Location: Putting density in the right place
What is Location Efficiency?
"Location Efficiency" is a term introduced by American researchers seeking to quantify the value of good neighbourhood design in reducing the need to drive(1) (2). It has since been used to describe how a mix of different factors can produce high quality places which save money, are more liveable, emit less carbon and are more adaptable to change over time (3).
Many different factors influence a place's location efficiency. These include accessibility to jobs and services, walkability to the daily needs of life, a well designed physical environment, good public transport links, and a mix of incomes and demographics. Getting this mix right reduces the need to own a car, reduces the amount one needs to drive, and therefore reduces carbon emissions. It also reduces household transportation costs, encourages a more healthy and active lifestyle, reduces congestion, and helps produce beautiful places where people are happy to live.
How to use the Location Efficiency Calculator
While often equated to the “feel” of a place, the elements of location efficiency are indeed quantifiable and can be measured through the use of well known statistical techniques. The Prince's Foundation for the Built Environment has created a statistical model for Greater London, which links different physical and social factors to the need to own a car and the number of kilometres driven per year(4).
We have developed a custom Flash tool called the Location Efficiency Calculator, which uses these statistics. The Location Efficiency Calculator measures location efficiency variables for any postcode in London and links these to car ownership, vehicle kilometers travelled, and transport-related carbon emissions.
Click here to use the Location Efficiency Calculator
Model details
The Location Efficiency Model was created using a common technique known as multiple regression analysis. Nine variables (five physical and four socio-economic) were compared to both auto ownership and vehicle kilometres traveled (VKT) to determine how they influenced the need to own a car and the amount driven per household. The nine variables used and their sources are summarised in the table below. You can also view and download maps of each variable in our Map Gallery.
This statistical model was able to explain over 95% of all car ownership and nearly 50% of all vehicle kilometres travelled, for every Middle Layer Super Output Area (MSOA) within the M25 Motorway(5). A variety of statistical tests were then conducted to test the robustness of the model (6), which performed very well. This suggests a reasonable level of confidence in its results.
Finally, the output of the model was then summarised by postcode, entered into a Sequel (SQL) database online, and then made available to the public through a custom designed Flash interface named the Location Efficiency Calculator, described above.
Variables and their sources used in the Greater London statistical model
| Variable | Meaning | Derivation | Source |
|---|---|---|---|
| Households per Residential Hectare | The number of households per hectare of residential land. | Number of households divided by the domestic use hectares for each Census Output Area (MSOA). | Number of People Living in Households (UV51): ONS 2001, Land Use Statistics (Generalised Land Use Database), 2005 |
| Job density | The number of jobs per hectare of commercial, retail and industrial land. | Daytime workplace population divided by the amount of commercial, retail, and industrial hectares per Census Output Area (MSOA). | Distance Travelled to Work - Workplace Population (UV80); ONS, 2001, Land Use Statistics (Generalised Land Use Database), 2005 |
| Percent transit users | Percent of working adults who commute using non-automobile modes (bus, tram, tube, train, ferry, walking or cycling). | Sum of adult working population using non-automobile modes of commute, divided by total adult working population per MSOA. | Method of Travel to Work - Resident Population (UV39); ONS, 2001 |
| Walkability | A measure of walkability and street network accessibility, as measured by Space Syntax network analysis. | Average local choice value of all street segments within the MSOA, calculated at radius 1200m. | Space Syntax Greater London Accessibility Map, Space Syntax Limited, 2007 |
| Income | The average weekly household income, in pounds. | Taken directly from ONS modelled income figures, 2004/5 Economic Census. | ONS, 2004/5, Income: Model-Based Estimates at MSOA Level |
| Household size | The number of adults per household, including families, sharers, and combined facilities. | Taken directly from ONS 2001 Census. | Number of People Living in Households (UV51): ONS 2001, Total Population; ONS, 2001 |
| Employees per household | The number of employeed adults per household. | Taken directly from ONS 2001 Census. | Economic Activity (UV28); ONS,2001, Number of People Living in Households (UV51): ONS 2001 |
| Average commute distance | The average length of commute, measured in kilometres, for each household in the survey area. | Taken directly from Transport for London's 2001 London Area Travel Survey (LATS). | London Area Travel Survey (LATS), Transport for London, 2001. |
| Number of commercial facitlities | The number of commercial, retail and service facilies per hectare. | Total area of commercial, retail, and service facilities (in square metres), divided by amount of non-residential land area. | Commercial and Industrial Floorspace and Rateable Value Statistics (2005 Revaluation), Generalised Land Use Database |
| VKT | Vehicles Kilometres Travelled, per year, per household. | Measured from the TfL LATS survey. | London Area Travel Survey (LATS), Transport for London, 2001. |
| CO2 | Transport related kilograms of CO2 emitted per household, per year. Measures only CO2 related to driving and travel, not home use, energy generation, or any other source. | Average VKT per household times 180 grams of CO2, times 260 working days per year. | The Center for Neighbourhood Technology |
References
And Peter Newman and Jeffrey Kenworthy (1989). Cities and Automobile Dependence: An International Sourcebook, Gower Publishing.
(2) For example, see the Center for Neighbourhood Technology's Location Efficiency work for the Prince's Foundation for the Built Environment's Walthamstow Master Plan:
Download the Walthamstow Masterplan Report [5.7 MB]
For further information on Walthamstow, also see our Walthamstow Project page, and the Location Efficiency case study of Walthamstow.
(3) The Center for Neighborhood Technology adapted the location efficiency model to create an Affordability Index, which combined location efficiency, transport mode choice, and household budgets. They found that households within more location efficient neighbourhoods spent less on transport needs and had more money left over for discretionary spending on other quality of life items. They also found that this extra money was often spent in nearby shops and services, further strengthening local economies and creating more sustainable long term economies for location efficiency neighbourhoods.
(4) The Prince's Foundation for the Built Environment model reproduced and improved upon an earlier study conducted for the Foundation by The Center for Neighborhood Technology. This work was completed as part of the Walthamstow Town Centre EbD, which can be downloaded here.
(5) The M25 Motorway is a 117 mile (188km) orbital motorway which encircles Greater London, United Kingdom.
(6) Four separate modeling techniques were used, including linear and non-linear multiple regression against a variety of dependent variables. Both stepwise regression and theory-driven general linear hypothesis tests (GLH tests) were conducted, as well as a variety of sensitivity analyses for the final model. Although the final model was found to be statistically significant and robust to variation, as in all statistical analysis, correlation does not imply causation
