The Tuntang Watershed is an important watershed in Central Java. Management of watersheds in the Tuntang stream is a priority for various parties to carry out. One of the things that threatens the sustainability of the Tuntang watershed is erosion. The erosion rate can lead to sediment accumulation and siltation in the Tuntang River reservoir, which can cause catastrophic flooding. Flood disaster mitigation caused by erosion needs to be done, one of which is by calculating the erosion rate per year that occurs in the Tuntang watershed. This study calcultated the predicted erosion rate (per year in the Tuntang watershed) using the Revised Universal Soil Loss Equation (RUSLE) method, processed using the Google Earth Engine (GEE). Google offers a cloud-storage technology called GEE. Programming in JavaScript is required to operate GEE. GEE is a petabyte-scale data-based tool that can be used to analyze and archive geospatial data that is open source. The computing environment is designed for the processing of geospatial data, including the depiction of spatial analysis of satellite imagery. Data for RUSLE is obtained from the database in GEE, and the results can be imaged on a map. According to the study's findings, the degree of soil erosion throughout the Tuntang Watershed was essentially constant, with Moderate erosion predominating in the majority of locations. Senjoyo Sub Watershed, Rowopening Sub Watershed, and Tuntang Hilir Sub Watershed are the primary locations with severe erosion. Rowopening Sub Watershed is the region that is the worst.
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In the literature about web survey methodology, significant e orts have been made to understand the role of time-invariant factors (e.g. gender, education and marital status) in (non-)response mechanisms. Time-invariant factors alone, however, cannot account for most variations in (non-)responses, especially fluctuations of response rates over time. This observation inspires us to investigate the counterpart of time-invariant factors, namely time-varying factors and the potential role they play in web survey (non-)response. Specifically, we study the effects of time, weather and societal trends (derived from Google Trends data) on the daily (non-)response patterns of the 2016 and 2017 Dutch Health Surveys. Using discrete-time survival analysis, we find, among others, that weekends, holidays, pleasant weather, disease outbreaks and terrorism salience are associated with fewer responses. Furthermore, we show that using these variables alone achieves satisfactory prediction accuracy of both daily and cumulative response rates when the trained model is applied to future unseen data. This approach has the further benefit of requiring only non-personal contextual information and thus involving no privacy issues. We discuss the implications of the study for survey research and data collection.
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When Google sold 3D geo-modeling software Sketch-up, a dedicated community of Google Earth developers were left behind. Is this a case of digital labor and exploitation or just an agreement based on mutual consent that ended, like relationships so often do?
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Door programma's als Google Earth zijn we gewend geraakt aan het perspectief van de satelliet, de god’s eye view. Wat doet dit met de relatie die we hebben tot onze omgeving? Door gebruik te maken van verschillende Google-applicaties reflecteren kunstenaars op de veranderende verhouding tot de buitenwereld.
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Pokémon Go, Facebook check-ins, Google Maps, public transport apps and especially smartphone apps are increasingly becoming traceable and locatable. As ‘check-in’, features in social media and games grow in popularity they pinpoint users in relation to everything else in the network, making physical context an essential input for online interactions. But what are the practical consequences of the increased proliferation of devices that can determine our location? Could one say that surveillance is already taken for granted as we passively provide our coordinates to others?
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