Why local data is the key to successful place making

Blog by Sally Kerr: “The COVID emergency has brought many challenges that were unimaginable a few months ago. The first priorities were safety and health, but when lockdown started one of the early issues was accessing and sharing local data to help everyone deal with and live through the emergency. Communities grappled with the scarcity of local data, finding it difficult to source for some services, food deliveries and goods. This was not a new issue, but the pandemic brought it into sharp relief.

Local data use covers a broad spectrum. People moving to a new area want information about the environment — schools, amenities, transport, crime rates and local health. For residents, continuing knowledge of business opening hours, events, local issues, council plans and roadworks remains important, not only for everyday living but to help understand issues and future plans that will change their environment. Really local data (hyperlocal data) is either fragmented or unavailable, making it difficult for local people to stay informed, whilst larger data sets about an area (e.g. population, school performance) are not always easy to understand or use. They sit in silos owned by different sectors, on disparate websites, usually collated for professional or research use.

Third sector organisations in a community will gather data relevant to their work such as contacts and event numbers but may not source wider data sets about the area, such as demographics, to improve their work. Using this data could strengthen future grant applications by validating their work. For Government or Health bodies carrying out place making community projects, there is a reliance on their own or national data sources supplemented with qualitative data snapshots. Their dependence on tried and tested sources is due to time and resource pressures but means there is no time to gather that rich seam of local data that profiles individual needs.

Imagine a future community where local data is collected and managed together for both official organisations and the community itself. Where there are shared aims and varied use. Current and relevant data would be accessible and easy to understand, provided in formats that suit the user — from data scientist to school child. A curated data hub would help citizens learn data skills and carry out collaborative projects on anything from air quality to local biodiversity, managing the data and offering increased insight and useful validation for wider decision making. Costs would be reduced with duplication and effort reduced….(More)”.

How Data-Driven Cities Respond Swiftly and Effectively to COVID-19

Blog Post by Jennifer Park, Lauren Su, Lisa Fiedler, and Madeleine Weatherhead: “Since January of this year, the novel coronavirus has swept rapidly throughout the United States, leaving no city untouched. To contain the virus’ spread and protect residents’ health and livelihoods, local leaders have had to act swiftly and decisively. It is a challenge in scope and scale unlike any other in recent history — and it has underscored the power of data to guide life-and-death decisions and build trust.

Take, for example, Los Angeles. As cities across the country began issuing states of emergency and acting to promote public health, Mayor Eric Garcetti quickly identified the city’s response priorities: supporting families, small businesses, healthcare workers, and unhoused Angelenos, and increasing the healthcare equipment and testing kits available for the city. Mayor Garcetti tapped his Chief Information Officer and Innovation Team to collect and analyze data, to inform decisions, and share real-time information publicly.

A snapshot of Los Angeles’ publicly shared data from one of the city’s daily COVID-19 summary briefings. Image courtesy of the City of Los Angeles’ Innovation Team.

The Mayor was soon conducting daily briefings, updating the public on the latest virus-related data and informing city residents about various decisions made by the city — from pausing parking rules enforcement to opening thousands of temporary shelter beds. He used data to justify key decisions, linking stay-at-home orders to a decrease in COVID-19 cases from week to week.

Los Angeles’ swift response built on an existing culture of leveraging data to set goals, make decisions, and communicate with the public. Its leaders are now seeing the positive impact of having invested in foundational data capacity — regular tracking of cases, hospital capacity, and infection rates have proven to be vital to helping and accelerating the city’s responses to COVID-19.

Other cities, too, have leaned on established data practices and infrastructure in their response efforts, both to the benefit of their residents and to lay a stronger foundation to guide recovery….(More)“.

Toward Inclusive Urban Technology

Report by Denise Linn Riedl: “Our cities are changing at an incredible pace. The technology being deployed on our sidewalks and streetlights has the potential to improve mobility, sustainability, connectivity, and city services.

Public value and public inclusion in this change, however, are not inevitable. Depending on how these technologies are deployed, they have the potential to increase inequities and distrust as much as they can create responsive government services.

Recognizing this tension, an initial coalition of local practitioners began collaborating in 2019 with the support of the Benton Institute for Broadband & Society. We combined knowledge of and personal experience with local governments to tackle a common question: What does procedural justice look like when cities deploy new technology?

This guide is meant for any local worker—inside or outside of government—who is helping to plan or implement technological change in their community. It’s a collection of experiences, cases, and best practices that we hope will be valuable and will make projects stronger, more sustainable, and more inclusive….(More)”.

Smart cities during COVID-19: How cities are turning to collective intelligence to enable smarter approaches to COVID-19.

