Studying the Impacts of Environmental Amenities and Hazards with Nationwide Property Data: Best Data Practices for Interpretable and Reproducible Analyses

2021 ◽  
Author(s):  
Christoph Nolte ◽  
Kevin J Boyle ◽  
Anita M Chaudhry ◽  
Christopher Clapp ◽  
Dennis Guignet ◽  
...  
Keyword(s):  
Environmental Amenities ◽  
Data Practices ◽  
Property Data

Welfare State Values and Public Service Media in the Era of Datafication

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Kaarina Nikunen ◽  
Jenni Hokka
Keyword(s):  
Public Service ◽  
Well Being ◽  
Welfare States ◽  
Social Solidarity ◽  
Media Environment ◽  
The Public ◽  
Media Companies ◽  
Data Practices ◽  
Public Media ◽  
New Public

Welfare states have historically been built on values of egalitarianism and universalism and through high taxation that provides free education, health care, and social security for all. Ideally, this encourages participation of all citizens and formation of inclusive public sphere. In this welfare model, the public service media are also considered some of the main institutions that serve the well-being of an entire society. That is, independent, publicly funded media companies are perceived to enhance equality, citizenship, and social solidarity by providing information and programming that is driven by public rather than commercial interest. This article explores how the public service media and their values of universality, equality, diversity, and quality are affected by datafication and a platformed media environment. It argues that the embeddedness of public service media in a platformed media environment produces complex and contradictory dependencies between public service media and commercial platforms. The embeddedness has resulted in simultaneous processes of adapting to social media logics and datafication within public service media as well as in attempts to create alternative public media value-driven data practices and new public media spaces.

PROMOTING FAIR DATA PRACTICES FOR THE GEOSCIENCES WITH JUPYTER NOTEBOOKS

2018 ◽  
Author(s):  
Kevin P. Russell ◽  
Keyword(s):  
Data Practices

scholarly journals Big data for climate action or climate action for big data?

2021 ◽  
Vol 8 (1) ◽  
pp. 205395172098203
Author(s):  
Maria I Espinoza ◽  
Melissa Aronczyk
Keyword(s):  
Big Data ◽  
Environmental Sustainability ◽  
Global Climate ◽  
Intergovernmental Organizations ◽  
Address Climate Change ◽  
Data Practices ◽  
Climate Action ◽  
User Data ◽  
Climate Crisis ◽  
Depth Interviews

Under the banner of “data for good,” companies in the technology, finance, and retail sectors supply their proprietary datasets to development agencies, NGOs, and intergovernmental organizations to help solve an array of social problems. We focus on the activities and implications of the Data for Climate Action campaign, a set of public–private collaborations that wield user data to design innovative responses to the global climate crisis. Drawing on in-depth interviews, first-hand observations at “data for good” events, intergovernmental and international organizational reports, and media publicity, we evaluate the logic driving Data for Climate Action initiatives, examining the implications of applying commercial datasets and expertise to environmental problems. Despite the increasing adoption of Data for Climate Action paradigms in government and public sector efforts to address climate change, we argue Data for Climate Action is better seen as a strategy to legitimate extractive, profit-oriented data practices by companies than a means to achieve global goals for environmental sustainability.

scholarly journals Evaluating the Impact of Floods on Housing Price Using a Spatial Matching Difference-In-Differences (SM-DID) Approach

2021 ◽  
Vol 13 (2) ◽  
pp. 804
Author(s):  
Jean Dubé ◽  
Maha AbdelHalim ◽  
Nicolas Devaux
Keyword(s):  
Housing Price ◽  
Hedonic Pricing ◽  
Environmental Amenities ◽  
Sale Price ◽  
Difference In Differences ◽  
Hedonic Pricing Model ◽  
The Difference ◽  
And Control ◽  
The Impact ◽  
The City

Many applications have relied on the hedonic pricing model (HPM) to measure the willingness-to-pay (WTP) for urban externalities and natural disasters. The classic HPM regresses housing price on a complete list of attributes/characteristics that include spatial or environmental amenities (or disamenities), such as floods, to retrieve the gradients of the market (marginal) WTP for such externalities. The aim of this paper is to propose an innovative methodological framework that extends the causal relations based on a spatial matching difference-in-differences (SM-DID) estimator, and which attempts to calculate the difference between sale price for similar goods within “treated” and “control” groups. To demonstrate the potential of the proposed spatial matching method, the researchers present an empirical investigation based on the case of a flood event recorded in the city of Laval (Québec, Canada) in 1998, using information on transactions occurring between 1995 and 2001. The research results show that the impact of flooding brings a negative premium on the housing price of about 20,000$ Canadian (CAN).

scholarly journals Thermal and Transport Properties of Molten Chloride Salts with Polarization Effect on Microstructure

