Search

Much new work in urban and regional economics has emphasized the importance of place prosperity. This study focuses on the determinants of adult poverty and the contribution of place prosperity in damping the intergenerational transmission of poverty. Childhood poverty is a major predictor of adult poverty. We consider how such intergenerational transmission is affected by metropolitan and neighborhood (census tract) prosperity. To capture the temporal dynamics of this process, the model explored here is recursive in nature. We use longitudinal microdata from the Panel Study of Income Dynamics. Location variables at the census tract and metropolitan levels, family variables, and poverty status are observed for our subjects over multiple years both in childhood and adulthood. Neighborhood and metropolitan prosperity are measured in terms of average incomes adjusted for purchasing power parity differences. The standardized neighborhood prosperity direct effect on adult poverty is strongly significant and its total effect is twice as large. On the other hand, the standardized direct effect of metropolitan prosperity and its total effect are small and insignificant. But even neighborhood effects are modest compared to standardized effects of childhood poverty, race, mother’s education and own education. At least with respect to these data, the recent emphasis on place variables would seem to be overstated.

This article reports on an integrated modeling exercise, conducted on behalf of the US Federal Highway Administration, on the potential for frequent automated transit shuttles (‘community transit’)...

Planning researchers traditionally conceptualize learning as cognitive changes in individuals. In this tradition, scholars assess learning with pre- and post-measures of understandings or beliefs. While valuable for documenting individual change, such methods leave unexamined the social processes in which planners think, act, and learn in groups, which often involve the use of technical tools. The present interdisciplinary research program used Learning Sciences research methods, including conversation analysis, interaction analysis, and visualization of discourse codes, to understand how tools like agent-based models and geographic information systems mediate learning in planning groups. The objective was to understand how the use of these tools in participatory planning can help stakeholders learn about complex environmental problems, to make more informed judgments about the future. The paper provides three cases that illustrate the capacity of such research methods to provide insights into planning gr...

State and local governments are increasingly considering the adoption of legislation to promote green infrastructure (e.g., bioswales, green roofs) for stormwater management. This interest emerges from higher frequencies of combined sewer outflows, floods and exposure of residents and habitat to polluted water resulting from growing urbanization and related pressure on stormwater management facilities. While this approach is promising, there are many unknowns about the effects of specific implementation aspects (e.g., scale, layout), particularly as urban settlements and climate conditions change over time. If green infrastructure is to be required by law, these aspects need to be better understood. We developed a spatially-explicit process-based model (the Landscape Green Infrastructure Design model, L-GriD) developed to understand how the design of green infrastructure may affect performance at a neighborhood scale, taking into consideration the magnitude of stormevents, and the spatial layout of different kinds of land cover. We inform the mechanisms in our model with established hydrological models. In contrast with watershed data-intensive models in one extreme and site level cost-savings calculators in the other, our model allows us to generalize principles for green infrastructure design and implementation at a neighborhood scale, to inform policy-making. Simulation results show that with as little as 10% surface coverage, green infrastructure can greatly contribute to runoff capture in small storms, but that the amount would need to be doubled or tripled to deal with larger storms in a similar way. When placement options are limited, layouts in which green infrastructure is dispersed across the landscape—particularly vegetated curb cuts—are more effective in reducing flooding in all storm types than clustered arrangements. As opportunities for green infrastructure placement increase and as precipitation increases, however, patterns that follow the flow-path and accumulation of water become more effective, which can be built on an underlying curb-cut layout. If space constraints prevented any of these layouts, random placement would still provide benefits over clustered layouts.

An earlier generation of planners turned to Rittel & Webber’s 1973 conception of “wicked problems” to explain why conventional scientific approaches failed to solve problems of pluralistic urban societies. More recently, “complex systems” analysis has attracted planners as an innovative approach to understanding metropolitan dynamics and its social and environmental impacts. Given the renewed scholarly interest in wicked problems, we asked: how can planners use the complex systems approach to tackle wicked problems? We re-evaluate Rittel and Webber’s arguments through the lens of complex systems, which provide a novel way to redefine wicked problems and engage their otherwise intractable, zero-sum impasses. The complex systems framework acknowledges and builds an understanding around the factors that give rise to wicked problems: interaction, heterogeneity, feedback, neighbourhood effects, and collective interest traps. This affinity allows complex systems tools to engage wicked problems more explicitly a...

Planners making groundwater plans often use scientific hydrological forecasts to estimate long term the risk of water depletion. We study a group of Chicago planners and stakeholders who learned to use and helped develop agent-based models (ABM) of coupled land-use change and groundwater flow, to explore the effects of resource use and policy on future groundwater availability. Using discourse analysis, we found planners learned to play with the ABM to judge complex interaction effects. The simulation results challenged prior policy commitments, and instead of reconsidering those commitments to achieve sustainability, participants set aside the ABM and the lessons learned with them. Visualizing patterns of objections and agreements in the dialogue enabled us to chart how clusters of participants gradually learned to grasp and interpret the simulated effects of individual and policy decisions even as they struggled to incorporate them into their deliberations.

The environmental planning literature proposes a set of ‘best management practices’ for urban development that assumes improvement in environmental quality as a result of specific urban patterns. These best management practices, however, often do not recognise finite biophysical limits and social impacts that urban patterns alone cannot overcome. To shed light on this debate, we explore the effects of different degrees of urban clustering on groundwater levels using a coupled land-use change and groundwater-flow model. Our simulations show that specific urban forms only slow down the impact on groundwater. As population increases, the pattern in which it is accommodated ceases to matter, and widespread depletion ensues. These results are predictable, yet current planning practice tends to take growth for granted and is reluctant to envision either no-growth scenarios or the prospect of depletion. We propose to use simulations such as those presented here to aid in policy discussions that allow decision ma...

