Paper by Stephen J. Redding: “This paper reviews recent quantitative urban models. These models are sufficiently rich to capture observed features of the data, such as many asymmetric locations and a rich geography of the transport network. Yet these models remain sufficiently tractable as to permit an analytical characterization of their theoretical properties. With only a small number of structural parameters (elasticities) to be estimated, they lend themselves to transparent identification. As they rationalize the observed spatial distribution of economic activity within cities, they can be used to undertake counterfactuals for the impact of empirically-realistic public-policy interventions on this observed distribution. Empirical applications include estimating the strength of agglomeration economies and evaluating the impact of transport infrastructure improvements (e.g., railroads, roads, Rapid Bus Transit Systems), zoning and land use regulations, place-based policies, and new technologies such as remote working…(More)”.
Addressing Data Challenges to Drive the Transformation of Smart Cities
Paper by Ekaterina Gilman et al: “Cities serve as vital hubs of economic activity and knowledge generation and dissemination. As such, cities bear a significant responsibility to uphold environmental protection measures while promoting the welfare and living comfort of their residents. There are diverse views on the development of smart cities, from integrating Information and Communication Technologies into urban environments for better operational decisions to supporting sustainability, wealth, and comfort of people. However, for all these cases, data are the key ingredient and enabler for the vision and realization of smart cities. This article explores the challenges associated with smart city data. We start with gaining an understanding of the concept of a smart city, how to measure that the city is a smart one, and what architectures and platforms exist to develop one. Afterwards, we research the challenges associated with the data of the cities, including availability, heterogeneity, management, analysis, privacy, and security. Finally, we discuss ethical issues. This article aims to serve as a “one-stop shop” covering data-related issues of smart cities with references for diving deeper into particular topics of interest…(More)”.
Long-term validation of inner-urban mobility metrics derived from Twitter/X
Paper by Steffen Knoblauch et al: “Urban mobility analysis using Twitter as a proxy has gained significant attention in various application fields; however, long-term validation studies are scarce. This paper addresses this gap by assessing the reliability of Twitter data for modeling inner-urban mobility dynamics over a 27-month period in the. metropolitan area of Rio de Janeiro, Brazil. The evaluation involves the validation of Twitter-derived mobility estimates at both temporal and spatial scales, employing over 1.6 × 1011 mobile phone records of around three million users during the non-stationary mobility period from April 2020 to. June 2022, which coincided with the COVID-19 pandemic. The results highlight the need for caution when using Twitter for short-term modeling of urban mobility flows. Short-term inference can be influenced by Twitter policy changes and the availability of publicly accessible tweets. On the other hand, this long-term study demonstrates that employing multiple mobility metrics simultaneously, analyzing dynamic and static mobility changes concurrently, and employing robust preprocessing techniques such as rolling window downsampling can enhance the inference capabilities of Twitter data. These novel insights gained from a long-term perspective are vital, as Twitter – rebranded to X in 2023 – is extensively used by researchers worldwide to infer human movement patterns. Since conclusions drawn from studies using Twitter could be used to inform public policy, emergency response, and urban planning, evaluating the reliability of this data is of utmost importance…(More)”.
