Paper by Amanda Pomeroy–Stevens, Bailey Goldman & Karen Grattan: “Rapidly growing cities face new and compounding health challenges, leading governments and donors to seek innovative ways to support healthier, more resilient urban growth. One such approach is the systems mapping process developed by Engaging Inquiry (EI) for the USAID-funded Building Healthy Cities project (BHC) in four cities in Asia. This paper provides details on the theory and methods of the process. While systems mapping is not new, the approach detailed in this paper has been uniquely adapted to the purpose of municipal planning. Strategic stakeholder engagement, including participatory workshops with a diverse group of stakeholders, is at the core of this approach and led to deeper insights, greater buy-in, and shared understanding of the city’s unique opportunities and challenges. This innovative mapping process is a powerful tool for defining municipal priorities within growing cities across the globe, where the situation is rapidly evolving. It can be used to provide evidence-based information on where to invest to gain the biggest impact on specific goals. This paper is part of a collection in this issue providing a detailed accounting of BHC’s systems mapping approach across four project cities…(More)”.
Artificial Intelligence in the City: Building Civic Engagement and Public Trust
Collection of essays edited by Ana Brandusescu, Ana, and Jess Reia: “After navigating various challenging policy and regulatory contexts over the years, in different regions, we joined efforts to create a space that offers possibilities for engagement focused on the expertise, experiences and hopes to shape the future of technology in urban areas. The AI in the City project emerged as an opportunity to connect people, organizations, and resources in the networks we built over the last decade of work on research and advocacy in tech policy. Sharing non-Western and Western perspectives from five continents, the contributors questioned, challenged, and envisioned ways public trust and meaningful civic engagement can flourish and persist as data and AI become increasingly pervasive in our lives. This collection of essays brings together a group of multidisciplinary scholars, activists, and practitioners working on a diverse range of initiatives to map strategies going forward. Divided into five parts, the collection brings into focus: 1) Meaningful engagement and public participation; 2) Addressing inequalities and building trust; 3) Public and private boundaries in tech policy; 4) Legal perspectives and mechanisms for accountability; and 5) New directions for local and urban governance. The focus on civil society and academia was deliberate: a way to listen to and learn with people who have dedicated many years to public interest advocacy, governance and policy that represents the interests of their communities…(More)”.
Crowdsourcing Initiatives in City Management: The Perspective of Polish Local Governments
Paper by Ewa Glińska, Halina Kiryluk and Karolina Ilczuk: “The past decade has seen a rise in the significance of the Internet facilitating the communication between local governments and local stakeholders. A growing role in this dialog has been played by crowdsourcing. The paper aims to identify areas, forms, and tools for the implementation of crowdsourcing in managing cities in Poland as well as the assessment of benefits provided by the use of crowdsourcing initiatives by representatives of municipal governments. The article utilized a quantitative study method of the survey realized on a sample of 176 city governments from Poland. Conducted studies have shown that crowdsourcing initiatives of cities concern such areas as culture, city image, spatial management, environmental protection, security, recreation and tourism as well as relations between entrepreneurs and city hall, transport and innovations. Forms of stakeholder engagement via crowdsourcing involve civic budgets, “voting/polls/surveys and interviews” as well as “debate/discussion/meeting, workshop, postulates and comments”. The larger the city the more often its representatives employ the forms of crowdsourcing listed above. Local governments most frequently carry out crowdsourcing initiatives by utilizing cities’ official web pages, social media, and special platforms dedicated to public consultations. The larger the city the greater the value placed on the utility of crowdsourcing…(More)”.
Crime Prediction Keeps Society Stuck in the Past
Article by Chris Gilliard: “…All of these policing systems operate on the assumption that the past determines the future. In Discriminating Data: Correlation, Neighborhoods, and the New Politics of Recognition, digital media scholar Wendy Hui Kyong Chun argues that the most common methods used by technologies such as PredPol and Chicago’s heat list to make predictions do nothing of the sort. Rather than anticipating what might happen out of the myriad and unknowable possibilities on which the very idea of a future depends, machine learning and other AI-based methods of statistical correlation “restrict the future to the past.” In other words, these systems prevent the future in order to “predict” it—they ensure that the future will be just the same as the past was.
