Allison Lassiter

In the last two decades, a variety of digital technologies have proliferated in cities. Urban planning educators must respond to this given local resources, constraints, and options. This commentary reviews curricular innovations being undertaken by planning faculty at five diverse institutions to advance pedagogy beyond analytics. Our contribution is to identify three general approaches to expand teaching on digital technologies: (1) undertake reforms within accredited planning programs, (2) develop new educational offerings, and (3) teach through engaged learning programs. We urge broader curricular innovation in the planning field to ensure the field’s relevance and impact in an increasingly technical future.

This study examines how research on smart water is contributing to climate-resilient municipal water systems around the globe. We identify smart water research trends over time, relationships with climate adaptation and mitigation goals, and applicability to places with developed or developing water and electrical infrastructure. To do so, we systematically review the literature, identifying research on Information Communication Technology-enabled technologies related to water supply, wastewater, and stormwater management. We assess the relationship between each study and climate adaptation and mitigation objectives: managing greater variation in water quantity, leading to scarcity and increased stormwater; managing declining water quality; and low-carbon water systems. We find 96 relevant studies and identify five major categories of research addressing climate adaptation and mitigation: monitoring, modeling, system design, system feedbacks, and uptake and implementation. We find there is a recent acceleration in smart water research, with a concentration of studies focused on modeling. There is an emphasis on water efficiency using data from Advanced Metering Infrastructure, which is most applicable to cities with developed water grids and consistent electrical supplies. Secondarily, there is a concentration of work using distributed sensors for early detection of water quality degradation, which is being done in all municipal contexts. There is far less research on uptake and implementation of smart approaches, especially at the institutional level. In addition, there is relatively little work that explicitly relates smart water technologies to reducing greenhouse gas emissions. While smart water approaches are applicable everywhere, there is a need to for expanded focus on areas without developed water grids or consistent electricity for smart water to meaningfully contribute to Sustainable Development Goal 6.

Many cities provide incentives for private landowners to install green stormwater infrastructure (GSI) to reduce stormwater runoff and deliver co-benefits of urban greening. We analyze how participation in a GSI subsidy program affects the spatial distribution of urban greening. The distributional effects manifest in two stages: program eligibility and participation decisions. Eligibility, determined by hydrological factors, is positively correlated with wealthier and whiter areas. Within eligible areas, the wealthiest households and least white neighborhoods have lower participation rates. The findings highlight the importance of considering eligibility and participation in balancing the joint goals of environmental quality and environmental justice.

To analyze types and patterns of greening trends across a city, this study seeks to identify a method of creating very high-resolution urban vegetation maps that scales over space and time. Vegetation poses unique challenges for image segmentation because it is patchy, has ragged boundaries, and high in-class heterogeneity. Existing and emerging public datasets with the spatial resolution necessary to identify granular urban vegetation lack a depth of affordable and accessible labeled training data, making unsupervised segmentation desirable. This study evaluates three unsupervised methods of segmenting urban vegetation: clustering with k-means using k-means++ seeding; clustering with a Gaussian Mixture Model (GMM); and an unsupervised, backpropagating convolutional neural network (CNN) with simple iterative linear clustering superpixels. When benchmarked against internal validity metrics and hand-coded data, k-means is more accurate than GMM and CNN in segmenting urban vegetation. K-means is not able to differentiate between water and shadows, however, and when this segment is important GMM is best for probabilistically identifying secondary land cover class membership. Though we find the unsupervised CNN shows high degrees of accuracy on built urban landscape features, its accuracy when segmenting vegetation does not justify its complexity. Despite limitations, for segmenting urban vegetation, k-means has the highest performance, is the simplest, and is more efficient than alternatives.

The management of stormwater runoff via distributed green infrastructures delivers a number of environmental services that go beyond the reduction of flood risk, which has been the focus of conventional stormwater systems. Not all of these services may be equally valued by the public, however. This paper estimates households' willingness to pay (WTP) for improvements in water security, stream health, recreational and amenity values, as well as reduction in flood risk and urban heat island effect. We use data from nearly 1000 personal interviews with residential homeowners in Melbourne and Sydney, Australia. Our results suggest that the WTP for the highest levels of all environmental services is A$799 per household per year. WTP is mainly driven by residents valuing improvements in local stream health, exemptions in water restrictions, the prevention of flash flooding, and decreased peak urban temperatures respectively at A$297, A$244, A$104 and A$65 per year. We further conduct a benefit transfer analysis and find that the WTP and compensating surplus are not significantly different between the study areas. Our findings provide additional support that stormwater management via green infrastructures have large nonmarket benefits and that, under certain conditions, benefit values can be transferred to different locations.