reduction Through Element-of-Interest (EOI) Extraction

Any large, multifaceted data collection that is challenging to handle with traditional management practices can be branded ‘Big Data.’ Any big data containing geo-referenced attributes can be considered big geospatial data. The increased proliferation of big geospatial data is currently reforming the geospatial industry into a data-driven enterprise. Challenges in the big spatial data domain can be summarized as the ‘Big Vs’ – variety, volume, velocity, veracity and value. Big spatial data sources can be considered in two broad classes, active and passive, as each is impacted to varying degrees. Some of these challenges may be alleviated by reducing unprocessed, or minimally processed, (raw) data to features, which we refer to as the extraction of Elements of Interest (EOI). In fact, many applications require EOI extraction from raw data to enable their basic employment. This chapter presents current state-of-the-art methods to create EOI from some types of georeferenced big data. We classify the data types into two realms: active and passive. Active data are those collected specifically for the purpose to which they are applied. Passive data are those collected for purposes other than those for which they are utilized, included those ‘collected’ for no particular purpose at all. The chapter then presents use cases from both the active and passive spatial realms, including the active applications of terrain feature extraction from digital elevation models and vegetation mapping from remotely-sensed imagery and passive applications like building identification from VGI and point-of-interest data mining from social networks for land use classification. Finally, the chapter concludes with future research needs.
Samantha T. Arundel, E. Lynn Usery

Chapter 6. Semantic Graphs to Reflect the Evolution of Geographic Divisions

Nowadays, the volume of data coming from the public sector is growing rapidly on the Open Data Web. Most of these data come from governmental agencies such as Statistical and Mapping Agencies. Together, these public institutions publish territorial statistics that are of utmost importance for policy-makers to conduct various analyses of their jurisdiction, in time and space, and observe its evolution over time. However, through times, all over the world, the geographic divisions that serve as a reference for recording territorial statistical values, are subject to change: their name, their belonging or their boundaries change for political or administrative reasons and at several subdivision levels (e.g., regions, districts, sub-districts). These changes lead to breaks in time-series and are source of both misinterpretations, and statistical biases when not properly documented. In this chapter, we investigate solutions relying on the Semantic Web technologies for the description of the evolution of geographic divisions over time. We investigate how these technologies may enhance the understanding of the territorial dynamics over time, providing statisticians, researchers, citizens with well-documented descriptions of territorial changes to conduct various analyses of the territories.
C. Bernard, C. Plumejeaud-Perreau, M. Villanova-Oliver, J. Gensel, H. Dao

Trajectories, Event and Movement Data


Chapter 7. Big Spatial Flow Data Analytics

Spatial flow data represent meaningful interaction activities between two regions, such as exchange of population, goods, capital, and information. In recent years, the widespread adoption of location-aware technologies such as the GPS-enabled smartphones amass flow data at individual level, along with much finer spatiotemporal granularity and abundant semantic information. The increasing availability of big spatial flow has brought us with unprecedented opportunities to study all kinds of spatial interaction phenomena from new perspectives, as well as intellectual challenges to develop visualization and analytical methods to handle its unique geographic and geometric characteristics. This chapter introduces a collection of the latest methods and techniques specifically designed for big spatial flow data. Three major families of methods are reviewed, namely geovisualization, spatial data mining, and spatial statistics, to give readers a comprehensive picture of the available approaches that serve different study purposes. One representative approach from each family is selected to elaborate, so the readers can gain a deeper understanding to readily use the methods and potentially develop their own in the future.
Ran Tao

Chapter 8. Semantic Trajectories Data Models

Semantic trajectories is a major paradigm for the representation of movement data, complementary to spatial trajectories. In this article, we introduce key concepts, focusing in particular on the structural properties of semantic trajectories. Hence, we discuss a possible taxonomy of semantic trajectory models, based on their purpose.
Maria Luisa Damiani