“If the captured and curated past is racist and sexist,” Chun writes, “these algorithms and models will only be verified as correct if they make sexist and racist predictions.” This is partly a description of the familiar garbage-in/garbage-out problem with all data analytics, but it’s something more: Ironically, the putatively “unbiased” technology sold to us by promoters is said to “work” precisely when it tells us that what is contingent in history is in fact inevitable and immutable. Rather than helping us to manage social problems like racism as we move forward, as the McDaniel case shows in microcosm, these systems demand that society not change, that things that we should try to fix instead must stay exactly as they are.
It’s a rather glaring observation that predictive policing tools are rarely if ever (with the possible exception of the parody “White Collar Crime Risk Zone” project) focused on wage theft or various white collar crimes, even though the dollar amounts of those types of offenses far outstrip property crimes in terms of dollar value by several orders of magnitude. This gap exists because of how crime exists in the popular imagination. For instance, news reports in recent weeks bludgeoned readers with reports of a so-called “crime wave” of shoplifting at high-end stores. Yet just this past February, Amazon agreed to pay regulators a whopping $61.7 million, the amount the FTC says the company shorted drivers in a two-and-a-half-year period. That story received a fraction of the coverage, and aside from the fine, there will be no additional charges.
The algorithmic crystal ball that promises to predict and forestall future crimes works from a fixed notion of what a criminal is, where crimes occur, and how they are prosecuted (if at all). Those parameters depend entirely on the power structure empowered to formulate them—and very often the explicit goal of those structures is to maintain existing racial and wealth hierarchies. This is the same set of carceral logics that allow the placement of children into gang databases, or the development of a computational tool to forecast which children will become criminals. The process of predicting the lives of children is about cementing existing realities rather than changing them. Entering children into a carceral ranking system is in itself an act of violence, but as in the case of McDaniel, it also nearly guarantees that the system that sees them as potential criminals will continue to enact violence on them throughout their lifetimes…(More)”.
Algorithm Claims to Predict Crime in US Cities Before It Happens
Article by Carrington York: “A new computer algorithm can now forecast crime in a big city near you — apparently.
The algorithm, which was formulated by social scientists at the University of Chicago and touts 90% accuracy, divides cities into 1,000-square-foot tiles, according to a study published in Nature Human Behavior. Researchers used historical data on violent crimes and property crimes from Chicago to test the model, which detects patterns over time in these tiled areas tries to predict future events. It performed just as well using data from other big cities, including Atlanta, Los Angeles and Philadelphia, the study showed.
The new tool contrasts with previous models for prediction, which depict crime as emerging from “hotspots” that spread to surrounding areas. Such an approach tends to miss the complex social environment of cities, as well as the nuanced relationship between crime and the effects of police enforcement, thus leaving room for bias, according to the report.
“It is hard to argue that bias isn’t there when people sit down and determine which patterns they will look at to predict crime because these patterns, by themselves, don’t mean anything,” said Ishanu Chattopadhyay, Assistant Professor of Medicine at the University of Chicago and senior author of the study. “But now, you can ask the algorithm complex questions like: ‘What happens to the rate of violent crime if property crimes go up?”
But Emily M. Bender, professor of linguistics at the University of Washington, said in a series of tweets that the focus should be on targeting underlying inequities rather than on predictive policing, while also noting that the research appears to ignore securities fraud or environmental crimes…(More)”
Mapping Urban Trees Across North America with the Auto Arborist Dataset
Google Blog: “Over four billion people live in cities around the globe, and while most people interact daily with others — at the grocery store, on public transit, at work — they may take for granted their frequent interactions with the diverse plants and animals that comprise fragile urban ecosystems. Trees in cities, called urban forests, provide critical benefits for public health and wellbeing and will prove integral to urban climate adaptation. They filter air and water, capture stormwater runoff, sequester atmospheric carbon dioxide, and limit erosion and drought. Shade from urban trees reduces energy-expensive cooling costs and mitigates urban heat islands. In the US alone, urban forests cover 127M acres and produce ecosystem services valued at $18 billion. But as the climate changes these ecosystems are increasingly under threat.
