Data In Digital Environment Research Articles

Tensions in Data Journey Activities: Mobilising, Processing, Producing, and Re-purposing Data in Environmental Assessment Practice

Digital data shape work practices as they travel through space, time, and social situations. In turn, workers shape the materiality of data through different data-oriented activities. In this paper, we draw on theory of data journeys to develop and present a conceptual framework of four data-intensive activities: Producing, mobilising, processing, and re-purposing. Together, those activities capture entanglements of data transformations and data-intensive practices. We develop the framework through a qualitative study of digitisation in Environmental Assessment (EA) practice. EA practitioners reflect on their experiences working with digital environmental data through ten semi-structured and two interactive interviews. The paper offers two main contributions. First, we develop a data journey theoretical framework and show that it is useful as a unit of analysis for uncovering tensions in data-oriented activities. Second, we use these insights to outline a set of tensions for future design activities in the area of data work practices.

Take a seed! Revealing Neolithic landscape and agricultural development in the Carpathian Basin through multivariate statistics and environmental modelling.

The Carpathian Basin represents the cradle of human agricultural development during the Neolithic period, when large parts were transformed into 'cultural landscapes' by first farmers from the Balkans. It is assumed that an Early Neolithic subsistence economy established along the hydrologic systems and on Chernozem soil patches, which developed from loess deposits. However, recent results from soil chemistry and geoarchaeological analyses raised the hypothesis that extensive Chernozem coverage developed from increased land-use activity and that Early Neolithic 'cultural' groups were not restricted to loess-covered surfaces but rather preferred hydromorphic soils that formed in the floodplains. This article performs multivariable statistics from large datasets of Neolithic sites in Hungary and allows tracing Early to Late Neolithic site preferences from digital environmental data. Quantitative analyses reveal a strong preference for hydromorphic soils, a significant avoidance of loess-covered areas, and no preference for Chernozem soils throughout the Early Neolithic followed by a strong transformation of site preferences during the Late Neolithic period. These results align with socio-cultural developments, large-scale mobility patterns, and land-use and surface transformation, which shaped the Carpathian Basin and paved the way for the agricultural revolution across Europe.

New data infrastructures for environmental monitoring in Myanmar: Is digital transparency good for governance?

The design and use of environmental data infrastructures, including software platforms, sensors, satellite data, mobile phone apps, and digitally generated visual representations, is increasingly inseparable from contemporary environmental governance. Such technologies are often intended to enable data transparency, which in turn is assumed to promote expanded participation in democratic governance. In this article, we investigate how environmental monitoring, as performed through domestic and globalized infrastructures that seek to make digital environmental data open and transparent, is playing out in Myanmar’s forest sector. New data infrastructures are inseparable from the proliferation of non-state actors involved in environmental governance amid the country’s transition from military surveillance state toward more liberal and democratic rule, yet participation is not universal. We argue that actors engage new platforms and tools based on different understandings of the role of increased data transparency in environmental governance, which in turn are structured by historical relations with and within the legacy of the surveillance state.

Data-driven quasi-interpolant spline surfaces for point cloud approximation

In this paper we investigate a local surface approximation, the Weighted Quasi Interpolant Spline Approximation (wQISA), specifically designed for large and noisy point clouds. We briefly describe the properties of the wQISA representation and introduce a novel data-driven implementation, which combines prediction capability and complexity efficiency. We provide an extended comparative analysis with other continuous approximations on real data, including different types of surfaces and levels of noise, such as 3D models, terrain data and digital environmental data.

