The authors present the results of a neighborhood-scaled exploratory study that tests the association of the
food environment and the built environment with women’s body mass index (BMI) in Erie County, New
York. The proximity of women’s homes to a supermarket relative to a convenience store is associated with
lower BMI. A diverse land use mix in a neighborhood is positively associated with women’s BMI, especially
when restaurants dominate nonresidential land use. The article offers suggestions for how food environments
may be improved using planning strategies
With the threat of wildfire hanging over many communities in the Western and Southern United States, wildfire mitigation is evolving into a significant public responsibility for rural and urban edge county governments. Regional governance is an important piece of the effort to reduce wildfire risks although still weakly developed as a policy arena. This project explores two dimensions in which planning support systems can support regional governance: assessing patterns of wildfire risk accumulation; and, evaluating land use planning alternatives and their effects on cumulative risk levels. These tools are examined for regional governance using a prototype planning information system, the Alternative Growth Futures (AGF) tool, a scenario-building approach developed at the University of Colorado Denver. The project develops a hybrid urban growth model that integrates logistic regression techniques and methods for simulation of growth alternatives. This model is used to evaluate the attractiveness of undeveloped building sites with respect to natural amenities, distance to primary urban services and site characteristics such as slope. The model and scenario-testing framework are reasonably robust and suggest that regional spatial accounting methods have potential as a framework for inter-governmental and public discussion around wildfire planning.
Much of the taxi route-planning literature has focused on driver strategies for finding passengers and determining the hot spot pick-up locations using historical global positioning system (GPS) trajectories of taxis based on driver experience, distance from the passenger drop-off location to the next passenger pick-up location and the waiting times at recommended locations for the next passenger. The present work, however, considers the average taxi travel speed mined from historical taxi GPS trajectory data and the allocation of cruising routes to more than one taxi driver in a small-scale region to neighboring pick-up locations. A spatio-temporal trajectory model with load balancing allocations is presented to not only explore pick-up/drop-off information but also provide taxi drivers with cruising routes to the recommended pick-up locations. In simulation experiments, our study shows that taxi drivers using cruising routes recommended by our spatio-temporal trajectory model can significantly reduce the average waiting time and travel less distance to quickly find their next passengers, and the load balancing strategy significantly alleviates road loads. These objective measures can help us better understand spatio-temporal traffic patterns and guide taxi navigation.
Race and class factors have been studied as underlying causes of segregation for many
years. Individual choices on race and economic constraints of living in one area versus
another play an important role in residential segregation. An attempt has not yet been
made to simulate the interplay of neighborhood racial and economic composition
in forming segregation using empirical micro-level data. Using City of Buffalo data,
this study explores how individuals’ housing location choices with respect to racial
composition and housing sale prices in their neighborhoods can give rise to aggregate
patterns of residential segregation and how segregation at one point in time was
contributing to increased segregation at later stages. The results show that observed
patterns of segregation in the city could plausibly arise from the interaction of racial
and economic factors. This study also demonstrates the application of such models
on exploring the possible effects of proposed integration efforts.
Over the past thirty years, recreation communities in many parts of the globe have gone through cycles of diversification and integration into complex recreation regions. As resort communities mature, they face increasing pressures on scarce recreational resources, demands for economic diversification, and changing attitudes toward tourism on the part of local residents. A variety of land-use management practices and economic development initiatives has emerged in resort towns in response to resource congestion and other growth issues. In this paper we explore alternative growth strategies through a simulation of housing decisions by primary actors in resort land markets. We use a multi-agent system to model the dynamics of growth regimes, assess the influence of recreational and town amenities, and evaluate the effect of alternative growth processes on long-term development patterns. Our case study area is Steamboat Springs and surrounding parts of Routt County, a four-season recreational region in northwestern Colorado.
Many resort communities in the U.S. Rocky Mountain West are experiencing rapid in-migration and growth because the natural and built amenities in those areas attracted people and investment. This study uses an agent-based model to explore how homeowners’ investment and reinvestment decisions are influenced by the level of investment and amenities available in their neighborhoods in a case study area of town of Breckenridge, Colorado to help understand the dynamics and the indirect spatial impacts of amenity-led mountain tour-ism development. This paper found that individual level of appreciation of amenities and continuing investment in a neighborhood attracted investment and reinvestment, and created pressure for high density resort housing development at the aggregate level. Agent-based model is a useful tool to simulate the dynamics behind the housing investment and reinvestment and to investigate the indirect spatial effects of high-density resort development.
Abstract. Agent-based models offer a promising framework for analyzing interactions between agents
and a heterogeneous landscape. Researchers have identified a complex of factors that influence
exurban development, including demographic shifts and location attractiveness of natural amenities
as a magnet to amenity-seeking migrants. Attractiveness is often defined in terms of local or on-lot
amenities, including scenic views, the availability of natural features, and low levels of noise. However, exurban-growth models have not fully incorporated a fundamental insight of this literature, that
the location behavior of exurban residents is sensitive to fine-grained variations in their biophysical
environment. In this study we evaluate how agents and households operate in exurban environments
and respond to biophysical features. We simulate household decision-making in terms of preferences
for features such as site accessibility, two-dimensional amenities, and three-dimensional scenic views.
Our results show that, as we build two-dimensional and three-dimensional landscape layers, our
model captures the characteristics of landscape change with increasing accuracy. This approach has
considerable potential to improve our ability to describe development dynamics in heterogeneous
Affinity propagation (AP) is a clustering algorithm for point data used in image recognition that can be used to solve various problems, such as initial class representative point selection, large-scale sparse matrix calculations, and large-scale data with fewer parameter settings. However, the AP clustering algorithm does not consider spatiotemporal information and multiple thematic attributes simultaneously, which leads to poor performance in discovering patterns from massive spatiotemporal points (e.g., trajectory points). To resolve this issue, a multidimensional spatiotemporal affinity propagation (MDST-AP) algorithm is proposed in this study. First, the similarity of spatial and nonspatial attributes is measured in Gaussian kernel space instead of Euclidean space, which helps address the multidimensional linear inseparability problem. Then, the Davies-Bouldin (DB) index is applied to optimize the parameter value of the MDST-AP algorithm, which is applied to analyze road congestion in Beijing via taxi trajectories. Experiments on different datasets and algorithms indicated that the MDST-AP algorithm can process multidimensional spatiotemporal data points faster and more effectively.