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 746
Author(s):  
Jianfeng Lu ◽  
Senfeng Yang ◽  
Gechuanqi Pan ◽  
Jing Ding ◽  
Shule Liu ◽  
...  
Keyword(s):  
Heat Capacity ◽  
Polarization Effect ◽  
Chloride Salt ◽  
Coulombic Interaction ◽  
Molten Chloride ◽  
Self Diffusion ◽  
Local Structures ◽  
Property Data ◽  
Chloride Salts ◽  
And Storage

Molten chloride salt is recognized as a promising heat transfer and storage medium in concentrating solar power in recent years, but there is a serious lack for thermal property data of molten chloride salts. In this work, local structures and thermal properties for molten chloride salt—including NaCl, MgCl2, and ZnCl2—were precisely simulated by Born–Mayer–Huggins (BMH) potential in a rigid ion model (RIM) and a polarizable ion model (PIM). Compared with experimental data, distances between cations, densities, and heat capacities of molten chloride slats calculated from PIM agree remarkably better than those from RIM. The polarization effect brings an extra contribution to screen large repulsive Coulombic interaction of cation–cation, and then it makes shorter distance between cations, larger density and lower heat capacity. For NaCl, MgCl2, and ZnCl2, PIM simulation deviations of distances between cations are respectively 3.8%, 3.7%, and 0.3%. The deviations of density and heat capacity for NaCl between PIM simulation and experiments are only 0.6% and 2.2%, and those for MgCl2 and ZnCl2 are 0.7–10.7%. As the temperature rises, the distance between cations increases and the structure turns into loose state, so the density and thermal conductivity decrease, while the ionic self-diffusion coefficient increases, which also agree well with the experimental results.

scholarly journals Data diaries: A situated approach to the study of data

2021 ◽  
Vol 8 (1) ◽  
pp. 205395172199603
Author(s):  
Nathaniel Tkacz ◽  
Mário Henrique da Mata Martins ◽  
João Porto de Albuquerque ◽  
Flávio Horita ◽  
Giovanni Dolif Neto
Keyword(s):  
Natural Hazards ◽  
Empirical Analysis ◽  
Ways Of Knowing ◽  
Data Intensive ◽  
Related Data ◽  
Data Practices ◽  
National Centre ◽  
The Creation ◽  
Situated Approach

This article adapts the ethnographic medium of the diary to develop a method for studying data and related data practices. The article focuses on the creation of one data diary, developed iteratively over three years in the context of a national centre for monitoring disasters and natural hazards in Brazil (Cemaden). We describe four points of focus involved in the creation of a data diary – spaces, interfaces, types and situations – before reflecting on the value of this method. We suggest data diaries (1) are able to capture the informal dimension of data-intensive organisations; (2) enable empirical analysis of the specific ways that data intervene in the unfolding of situations; and (3) as a document, data diaries can foster interdisciplinary and inter-expert dialogue by bridging different ways of knowing data.

scholarly journals A Comparative Study of Various Methods of Handling Missing Data in UNSODA

Agriculture ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 727
Author(s):  
Yingpeng Fu ◽  
Hongjian Liao ◽  
Longlong Lv
Keyword(s):  
Missing Data ◽  
Missing Values ◽  
Soil Property ◽  
Particle Density ◽  
Organic Matter Content ◽  
Nonparametric Tests ◽  
Matter Content ◽  
Support Vector ◽  
Soil Database ◽  
Property Data

UNSODA, a free international soil database, is very popular and has been used in many fields. However, missing soil property data have limited the utility of this dataset, especially for data-driven models. Here, three machine learning-based methods, i.e., random forest (RF) regression, support vector (SVR) regression, and artificial neural network (ANN) regression, and two statistics-based methods, i.e., mean and multiple imputation (MI), were used to impute the missing soil property data, including pH, saturated hydraulic conductivity (SHC), organic matter content (OMC), porosity (PO), and particle density (PD). The missing upper depths (DU) and lower depths (DL) for the sampling locations were also imputed. Before imputing the missing values in UNSODA, a missing value simulation was performed and evaluated quantitatively. Next, nonparametric tests and multiple linear regression were performed to qualitatively evaluate the reliability of these five imputation methods. Results showed that RMSEs and MAEs of all features fluctuated within acceptable ranges. RF imputation and MI presented the lowest RMSEs and MAEs; both methods are good at explaining the variability of data. The standard error, coefficient of variance, and standard deviation decreased significantly after imputation, and there were no significant differences before and after imputation. Together, DU, pH, SHC, OMC, PO, and PD explained 91.0%, 63.9%, 88.5%, 59.4%, and 90.2% of the variation in BD using RF, SVR, ANN, mean, and MI, respectively; and this value was 99.8% when missing values were discarded. This study suggests that the RF and MI methods may be better for imputing the missing data in UNSODA.

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