Protection of Personal Data in Digital Environment

In recent years, the application of legislation in the field of personal data has become the focus of attention of legal scholars. With the development of digital technologies, the problem of protection of personal data becomes especially urgent. The importance of personal data is so great that some scholars treat them as intangible goods. In order to protect the interests of citizens, our State takes measures to localize citizens’ personal data by statutory regulation of the Russian segment of the Internet. Such remedies as the right to be forgotten and personal data anonymization are also applied.However, the practice, including judicial practice, shows that the available means of protection of personal data are insufficient in the context of new technologies. However, the practice of application of laws on personal data reveals a number of problems that need to be addressed. The attribution of specific information about natural persons to personal data leads to a number of questions with regard to the practice of the activities of state bodies. Under currently effective Article 3 of the Federal Law, the term personal data refers to any information relating directly or indirectly to a certain or definable natural person (subject of personal data). At the same time, the law does not specify which data about an individual refers to personal data. Due to this broad understanding of personal data, questions arise concerning the attribution of paticular information about an individual to personal data. In this regard, the definition of criteria for the attribution of specific information about a person to personal data becomes an important theoretical task.The issues of primary concern include: 1) strengthening of responsibility for violation of personal data legislation; 2) giving priority to the issue of neutrality of the Internet, 3) solving the problem of the balance between direct access to publicly available data and the need to protect personal data. In the author’s opinion, it is necessary to ensure by means of comprehensive measures the priority of protection of personal data of citizens. This problem is of particular importance in connection with the elaboration of new laws on the digital profile of citizens.

Multispectral canopy reflectance improves spatial distribution models of Amazonian understory species

Species distribution models are required for the research and management of biodiversity in the hyperdiverse tropical forests, but reliable and ecologically relevant digital environmental data layers are not always available. We here assess the usefulness of multispectral canopy reflectance (Landsat) relative to climate data in modelling understory plant species distributions in tropical rainforests. We used a large dataset of quantitative fern and lycophyte species inventories across lowland Amazonia as the basis for species distribution modelling (SDM). As predictors, we used CHELSA climatic variables and canopy reflectance values from a recent basin‐wide composite of Landsat TM/ETM+ images both separately and in combination. We also investigated how species accumulate over sites when environmental distances were expressed in terms of climatic or surface reflectance variables. When species accumulation curves were constructed such that differences in Landsat reflectance among the selected plots were maximised, species accumulated faster than when climatic differences were maximised or plots were selected in a random order. Sixty‐nine species were sufficiently frequent for species distribution modelling. For most of them, adequate SDMs were obtained whether the models were based on CHELSA data only, Landsat data only or both combined. Model performance was not influenced by species’ prevalence or abundance. Adding Landsat‐based environmental data layers overall improved the discriminatory capacity of SDMs compared to climate‐only models, especially for soil specialist species. Our results show that canopy surface reflectance obtained by multispectral sensors can provide studies of tropical ecology, as exemplified by SDMs, much higher thematic (taxonomic) detail than is generally assumed. Furthermore, multispectral datasets complement the traditionally used climatic layers in analyses requiring information on environmental site conditions. We demonstrate the utility of freely available, global remote sensing data for biogeographical studies that can aid conservation planning and biodiversity management.

Comparing methods for the approximation of rainfall fields in environmental applications

Digital environmental data are becoming commonplace and the amount of information they provide is complex to process, due to the size, variety, and dynamic nature of the data captured by sensing devices. The paper discusses an evaluation framework for comparing methods to approximate observed rain data, in real conditions of sparsity of the observations. The novelty brought by this experimental study stands in the geographical area and heterogeneity of the data used for evaluation, aspects which challenge all approximation methods. The Liguria region, located in the north-west of Italy, is a complex area for the orography and the closeness to the sea, which cause complex hydro-meteorological events. The observed rain data are highly heterogeneous: two data sets come from measured rain gathered from two different rain gauge networks, with different characteristics and spatial distributions over the Liguria region; the third data set come from weather radar, with a more regular coverage of the same region but a different veracity. Finally, another novelty of the paper is brought by the proposal of an application-oriented perspective on the comparison. The approximation models the rain field, whose maxima and their evolution is essential for an effective monitoring of meteorological events. Therefore, we adapt a storm tracking technique to the analysis of the displacement of maxima computed by the different methods, used as a dissimilarity measure among the approximation methods analyzed.

Scientific Stewardship in the Open Data and Big Data Era — Roles and Responsibilities of Stewards and Other Major Product Stakeholders

Ensuring and improving quality and usability is an important part of scientific stewardship of digital environmental data products, but the roles of the responsible parties — those who manage quality and usability — have been evolving over time and have not always been clearly defined. Recognizing that in the Open Data and Big Data era, effective long-term scientific stewardship of data products requires an integrated and coordinated team effort of experts in multiple knowledge domains — data management, science, and technology — we introduce the following stewardship roles for each of these domains: data steward, scientific steward, and technology steward. This article defines their roles and high-level responsibilities as well as the responsibilities of other major product stakeholders, including data originators and distributors. Defining roles and formalizing responsibilities will facilitate the process of curating and communicating quality information to users. Clearly defined roles will allow effective cross-disciplinary communication and better resource allocation for data stewardship, supporting organizations in meeting the challenges of stewarding digital environmental data products in the Open Data and Big Data era.

