Environment and Planning B: Urban Analytics and City Science

The impact of urban form on residential space-conditioning energy use has been controversial in recent planning literature. This study empirically evaluates the association between urban form and residential energy use, focusing particularly on residential electricity use for space cooling in the City of Sacramento, California. We characterize urban form, property conditions, and demographic and socioeconomic characteristics by applying spatial metrics embedded within a geographic information system where LiDAR data effectively include each building and the surrounding vegetation. A statistical model is applied to assess the relationship between these explanatory variables and the estimated summer air-conditioning energy use. Controlling for other variables, higher population density, east—west street orientation, higher green space density, larger vegetation on the east, south, and especially the west sides of houses, appears to have statistically significant effects on reducing summer cooling energy use. This study quantifies the built environment impact on the energy demand of air conditioning and informs planners as they craft urban planning and design policies for energy conservation.

Megaprojects are large, costly, complex infrastructure projects. To assess the financial viability of a megaproject, a cost—benefit analysis (CBA) is usually performed; the results depend upon the accuracy of the cost estimations and the predictive models used to forecast future demand for the use of the infrastructure. The outcomes of the models are very vulnerable to unexpected events. As a result, the CBA may become unreliable and give an unrealistic picture of the financial viability of a project. An alternative way of policy making that tries to take uncertainty into account is the dynamic adaptive policy (DAP) approach. This approach involves a systematic method for designing and implementing a policy over time that is based on a clear set of constraints and objectives and that involves monitoring the environment, gathering information, and adjusting and readjusting to new circumstances. The efficacy of this type of policy making has already been shown, but whether DAP leads to a better cost—benefit performance of megaprojects is unknown. In this paper we focus on answering two research questions: How can CBA be applied to DAP? How good is the cost—benefit performance of megaprojects when using DAP compared with the cost—benefit performance when using the static policy-making approach? In this paper a framework based on real options theory is specified, enabling a CBA to be performed on a dynamic adaptive policy. This framework is then applied to a case involving Schiphol Airport, Amsterdam, to compare the cost—benefit performance of the static policy with the cost—benefit performance using the DAP approach. For this case, the cost—benefit performance of the megaproject under the DAP approach turns out to be better compared with its performance under the static policy. This result provides a first indication that adaptive policies might be able to improve the cost—benefit performance of megaprojects.

A data-adaptive multiscale spatial decomposition model is proposed to deal with skew-distributed data (eg, population or GDP). Relying on the filtering characteristics according to the bandwidth change of kernel smoothing, the parallel spatial kernel smoothing with different bandwidths is constructed as spatial filter banks for filtering spatial variations at different spatial scales. The filtering residual, a function changing with the spatial scale, is then extracted by parallel spatial kernel smoothing. With a change point detection model based on the second deviation, standard deviation of the residential data is selected for identifying robust significant scales. Then we present the iterative algorithm to extract and remove significant spatial variations at different scales. With well-designed stop criteria, the full hierarchical spatial scale structure in the original spatial process can be adaptively established without assigning the decomposition levels artificially. The computation processes and the statistical and spatial distribution characteristics are demonstrated with case studies of 2003 Chinese population data and GDP data, and the results show that the proposed model is suitable for decomposing the spatial data with spatial heterogeneity. Comparison with the 2D wavelet decomposition suggests that our model has better data-adaptive and shape-preserving ability.

There has been a great deal of research on the relationship between the built environment and walking activity, but many studies have produced inconclusive or conflicting results. In this research we aim to enrich the association of the built environment with walking activity by examining its impact in Seoul, a Korean megacity characterized by high-density development and a well-equipped public transportation system. The results show that neighborhoods with a relatively higher land-use mix and relatively greater access to public transportation have a significantly positive association with walking activity for destinations that are within a 500 m radius of residences. However, no positive association was found between development density by land use and walking activity. Overall, the results of this study indicate that the relationship between the built environment and walking activity differs by neighborhood scale and the urban built environment in terms of density and public transportation across countries.

