Data-centric Regenerative Built Environment

Big Data for Sustainable Regeneration

Saeed Banihashemi, Sepideh Zarepour Sohi

• Publisher: Routledge
• Year: 2022
• ISBN:
  9781000593198
• Edition: 1
• Topic: Computer Science
• Subtopic: Databases
• Index: Computer Science

This book examines the use of big data in regenerative urban environment and how data helps in functional planning and design solutions.

This book is one of the first endeavors to present the data-driven methods for regenerative built environments and integrate it with the novel design solutions. It looks at four specific areas in which data is used – urban land use, transportation and traffic, environmental concerns and social issues – and draws on the theoretical literature concerning regenerative built environments to explain how the power of big data can achieve the systematic integration of urban design solutions. It then applies an in-depth case study method on Asian metropolises including Beijing and Tehran to bring the developed innovation into a research-led practical context.

This book is a useful reference for anyone interested in driving sustainable regeneration of our urban environments through big data-centric design solutions.

1 Classics of Data-Centric Regenerative Built Environment

DOI: 10.4324/9781003139942-1

Introduction

Humans and their activities are the focal point of city formation and urban development. Urban growth and development, in addition to providing opportunities for people to have a better life, can create some problems and issues in various aspects if they are not sustainable. As a consequence, it is necessary to develop urban areas in a sustainable manner and meanwhile, regenerate cities to resolve the urban social, economic, physical and environmental problems that are due to uncoordinated growth and development.

With the current technology trends and advent of big data with its three main features of volume, velocity and variety, it is feasible to understand a real perception of patterns of human behaviour from high spatial and temporal resolutions. Understanding the residents’ patterns through the main urban environmental, functional, traffic and transportation components, and the social aspects which develop a real-time sustainable city can be immensely effective for the regeneration of urban areas.

This chapter, therefore, theorizes what a data-centric approach entails for the sustainable urban regeneration of these components, how big data can be integrated in the design and planning process and what types of technologies contribute to sustainable urban land use, transportation, environmental and social aspects.

Urban Development and Regeneration

Sustainable development is seminally delineated as the ‘development that meets the requirements of the present without compromising the ability of future generations to meet their own needs’. With this respect, sustainable urban development was further coined in the early 1990s after the construction of global industrial cities and permeated into the urban planning principles to get cities and human settlements to become more inclusive, safe, resilient and sustainable [1]. To address these objectives, sustainable urban development has been directed towards the harmonized development of three fundamental factors of the environment, economy and society [2].

On top of that, urban regeneration seats where the ‘regeneration’ was first applied by the British Government during the 1970s to launch a new phase of national policies aimed at enhancing the employment of the middle class of workers and, at the same time, resolving their housing issues [3]. The concept of urban regeneration has become progressively a portion of national and local policies for reuse and rectification of the built environment [4] and can be equivalent with urban rehabilitation and urban renovation, as well [5].

The sustainable urban development requires improving strategies to regenerate urban areas. Urban regeneration is, therefore, regarded as a multidisciplinary approach that comprises policy-making and implementation processes in the field of urban planning, urban design, transportation, urban economy, urban development, sustainable solution and housing design [6]. It encompasses the rebirth or renewal of urban areas and settlements where the overall goal is to obtain high quality, well-designed and sustainable places for people to live, work and enjoy. This concept also looks for enhancing people’s quality of life in downstream and raising the economic growth in upstream [5, 7]. Moreover, during the process of urban regeneration, it is necessary to have a profound theoretical intuition incorporated with actions to the resolution of urban problems and minimize the harmful effects on the natural environment [8]. It is argued that urban regeneration proposes fundamental opportunities to deliver sustainable cities and achieve sustainable development [5, 9].

The regeneration of urban areas can include various actions such as:

• The renovation of historic regions,
• The improvement of living quality in residential areas,
• The renewal of public spaces including squares and parks,
• The modernization of urban infrastructures such as water networks, gas, electricity and transportation [4].

Big Data Technology

The progress and development of technology has immersed us in a huge amount of data. The age of ‘Big Data’ is coming and altering our perception from the world [10] as its application is getting more and more popular [11]. Big data was originally introduced by John-Mashey, the retired former chief scientist at Silicon Graphics, to explain the management and analysis of enormous datasets [12]. It is stated that this concept emerged as a term concerning visualization around 20 years ago. ‘Visualization implements a new challenge for computer systems: datasets are commonly quite enormous, occupy the capacities of main memory, local disk, and even remote disk. We call this the problem of big data’ [13].

There is no specific and agreed formal definition in industry or academia about what is generally intended with the term of big data, same as the many terms applied to advancing technology [11, 14]. But, it is evident that ‘Big Data’ sets are now applied in different areas such as physics, genomics, industry sectors and government agencies [11].

Big data, as its name proposes, is mainly specified by size or volume [13]. It points to enormous datasets that may be analyzed to disclose patterns, trends, dependencies and principles relating to human behaviour and interactions [10] and unpredictable qualitative and quantitative relationships that would not have been feasible through smaller datasets [11]. Big data also attracts our attention to changes occurring in much smaller time spans [13]. It is connected to structured and unstructured data and spontaneously generated as a part of transactional, operational, planning and social activities [15]. Apparently, it is hard to analyze such volume of data through conventional data-processing methods [16].

TechAmerica Foundation of Federal Big Data Commission defines big data as a term that explains huge volumes of high velocity, complicated and variable data that need advanced techniques and technologies to empower the capture, storage, distribution, management and analysis of the information [17]. From the spatial point of view, Batty describes big data as any data that cannot fit into an Excel spreadsheet and is regularly created in a relation with space and time [18]. In a more ubiquitous definition, IBM, in 2013, characterized big data as a data which originates from everywhere, for instance, from the sensors used to collect climatic information, posts or comments in social media networks, digital pictures and videos, financial transaction records and cell phones GPS signals [17]. In another definition, big data is a term indicating enormous and complicated datasets that need adapting to common approaches or expanding to absolutely new methods for their analysis [16, 19]. Practically speaking, it is stated that if the size of data overpasses the capacities of the standard data management tools, it will be considered as big data [10].

Doug Laney [20] characterizes big data with three main and salient features to distinct it from traditional and small data based on the features of:

• Volume: containing large quantities of data and massive in volume, including terabytes or petabytes of data,
• Velocity: the speed at which data is transferred (created in or near real-time and having temporality),
• Variety: various types of data (structured, semi-structured, non-structured and spatial-temporal datasets).

There are also other attributes to big data including:

• Veracity: this concerns the quality of data which has an influence on the accuracy of analysis and means that data can be messy, noisy and may have faults and uncertainties,
• Variability: data whose meaning can be regularly changing in relation to the context in which they are created and which show an amount of inconsistency,
• Scalability and extensionality: having the flexibility in increasing the size or adding new data fine-grained in resolution, to be as detailed as possible,
• Value: many insights can be obtained,
• Rationality: enabling the inclusion of different datasets [12, 13, 21] (Figure 1.1).

  

  Figure 1.1 Features of big data technology

Tools...

Table of contents

• Cover
• Half Title
• Series
• Title
• Copyright
• Dedication
• Contents
• List of Figures
• List of Tables
• Preface
• List of Abbreviations
• 1 Classics of Data-Centric Regenerative Built Environment
• 2 Big Data in Urban Land Use Regeneration
• 3 Big Data in Urban Traffic and Transportation
• 4 Big Data and Urban Environmental Sustainability
• 5 Big Data and Urban Social Sustainability
• 6 Data-Focused Visionary Leap for the Future Built Environment
• Index