Advancing Sustainable Urbanism Processes: The Key Practical and Analytical Applications of Big Data for Urban Systems and Domains

2019 ◽  
pp. 221-252
Author(s):  
Simon Elias Bibri
Keyword(s):  
Big Data ◽  
Urban Systems ◽  
Analytical Applications ◽  
Sustainable Urbanism

scholarly journals Out of the loop? On the radical and the routine in urban big data

Urban Studies ◽  
2021 ◽  
pp. 004209802110140
Author(s):  
Sarah Barns
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Computational Models ◽  
Data Driven ◽  
Urban Systems ◽  
Out Of The Loop ◽  
Urban Futures ◽  
Urban Behaviour ◽  
The City

This commentary interrogates what it means for routine urban behaviours to now be replicating themselves computationally. The emergence of autonomous or artificial intelligence points to the powerful role of big data in the city, as increasingly powerful computational models are now capable of replicating and reproducing existing spatial patterns and activities. I discuss these emergent urban systems of learned or trained intelligence as being at once radical and routine. Just as the material and behavioural conditions that give rise to urban big data demand attention, so do the generative design principles of data-driven models of urban behaviour, as they are increasingly put to use in the production of replicable, autonomous urban futures.

scholarly journals Measuring Functional Urban Shrinkage with Multi-Source Geospatial Big Data: A Case Study of the Beijing-Tianjin-Hebei Megaregion

2020 ◽  
Vol 12 (16) ◽  
pp. 2513 ◽  
Author(s):  
Qiwei Ma ◽  
Zhaoya Gong ◽  
Jing Kang ◽  
Ran Tao ◽  
Anrong Dang
Keyword(s):  
Big Data ◽  
Urban Areas ◽  
Spatial Clustering ◽  
Landscape Fragmentation ◽  
Analytical Framework ◽  
Urban Land Use ◽  
Urban Systems ◽  
Landsat 8 ◽  
Clustering Methods

Most of the shrinking cities experience an unbalanced deurbanization across different urban areas in cities. However, traditional ways of measuring urban shrinkage are focused on tracking population loss at the city level and are unable to capture the spatially heterogeneous shrinking patterns inside a city. Consequently, the spatial mechanism and patterns of urban shrinkage inside a city remain less understood, which is unhelpful for developing accommodation strategies for shrinkage. The smart city initiatives and practices have provided a rich pool of geospatial big data resources and technologies to tackle the complexity of urban systems. Given this context, we propose a new measure for the delineation of shrinking areas within cities by introducing a new concept of functional urban shrinkage, which aims to capture the mismatch between urban built-up areas and the areas where significantly intensive human activities take place. Taking advantage of a data fusion approach to integrating multi-source geospatial big data and survey data, a general analytical framework is developed to construct functional shrinkage measures. Specifically, Landsat-8 remote sensing images were used for extracting urban built-up areas by supervised neural network classifications and Geographic Information System tools, while cellular signaling data from China Unicom Inc. was used to depict human activity areas generated by spatial clustering methods. Combining geospatial big data with urban land-use functions obtained from land surveys and Points-Of-Interests data, the framework further enables the comparison between cities from dimensions characterized by indices of spatial and urban functional characteristics and the landscape fragmentation; thus, it has the capacity to facilitate an in-depth investigation of fundamental causes and internal mechanisms of urban shrinkage. With a case study of the Beijing-Tianjin-Hebei megaregion using data from various sources collected for the year of 2018, we demonstrate the validity of this approach and its potential generalizability for other spatial contexts in facilitating timely and better-informed planning decision support.

scholarly journals Opportunities and Challenges of Geospatial Analysis for Promoting Urban Livability in the Era of Big Data and Machine Learning

2020 ◽  
Vol 9 (12) ◽  
pp. 752
Author(s):  
Anna Kovacs-Györi ◽  
Alina Ristea ◽  
Clemens Havas ◽  
Michael Mehaffy ◽  
Hartwig H. Hochmair ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Science ◽  
Temporal Analysis ◽  
Data Sources ◽  
Urban Systems ◽  
Urban Dynamics ◽  
Analysis Methods ◽  
Data Driven Approach ◽  
Spatio Temporal

