Ok, I know diddly squat about Ghana other than I enjoyed my visit, and I also know diddly squat about flooding, but I LOVE how these authors use assemblage as both a theoretical approach and *almost* a method in constructing the comparative case studies of Agbogbloshie and Old Fadama, two informal settlements.

Assemblage theory is an ontological approach developed by Giles Deleuze Félix Guattari in their book A Thousand Plateaus: Capitalism and Schizophrenia. I do not feel qualified to really present the approach, but it seems to me that Amoaka and Frimpong Boamah do a great job of taking it down from the clouds, as it were, and really unlocking the potential of the approach to help us understand how and why vulnerability to flooding happens. It releases the research from the demands of a paradigm: you don’t have to have supply or demand variables. You just examine the variables, knowing they are mutually constituted and influencing, and explore how they are assembling in the context.

My PhD students read the paper with me. There are a couple places in the cases where I think the narrative gets a muddled, but that happens to all of us and it doesn’t negate the fact that this is a very nice exemplar of really using theory to deepen empirical work.

Here is the citation and the link. It’s behind a paywall, but , I suspect we can find you a copy of it if you request it:

Clifford Amoako & Emmanuel Frimpong Boamah (2020) Becoming Vulnerable to Flooding: An Urban Assemblage View of Flooding in an African City, Planning Theory & Practice, 21:3, 371-391, DOI: 10.1080/14649357.2020.1776377

Assemblage thinking has emerged over the last two decades as an important theoretical framework to interrogate emerging complex socio-material phenomenon in cities. This paper deploys the assemblage lens to unpack the vulnerability of informal communities to flood hazards in an African city. Focusing on Agbogbloshie and Old Fadama, the largest informal settlements in Accra, Ghana, this paper employs multiple methods including archival analysis, institutional surveys, focus group discussions, and mini-workshops to study the processes of exposure and vulnerability to flood hazards in these two communities. We find that being vulnerable to flood hazards in these informal settlements emerges from historically contingent, co-constitutive processes and actants: the city officials’ modernist imaginaries and socio-cultural identities of residents in informal settlements; the social material conditions experienced by residents in these settlements; and the translocal learning networks of government and non-government actors that simultaneously (re)produce oppressive urban planning policies and grassroots resistance to these policies. The paper concludes with a call to urban planners and allied built environment practitioners to understand flood vulnerability as both a process and product of these complex interactions.

I admit, I am one of those people who does all the eye-rolling when city science comes up because it way-too-often comes in the following form: Planners Have Failed to Solve the City, and Thus SCIENTISTS with their RIGOR are here to help. And then it boils down to a bunch of atheoretical and dehumanized equations, sometimes with BIG DATA attached.

In this “debate” piece, James Duminy and Susan Parnell say “not so fast, and don’t be so darn biased in your thinking” and they are, in general, right that knee-jerk dismissals are lazy and, over time, likely to be wrong. Now, I have to say, I am not convinced ulitimately by what they have here–they have reconceptualized science in ways that I suspect are really useful in order that there might be a possibility of city science, which is theoretically intereting but I suspect would make many a scientist get squinky. (That doesn’t disqualify the reconceptualization.) I do think they are onto something when they say perhaps the general model for a city science could come from citizen science (interesting). That releases the possibiities from the strangulation of academic hierachies in the first place. And they are right; if you dismiss it as impossible, you miss what it is possible to show with it.

I always like essays that make me examine my own intellectual biases and this one did it.

This baby does NOT have a paywall so you can go ahead and read it from here.

James Duminy & Susan Parnell (2020) City Science: A Chaotic Concept – And an Enduring Imperative, Planning Theory & Practice, 21:4, 648-655, DOI: 10.1080/14649357.2020.1802155

Debates surrounding the ‘new’ city sciences are polarized. On the one hand, a new generation of tech-savvy data scientists, spatial modellers, and analysts confidently express their ability to predict and explain city processes at unprecedented scales of complexity. On the other hand, those trained to see the world as fundamentally shaped by contingent meanings and subjectivities may see in such approaches little more than old positivism in new bottles, or perhaps a hubristic overstep of urban non-specialists onto their turf (Derudder & Van Meeteren, 2019).

A few years back, I honestly told journal editors I couldn’t review any more housing voucher studies. The field was crowded, lots of people could review the papers in question, and virtually all of them would be more qualified and more interested than me in housing vouchers. I think vouchers are solid policy, but I”ve run out things to say about them. If we aren’t going to fund them generously, there are hard limits as to what they can do for affordability, granted the wage and housing scarcity environments of large cities. (A true statement about any income support policy.)

Thus it always makes me happy to see housing research that reaches into the greater social and economic context of affordability as a context. Now, I am economically very lucky, but every time I open my utility bill in southern California, I have to take several deep breaths and sit down because my head *swims*. Utilitie are expensive, and while I know my specific context is special, utilties for rental houses can’t be that much less mine. Furthermore, one of the ways that climate change is going to hurt people is through their utility bills, where at least some are going to have to make choices between paying more and letting Grandma struggle during heat waves.

