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Land
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Sustainable Urbanism in the Era of Big Data: A Data-Driven...
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Sustainable Urbanism in the Era of Big Data: A Data-Driven Approach to Strategic Planning

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Urban Contexts and Urban-Rural Interactions".

Deadline for manuscript submissions: closed (10 April 2022) | Viewed by 18595

Special Issue Editor

Dr. Simon Elias Bibri

E-Mail Website
Guest Editor
Department of Computer Science and Department of Planning and Architecture, Norwegian University of Science and Technology, 7491 Trondheim, Norway
Interests: sustainable development; urban sustainability; urban planning and design; smart urban governance; big data science and analytics; urban science; the Internet of Things (IoT); urban computing and intelligence; data-driven smart sustainable cities; sustainable cities (e.g., eco–city, low-carbon city, green city, compact city); smart cities (e.g., real–time city, data–driven city, ubiquitous city); integrated renewable energy and smart energy technologies; data-driven smart solutions for environmental sustainability; environmental innovations and sustainable energy transitions; sustainability transitions and socio-technical shifts; science, technology, and innovation studies; circular economy and business model innovation for sustainability; technological and sectoral innovation systems; and technology, innovation, and environmental policies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The focus of this Special Issue is data-driven smart sustainable urbanism. This emerging strategic approach to sustainable urban planning and development seeks to make actual progress towards achieving the goals of sustainability—with the support of advanced ICT. Sustainable urbanism is increasingly emphasizing the value of big data technology as a form of advanced ICT in improving and advancing sustainability.

The abundance of urban data, coupled with their analytical power, opens up new opportunities for innovative approaches to planning in sustainable cities. New sources of data coordinated with urban policy and management can be applied by following the fundamental principles of engineering science and applied science to achieve more effective solutions to the kind of wicked problems inherent in the planning of the existing models of sustainable urbanism, such as compact cities, eco-cities, green cities, environmental cities, and symbiotic cities. While planning cannot reproduce the characteristics of sustainable cities that have been developed based on incremental and interactive processes involving many stakeholders over time, the primary role of big data lies in enabling information flows and channels, coordination mechanisms, cooperative communication, learning and sharing processes, involving divergent constituents and heterogenous collective and individual actors as data agents, and above all, well-informed and fact-based decisions. In addition, big data can be used as evidence base for formulating urban policies, plans, and strategies themselves, as well as for tracking their effectiveness and modeling and simulating future development projects.

The applications of big data technologies for strategic planning relate to infrastructure and land use in terms of the analysis of population data and the evaluation of the potential impacts of urban growth to allow sustainable cities to take into account emerging demand from the population for certain venues and to prioritize initiatives and allocate resources appropriately. The data-driven approach to strategic planning enables the development of new districts, streets, buildings, green areas, facilities, public transport routes, distribution wires on poles or underground, waste sorting stations, and road infrastructures, based on the information collected on human mobility, physical movement, intensive activity, and residents’ expectations. In addition, the planning of sustainable cities can benefit from integrating the data regarding the various uses of urban areas to build scenarios in response to the need for urban revitalization, renewal, and redevelopment. This integration makes it possible to improve the way in which urban areas meet the needs of the residents, to share environmental and social practices, to enhance participation and consultation, and to engage in dialogue with the residents. For example, by building in favor of cycling and walking in response to the needs of residents, car traffic can be reduced in the city center, leading to better health among the residents. Likewise, developing green areas as part of strategic nodes can help create meeting places and also play the role of air cleaners, water collectors, and noise reducers, in addition to providing ecosystem services.

This Special Issue of Land aims to offer a platform for enhancing research and practice in data-driven smart sustainable urbanism by bringing an informed understanding of the subject to scholars, policymakers, practitioners, and futurists. It seeks contributions—in the form of research articles, literature reviews, case reports, futures studies, short communications, project reports, and discussion papers—that offer insights into data-driven smart sustainable urbanism. The scope of this Special Issue includes, but is not limited to, the following broad topics:

  • The value of big data in formalizing urban planning;
  • Complexity science and big data analytics for understanding urban complexity;
  • Geographic mapping and analysis;
  • Monitoring the condition and composition of green space in urban areas;
  • Energy usage patterns analysis and prediction;
  • Big data analytics and GIS uses in waste management inefficiency identification;
  • Big data analytics and GIS uses in transportation inefficiency identification;
  • Modeling and simulating the transport and traffic flows of sustainable cities;
  • Transportation and traffic patterns analysis and prediction;
  • Energy demands analysis and prediction;
  • Land-use impact analysis;
  • Data-driven smart approach to strategic planning of building energy retrofitting;
  • Data-driven smart urban metabolism;
  • Data-driven policy formulation;
  • Modeling and simulation of intensification projects;
  • Theoretical and disciplinary dimensions of data-driven smart sustainable urbanism;
  • Environmental, economic, social, and political dimensions of data-driven smart sustainable urbanism;
  • Opportunities and challenges for designing and developing data-driven smart sustainable cities;
  • Societal and scientific challenges, opportunities, and barriers for using real-time data and analytics in sustainable cities;
  • The effects of instrumenting the built environment with sensor technologies for datafication purposes.