Article by Peter Baeck and Sophie Reynolds: One of the most prominent examples of how technology and data is being used to empower citizens is happening in Seoul. Here the city has used its ‘citizens as mayors’ philosophy for smart cities; an approach which aims to equip citizens with the same real-time access to information as the mayor. Seoul has gone further than most cities in making information about the COVID-19 outbreak in the city accessible to citizens. Its dashboard is updated multiple times daily and allows citizens to access the latest anonymised information on confirmed patients’ age, gender and dates of where they visited and when, after developing symptoms. Citizens can access even more detailed information; down to visited restaurants and cinema seat numbers.

The goal is to provide citizens with the information needed to take precautionary measures, self-monitor and report if they start showing symptoms after visiting one of the “infection points.” To help allay people’s fears and reduce the stigma associated with businesses that have been identified as “infection points”, the city government also provides citizens with information about the nearest testing clinics and makes “clean zones” (places that have been disinfected after visits by confirmed patients) searchable for users.

In addition to national and institutional responses there are (at least) five ways collective intelligence approaches are helping city governments, companies and urban communities in the fight against COVID-19:

1. Open sharing with citizens about the spread and management of COVID-19:

Based on open data provided by public agencies, private sector companies are using the city as a platform to develop their own real-time dashboards and mobile apps to further increase public awareness and effectively disseminate disease information. This has been the case with Corona NOWCorona MapCorona 100m in Seoul, Korea – which allow people to visualise data on confirmed coronavirus patients, along with patients’ nationality, gender, age, which places the patient has visited, and how close citizens are to these coronavirus patients. Developer Lee Jun-young who created the Corona Map app, said he built it because he found that the official government data was too difficult to understand.

Meanwhile in city state Singapore, the dashboard developed by UpCode scrapes data provided by the Singapore Ministry of Health’s own dashboard (which is exceptionally transparent about coronavirus case data) to make it cleaner and easier to navigate, and vastly more insightful. For instance, it allows you to learn about the average recovery time for those infected.

UpCode is making its platform available for others to re-use in other contexts.

2. Mobilising community-led responses to tackle COVID-19

Crowdfunding is being used in a variety of ways to get short-term targeted funding to a range of worthy causes opened up by the COVID-19 crisis. Examples include helping to fundraise for community activities for those directly affected by the crisis, backing tools and products that can address the crisis (such as buying PPE) and pre-purchasing products and services from local shops and artists. A significant proportion of the UK’s 1,000 plus mutual aid initiatives are now turning to crowdfunding as a way to rapidly respond to the new and emerging needs occurring at the city-wide and hyperlocal (i.e. streets and neighbourhood) levels.

Aberdeen City Mutual Aid group set up a crowdfunded community fund to cover the costs of creating a network of volunteers across the city, as well as any expenses incurred at food shops, fuel costs for deliveries and purchasing other necessary supplies. Similarly, the Feed the Heroes campaign was launched with an initial goal of raising €250 to pay for food deliveries for frontline staff who are putting in extra hours at the Mater Hospital, Dublin during the coronavirus outbreak….(More)”.

Governing Privacy in the Datafied City

Paper by Ira Rubinstein and Bilyana Petkova: “Privacy — understood in terms of freedom from identification, surveillance and profiling — is a precondition of the diversity and tolerance that define the urban experience, But with “smart” technologies eroding the anonymity of city sidewalks and streets, and turning them into surveilled spaces, are cities the first to get caught in the line of fire? Alternatively, are cities the final bastions of privacy? Will the interaction of tech companies and city governments lead cities worldwide to converge around the privatization of public spaces and monetization of data with little to no privacy protections? Or will we see different city identities take root based on local resistance and legal action?

This Article delves into these questions from a federalist and localist angle. In contrast to other fields in which American cities lack the formal authority to govern, we show that cities still enjoy ample powers when it comes to privacy regulation. Fiscal concerns, rather than state or federal preemption, play a role in privacy regulation, and the question becomes one of how cities make use of existing powers. Populous cosmopolitan cities, with a sizeable market share and significant political and cultural clout, are in particularly noteworthy positions to take advantage of agglomeration effects and drive hard deals when interacting with private firms. Nevertheless, there are currently no privacy front runners or privacy laggards; instead, cities engage in “privacy activism” and “data stewardship.”

First, as privacy activists, U.S. cities use public interest litigation to defend their citizens’ personal information in high profile political participation and consumer protection cases. Examples include legal challenges to the citizenship question in the 2020 Census, and to instances of data breach including Facebook third-party data sharing practices and the Equifax data breach. We link the Census 2020 data wars to sanctuary cities’ battles with the federal administration to demonstrate that political dissent and cities’ social capital — diversity — are intrinsically linked to privacy. Regarding the string of data breach cases, cities expand their experimentation zone by litigating privacy interests against private parties.

