Paper by Massimiliano Luca, Gian Maria Campedelli, Simone Centellegher, Michele Tizzoni, and Bruno Lepri: “Urban agglomerations are constantly and rapidly evolving ecosystems, with globalization and increasing urbanization posing new challenges in sustainable urban development well summarized in the United Nations’ Sustainable Development Goals (SDGs). The advent of the digital age generated by modern alternative data sources provides new tools to tackle these challenges with spatio-temporal scales that were previously unavailable with census statistics. In this review, we present how new digital data sources are employed to provide data-driven insights to study and track (i) urban crime and public safety; (ii) socioeconomic inequalities and segregation; and (iii) public health, with a particular focus on the city scale…(More)”.
Can Mobility of Care Be Identified From Transit Fare Card Data? A Case Study In Washington D.C.
Paper by Daniela Shuman, et al: “Studies in the literature have found significant differences in travel behavior by gender on public transit that are largely attributable to household and care responsibilities falling disproportionately on women. While the majority of studies have relied on survey and qualitative data to assess “mobility of care”, we propose a novel data-driven workflow utilizing transit fare card transactions, name-based gender inference, and geospatial analysis to identify mobility of care trip making. We find that the share of women travelers trip-chaining in the direct vicinity of mobility of care places of interest is 10% – 15% higher than men….(More)”.
How a small news site built an innovative data project to visualise the impact of climate change on Uruguay’s capital
Interview by Marina Adami: “La ciudad sumergida (The submerged city), an investigation produced by Uruguayan science and technology news site Amenaza Roboto, is one of the winners of this year’s Sigma Awards for data journalism. The project uses maps of the country’s capital, Montevideo, to create impressive visualisations of the impact sea level rises are predicted to have on the city and its infrastructure. The project is a first of its kind for Uruguay, a small South American country in which data journalism is still a novelty. It is also a good example of a way news outlets can investigate and communicate the disastrous effects of climate change in local communities.
I spoke to Miguel Dobrich, a journalist, educator and digital entrepreneur who worked on the project together with colleagues Gabriel Farías, Natalie Aubet and Nahuel Lamas, to find out what lessons other outlets can take from this project and from Amenaza Roboto’s experiments with analysing public data, collaborating with scientists, and keeping the focus on their communities….(More)”
Global Data Stewardship
On-line Course by Stefaan G. Verhulst: “Creating a systematic and sustainable data access program is critical for data stewardship. What you do with your data, how you reuse it, and how you make it available to the general public can help others reimagine what’s possible for data sharing and cross-sector data collaboration. In this course, instructor Stefaan Verhulst shows you how to develop and manage data reuse initiatives as a competent and responsible global data steward.
Following the insights of current research and practical, real-world examples, learn about the growing importance of data stewardship, data supply, and data demand to understand the value proposition and societal case for data reuse. Get tips on designing and implementing data collaboration models, governance framework, and infrastructure, as well as best practices for measuring, sunsetting, and supporting data reuse initiatives. Upon completing this course, you’ll be ready to start pushing your new skill set and continue your data stewardship learning journey….(More)”
Big data proves mobility is not gender-neutral
Blog by Ellin Ivarsson, Aiga Stokenberg and Juan Ignacio Fulponi: “All over the world, there is growing evidence showing that women and men travel differently. While there are many reasons behind this, one key factor is the persistence of traditional gender norms and roles that translate into different household responsibilities, different work schedules, and, ultimately, different mobility needs. Greater overall risk aversion and sensitivity to safety issues also play an important role in how women get around. Yet gender often remains an afterthought in the transport sector, meaning most policies or infrastructure investment plans are not designed to take into account the specific mobility needs of women.
The good news is that big data can help change that. In a recent study, the World Bank Transport team combined several data sources to analyze how women travel around the Buenos Aires Metropolitan Area (AMBA), including mobile phone signal data, congestion data from Waze, public transport smart card data, and data from a survey implemented by the team in early 2022 with over 20,300 car and motorcycle users.
Our research revealed that, on average, women in AMBA travel less often than men, travel shorter distances, and tend to engage in more complex trips with multiple stops and purposes. On average, 65 percent of the trips made by women are shorter than 5 kilometers, compared to 60 percent among men. Also, women’s hourly travel patterns are different, with 10 percent more trips than men during the mid-day off-peak hour, mostly originating in central AMBA. This reflects the larger burden of household responsibilities faced by women – such as picking children up from school – and the fact that women tend to work more irregular hours…(More)” See also Gender gaps in urban mobility.
Digital Equity 2.0: How to Close the Data Divide
Report by Gillian Diebold: “For the last decade, closing the digital divide, or the gap between those subscribing to broadband and those not subscribing, has been a top priority for policymakers. But high-speed Internet and computing device access are no longer the only barriers to fully participating and benefiting from the digital economy. Data is also increasingly essential, including in health care, financial services, and education. Like the digital divide, a gap has emerged between the data haves and the data have-nots, and this gap has introduced a new set of inequities: the data divide.
Policymakers have put a great deal of effort into closing the digital divide, and there is now near-universal acceptance of the notion that obtaining widespread Internet access generates social and economic benefits. But closing the data divide has received little attention. Moreover, efforts to improve data collection are typically overshadowed by privacy advocates’ warnings against collecting any data. In fact, unlike the digital divide, many ignore the data divide or argue that the way to close it is to collect vastly less data.1 But without substantial efforts to increase data representation and access, certain individuals and communities will be left behind in an increasingly data-driven world.