Chapter 9. Multi-attribute Trajectory Data Management

Motivated by the trend of enriching the knowledge about movement data, this chapter introduces representing and querying multi-attribute trajectories in a database system. Such a trajectory contains a sequence of time-stamped locations and a set of descriptive attributes. Multi-attribute trajectories mainly deal with attributes describing characteristics of objects, i.e., attributes that are not relevant to locations. This differs from semantic and activity trajectories in the literature that focus on location-dependent information. A range of novel queries are incurred that integrate both spatio-temporal data and attributes into the evaluation. To enhance the query performance, a hybrid and flexible index is developed to manage multi-attribute trajectories. Efficiently updating the structure is also presented. Query algorithms are proposed, accompanied with optimization strategies. Furthermore, the chapter introduces the system development, reports the performance evaluation and points out future directions.
Jianqiu Xu

Chapter 10. Mining Colocation from Big Geo-Spatial Event Data on GPU

This chapter investigates the colocation pattern mining problem for big spatial event data. Colocation patterns refer to subsets of spatial features whose instances are frequently located together. The problem is important in many applications such as analyzing relationships of crimes or disease with various environmental factors, but is computationally challenging due to a large number of instances, the potentially exponential number of candidate patterns, and high computational cost in generating pattern instances. Existing colocation mining algorithms (e.g., Apriori algorithm, multi-resolution filter, partial join and joinless approaches) are mostly sequential, and thus can be insufficient for big spatial event data. Recently, parallel colocation mining algorithms have been developed based on the Map-reduce framework. However, these algorithms need a large number of nodes to scale up, which is economically expensive, and their reducer nodes have a bottleneck of aggregating all instances of the same colocation patterns. Another work proposes a parallel colocation mining algorithm on GPU based on the iCPI tree and the joinless approach, but assumes that the number of neighbors for each instance is within a small constant, and thus may be inefficient when instances are dense and unevenly distributed. To address these limitations, we introduce a grid-based GPU colocation mining algorithm that includes a cell aggregate based upper bound filter, and two refinement algorithms. We prove the correctness and completes of the GPU algorithms. Experimental results on both real world data and synthetic data show that the GPU algorithms are promising with over 30 times speedup on up to millions of instances.
Arpan Man Sainju, Zhe Jiang

Chapter 11. Automatic Urban Road Network Extraction From Massive GPS Trajectories of Taxis

Urban road networks are fundamental transportation infrastructures in daily life and essential in digital maps to support vehicle routing and navigation. Traditional methods of map vector data generation based on surveyor’s field work and map digitalization are costly and have a long update period. In the Big Data age, large-scale GPS-enabled taxi trajectories and high-volume ride-sharing datasets are increasingly available. These datasets provide high-resolution spatiotemporal information about urban traffic along road networks. In this study, we present a novel geospatial-big-data-driven framework that includes trajectory compression, clustering, and vectorization to automatically generate urban road geometric information. A case study is conducted using a large-scale DiDi ride-sharing GPS dataset in the city of Chengdu in China. We compare the results of our automatic extraction method with the road layer downloaded from OpenStreetMap. We measure the quality and demonstrate the effectiveness of our road extraction method regarding accuracy, spatial coverage and connectivity. The proposed framework shows a good potential to update fundamental road transportation information for smart-city development and intelligent transportation management using geospatial big data.
Song Gao, Mingxiao Li, Jinmeng Rao, Gengchen Mai, Timothy Prestby, Joseph Marks, Yingjie Hu

Chapter 12. Exploratory Analysis of Massive Movement Data

Movement is a dynamic process that is increasingly being monitored by a variety of tracking systems. As a consequence, analysts who are trying to understand movement processes are confronted with growing movement datasets. Yet, there are no established analysis tools that support analysts in understanding these datasets and in asking the right analysis questions. Exploratory data analysis (EDA) is an approach that helps analysts to identify the main characteristics of datasets and to generate hypothesis. This chapter provides an overview of common tasks related to the exploratory analysis of massive movement datasets in GIScience and GISystems. It lays out conceptual and technical challenges related to these tasks and provides recommendations for performing exploratory analysis of massive movement data.
Anita Graser, Melitta Dragaschnig, Hannes Koller