Second, cities as data stewards use data to regulate their urban environment. As providers of municipal services, they collect, analyze and act on a broad range of data about local citizens or cut deals with tech companies to enhance transit, housing, utility, telecom, and environmental services by making them smart while requiring firms like Uber and Airbnb to share data with city officials. This has proven contentious at times but in both North American and European cities, open data and more cooperative forms of data sharing between the city, commercial actors, and the public have emerged, spearheaded by a transportation data trust in Seattle. This Article contrasts the Seattle approach with the governance and privacy deficiencies accompanying the privately-led Quayside smart city project in Toronto. Finally, this Article finds the data trust model of data sharing to hold promise, not least since the European rhetoric of exclusively city-owned data presented by Barcelona might prove difficult to realize in practice….(More)”.

Strategies for Urban Network Learning: International Practices and Theoretical Reflections

Book edited by Leon van den Dool: This book presents international experiences in urban network learning. It is vital for cities to learn as it is necessary to constantly adapt and improve public performance and address complex challenges in a constantly changing environment. It is therefore highly relevant to gain more insight into how cities can learn. Cities address problems and challenges in networks of co-operation between existing and new actors, such as state actors, market players and civil society. This book presents various learning environments and methods for urban network learning, and aims to learn from experiences across the globe. How does learning take place in these urban networks? What factors and situations help or hinder these learning practices? Can we move from intuition to a strategy to improve urban network learning?…(More)”.

Exploring the role of data in post-Covid recovery

Blog by Eddie Copeland: “…how might we think about exploring the Amplify box in the diagram above? I’d suggest three approaches are likely to emerge:

Image outlines three headings: Specific fixes, new opportunities, generic capabilities

Let’s discuss these in the context of data.

Specific Fixes — A number of urgent data requests have arisen during Covid where it’s been apparent that councils simply don’t have the data they need. One example is how local authorities have needed to distribute business support grants. Many have discovered that while they have good records of local companies on their business rates database, they lack email or bank details for the majority. That makes it incredibly difficult to get payments out promptly. We can and should fix specific issues like this and ensure councils have those details in future.

New Opportunities — A crisis also prompts us to think about how things could be done differently and better. Perhaps the single greatest new opportunity we could aim to realise on a data front would be shifting from static to dynamic (if not real-time) data on a greater range of issues. As public sector staff, from CEOs to front line workers, have sought to respond to the crisis, the limitations of relying on static weekly, monthly or annual figures have been laid bare. As factors such as transport usage, high street activity and use of public spaces become deeply important in understanding the nature of recovery, more dynamic data could make a real difference.

Generic Capabilities — While the first two categories of activity are worth pursuing, I’d argue the single most positive legacy that could come out of a crisis is that we put in place generic capabilities — core foundation stones — that make us better able to respond to whatever comes next. Some of those capabilities will be about what individual councils need to have in place to use data well. However, given that few crises respect local authority boundaries, arguably the most important set of capabilities concern how different organisations can collaborate with data.

Putting in place the foundation stones for data collaboration

For years there has been discussion about the factors that make data collaboration between different public sector bodies hard.

Five stand out.

  1. Technology — some technologies make it hard to get the data out (e.g. lack of APIs); worse, some suppliers charge councils to access their own data.
  2. Data standards — the use of different standards, formats and conventions for recording data, and the lack of common identifiers like Unique Property Reference Numbers (UPRNs) makes it hard to compare, link or match records.
  3. Information Governance (IG) — Ensuring that London’s public sector organisations can use data in a way that’s legal, ethical and secure — in short, worthy of citizens’ trust and confidence — is key. Yet councils’ different approaches to IG can make the process take a long time — sometimes months.
  4. Ways of working — councils’ different processes require and produce different data.
  5. Lack of skills — when data skills are at a premium, councils understandably need staff with data competencies to work predominantly on internal projects, with little time available for collaboration.

There’s a host of reasons why progress to resolve these barriers has been slow. But perhaps the greatest is the perception that the effort required to address them is greater than the reward of doing so…(More)” –

See also Call For Action here

The tricky math of lifting coronavirus lockdowns

James Temple at MIT Technology Review: “…A crucial point of the work—which Steinhardt and MIT’s Andrew Ilyas​ wrote up in a draft paper that hasn’t yet been published or peer-reviewed—is that communities need to get much better at tracking infections. “With the data we currently have, we actually just don’t know what the level of safe mobility is,” Steinhardt says. “We need much better mechanisms for tracking prevalence in order to do any of this safely.”

