Complex networks can be used for modeling street meshes and urban agglomerates. With such a model, many aspects of a city can be investigated to promote a better quality of life to its citizens. Along these lines, this paper proposes a set of distance-based pattern-discovery algorithmic instruments to improve urban structures modeled as complex networks, detecting nodes that lack access from to points of interest in a given city. Furthermore, we introduce a greedy algorithm that is able to recommend improvements to the structure of a city by suggesting where points of interest are to be placed. We contribute to a thorough process to deal with complex networks, including mathematical modeling and algorithmic innovation. The set of our contributions introduces a systematic manner to treat a recurrent problem of broad interest in cities.
A vast number of studies have been conducted to analyze inherent properties and behaviors found in cities. For instance, multiple metrics have been adopted to explain their structural conditions @cite_11 , their intense traffic of vehicles @cite_2 , and the emergence of collective behavior @cite_0 . In other studies, the authors centered on the geometrical perspective of the network @cite_12 , and on the elements positioning @cite_10 @cite_5 . Furthermore, there are those who reviewed the role of the city elements @cite_9 @cite_15 , that addressed the support to the urban planning and design @cite_13 @cite_18 , and that improved the facility-location analysis and planning of street meshes @cite_8 . In addition to the ones that inspected the effectiveness of the underground systems @cite_16 and the improvement of long-range connections @cite_14 , besides those who defined the concept of accessibility through complex networks and cities @cite_17 and that tested the centrality of cities considering their space syntax @cite_3 @cite_6 @cite_1 .
@cite_13 @cite_18 @cite_14 @cite_8 @cite_9 @cite_1 @cite_17 @cite_3 @cite_6 @cite_0 @cite_2 @cite_5 @cite_15 @cite_16 @cite_10 @cite_12 @cite_11
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Urbanisation is a fundamental phenomenon whose quantitative characterisation is still inadequate. We report here the empirical analysis of a unique data set regarding almost 200 years of evolution of the road network in a large area located north of Milan (Italy). We find that urbanisation is characterised by the homogenisation of cell shapes, and by the stability throughout time of high–centrality roads which constitute the backbone of the urban structure, confirming the importance of historical paths. We show quantitatively that the growth of the network is governed by two elementary processes: (i) ‘densification’, corresponding to an increase in the local density of roads around existing urban centres and (ii) ‘exploration’, whereby new roads trigger the spatial evolution of the urbanisation front. The empirical identification of such simple elementary mechanisms suggests the existence of general, simple properties of urbanisation and opens new directions for its modelling and quantitative description. Abstract Multidimensional scaling is applied in order to visualize an analogue of the small-world effect implied by edges having different displacement velocities in transportation networks. Our findings are illustrated for two real-world systems, namely the London urban network (streets and underground) and the US highway network enhanced by some of the main US airlines routes. We also show that the travel time in these two networks is drastically changed by attacks targeting the edges with large displacement velocities. Abstract As a result of multiple land use types, spatial heterogeneity, and conflicts of interest among multiple participants, multi-site land use allocation becomes a complex and significant optimization issue. We propose an improved Genetic Algorithm (GA) to deal with multi-site land use allocation, in which maximum economic benefit, maximum ecological benefit, maximum suitability, and maximum compactness were formulated as optimal objectives; and residential space demand and some regulatory knowledge were set as constraints. A Goal Programming model with a reference point form was used to manage trade-offs among multiple objectives. In order to improve the efficiency of the common GA applied to multi-site land use allocation, two crossover steps and two mutation operations were designed. This paper presents an application of the improved GA to the Regional District of Central Okanagan in Canada. Results showed that the proposed GA exhibited good robustness and could generate any optimal land use scenario according to stakeholders' preferred objectives, thus having the potential to provide interactive technical support for land use planning. We study centrality in urban street patterns of different world cities represented as networks in geographical space. The results indicate that a spatial analysis based on a set of four centrality indices allows an extended visualization and characterization of the city structure. A hierarchical clustering analysis based on the distributions of centrality has a certain capacity to distinguish different classes of cities. In particular, self-organized cities exhibit scale-free properties similar to those found in nonspatial networks, while planned cities do not. This Letter describes a method for the quantification of the diversity of non-linear dynamics in complex networks as a consequence of self-avoiding random walks. The methodology is analyzed in the context of theoretical models and illustrated with respect to the characterization of the accessibility in urban streets. Centrality has revealed crucial for understanding the structural properties of complex relational networks. Centrality is also relevant for various spatial factors affecting human life and behaviors in cities. Here, we present a comprehensive study of centrality distributions over geographic networks of urban streets. Five different measures of centrality, namely degree, closeness, betweenness, straightness and information, are compared over 18 1-square-mile samples of different world cities. Samples are represented by primal geographic graphs, i.e., valued graphs defined by metric rather than topologic distance where intersections are turned into nodes and streets into edges. The spatial behavior of centrality indices over the networks is investigated graphically by means of color-coded maps. The results indicate that a spatial analysis, that we term multiple centrality assessment, grounded not on a single but on a set of different centrality indices, allows an extended comprehension of the city structur... To summarize, our approach yields important insights into urban phenomena generated by human movements. It represents a quantitative approach to urban analysis, which explicitly identifies ongoing urban transformations. Abstract Purpose. The purpose of this study was to examine associations between street connectivity and road traffic speed and neighborhood residents' use of parks and park-based physical activity. Design. Cross-sectional. Setting. Kansas City, Missouri. Subjects. Participants were 893 adults from randomly selected households. Measures. Both self-reported park use and park-based physical activity were dichotomized as some versus none. Intersection density was calculated around each participant, and network analysis was used to determine whether participants had to travel on or cross a road with traffic speed greater than 35 miles per hour (mph) to reach the closest park. Analysis. Multilevel logistic regression examined the association between intersection density and traffic speed wit park use and park-based physical activity. Results. Compared to those in the lowest intersection density quartile, participants in the third and fourth quartiles were more likely to use parks and to engage in physical activ... Urban street patterns form planar networks whose empirical properties cannot be accounted for by simple models such as regular grids or Voronoi tesselations. Striking statistical regularities across different cities have been recently empirically found, suggesting that a general and detail-independent mechanism may be in action. We propose a simple model based on a local optimization process combined with ideas previously proposed in studies of leaf pattern formation. The statistical properties of this model are in good agreement with the observed empirical patterns. Our results thus suggest that in the absence of a global design strategy, the evolution of many different transportation networks indeed follows a simple universal mechanism. We investigate the growth dynamics of Greater London defined by the administrative boundary of the Greater London Authority, based on the evolution of its street network during the last two centuries. This is done by employing a unique dataset, consisting of the planar graph representation of nine time slices of Greater London's road network spanning 224 years, from 1786 to 2010. Within this time-frame, we address the concept of the metropolitan area or city in physical terms, in that urban evolution reveals observable transitions in the distribution of relevant geometrical properties. Given that London has a hard boundary enforced by its long standing green belt, we show that its street network dynamics can be described as a fractal space-filling phenomena up to a capacitated limit, whence its growth can be predicted with a striking level of accuracy. This observation is confirmed by the analytical calculation of key topological properties of the planar graph, such as the topological growth of the network and its average connectivity. This study thus represents an example of a strong violation of Gibrat's law. In particular, we are able to show analytically how London evolves from a more loop-like structure, typical of planned cities, toward a more tree-like structure, typical of self-organized cities. These observations are relevant to the discourse on sustainable urban planning with respect to the control of urban sprawl in many large cities which have developed under the conditions of spatial constraints imposed by green belts and hard urban boundaries.