Environment and Planning B: Urban Analytics and City Science

In recent decades several methods have been proposed to simulate land-use changes in a spatially explicit way. In these models land is generally represented on a lattice with cell states indicating the predominant land use. Since a cell can have only one state, mixed land uses and different densities of one land use can only be introduced superficially, as separate cell states. In this paper we describe a cellular automata model that simulates dynamics in both land uses and activities, where activities represent quantitative information, such as the number of inhabitants at a location. Therefore each cell has associated with it (1) a value representing one of a finite set of land-use classes, and (2) a vector of numerical values representing the quantity of each modelled activity that is present at that location. This allows simulation of incremental changes as well as mixed land uses. The proposed model is tested with a synthetic application that uses two activities: population and jobs. It simulates the emergence of human settlements over time from local interactions between activities and land uses. Assessment of results indicates that the model generates realistic urbanization patterns.

Land-use modelling and spatial scenarios have gained attention as a means to meet the challenge of reducing uncertainty in spatial planning and decision making. Many of the recent modelling efforts incorporate cellular automata to accomplish spatially explicit land-use-change modelling. Spatial interaction between neighbouring land uses is an important component in urban cellular automata. Nevertheless, this component is often calibrated through trial-and-error estimation. The aim of this project has been to develop an empirically derived landscape metric supporting cellular-automata-based land-use modelling. Through access to very detailed urban land-use data it has been possible to derive neighbourhood rules empirically, and test their sensitivity to the land-use classification applied, the regional variability of the rules, and their time variance. The developed methodology can be implemented easily and thus used as a much needed replacement for the various trial-and-error approaches that are often applied in land-use modelling.

After the fall of the Berlin wall in 1989, demographic decline and urban shrinkage brought massive changes in the housing stock in East German cities. Urban planners and policy makers face complex problems caused by the resulting vacancies and demolitions and the handling of urban brownfields in the inner city. At the same time, cities are under ongoing pressure of suburbanisation. Because existing models focus mainly on demographic and urban growth and their impact on housing stocks, we present a simulation model that is able to compute both growth and shrinkage processes. We uncover nonlinear dynamics and feedbacks between demography, housing preference, and supply of housing space. The simulation results show that, despite population decline, the increasing number of single households leads to a growing total housing demand in the central parts of the study area. Beyond this area, residential vacancies in multistorey housing segments will remain regardless of population growth. At the same time, the simulations show that, despite population shrinkage and an overall oversupply of flats, there is a negative net demand for flats in affordable prefabricated housing estates as the percentage of low-income households increases. These findings will help planners modify or adapt their visions of the residential function in shrinking cities and to adjust current programmes of renewal and restructuring.

Density gradients have been a common approach when estimating decentralisation processes with monocentric models. In this study the gradient approach is applied to measure the traditional pattern of commuting to the centre from surrounding areas. Availability of empirical origin–destination data on commuting enables comparisons between increasing numbers of different urban areas. Empirical data on commuting patterns in nine cities from Finland and the United Kingdom are used. In the model the probability of commuting depends on the distance to the centre. The result is the parameterisation of the distance-decay curve of commuting. The estimated parameter values enable identification of different urban structures.

Inspired by the behavior of slime mold cells, Paul Krugman developed a simple one-dimensional model in which moving firms self-organize into cities. In this paper I show that extending the model into two dimensions significantly improves its realism. Cities in the two-dimensional model are similar in several respects to real cities: they grow and decline, they cluster near rivers and coasts, and, given certain parameters, their distribution follows Zipf's law. A calibration exercise, however, suggests that observed levels of agglomeration must be due to factors beyond those included in the model.

Siting a linear facility such as a highway or a pipeline often requires a preliminary study in which one or several corridors are identified. Here we construct corridors as a collection of adjacent polygons specifying a ‘path’ from origin s to destination t. Formally, we make use of a graph, called the connectivity graph, in which vertices correspond to polygons and edges to adjacent polygons. Within this formal representation, a corridor corresponds to an s to t path in the connectivity graph. The corridors are evaluated on two criteria: (1) a quantitative criterion measuring the length of the corridor, and (2) a qualitative criterion measuring the quality of the corridor with respect to the suitability of crossing its component polygons. We first introduce a three-phase approach based on a coupling between a geographical information system (GIS) and multicriteria evaluation and devoted to handling biobjective corridor siting problems. Then, the proposed approach is validated through an example of a real-world application.