The introduction of Bus Rapid Transit (BRT), typically involving the use of exclusive bus lanes and related bus priority measures, is increasingly advocated as a flexible and cost-effective way of improving the attractiveness of public transit in congested urban areas by reducing travel times and variability. These schemes typically involve the reallocation of road space for exclusive use by buses, presenting commuters with potentially competing incentives: buses on BRT routes can run faster and more efficiently than buses running in general traffic, potentially attracting commuters to public transit and reducing congestion through modal shift from cars. However, a secondary impact may also exist; remaining car users may be presented with less congested road space, improving their journey times and simultaneously acting as an incentive for some bus-users to revert to the car. To investigate the potential for these primary and secondary impacts, we develop a prototype agent-based model to investigate the nature of these interactions and how they play out into system-wide patterns of modal share and travel times. The model allows us to test the effects of multiple assumptions about the behaviors of individual agents as they respond to different incentives introduced by BRT policy changes, such as the implementation of exclusive bus lanes, increased bus frequency, pre-boarding ticket machines and express stops, separately and together. We find that, under our assumptions, these policies can result in significant improvements in terms of individual journey times, modal shift, and length of rush hour. We see that the addition of an exclusive bus lane results in significant improvements for both car users and bus riders. Informed with appropriate empirical data relating to the behavior of individual agents, the geography and the specific policy interventions, the model has the potential to aid policymakers in examining the effectiveness of different BRT schemes, applied to broader environments.

An important debate in the literature on exurban sprawl is whether low-density development results from residential demand, as operationalized by developers, or from exclusionary zoning policies. Central to this debate is the purpose of zoning, which could alternatively be a mechanism to increase the utility of residents by separating land uses and reducing spillover effects of development, or an obstacle to market mechanisms that would otherwise allow the realization of residential preferences. To shed light on this debate, we developed an agent-based model of land-use change to study how the combined effects of zoning-enforcement levels, density preferences, preference heterogeneity, and negative externalities from development affect exurban development and the utility of residents. Our computational experiments show that sprawl is not inevitable, even when most of the population prefers low densities. The presence of negative externalities consistently encourage sprawl while decreasing average utility and flattening the utility distribution. Zoning can reduce sprawl by concentrating development in specific areas, but in doing so decreases average utility and increases inequality. Zoning does not internalize externalities; instead, it contains externalities in areas of different development density so that residents bear the burden of the external effects of the density they prefer. Effects vary with residential preference distributions and levels of zoning enforcement. These initial investigations can help inform policy makers about the conditions under which zoning enforcement is preferable to free-market development and vice versa. Future work will focus on the environmental impacts of different settlement patterns and the role land-use and market-based policies play in this relationship.

Theoretical urban policy literature predicts the likelihood of free riding in the management of common goods such as forested open space; such outcome is often characterized as a Prisoner's Dilemma game. Numerous cases exist in which neighboring jurisdictions cooperate to maintain public goods, challenging the expected results, yet theoretical explanations of these cases have not been fully developed. In this paper, we use an agent-based model to explore how underlying micro-behaviors affect the payoffs obtained by two neighboring municipalities in a hypothetical exurban area. Payoffs are measured in terms of regional forested space and of local tax revenue at the end of the agent-based simulations; the municipalities affect these payoffs through their choice of residential zoning policies and the spillover effect between the neighboring jurisdictions. Zoning restrictions influence the conversion of farmland into residential subdivisions of different types, and consequently the location of heterogeneous residential households in the region. Developers and residents respond to the changing landscape characteristics, thus establishing a feedback between early and future land-use patterns. The structure of the simulated payoffs is analyzed using standard game theory. Our analysis shows that a variety of games, in addition to Prisoner's Dilemma, can emerge between the neighboring jurisdictions. Other games encourage coordination or subsidization, offering some explanations for the unexpected observations. The game realized in any given context depends on the initial characteristics of the landscape, the value given to the objectives each township seeks to maximize, and the income distribution of the population.

Environmental degradation is often defined as a public goods problem, emerging when property rights are not clearly defined and costs are externalized to other parties. Proposing corrective regulation that enforces technological fixes or market-based approaches is often met with political resistance and doubts about its effectiveness. This is partly due to the complexity of interacting physical and socio-economic components that obscure the impacts of human decision-making on environmental functions. Yet, understanding the complexity of integrated human-environmental systems can help planners and stakeholders frame environmental problems, view their role in them and design effective policies to address them. This article examines the potential and limitations of agent-based models as metaphors that can contribute to the understanding of such complex systems, illustrating the argument with a hypothetical application in groundwater management.

In this paper, we identify two distinct notions of accuracy of land-use models and highlight a tension between them. A model can have predictive accuracy: its predicted land-use pattern can be highly correlated with the actual land-use pattern. A model can also have process accuracy: the process by which locations or land-use patterns are determined can be consistent with real world processes. To balance these two potentially conflicting motivations, we introduce the concept of the invariant region, i.e., the area where land-use type is almost certain, and thus path independent; and the variant region, i.e., the area where land use depends on a particular series of events, and is thus path dependent. We demonstrate our methods using an agent-based land-use model and using multitemporal land-use data collected for Washtenaw County, Michigan, USA. The results indicate that, using the methods we describe, researchers can improve their ability to communicate how well their model performs, the situations or instances in which it does not perform well, and the cases in which it is relatively unlikely to predict well because of either path dependence or stochastic uncertainty.