New technologies for urban designers: the VENUE project

In this report, we first outline the basic idea of VENUE. This involves developing digital tools froma foundation of geographic information systems (GIS) software which we then apply to urbandesign, a subject area and profession which has little tradition in using such tools. Our project wasto develop two types of tool, namely functional analysis based on embedding models of movementin local environments into GIS based on ideas from the field of space syntax; and secondlyfashioning these ideas in a wider digital context in which the entire range of GIS technologies werebrought to bear at the local scale. By local scale, we mean the representation of urban environmentsfrom about 1: 500 to around 1: 2500

The structure of Inter-Urban traffic: A weighted network analysis

We study the structure of the network representing the interurban commuting traffic of the Sardinia region, Italy, which amounts to 375 municipalities and 1,600,000 inhabitants. We use a weighted network representation where vertices correspond to towns and the edges to the actual commuting flows among those. We characterize quantitatively both the topological and weighted properties of the resulting network. Interestingly, the statistical properties of commuting traffic exhibit complex features and non-trivial relations with the underlying topology. We characterize quantitatively the traffic backbone among large cities and we give evidences for a very high heterogeneity of the commuter flows around large cities. We also discuss the interplay between the topological and dynamical properties of the network as well as their relation with socio-demographic variables such as population and monthly income. This analysis may be useful at various stages in environmental planning and provides analytical tools for a wide spectrum of applications ranging from impact evaluation to decision-making and planning support.Comment: 12 pages, 12 figures, 4 tables; 1 missing ref added and minor revision

Visualization in spatial modeling

This chapter deals with issues arising from a central theme in contemporary computer modeling - visualization. We first tie visualization to varieties of modeling along the continuum from iconic to symbolic and then focus on the notion that our models are so intrinsically complex that there are many different types of visualization that might be developed in their understanding and implementation. This focuses the debate on the very way of 'doing science' in that patterns and processes of any complexity can be better understood through visualizing the data, the simulations, and the outcomes that such models generate. As we have grown more sensitive to the problem of complexity in all systems, we are more aware that the twin goals of parsimony and verifiability which have dominated scientific theory since the 'Enlightenment' are up for grabs: good theories and models must 'look right' despite what our statistics and causal logics tell us. Visualization is the cutting edge of this new way of thinking about science but its styles vary enormously with context. Here we define three varieties: visualization of complicated systems to make things simple or at least explicable, which is the role of pedagogy; visualization to explore unanticipated outcomes and to refine processes that interact in unanticipated ways; and visualization to enable end users with no prior understanding of the science but a deep understanding of the problem to engage in using models for prediction, prescription, and control. We illustrate these themes with a model of an agricultural market which is the basis of modern urban economics - the von Thünen model of land rent and density; a model of urban development based on interacting spatial and temporal processes of land development - the DUEM model; and a pedestrian model of human movement at the fine scale where control of such movements to meet standards of public safety is intrinsically part of the model about which the controllers know intimately. © Springer-Verlag Berlin Heidelberg 2006

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Emergent topological and dynamical properties of a real inter-municipal commuting network - perspectives for policy-making and planning

A variety of phenomena can be explained by means of a description of the features of their underlying network structure. In addition, a large number of scientists (see the reviews, eg. Barabasi, 2002; Watts, 2003) demonstrated the emergence of large-scale properties common to many different systems. These various results and studies led to what can be termed as the “new science of complex networks” and to emergence of the new “age of connectivity”. In the realms of urban and environmental planning, spatial analysis and regional science, many scientists have shown in the past years an increasing interest for the research developments on complex networks. Their studies range from theoretical statements on the need to apply complex network analysis to spatial phenomena (Salingaros, 2001) to empirical studies on quantitative research about urban space syntax (Jiang and Claramunt, 2004). Concerning transportation systems analysis, interesting results have been recently obtained on subway networks (Latora and Marchiori, 2002; Gastner and Newman, 2004) and airports (Barrat et al, 2004). In this paper, we study the inter-municipal commuting network of Sardinia (Italy). In this complex weighted network, the nodes correspond to urban centres while the weight of the links between two municipalities represents the flow of individuals between them. Following the analysis developed by Barrat et al. (2004), we investigate the topological and dynamical properties of this complex weighted network. The topology of this network can be accurately described by a regular small-world network while the traffic structure is very rich and reveals highly complex traffic patterns. Finally, in the perspective of policy-making and planning, we compare the emerging network behaviors with the geographical, social and demographical aspects of the transportation system.

