Official contact tracing apps have been implemented and recommended for use across nations to track and contain the spread of COVID-19. Such apps can be effective if people are willing to use them. Accordingly, many attempts are being made to motivate citizens to make use of the officially recommended apps. The present research sought to contribute to an understanding of the preconditions under which people are willing to use this app (i.e., their use intentions and actual use). To go beyond personal motives in favor of app use, it takes people’s social relationships into account; doing so, it argues that the more people identify with the beneficiaries of app use (i.e., people living close by in their social environment) and with the source recommending the app (i.e., members of the government), the more likely they will be to accept the officially recommended contact tracing app. Before, right after, and five months after the official contact tracing app was launched in Germany, a total of 1044 people participated in three separate studies. Structural equation modeling tested and supported the hypotheses, examining the same model in all studies at these critical points in time.

Introduction

Welcome to the German Language Visual Speech Dataset! This dataset is a collection of diverse, single-person unscripted spoken videos supporting research in visual speech recognition, emotion detection, and multimodal communication.

Dataset Content

This visual speech dataset contains 1000 videos in German language each paired with a corresponding high-fidelity audio track. Each participant is answering a specific question in a video in an unscripted and spontaneous nature.

Video Data

While recording each video extensive guidelines are kept in mind to maintain the quality and diversity.

Metadata

The dataset provides comprehensive metadata for each video recording and participant:

This Location Data & Foot traffic dataset available for all countries include enriched raw mobility data and visitation at POIs to answer questions such as:

-How often do people visit a location? (daily, monthly, absolute, and averages). -What type of places do they visit ? (parks, schools, hospitals, etc) -Which social characteristics do people have in a certain POI? - Breakdown by type: residents, workers, visitors. -What's their mobility like enduring night hours & day hours?
-What's the frequency of the visits partition by day of the week and hour of the day?

Extra insights -Visitors´ relative income Level. -Visitors´ preferences as derived by their visits to shopping, parks, sports facilities, churches, among others.

Overview & Key Concepts Each record corresponds to a ping from a mobile device, at a particular moment in time and at a particular latitude and longitude. We procure this data from reliable technology partners, which obtain it through partnerships with location-aware apps. All the process is compliant with applicable privacy laws.

We clean and process these massive datasets with a number of complex, computer-intensive calculations to make them easier to use in different data science and machine learning applications, especially those related to understanding customer behavior.

Featured attributes of the data Device speed: based on the distance between each observation and the previous one, we estimate the speed at which the device is moving. This is particularly useful to differentiate between vehicles, pedestrians, and stationery observations.

Night base of the device: we calculate the approximated location of where the device spends the night, which is usually their home neighborhood.

Day base of the device: we calculate the most common daylight location during weekdays, which is usually their work location.

Income level: we use the night neighborhood of the device, and intersect it with available socioeconomic data, to infer the device’s income level. Depending on the country, and the availability of good census data, this figure ranges from a relative wealth index to a currency-calculated income.

POI visited: we intersect each observation with a number of POI databases, to estimate check-ins to different locations. POI databases can vary significantly, in scope and depth, between countries.

Category of visited POI: for each observation that can be attributable to a POI, we also include a standardized location category (park, hospital, among others). Coverage: Worldwide.

Delivery schemas We can deliver the data in three different formats:

Full dataset: one record per mobile ping. These datasets are very large, and should only be consumed by experienced teams with large computing budgets.

Visitation stream: one record per attributable visit. This dataset is considerably smaller than the full one but retains most of the more valuable elements in the dataset. This helps understand who visited a specific POI, characterize and understand the consumer's behavior.

Audience profiles: one record per mobile device in a given period of time (usually monthly). All the visitation stream is aggregated by category. This is the most condensed version of the dataset and is very useful to quickly understand the types of consumers in a particular area and to create cohorts of users.

Our dataset gives access to the most precise data thanks to the power of our advanced algorithms. We use massive, precise and representative geolocation data from mobile applications that we aggregate, standardize and couple with manual counts to offer the most reliable analysis. This data product contains footfall data as well as shopping center names, city, postal code and geographies for shopping centers in Belgium / England / France / Germany / Italy / Netherlands / Spain, over the past several years. Use Cases: Foot Traffic Analytics Foot Traffic Analytics Territory Planning Gain detailed insights into pedestrian traffic across diverse locations, such as addresses, shopping centers, and shopping areas, to make strategic decisions for your location strategy. Identify high-traffic areas to optimize site selection and expansion plans. Competition Analytics Benchmark footfall within the shopping centers of your competitors, enabling informed business decisions. Understand competitor performance and identify opportunities for market share growth by analyzing comparative traffic patterns. Marketing Targeting Enhance your marketing strategies by analyzing footfall data to identify high-traffic areas and peak times. Target your marketing and promotional efforts more effectively by understanding where and when to reach your audience, maximizing engagement and conversion rates.. Urban and Infrastructure Planning Support urban and infrastructure planning by providing data on pedestrian traffic flows. Help city planners and developers design more efficient public spaces, transportation hubs, and commercial areas by understanding how people move through different environments.