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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.

Conservation organizations undertaking ecological restoration and the lands they manage constitute a social-ecological system (SES). We implemented SES analysis to examine the relationship between diversity in organizational structure and restoration planning processes, and vegetation outcomes on the ground. Understanding the restoration consequences of multiple approaches to planning and implementation is relevant to assessing the resilience of this SES, especially if disagreements about the effectiveness of some approaches lead to conflict in the socio-political arena. We studied 10 conservation organizations in the Chicago Wilderness region that are restoring Midwestern oak woodlands of global conservation concern. Despite the institutional diversity of these organizations, we found little relationship between restoration planning and vegetation outcomes. This result has implications for the resilience of restoration as an SES, since similar outcomes from diverse processes should increase resilience of this SES, especially when controversial restoration practices are employed, and when priorities and funding levels change.

Understanding the factors that determine invasion success for non‐native plants is crucial for maintaining global biodiversity and ecosystem functioning. One hypothesized mechanism by which many exotic plants can become invasive is through the disruption of key plant–mycorrhizal mutualisms, yet few studies have investigated how these disruptions can lead to invader success. We present an individual‐based model to examine how mutualism strengths between a native plant (Impatiens capensis) and mycorrhizal fungus can influence invasion success for a widespread plant invader, Alliaria petiolata (garlic mustard). Two questions were investigated as follows: (a) How does the strength of the mutualism between the native I. capensis and a mycorrhizal fungus affect resistance (i.e., native plant maintaining >60% of final equilibrium plant density) to garlic mustard invasion? (b) Is there a non‐linear relationship between initial garlic mustard density and invasiveness (i.e., garlic mustard representing >60% of final equilibrium plant density)? Our findings indicate that either low (i.e., facultative) or high (i.e., obligate) mutualism strengths between the native plant and mycorrhizal fungus were more likely to lead to garlic mustard invasiveness than intermediate levels, which resulted in higher resistance to garlic mustard invasion. Intermediate mutualism strengths allowed I. capensis to take advantage of increased fitness when the fungus was present but remained competitive enough to sustain high numbers without the fungus. Though strong mutualisms had the highest fitness without the invader, they proved most susceptible to invasion because the loss of the mycorrhizal fungus resulted in a reproductive output too low to compete with garlic mustard. Weak mutualisms were more competitive than strong mutualisms but still led to garlic mustard invasion. Furthermore, we found that under intermediate mutualism strengths, the initial density of garlic mustard (as a proxy for different levels of plant invasion) did not influence its invasion success, as high initial densities of garlic mustard did not lead to it becoming dominant. Our results indicate that plants that form weak or strong mutualisms with mycorrhizal fungi are most vulnerable to invasion, whereas intermediate mutualisms provide the highest resistance to an allelopathic invader.

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’)...

In environmental participatory modeling (PM), both computer and non-computer-based modeling techniques are used to aid participatory problem description, solution, and decision-making actions in environmental contexts. Although many PM case studies have been published, few efforts have sought to systematically describe and understand dominant PM processes or establish best practices for PM. As a first step, we have reviewed a random sample of environmental PM case study articles (n = 60) using a novel PM process evaluation instrument. We found that significant work likely remains for PM to fully support participatory and integrated planning processes. While PM reports systematically address knowledge integration and learning, they often neglect the facilitation of a multi-value perspective within a democratic process, and the integration across organizations within a governance system. If not reported, we suspect these aspects are also neglected in practice. We conclude with key research and practice issues for improving PM as an approach for real-world participatory planning and governance.

Including stakeholders in environmental model building and analysis is an increasingly popular approach to understanding ecological change. This is because stakeholders often hold valuable knowledge about socio-environmental dynamics and collaborative forms of modeling produce important boundary objects used to collectively reason about environmental problems. Although the number of participatory modeling (PM) case studies and the number of researchers adopting these approaches has grown in recent years, the lack of standardized reporting and limited reproducibility have prevented PM's establishment and advancement as a cohesive field of study. We suggest a four-dimensional framework (4P) that includes reporting on dimensions of (1) the Purpose for selecting a PM approach (the why); (2) the Process by which the public was involved in model building or evaluation (the how); (3) the Partnerships formed (the who); and (4) the Products that resulted from these efforts (the what). We highlight four case studies that use common PM software-based approaches (fuzzy cognitive mapping, agent-based modeling, system dynamics, and participatory geospatial modeling) to understand human–environment interactions and the consequences of ecological changes, including bushmeat hunting in Tanzania and Cameroon, agricultural production and deforestation in Zambia, and groundwater management in India. We demonstrate how standardizing communication about PM case studies can lead to innovation and new insights about model-based reasoning in support of ecological policy development. We suggest that our 4P framework and reporting approach provides a way for new hypotheses to be identified and tested in the growing field of PM.

