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Quiz yourself on Human Geography FieldworkGet your flashcardsBuild my revision planTrack my progress⛓️ Why EQS Scores Should Change With Distance From the Centre
The Burgess model gives you a theoretical reason for the pattern you expect to find. Understanding the cause chain that links urban land use theory to your fieldwork prediction is essential for scoring Level 3 on exam questions that ask you to explain your results.
High demand for central land → high land values in CBD
The centre of a city is the most accessible point — reachable by the most transport routes. This makes it attractive to businesses that depend on footfall or face-to-face interaction. As demand for central locations drives up prices, only high-value uses (offices, major retailers) can afford to locate there. Residential housing is displaced to the edges.
The centre of a city is the most accessible point — reachable by the most transport routes. This makes it attractive to businesses that depend on footfall or face-to-face interaction. As demand for central locations drives up prices, only high-value uses (offices, major retailers) can afford to locate there. Residential housing is displaced to the edges.
High land values → intensive use, dense development, little green space
When land is expensive, developers build up rather than out, and every square metre of ground is used. The CBD therefore has tall buildings, minimal green space, high traffic volumes, and high noise levels. These factors drive down EQS scores for criteria like green space, noise, and aesthetic appeal — even though the area is economically vibrant.
When land is expensive, developers build up rather than out, and every square metre of ground is used. The CBD therefore has tall buildings, minimal green space, high traffic volumes, and high noise levels. These factors drive down EQS scores for criteria like green space, noise, and aesthetic appeal — even though the area is economically vibrant.
Distance from centre → lower land values → different development patterns
As you move outward, land values fall. The inner city (Zone 2) was historically used for industrial purposes and dense worker housing — neither designed with environmental quality in mind. Buildings are older and often less well-maintained. Multiple deprivation indices show the highest concentrations of poverty in inner-city zones, meaning residents have less capacity to maintain and improve their environment.
As you move outward, land values fall. The inner city (Zone 2) was historically used for industrial purposes and dense worker housing — neither designed with environmental quality in mind. Buildings are older and often less well-maintained. Multiple deprivation indices show the highest concentrations of poverty in inner-city zones, meaning residents have less capacity to maintain and improve their environment.
Outer suburbs → newer, lower-density, owner-occupied housing
Land is cheaper, so housing is lower-density. Gardens, verges, parks, and trees are more common. Owner-occupiers have an economic incentive to maintain their properties, so building condition scores are higher. Traffic is lower-volume (residential streets rather than arterial roads). Noise and litter scores improve.
Land is cheaper, so housing is lower-density. Gardens, verges, parks, and trees are more common. Owner-occupiers have an economic incentive to maintain their properties, so building condition scores are higher. Traffic is lower-volume (residential streets rather than arterial roads). Noise and litter scores improve.
Prediction: EQS total score increases with distance from CBD
The model therefore predicts a positive relationship between distance and EQS score. Geographers test this using Spearman's rank correlation coefficient — a statistical test that quantifies the strength of the relationship between two ranked variables. A strong positive correlation (rs close to +1) supports the Burgess model. Anomalies — points that fall well off the expected trend line — indicate places where local factors override the general pattern.
The model therefore predicts a positive relationship between distance and EQS score. Geographers test this using Spearman's rank correlation coefficient — a statistical test that quantifies the strength of the relationship between two ranked variables. A strong positive correlation (rs close to +1) supports the Burgess model. Anomalies — points that fall well off the expected trend line — indicate places where local factors override the general pattern.
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