This report describes the multipronged efforts needed to address digital inequity. For the digital divide, policymakers have expanded digital connectivity, increased digital literacy, and improved access to digital devices. For the data divide, policymakers should similarly take a holistic approach, including by balancing privacy and data innovation, increasing data collection efforts across a wide array of fronts, enhancing access to data, improving data quality, and improving data analytics efforts. Applying lessons from the digital divide to this new challenge will help policymakers design effective and efficient policy and create a more equitable and effective data economy for all Americans…(More)”.
3 barriers to successful data collaboratives
Article by Federico Bartolomucci: “Data collaboratives have proliferated in recent years as effective means of promoting the use of data for social good. This type of social partnership involves actors from the private, public, and not-for-profit sectors working together to leverage public or private data to enhance collective capacity to address societal and environmental challenges. The California Data Collaborative for instance, combines the data of numerous Californian water managers to enhance data-informed policy and decision making.
But, in my years as a researcher studying more than a hundred cases of data collaboratives, I have observed widespread feelings of isolation among collaborating partners due to the absence of success-proven reference models. …Below, I provide an overview of three governance challenges faced by practitioners, as well as recommendations for addressing them. In doing so, I encourage every practitioner embarking on a data collaborative initiative to reflect on these challenges and create ad-hoc strategies to address them…
1. Overly relying on grant funding limits a collaborative’s options.
Data Collaboratives are typically conceived as not-for-profit projects, relying solely on grant funding from the founding partners. This is the case, for example, with TD1_Index, a global collaboration that seeks to gather data on Type 1 diabetes, raise awareness, and advance research on the topic. Although grant funding schemas work in some cases (like in that of T1D_Index), relying solely on grant funding makes a data collaborative heavily dependent on the willingness of one or more partners to sustain its activities and hinders its ability to achieve operational and decisional autonomy.
Operational and decisional autonomy indeed appears to be a beneficial condition for a collaborative to develop trust, involve other partners, and continuously adapt its activities and structure to external events—characteristics required for operating in a highly innovative sector.
Hybrid business models that combine grant funding with revenue-generating activities indicate a promising evolutionary path. The simplest way to do this is to monetize data analysis and data stewardship services. The ActNow Coalition, a U.S.-based not-for-profit organization, combines donations with client-funded initiatives in which the team provides data collection, analysis, and visualization services. Offering these types of services generates revenues for the collaborative and gaining access to them is among the most compelling incentives for partners to join the collaboration.
In studying data collaboratives around the world, two models emerge as most effective: (1) pay-per-use models, in which collaboration partners can access data-related services on demand (see Civity NL and their project Sniffer Bike) and (2) membership models, in which participation in the collaborative entitles partners to access certain services under predefined conditions (see the California Data Collaborative).
2. Demonstrating impact is key to a collaborative’s survival.
As partners’ participation in data collaboratives is primarily motivated by a shared social purpose, the collaborative’s ability to demonstrate its efficacy in achieving its purpose means being able to defend its raison d’être. Demonstrating impact enables collaboratives to retain existing partners, renew commitments, and recruit new partners…(More)”.
Data Sharing Between Public and Private Sectors: When Local Governments Seek Information from the Sharing Economy.
Paper by the Centre for Information Policy Leadership: “…addresses the growing trend of localities requesting (and sometimes mandating) that data collected by the private sector be shared with the localities themselves. Such requests are generally not in the context of law enforcement or national security matters, but rather are part of an effort to further the public interest or promote a public good.
To the extent such requests are overly broad or not specifically tailored to the stated public interest, CIPL believes that the public sector’s adoption of accountability measures—which CIPL has repeatedly promoted for the private sector—can advance responsible data sharing practices between the two sectors. It can also strengthen the public’s confidence in data-driven initiatives that seek to improve their communities…(More)”.
Spatial data trusts: an emerging governance framework for sharing spatial data
Paper by Nenad Radosevic et al: “Data Trusts are an important emerging approach to enabling the much wider sharing of data from many different sources and for many different purposes, backed by the confidence of clear and unambiguous data governance. Data Trusts combine the technical infrastructure for sharing data with the governance framework of a legal trust. The concept of a data Trust applied specifically to spatial data offers significant opportunities for new and future applications, addressing some longstanding barriers to data sharing, such as location privacy and data sovereignty. This paper introduces and explores the concept of a ‘spatial data Trust’ by identifying and explaining the key functions and characteristics required to underpin a data Trust for spatial data. The work identifies five key features of spatial data Trusts that demand specific attention and connects these features to a history of relevant work in the field, including spatial data infrastructures (SDIs), location privacy, and spatial data quality. The conclusions identify several key strands of research for the future development of this rapidly emerging framework for spatial data sharing…(More)”.
Unlocking the Power of Data Refineries for Social Impact
Essay by Jason Saul & Kriss Deiglmeier: “In 2021, US companies generated $2.77 trillion in profits—the largest ever recorded in history. This is a significant increase since 2000 when corporate profits totaled $786 billion. Social progress, on the other hand, shows a very different picture. From 2000 to 2021, progress on the United Nations Sustainable Development Goals has been anemic, registering less than 10 percent growth over 20 years.
What explains this massive split between the corporate and the social sectors? One explanation could be the role of data. In other words, companies are benefiting from a culture of using data to make decisions. Some refer to this as the “data divide”—the increasing gap between the use of data to maximize profit and the use of data to solve social problems…
Our theory is that there is something more systemic going on. Even if nonprofit practitioners and policy makers had the budget, capacity, and cultural appetite to use data; does the data they need even exist in the form they need it? We submit that the answer to this question is a resounding no. Usable data doesn’t yet exist for the sector because the sector lacks a fully functioning data ecosystem to create, analyze, and use data at the same level of effectiveness as the commercial sector…(More)”.