Statistics, Uncertainty and Data Quality


Chapter 13. Spatio-Temporal Data Quality: Experience from Provision of DOT Traveler Information

The motivation for this chapter stems from a 15-year collaboration between the California Department of Transportation (Caltrans) and the Western Transportation Institute, and now Utah State University. Ian Turnbull, recently retired from Caltrans, set the standard of “accurate, timely and reliable” for all projects he was involved with, particularly those in remote, rural areas of California, and related collaborations. Ian’s team, now headed by Jeremiah Pearce, is responsible for the operation of a vast field sensor network and the provision of weather sensor data, CCTV images, chain control messages, changeable message sign (CMS) messages, and just about every other type of traveler information related to Caltrans District 2, particularly in remote, rural areas of Northeastern California. In turn, Sean Campbell, also from Caltrans, makes this and similar data from the rest of the state available to third-party providers of traveler information and other related applications. The experience gained in working with Ian, Sean, Jeremiah and others on the provision of traveler information and the development of systems for DOT operations and maintenance personal has been invaluable for recognizing the need for and the challenges of providing quality spatio-temporal data.
Douglas Galarus, Ian Turnbull, Sean Campbell, Jeremiah Pearce, Leann Koon

Chapter 14. Uncertain Spatial Data Management: An Overview

Both the current trends in technology such as smart phones, general mobile devices, stationary sensors, and satellites as we as a new user mentality of using this technology to voluntarily share enriched location information produces a flood of geo-spatial and geo-spatio-temporal data. This data flood provides a tremendous potential of discovering new and useful knowledge. But in addition to the fact that measurements are imprecise, spatial data is often interpolated between discrete observations. To reduce communication and bandwidth utilization, data is often subjected to a reduction, thereby eliminating some of the known/recorded values. These issues introduce the notion of uncertainty in the context of spatio-temporal data management, an aspect raising imminent need for scalable and flexible solutions. The main scope of this chapter is to survey existing techniques for managing, querying, and mining uncertain spatio-temporal data. First, this chapter surveys common data representations for uncertain data, explains the commonly used possible worlds semantics to interpret an uncertain database, and surveys existing system to process uncertain data. Then this chapter defines the notion of different probabilistic result semantics to distinguish the task of enrich individual objects with probabilities rather than enriched entire results with probabilities. To distinguish between result semantics is important, as for many queries, the problem of computing object-level result probabilities can be done efficiently, whereas the problem of computing probabilities of entire results is often exponentially hard. Then, this chapter provides an overview over probabilistic query predicates to quantify the required probability of a result to be included in the result.
Finally, this chapter introduces a novel paradigm to efficiently answer any kind of query on uncertain data: the Paradigm of Equivalent Worlds, which groups the exponential set of possible database worlds into a polynomial number of set of equivalent worlds that can be processed efficiently. Examples and use-cases of querying uncertain spatial data are provided using the example of uncertain range queries.
Andreas Züfle

Chapter 15. Spatial Statistics, or How to Extract Knowledge from Data

In this paper, we give an overview of selected statistical models to draw (statistically significant) conclusions from data. This is particularly important to prove theoretical/physical models. For this reason, we initially describe classical modeling approaches in geostatistics and spatial econometrics. Moreover, we show how large spatial and spatiotemporal data can be modeled by these approaches. Therefore, we sketch selected suitable methods and their computational implementation. Thus, the paper should give a general guideline to analyze big geospatial data, where “big data” refers to the total number of spatially dependent observations, and to draw conclusions from these data. Finally, we compare two of these approaches by applying them to an empirical data set. To be precise, we model the particulate matter concentration (PM2.5) in Colorado.
Anna Antoniuk, Miryam S. Merk, Philipp Otto

