Quantitative Place Research

While qualitative methods capture the subjective experience of place, quantitative approaches provide measurable, comparable data about place characteristics. This lesson examines the key quantitative datasets, classification systems, and analytical techniques used in place studies at A-Level, along with their strengths and limitations.

---

Census Data Analysis

The UK Census is conducted every ten years (most recently in 2021) and provides the most comprehensive quantitative dataset for analysing places.

Key Variables Available at Small-Area Level

Category	Variables	Use in Place Studies
Population	Total population, age structure, sex ratio	Identifying demographic profiles; ageing/youthful places
Ethnicity	Ethnic group, national identity, country of birth	Analysing diversity and segregation
Housing	Tenure (owned/rented), type (detached/flat), number of rooms	Comparing housing markets; identifying deprivation
Employment	Economic activity, occupation, industry, hours worked	Assessing economic health; identifying dominant sectors
Education	Qualifications, student status	Proxy for social class and human capital
Health	Self-assessed health, disability, unpaid care	Identifying health inequalities
Transport	Mode of travel to work, car ownership	Assessing connectivity and mobility
Deprivation	Derived from multiple indicators (see IMD below)	Identifying and comparing levels of disadvantage

Geographical Units

Census data is available at multiple spatial scales:

• Output Areas (OAs) — smallest unit; approximately 125 households (England and Wales)
• Lower Super Output Areas (LSOAs) — approximately 650 households; the unit used for the Index of Multiple Deprivation
• Middle Super Output Areas (MSOAs) — approximately 3,000 households
• Local Authority Districts — administrative areas
• Regions — e.g., North West, South East

Using Census Data in Place Studies

1. Place profiles — compile data for a specific area to create a quantitative portrait
2. Comparison — compare two or more places on the same variables (e.g., contrasting a deprived urban neighbourhood with an affluent rural village)
3. Change over time — compare census data from different years (2001, 2011, 2021) to identify trends
4. Spatial patterns — map census data to identify clusters, gradients, and anomalies

Accessing Census Data

• NOMIS (Office for National Statistics) — official source for census and labour market data
• Census maps — interactive mapping tools showing spatial distribution of variables
• DataShine — visual tool for exploring census data at neighbourhood level

---

Index of Multiple Deprivation (IMD)

The Index of Multiple Deprivation is the official measure of relative deprivation in England. It ranks every LSOA (32,844 in total) from most deprived (1) to least deprived.

The Seven Domains

Domain	Weight	What It Measures
Income	22.5%	Proportion of people on low incomes (benefits, tax credits)
Employment	22.5%	Proportion of working-age people involuntarily excluded from the labour market
Education, Skills and Training	13.5%	Educational attainment and skills among children and adults
Health Deprivation and Disability	13.5%	Premature death, illness, and disability
Crime	9.3%	Rates of recorded crime (violence, burglary, theft, criminal damage)
Barriers to Housing and Services	9.3%	Affordability, overcrowding, homelessness; distance to services
Living Environment	9.3%	Housing quality (central heating, condition); outdoor environment (air quality, road accidents)

Using the IMD in Place Studies

• Identify the most and least deprived areas within a local authority or region
• Compare domains — an area may rank highly deprived on income but not on crime, revealing a more nuanced picture
• Track change — compare IMD rankings over time (published in 2004, 2007, 2010, 2015, 2019)
• Map deprivation — GIS mapping of IMD data reveals spatial concentrations of disadvantage

Limitations of the IMD

• Measures relative deprivation — it ranks areas against each other rather than measuring absolute poverty
• Ecological fallacy — not everyone in a deprived area is deprived, and not everyone in a wealthy area is wealthy
• LSOA boundaries are arbitrary — they may split coherent communities or combine very different areas
• Does not capture subjective experience — a statistically deprived area may have strong community bonds and high resident satisfaction
• Updated irregularly — data can be several years out of date

---

GIS Mapping

Geographic Information Systems (GIS) are digital tools for storing, analysing, and visualising spatial data. GIS is increasingly central to place research.

Applications in Place Studies

• Choropleth maps — shading areas by data values (e.g., IMD deciles, ethnic diversity, house prices)
• Dot density maps — showing the distribution of specific features (e.g., fast food outlets, listed buildings, crime incidents)
• Buffer analysis — identifying areas within a specified distance of a feature (e.g., households within 1km of a GP surgery)
• Overlay analysis — combining multiple data layers to identify spatial relationships (e.g., overlaying flood risk with deprivation data)
• Change detection — comparing datasets from different time periods to identify spatial change

GIS Software