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Mapping ‘Women in Technology' Issue Networks across Bulgarian, Croatian, and Serbian National Google(s)

This article explores the intersection between women and technology with an experimental research design that uses online search engine data and digital methods (Rogers 2002, 2004, 2013). We respond to Blagojevic’s (2016) call for online mapping of gender equality stakeholders by incorporating the practice of ‘issue mapping’, which Rogers et al. (2015) conceptualise as a series of techniques that can be used to map the network of actors around a public issue, and to understand the ways they associate with one another. Specifically, we apply the software tool IssueCrawler and its co-link analysis of relevant queries to study national Google search result pages for Bulgaria, Croatia and Serbia. We ask, what types of stakeholders are prevalent around the topic of ‘women in technology’ in the local contexts (demarcated by the national Google result pages) of these three countries? Are they country-specific or do they cross national borders? To what extent do they associate with each other? Which actors are in the centre of the identified networks and which are on the periphery? The authors found that the issue networks of all three countries were heavily dominated by media and government actors, followed by business, entrepreneurial and nongovernmental sites, and websites containing information on EU grants. The national specificity, however, was mostly embedded in the groupings of these actors; whether they were densely or loosely interlinked with each other, and whether they were present or absent from the maps.

Branding and Google Trends: Software companies’ names, the metaverse and virtual reality.

Probability of Erosion Utilizing Google Earth Engine and the RUSLE Method in the Tuntang Watershed

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.

The role of time, weather and Google Trends in understanding and predicting web survey response

In the literature about web survey methodology, significant eorts 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.

Google Earth, surveillance, and the power of digital cartography

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?

I, (for one), welcome our new (Google) overlords

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?