Urban mobility demand profiles: Time series for cars and bike-sharing use as a resource for transport and energy modeling

The transport sector is currently facing a significant transition, with strong drivers including decarbonization and digitalization trends, especially in urban passenger transport. The availability of monitoring data is at the basis of the development of optimization models supporting an enhanced urban mobility, with multiple benefits including lower pollutants and CO2 emissions, lower energy consumption, better transport management and land space use. This paper presents two datasets that represent time series with a high temporal resolution (five-minute time step) both for vehicles and bike sharing use in the city of Turin, located in Northern Italy. These high-resolution profiles have been obtained by the collection and elaboration of available online resources providing live information on traffic monitoring and bike sharing docking stations. The data are provided for the entire year 2018, and they represent an interesting basis for the evaluation of seasonal and daily variability patterns in urban mobility. These data may be used for different applications, ranging from the chronological distribution of mobility demand, to the estimation of passenger transport flows for the development of transport models in urban contexts. Moreover, traffic profiles are at the basis for the modeling of electric vehicles charging strategies and their interaction with the power grid

Explicit diversification of event aspects for temporal summarization

During major events, such as emergencies and disasters, a large volume of information is reported on newswire and social media platforms. Temporal summarization (TS) approaches are used to automatically produce concise overviews of such events by extracting text snippets from related articles over time. Current TS approaches rely on a combination of event relevance and textual novelty for snippet selection. However, for events that span multiple days, textual novelty is often a poor criterion for selecting snippets, since many snippets are textually unique but are semantically redundant or non-informative. In this article, we propose a framework for the diversification of snippets using explicit event aspects, building on recent works in search result diversification. In particular, we first propose two techniques to identify explicit aspects that a user might want to see covered in a summary for different types of event. We then extend a state-of-the-art explicit diversification framework to maximize the coverage of these aspects when selecting summary snippets for unseen events. Through experimentation over the TREC TS 2013, 2014, and 2015 datasets, we show that explicit diversification for temporal summarization significantly outperforms classical novelty-based diversification, as the use of explicit event aspects reduces the amount of redundant and off-topic snippets returned, while also increasing summary timeliness

Technology for Good: Innovative Use of Technology by Charities

Technology for Good identifies ten technologies being used by charitable organizations in innovative ways. The report briefly introduces each technology and provides examples of how those technologies are being used.Examples are drawn from a broad spectrum of organizations working on widely varied issues around the globe. This makes Technology for Good a unique repository of inspiration for the public and private sectors, funders, and other change makers who support the creation and use of technology for social good

LEVERAGING PRIVATE DATA FOR PUBLIC GOOD: A Descriptive Analysis and Typology of Existing Practices

To address the challenges of our times, we need both new solutions and new ways to develop those solutions. Data will play a central role in this process. Yet, much of the most useful, timely and comprehensive data that could help transform the way we make decisions or solve public problems resides with the private sector in the form of call detail records, online purchases, sensor data, social media data, and other assets. If we truly want to harness the potential of data to improve people's lives, we need to understand and find ways to unlock and re-use this private data for public good.In what follows, we analyze the current practice of "data collaboratives," an emerging form of collaboration in which a private-sector entity's data is leveraged in partnership with other entities from the public sector, civil society or academia for public good. The GovLab coined the term "data collaborative" in 2015.The potential and realized contributions of data collaboratives stem from how the supply of and demand for data are widely dispersed—spread across government, the private sector, and civil society—and often poorly matched. While most commentary on the data era's shortcomings focuses on the potential misuse of data, one of the key challenges of our data age actually lies in a persistent failure to re-use data responsibly for public good. This failure results in tremendous inefficiencies and lost potential.Data collaboratives, when designed responsibly, are key to addressing this shortcoming. They draw together otherwise siloed data and a dispersed range of expertise, matching supply and demand and ensuring that relevant institutions and individuals are using and analyzing data in ways that maximize the possibility of new, innovative social solutions.While we have seen an uptake in normative discussions on how data should be shared, little analysis exists of the actual practice. Over the last few years, we have identified, curated and documented more than 150 data collaboratives deployed around the world to address societal challenges as varied as urban mobility, public health, and corruption. These cases are stored on our Data Collaboratives Explorer, the largest such repository on the topic.This paper seeks to answer the central question: What institutional arrangements and operational dynamics enable private-sector data holders to collaborate with external parties to create new public value

Hydrolink 4/2022. Citizen science

Topic: Citizen Scienc

Earth Observation Open Science and Innovation

geospatial analytics; social observatory; big earth data; open data; citizen science; open innovation; earth system science; crowdsourced geospatial data; citizen science; science in society; data scienc