Data-Driven Smart Sustainable Urbanism and Data-Intensive Urban Sustainability Science: New Approaches to Tackling Urban Complexities

2020 ◽  
pp. 175-190
Author(s):  
Simon Elias Bibri
Keyword(s):  
Sustainability Science ◽  
Urban Sustainability ◽  
Data Driven ◽  
Data Intensive ◽  
New Approaches ◽  
Sustainable Urbanism

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.

scholarly journals The core academic and scientific disciplines underlying data-driven smart sustainable urbanism: an interdisciplinary and transdisciplinary framework

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Simon Elias Bibri
Keyword(s):  
The Body ◽  
Data Driven ◽  
New Era ◽  
The Core ◽  
Sustainable Urbanism ◽  
Scientific Disciplines ◽  
Specific Direction ◽  
Research Problems ◽  
Urban Planning And Development ◽  
Application Potential

AbstractA new era is presently unfolding wherein both smart urbanism and sustainable urbanism processes and practices are becoming highly responsive to a form of data-driven urbanism under what has to be identified as data-driven smart sustainable urbanism. This flourishing field of research is profoundly interdisciplinary and transdisciplinary in nature. It operates out of the understanding that advances in knowledge necessitate pursuing multifaceted questions that can only be resolved from the vantage point of interdisciplinarity and transdisciplinarity. This implies that the research problems within the field of data-driven smart sustainable urbanism are inherently too complex and dynamic to be addressed by single disciplines. As this field is not a specific direction of research, it does not have a unitary disciplinary framework in terms of a uniform set of the academic and scientific disciplines from which the underlying theories can be drawn. These theories constitute a unified foundation for the practice of data-driven smart sustainable urbanism. Therefore, it is of significant importance to develop an interdisciplinary and transdisciplinary framework. With that in regard, this paper identifies, describes, discusses, evaluates, and thematically organizes the core academic and scientific disciplines underlying the field of data-driven smart sustainable urbanism. This work provides an important lens through which to understand the set of established and emerging disciplines that have high integration, fusion, and application potential for informing the processes and practices of data-driven smart sustainable urbanism. As such, it provides fertile insights into the core foundational principles of data-driven smart sustainable urbanism as an applied domain in terms of its scientific, technological, and computational strands. The novelty of the proposed framework lies in its original contribution to the body of foundational knowledge of an emerging field of urban planning and development.

scholarly journals A novel model for data-driven smart sustainable cities of the future: the institutional transformations required for balancing and advancing the three goals of sustainability

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Simon Elias Bibri
Keyword(s):  
Smart Cities ◽  
Case Study Research ◽  
Development Planning ◽  
Three Dimensions ◽  
Data Driven ◽  
Sustainable Cities ◽  
Sustainable Urbanism ◽  
The Future ◽  
Novel Model ◽  
Smart Urbanism

AbstractIn recent years, it has become increasingly feasible to achieve important improvements of sustainability by integrating sustainable urbanism with smart urbanism thanks to the proven role and synergic potential of data-driven technologies. Indeed, the processes and practices of both of these approaches to urban planning and development are becoming highly responsive to a form of data-driven urbanism, giving rise to a new phenomenon known as “data-driven smart sustainable urbanism.” Underlying this emerging approach is the idea of combining and integrating the strengths of sustainable cities and smart cities and harnessing the synergies of their strategies and solutions in ways that enable sustainable cities to optimize, enhance, and maintain their performance on the basis of the innovative data-driven technologies offered by smart cities. These strengths and synergies can be clearly demonstrated by combining the advantages of sustainable urbanism and smart urbanism. To enable such combination, major institutional transformations are required in terms of enhanced and new practices and competences. Based on case study research, this paper identifies, distills, and enumerates the key benefits, potentials, and opportunities of sustainable cities and smart cities with respect to the three dimensions of sustainability, as well as the key institutional transformations needed to support the balancing of these dimensions and to enable the introduction of data-driven technology and the adoption of applied data-driven solutions in city operational management and development planning. This paper is an integral part of a futures study that aims to analyze, investigate, and develop a novel model for data-driven smart sustainable cities of the future. I argue that the emerging data-driven technologies for sustainability as innovative niches are reconfiguring the socio-technical landscape of institutions, as well as providing insights to policymakers into pathways for strengthening existing institutionalized practices and competences and developing and establishing new ones. This is necessary for balancing and advancing the goals of sustainability and thus achieving a desirable future.

