How Integrated Data Can Support COVID-19 Crisis and Recovery

Curated on Posted on by Stefaan Verhulst

Blog by Actionable Intelligence for Social Policy (AISP): “…State and local leaders are called upon to respond to the immediate harms of COVID-19. Yet with a looming recession threatening to undo gains among marginalized groups — particularly the Black middle class — tools to understand and disrupt long-term impacts on economic mobility and well-being are also urgently needed.

Administrative data[3] — the information collected during the course of routine service delivery, program administration, and business operations — provide an essential tool to help policymakers, community leaders, and researchers understand short- and long-term impacts of the pandemic. Several jurisdictions now have the capacity to link administrative data across programs in order to better understand how individuals interact with multiple systems, study longitudinal outcomes, and identify vulnerable subpopulations. As the COVID-19 crisis reveals weaknesses in the U.S. social safety net, states and localities with integrated administrative data infrastructure can use their capacity to identify populations and needs otherwise overlooked. Youth who “age out” of the child welfare system or individuals experiencing chronic homelessness often remain invisible when using traditional methods, aggregate data, or administrative records from a single source.

This blogpost demonstrates how nimble state and local data integration efforts have leveraged their capacity to quickly respond to and understand the impacts of COVID-19, while also reflecting on what can be done to mitigate harm and shift thinking about social welfare and the safety net….(More)”.

Encouraging Participation and Cooperation in Contact Tracing

Curated on Posted on by Stefaan Verhulst

Lessons from Survey Research by National Academies of Sciences, Engineering, and Medicine: “Contact tracing shares important features with the collection of survey data, as well as the taking of the U.S. Census. This rapid expert consultation suggests proven strategies from survey research that decision makers can use to encourage participation in and cooperation with contact tracing efforts along two fronts: encouraging individuals to respond to outreach from health department officials regarding participation in contact tracing and case investigation, and encouraging those who do participate to share information about people whom they may have exposed to COVID-19.

Encouraging Participation and Cooperation in Contact Tracing is intended to help decision makers in local public health departments and local governments increase participation and cooperation in contact tracing related to COVID-19. This publication focuses on contact tracing methods that involve phone, text, or email interviews with people who have tested positive and with others they may have exposed to the virus…(More)”.

The Broken Algorithm That Poisoned American Transportation

Curated on Posted on by Stefaan Verhulst

Aaron Gordon at Vice: “…The Louisville highway project is hardly the first time travel demand models have missed the mark. Despite them being a legally required portion of any transportation infrastructure project that gets federal dollars, it is one of urban planning’s worst kept secrets that these models are error-prone at best and fundamentally flawed at worst.

Recently, I asked Renn how important those initial, rosy traffic forecasts of double-digit growth were to the boondoggle actually getting built.

“I think it was very important,” Renn said. “Because I don’t believe they could have gotten approval to build the project if they had not had traffic forecasts that said traffic across the river is going to increase substantially. If there isn’t going to be an increase in traffic, how do you justify building two bridges?”

ravel demand models come in different shapes and sizes. They can cover entire metro regions spanning across state lines or tackle a small stretch of a suburban roadway. And they have gotten more complicated over time. But they are rooted in what’s called the Four Step process, a rough approximation of how humans make decisions about getting from A to B. At the end, the model spits out numbers estimating how many trips there will be along certain routes.

As befits its name, the model goes through four steps in order to arrive at that number. First, it generates a kind of algorithmic map based on expected land use patterns (businesses will generate more trips than homes) and socio-economic factors (for example, high rates of employment will generate more trips than lower ones). Then it will estimate where people will generally be coming from and going to. The third step is to guess how they will get there, and the fourth is to then plot their actual routes, based mostly on travel time. The end result is a number of how many trips there will be in the project area and how long it will take to get around. Engineers and planners will then add a new highway, transit line, bridge, or other travel infrastructure to the model and see how things change. Or they will change the numbers in the first step to account for expected population or employment growth into the future. Often, these numbers are then used by policymakers to justify a given project, whether it’s a highway expansion or a light rail line…(More)”.

AI technologies — like police facial recognition — discriminate against people of colour

Curated on Posted on by Stefaan Verhulst

Jane Bailey et al at The Conversation: “…In his game-changing 1993 book, The Panoptic Sort, scholar Oscar Gandy warned that “complex technology [that] involves the collection, processing and sharing of information about individuals and groups that is generated through their daily lives … is used to coordinate and control their access to the goods and services that define life in the modern capitalist economy.” Law enforcement uses it to pluck suspects from the general public, and private organizations use it to determine whether we have access to things like banking and employment.

Gandy prophetically warned that, if left unchecked, this form of “cybernetic triage” would exponentially disadvantage members of equality-seeking communities — for example, groups that are racialized or socio-economically disadvantaged — both in terms of what would be allocated to them and how they might come to understand themselves.

