Geodemographics and spatial interaction: an integrated model for higher education

Singleton, Alex; Wilson, Alan; O’Brien, Oliver

doi:10.1007/s10109-010-0141-5

Cited by 30 publications

(24 citation statements)

References 23 publications

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“…Although geodemographic classification have received criticism (e.g. Goss 1995), they have developed and sustained a reputedly robust pedigree (Birkin et al 2002) in both the public and private sectors (Longley 2005), with numerous successful areas of application including health (Petersen et al 2011), retail (Thompson et al 2012), education (Singleton et al 2012), planning (Batey and Brown 2007) and policing (Ashby and Longley 2005).…”

Section: Introductionmentioning

confidence: 99%

The stability of geodemographic cluster assignments over an intercensal period

2016

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A geodemographic classification provides a set of categorical summaries of the built and socio-economic characteristics of small geographic areas. Many classifications, including that developed in this paper, are created entirely from data extracted from a single decennial census of population. Such classifications are often criticised as becoming less useful over time because of the changing composition of small geographic areas. This paper presents a methodology for exploring the veracity of this assertion, by examining changes in UK census-based geodemographic indicators over time, as well as a substantive interpretation of the overall results. We present an innovative methodology that classifies both 2001 and 2011 census data inputs utilising a unified geography and set of attributes to create a classification that spans both census periods. Through this classification, we examine the temporal stability of the clusters and whether other secondary data sources and internal measures might usefully indicate local uncertainties in such a classification during an intercensal period.

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Section: Introductionmentioning

confidence: 99%

The stability of geodemographic cluster assignments over an intercensal period

2016

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“…Singleton et al (2012) modelaron la participación en la Educación Superior con un modelado espacial interactivo conectado a la clasificación demográfica. El objetivo principal era poder observar la aplicación hipotética de diversas políticas.…”

Section: Wang Et Al (2009) Desarrollaron Un Sistema En Launclassified

Análisis De La Colaboración Científica en La Universidad Politécnica De Valencia

Fombuena¹

2017

Proceedings - 1st Congress in Geomatics Engineering - CIGeo

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Abstract:The work presented in this paper is focused on employing maps for the visualization of the collaboration network of the Polytechnic University of Valencia (Spain). In this paper, a collaboration is understood as the scientific publications where at least one author is affiliated in the aforementioned university. Furthermore, a visual analysis using the maps created is performed with the goal of observing the proximity of the institutions collaborating with said university, the intensity of said collaborations, and supporting future decision-making processes for new research projects. Key words: Geomatics, GIS, Higher Education Resumen:El trabajo presentado en este artículo se centra en la visualización de la red de instituciones con las que colabora la Universidad Politécnica de Valencia a través de mapas. Entendiendo como colaboraciones las publicaciones científicas en las que al menos un autor pertenece a dicha institución. Además, se expone un análisis visual mediante los mapas creados con el objetivo final de observar la proximidad de las instituciones colaboradoras con la universidad y la intensidad de las colaboraciones, que puedan servir de apoyo a la toma de decisiones para futuros proyectos de investigación.

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“…One of the challenges of deriving meaning from attribute poor but spatially rich interaction data such as the telecommunications interactions used here or by Reades and Smith (2014), taxi journeys (Peng et al 2012) or flows of students to University (Singleton et al 2010) is that without other contextual information on the origins and destinations, the interactions remain just flows of information, traffic or people. To overcome this, despite using data from quite different contexts, Reades and Smith (2014), Peng et al (2012) and Singleton et al (2010) all made use of different area classifications (derived from alternative data sources) situate the flows they were analysing.…”

Section: Introductionmentioning

confidence: 99%

“…To overcome this, despite using data from quite different contexts, Reades and Smith (2014), Peng et al (2012) and Singleton et al (2010) all made use of different area classifications (derived from alternative data sources) situate the flows they were analysing. The challenge we face in Senegal is that contextual data is scarce and area classifications non-existent, and so developing a new classification based on available infrastructural data should provide a means to contextualise the telecommunication interactions observed.…”

Section: Introductionmentioning

confidence: 99%

New Forms of Data for Understanding Urban Activity in Developing Countries

Manley

Dennett

2018

Appl. Spatial Analysis

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Emerging forms of data offer new opportunities for developing a deeper understanding of poorly understood social and spatial processes. This is no more important than in developing countries, where large-scale data collection and processing has been relatively limited. In this paper, we explore how two new datasets can be used to enhance our understanding of human activity and communication interactions in Dakar, Senegal. Starting from a premise of little contextual knowledge about the setting in which we are working, we explore how much these data, combined with novel quantitative methods, are able to inform us about the urban environment in question. Fine-grained infrastructural data are combined with k-means clustering to produce an 11-class land use classification, distinguishing dense and sparse, single and mixed use regions. Using these classifications and over 1.5 billion mobile phone call records, patterns of activity and interaction within and between land use types are analysed. These analyses reveal strong activity associated with high density commercial, governmental and administrative regions. These regions are also identified as relatively strong 'attractors' of communication, and wider patterns show higher interactions between areas with similar land use characteristics. Analyses of dynamic activity and interaction patterns highlight the movement of people from workplace zones to residential areas. Importantly, these analyses reflect the expected patterns of urban activity, providing some validation of the data, methods, and the empirical approach. The paper concludes in addressing the strengths and potential of these approaches, while recognising current limitations and areas for further work.

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Geodemographics and spatial interaction: an integrated model for higher education

Cited by 30 publications

References 23 publications

The stability of geodemographic cluster assignments over an intercensal period

The stability of geodemographic cluster assignments over an intercensal period

Análisis De La Colaboración Científica en La Universidad Politécnica De Valencia

New Forms of Data for Understanding Urban Activity in Developing Countries

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