Urban forest monitoring — measuring the size, health, and species distribution of trees in cities over time — allows researchers and policymakers to (1) quantify ecosystem services, including air quality improvement, carbon sequestration, and benefits to public health; (2) track damage from extreme weather events; and (3) target planting to improve robustness to climate change, disease and infestation.
However, many cities lack even basic data about the location and species of their trees. …
Today we introduce the Auto Arborist Dataset, a multiview urban tree classification dataset that, at ~2.6 million trees and >320 genera, is two orders of magnitude larger than those in prior work. To build the dataset, we pulled from public tree censuses from 23 North American cities (shown above) and merged these records with Street View and overhead RGB imagery. As the first urban forest dataset to cover multiple cities, we analyze in detail how forest models can generalize with respect to geographic distribution shifts, crucial to building systems that scale. We are releasing all 2.6M tree records publicly, along with aerial and ground-level imagery for 1M trees…(More)”
We need smarter cities, not “smart cities”
Article by Riad Meddebarchive and Calum Handforth: “This more expansive concept of what a smart city is encompasses a wide range of urban innovations. Singapore, which is exploring high-tech approaches such as drone deliveries and virtual-reality modeling, is one type of smart city. Curitiba, Brazil—a pioneer of the bus rapid transit system—is another. Harare, the capital of Zimbabwe, with its passively cooled shopping center designed in 1996, is a smart city, as are the “sponge cities” across China that use nature-based solutions to manage rainfall and floodwater.
Where technology can play a role, it must be applied thoughtfully and holistically—taking into account the needs, realities, and aspirations of city residents. Guatemala City, in collaboration with our country office team at the UN Development Programme, is using this approach to improve how city infrastructure—including parks and lighting—is managed. The city is standardizing materials and designs to reduce costs and labor, and streamlining approval and allocation processes to increase the speed and quality of repairs and maintenance. Everything is driven by the needs of its citizens. Elsewhere in Latin America, cities are going beyond quantitative variables to take into account well-being and other nuanced outcomes.
In her 1961 book The Death and Life of Great American Cities, Jane Jacobs, the pioneering American urbanist, discussed the importance of sidewalks. In the context of the city, they are conduits for adventure, social interaction, and unexpected encounters—what Jacobs termed the “sidewalk ballet.” Just as literal sidewalks are crucial to the urban experience, so is the larger idea of connection between elements.
Truly smart cities recognize the ambiguity of lives and livelihoods, and they are driven by outcomes beyond the implementation of “solutions.”
However, too often we see “smart cities” focus on discrete deployments of technology rather than this connective tissue. We end up with cities defined by “use cases” or “platforms.” Practically speaking, the vision of a tech-centric city is conceptually, financially, and logistically out of reach for many places. This can lead officials and innovators to dismiss the city’s real and substantial potential to reduce poverty while enhancing inclusion and sustainability.
In our work at the UN Development Programme, we focus on the interplay between different components of a truly smart city—the community, the local government, and the private sector. We also explore the different assets made available by this broader definition: high-tech innovations, yes, but also low-cost, low-tech innovations and nature-based solutions. Big data, but also the qualitative, richer detail behind the data points. The connections and “sidewalks”—not just the use cases or pilot programs. We see our work as an attempt to start redefining smart cities and increasing the size, scope, and usefulness of our urban development tool kit…(More)”.
Aligning investment and values: How an Economic Value Atlas can map regional strategies
Report by Adie Tomer and Caroline George: “Traditional built environment and economic development practices are falling short in the face of a convergent set of environmental, economic, and social challenges. With each passing year, more communities find themselves vulnerable to extreme weather events; income disparities continue to rise, leaving too many households unable to afford essential services; and employers, especially many young and minority-owned businesses, often struggle to find talented workers and access financial capital.
Public, private, and civic leaders increasingly recognize that achieving inclusive growth and designing resilient communities require more than recruiting out-of-town businesses or attempting to reduce highway congestion. Those leaders need a new kind of policy playbook—one that addresses the cross-sectoral challenges regions face and designs strategies across disciplines.
