Environment and Planning B: Urban Analytics and City Science

This paper examines the relationship between patterns of trip chaining and urban form. The goal is to examine whether lower density environments are related to more frequent reliance upon trip chaining and more complex tours. The analysis uses the 2001 National Household Travel Survey to evaluate household individual travel and trip characteristics alongside a basic measure of residential density. Two estimation techniques, the ordered probit and the negative binomial model, are used to evaluate the factors associated with the tendency to combine trips into more complex tours, measured as the number of stops. The results indicate that, accounting for key household and traveler characteristics, lower density environments lead both to a greater reliance upon trip chaining and to tours that involve more stops along the way. This is followed by a household level analysis of tour generation. Crane and Krizek have suggested that more accessible areas will tend to generate more tours. However, we found no evidence for this in our analysis.

Previously, GIS-based visibility analysis has been conducted mainly in two dimensions, based on the concept of an isovist in the built environment or the concept of a viewshed in terrain and landscape analysis. The Viewsphere, a GIS approach towards 3D visibility analysis is proposed for measuring visible urban space quantitatively in a way that is different from its predecessors, the isovist and the viewshed. A test case of Singapore's urban space was conducted by evaluating the visibility of three alternative urban design scenarios and their potential impacts on the visual quality of open space. Both directional and nondirectional approaches were applied to the mapping of visibility based on the 2D and 3D indices. The proposition that 3D visibility indices are more effective than 2D indices was verified. The findings show that the 3D indices are sensitive to the changes of z-dimension.

This paper draws on the importance of public participation in improving several aspects of the land-use planning and decision-making process and regards the lack of adequate tools as an important barrier hindering effective implementation of participation in planning practice. The hypothesis is that extensive research into local knowledge, especially regarding values and goals, followed by a careful and focused preparation of expert proposals could improve the effectiveness of the participatory process. The paper therefore focuses on the development of methodologies to obtain knowledge from local residents and to integrate it effectively with expert knowledge to produce an input for the communicative process, whereby interaction and communication can bring about consensual planning proposals. The methodology discussed in this paper is an innovative combination of several tools already recognized within spatial and/or participatory planning, such as public surveys and participatory workshops. Connecting these tools can be shown to ameliorate their individual drawbacks and to achieve synergetic effects in all aspects. The use of support tools, such as cognitive mapping, statistical analysis, and suitability modeling, to assist these processes is also discussed. The study was implemented as a pilot project testing the proposed methodology in a case study of land-use planning for a local community in Slovenia, which is currently undergoing significant change from a rural community into an urban community. The results have shown that the use of a traditional questionnaire, combined with the mapping of chosen responses, proved effective in the acquisition of existing local knowledge. The result also showed that in-depth analysis of local knowledge and values and consideration of both in preparing alternative expert planning proposals proved to be a valuable input into traditional participation workshops, thus fostering an interactive participative planning process, whereby conflicts could be resolved while searching for a consensual solution.

Technology incubators have emerged in many places as a tool in facilitating the establishment and survival of high-technology firms. Some incubators develop quickly and produce a fast-increasing number of new ventures, while others remain stable in size. Despite a growing public investment in technology incubators, systematic studies of the factors determining their growth are scarce, meaning that policy decisions are taken without sufficient practical insights into critical conditions for growth. In response to that situation, we explore several factors in determining differences in growth patterns. We use a quantitative approach derived from the field of artificial intelligence that matches with meta-analysis and qualitative (and sometimes fuzzy) data—that is, rough set analysis. Benefits and challenges of rough set analysis are discussed, including experience with a stepwise procedure with various accuracy checks. The findings suggest that a strong performance of incubators mainly rests on diversity in stakeholder involvement and a location in nonmetropolitan areas. Rough set analysis turns out to be a helpful tool in comparative project analysis, but there is still a need for standardization of measures used in the interpretation of the results.

People experience and memorize space primarily with the help of landmarks. These landmarks have structural salience, besides visual and semantic salience. When people move in urban space they perceive first the street network as structuring this space. Therefore, streets are a good candidate for investigating structural salience. This paper investigates different structural representations of the urban fabric, and measures to describe the structural salience especially of elements of the street network and dependent elements. The measures are taken from topology and network analysis. The goal is to identify a generic model of structural salience for urban elements that favors the automatic identification of references for route directions. The proposed model is illustrated by a case study applied to a small city in northern France.

Traditionally, researchers have used elaborate regression models to simulate the retail petrol market. Such models are limited in their ability to model individual behaviour and geographical influences. Heppenstall et al presented a novel agent-based framework for modelling individual petrol stations as agents and integrated important additional system behaviour through the use of established methodologies such as spatial interaction models. The parameters for this model were initially determined by the use of real data analysis and experimentation. This paper explores the parameterisation and verification of the model through data analysis and by use of a genetic algorithm (GA). The results show that a GA can be used to produce not just an optimised match, but results that match those derived by expert analysis through rational exploration. This may suggest that despite the apparent nonlinear and complex nature of the system, there are a limited number of optimal or near optimal behaviours given its constraints, and that both user-driven and GA solutions converge on them.

In the planning of apartment houses in Seoul, it is typical that the stereotyped building form strongly regulates the spatial arrangement of the interior. This research therefore attempts to combine two different approaches, namely the formal approach and the spatial approach, for the comprehensive understanding of the architectural grammar. To bridge the technical gap between these two approaches, a new graph-theoretical method is introduced by which configurational information is linked to a shape generation model. From a series of analyses it is found that there exist clear principles that generate all the typical house plans in the city.

In this study we investigate spatial dimensions of housing-market dynamics in the City of Milwaukee by modeling the determinants of housing prices. From the 2003 Master Property data file of the city, two sets of owner-occupied single-family houses were randomly selected (one to construct the models, and the other to rest the models). Besides conventional housing attributes, remote-sensing information, in particular the fractions of soil and impervious surface representing degraded neighborhood environment conditions, is added to improve the model. Spatial regression and geographically weighted regression approaches are employed to examine spatial dependence and heterogeneity. Results reveal that these spatial models tend to perform better, especially in terms of model performance and predictive accuracy, than the ordinary least squares estimates.

A critical infrastructure (CI) is an array of assets and systems that, if disrupted, would threaten national security, economy, public health and safety, and way of life. Essential to the practice of critical infrastructure planning and drills are two pieces of knowledge. One concerns the interactions within a CI system (intradomain interdependencies), and the other concerns the interactions among the CI systems (cross-domain interdependencies). A thorough understanding of these two interwoven CI interdependencies is crucial to such tasks as vulnerability assessment, scenario composition, and homeland security drills. In this paper we present a new approach that facilitates the learning of the interdependencies. Employing a loosely coupled system of GIS and an ontology-based object modeling system developed in this study, it represents and visualizes the intradomain and cross-domain CI interdependencies both diagrammatically and geographically. The system and its knowledge representation methodology were tested through a case study in the Southeastern United States.