Kontokosta, Reina, and Bonczak take up this question about how utility costs factor into the transportation-housing cost nexus using a dataset that planners don’t tend to use much.

Here is the citation and the link. It’s behind a paywall, but if you ask me or the author, I suspect we can find you a copy of it between the two of us.

Constantine E. Kontokosta, Vincent J. Reina & Bartosz Bonczak (2020) Energy Cost Burdens for Low-Income and Minority Households, Journal of the American Planning Association, 86:1, 89-105, DOI: 10.1080/01944363.2019.1647446

Problem, research strategy, and findings: Of the three primary components of housing affordability measures—rent, transportation, and utilities—utility costs are the least understood yet are the one area where the cost burden can be reduced without household relocation. Existing data sources to estimate energy costs are limited to surveys with small samples and low spatial and temporal resolution, such as the American Housing Survey and the Residential Energy Consumption Survey. In this study, we present a new method for small-area estimates of household energy cost burdens (ECBs) that leverages actual building energy use data for approximately 13,000 multifamily properties across five U.S. cities and links energy costs to savings opportunities by analyzing 3,000 energy audit reports. We examine differentials in cost burdens across household demographic and socioeconomic characteristics and analyze spatial, regional, and building-level variations in energy use and expenditures. Our results show the average low-income household has an ECB of 7%, whereas higher income households have an average burden of 2%. Notably, even within defined income bands, minority households experience higher ECBs than non-Hispanic White households. For lower income households, low-cost energy improvements could reduce energy costs by as much as $1,500 per year.

Takeaway for practice: In this study we attempt to shift the focus of energy efficiency investments to their impact on household cost burdens and overall housing affordability. Our analysis explores new and unique data generated from measurement-driven urban energy policies and shows low-income households disproportionately bear the burden of poor-quality and energy-inefficient housing. Cities can use these new data resources and methods to develop equity-based energy policies that treat energy efficiency and climate mitigation as issues of environmental justice and that apply data-driven, targeted policies to improve quality of life for the most vulnerable urban residents.

I’m interested in forecasting in the same way I am interested in divination. If planning is really about trying to create future imaginaries and understanding futures, forecasting is its quantitive arm. Today’s entry in the stuff I’ve enjoyed reading this year is about how experts who work with forecasts understand them. Transit New Start project forecasts have improved over time—which makes sense. The more people do, the better we get at it; the more projects we start, the more data on passenger behavior we get.

(People used to get mad at me because I was appalled at the CA HSR cost projections and not the ridership projections, but I stand my ground on that. HSR in CA would be an entirely new service. But we should know full damn well how much it costs to pour concrete in California. Passenger forecasts are extremely hard in my mind, for a lot of reasons that not the fault of either the analysts or the project promoters. That said, the incentive to diddle them is real and there’s a reason for the upward bias.)

This is a neat paper because she’s able to interview at least one person associated with all of the 82 New Start Projects that have been funded. It’s nice to see them all examined in retrospect. I look forward to seeing more of Voulgaris’ ideas here. One thing I’ve really wished for is that planners are not the only people who introduce new services, and I wonder sometimes if the world of market research and social marketing would yield some interesting insights on the field of passenger forecasts in transit and in influencing people to try transit.

Here is the citation and the link. It’s behind a paywall, but if you ask me or the author, I suspect we can find you a copy of it between the two of us.

Carole Turley Voulgaris (2020) What Is a Forecast for?, Journal of the American Planning Association, 86:4, 458-469, DOI: 10.1080/01944363.2020.1746191

The forecasts transit agencies submit in support of applications for federal New Starts funding have historically overestimated ridership, as have ridership forecasts for rail projects in several countries and contexts. Forecast accuracy for New Starts projects has improved over time. Understanding the motivations of forecasters to produce accurate or biased forecasts can help forecast users determine whether to trust new forecasts. For this study I interviewed 13 transit professionals who have helped prepare or evaluate applications for federal New Starts funds. This sample includes interviewees who have had varying levels of involvement in all 82 New Starts projects that opened between 1976 and 2016. I recruited interviewees through a snowball sampling method; my interviews focus on the interviewees’ perspectives on how New Starts project evaluation and ridership forecasting has changed over time. Interview results suggest that ridership forecasters’ motivations to produce accurate forecasts may have increased with increased transparency, increased influence on local decision making, and decreased influence on external (federal) funding.

Takeaway for practice: Planners can evaluate the likely trustworthiness of forecasts based on transparency, internal influence, and external influence. If forecast users cannot easily determine a forecast’s key inputs and assumptions, if the forecaster has been tasked with producing a forecast to justify a predetermined action, and if an unfavorable forecast would circumvent decisions by the forecaster’s immediate client, forecasts should viewed with skepticism. Planners should seek to alter conditions that may create these conflicts of interest. Forecasters seem to be willing and able to improve forecast accuracy when the demand for accurate forecasts increases.