Information Retrieval from Multimedia Spatial Datasets


Chapter 16. A Survey of Textual Data & Geospatial Technology

While address geocoding has a long-established track record of research as well as industry deployment as part of GIS systems and online Web services, postal addresses are not the only way to textually encode geographic footprints. For instance, toponyms in particular can be recognized in prose text and “toponym resolution” (Leidner, Comput Environ Urban Syst 30(4):400--417, 2006; Toponym resolution in text: annotation, evaluation and applications of spatial grounding of place names. Ph.D. thesis, School of Informatics, University of Edinburgh, Edinburgh, 2007) has been defined as the mapping of a name for a place to a spatial footprint, enables better spatial applications. In this chapter, we review a range of alternative methods to arrive at a geographic footprints, starting out from different types of input such as available unstructured text and structured meta-data such as postal addresses, toponym mentions and geographic phrase resolution as well as KML, GeoRDF and other structured metadata formats. A range of application types are then described that are supported by georeferencing technologies to show the potential inherent in computing with geospatial data at scale.
Jochen L. Leidner

Chapter 17. Harnessing Heterogeneous Big Geospatial Data

The heterogeneity of geospatial datasets is a mixed blessing in that it theoretically enables researchers to gain a more holistic picture by providing different (cultural) perspectives, media formats, resolutions, thematic coverage, and so on, but at the same time practice shows that this heterogeneity may hinder the successful combination of data, e.g., due to differences in data representation and underlying conceptual models. Three different aspects are usually distinguished in processing big geospatial data from heterogeneous sources, namely geospatial data conflation, integration, and enrichment. Each step is a progression on the previous one by taking the result of the last step, extracting useful information, and incorporating additional information to solve specific questions. This chapter introduces and clarifies the scope and goal of each of these aspects, presents existing methods, and outlines current research trends.
Bo Yan, Gengchen Mai, Yingjie Hu, Krzysztof Janowicz

Chapter 18. Big Historical Geodata for Urban and Environmental Research

Historical geoinformation is a valuable resource for various scientific disciplines, ranging from urban and environmental research to the emerging field of digital humanities. This chapter elucidates potentials and applications of big geospatial data which it has recently become possible to automatically retrieve from historical records. Large volumes of historical textual and cartographic documents are currently being made digitally accessible by libraries and other institutions. With the help of computer vision and image analysis techniques, the hitherto only implicitly, i.e. human-readable, contained historical geoinformation can be made machine-readable and can hence be spatiotemporally analyzed and associated with current big geospatial databases filled with satellite imagery, digital maps or user-generated geocoded content. The chapter begins with an overview of existing geohistorical data sources and processing approaches and describes challenges posed by the sheer number and diversity of the sources. The main part is dedicated to potentials and applications of the derived geoinformation in the various environmental research domains, such as long-term land change monitoring, sustainability research, and Earth system modeling for studying the complex human-environment interactions between land, climate change, ecosystem and biodiversity changes during the Anthropocene.
Hendrik Herold

Chapter 19. Harvesting Big Geospatial Data from Natural Language Texts

A vast amount of geospatial data exists in natural language texts, such as newspapers, Wikipedia articles, social media posts, travel blogs, online reviews, and historical archives. Compared with more traditional and structured geospatial data, such as those collected by the US Geological Survey and the national statistics offices, geospatial data harvested from these unstructured texts have unique merits. They capture valuable human experiences toward places, reflect near real-time situations in different geographic areas, or record important historical information that is otherwise not available. In addition, geospatial data from these unstructured texts are often big, in terms of their volume, velocity, and variety. This chapter presents the motivations of harvesting big geospatial data from natural language texts, describes typical methods and tools for doing so, summarizes a number of existing applications, and discusses challenges and future directions.
Yingjie Hu, Benjamin Adams

Chapter 20. Automating Information Extraction from Large Historical Topographic Map Archives: New Opportunities and Challenges