Developing and applying a smart city for development model : the case of COR in Rio de Janeiro

The present thesis addresses how smart city initiatives can positively impact development, with a special emphasis on developing countries in Latin America. Existing definitions and maturity models have a very strong focus on the mere use of Information and Communication Technology (ICT) and ignore the special needs and factors to be considered in developing countries. By using the extant literature on Smart Cities and Information and Communication Technology for Development (ICT4D) as a related area from which to learn, a comprehensive Smart City for Development (SC4D) model is introduced and then exemplified via its application to a Latin American smart city initiative. The thesis argues that a favorable ecosystem for SC4D is one that is backed by both national and local sustainability, infrastructure, human capital, services, apps, and data. Moreover, successful SC4D initiatives include bottom-up approaches, citizen participation, a fit with both the national and the local culture, as well as a fit with the United Nations’ Sustainable Development Goals and the Capability Approach developed by Amartya Sen.A presente dissertação analisa como as iniciativas de cidades inteligentes podem ter impacto no desenvolvimento de países, com ênfase especial nos países em desenvolvimento da América Latina. As definições e os modelos de maturidade atualmente existentes para Cidade Inteligente têm um foco muito forte no uso de Tecnologias da Informação e da Comunicação (ICT), ignorando as necessidades especiais e os fatores a serem considerados nos países em desenvolvimento. Fazendo uso da literatura existente e da revisão da literatura sobre Tecnologias da Informação e da Comunicação para Desenvolvimento (ICT4D), como área relacionada, um modelo abrangente de Cidade Inteligente para Desenvolvimento (SC4D) é apresentado e então exemplificado por meio da sua aplicação em uma iniciativa de Cidade Inteligente na América Latina. O trabalho argumenta que um ecossistema favorável para o SC4D é aquele apoiado pela sustentabilidade nacional e local, infraestrutura, capital humano, serviços, aplicativos e dados. Além disso, as iniciativas de sucesso de SC4D incluem abordagens bottom-up, participação dos cidadãos, adequação à cultura nacional e local, bem como adequação aos Objetivos de Desenvolvimento Sustentável das Nações Unidas e à Abordagem das Capacidades de Amartya Sen

Crowdsourced Data Mining for Urban Activity: A Review of Data Sources, Applications and Methods

The penetration of devices integrated with location-based services and internet services has generated massive data about the everyday life of citizens and tracked their activities happening in cities. Crowdsourced data, such as social media data, POIs data and collaborative websites, generated by the crowd, has become fine-grained proxy data of urban activity and widely used in research in urban studies. However, due to the heterogeneity of data types of crowdsourced data and the limitation of previous studies mainly focusing on a specific application, a systematic review of crowdsourced data mining for urban activity is still lacking. In order to fill the gap, this paper conducts a literature search in the Web of Science database, selecting 226 highly related papers published between 2013 and 2019. Based on those papers, the review firstly conducts a bibliometric analysis identifying underpinning domains, pivot scholars and papers around this topic. The review also synthesises previous research into three parts: main applications of different data sources and data fusion; application of spatial analysis in mobility patterns, functional areas and event detection; application of socio-demographic and perception analysis in city attractiveness, demographic characteristics and sentiment analysis. The challenges of this type of data are also discussed in the end. This study provides a systematic and current review for both researchers and practitioners interested in the applications of crowdsourced data mining for urban activity.This research is funded by a scholarship from the China Scholarship Counci