Understanding local government responsible AI strategy: An international municipal policy document analysis
Paper by Anne David et al: “The burgeoning capabilities of artificial intelligence (AI) have prompted numerous local governments worldwide to consider its integration into their operations. Nevertheless, instances of notable AI failures have heightened ethical concerns, emphasising the imperative for local governments to approach the adoption of AI technologies in a responsible manner. While local government AI guidelines endeavour to incorporate characteristics of responsible innovation and technology (RIT), it remains essential to assess the extent to which these characteristics have been integrated into policy guidelines to facilitate more effective AI governance in the future. This study closely examines local government policy documents (n = 26) through the lens of RIT, employing directed content analysis with thematic data analysis software. The results reveal that: (a) Not all RIT characteristics have been given equal consideration in these policy documents; (b) Participatory and deliberate considerations were the most frequently mentioned responsible AI characteristics in policy documents; (c) Adaptable, explainable, sustainable, and accountable considerations were the least present responsible AI characteristics in policy documents; (d) Many of the considerations overlapped with each other as local governments were at the early stages of identifying them. Furthermore, the paper summarised strategies aimed at assisting local authorities in identifying their strengths and weaknesses in responsible AI characteristics, thereby facilitating their transformation into governing entities with responsible AI practices. The study informs local government policymakers, practitioners, and researchers on the critical aspects of responsible AI policymaking…(More)” See also: AI Localism
City Tech
Book by Rob Walker: “The world is rapidly urbanizing, and experts predict that up to 80 percent of the population will live in cities by 2050. To accommodate that growth while ensuring quality of life for all residents, cities are increasingly turning to technology. From apps that make it easier for citizens to pitch in on civic improvement projects to comprehensive plans for smarter streets and neighborhoods, new tools and approaches are taking root across the United States and around the world. In this thoughtful, inquisitive collection, Rob Walker—former New York Times columnist and author of the City Tech column for Land Lines magazine—investigates the new technologies afoot and their implications for planners, policymakers, residents, and the virtual and literal landscapes of the cities we call home…(More)”
Federal Court Invalidates NYC Law Requiring Food Delivery Apps to Share Customer Data with Restaurants
Article by Hunton, Andrews, Kurth: “On September 24, 2024, a federal district court held that New York City’s “Customer Data Law” violates the First Amendment. Passed in the summer of 2021, the law requires food-delivery apps to share customer-specific data with restaurants that prepare delivered meals.
The New York City Council enacted the Customer Data Law to boost the local restaurant industry in the wake of the pandemic. The law requires food-delivery apps to provide restaurants (upon the restaurants’ request) with each diner’s full name, email address, phone number, delivery address, and order contents. Customers may opt out of such sharing. The law’s supporters argue that requiring such disclosure addresses exploitation by the delivery apps and helps restaurants advertise more effectively.
Normally, when a customer places an order through a food-delivery app, the app provides the restaurant with the customer’s first name, last initial and food order. Food-delivery apps share aggregate data analytics with restaurants but generally do not share customer-specific data beyond the information necessary to fulfill an order. Some apps, for example, provide restaurants with data related to their menu performance, customer feedback and daily operations.
Major food-delivery app companies challenged the Customer Data Law, arguing that its data sharing requirement compels speech impermissibly under the First Amendment. Siding with the apps, the U.S. District Court for the Southern District of New York declared the city’s law invalid, holding that its data sharing requirement is not appropriately tailored to a substantial government interest…(More)”.
Need for Co-creating Urban Data Collaborative
Blog by Gaurav Godhwani: “…The Government of India has initiated various urban reforms for our cities like — Atal Mission for Rejuvenation and Urban Transformation 2.0 (AMRUT 2.0), Smart Cities Mission (SCM), Swachh Bharat Mission 2.0 (SBM-Urban 2.0) and development of Urban & Industrial Corridors. To help empower cities with data, the Ministry of Housing & Urban Affairs(MoHUA) has also launched various data initiatives including — DataSmart Cities Strategy, Data Maturity Assessment Framework, Smart Cities Open Data Portal, City Innovation Exchange, India Urban Data Exchange and the India Urban Observatory.
Unfortunately, most of the urban data remains in silos and capacities for our cities to harness urban data to improve decision-making, strengthen citizen participation continues to be limited. As per the last Data Maturity Assessment Framework (DMAF) assessment conducted in November 2020 by MoHUA, among 100 smart cities only 45 cities have drafted/ approved their City Data Policies with just 32 cities having a dedicated data budget in 2020–21 for data-related activities. Moreover, in-terms of fostering data collaborations, only 12 cities formed data alliances to achieve tangible outcomes. We hope smart cities continue this practice by conducting a yearly self-assessment to progress in their journey to harness data for improving their urban planning.