Does Automated Enforcement Presence Impact Collisions and Crime?

2019 ◽  
Vol 2673 (10) ◽  
pp. 522-531
Author(s):  
Shewkar Ibrahim ◽  
Tarek Sayed
Keyword(s):  
Traffic Safety ◽  
Multiple Objectives ◽  
Data Driven ◽  
Limited Resources ◽  
Traffic Enforcement ◽  
Lognormal Model ◽  
Deployment Strategy ◽  
New Approaches ◽  
Efficiency And Effectiveness ◽  
The City

Enforcement agencies generally operate under a strict budget and with limited resources. For this reason, they are continually searching for new approaches to maximize the efficiency and effectiveness of their deployment. The Data-Driven Approaches to Crime and Traffic Safety approach attempts to identify opportunities where increased visibility of traffic enforcement can lead to a reduction in collision frequencies as well as criminal incidents. Previous research developed functions to model collisions and crime separately, despite evidence suggesting that the two events could be correlated. Additionally, there is little knowledge of the implications of automated enforcement programs on crime. This study developed a Multivariate Poisson-Lognormal model for the city of Edmonton to quantify the correlation between collisions and crime and to determine whether automated enforcement programs can also reduce crime within a neighborhood. The results of this study found a high correlation between collisions and crime of 0.72 which indicates that collision hotspots were also likely to be crime hotspots. The results of this paper also showed that increased enforcement presence resulted in reductions not only in collisions but also in crime. If a single deployment can achieve multiple objectives (e.g., reducing crime and collisions), then optimizing an agency’s deployment strategy would decrease the demand on their resources and allow them to achieve more with less.

Evolving the Materials Genome: How Machine Learning Is Fueling the Next Generation of Materials Discovery

2020 ◽  
Vol 50 (1) ◽  
pp. 1-25 ◽  
Author(s):  
Changwon Suh ◽  
Clyde Fare ◽  
James A. Warren ◽  
Edward O. Pyzer-Knapp
Keyword(s):  
Machine Learning ◽  
Materials Science ◽  
State Of The Art ◽  
Learning Technologies ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Computational Learning ◽  
Data Intensive ◽  
Research Activities ◽  
Materials Genome

Machine learning, applied to chemical and materials data, is transforming the field of materials discovery and design, yet significant work is still required to fully take advantage of machine learning algorithms, tools, and methods. Here, we review the accomplishments to date of the community and assess the maturity of state-of-the-art, data-intensive research activities that combine perspectives from materials science and chemistry. We focus on three major themes—learning to see, learning to estimate, and learning to search materials—to show how advanced computational learning technologies are rapidly and successfully used to solve materials and chemistry problems. Additionally, we discuss a clear path toward a future where data-driven approaches to materials discovery and design are standard practice.

scholarly journals Productive myopia: Racialized organizations and edtech

2021 ◽  
Vol 8 (2) ◽  
pp. 205395172110504
Author(s):  
Roderic Crooks
Keyword(s):  
Public Schools ◽  
Los Angeles ◽  
Digital Data ◽  
Data Driven ◽  
Data Intensive ◽  
For Profit ◽  
Goods And Services ◽  
Data Team ◽  
Racial Projects ◽  
Charter Management Organization

This paper reports on a two-year, field-based study set in a charter management organization (CMO-LAX), a not-for-profit educational organization that operates 18 public schools exclusively in the Black and Latinx communities of South and East Los Angeles. At CMO-LAX, the nine-member Data Team pursues the organization's avowed mission of making public schools data-driven, primarily through the aggregation, analysis, and visualization of digital data derived from quotidian educational activities. This paper draws on the theory of racialized organizations to characterize aspects of data-driven management of public education as practiced by CMO-LAX. I explore two examples of how CMO-LAX shapes data to support racial projects: the reconstruction of the figure of chronic truants and the incorporation of this figure in a calculative regime of student accomplishment. Organizational uses of data support a strategy I call productive myopia, a way of pursuing racial projects via seemingly independent, objective quantifications. This strategy allows the organization to claim to mitigate racial projects and, simultaneously, to accommodate them. This paper concludes by arguing for approaches to research and practice that center racial projects, particularly when data-intensive tools and platforms are incorporated into the provision of public goods and services such as education.

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