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.

Transit in the United States often suffers from the problem of inability to deliver travelers all the way from their point of origin to their destination. This “last-mile” problem is thought to deter transit use among riders with auto access, even when high quality transit service is provided for the majority of the trip distance. This study explores how transportation improvements, including automated driverless shuttles between origins of trips and nearby transit stations, and physical improvements enhancing pedestrians’ and cyclists’ commute might help overcome the last-mile problem particularly as they interact with policy shifts including changing in parking and fuel costs. To conduct this study, the authors developed an agent-based model representing the commuters and their preferences for different aspects of transportation disutility, namely cost, time and safety. Commuters in the model assess their transportation options in light of their preferences, the characteristics of their environment, and the various modes available to them. The model is calibrated with data from four Chicago neighborhoods, representing four different combinations of land-use patterns and household income. Simulations suggest significant potential for the combined shuttles and urban design improvements to shift downtown commuters to non-automotive modes (between 12 and 21 percentage point reduction in driving in three out of four neighborhoods). Less dense neighborhoods were more sensitive to higher parking costs, streetscape improvements and shuttle service than the denser and more pedestrian-oriented neighborhoods. Distance from the station encouraged driving, but the presence of shuttles encouraged shifts towards transit. Streetscape improvements tended to support transit use closer to train stations. In addition to anticipating a range of likely mode choice outcomes, the agent-based modeling approach facilitates exploration of the mechanism underlying travelers’ behavior. Rather than modeling through data fitting, this approach involved formulating theory of behavior first, using data to parameterize the conceptual model, and running simulations to see how the outputs would match observations. When discrepancies arose, the authors advanced the theory and reformulated the conceptual model to explain them. In this way, the authors found that a dense bus service shuttling travelers towards the commuter train station with express service downtown was critical in encouraging transit use, and extensive bus coverage throughout another neighborhood encouraged bus use to access downtown. Bike penalties representing various difficulties inherent to this mode (e.g., lack of physical fitness, the need for showering facilities at the destination, etc.) needed to be adjusted to higher values than those typically found in the literature, suggesting greater barriers to biking in this metropolitan area. Finally, the authors had hypothesized that pedestrian and biker presence would represent an important feedback promoting shifts away from driving, but this was not the case. Further in-depth empirical research is needed to improve the conceptual models of this feedback, and to understand how policy can leverage it to encourage greater transit, pedestrian and bicycle use.

We present Hydroman, a flexible spatially explicit model coupling human and hydrological processes to explore shallow water tables and land cover interactions in flat agricultural landscapes, modeled after the Argentine Pampas. With fewer parameters, Hydroman aligned well with established hydrological models, and was validated with observed water table patterns and crop yield data. Simulations with different climate, phreatic and land cover conditions confirmed that climate remains the main driver, but crops also influence water levels and yields, depending on the growing cycle. We also examined the impacts of two alternative sowing strategies. Risk aversion proves robust in minimizing crop losses, but often results in less sowing, exacerbating flooding. Strict rotators risk more, but help stabilize the groundwater levels. Reintroducing pasture further stabilizes the system. Future work will engage farmers to derive and assess land cover strategies that maximize yield and minimize losses, and transfer our modeling approach to other applications.

© 2018 Elsevier Ltd Various tools and methods are used in participatory modelling, at different stages of the process and for different purposes. The diversity of tools and methods can create challenges for stakeholders and modelers when selecting the ones most appropriate for their projects. We offer a systematic overview, assessment, and categorization of methods to assist modelers and stakeholders with their choices and decisions. Most available literature provides little justification or information on the reasons for the use of particular methods or tools in a given study. In most of the cases, it seems that the prior experience and skills of the modelers had a dominant effect on the selection of the methods used. While we have not found any real evidence of this approach being wrong, we do think that putting more thought into the method selection process and choosing the most appropriate method for the project can produce better results. Based on expert opinion and a survey of modelers engaged in participatory processes, we offer practical guidelines to improve decisions about method selection at different stages of the participatory modeling process.

