AUM2020 Global Workshop: Session 1: City analytics (1)

Duration: 1 hour 55 mins

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Description:	Session outline: 1. Familiar strangers and urban analytics: mapping, measuring and monitoring Prof. Jonathan Corcoran (The University of Queensland) 2. Vertical Land Use Mix and Industrial Ecosystem Dr Hoon Han (University of New South Wale) 3. Polycentric urban growth and evolving access to jobs and social infrastructure: the role of new data sources, and implications of unexpected extreme events (e.g. COVID-19) Dr Somwrita Sarkar (University of Sydney) Convenor: Prof. Christopher Pettit (University of New South Wales) Discussant: Prof. Sharon Biermann (University of Western Australia) Host: Dr Ying Jin (University of Cambridge)

Description:

Session outline:
1. Familiar strangers and urban analytics: mapping, measuring and monitoring
Prof. Jonathan Corcoran (The University of Queensland)

2. Vertical Land Use Mix and Industrial Ecosystem
Dr Hoon Han (University of New South Wale)

3. Polycentric urban growth and evolving access to jobs and social infrastructure: the role of new data sources, and implications of unexpected extreme events (e.g. COVID-19)
Dr Somwrita Sarkar (University of Sydney)

Convenor: Prof. Christopher Pettit (University of New South Wales)
Discussant: Prof. Sharon Biermann (University of Western Australia)
Host: Dr Ying Jin (University of Cambridge)


Created:	2021-02-22 11:42
Collection:	Martin Centre AUM2020: Modelling the New Urban World
Publisher:	University of Cambridge
Copyright:	The Martin Centre
Language:	eng (English)
Keywords:	AUM2020; Online Global Workshop; Modelling the New Urban World; Architecture; Martin Centre;


Abstract:	1. Our everyday urban lives often entail encountering a familiar stranger – this is someone who we recognise but have never spoken to – a phenomenon known to hold important social benefits. The emergence of large scale big data sources present new and exciting opportunities as well as computational challenges through which we can both capture and measure familiar strangers across metropolises. This presentation will draw on one source of big data – a large transit smart card database -–and reveal how familiar stranger encounters are important in shaping opportunities for crime within the context of a transit environment. 2. Economic restructuring has significantly affected the industrial ecosystem and land use planning in world cities. Over the past decades urban planners, industrial ecologists and economic geographers investigated a sustainable form of industrial ecosystem in certain regions by examining a group of companies, or industry clusters, coordinate their resource management and inter-related activities to maximise collective economic and environmental benefit (Ashton, 2009). For instance, the death of certain industries (e.g. labour-intensive manufacturing) from global economic restructuring, with declining post-industrial towns, and the emergence of technological innovation replacing conventional jobs by automation and specialised jobs by AI have both led to new forms of industrial ecosystem in global cities. Some urban scientists and geographers in the study area interpreted industrial ecosystem within the broader context of agglomeration economy and comparative advantage of one industry over the others. They defined industrial ecosystem as complex systems which dynamic interactions among industrial actors and exogenous forces cause relational structures to develop and evolve over time, and in a long term it is adjustable to the system’s sustainability. Existing studies in industrial ecosystem focus on patterns of horizontal land or industrial mix in certain regions or precincts. There is limited understanding of vertical mixed use within high rise buildings such as office buildings, particularly in CBDs where high rise buildings are built on the use of valuable and constrained land. 3. Ease of access to jobs, a well-balanced accessibility profile between residential/housing profiles and labour markets, and ease of access to basic social infrastructure (e.g. schools, hospitals, libraries, parks etc.) is critical to liveability and community wellbeing in well-functioning cities. In this talk, I present work that brings together new big data sources, combined with traditional ones, to develop new analytic methods for measuring polycentric urban growth, fine spatio-temporal scale population estimation based on on-going building activity in rapidly growing cities followed by accessibility computations to jobs, labour, and developing social infrastructure in these fast-changing urban areas. I will discuss the importance of these new data sources for fast planning and monitoring of urban growth and polycentric development, how extreme unexpected events such as COVID can unexpectedly affect modelling and estimation processes, with a view to thereby developing more adaptive, responsive, and resilient models and algorithms.

Abstract:

1. Our everyday urban lives often entail encountering a familiar stranger – this is someone who we recognise but have never spoken to – a phenomenon known to hold important social benefits. The emergence of large scale big data sources present new and exciting opportunities as well as computational challenges through which we can both capture and measure familiar strangers across metropolises. This presentation will draw on one source of big data – a large transit smart card database -–and reveal how familiar stranger encounters are important in shaping opportunities for crime within the context of a transit environment.

2. Economic restructuring has significantly affected the industrial ecosystem and land use planning in world cities. Over the past decades urban planners, industrial ecologists and economic geographers investigated a sustainable form of industrial ecosystem in certain regions by examining a group of companies, or industry clusters, coordinate their resource management and inter-related activities to maximise collective economic and environmental benefit (Ashton, 2009). For instance, the death of certain industries (e.g. labour-intensive manufacturing) from global economic restructuring, with declining post-industrial towns, and the emergence of technological innovation replacing conventional jobs by automation and specialised jobs by AI have both led to new forms of industrial ecosystem in global cities.
Some urban scientists and geographers in the study area interpreted industrial ecosystem within the broader context of agglomeration economy and comparative advantage of one industry over the others. They defined industrial ecosystem as complex systems which dynamic interactions among industrial actors and exogenous forces cause relational structures to develop and evolve over time, and in a long term it is adjustable to the system’s sustainability. Existing studies in industrial ecosystem focus on patterns of horizontal land or industrial mix in certain regions or precincts.
There is limited understanding of vertical mixed use within high rise buildings such as office buildings, particularly in CBDs where high rise buildings are built on the use of valuable and constrained land.

3. Ease of access to jobs, a well-balanced accessibility profile between residential/housing profiles and labour markets, and ease of access to basic social infrastructure (e.g. schools, hospitals, libraries, parks etc.) is critical to liveability and community wellbeing in well-functioning cities. In this talk, I present work that brings together new big data sources, combined with traditional ones, to develop new analytic methods for measuring polycentric urban growth, fine spatio-temporal scale population estimation based on on-going building activity in rapidly growing cities followed by accessibility computations to jobs, labour, and developing social infrastructure in these fast-changing urban areas. I will discuss the importance of these new data sources for fast planning and monitoring of urban growth and polycentric development, how extreme unexpected events such as COVID can unexpectedly affect modelling and estimation processes, with a view to thereby developing more adaptive, responsive, and resilient models and algorithms.

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