A Matter of Perspective? The Impact of Analysis Configurations on Testing the Agenda-Setting Hypothesis

The media's capacity to stimulate public concern and create a common ground for issues can counteract the fragmentation of society. Assessing the intactness of the media's agenda-setting function can be an important diagnostic tool for scholars. However, the manifold design choices in agenda-setting research raise the question of how design choice impacts analysis results and potentially leads to methodological artefacts. I compare how the choice between 20 plausible analysis configurations impacts tests of the agenda-setting hypothesis, coefficients, and explanatory power. I also explore changes in agenda-setting effect size over time. I develop a typology of analysis configurations from five basic study design types by four ways of linking content analysis to survey data (5 × 4 = 20). The following design types are compared: three single-survey/between designs (aggregate-cross-sectional, aggregate-longitudinal, and individual-level) and two panel-survey/within designs (aggregate-change and individual-change). I draw on the German Longitudinal Election Study data (2009, 2013, and 2017). All 20 tests of the agenda-setting hypothesis support the hypothesis, independent of the analytical configuration used. The choice of analysis configuration substantially impacts the coefficients and explanatory power attributed to media salience. The individual-level analyses indicate that agenda-setting effects became significantly weaker at later elections, though not linearly. This study provides strong empirical support for the agenda-setting hypothesis independent of design choice

Self-Inflicted Deprivation? Quality-as-Sent and Quality-as-Received in German News Media

Both the news media and citizens have been blamed for citizens’ lack of political sophistication. Citizens’ information source choices can certainly contribute to suboptimal results of opinion formation when citizens’ media menus feature few, redundant, or poor-quality outlets. How strongly news consumers’ choices affect the quality of information they receive has rarely been investigated, however. The study uses a novel method investigating how content-as-sent translates into content-as-received that is applicable to high-choice information environments. It explores quality-as-sent and quality-as-received in a content analysis that is combined with survey data on news use. This study focuses on ‘selection quality’ measured in terms of scope and balance of subtopic units, information units, and protagonist statements sent/received. Regarding quality-as-sent, the scope of news proves to be lowest in TV news and substantially greater for online news and newspapers; imbalance of coverage varies only moderately between outlets. As for quality-as-received, the scope citizens received was only a small fraction of what the news outlets provided in combination or what the highest-quality news outlet provided, but was close to what one average news outlet provided. There was substantial stratification in the extent to which news coverage quality materializes at the recipient level. Scope-as-received grew mainly with using more news, relatively independent of which specific news outlets were used. Imbalance-as-received, however, was a function of the use of specific outlet types and specific outlets rather than the general extent of news use. Using additional news media improved the quality-as-received, invalidating the notion that different news outlets merely provide “more of the same.

Selection of Unlabeled Source Domains for Domain Adaptation in Remote Sensing

In the context of supervised learning techniques, it can be desirable to utilize existing prior knowledge from a source domain to estimate a target variable in a target domain by exploiting the concept of domain adaptation. This is done to alleviate the costly compilation of prior knowledge, i.e., training data. Here, our goal is to select a single source domain for domain adaptation from multiple potentially helpful but unlabeled source domains. The training data is solely obtained for a source domain if it was identified as being relevant for estimating the target variable in the corresponding target domain by a selection mechanism. From a methodological point of view, we propose unsupervised source selection by voting from (an ensemble of) similarity metrics that follow aligned marginal distributions regarding image features of source and target domains. Thereby, we also propose an unsupervised pruning heuristic to solely include robust similarity metrics in an ensemble voting scheme. We provide an evaluation of the methods by learning models from training data sets created with Level-of-Detail-1 building models and regress built-up density and height on Sentinel-2 satellite imagery. To evaluate the domain adaptation capability, we learn and apply models interchangeably for the four largest cities in Germany. Experimental results underline the capability of the methods to obtain more frequently higher accuracy levels with an improvement of up to almost 10 percentage points regarding the most robust selection mechanisms compared to random source-target domain selections

Automatic Training Set Compilation with Multisource Geodata for DTM Generation from the TanDEM-X DSM

The TanDEM-X mission (TDM) is a spaceborne radar interferometer which delivers a global digital surface model (DSM) with a spatial resolution of 0.4 arcsec. In this letter, we propose an automatic workflow for digital terrain model (DTM) generation from TDM DSM data through additional consideration of Sentinel-2 imagery and open-source geospatial vector data. The method includes the automatic and robust compilation of training samples by imposing dedicated criteria on the multisource geodata for subsequent learning of a classification model. The model is capable of supporting the accurate distinction of elevated objects (OBJ) and bare earth (BE) measurements in the TDM DSM. Finally, a DTM is interpolated from identified BE measurements. Experimental results obtained from a test site which covers a complex and heterogeneous built environment of Santiago de Chile, Chile, underline the usefulness of the proposed workflow, since it allows for substantially increased accuracies compared to a morphological filter-based method

Stability characterization of the response of white storks foraging behavior to vegetation dynamics retrieved from Landsat time series

Agricultural activities cause rapid changes in vegetation development at local and regional scales. Those modifications affect the small-scale behavior of animals, like the foraging ground usage of breeding white storks. Only recently, a novel approach, that enables to quantify the relationship between mowing and harvesting activities and a prolonged foraging time of storks by combining remote sensing time series with GPS telemetry, has been proposed. This study examines the stability of this approach. We investigate two potential influencing factors: different vegetation indices and time lags over which vegetation dynamics were retrieved. Mostly independent from the vegetation index and time lag, we observed that storks spent large proportions of foraging time in areas characterized by a recent drop in vegetation indices, indicative for a preferred usage after harvesting and mowing events. This suggest that the proposed approach is relatively stable and hence, provides a reasonable basis to investigate the effects of anthropogenic vegetation alterations on animal behavior at small spatiotemporal scales

New approaches for using satellite observations in numerical weather prediction

Satellite observations provide high-resolution information on the state of the atmosphere. This thesis examines two novel approaches for using satellite observations in numerical weather prediction. The primary observations used are provided by the SEVIRI instrument on EUMETSAT's geostationary MSG satellite. Forward operators are applied to the model output of Deutscher Wetterdienst's regional numerical weather forecasting system to generate synthetic visible and infrared satellite images. The first approach combines two complementary satellite channels providing a wealth of information to better understand the model representation of clouds. The second approach assimilates visible satellite observations for the improvement of model initial conditions and subsequent forecasts