Urban systems involve a multitude of closely intertwined components, which are more measurable than before due to new sensors, data collection, and spatio-temporal analysis methods. Turning these data into knowledge to facilitate planning efforts in addressing current challenges of urban complex systems requires advanced interdisciplinary analysis methods, such as urban informatics or urban data science. Yet, by applying a purely data-driven approach, it is too easy to get lost in the ‘forest’ of data, and to miss the ‘trees’ of successful, livable cities that are the ultimate aim of urban planning. This paper assesses how geospatial data, and urban analysis, using a mixed methods approach, can help to better understand urban dynamics and human behavior, and how it can assist planning efforts to improve livability. Based on reviewing state-of-the-art research the paper goes one step further and also addresses the potential as well as limitations of new data sources in urban analytics to get a better overview of the whole ‘forest’ of these new data sources and analysis methods. The main discussion revolves around the reliability of using big data from social media platforms or sensors, and how information can be extracted from massive amounts of data through novel analysis methods, such as machine learning, for better-informed decision making aiming at urban livability improvement.

Urban systems and the role of big data

2020 ◽  
pp. 23-58
Author(s):  
Yoshiki Yamagata ◽  
Perry P.J. Yang ◽  
Soowon Chang ◽  
Michael B. Tobey ◽  
Robert B. Binder ◽  
...  
Keyword(s):  
Big Data ◽  
Urban Systems

scholarly journals The taming of chaos: Optimal cities and the state of the art in urban systems research

Urban Studies ◽  
2021 ◽  
pp. 004209802110128
Author(s):  
Linnet Taylor
Keyword(s):  
Big Data ◽  
Urban Systems ◽  
Data Sets ◽  
Critical Approach ◽  
City Life ◽  
Urban Dynamics ◽  
Systems Research ◽  
New Perspective ◽  
Technology Users ◽  
The City

What can urban big data research tell us about cities? While studying cities as complex systems offers a new perspective on urban dynamics, we should dig deeper into the epistemological claims made by these studies and ask what it means to distance the urban researcher from the city. Big data research has the tendency to flatten our perspective: it shows us technology users and their interactions with digital systems but does so often at the expense of the informal and irregular aspects of city life. It also presents us with the city as optimisable system, offering up the chance to engineer it for particular forms of efficiency or productivity. Both optimisation itself, and the process of ordering of the city for optimisation, confer political and economic power and produce a hierarchy of interests. This commentary advocates that researchers connect systems research to questions of structure and power. To do this requires a critical approach to what is missing, what is implied by the choices about which data to collect and how to make them available, and an understanding of the ontologies that shape both the data sets and the urban spaces they describe.

scholarly journals «SMART CITY» IN THE CONTEXT OF INTELEGENT SYSTEM AND BIG DATA: STRATEGIES, RISKS

2021 ◽  
Vol 1 (161) ◽  
pp. 241-249
Author(s):  
V. Boyko ◽  
M. Vasilenko
Keyword(s):  
Big Data ◽  
Urban Environment ◽  
Rural Areas ◽  
Smart City ◽  
Point Of View ◽  
Urban Systems ◽  
Management Decision ◽  
Broadband Internet ◽  
Positioning Systems

According to UN forecasts, by 2050 more than two-thirds of the world’s population will live in cities. Urban and rural areas are evolving and their evolution are based on wide use of broadband Internet systems, cloud computing platforms, geoinformation and geo-positioning systems, high-load computing clusters, wireless telecommunications, “Internet of Things” systems and other technological and information innovations. With the increasing complexity and cohesion of urban systems, the cost of management decisions - and the associated cost of decision errors - has increased significantly. The time for deciding has on the contrary decreased. Incoming data may be deliberately inaccurate, unreliable, clogged with random and intentional interference. And in many cases, it is the management decision that is a critical factor for development and proper functioning of the urban system especially in the context of the formation of a smart city infrastructure. The paper studies use cases of artificial intelligence systems (AI) for processing big data and decision support as a solution to the problems listed above. Use of AI systems allow collecting and cleaning data to obtain a reliable information landscape of the urban systems. Further, on the basis of the obtained picture, AI systems can be used for operational analysis and response to emerging crisis situations, for analyzing the medium-term perspective and balancing the optimal use of urban resources, for long-term planning of the urban environment development. Currently, according to experts, there are two main strategies for the development of information systems - vertical and horizontal. The article analyzes the possibility of applying these two strategies to the use of AI in an urban environment. Using the example of the implementation experience (ET City Brain), on the one hand, conclusions can be drawn about the long-term benefits of such an implementation, on the other, about the risks associated with "vendor lock-in" and the associated problems. One of the biggest risks is the subsequent monopolization of the management system, which transfers part of the power from city structures to the owners of the information system, who, in such conditions, gain the right to vote and leverage on municipalities. It is shown that maximal use of open data and open source software solutions are the most beneficial from the point of view from the point of view of the city and urban systems as stakeholders in the formation of a smart city.