Seeding Urban Data Collaborative to advance City-level Data Engagements
There is a need to bring together a diverse set of stakeholders including governments, civil societies, academia, businesses and startups, volunteer groups and more to share and exchange urban data in a secure, standardised and interoperable manner, deriving more value from re-using data for participatory urban development. Along with improving data sharing among these stakeholders, it is necessary to regularly convene, ideate and conduct capacity building sessions and institutionalise data practices.
Urban Data Collaborative can bring together such diverse stakeholders who could address some of these perennial challenges in the ecosystem while spurring innovation…(More)”
What roles can democracy labs play in co-creating democratic innovations for sustainability?
Article by Inês Campos et al: “This perspective essay proposes Democracy Labs as new processes for developing democratic innovations that help tackle complex socio-ecological challenges within an increasingly unequal and polarised society, against the backdrop of democratic backsliding. Next to the current socio-ecological crisis, rapid technological innovations present both opportunities and challenges for democracy and call for democratic innovations. These innovations (e.g., mini-publics, collaborative governance and e-participation) offer alternative mechanisms for democratic participation and new forms of active citizenship, as well as new feedback mechanisms between citizens and traditional institutions of representative democracy. This essay thus introduces Democracy Labs, as citizen-centred processes for co-creating democratic innovations to inspire future transdisciplinary research and practice for a more inclusive and sustainable democracy. The approach is illustrated with examples from a Democracy Lab in Lisbon, reflecting on requirements for recruiting participants, the relevance of combining sensitising, reflection and ideation stages, and the importance of careful communication and facilitation processes guiding participants through co-creation activities…(More)”
Mapping AI Narratives at the Local Level
Article for Urban AI: “In May 2024, Nantes Métropole (France) launched a pioneering initiative titled “Nantes Débat de l’IA” (meaning “Nantes is Debating AI”). This year-long project is designed to curate the organization of events dedicated to artificial intelligence (AI) across the territory. The primary aim of this initiative is to foster dialogue among local stakeholders, enabling them to engage in meaningful discussions, exchange ideas, and develop a shared understanding of AI’s impact on the region.
Over the course of one year, the Nantes metropolitan area will host around sixty events focused on AI, bringing together a wide range of participants, including policymakers, businesses, researchers, and civil society. These events provide a platform for these diverse actors to share their perspectives, debate critical issues, and explore the potential opportunities and challenges AI presents. Through this collaborative process, the goal is to cultivate a common culture around AI, ensuring that all relevant voices are heard as the city navigates to integrate this transformative technology…(More)”.
Utilizing big data without domain knowledge impacts public health decision-making
Paper by Miao Zhang, Salman Rahman, Vishwali Mhasawade and Rumi Chunara: “…New data sources and AI methods for extracting information are increasingly abundant and relevant to decision-making across societal applications. A notable example is street view imagery, available in over 100 countries, and purported to inform built environment interventions (e.g., adding sidewalks) for community health outcomes. However, biases can arise when decision-making does not account for data robustness or relies on spurious correlations. To investigate this risk, we analyzed 2.02 million Google Street View (GSV) images alongside health, demographic, and socioeconomic data from New York City. Findings demonstrate robustness challenges; built environment characteristics inferred from GSV labels at the intracity level often do not align with ground truth. Moreover, as average individual-level behavior of physical inactivity significantly mediates the impact of built environment features by census tract, intervention on features measured by GSV would be misestimated without proper model specification and consideration of this mediation mechanism. Using a causal framework accounting for these mediators, we determined that intervening by improving 10% of samples in the two lowest tertiles of physical inactivity would lead to a 4.17 (95% CI 3.84–4.55) or 17.2 (95% CI 14.4–21.3) times greater decrease in the prevalence of obesity or diabetes, respectively, compared to the same proportional intervention on the number of crosswalks by census tract. This study highlights critical issues of robustness and model specification in using emergent data sources, showing the data may not measure what is intended, and ignoring mediators can result in biased intervention effect estimates…(More)”