A COMPARISON OF METHODS FOR THE APPROXIMATION AND ANALYSIS OF RAINFALL FIELDS IN ENVIRONMENTAL APPLICATIONS

Abstract. Digital environmental data are becoming commonplace and the amount of information they provide is huge, yet complex to process, due to the size, variety, and dynamic nature of the data captured by the available sensing devices. Making use of the data largely relies on the availability of efficient methods to extract meaningful information, and requires to process the environmental events at the speed data are acquired. This paper focuses on the evaluation of methods to approximate observed rain data, in real conditions of sparsity of the observations. The novelty stands in the selection of a particularly complex area, Liguria region, located in the north-west of Italy, where the orography and the closeness to the sea causes complex hydro-meteorological events. Approximation results are compared on a fine granularity in terms of cumulated rain interval used, gathered from two different rain gauge networks, with different characteristics and spatial distribution. Moreover, beside traditional cross-validation comparison, we provide a qualitative comparison based on the analysis of the number and location of maxima of the approximation. Rain maxima are indeed crucial features of rain fields needed for storm tracking, to support effective monitoring of meteorological events.

A Unified Framework for Measuring Stewardship Practices Applied to Digital Environmental Datasets

This paper presents a stewardship maturity assessment model in the form of a matrix for digital environmental datasets. Nine key components are identified based on requirements imposed on digital environmental data and information that are cared for and disseminated by U.S. Federal agencies by U.S. law, i.e., Information Quality Act of 2001, agencies’ guidance, expert bodies’ recommendations, and users. These components include: preservability, accessibility, usability, production sustainability, data quality assurance, data quality control/monitoring, data quality assessment, transparency/traceability, and data integrity. A five-level progressive maturity scale is then defined for each component associated with measurable practices applied to individual datasets, representing Ad Hoc, Minimal, Intermediate, Advanced, and Optimal stages. The rationale for each key component and its maturity levels is described. This maturity model, leveraging community best practices and standards, provides a unified framework for assessing scientific data stewardship. It can be used to create a stewardship maturity scoreboard of dataset(s) and a roadmap for scientific data stewardship improvement or to provide data quality and usability information to users, stakeholders, and decision makers.

Harnessing the world's biodiversity data: promise and peril in ecological niche modeling of species distributions

Recent advances allow harnessing enormous stores of biological and environmental data to model species niches and geographic distributions. Natural history museums hold specimens that represent the only information available for most species. Ecological niche models (sometimes termed species distribution models) combine such information with digital environmental data (especially climatic) to offer key insights for conservation biology, management of invasive species, zoonotic human diseases, and other pressing environmental problems. Five major pitfalls seriously hinder such research, especially for cross-space or cross-time uses: (1) incorrect taxonomic identifications; (2) lacking or inadequate databasing and georeferences; (3) effects of sampling bias across geography; (4) violation of assumptions related to selection of the study region; and (5) problems regarding model evaluation to identify optimal model complexity. Large-scale initiatives regarding data availability and quality, technological development, and capacity building should allow high-quality modeling on a scale commensurate with the enormous potential of and need for these techniques.

Corridor and Eis Studies Enter the CADD Age

Until recently, most major corridor studies and studies of environmental impact statements have been done using traditional manual methods. The major stumbling block to performing corridor and EIS studies cost‐effectively on computer‐aided design and drafting (CADD) has been that most of the base data was not available in a digital format compatible with most CADD systems. The only methods for inputting this base data were to either digitize or scan a large amount of data, each of which can be costly and time consuming. Now, however, a number of government agencies and private firms have been developing environmental and topographic data into a digital format resulting in cost effective data bases for corridor and EIS studies. As the technology and variety of data bases changes the way we think about our work, it will also change the way we organize resources to accomplish the work. This paper covers sources of digital environmental data, how the data can be used, basic procedures for performing corridor and EIS projects on CADD, and an example of how the Sverdrup Corporation is using CADD on planning projects.