Some 25 years later, we’re now living with the panoptic sort on steroids. And examples of its negative effects on equality-seeking communities abound, such as the false identification of Williams.

Pre-existing bias

This sorting using algorithms infiltrates the most fundamental aspects of everyday life, occasioning both direct and structural violence in its wake.

The direct violence experienced by Williams is immediately evident in the events surrounding his arrest and detention, and the individual harms he experienced are obvious and can be traced to the actions of police who chose to rely on the technology’s “match” to make an arrest. More insidious is the structural violence perpetrated through facial recognition technology and other digital technologies that rate, match, categorize and sort individuals in ways that magnify pre-existing discriminatory patterns.

Structural violence harms are less obvious and less direct, and cause injury to equality-seeking groups through systematic denial to power, resources and opportunity. Simultaneously, it increases direct risk and harm to individual members of those groups.

Predictive policing uses algorithmic processing of historical data to predict when and where new crimes are likely to occur, assigns police resources accordingly and embeds enhanced police surveillance into communities, usually in lower-income and racialized neighbourhoods. This increases the chances that any criminal activity — including less serious criminal activity that might otherwise prompt no police response — will be detected and punished, ultimately limiting the life chances of the people who live within that environment….(More)”.

Where are there gaps in gender data in five Latin American and Caribbean countries?

Curated on Posted on by Stefaan Verhulst

Data2X: “This report builds on our 2019 technical report, Bridging the Gap: Mapping Gender Data Availability in Africa, but shifts the geographic focus to Latin America and the Caribbean (LAC).

It reports on the availability of gender data in Colombia, Costa Rica, the Dominican Republic, Jamaica, and Paraguay at the international, national, and microdata levels, and it assesses the availability of 93 gender indicators, their disaggregations, and their frequency of observation in international and national databases and publications.

Additionally, with the assistance of the UN Economic Commission for Latin America (ECLAC), the report documents the availability of statistical indicators to support gender development plans in the five countries.

Through this report, we hope to help move the development community one step closer to producing high-quality and policy-relevant gender indicators to inform better decisions….Read the report.“

Internet Searches for Acute Anxiety During the Early Stages of the COVID-19 Pandemic

Curated on Posted on by Stefaan Verhulst

Paper by John W. Ayers et al: “There is widespread concern that the coronavirus disease 2019 (COVID-19) pandemic may harm population mental health, chiefly owing to anxiety about the disease and its societal fallout. But traditional population mental health surveillance (eg, telephone surveys, medical records) is time consuming, expensive, and may miss persons who do not participate or seek care. To evaluate the association of COVID-19 with anxiety on a population basis, we examined internet searches indicative of acute anxiety during the early stages of the COVID-19 pandemic.Methods

The analysis relied on nonidentifiable, aggregate, public data and was exempted by the University of California San Diego Human Research Protections Program. Acute anxiety, including colloquially called anxiety attacks or panic attacks, was monitored because of its higher prevalence relative to other mental health problems. It can lead to other mental health problems (including depression), it is triggered by outside stressors, and it is socially contagious. Using Google Trends (https://trends.google.com/trends) we monitored the daily fraction of all internet searches (thereby adjusting the results for any change in total queries) that included the terms anxiety or panic in combination with attack (including panic attack, signs of anxiety attack, anxiety attack symptoms) that originated from the US from January 1, 2004, through May 4, 2020. Raw search counts were inferred using Comscore estimates (comscore.com).

We compared search volumes after President Trump declared a national COVID-19 emergency on March 13, 2020, with expected search volumes if COVID-19 had not occurred, thereby taking into account the historical trend and periodicity in the data. Expected volumes were computed using an autoregressive integrated moving average model,4 based on historical trends from January 1, 2004 to March 12, 2020, to predict counterfactual trends for March 13, 2020 to May 9, 2020. The expected volumes with prediction intervals (PIs) and ratio of observed and expected volumes with bootstrap CIs were computed using R statistical software (version 3.5.3, R Foundation). The results were similar if we varied our interruption date plus or minus 1 week….(More)”.

This app is helping mothers in the Brazilian favelas survive the pandemic

Curated on Posted on by Stefaan Verhulst


Daniel Avelar at Open Democracy: “As Brazil faces one of the worst COVID-19 outbreaks in the world, a smartphone app is helping residents of impoverished areas known as favelas survive the virus threat amid sudden mass unemployment.

So far, the Latin American country has recorded over 115.000 deaths caused by COVID-19. The shutdown of economic activity wiped out 7.8 million jobs, mostly affecting low-skilled informal workers who form the bulk of the population in the favelas. Emergency income distributed by the government is limited to 60% of the minimum wage, so families are struggling to make ends meet.

Many blame president Jair Bolsonaro for the tragedy. Bolsonaro, a far-right populist, has consistently rallied against science-based policies in the management of the pandemic and pushed for an end to stay-at-home orders. A precocious reopening of the economy is likely to increase infection rates and cause more deaths.