The analysis relies on other noisy and less-than-optimal measurements as well, including using hospitalization admissions and deaths to estimate disease prevalence before the lockdowns. They also had to make informed assumptions, which others might disagree with, about how much shelter-in-place rules have altered the spread of the disease. Much of the overall uncertainty is due to the spottiness of testing to date. If case counts are rising, but so is testing, it’s difficult to decipher whether infections are still increasing or a greater proportion of infected people are being evaluated.

This produces some confusing results in the study for any policymaker looking for clear direction. Notably, in Los Angeles, the estimated growth rate of the disease since the shelter-in-place order went into effect ranges from negative to positive. This suggests either that the city could start loosening restrictions or that it needs to tighten them further.

Ultimately, the researchers stress that communities need to build up disease surveillance measures to reduce this uncertainty, and strike an appropriate balance between reopening the economy and minimizing public health risks.

They propose several ways to do so, including conducting virological testing on a random sample of some 20,000 people per day in a given area; setting up wide-scale online surveys that ask people to report potential symptoms, similar to what Carnegie Mellon researchers are doing through efforts with both Facebook and Google; and potentially testing for the prevalence of viral material in wastewater, a technique that has “sounded the alarm” on polio outbreaks in the past.

A team of researchers affiliated with MIT, Harvard, and startup Biobot Analytics recently analyzed water samples from a Massachusetts treatment facility, and detected levels of the coronavirus that were “significantly higher” than expected on the basis of confirmed cases in the state, according to a non-peer-reviewed paper released earlier this month….(More)”.

The Routledge Companion to Smart Cities

Book edited by Katharine S. Willis, and Alessandro Aurigi: “The Routledge Companion to Smart Cities explores the question of what it means for a city to be ‘smart’, raises some of the tensions emerging in smart city developments and considers the implications for future ways of inhabiting and understanding the urban condition. The volume draws together a critical and cross-disciplinary overview of the emerging topic of smart cities and explores it from a range of theoretical and empirical viewpoints.

This timely book brings together key thinkers and projects from a wide range of fields and perspectives into one volume to provide a valuable resource that would enable the reader to take their own critical position within the topic. To situate the topic of the smart city for the reader and establish key concepts, the volume sets out the various interpretations and aspects of what constitutes and defines smart cities. It investigates and considers the range of factors that shape the characteristics of smart cities and draws together different disciplinary perspectives. The consideration of what shapes the smart city is explored through discussing three broad ‘parts’ – issues of governance, the nature of urban development and how visions are realised – and includes chapters that draw on empirical studies to frame the discussion with an understanding not just of the nature of the smart city but also how it is studied, understood and reflected upon.

The Companion will appeal to academics and advanced undergraduates and postgraduates from across many disciplines including Urban Studies, Geography, Urban Planning, Sociology and Architecture, by providing state of the art reviews of key themes by leading scholars in the field, arranged under clearly themed sections….(More)”.

Developing better Civic Services through Crowdsourcing: The Twitter Case Study

Paper by Srushti Wadekar, Kunal Thapar, Komal Barge, Rahul Singh, Devanshu Mishra and Sabah Mohammed: “Civic technology is a fast-developing segment that holds huge potential for a new generation of startups. A recent survey report on civic technology noted that the sector saw $430 million in investment in just the last two years. It’s not just a new market ripe with opportunity it’s crucial to our democracy. Crowdsourcing has proven to be an effective supplementary mechanism for public engagement in city government in order to use mutual knowledge in online communities to address such issues as a means of engaging people in urban design. Government needs new alternatives — alternatives of modern, superior tools and services that are offered at reasonable rates.

An effective and easy-to-use civic technology platform enables wide participation. Response to, and a ‘conversation’ with, the users is very crucial for engagement, as is a feeling of being part of a society. These findings can contribute to the future design of civic technology platforms. In this research, we are trying to introduce a crowdsourcing platform, which will be helpful to people who are facing problems in their everyday practice because of the government services. This platform will gather the information from the trending twitter tweets for last month or so and try to identify which challenges public is confronting. Twitter for crowdsourcing as it is a simple social platform for questions and for the people who see the tweet to get an instant answer. These problems will be analyzed based on their significance which then will be made open to public for its solutions. The findings demonstrate how crowdsourcing tends to boost community engagement, enhances citizens ‘ views of their town and thus tends us find ways to enhance the city’s competitiveness, which faces some serious problems. Using of topic modeling with Latent Dirichlet Allocation (LDA) algorithm helped get categorized civic technology topics which was then validated by simple classification algorithm. While working on this research, we encountered some issues regarding to the tools that were available which we have discussed in the ‘Counter arguments’ section….(More)”.