Every city is—quoting Plato—divided into two, one city of the poor, the other of the rich. In this study we test whether the economic urban divide is reflected in the digital sphere of cities. Because, especially in dynamically growing cities, ready-to-use comprehensive data sets on the urban poor, as well as on the digital divide, are not existent, we use proxies: we spatially delimit the urban poor using settlement characteristics derived from remote sensing data. The digital divide is targeted by geolocated Twitter data. Based on a sample of eight cities across the globe, we spatially test whether areas of the urban poor are more likely to be digital cold spots. Over the course of time, we analyze whether temporal signatures in poor urban areas differ from formal environments. We find that the economic divide influences digital participation in public life. Less residents of morphological slums are found to be digitally oriented (“are digitally left behind”) as compared to residents of formal settlements. However, among the few twitter users in morphological slums, we find their temporal behavior similar to the twitter users in formal settlements. In general, we conclude this discussion, this study exemplifies that the combination of both heterogeneous data sets allows for extending the capabilities of individual disciplines for research towards urban poverty.

Background: In modern societies, noise is ubiquitous. It is an annoyance and can have a negative impact on human health as well as on the environment. Despite increasing evidence of its negative impacts, spatial knowledge about noise distribution remains limited. Up to now, noise mapping is frequently inhibited by the necessary resources and therefore limited to selected areas.Objective: Based on the assumption, that prevalent noise is determined by the arrangement of sources and the surrounding environment in which the sound propagates, we build a geostatistical model representing these parameters. Aiming for a large-scale noise mapping approach, we utilize publicly available data, context-aware feature engineering and a linear land-use regression (LUR) model.Methods: Compliant to the European Noise Directive 2002/49/EG, we work at a high spatial granularity of 10 × 10-m resolution. As reference, we use the day–evening–night noise level indicatorL_den. Therewith, we carry out 2000 virtual field campaigns simulating different sampling schemes and introduce spatial cross-validation concepts to test the transferability to new areas.Results: The experimental results suggest the necessity for more than 500 samples stratified over the different noise levels to produce a representative model. Eventually, using 21 selected variables, our model was able to explain large proportions of the yearly averaged road noise (L_den) variability (R² = 0.702) with a mean absolute error of 4.24 dB(A), 3.84 dB(A) for build-up areas, respectively. In applying this best performing model for an area-wide prediction, we spatially close the blank spots in existing noise maps with continuous noise levels for the entire range from 24 to 106 dB(A).Significance: This data is new, particular for small communities that have not been mapped sufficiently in Europe so far. In conjunction, our findings also supplement conventionally sampled studies using physical microphones and spatially blocked cross-validations.

The ecological validity of soundscape research relies, among other issues, on the selection of measurement sites that are contextually significant to the space under investigation. Traditional site selection methods in environmental studies, while insightful, often lack a structured approach, leading to inconsistency and potential bias in identifying optimal sites for acoustic measurements. To overcome this, we present a novel data-driven approach using Enclosed Tessellation (ET) and unsupervised learning. Our method classifies urban areas to identify locations that best capture the diversity of environmental and acoustic conditions based on urban form, function, and environmental and topographic variables across a city-region. We use purposive sampling to locate these most appropriate sites for acoustic measurements adapted to specific research questions. This approach not only ensures a meaningful range of environmental conditions but also increases the reliability and validity of research findings, thereby improving our understanding of urban health and well-being.

Motorized traffic often causes road noise directly in front of our homes and windows. Yet long-term exposure to noise impact life's quality and can potentially cause negative effects on human health. Furthermore, social and behavioral effects have been measured. To protect people's health and well-being from such noise, the European Noise Directive (END, 2002/49/EC) obliges countries to produce strategic noise maps every five years for large agglomerations and along major roads, which are then used for noise action planning. Besides that, the official noise maps are a valuable data source for environmental exposure analyses. However, the END has some limitations. The definition of urban agglomerations is vague, different input parameterizations lead to data inconsistencies across administrative units, undefined post processing methods introduce geometric artifacts, and topological errors incompliant to the common Simple Features Implementation Specification hinder working with the published geodata. The aim of this article is to provide practical insights for end-users and stipulate for concise regulations. Moreover, we highlight that these variations limit the comparability of maps in environmental impact assessments. We compile 84 separate noise assessments in Germany reported according to the END to review shape and structure of the geographic data. Graphical representations are used to show in particular how vertices are connected to polygons in noise contour maps and that these geometric alterations effect the eventual statistics on exposed population shares. We aggregate spatial metrics to assess the reported data's spatial properties in an automatic manner, e.g. when receiving data in future mapping rounds. Along with our quality assessment, a nation-wide dataset on road traffic noise was produced. Depicting the yearly averaged noise level indicator L_den, which integrates exposure at day, evening and night, for 2017, it serves as common ground for environmental health analyses. The examination of different raster to polygon conversion implementations is fundamental to other geodata managers outside the domain of noise mapping, as well.