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.

Artificial lighting has allowed the decoupling of human activities from natural daylight hours. Electricity utilized for artificial lighting accounts for 18.8% of U.S. electricity consumption. Compact fluorescent lamp (CFL) and light-emitting diode (LED) options are more efficient and have longer lifetimes than conventional incandescent bulbs, but the question remains about the actual energy savings likely to be realized through more efficient lighting delivery systems. This uncertainty influences the rate of adoption and use of efficient lighting technology (and thus the extent and time lags of efficiency gains). Once adopted, gains in efficiency can lead to rebound effects that eliminate these gains and, paradoxically, lock society into increased use of energy. In this study, an agent-based model and complex systems approach is used to understand how available information and perceptions of different lighting options influence adoption and use, and the potential impact of the rebound effect to reduce the energy savings of energy-efficient lighting options in a residential setting. Individual households and their decisions are modeled to create overall population-level consumption data. The multifunctionality of LED lighting may cause consumers to use significantly more light, creating the potential for both rebound and backfire to occur. The results indicate that the adoption of CFL and LED lighting will decrease residential energy consumption if consumers continue to use the same amount or slightly more light; however, when an expansion of lit spaces is included or a large increase in lighting usage occurs, energy consumption will increase and, over time, reduce or completely erode energy savings.

Modeling is essential to characterize and explore complex societal and environmental issues in systematic and collaborative ways. Socio-environmental systems (SES) modeling integrates knowledge and perspectives into conceptual and computational tools that explicitly recognize how human decisions affect the environment. Depending on the modeling purpose, many SES modelers also realize that involvement of stakeholders and experts is fundamental to support social learning and decision-making processes for achieving improved environmental and social outcomes. The contribution of this paper lies in identifying and formulating grand challenges that need to be overcome to accelerate the development and adaptation of SES modeling. Eight challenges are delineated: bridging epistemologies across disciplines; multi-dimensional uncertainty assessment and management; scales and scaling issues; combining qualitative and quantitative methods and data; furthering the adoption and impacts of SES modeling on policy; capturing structural changes; representing human dimensions in SES; and leveraging new data types and sources. These challenges limit our ability to effectively use SES modeling to provide the knowledge and information essential for supporting decision making. Whereas some of these challenges are not unique to SES modeling and may be pervasive in other scientific fields, they still act as barriers as well as research opportunities for the SES modeling community. For each challenge, we outline basic steps that can be taken to surmount the underpinning barriers. Thus, the paper identifies priority research areas in SES modeling, chiefly related to progressing modeling products, processes and practices.

Big Data is the term being used to describe a wide spectrum of observational or “naturally-occurring” data generated through transactional, operational, planning and social activities that are not specifically designed for research. Due to the structure and access conditions associated with such data, their use for research and analysis becomes significantly complicated. New sources of Big Data are rapidly emerging as a result of technological, institutional, social, and business innovations. The objective of this background paper is to describe emerging sources of Big Data, their use in urban research, and the challenges that arise with their use. To a certain extent, Big Data in the urban context has become narrowly associated with sensor (e.g., Internet of Things) or socially generated (e.g., social media or citizen science) data. However, there are many other sources of observational data that are meaningful to different groups of urban researchers and user communities. Examples include privately held transactions data, confidential administrative micro-data, data from arts and humanities collections, and hybrid data consisting of synthetic or linked data.

The chapters in this book were first presented in a 2-day workshop on Big Data and Urban Informatics held at the University of Illinois at Chicago in 2014. The workshop, sponsored by the National Science Foundation, brought together approximately 150 educators, practitioners and students from 91 different institutions in 11 countries. Participants represented a variety of academic disciplines including Urban Planning, Computer Science, Civil Engineering, Economics, Statistics, and Geography and provided a unique opportunity for discussions by urban social scientists and data scientists interested in the use of Big Data to address urban challenges. The papers in this volume are a selected subset of those presented at the workshop and have gone through a peer-review process.