Efrat Blumenfeld-Lieberthal

The Dynamics of the Tel Aviv Morphology

In this paper we suggest an approach for understanding the spatial behavior and structure of cities. It views cities as physical objects and is based on urban morphology alone. The units of examination are urban clusters instead of municipalities defined by politically determined boundaries. Clusters are defined as contiguous built-up urban areas. We present characteristics of clusters, including their morphology. Previous work that analyzed urban clusters focused on the Pareto distribution of clusters and on the behavior of the biggest cluster. Our work presents a more thorough description of the characteristics of urban clusters. By means of historic data of the Tel Aviv metropolis we present cluster statistics and we study their dynamics. We present characteristics of the clusters from 1935 to 2000, including their number, rank-size distribution, and morphology through the area–perimeter relation. These indicators present important anomalies in 1964 and 1985. Our study suggests that the urban cluster approach can be used as a tool to study urban phenomena and we hope that through them we shall be able to investigate economic and social phenomena as well.

Beyond the power law – a new approach to analyze city size distributions

This work proposes a new approach to analyze the city size distribution (CSD). We present a general equation for the rank size logarithmic plot, with a new positive exponent α. When α = 1, the Pareto distribution is yielded; when α ≠ 1, the log of the curves exhibits a concave distribution. We studied the CSDs of 41 cases in 35 countries (in several countries we examined cities and metropolitan areas or agglomerations) in order to apply our new equation. We determined accurately the exponent α for 31 cases. In 18 cases we received α = 1, in one case α 1. However, for the other cases, either the distributions were not homogeneous, or the data exhibited significant fluctuations which precluded a good determination of the exponent α. Based on this analysis, we developed a series of models (based on the models of town growth of Gabaix and of Blank and Solomon) in order to describe the different CSDs. The results of these models include power laws as well as cases that are represented by concave distributions on a logarithmic plot of the rank size.

The end of a paradigm: is Zipf's law universal?

It is largely accepted among geographers and economists that the City Size Distribution (CSD) is well described by a power law, i.e., Zipf’s law. This opinion is shared by this community in a manner it could be treated as a paradigm. In reality, however, Zipf’s law is not always observed (even as an approximation), and we prefer to adopt a classification of the CSD into three classes. In this work, we present the characteristics of these classes and give some examples for them. We use the Israeli system of cities as an interesting case study in which the same ensemble of cities passes from one class to another. We relate this change to the urbanization process that occurred in Israel from the 1960s onwards.

FROM LOGNORMAL DISTRIBUTION TO POWER LAW: A NEW CLASSIFICATION OF THE SIZE DISTRIBUTIONS

We propose a new classification of the size distributions of entities based on an exponent α defined from the shape of the log–log Rank Size plot. From an inspection of a large number of cases in different fields, one finds three possibilities: α = 1 giving a power law, α > 1 (parabola like curve) and 0

Network Analysis of Rat Spatial Cognition: Behaviorally-Established Symmetry in a Physically Asymmetrical Environment

Background We set out to solve two inherent problems in the study of animal spatial cognition (i) What is a “place”?; and (ii) whether behaviors that are not revealed as differing by one methodology could be revealed as different when analyzed using a different approach. Methodology We applied network analysis to scrutinize spatial behavior of rats tested in either a symmetrical or asymmetrical layout of 4, 8, or 12 objects placed along the perimeter of a round arena. We considered locations as the units of the network (nodes), and passes between locations as the links within the network. Principal Findings While there were only minor activity differences between rats tested in the symmetrical or asymmetrical object layouts, network analysis revealed substantial differences. Viewing ‘location’ as a cluster of stopping coordinates, the key locations (large clusters of stopping coordinates) were at the objects in both layouts with 4 objects. However, in the asymmetrical layout with 4 objects, additional key locations were spaced by the rats between the objects, forming symmetry among the key locations. It was as if the rats had behaviorally imposed symmetry on the physically asymmetrical environment. Based on a previous finding that wayfinding is easier in symmetrical environments, we suggest that when the physical attributes of the environment were not symmetrical, the rats established a symmetric layout of key locations, thereby acquiring a more legible environment despite its complex physical structure. Conclusions and Significance The present study adds a behavioral definition for “location”, a term that so far has been mostly discussed according to its physical attributes or neurobiological correlates (e.g. - place and grid neurons). Moreover, network analysis enabled the assessment of the importance of a location, even when that location did not display any distinctive physical properties.