Historical maps constitute unique sources of retrospective geographic information. Recently, several archives containing historical map series covering large spatial and temporal extents have been systematically scanned and made available to the public. The spatial-temporal information contained in such archives represents valuable information for a myriad of scientific applications. However, this geographic information needs to be unlocked and provided in analysis-ready geospatial data formats using adequate extraction and recognition techniques that can handle the typically very large volumes of complex data and thus, requiring high degrees of automation. Whereas traditional approaches for information extraction from map documents typically involve a certain degree of user interaction, recently, a number of methods has been proposed aiming to overcome such shortcomings and to fully automate these information extraction tasks based on machine learning methods and the automated generation of training data, among others. In this chapter, we provide an overview of these recent trends, on existing, publicly available map archives, and the opportunities and challenges associated with these developments.
Johannes H. Uhl, Weiwei Duan

Governance, Infrastructures and Society


Chapter 21. The Integration of Decision Maker’s Requirements to Develop a Spatial Data Warehouse

Nowadays, technologies such as positioning systems and Internet make it easier to produce and access to geographic information. During the recent years, this fact led to an increasing availability of diverse, heterogeneous and distributed spatial data sources. Those sources contain information collected at different times and use different techniques to aliment spatial data warehouses (SDWs) . The specificities of the SDWs are: (1) The nature of the spatial data requires taking into account possible incompatibilities: the spatial reference (position, shape, orientation, size), the reference systems, in the measure units, the spatial uncertainty, the precision, the size, etc. (2) Other elements to be considered in a warehouse of spatial data: the topology, the spatial integrity constraints, the consistency between scales, etc.
Sana Ezzedine, Sami Yassine Turki, Sami Faiz

Chapter 22. Smart Cities

Recent years have seen an increase in varied social and urban issues such as shortages, unequal resource allocations, and unpredictable weather and geopolitics. Advances in technology in both data management and collection (particularly from sensors and mobile devices), and Artificial Intelligence areas like Semantic Web and Natural Language Processing, present an unprecedented opportunity for addressing such social and urban problems without massive infrastructural investments. Within the context of an urban region, a ‘Smart City’ approach to these issues considers both the collective scope of individual problems and their interdependencies, and practical (including technological) steps that present a reasonable solution. In this chapter, we will describe both the scope of Smart Cities i.e. what defines a Smart City, according to various discussions in the literature, alternate definitions, nomenclatures and scope of Smart Cities, and the technologies and concerns that fall within the scope of making Smart Cities a commonplace reality. Special attention will be paid to the application of Artificial Intelligence and Big Data in implementing the Smart Cities vision.
Mayank Kejriwal

Chapter 23. The 4th Paradigm in Multiscale Data Representation: Modernizing the National Geospatial Data Infrastructure

The need of citizens in any nation to access geospatial data in readily usable form is critical to societal well-being, and in the United States (US), demands for information by scientists, students, professionals and citizens continue to grow. Areas such as public health, urbanization, resource management, economic development and environmental management require a variety of data collected from many sources to identify problems, monitor trends and propose solutions. Such information needs and demands have driven the coordination of federal and regional government agencies with respective private sector participation to develop national geospatial data infrastructures in many countries.
Barbara P. Buttenfield, Lawrence V. Stanislawski, Barry J. Kronenfeld, Ethan Shavers

Chapter 24. INSPIRE: The Entry Point to Europe’s Big Geospatial Data Infrastructure

Initiated in 2007, the INSPIRE Directive has set a legal framework to create a European-wide Spatial Data Infrastructure (SDI) to support the European Union (EU) environmental policies. This chapter analyses the INSPIRE infrastructure from a Big Geospatial Data perspective, describing how data is shared in an interoperable way by public sector organisations in the EU Member States and how it is made available in and accessible within the infrastructure. The INSPIRE Geoportal, which is the entry point to the whole infrastructure, is presented in detail. To justify its nature of a Big Geospatial Data infrastructure, the characteristics of INSPIRE data are mapped to those of Big Data’s six ‘Vs’. Despite many good results achieved in terms of data sharing, some challenges still remain related to data consumption from the user side. The chapter concludes with a dedicated discussion on how INSPIRE, and traditional SDIs in general, should evolve into modern data ecosystems to address these challenges while also embracing the modern practices of data sharing through the web.
Marco Minghini, Vlado Cetl, Alexander Kotsev, Robert Tomas, Michael Lutz
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