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- Language: English
This study reviews scholarly papers and case studies on urban vacant land to gain a stronger understanding of its public value in terms of the ecological and social benefits it can bring. This literature review offers a conceptual overview of the potential benefits of vacant land with the goal of addressing gaps in knowledge about vacant land and to provide suggestions to planners and designers on how vacant properties can be integrated with other green infrastructure in cities. There are many opportunities to redevelop vacant land to enhance its ecological and social value, and many design professionals and scholars are becoming interested in finding new ways to exploit this potential, especially with regard to planning and design. A better appreciation of the public value of urban vacant land is vital for any effort to identify alternative strategies to optimize the way these spaces are utilized for both short-term and long-term uses to support urban regeneration and renewal. This study will help planners and designers to understand and plan for urban vacant land, leading to better utilization of these spaces and opening up alternative creative approaches to envisioning space and landscape design in our urban environments.
Background:
Data assimilation refers to methods for updating the state vector (initial condition) of a complex spatiotemporal model (such as a numerical weather model) by combining new observations with one or more prior forecasts. We consider the potential feasibility of this approach for making short-term (60-day) forecasts of the growth and spread of a malignant brain cancer (glioblastoma multiforme) in individual patient cases, where the observations are synthetic magnetic resonance images of a hypothetical tumor.
Results:
We apply a modern state estimation algorithm (the Local Ensemble Transform Kalman Filter), previously developed for numerical weather prediction, to two different mathematical models of glioblastoma, taking into account likely errors in model parameters and measurement uncertainties in magnetic resonance imaging. The filter can accurately shadow the growth of a representative synthetic tumor for 360 days (six 60-day forecast/update cycles) in the presence of a moderate degree of systematic model error and measurement noise.
Conclusions:
The mathematical methodology described here may prove useful for other modeling efforts in biology and oncology. An accurate forecast system for glioblastoma may prove useful in clinical settings for treatment planning and patient counseling.