Physical, Behavioral and Spatiotemporal Perspectives of Home in Humans and Other Animals

Home is usually considered as a physical construct of residence. In both humans and non-humans it has a functional partitioning into room for living, storage, toilets and other defined activities or services. Home is first and foremost where a set of behaviors are performed at rates higher than anywhere else; in rats, home is defined by sleeping, long stays, food hoarding and parental behavior. Another conspicuous feature of home is identity, which is constituted by the collection of inanimate objects, furnishings and gadgets that personalize each individual’s home. Security is another aspect: home is where you feel safe, and your privacy is protected. Moreover, home behavior is a strong trait that it is manifested even when a physical home is lacking, such as in the case of homeless humans and other animals. Finally, spatiotemporal behavior in the living environment is organized in relation to the home. Indeed, home is a hub for activity, with both humans and non-humans taking trips out from and back to their home, traveling regularly along the same paths and usually stopping at the same locations along them. While there are obvious differences between humans and animals, there are many similarities, and by focusing on the latter, it is suggested that similar biobehavioral systems in humans and non-humans account for the convergence of home behavior to these similar traits.

Using virtual reality environments to unveil the imageability of the city in homogenous and heterogeneous environments

Abstract The presented work explores the way the information conveyed by the morphology of urban artifacts affects the way we perceive the built environment. The above exploration is implemented by means of Virtual Reality Environments we have specifically developed for this purpose. We compare heterogeneous environments characterized by a heavy tailed distribution with homogeneous environments that are characterized by normal distribution and show that the former provide higher level of information and thus better imageability than the latter.

A Simulation Model for Intra-Urban Movements

Human mobility patterns (HMP) have become of interest to a variety of disciplines. The increasing availability of empirical data enables researchers to analyze patterns of people’s movements. Recent work suggested that HMP follow a Levy-flight distribution and present regularity. Here, we present an innovative agent-based model that simulates HMP for various purposes. It is based on the combination of regular movements with spatial considerations, represented by an expanded gravitation model. The agents in this model have different attributes that affect their choice of destination and the duration they stay in each location. Thus, their movement mimics real-life situations. This is a stochastic, bottom-up model, yet it yields HMP that qualitatively fit HMP empirical data in terms of individuals, as well as the entire population. Our results also correspond to real-life phenomena in terms of urban spatial dynamics, that is, the emergence of popular locations in the city due to bottom-up behavior of people. Our model is novel in being based on the assumption that HMP are space-dependent as well as follow high regularity. To our knowledge, we are the first to succeed in simulating HMP not only at the inter-city scale but also at the intra-urban one.

Network Cities: A Complexity-Network Approach to Urban Dynamics and Development

The aim of this proposed research is to develop an urban simulation model (USM) specifically designed to study the evolution and dynamics of systems of cities. It is innovative in three respects: First, in its structure – the proposed model is built as a superposition of two types of models; the first is an Agent Based Urban Simulation Model (ABUSM) that simulates the movement and interaction of agents in the urban space and the second is a network model that simulates the resultant urban network as it evolves. Secondly, it is innovative in the specific behavior of its agents – urban agents in our model act locally (as usual) but in order to do so, they perceive the city globally, i.e. they “think globally and act locally”. In our model, the local activities and interactions of agents give rise to the global urban structure and network that in turn affects the agents’ cognition, behavior, movement, and action in the city and so on in circular causality. The third aspect of innovation is connected with the specific urban phenomena it simulates – the vast majority of USM simulate the growth and expansion of urban systems but few simulate the reverse process of re-urbanization and gentrification; our model simultaneously captures the two processes and the interplay between them.

Macro and Micro Dynamics of the City Size Distribution

Complex systems evolve and grow from the bottom up. Their key characteristic is emergence in that the actions of the systems basic elements are uncoordinated yet their effects at greater scales appear organized. Hence we say that a complex system exhibits order at higher scales which is usually measurable using some scale-free characteristics. In city systems for example, it is clear that there is a hierarchy of sizes and that these sizes follow a scaling law which can be approximated by a power law. Within cities, different types of centre also follow such scaling not only in terms of their sizes but also in terms of their frequency and spacing. Such systems are sometimes said to exhibit self-similarity which means that if the system is examined at different scales, it appears the same; that is if a system has a certain pattern at one scale, this pattern can be transformed to another scale by enlargement or contraction so that it is impossible to see the difference between the two scales. Self-similarity is a key feature of geometries that are said to be fractal and in terms of cities, such fractal patterns have been widely observed (Axtell 2001). In this chapter we will exploit this fact by examining the pattern of city sizes which have a characteristic signature which is a power law. This signature which is sometimes referred to as the rank size rule is one of the most fundamental features of complexity in that many systems in the physical, natural and social world exhibit such scaling.