The following is a transcript for a podcast as part of the series called “Viewpoints on Resilient and Equitable Responses to the Pandemic” produced by Center for Urban and Regional Studies.
In March 2020, the New York Times reported on a smartphone application that is being rolled out in Hangzhou, China in response to the COVID-19 pandemic. The Alipay Health Code, as China’s official news media has called the system, is a project by the local government with the help of e-commerce giant Alibaba.
In 2018, as I was setting up this website, I had a conversation about land suitabilty analysis over email with Lew Hopkins, who was my doctoral advisor when I was at Illinois. I want to capture this conversation on this site, as a caveat, instead of sitting in my Inbox. The emails are slightly edited.
I am in the process of getting some blog posts up regarding planning methods and I was imagining writing one about land suitability analysis.
The measurement and characterization of urbanization crucially depends upon defining what counts as urban. According to The Indian Planning Commission, less than a third of the Indian population lives in urban areas, and while Indian cities are increasingly important to the economy, India is perceived fundamentally as a rural country. In this paper, we show that this received wisdom is an artefact of the definition of urbanity and the official statistics vastly undercount the level of urbanization and its importance for development policies in India. We begin by creating temporally-consistent, high-resolution population maps from sub district level population data available from the Indian Census for 2001 and 2011. The modeling framework is a two-step process that applies a Random Forest-based model to generate a prediction weighting layer subsequently used to inform a gridded dasymetric redistribution of original census counts at 100 m resolution (Stevens et al. 2015). We then apply density thresholds, contiguity conditions, distance based clustering and minimum population sizes to construct urban agglomerations for the entire country. Compared to the official estimates, we find that this approach counts 8%-30% (depending on thresholds) more urban population in 2011. We find large urban agglomerations that span large portions of Kerala and the Gangetic plain. Thus, while official estimates count more cities in the country, we delineate fewer cities but large urban regions that span jurisdictional boundaries. This has implication for urban policies.
Why are many plans not implemented? Common explanations for this question are planners have little power, or they failed to account for political or environmental uncertainty, or they failed to include to enough voices during the planning process. Weaving different strands of implementation and strategic planning literature, I provide an alternative account by challenging the premise that plans realise their potential only when they are implemented. I argue that theoretical frameworks that we base our understanding of plans and their purposes do not allow us to explain the ways in which plans are used. Monitoring implementation of plans, presupposes that we know what plans are there to monitor. It privileges published plans and ignores all the other plans that guide urban development. By jettisoning implementation as a key criterion by which to evaluate the effectiveness of plans, we can begin to focus on myriad of ways in which plans are used by plan makers as well as others. A better question to ask is, ``How are these plans used and when are they useful?” In asking those questions, we can create different evaluative frameworks for different types of plans. Some unimplementable plans are worth making.
We aim to find agglomerations of U.S. counties that are partitioned by commuting patterns by representing inter-county commuting patterns as a weighted network. We develop and use a community detection method based on the configuration model to identify significant clusters of nodes in a weighted network that prominently feature self-loops which represent same-county commuting. After we apply this method to county level commuting data from 2010, we find regions that are significantly different from existing delineations such as Metropolitian Statistical Areas and Megaregions. Our method identifies regions with varying sizes as well as highly overlapping regions. Some counties belong to as many as six different statistically significant clusters but some do not belong to any. Our results offer an alternative way of categorizing economic regions from existing methods and suggest that geographical delineations should be rethought.
Understanding the spatial pattern of energy consumption within buildings is essential to urban energy planning and management. This study explores the spatial complexity of residential energy usage intensity, with a focus on urban form and geomorphometry attributes. Using spatial regression models, we find that while vegetation and isolation have more local impact on energy intensity, urban porosity and roughness length have consistent spillover effects on building electricity usage intensity in Chicago. Additionally, these relationships are seasonally varied. The results highlight the importance of spatially explicit policies and clear urban design and form frameworks for improving urban energy efficiency.