scholarly journals Evolution of Urban Patterns: Urban Morphology as an Open Reproducible Data Science

2021 ◽  
Author(s):  
Martin Fleischmann ◽  
Alessandra Feliciotti ◽  
William Kerr
Keyword(s):  
Data Science ◽  
Urban Morphology ◽  
Urban Patterns

scholarly journals Assessment of the process of urban transformation in Baghdad city form and function.

2017 ◽  
Author(s):  
Mazin Al-Saffar
Keyword(s):  
Public Space ◽  
Urban Morphology ◽  
Urban Transformation ◽  
Factors Affecting ◽  
Form And Function ◽  
Urban Patterns ◽  
Private And Public ◽  
History Of ◽  
And Function ◽  
The City

During the 21st century, urban transformation of cities has been intensely affected by flows of socio-economic and technological processes. Through the centuries, such as all historical places in Mesopotamia, Baghdad has given an outstanding example of dramatic evolution. The city, which stands on the river Tigris, faced various transformation processes in the culture and physical environment due to social and political reasons. The transformation of Baghdad city is a very complicated process driven by various factors affecting the homogeneity of the old urban fabric. Reconfiguration and the production of new urban typologies within the heritage fabric were the most fundamental effects. The outcome was different spatial languages competing with each other. This transformation changed the relations and hierarchies among spaces, which allowed more flexibility and accessibility between private and public space. The main purpose of this study is to examine how Baghdad city emerged and to develop a comprehensive understanding of the history of urban transformation in the context of city change. To achieve this aim, this paper will utilise urban morphology to explain how Baghdad transformed from a geometric city (the Round City AD762 by Caliph Al-Mansur) to an organic form and then from a traditional city to the modern metropolis. It will seek to analyse the process of urban transformation in Baghdad and show different types of urban patterns. Moreover, this paper will try to illustrate how the new way of transportation represented by the car has affected the historic centre and changed the structural system of Baghdad.

scholarly journals Spatial Information and the Legibility of Urban Form: Big Data in Urban Morphology

2019 ◽  
Author(s):  
Geoff Boeing
Keyword(s):  
Big Data ◽  
Urban Planning ◽  
Urban Form ◽  
Data Science ◽  
Spatial Information ◽  
Smart Cities ◽  
Urban Morphology ◽  
Urban Fabric ◽  
Form Analysis ◽  
Study Sites

Urban planning and morphology have relied on analytical cartography and visual communication tools for centuries to illustrate spatial patterns, propose designs, compare alternatives, and engage the public. Classic urban form visualizations – from Giambattista Nolli’s ichnographic maps of Rome to Allan Jacobs’s figure-ground diagrams of city streets – have compressed physical urban complexity into easily comprehensible information artifacts. Today we can enhance these traditional workflows through the Smart Cities paradigm of understanding cities via user-generated content and harvested data in an information management context. New spatial technology platforms and big data offer new lenses to understand, evaluate, monitor, and manage urban form and evolution. This paper builds on the theoretical framework of visual cultures in urban planning and morphology to introduce and situate computational data science processes for exploring urban fabric patterns and spatial order. It demonstrates these workflows with OSMnx and data from OpenStreetMap, a collaborative spatial information system and mapping platform, to examine street network patterns, orientations, and configurations in different study sites around the world, considering what these reveal about the urban fabric. The age of ubiquitous urban data and computational toolkits opens up a new era of worldwide urban form analysis from integrated quantitative and qualitative perspectives.

scholarly journals Methodological foundation of a numerical taxonomy of urban form

2021 ◽  
pp. 239980832110598
Author(s):  
Martin Fleischmann ◽  
Alessandra Feliciotti ◽  
Ombretta Romice ◽  
Sergio Porta
Keyword(s):  
Urban Space ◽  
Urban Form ◽  
Urban Areas ◽  
Numerical Taxonomy ◽  
Data Science ◽  
Voronoi Tessellation ◽  
Hierarchical Classification ◽  
Urban Morphology ◽  
Morphological Studies