In an attempt to stop the looming humanitarian catastrophe, a coalition of activists in the favelas and corporate partners developed an app that is facilitating the distribution of food and emergency income to thousands of women spearheading families. The app has a facial recognition feature that helps volunteers identify and register recipients of aid and prevents fraud.

So far, the Favela Mothers project has distributed the equivalent to US$ 26 million in food parcels and cash allowances to more than 1.1 million families in 5,000 neighborhoods across the country….(More)”.

Data Mining on Open Public Transit Data for Transportation Analytics During Pre-COVID-19 Era and COVID-19 Era

Curated on Posted on by Stefaan Verhulst

Paper by Carson K. Leung et al: “As the urbanization of the world continues and the population of cities rise, the issue of how to effectively move all these people around the city becomes much more important. In order to use the limited space in a city most efficiently, many cities and their residents are increasingly looking towards public transportation as the solution. In this paper, we focus on the public bus system as the primary form of public transit. In particular, we examine open public transit data for the Canadian city of Winnipeg. We mine and conduct transportation analytics on data prior to the coronavirus disease 2019 (COVID-19) situation and during the COVID-19 situation. By discovering how often and when buses were reported to be too full to take on new passengers at bus stops, analysts can get an insight of which routes and destinations are the busiest. This information would help decision makers make appropriate actions (e.g., add extra bus for those busiest routines). This results in a better and more convenient transit system towards a smart city. Moreover, during the COVID-19 era, it leads to additional benefits of contributing to safer buses services and bus waiting experiences while maintaining social distancing…(More)”.

Sandboxing Nature: How Regulatory Sandboxes Could Help Restore Species, Enhance Water Quality and Build Better Habitats Faster

Curated on Posted on by Stefaan Verhulst

White Paper by Phoebe Higgins & Timothy Male: “Late in 2017, the United Kingdom’s energy regulator, Ofgem, gave fast approval for a new project allowing residents to buy and sell renewable energy from solar panels and batteries within their own apartment buildings. Normally, this would not be legal since UK energy rules dictate that locally generated energy can only be used by the owner or sold back to the grid at a relatively low price. However, the earlier establishment of a regulatory sandbox for such energy delivery modernizations created a path to try something new and get it approved quickly. In April 2018, only a few months after project initiation, the first peer-to-peer energy trades within apartment complexes started.

Energy policy is not the only space where rules need fast modification to make allowances for all the novelty arising in the world today. The protection and restoration of our water, healthy soil and wildlife resources are static processes, starved for creativity. A United Nations’ panel recently reported on the extinction risks that face more than one million species around the globe. In a 2009 National Rivers and Streams Assessment, the EPA reported that 46 percent of U.S. waterways were in ‘poor’ biological condition, and more than 40 percent were polluted with high levels of nitrogen or phosphorus.

Innovators have big ideas that could help with these problems, but ponderous regulatory systems and older generations of bureaucrats aren’t used to the fast pace of new technologies, tools and products. Often, it is a simple thing—one word or phrase in a policy or regulation—that is a barrier to a new technology or technique being widely used. However, one sentence can be just as hard and slow to change as a whole law. Rather than simply accept this regulatory status quo, we believe in the need to find, nurture and learn from new concepts even when it means deliberately
breaking old rules.

Regulatory sandboxes like the one in the United Kingdom open the door to testing new approaches within a controlled environment. While they don’t ensure success, they make it possible for new technologies and tools to be explored in real-world settings. Not just so that innovators can learn, but also to allow government bureaucracies to catch up to the present and adapt to the future. Our planet and country need more opportunities to do this….(More)“

Landlord Tech Watch

Curated on Posted on by Stefaan Verhulst

About: “Landlord Tech—what the real estate industry describes as residential property technology, is leading to new forms of housing injustice. Property technology, or “proptech,” has grown dramatically since 2008, and applies to residential, commercial, and industrial buildings, effectively merging the real estate, technology, and finance industries. By employing digital surveillance, data collection, data accumulation, artificial intelligence, dashboards, and platform real estate in tenant housing and neighborhoods, Landlord Tech increases the power of landlords while disempowering tenants and those seeking shelter.

There are few laws and regulations governing the collection and use of data in the context of Landlord Tech. Because it is generally sold to landlords and property managers, not tenants, Landlord Tech is often installed without notifying or discussing potential harms with tenants and community members. These harms include the possibility that sensitive and personal data can be handed over to the police, ICE, or other law enforcement and government agencies. Landlord Tech can also be used to automate evictions, racial profiling, and tenant harassment. In addition, Landlord Tech is used to abet real estate speculation and gentrification, making buildings more desirable to whiter and wealthier tenants, while feeding real estate and tech companies with property – be that data or real estate. Landlord Tech tracking platforms have increasingly been marketed to landlords as solutions to Covid-19, leading to new forms of residential surveillance….(More)”.