An Economic Value Atlas, or EVA, is part of that playbook. An EVA is a regional engagement, value-setting, and measurement process culminating in an interactive regional map that indexes neighborhood-level, value-based performance metrics. The overall framework helps practitioners delve into geographic disparities in how the region is living up to its values—opening the door to more equitable, place-based decisionmaking for business, infrastructure, and land use purposes…
The EVA framework consists of five phases of work, each of which can be adjusted based on unique local conditions:
- The EVA’s leadership team sets a stakeholder table with a diverse collection of regional voices to serve as the board of directors for the EVA process.
- The leadership team and stakeholder table develop a shared vision—a collection of specific long-term goals a region would like to achieve.
- A research-driven team translates values into indicators and metrics using sets of categorical indicators and quantitative metrics that reflect the goals stakeholders would like to achieve.
- A coding team develops and launches EVA software, which uses dynamic and flexible data to benchmark neighborhood performance relative to regional goals.
- The leadership team works with government and civic leaders to inform and guide policy and investment decisions using EVA outputs.
Critically, the EVA framework is designed to deliver results…(More)”
Parallel Worlds: Revealing the Inequity of Access to Urban Spaces in Mexico City Through Mobility Data
Paper by Emmanuel Letouzé et al: “The near-ubiquitous use of mobile devices generates mobility data that can paint pictures of urban behavior at unprecedented levels of granularity and complexity. In the current period of intense sociopolitical polarization, mobility data can help reveal which urban spaces serve to attenuate or accentuate socioeconomic divides. If urban spaces served to bridge class divides, people from different socioeconomic groups would be prone to mingle in areas further removed from their homes, creating opportunities for sharing experiences in the physical world. In an opposing scenario, people would remain among neighbors and peers, creating “local urban bubbles” that reflect and reinforce social inequities and their adverse effects on social mixity, cohesion, and trust. These questions are especially salient in cities with high levels of socioeconomic inequality, such as Mexico City.
Building on a joint research project between Data-Pop Alliance and Oxfam Mexico titled “Mundos Paralelos” [Parallel Worlds], this paper leverages privacy-preserving mobility data to unveil the unequal use and appropriation of urban spaces by the inhabitants of Mexico City. This joint research harnesses a year (2018–2019) of anonymized mobility data to perform mobility and behavioral analysis of specific groups at high spatial resolution. Its main findings suggest that Mexico City is a spatially fragmented, even segregated city: although distinct socioeconomic groups do meet in certain spaces, a pattern emerges where certain points of interest are exclusive to the high- and low-income groups analyzed in this paper. The results demonstrate that spatial inequality in Mexico City is marked by unequal access to government services and cultural sites, which translates into unequal experiences of urban life and biased access to the city. The paper concludes with a series of public policy recommendations to foster a more equitable and inclusive appropriation of public space…(More)”.
Satellites zoom in on cities’ hottest neighborhoods to help combat the urban heat island effect
Article by Daniel P. Johnson: “Spend time in a city in summer and you can feel the urban heat rising from the pavement and radiating from buildings. Cities are generally hotter than surrounding rural areas, but even within cities, some residential neighborhoods get dangerously warmer than others just a few miles away.
Within these “micro-urban heat islands,” communities can experience heat wave conditions well before officials declare a heat emergency.
I use Earth-observing satellites and population data to map these hot spots, often on projects with NASA. Satellites like the Landsat program have become crucial for pinpointing urban risks so cities can prepare for and respond to extreme heat, a top weather-related killer.
Among the many things we’ve been able to track with increasingly detailed satellite data is that the hottest neighborhoods are typically low-income and often have predominantly Black or Hispanic residents….
With rising global temperatures increasing the likelihood of dangerous heat waves, cities need to know which neighborhoods are at high risk. Excessive heat can lead to dehydration, heat exhaustion, heat stroke and even death with prolonged exposure, and the most at-risk residents often lack financial resources to adapt.
Satellite instruments can identify communities vulnerable to extreme heat because they can measure and map the surface urban heat island in high detail.
For example, industrial and commercial zones are frequently among the hottest areas in cities. They typically have fewer trees to cool the air and more pavement and buildings to retain and radiate heat…(More)”