Every number paints a picture: Unraveling socioeconomic variations within an urban area

This project idea was contributed by Jeff Stanfield.

Setting the Scene

Block of flats in Woolston

Central to the specifications of all GCSE geography syllabuses is the requirement for students to investigate and interrogate geographical data in a wide range of formats. For example, the AQA GCSE Geography B draft specification states that students should be able to ‘Use (census) data to identify socio-economic variations in one urban area.’

Not only does specific data detection work help to hone and deepen geographical knowledge and understanding, it also supports the development, enhancement and application of numeracy; a key functional skill.

Within this example students interrogate socio-economic data for different wards within a selected urban area (in this case Southampton) as part of their more detailed investigation into socio-economic variations within settlements.

The activity outlined is of a generic nature and thus can be easily replicated for any other urban area; namely, a local settlement which has been selected as a multi – functional case study by your department.

Simplified data for one of the wards selected (Woolston) is given in this summary document. This data can also be obtained from the website of the Office for Neighbourhood Statistics via the use of post codes. The site yields both 10% and 100% ward data for the 2001 census. Information for a previous census can also be obtained to investigate socio-economic change over time if that is required.

Using Neighbourhood Statistics

This main frame for Neighbourhood Statistics gives access to summary and complete ward data via use of postcodes.

Within the summary information the map extract shows the location of the postcode site within the ward. A swing meter indicates the total deprivation measured against all 32,482 wards.  It also indicates the ward position with regard to income, employment, health, education, housing/services, crime and the environment.

100% data gives complex information on wards. Source: National Statistics website Crown copyright material is reproduced with the permission of the Controller Office of Public Sector Information (OPSI).

Even the simplified socio-economic data on this site is relatively complex for a significant number of students. However, it can be carefully selected and modified as required as exemplified in this document. For gifted and talented students the site allows them the opportunity to review some quite sophisticated and challenging data sets in a more personalised and independent fashion.

Key Geography Objectives

• To evaluate, analyse and sythesise information from geographical data sets
• To cross reference secondary geographical resources (data tables, ground shot photographs, maps and aerial photographs)
• To describe and compare the layout, character and socio-economic structure of different parts of an urban area
• To reach plausible conclusions concerning socio economic differences within urban areas

Key ICT Objectives

• To investigate and gather information from an electronic data base/National Census site
• To cross reference a range of digital information
• To communicate findings using a digital medium

Developing the activity

• For your chosen urban settlement select four or five different wards moving out from the centre. For example one in the CBD, inner city, suburbs, edge of city estate and urban fringe etc. Examples chosen will depend very much on the perceived or known character/socio-economic breakdown of your selected urban area. Postcodes can be used to direct students to specific points from which the ward can be defined and named.
• For each ward collect the following materials either as hard copies, on CD, or stored for retrieval on the school intranet/network:
  1. a map extract from an online mapping site for example Streetmap or others.
  2. a vertical aerial picture and if possible an oblique aerial picture using Bing Maps
  3. 5 – 10 ground shots. You could use home made digital images. Ground shots can also be obtained from the Geograph site.

  Alternatively, list sites to be utilised by students during their investigation enabling them to collect similar information. This will ensure that lesson time is used effectively and efficiently.

• For less able students use Neighbourhood Statistics to produce a simple table displaying socio-economic data for each selected ward, such as the one prepared for Woolston below.

Ward info for Woolston with pictures

Running the Activity

This activity can be run either in a classroom with no access to ICT by using a pre-prepared summary document such as the one above, or in a computer room using saved digital resources or web based searches.

Introduction to the lesson will include appropriate starter activity and agreed procedures for sharing learning objectives with students. The group is split into urban data detective teams. The number of teams will be dependent on the number of wards for which resources have been collected, or the number of differing socio-economic zones to be explored.

Each team is allocated a set of ward data (see example) for interrogation. A member of staff gives support on interrogating the data as necessary. What would you expect to see if you visited this ward etc? What are the key characteristics of the ward with regard to selected socio-economic data?

Teams are given time to investigate their socio-economic data sets and examine population structure, occupation type, ethnicity, types of houses, house prices, etc. From the information that the students have deduced from their data sets they then select which map, aerial photograph(s) and ground shots from the resource bank they think matches their ward.

Students record reasons for their choices, either manually or electronically, selecting an appropriate but personalised format, together with a summary of the key characteristics of their urban area. It does not matter if the students select some ‘incorrect’ resources for their ward; the activity is about interrogating data and drawing plausible conclusions from it and cross referencing secondary sources.

A plenary review of existing findings and resolving any key issues. Following this a subsequent session will involve detailed presentation of findings of data interrogated by each team, evaluation of geographical resources collected to exemplify their ward and ‘trading’ of resources as necessary by agreement.

Students then use digital learning resources to develop a detailed report on the key socio-economic characteristics of their ward. The group findings are placed on the school’s VLE so as to be accessible for all students. Where departments have easy access to a batch of computers and the staff and students have high level skills in the use of ICT, findings can be displayed via use of a GIS such as Aegis.

More able, and gifted and talented geography students can plan their enquiry/investigation of an urban area independently from start to finish. In this case the member of staff acts fully in the role of learning mentor rather than learning facilitator. Alternatively they may wish to use similar digital learning resources to those listed above to investigate socio economic differences within a single ward; a complex geographical detection task.