Can geocomputation save urban simulation? Throw some agents into the mixture, simmer and wait ...

There are indications that the current generation of simulation models in practical, operational uses has reached the limits of its usefulness under existing specifications. The relative stasis in operational urban modeling contrasts with simulation efforts in other disciplines, where techniques, theories, and ideas drawn from computation and complexity studies are revitalizing the ways in which we conceptualize, understand, and model real-world phenomena. Many of these concepts and methodologies are applicable to operational urban systems simulation. Indeed, in many cases, ideas from computation and complexity studies—often clustered under the collective term of geocomputation, as they apply to geography—are ideally suited to the simulation of urban dynamics. However, there exist several obstructions to their successful use in operational urban geographic simulation, particularly as regards the capacity of these methodologies to handle top-down dynamics in urban systems. This paper presents a framework for developing a hybrid model for urban geographic simulation and discusses some of the imposing barriers against innovation in this field. The framework infuses approaches derived from geocomputation and complexity with standard techniques that have been tried and tested in operational land-use and transport simulation. Macro-scale dynamics that operate from the topdown are handled by traditional land-use and transport models, while micro-scale dynamics that work from the bottom-up are delegated to agent-based models and cellular automata. The two methodologies are fused in a modular fashion using a system of feedback mechanisms. As a proof-of-concept exercise, a micro-model of residential location has been developed with a view to hybridization. The model mixes cellular automata and multi-agent approaches and is formulated so as to interface with meso-models at a higher scale

Introducing Preservice STEM Teachers to Computer Science: A Narrative of Theoretically Oriented Design

This paper narrates the process of designing a curricular unit that serves to introduce preservice science, technology, engineering, and mathematics (STEM) teachers to computer science (CS) education. Unlike most literature that focuses on results and findings, this paper explains how a justice-centered approach to CS education informed decisions about the theoretical underpinnings of curricular design choices. Situated in issues related to the gentrification of Austin, Texas, the described curricular unit explores how the increased use of CS and growth of the technology sector are having a direct impact on the historically marginalized residents of East Austin. Connected by a theme that maps are both a form of data visualization and political artifact, the described curricular unit uses CS as a tool to: critique the macro-ethics of politics and society; provide a CS learning environment that can be responsive to the multiple social identities of students; and connect CS to larger struggles for justice and liberation.Educatio

From buildings to cities: techniques for the multi-scale analysis of urban form and function

The built environment is a significant factor in many urban processes, yet direct measures of built form are seldom used in geographical studies. Representation and analysis of urban form and function could provide new insights and improve the evidence base for research. So far progress has been slow due to limited data availability, computational demands, and a lack of methods to integrate built environment data with aggregate geographical analysis. Spatial data and computational improvements are overcoming some of these problems, but there remains a need for techniques to process and aggregate urban form data. Here we develop a Built Environment Model of urban function and dwelling type classifications for Greater London, based on detailed topographic and address-based data (sourced from Ordnance Survey MasterMap). The multi-scale approach allows the Built Environment Model to be viewed at fine-scales for local planning contexts, and at city-wide scales for aggregate geographical analysis, allowing an improved understanding of urban processes. This flexibility is illustrated in the two examples, that of urban function and residential type analysis, where both local-scale urban clustering and city-wide trends in density and agglomeration are shown. While we demonstrate the multi-scale Built Environment Model to be a viable approach, a number of accuracy issues are identified, including the limitations of 2D data, inaccuracies in commercial function data and problems with temporal attribution. These limitations currently restrict the more advanced applications of the Built Environment Model

A theory driven, spatially explicit agent-based simulation to model the economic and social implications of urban regeneration

We model the economic mechanics of housing regeneration employing the rent-gap theory proposed by Neil Smith in 1979. We discuss the conditions for successful regeneration in theory, using an abstract representation of a city, then try and evaluate the possible outcomes of an actual regeneration programme in Salford, England in terms of property prices and area social composition

The individual agent makes a difference in segregation simulation

Urban social segregation modeling from the bottom up attempts at understanding the processes which take place when residents look for a new home. This micro-scale perspective thus requires implementing actual individual agents instead of socially unified communities with similar or identical behavior. Complementary, meso- and macro-scale determinants such as housing markets, estate agencies, urban planning institutions, and societal life-style preferences must be incorporated in order to comprehensively and adequately simulate residential mobility. The paper presents an attempt to simulate urban socio-spatial segregation for the city of Salzburg, Austria, by consistently taking the individual household scale into account. We first apply the beneficial features of a Schelling-style simulation model by also taking macro-social regularities into account. This is followed by a description of an adapted segregation model that includes the mentioned requirements. The paper concludes with an extensive presentation and discussion of the model results achieved so fa

Risky Business: Sustainability and Industrial Land Use across Seattle’s Gentrifying Riskscape

This paper examines the spatial and temporal trajectories of Seattle’s industrial land use restructuring and the shifting riskscape in Seattle, WA, a commonly recognized urban model of sustainability. Drawing on the perspective of sustainability as a conflicted process, this research explored the intersections of urban industrial and nonindustrial land use planning, gentrification, and environmental injustice. In the first part of our research, we combine geographic cluster analysis and longitudinal air toxic emission comparisons to quantitatively investigate socioeconomic changes in Seattle Census block-groups between 1990, 2000, and 2009 coupled with measures of pollution volume and its relative potential risk. Second, we qualitatively examine Seattle’s historical land use policies and planning and the growing tension between industrial and nonindustrial land use. The gentrification, green cities, and growth management conflicts embedded within sustainability/livability lead to pollution exposure risk and socioeconomic vulnerability converging in the same areas and reveal one of Seattle’s significant environmental challenges. Our mixed-method approach can guide future urban sustainability studies to more effectively examine the connections between land use planning, industrial displacement, and environmental injustice. Our results also help sustainable development practitioners recognize that a more just sustainability in Seattle and beyond will require more planning and policy attention to mitigate obscured industrial land use conflicts

Spatial Heterogeneity in Spillover Effects of Assisted and Unassisted Rental Housing

Three new contributions are added to the literature on subsidized rental housing impacts on nearby property values: 1) A primary focus on the spatial heterogeneity of these effects which warrants caution regarding citywide results; 2) an analysis by zoning area, and 3) a comparison of impacts with unsubsidized apartments. An adjusted-interrupted time series (difference-in-difference) model is estimated with a comprehensive dataset for Seattle, WA (1987-97). Contrary to NIMBY expectations, the predominant impact is an upgrading effect of lower-value areas. However, spillover effects are very sensitive to how data are pooled across space: The citywide upgrading effects are driven by poorer pockets adjacent to affluent areas with no or small effects in more diverse low- and medium income areas. They only occur in single-family, not multi-family zones. The only negative effects were associated with vouchers in one of the affluent areas. Impacts of unsubsidized rentals are very similar to those of subsidized ones, suggesting an independent effect beyond subsidy status. These findings are explained with Seattle's dispersion and good neighbor policies, with gentrification pressures as a possible alternative explanation. Site visits confirmed the location of subsidized sites in lower-value areas and the higher maintenance quality of subsidized vis-à-vis unsubsidized units.