Dr. Simon Elias Bibri
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. Land is an international peer-reviewed open access monthly 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 2600 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

  • sustainable urbanism
  • data-driven urbanism
  • smart urbanism
  • urban analytics
  • urban sustainability
  • urban policy
  • strategic planning
  • sustainable planning
  • infrastructure planning
  • land use planning
  • environmental planning
  • geographic information system (GIS)
  • multi-agent simulation

Published Papers (5 papers)

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Research

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30 pages, 7272 KiB  
Article
Exploring the Contributions by Transportation Features to Urban Economy: An Experiment of a Scalable Tree-Boosting Algorithm with Big Data
by Changju Lee and Sunghoon Lee
Land 2022, 11(4), 577; https://0-doi-org.brum.beds.ac.uk/10.3390/land11040577 - 14 Apr 2022
Cited by 8 | Viewed by 2604
Abstract
Previous studies regarding transportation impacts on economic development in urban areas have three major issues—the limited scope of analysis mostly with the change of property values, the exclusion of smart transportation systems as features despite their potential for urban areas, and stereotyped approaches [...] Read more.
Previous studies regarding transportation impacts on economic development in urban areas have three major issues—the limited scope of analysis mostly with the change of property values, the exclusion of smart transportation systems as features despite their potential for urban areas, and stereotyped approaches with limited types of variables. To surmount such limitations, this research adopted the concept of Big Data with machine learning techniques. As such, a total of 67 features from main categories, including the change of business, geographical boundary, socio-economic, land value, transportation, smart transportation, sales, and floating population were analyzed with XGBoost and SHAP algorithms. Given that the rise and fall of business is a major consideration for economic development in urban areas, the change in the total number of sales was selected as a target value. As a result, sales-related features showed the largest contribution to the rise of business, among others. It was also noted that features related to smart transportation systems obviously affected the success of business, even more than traditional ones from transportation. It is thus expected that the findings from this research will provide insights for decision-makers and researchers to make customized policies for boosting economic development in urban areas that are a major part of the urban economy to achieve sustainability. Full article
(This article belongs to the Special Issue Sustainable Urbanism in the Era of Big Data: A Data-Driven Approach to Strategic Planning)
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19 pages, 12392 KiB  
Article
Measuring the Correlation between Human Activity Density and Streetscape Perceptions: An Analysis Based on Baidu Street View Images in Zhengzhou, China
by Yilei Tao, Ying Wang, Xinyu Wang, Guohang Tian and Shumei Zhang
Land 2022, 11(3), 400; https://0-doi-org.brum.beds.ac.uk/10.3390/land11030400 - 09 Mar 2022
Cited by 10 | Viewed by 3044
Abstract
Although investigators are using data sources to describe the visual characteristics of streets, few researchers have linked human perceptions of the street environment with human activity density. This study proposes a conceptualized analytical framework that explains the relationship between human activity density and [...] Read more.
Although investigators are using data sources to describe the visual characteristics of streets, few researchers have linked human perceptions of the street environment with human activity density. This study proposes a conceptualized analytical framework that explains the relationship between human activity density and the visual characteristics of the streetscape. The image-segmentation model DeepLabv3+ automatically extracts each pixel’s semantic information and classifies visual elements from 120,012 collected panoramic street view images of Zhengzhou, China, using the entropy weighting method and weighted superposition to calculate the street perception summary score. This deep learning approach can successfully describe the semantics of streets and the connection between population density and street perception. The study provides a new quantitative method for urban planning and the development of high-density cities. Full article
(This article belongs to the Special Issue Sustainable Urbanism in the Era of Big Data: A Data-Driven Approach to Strategic Planning)
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20 pages, 3499 KiB  
Article
Incorporating Multi-Modal Travel Planning into an Agent-Based Model: A Case Study at the Train Station Kellinghusenstraße in Hamburg
by Ulfia Annette Lenfers, Nima Ahmady-Moghaddam, Daniel Glake, Florian Ocker, Jonathan Ströbele and Thomas Clemen
Land 2021, 10(11), 1179; https://0-doi-org.brum.beds.ac.uk/10.3390/land10111179 - 03 Nov 2021
Cited by 4 | Viewed by 2551
Abstract
Models can provide valuable decision support in the ongoing effort to create a sustainable and effective modality mix in urban settings. Modern transportation infrastructures must meaningfully combine public transport with other mobility initiatives such as shared and on-demand systems. The increase of options [...] Read more.
Models can provide valuable decision support in the ongoing effort to create a sustainable and effective modality mix in urban settings. Modern transportation infrastructures must meaningfully combine public transport with other mobility initiatives such as shared and on-demand systems. The increase of options and possibilities in multi-modal travel implies an increase in complexity when planning and implementing such an infrastructure. Multi-agent systems are well-suited for addressing questions that require an understanding of movement patterns and decision processes at the individual level. Such models should feature intelligent software agents with flexible internal logic and accurately represent the core functionalities of new modalities. We present a model in which agents can choose between owned modalities, station-based bike sharing modalities, and free-floating car sharing modalities as they exit the public transportation system and seek to finish their personal multi-modal trip. Agents move on a multi-modal road network where dynamic constraints in route planning are evaluated based on an agent’s query. Modality switch points (MSPs) along the route indicate the locations at which an agent can switch from one modality to the next (e.g., a bike rental station to return a used rental bike and continue on foot). The technical implementation of MSPs within the road network was a central focus in this work. To test their efficacy in a controlled experimental setting, agents optimized only the travel time of their multi-modal routes. However, the functionalities of the model enable the implementation of different optimization criteria (e.g., financial considerations or climate neutrality) and unique agent preferences as well. Our findings show that the implemented MSPs enable agents to switch between modalities at any time, allowing for the kind of versatile, individual, and spontaneous travel that is common in modern multi-modal settings. Full article
(This article belongs to the Special Issue Sustainable Urbanism in the Era of Big Data: A Data-Driven Approach to Strategic Planning)
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20 pages, 4369 KiB  
Article
Revealing Characteristics of the Spatial Structure of Megacities at Multiple Scales with Jobs-Housing Big Data: A Case Study of Tianjin, China
by Ruixi Dong and Fengying Yan
Land 2021, 10(11), 1144; https://0-doi-org.brum.beds.ac.uk/10.3390/land10111144 - 27 Oct 2021
Cited by 4 | Viewed by 1935
Abstract
Urban spatial structure reflects the organization of urban land use and is closely related to the travel patterns of residents. The characteristics of urban spatial structure include both static and dynamic aspects. The static characteristics of urban spatial structure reflect the morphological features [...] Read more.
Urban spatial structure reflects the organization of urban land use and is closely related to the travel patterns of residents. The characteristics of urban spatial structure include both static and dynamic aspects. The static characteristics of urban spatial structure reflect the morphological features of space, and the dynamic characteristics of urban spatial structure reflect intra-city functional linkages. With the continuous agglomeration of population and industries; megacities have become the core spatial carriers leading China’s social and economic development; and their urban spatial structure has also been reconstructed. However; there is still a certain lack of understanding of the characteristics of the spatial structure of China’s megacities. This study aimed to reveal characteristics of the spatial structure of Chinese megacities at different scales using jobs-housing big data. To achieve this goal, spatial autocorrelation and a geographically weighted regression (GWR) model were applied to reveal static polycentricity, and community detection was used to reveal dynamic commuting communities. The distribution of jobs in urban space and jobs–housing balance levels in commuting communities were further analyzed. Experiments were conducted in Tianjin, China. We found that: (1) the static characteristics of the spatial structure of megacities presented the coexistence of polycentricity and a high degree of dispersion at macro- and meso-scales; (2) the dynamic characteristics of the spatial structure of megacities revealed two types of commuting communities at macro- and meso-scales and most commuting communities had a good jobs-housing balance. These findings can be referenced by urban managers and planners to formulate relevant policies for spatial distribution optimization of urban functions and transportation development at different spatial scales. Full article
(This article belongs to the Special Issue Sustainable Urbanism in the Era of Big Data: A Data-Driven Approach to Strategic Planning)
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Review

