Search

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.

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’)...

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.

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.

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.

This paper examines the effect of land use regulations on travel behavior by using agent-based modeling. A simulation model for a hypothetical urban area loosely based on the Chicago, Illinois, metropolitan area was used to study the impact of six land use regulation scenarios on transit use and urban form. The key features and techniques of the model development and the scenarios tested are described. The results from the simulations showed that although the land use regulations that were designed to increase the density near the transit station or in and near the urban core were able to achieve the intended land use patterns, they did not increase the transit mode share for the region in a significant manner. More detailed examination of the output revealed that as long as the rules for mode choice, the distribution of employment, and the transit network remained unchanged, land use regulations that affect residential locations produced limited effects on transit use.

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.