Great Britain Transport, Employment Access Datasets for small-area Urban Area Analytics

Published: 30-08-2019| Version 2 | DOI: 10.17632/tvnnb7pv8b.2
Contributor:
Obinna Anejionu

Description

This paper provides a brief description of four new forms of key datasets relevant to urban analytics studies namely: Transport, Housing and Employment Accessibility and Education, covering Great Britain, developed by the Urban Big Data Centre (UBDC). Full details of the research related to this paper are contained in “Spatial urban data system: A cloud-enabled big data infrastructure for social and economic urban analytics”[1]The transport Dataset contains public transport availability (PTA) indicators at both the stop/station and small-area levels (lower layer super output area (LSOA) and middle layer super output area (MSOA)). The employment dataset provides information on the number of people with access to employment within specific distances from each output area. The housing datasets contains quarterly house rent and sales prices aggregated at output area level (MSOA). The education data contains secondary school (Greater Glasgow Area, Scotland) and Higher Education (Great Britain) student-level data. In addition to all educational outcomes at school stages S4-S6, the secondary school pupil data consists of age, gender, nationality and ethnic background, level of English, attendance, post-school destinations, and receipt of Gaelic education. This is augmented by individual schools’ data consisting of staffing levels, proportions of pupils’ speaking particular languages at home, religious denomination, distance travelled by students from home, and accessibility to greenspace from both the home and school neighbourhoods. The higher education (HE) dataset consists of home and term-time locations (at postcode sector level), subject studied, level and mode of study of courses, level and classification of qualification, and post-HE destination. The theoretical background for measuring the datasets at small area levels is also presented in this paper. Additionally, a variety of raw data used to produce some of the datasets (e.g. PTA) is also introduced to enable interested readers to reproduce them.

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