scholarly journals A Review of Human Mobility Research Based on Big Data and Its Implication for Smart City Development

2020 ◽  
Vol 10 (1) ◽  
pp. 13
Author(s):  
Anqi Wang ◽  
Anshu Zhang ◽  
Edwin H. W. Chan ◽  
Wenzhong Shi ◽  
Xiaolin Zhou ◽  
...  
Keyword(s):  
Big Data ◽  
Hong Kong ◽  
Smart City ◽  
Data Analytics ◽  
Human Mobility ◽  
Management Policy ◽  
Urban Systems ◽  
City Management ◽  
City Development ◽  
Quality Of Data

Along with the increase of big data and the advancement of technologies, comprehensive data-driven knowledge of urban systems is becoming more attainable, yet the connection between big-data research and its application e.g., in smart city development, is not clearly articulated. Focusing on Human Mobility, one of the most frequently investigated applications of big data analytics, a framework for linking international academic research and city-level management policy was established and applied to the case of Hong Kong. Literature regarding human mobility research using big data are reviewed. These studies contribute to (1) discovering the spatial-temporal phenomenon, (2) identifying the difference in human behaviour or spatial attributes, (3) explaining the dynamic of mobility, and (4) applying to city management. Then, the application of the research to smart city development are scrutinised based on email queries to various governmental departments in Hong Kong. The identified challenges include data isolation, data unavailability, gaming between costs and quality of data, limited knowledge derived from rich data, as well as estrangement between public and private sectors. With further improvement in the practical value of data analytics and the utilization of data sourced from multiple sectors, paths to achieve smarter cities from policymaking perspectives are highlighted.

scholarly journals The Sciences Underlying Smart Sustainable Urbanism: Unprecedented Paradigmatic and Scholarly Shifts in Light of Big Data Science and Analytics

Smart Cities ◽  
2019 ◽  
Vol 2 (2) ◽  
pp. 179-213 ◽  
Author(s):  
Simon Elias Bibri
Keyword(s):  
Big Data ◽  
Data Science ◽  
Smart Cities ◽  
Urban Sustainability ◽  
Urban Life ◽  
Transformative Power ◽  
Management Planning ◽  
Data Intensive ◽  
Sustainable Urbanism ◽  
The Face

As a new area of science and technology (S&T), big data science and analytics embodies an unprecedentedly transformative power—which is manifested not only in the form of revolutionizing science and transforming knowledge, but also in advancing social practices, catalyzing major shifts, and fostering societal transitions. Of particular relevance, it is instigating a massive change in the way both smart cities and sustainable cities are understood, studied, planned, operated, and managed to improve and maintain sustainability in the face of expanding urbanization. This relates to what has been dubbed data-driven smart sustainable urbanism, an emerging approach that is based on a computational understanding of city systems that reduces urban life to logical and algorithmic rules and procedures, as well as employs a new scientific method based on data-intensive science, while also harnessing urban big data to provide a more holistic and integrated view and synoptic intelligence of the city. This paper examines the unprecedented paradigmatic and scholarly shifts that the sciences underlying smart sustainable urbanism are undergoing in light of big data science and analytics and the underlying enabling technologies, as well as discusses how these shifts intertwine with and affect one another in the context of sustainability. I argue that data-intensive science, as a new epistemological shift, is fundamentally changing the scientific and practical foundations of urban sustainability. In specific terms, the new urban science—as underpinned by sustainability science and urban sustainability—is increasingly making cities more sustainable, resilient, efficient, and livable by rendering them more measurable, knowable, and tractable in terms of their operational functioning, management, planning, design, and development.

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