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This rapid response surveillance project was funded by the National Science Foundation (NSF) to collect “perishable” data on egress behaviors and neighborhood conditions surrounding healthcare centers (HCCs) in New York City (NYC) during the initial NYC COVID-19 PAUSE ordinance from March 22nd to May 19th, 2020. Anonymized data on NYC HCC egress behaviors were collected by observational field workers using phone-based mapping applications. Each egress trip record includes the day of week, time of day, destination category type, along with an array of behavioral outcome categories, ambient weather conditions and socio-economic factors. Egress trajectories with precise estimates of distance traveled and the spatial dispersion or “spread” around each HCC were added via post-processing. The data collection and cleaning process resulted in 5,030 individual egress records from 18 facilities over a 9-week period.

 

Street view imagery databases such as Google Street View, Mapillary, and Karta View provide great spatial and temporal coverage for many cities globally. Those data, when coupled with appropriate computer vision algorithms, can provide an effective means to analyse aspects of the urban environment at scale. As an effort to enhance current practices in urban flood risk assessment, this project investigates a potential use of street view imagery data to identify building features that indicate buildings’ vulnerability to flooding (e.g., basements and semi-basements). In particular, this paper discusses (1) building features indicating the presence of basement structures, (2) available imagery data sources capturing those features, and (3) computer vision algorithms capable of automatically detecting the features of interest. The paper also reviews existing methods for reconstructing geometry representations of the extracted features from images and potential approaches to account for data quality issues. Preliminary experiments were conducted, which confirmed the usability of the freely available Mapillary images for detecting basement railings as an example type of basement features, as well as geolocating the features.

 

A utilidor is a ‘system of systems’ infrastructural solution to the ‘subsurface spaghetti’ problem resulting from direct burial of utility transmission infrastructure beneath the public right of way (PROW). The transition from direct burial to utilidors in older, dense American cities has generally not occurred, despite the potential to increase system performance in a long-term, !nancially and environmentally sustainable manner, because it would require reform of local planning practices and of utility pricing to support !nancing within a complex regulatory system. Utilidor adoption in New York City (NYC) would be a signi!cant local infrastructure transition, amplifying the need for localitybased research, that would occur while each utility sector undergoes its own infrastructure transitions, thereby increasing the level of regulatory complexity. This paper applies transitions analysis, recursive collective action theory, and capacity to act analysis to NYC’s experience with its PROW subsurface spaghetti problem and utilidor implementation to demonstrate a placebased methodology that identi!es speci!c sources of resistance to innovative subsurface design and feasible pathways for resolving them. This methodology would be transferable for application to other American cities or classes of American cities to supplement the limits of generalised subsurface and subsurface resource integration research for practitioner application. 

We present an overview of four challenging research areas in multiscale physics and engineering as well as four data science topics that may be developed for addressing these challenges. We focus on multiscale spatiotemporal problems in light of the importance of understanding the accompanying scientific processes and engineering ideas, where “multiscale” refers to concurrent, non-trivial and coupled models over scales separated by orders of magnitude in either space, time, energy, momenta, or any other relevant parameter. Specifically, we consider problems where the data may be obtained at various resolutions; analyzing such data and constructing coupled models led to open research questions in various applications of data science. Numeric studies are reported for one of the data science techniques discussed here for illustration, namely, on approximate Bayesian computations. 

An adaptive, adversarial methodology is developed for the optimal transport problem between two distributions $\mu $ and $\nu $, known only through a finite set of independent samples $(x_i)_{i=1..n}$ and $(y_j)_{j=1..m}$. The methodology automatically creates features that adapt to the data, thus avoiding reliance on a priori knowledge of the distributions underlying the data. Specifically, instead of a discrete point-by-point assignment, the new procedure seeks an optimal map $T(x)$ defined for all $x$, minimizing the Kullback–Leibler divergence between $(T(x_i))$ and the target $(y_j)$. The relative entropy is given a sample-based, variational characterization, thereby creating an adversarial setting: as one player seeks to push forward one distribution to the other, the second player develops features that focus on those areas where the two distributions fail to match. The procedure solves local problems that seek the optimal transfer between consecutive, intermediate distributions between $\mu $ and $\nu $. As a result, maps of arbitrary complexity can be built by composing the simple maps used for each local problem. Displaced interpolation is used to guarantee global from local optimality. The procedure is illustrated through synthetic examples in one and two dimensions.