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Urban informatics appears to be a suitable area for the application of digital twins. Definitions of the term share some characteristics, but these definitions do not agree on what exactly constitutes a digital twin. The term has the potential to be misleading unless adequate attention is paid to the inherent uncertainty in any replica of a real system. The question of uncertainty is addressed, together with some of the issues that make its quantification problematic. Digital twins for urban informatics pose questions of purpose, governance, and ethics. In the final section the paper suggests some research issues that will need to be addressed if digital twins are to be successful. [ABSTRACT FROM AUTHOR]

Review of the special issue papers The eight research papers included in this special issue cover a breath of literature review, new methodology, applications, and perspectives of scale. Wang and Wu discuss computational challenges in modeling spatial and temporal dimensions of big data (data captured at fine spatial and temporal scales) in discrete-choice models. To achieve this, the authors collected and reconciled multiple years of geospatial data related to urbanization and surveys of children's health from multiple sources and at different scales, into a uniform spatial frame to exploit their correlations. Following these methodological discussions, this special issue includes three interesting application papers that adopt scale-aware analysis. [Extracted from the article]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

We study the problem of synthesizing lockdown policies—schedules of maximum capacities for different types of activity sites—to minimize the number of deceased individuals due to a pandemic within a given metropolitan statistical area (MSA) while controlling the severity of the imposed lockdown. To synthesize and evaluate lockdown policies, we develop a multiscale susceptible, infected, recovered, and deceased model that partitions a given MSA into geographic subregions, and that incorporates data on the behaviors of the populations of these subregions. This modeling approach allows for the analysis of heterogeneous lockdown policies that vary across the different types of activity sites within each subregion of the MSA. We formulate the synthesis of optimal lockdown policies as a nonconvex optimization problem and we develop an iterative algorithm that addresses this nonconvexity through sequential convex programming. We empirically demonstrate the effectiveness of the developed approach by applying it to six of the largest MSAs in the United States. The developed heterogeneous lockdown policies not only reduce the number of deceased individuals by up to 45 percent over a 100 day period in comparison with three baseline lockdown policies that are less heterogeneous, but they also impose lockdowns that are less severe. [ABSTRACT FROM AUTHOR]

The vector assignment p-median problem (VAPMP) (Weaver and Church in Transp Sci 19(1):58-74, ) was one of the first location-allocation models developed to handle split assignment of a demand to multiple facilities. The underlying construct of the VAPMP has been subsequently used in a number of reliable facility location and backup location models. Although in many applications the chance that a facility fails may vary substantially with locations, many existing models have assumed a uniform failure probability across all sites. As an improvement, this paper proposes a new model, the expected p-median problem as a generalization of existing approaches by explicitly considering site-dependent failure probabilities. Multi-level closest assignment constraints and two efficient integer linear programming (ILP) formulations are introduced. While prior research generally concludes that similar problems are not integer-friendly and cannot be solved by ILP software, computational results show that our model can be used to solve medium-sized location problems optimally using existing ILP software. Moreover, the new model can be used to formulate other reliable or expected location problems with consideration of site-dependent failure probabilities. [ABSTRACT FROM AUTHOR]

With the direct linkage to a travel map system, geostatistical techniques have been recently adopted for urban travel time estimation. Some important traffic characteristics of urban transportation networks, however, have not been adequately addressed in these studies. As an improvement over the existing studies, this study incorporates the directional effect of traffic into several commonly used geostatistical models for travel time estimation. We show that model performance can be significantly enhanced when flow specific properties are explicitly considered in constructing the associated interpolation models. The developed methodology is applied to a set of traffic data collected in the city of Tucson, Arizona during the rush hours. Results demonstrate an average of 20 % reduction in RMSE compared with those by the traditional approaches. [ABSTRACT FROM AUTHOR]

Estrogen promotes growth of estrogen receptor-positive (ER+) breast tumors. However, epidemiological studies examining the prognostic characteristics of breast cancer in postmenopausal women receiving hormone replacement therapy reveal a significant decrease in tumor dissemination, suggesting that estrogen has potential protective effects against cancer cell invasion. Here, we show that estrogen suppresses invasion of ER+ breast cancer cells by increasing transcription of the Ena/VASP protein, EVL, which promotes the generation of suppressive cortical actin bundles that inhibit motility dynamics, and is crucial for the ER-mediated suppression of invasion in vitro and in vivo. Interestingly, despite its benefits in suppressing tumor growth, anti-estrogenic endocrine therapy decreases EVL expression and increases local invasion in patients. Our results highlight the dichotomous effects of estrogen on tumor progression and suggest that, in contrast to its established role in promoting growth of ER+ tumors, estrogen has a significant role in suppressing invasion through actin cytoskeletal remodeling. Whilst estrogen is known to be tumorigenic in some breast cancer, in some contexts it can be protective against invasion and dissemination. Here, the authors show estrogen can promote generation of Suppressive Cortical Actin Bundles that can inhibit motility dynamics through EVL-mediated actin cytoskeletal remodeling. [ABSTRACT FROM AUTHOR]