A new geodemographic classification of commuting flows for England and Wales

Hincks, Stephen; Kingston, Richard; Webb, Brian; Wong, Cecilia

doi:10.1080/13658816.2017.1407416

Cited by 27 publications

(14 citation statements)

References 34 publications

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“…Prior to the PCA and GWPCA, indicator data sets were normalized using fractional rank followed by an inverse distance normalization (Hincks et al 2017). Normalized data sets were then standardized using z scores, defined as the number of standard deviations the data point is from the mean value.…”

Section: An Index Of Sociospatial Vulnerability To Energy Povertymentioning

confidence: 99%

The Spatially Varying Components of Vulnerability to Energy Poverty

Robinson

Lindley

Bouzarovski

2019

Annals of the American Association of Geographers

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A household's vulnerability to energy poverty is socially and spatially variable. Efforts to measure energy poverty, however, have focused on narrow, expenditure-based metrics or area-based targeting. These metrics are not spatial per se, because the relative importance of drivers does not vary between neighborhoods to reflect localized challenges. Despite recent advancements in geographically weighted methodologies that have the potential to yield important information about the sociospatial distribution of vulnerability to energy poverty, the phenomenon has not been approached from this perspective. For a case study of England, global principal component analysis (PCA) and local geographically weighted PCA (GWPCA) are applied to a suite of neighborhood-scale vulnerability indicators. The explicit spatiality of this methodological approach addresses a common criticism of vulnerability assessments. The global PCA reaffirms the importance of well-established vulnerabilities, including older age, disability, and energy efficiency. It also demonstrates striking new evidence of vulnerabilities among precarious and transient households that are less well understood and have become starker during austerity. In contrast, rather than providing a single estimate of propensity to energy poverty for neighborhoods based on a national understanding of what drives the condition, the GWPCA identifies a diverse array of vulnerability factors of greatest importance in different locales. These local results destabilize the geographical configurations of an urban-rural and north-south divide that typify understandings of deprivation in this context. The geographically weighted approach therefore draws attention to vulnerabilities often hidden in policymaking, allowing for reflection on the applicability of spatially constituted methodologies to wider social vulnerability assessments.

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Section: An Index Of Sociospatial Vulnerability To Energy Povertymentioning

confidence: 99%

The Spatially Varying Components of Vulnerability to Energy Poverty

Robinson

Lindley

Bouzarovski

2019

Annals of the American Association of Geographers

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“…By building a complex network model of traffic flow [16], the regional divisions of the city are used as graph nodes to quantify the connected edges of the graph by the joint similarity of movement patterns, space, and time. Using some effective detection algorithms, it is easy to identify the travel trajectories of human flows in different regions and estimate the economic and social functions of these regions [17].…”

Section: Related Workmentioning

confidence: 99%

Urban Fine-Grained Spatial Structure Detection Based on a New Traffic Flow Interaction Analysis Framework

Zhang

Zheng

Chen

et al. 2021

IJGI

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The urban structure is the spatial reflection of various economic and cultural factors acting on the urban territory. Different from the physical structure, urban structure is closely related to the population mobility. Taxi trajectories are widely distributed, completely spontaneous, closely related to travel needs, and massive in data volume. Mining it not only can help us better understand the flow pattern of a city, but also provides a new perspective for interpreting the urban structure. On the basis of massive taxi trajectory data in Chengdu, we introduce a network science approach to analysis, propose a new framework for interaction analysis, and model the intrinsic connections within cities. The spatial grid of fine particles and the trajectory connections between them are used to resolve the urban structure. The results show that: (1) Based on 200,000 taxi trajectories, we constructed a spatial network of traffic flow using the interaction analysis framework and extracted the cold hot spots among them. (2) We divide the 400 traffic flow network nodes into 6 communities. Community 2 has high centrality and density, and belongs to the core built-up area of the city. (3) A traffic direction field is proposed to describe the direction of the traffic flow network, and the direction of traffic flow roughly presents an inflow from northeast to southwest and an outflow from southeast to northwest of the study area. The interaction analysis framework proposed in this study can be applied to other cities or other research areas (e.g., population migration), and it could extract the directional nature of the network as well as the hierarchical structure of the city.

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“…Very few published studies have attempted to produce classifications of small areas based solely on workplace characteristics; where this has been attempted it has been in support of specific research objectives or without workplace-based small area data or boundaries. Hincks et al (2018) produce a classification of commuting flows using census travel to work data, characterising the types of commuter travelling from origin and into destination areas. However, their analysis is based on Middle Layer Super Output Areas (MSOAs, mean population 7806) and thus does not make use of the highest spatial resolution residence or workplace data available.…”

Section: Reviewmentioning

confidence: 99%

Developing a National Geodemographic Classification of Workplace Zones

Cockings

Martin

Harfoot

2020

Appl. Spatial Analysis

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Geodemographics conventionally refers to the classification of geographical areas based on the socioeconomic characteristics of their residents. In this paper, we develop the novel concept of a classification based on the characteristics of workers and workplaces. The paper describes the implementation of this concept at the small area level for the whole of the UK, which has involved reconciliation of three slightly different national censuses. It presents a summary of the resulting classification (a Classification of Workplace Zones for the UK (COWZ-UK)) and an innovative validation exercise based on comparison with a very large digital mapping dataset containing specific workplace locations. The openly available classification provides important new insights into the characteristics of workers and workplaces at the small area level across the UK, which will be useful for analysts in a range of sectors, including health, local government, transport and commerce. The generic concept of a classification based on the characteristics of workers and workplaces within a set of workplace zones is transferable to other countries, with refinement to reflect context- and country-specific phenomena. The concept can be readily implemented by census agencies or other data providers where individual level worker and workplace data are available.

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A new geodemographic classification of commuting flows for England and Wales

Cited by 27 publications

References 34 publications

The Spatially Varying Components of Vulnerability to Energy Poverty

The Spatially Varying Components of Vulnerability to Energy Poverty

Urban Fine-Grained Spatial Structure Detection Based on a New Traffic Flow Interaction Analysis Framework

Developing a National Geodemographic Classification of Workplace Zones

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