In this paper we develop a positive theory of network connectivity, seeking to provide the microfoundations of alternative network topologies as the result of self-interested actors. By building roads, landowners hope to increase their parcels' accessibility and economic value. A simulation model is performed on a grid-like land-use layer with a downtown in the center. The degree to which the networks are tree-like is evaluated. This research posits that road networks experience an evolutionary process where a tree-like structure first emerges around the centered parcel before the network pushes outward to the periphery. Road network topology becomes increasingly connected as the accessibility value of reaching other parcels increases. The results demonstrate that, even without a centralized authority, road networks can display the property of self-organization and evolution, and that, in the absence of intervention, the degree to which a network structure is tree-like or web-like results from the underlying economies.

A large body of research has examined relationships between accessibility to green space and a variety of health outcomes with many researchers finding benefits in terms of levels of physical activity and relationships with levels of obesity, mental health, and other health conditions. Such studies often use spatial analytical techniques to examine relationships between distance to such spaces and health data collated at an individual survey respondent's home address or, more commonly, derived from area-based census measures summarised at a centroid. Generally, such measures are becoming more sophisticated and have moved on from the use of straightforward Euclidean-based measures to those based on network distance. However, few studies tend to use a combination of approaches or seek to establish the implications of incorporating alternative measures of accessibility on potential relationships. Using a database of green spaces (and associated attributes) and a detailed network dataset for the city of Cardiff, Wales, we examine the sensitivity of findings to the ways in which different metrics are calculated. This is illustrated by examining the variations in association between such metrics and a census-based deprivation index widely used in health studies to measure socioeconomic conditions. Our findings demonstrate that not only will the distances to green spaces vary according to the methodologies adopted but that any study that aims to investigate relationships with attributes of the nearest green space should acknowledge that matches may vary widely according to the techniques used. We conclude by warning against the use of inappropriate methodologies in examining access to green space which may directly influence directions (and levels) of association and hence may limit their relevance in wider geographical contexts.

There is a growing interest in increasing walking in urban areas, partly to reduce pollution and other problems related to transportation by cars, and partly to improve public health (through reasonable exercise such as walking). In this study several factors that influence the amount of pedestrian movement in Tehran (Iran) are explored. Data were collected through questionnaires and interviews, and included sociodemographic indicators, people's perceptions of the neighborhoods where they live or work, and daily walking time in District 6 of the City of Tehran. The results of the study show that security, street connectivity, public health education, and sociodemographic indicators such as age and education influence pedestrian movement in residential areas. Local sociocultural behavior and indicators such as age and education were found to be the most influential in the commercial areas in the study. On the other hand, the respondents' behavior showed that there is a surprisingly low tendency in the City of Tehran to walk out of choice. Almost all pedestrian movement appears to be in response to a need or an obligation to walk, such as for business or essential shopping.

Embedding parts is a key problem in computing when dealing with continuous matter such as shapes rather than discrete matter such as symbols. For computing part relations such as embedding, a technical framework that uses weighted shapes is introduced and implemented. In the proposed framework, for any given two-dimensional shape, the entire canvas is defined as a weighted shape and serves as a registration mark in detecting embedded parts. The approach treats shapes as perceived wholes rather than composed and eliminates the technical distinction between shape categories such as line, curve, or plane. The implementation is shown for two-dimensional shapes but is extendable to three dimensions. As demonstrated on a Seljuk geometric pattern, the framework allows for embedding multiple and various perceived wholes, thus exploring emerging shapes and shape relations to be used for analysis and synthesis in design.

The paper focuses on a case study of delineating census tracts (CTs) in the Census Metropolitan area of London, Ontario, Canada. The procedure for defining the actual pattern of CTs by a local committee and Statistics Canada has involved such consideration as the compactness of CTs and their population-based and area-based uniformity as well as some subjective aspects. The actual pattern shows that compactness of CTs has been achieved at the expense of uniformity in population and areal sizes. The paper proposes an integer-coded multiobjective genetic algorithm for aggregating census units with the expectation of obtaining a higher level of compactness and population/area uniformity of CTs through an optimization technique. Square-shape and circular-shape compactness of CTs are examined under different scenarios. The results indicate that the proposed genetic algorithm can provide solutions that are considerably better in terms of the Pareto-optimality principle than the actual pattern of CTs.

According to two recent studies, Thomas Schelling's model of segregation is only weakly affected by the underlying spatial structure whatever its complexity. Such a conclusion is important from an urban planning perspective as it suggests that only a very restricted range of possible actions, if any, would be able to contribute to limiting social segregation, unless individual preferences are significantly modified. My own simulations show that, using appropriate graph-based spatial structures, one can reveal significant spatial effects and thus provide alternative planning insights. Cliques in networks indeed play a significant role, reinforcing segregation effects in Schelling's model. Introducing a small amount of noise into the model permits us to reveal this effect more precisely, without modifying the global behavior of the initial model. Furthermore, I show how a logistic model describes in a concise but precise way this global behavior at an aggregated level.