Cities are complex products of human culture, characterised by a startling diversity of visible traits. Their form is constantly evolving, reflecting changing human needs and local contingencies, manifested in space by many urban patterns. Urban morphology laid the foundation for understanding many such patterns, largely relying on qualitative research methods to extract distinct spatial identities of urban areas. However, the manual, labour-intensive and subjective nature of such approaches represents an impediment to the development of a scalable, replicable and data-driven urban form characterisation. Recently, advances in geographic data science and the availability of digital mapping products open the opportunity to overcome such limitations. And yet, our current capacity to systematically capture the heterogeneity of spatial patterns remains limited in terms of spatial parameters included in the analysis and hardly scalable due to the highly labour-intensive nature of the task. In this paper, we present a method for numerical taxonomy of urban form derived from biological systematics, which allows the rigorous detection and classification of urban types. Initially, we produce a rich numerical characterisation of urban space from minimal data input, minimising limitations due to inconsistent data quality and availability. These are street network, building footprint and morphological tessellation, a spatial unit derivative of Voronoi tessellation, obtained from building footprints. Hence, we derive homogeneous urban tissue types and, by determining overall morphological similarity between them, generate a hierarchical classification of urban form. After framing and presenting the method, we test it on two cities – Prague and Amsterdam – and discuss potential applications and further developments. The proposed classification method represents a step towards the development of an extensive, scalable numerical taxonomy of urban form and opens the way to more rigorous comparative morphological studies and explorations into the relationship between urban space and phenomena as diverse as environmental performance, health and place attractiveness.

Model-Based Clustering and Classification for Data Science

2019 ◽  
Author(s):  
Charles Bouveyron ◽  
Gilles Celeux ◽  
T. Brendan Murphy ◽  
Adrian E. Raftery
Keyword(s):  
Data Science ◽  
Model Based Clustering ◽  
Model Based ◽  
Clustering And Classification

Issues in security and privacy of big data

2018 ◽  
Vol 7 (12) ◽  
pp. 1
Author(s):  
Shaveta Bhatia
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Approximate Method ◽  
Biomedical Research ◽  
Cyber Security ◽  
Data Science ◽  
Third Party ◽  
Security And Privacy ◽  
Security Threats ◽  
The Third

 The epoch of the big data presents many opportunities for the development in the range of data science, biomedical research cyber security, and cloud computing. Nowadays the big data gained popularity.  It also invites many provocations and upshot in the security and privacy of the big data. There are various type of threats, attacks such as leakage of data, the third party tries to access, viruses and vulnerability that stand against the security of the big data. This paper will discuss about the security threats and their approximate method in the field of biomedical research, cyber security and cloud computing.

About digital transformation of the transport industry in Russia

2020 ◽  
Vol 19 (3) ◽  
pp. 95-105
Author(s):  
Natalia V. Vysotskaya ◽  
T. V. Kyrbatskaya
Keyword(s):  
Artificial Intelligence ◽  
Business Model ◽  
Data Science ◽  
Digital Transformation ◽  
Digital Culture ◽  
Blockchain Technology ◽  
Community Approach ◽  
Non Linear ◽  
Transport Industry ◽  
Artificial Intelligence Systems

The article is devoted to the consideration of the main directions of digital transformation of the transport industry in Russia. It is proposed in the process of digital transformation to integrate the community approach into the company's business model using blockchain technology and methods and results of data science; complement the new digital culture with a digital team and new communities that help management solve business problems; focus the attention of the company's management on its employees and develop those competencies in them that robots and artificial intelligence systems cannot implement: develop algorithmic, computable and non-linear thinking in all employees of the company.

Data Mining and Statistics in Data Science

2019 ◽  
Vol 5 (30) ◽  
pp. 960-968
Author(s):  
Güner Gözde KILIÇ
Keyword(s):  
Data Mining ◽  
Data Science
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