I am very excited by the massive expansion of Google Street View, and is a good opportunity to present my street chronology:

1980 – 1988
Alnwick, Northumberland

1988-1990
Alnmouth, Northumberland

1990-1993
Allestree, Derby

1993-1998
Shenley Church …

What’s the data source for this visualization? Is it available for download?

This paper reports on a cross-cultural outreach activity of the current UK ‘Spatial Literacy in Teaching’ (SPLINT) Centre of Excellence in Teaching and Learning (CETL), a past UK Economic and Social Research Council (ESRC) grant, and shared interests in family names between Japanese and UK academics. It describes a pedagogic programme developed for Japanese postgraduates and advanced undergraduates that entailed quantitative and qualitative analysis of the spatial distributions of Japanese family names. The authors describe some specific semantic, procedural and theoretical issues and, more generally, suggest how names analysis provides a common framework for engaging student interest in GIS.

Paul A. Longley; Alex D. Singleton; Keiji Yano; Tomoki Nakaya

Longley, Paul A., A.D. Singleton, Keiji Yano, and Tomoki Nakaya. 2010. “Lost in Translation: Cross-Cultural Experiences in Teaching Geo-Genealogy.” Journal of Geography in Higher Education 34 (1): 21–38. http://dx.doi.org/10.1080/03098260902982476.

This is my first post, let’s give it a try

You can even track a commercial flight:<br />http://www.youtube.com/watch?v=cgt6j1nbyuU

Geographically Weighted Regression (GWR) is a method of spatial statistical analysis used to explore geographical differences in the effect of one or more predictor variables upon a response variable. However, as a form of local analysis, it does not scale well to (especially) large data sets because of the repeated processes of fitting and then comparing multiple regression surfaces. A solution is to make use of developing grid infrastructures, such as that provided by the National Grid Service (NGS) in the UK, treating GWR as an “embarrassing parallel” problem and building on existing software platforms to provide a bridge between an open source implementation of GWR (in R) and the grid system. To demonstrate the approach, we apply it to a case study of participation in Higher Education, using GWR to detect spatial variation in social, cultural and demographic indicators of participation.

Harris, Richard, A.D. Singleton, Daniel Grose, Chris Brunsdon, and P.A. Longley. 2010. “Grid-enabling Geographically Weighted Regression: A Case Study of Participation in Higher Education in England.” Transactions in GIS 14 (1): 43–61. http://dx.doi.org/10.1111/j.1467-9671.2009.01181.x.

Abstracts are invited for a session at the annual conference of the the Royal Geographical Society – Institute of British Geographers in 2010 on the spatial dimensions of health. The session is jointly sponsored by the QMRG as well as the Health geography research group (HGRG) of the RGS. Details are as follows: The Spatial […]

This is a talk given at the University of Tokyo on 5th December 2009.

Geocomputation 2009, UNSW – Sydney, 2nd Dec.

Thanks for this information!

Yesterday I spoke at an OAC User Group event at the RSS about geographic visualisation and area classification more generally. The slides as usual are on slideshare!

Here is my talk from the 39th RSAI:BIS conference on 2-4 September 2009, Limerick, Ireland.

I have just finished at the RGS conference which was hosted in the fantastic city of Manchester. I thought the line up this year was very good indeed and there were lots of interesting talks. The UCL talks from CASA comprised the QMRG session I organis…

Although viewed by business and commerce as successful solutions, geodemographic profiling of neighbourhoods has attracted wide-ranging criticism in the academic literature. This paper addresses some specific concerns that arise because the derivation of classifications is rarely transparent and open to scrutiny or challenge. The substantive focus of the research reported in this paper is a nationwide geodemographic classification of how people engage with new information and communication technologies (ICTs). In response to the critique of geodemographics as a ‘black box’ technology, we describe how the classification was opened up to public scrutiny and how we conducted a major consultation exercise into the reliability of its results. We assess the message of the 50,000+ searches and 3952 responses collected during the consultation exercise, in terms of possible systematic errors in the shape and detail of the classification. Unusually for Internet-based surveys, we also investigate the likely reliability of the response information received and identify ways in which the outcome of consultation might be used to improve the classification. We believe that this is the first-ever large-scale consultation survey of the validity and remit of a geodemographic classification and that it may have wider implications for the creation of geodemographic classifications.

Longley, P.A., and A.D. Singleton. 2009. “Classification Through Consultation: Public Views Of The Geography Of The E-Society.” International Journal of Geographical Information Science 23 (6): 737–763. http://dx.doi.org/10.1080/13658810701704652.

This paper explores the use of geodemographic classifications to investigate the social, economic and spatial dimensions of participation in Higher Education (HE). Education is a public service that confers very significant and tangible benefits upon receiving individuals: as such, we argue that understanding the geodemography of educational opportunity requires an application-specific classification that exploits under-used educational data sources. We develop a classification for the UK higher education sector, and apply it to the Gospel Oak area of London. We discuss the wider merits of sector specific applications of geodemographics and enumerate the advantages of bespoke classifications for applications in public service provision.

Singleton, A.D., and P.A. Longley. 2009. “Creating Open Source Geodemographics – Refining a National Classification of Census Output Areas for Applications in Higher Education.” Papers in Regional Science 88 (3): 643–666. http://dx.doi.org/10.1111/j.1435-5957.2008.00197.x.