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Urban Science
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Urban Modeling and Simulation
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Urban Modeling and Simulation

A special issue of Urban Science (ISSN 2413-8851).

Deadline for manuscript submissions: closed (1 November 2017) | Viewed by 29798

Special Issue Editor

Dr. Shade T. Shutters

E-Mail Website
Guest Editor
Global Security Initiative, Arizona State University, Tempe, AZ 85287, USA
Interests: urban dynamics; urban labor structure; interurban and intraurban networks; big data analytics; urban scaling; science of cities; urban sustainability; urban resilience; food-water security; urban economic security

Special Issue Information

Dear Colleagues,

Extraordinary increases in computational ability and data availability in the last two decades have led to revolutionary advances in the simulation and modelling of complex systems. Techniques, such as agent-based modelling and systems dynamic modelling, have taken advantage of these advances to make major contributions to scientific diverse areas such as personalized medicine, computational chemistry, social dynamics, or behavioral economics. Perhaps the epitome of complex systems is the city, with its dynamic web of interacting human, institutional, environmental, and physical systems, each of which is itself a complex and constantly evolving system. This Special Issue aims to highlight cases where advances in simulation and modelling have been brought to bear on theoretical questions of urban systems and how those cases have advanced urban science. In particular, this Special Issue welcomes papers describing, among others, systems dynamic models, agent-based models, or purely mathematical models of urban systems, pertaining to the following themes:

  • Sustainability

  • Economic models

  • Data-driven decision models of urban systems

  • Urban resilience

  • Social dynamics in cities

  • Traffic flow models

  • Migration and/or social stratification

  • Inequality of wages, income, or resource access

  • Resource distribution and flows

  • Urban networks, either between and within cities

Dr. Shade T. Shutters
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Urban Science is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • agent-based modelling;

  • networks;

  • systems dynamics;

  • predictive analytics;

  • smart cities;

  • big data;

  • computational economics;

  • decision models

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Editorial

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4 pages, 286 KiB  
Editorial
Urban Science: Putting the “Smart” in Smart Cities
by Shade T. Shutters
Urban Sci. 2018, 2(4), 94; https://doi.org/10.3390/urbansci2040094 - 20 Sep 2018
Cited by 3 | Viewed by 4773
Abstract
Increased use of sensors and social data collection methods have provided cites with unprecedented amounts of data. Yet, data alone is no guarantee that cities will make smarter decisions and many of what we call smart cities would be more accurately described as [...] Read more.
Increased use of sensors and social data collection methods have provided cites with unprecedented amounts of data. Yet, data alone is no guarantee that cities will make smarter decisions and many of what we call smart cities would be more accurately described as data-driven cities. Parallel advances in theory are needed to make sense of those novel data streams and computationally intensive decision support models are needed to guide decision makers through the avalanche of new data. Fortunately, extraordinary increases in computational ability and data availability in the last two decades have led to revolutionary advances in the simulation and modeling of complex systems. Techniques, such as agent-based modeling and systems dynamic modeling, have taken advantage of these advances to make major contributions to diverse disciplines such as personalized medicine, computational chemistry, social dynamics, or behavioral economics. Urban systems, with dynamic webs of interacting human, institutional, environmental, and physical systems, are particularly suited to the application of these advanced modeling and simulation techniques. Contributions to this special issue highlight the use of such techniques and are particularly timely as an emerging science of cities begins to crystallize. Full article
(This article belongs to the Special Issue Urban Modeling and Simulation)
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Research

