Urban Resilience and Urban Sustainability

The urban system has been a subject of much attention and inquiry, more so in the past two or so decades. The design of urban systems and juxtaposition of humans with nature has been scrutinized and critically examined by several means and modes. To replicate the natural systems’ balance post human intervention on thus is what societies have strived for, for a long time now (Redman, 2010). The emergence of sustainability in the realm of urban living and more recently the attention imparted to the long known concept of resilient living have been a corollary of this quest. The trend in sustainable development and resilient systems planning of devising frameworks and drawing indicators to help define these vast concepts and help make their manifestation and measurement more tangible is evolving and developing more and more in academic circles and planning disciplines alike (see Alberti, 2012; Arup, 2015). However, the lack of a definite norm and basic sustainability and resilience requirements worldwide has made it more and more difficult for built and planning manifestations to respond directly to these broad-ended goals. It is hypothesized thus that a required innovative framework that incorporates the concepts of sustainability, resilience, a balanced ecological living and an efficient urban system be coined so that these concepts are reflected in manifestations of urban living and not lost in translation. An analysis of existing indicators and frameworks created by both academics and practitioners that currently define sustainability and resilience in urban development are used in identifying gaps in the current methodology adopted by cities in planning for sustainable-resilient living. A new framework and approach is attempted by incorporating the missing links. It is expected that this integrative framework - measuring and defining a resilient, sustainable and more responsible urban systems living - is a paradigmatic shift in the way planning policies are ideated and manifested in cities

Investigating the Effectiveness of Peanut Hull as Biosorbent of Lead (Pb) from Water

Lead contamination poses a major threat to health and environmental well-being. The remediation of this heavy metal from water sources is essential to safeguard health and ensure access to clean water. In this study, Peanut hull was used as a biosorbent for lead (Pb) removal from water. It focuses on optimizing various parameters important for lead removal. Statistical analysis, such as the Kruskal-Wallis test, was done to assess the significance of these parameters on lead biosorption, and an inverse variance weighting technique was employed to derive the weighted contribution of each variable for fixed Pb removal categories in the range of 80-100% and 80% (below). On analysis, it was found that factors such as pH and biomass dosage played major roles in lead removal. Furthermore, Scanning Electron Microscopy (SEM) and Energy-dispersive X-ray Spectroscopy (EDS), were done to find out changes in the structural and elemental characteristics of peanut hull after lead sequestration. Overall, this study highlights the potential of peanut hull as a promising biosorbent for lead removal from water, thereby offering a sustainable solution to water contamination with heavy metals

Replication files for Poisson MGWR

Files to replicate the simulation and NYC Covid data study.</p

A Geographical Perspective on Simpson’s Paradox

The concept of scale is inherent to, and consequential for, the modeling of geographical processes. However, scale also causes huge problems because the results of many types of spatial analysis appear to be dependent on the scale of the units for which data are reported (measurement scale). With the advent of local models and the fundamental difference in their scale of application compared to global models, this issue is exacerbated in unexpected ways. For example, a global model and local model calibrated using data measured at the same aggregation scale can also result in different and sometimes contradictory inferences (the classic Simpson’s Paradox). Here we provide a geographical perspective on why and how contrasting inferences might result from the calibration of a local and global model using the same data. Further, we examine the viability of such an occurrence using a synthetic experiment and two empirical examples. Finally, we discuss how such a perspective might inform the analyst’s conundrum: when the respective inferences run counter to one another, do we believe the local or global model results

Do places have value?: Quantifying the intrinsic value of housing neighborhoods using MGWR

Real estate market analysis has long been an active area of inquiry and one that reveals much about people’s preferences regarding housing attributes. It is well-known that house prices tend to exhibit strong spatial dependency and that they vary across space due to differences in structural and neighborhood characteristics. It is perhaps less well-known but gaining recognition that the influence of various structural and neighborhood characteristics on house prices might vary over space. However, very few, if any, applications in real estate research have recognized and measured the spatial scales over which different factors affect house prices or been able to quantify the ‘intangible’ impacts certain locations have on house prices. Using house price data in King County, WA, this research applies a multiscale extension to GWR, multiscale geographically weighted regression (MGWR), to measure and investigate spatial variations in the processes affecting house prices at varying scales. In a novel attempt, this research quantifies the intrinsic value certain locations have beyond the determinants used to define traditional hedonic price models. The research also demonstrates the utility of MGWR to hedonic price analysis and its ability to identify intricate housing submarkets often overlooked by other techniques