Urban land-use modeling methods have experienced substantial improvements in the last several decades. With the advancement of urban land-use change theories and modeling techniques, a considerable number of models have been developed. The relatively young approach, agent-based modeling, provides urban land-use models with some new features and can help address the challenges faced by traditional models. Applications of agent-based models to study urban dynamics have increased steadily over the last twenty years. To offer a retrospective on the developments in agent-based models (ABMs) of urban residential choices, we review fifty-one relevant models that fall into three general categories: (i) urban land-use models based on classical theories; (ii) different stages of the urbanization process; and (iii) integrated agent-based and microsimulation models. We summarize and compare the main features of these fifty-one models within each category. This review focuses on three fundamental new features introduced byABMs. The first is agent heterogeneity with particular attention to the method of introducing heterogeneity in agents' attributes and behaviors. The second is the representation of land-market processes, namely preferences, resources constraints, competitive bidding, and endogenous relocation. The third is the method of measuring the extensive model outputs. In addition, we outline accompanying challenges to, and open questions for, incorporating these new features. We conclude that, by modeling agent heterogeneity and land markets, and by exploiting a much broader dimension of output, we will enhance our understanding of urban land-use change and are hopefully able to improve model fitness and robustness.

This paper presents a generative grammar producing a language of patterns for the south façade of a prototype sustainable house. The patterns are produced through the activation of the electrochromic material that is applied on the windowpanes of the façade. The class of the performatively effective configurations of the façade is approached as a visual language and the productive (generation), combinatorial (enumeration) and performative (verification) attributes of this language are examined. Random, performance driven, patterns could supply sufficient interior daylight without acknowledging the visual potential of façade pattern generation. The uniqueness of the chosen approach is that the shape grammar encodes the performative constraints pertaining to the generation of façade patterns in a visual manner by associating principles of two-dimensional pattern generation to levels of illuminance.

In Portugal the distribution of physicians is considered an appropriate proxy for the distribution of the actual hospital resources and additional information on hospital supply is mostly unavailable, while health care utilization data are also usually absent. A suitable method that can be used to analyze patients' access to hospital health care in countries with such characteristics is the two-step floating catchment area (2SFCA) method, since it requires only the number of physicians to represent supply and the population size to estimate demand. An improved version of the 2SFCA method is the kernel density 2SFCA (KD2SFCA) method. However, this method was not developed to analyze access to health care and it computes scores that express only the spatial access dimensions of proximity and availability. In this paper we present a new method, based on the KD2SFCA method, which improves health care access analysis and better identifies populations that are less empowered to use health care. We adapt the KD2SFCA method for the context of health care access analysis and extend it to capture additional access dimensions. We applied the extended method to the Portuguese hospital health care sector in a case study, and compared its results with those obtained with the KD2SFCA method. Our method was able to improve the identification of the less empowered populations and discovered that they represent 8.1% of the total population, instead of 4.6%, and reside in sixteen of the eighteen Portuguese districts, instead of in thirteen, as identified by the original KD2SFCA method. By improving the KD2SFCA method for the identification of the less empowered populations, our method can be a first step in an endeavor to identify opportunities to increase the health care supply or to redistribute supply resources, with the objective of increasing the access of those deprived populations.

Using 2007 travel-diary data from metropolitan Chicago, I investigate what aspects of urban form contribute most to community completeness, as defined by internal tour capture for nonwork tours. I examine two distinct geographic scales: census-defined ‘places’, and synthetically constructed ‘centered communities’. Centered communities are defined as nonwork travel sheds centered upon well-defined concentrations of activity. Higher accessibility share (a new urban form measure defined in the paper) and higher mixed use both significantly predict greater community completeness, as do higher levels of residential or employment density. Furthermore, I find that mixed-use measures describe something other than simple proximity to job-based attractions; these measures also address the appropriate balance of activities necessary for a complete community. To build more-complete communities, planners need to ensure that local accessibility to a variety of destinations of interest is high relative to the regional accessibility to these same types of destinations outside the community.