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19 pages, 1457 KiB  
Article
Determining Factors for Slum Growth with Predictive Data Mining Methods
by John Friesen, Lea Rausch, Peter F. Pelz and Johannes Fürnkranz
Urban Sci. 2018, 2(3), 81; https://doi.org/10.3390/urbansci2030081 - 29 Aug 2018
Cited by 15 | Viewed by 6310
Abstract
Currently, more than half of the world’s population lives in cities. Out of these more than four billion people, almost one quarter live in slums or informal settlements. In order to improve living conditions and provide possible solutions for the major problems in [...] Read more.
Currently, more than half of the world’s population lives in cities. Out of these more than four billion people, almost one quarter live in slums or informal settlements. In order to improve living conditions and provide possible solutions for the major problems in slums (e.g., insufficient infrastructure), it is important to understand the current situation of this form of settlement and its development. There are many different models that attempt to simulate the development of slums. In this paper, we present data mining models that correlate information about the temporal development of slums with other economic, ecologic, and demographic factors in order to identify dependencies. Different learning algorithms, such as decision rules and decision trees, are used to learn descriptive models for slum development from data, and the results are evaluated with commonly used attribute evaluation methods known from data mining. The results confirm various previously made statements about slum development in a quantitative way, such as the fact that slum development is very strongly linked to the demographic development of a country. Applying the introduced classification models to the most recent data for different regions, it can be shown that the slum development in Africa is expected to be above average. Full article
(This article belongs to the Special Issue Urban Modeling and Simulation)
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533 KiB  
Article
Seeing the Forest through the Trees: Sociocultural Factors of Dense Urban Spaces
by Richard L. Wolfel, Amy Richmond and Peter Grazaitis
Urban Sci. 2017, 1(4), 40; https://doi.org/10.3390/urbansci1040040 - 19 Dec 2017
Cited by 2 | Viewed by 5114
Abstract
Coming to terms with the complexity of dense urban areas represents one of the major challenges people, organizations and governments will face in the next few decades. Defining, explaining and modeling socio-cultural factors associated with the development of dense urban regions will be [...] Read more.
Coming to terms with the complexity of dense urban areas represents one of the major challenges people, organizations and governments will face in the next few decades. Defining, explaining and modeling socio-cultural factors associated with the development of dense urban regions will be among the most complex problems researchers will face when studying dense urban areas. In this paper, we seek to open the discussion and begin to define the modeling process by conducting a literature review and creating a conceptual framework based on Verba, Binder, Coleman, La Palombara, Pye, & Weiner’s (1971) model of political development. The model emphasizes six key elements of political development, which we use as a point of departure to begin to identify key socio-cultural factors of dense urban areas. Our framework also embarks on identifying a difference between factor relationships in loosely and tightly integrated cities. The interrelationship between variables and the recursive nature of variables are some of the major difficulties we identify when it comes to modeling sociocultural dynamics in dense urban areas. Full article
(This article belongs to the Special Issue Urban Modeling and Simulation)
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10137 KiB  
Article
An Integrated Modeling Approach Combining Multifractal Urban Planning with a Space Syntax Perspective
by Claudia Yamu and Akkelies Van Nes
Urban Sci. 2017, 1(4), 37; https://doi.org/10.3390/urbansci1040037 - 1 Dec 2017
Cited by 18 | Viewed by 6911
Abstract
The United Nations Paris agreement of 2015 highlighted the need for urban planning to prevent and contain urban sprawl so as to reduce trip lengths through an efficient distribution of agglomerations and a well-balanced urban pattern distribution, all while considering travel behavior and [...] Read more.
The United Nations Paris agreement of 2015 highlighted the need for urban planning to prevent and contain urban sprawl so as to reduce trip lengths through an efficient distribution of agglomerations and a well-balanced urban pattern distribution, all while considering travel behavior and accessibility to green areas, services, and facilities on different temporal scales. For the Vienna-Bratislava metropolitan region, our integrated modeling approach uses a combination of multifractal spatial modeling along with a space syntax perspective. Multifractal strategies are intrinsically multiscalar and adhere to five planning principles: hierarchical (polycentric) urban development to manage urban sprawl; sustainable transit-oriented development; locally well-balanced urban pattern and functions distribution to enhance vital urban systems, local centers, and neighborhoods; penetration of green areas into built-up areas; and the preservation of large interconnected networks of green areas to conserve biodiversity. Adding space syntax modeling to a multifractal strategy integrates how space relates to functional patterns based on centrality, thus applying a socio-spatial perspective. In this paper, we used the following workflow for an integrated modeling approach: (1) Space syntax to identify the urban systems’ hierarchy and so determine a spatial strategy regionally; (2) Fractalopolis to create a multifractal development plan for potential urbanization; and (3) Space syntax to design a strategic urban master plan for locating new housing and facilities vis-à-vis socioeconomic factors. Full article
(This article belongs to the Special Issue Urban Modeling and Simulation)
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Review

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23 pages, 15750 KiB  
Review
Projecting Land-Use and Land Cover Change in a Subtropical Urban Watershed
by John J. Lagrosa IV, Wayne C. Zipperer and Michael G. Andreu
Urban Sci. 2018, 2(1), 11; https://doi.org/10.3390/urbansci2010011 - 31 Jan 2018
Cited by 11 | Viewed by 4523
Abstract
Urban landscapes are heterogeneous mosaics that develop via significant land-use and land cover (LULC) change. Current LULC models project future landscape patterns, but generally avoid urban landscapes due to heterogeneity. To project LULC change for an urban landscape, we parameterize an established LULC [...] Read more.
Urban landscapes are heterogeneous mosaics that develop via significant land-use and land cover (LULC) change. Current LULC models project future landscape patterns, but generally avoid urban landscapes due to heterogeneity. To project LULC change for an urban landscape, we parameterize an established LULC model (Dyna-CLUE) under baseline conditions (continued current trends) for a sub-tropical urban watershed in Tampa, FL. Change was modeled for 2012–2016 with observed data from 1995–2011. An ecosystem services-centric classification was used to define 9 LULC classes. Dyna-CLUE projects change using two modules: non-spatial quantity and spatial reallocation. The data-intensive spatial module requires a binomial logistic regression of socioecological driving factors, maps of restricted areas, and conversion settings, which control the sensitivity of class-to-class conversions. Observed quantity trends showed a decrease in area for agriculture, rangeland and upland forests by 49%, 56% and 27% respectively with a 22% increase in residential and 8% increase in built areas, primarily during 1995–2004. The spatial module projected future change to occur mostly in the relatively rural northeastern section of the watershed. Receiver-operating characteristic curves to evaluate driving factors averaged an area of 0.73 across classes. The manipulation of these baseline trends as constrained scenarios will not only enable urban planners to project future patterns under many ecological, economic and sociological conditions, but also examine changes in urban ecosystem services. Full article
(This article belongs to the Special Issue Urban Modeling and Simulation)
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