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19 pages, 319 KiB  
Review
Impact of AI-Based Tools and Urban Big Data Analytics on the Design and Planning of Cities
by Dorota Kamrowska-Załuska
Land 2021, 10(11), 1209; https://0-doi-org.brum.beds.ac.uk/10.3390/land10111209 - 08 Nov 2021
Cited by 15 | Viewed by 6286
Abstract
Wide access to large volumes of urban big data and artificial intelligence (AI)-based tools allow performing new analyses that were previously impossible due to the lack of data or their high aggregation. This paper aims to assess the possibilities of the use of [...] Read more.
Wide access to large volumes of urban big data and artificial intelligence (AI)-based tools allow performing new analyses that were previously impossible due to the lack of data or their high aggregation. This paper aims to assess the possibilities of the use of urban big data analytics based on AI-related tools to support the design and planning of cities. To this end, the author introduces a conceptual framework to assess the influence of the emergence of these tools on the design and planning of the cities in the context of urban change. In this paper, the implications of the application of artificial-intelligence-based tools and geo-localised big data, both in solving specific research problems in the field of urban planning and design as well as on planning practice, are discussed. The paper is concluded with both cognitive conclusions and recommendations for planning practice. It is directed towards urban planners interested in the emerging urban big data analytics based on AI-related tools and towards urban theorists working on new methods of describing urban change. Full article
(This article belongs to the Special Issue Sustainable Urbanism in the Era of Big Data: A Data-Driven Approach to Strategic Planning)
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