Skip to main content

Using spatial equity analysis in the process evaluation of environmental interventions to tackle obesity: the healthy towns programme in England



Process evaluations of environmental public health interventions tend not to consider issues of spatial equity in programme delivery. However, an intervention is unlikely to be effective if it is not accessible to those in need. Methods are required to enable these considerations to be integrated into evaluations. Using the Healthy Towns programme in England, we demonstrate the potential of spatial equity analysis in the evaluation of environmental interventions for diet and physical activity, examining whether the programme was delivered to those in greatest need.


Locations of new physical infrastructure, such as cycle lanes, gyms and allotments, were mapped using a geographic information system. A targeting ratio was computed to indicate how well-located the infrastructure was in relation to those at whom it was specifically aimed, as detailed in the relevant project documentation, as well as to generally disadvantaged populations defined in terms of UK Census data on deprivation, age and ethnicity. Differences in targeting were examined using Kruskal-Wallis and t-tests.


The 183 separate intervention components identified were generally well located, with estimated targeting ratios above unity for all population groups of need, except for black and ethnic minorities and children aged 5–19 years. There was no evidence that clustering of population groups influenced targeting, or that trade-offs existed when components were specifically targeted at more than one group.


The analysis of spatial equity is a valuable initial stage in assessing the provision of environmental interventions. The Healthy Towns programme can be described as well targeted in that interventions were for the most part located near populations of need.


Effective public health interventions should narrow inequalities by having a positive impact on the health of populations most in need. However, this often does not occur, and interventions may sometimes widen inequalities[1]. These intervention generated inequalities may be more pronounced for certain classes of intervention, such as those reliant on voluntary behaviour change, which may result in lower uptake among more disadvantaged groups[2]. This has been shown in interventions such as those to promote breast feeding[3], seatbelt use[4] and cancer screening[5]. Furthermore, the effectiveness of interventions may be socially patterned, as found by studies of smoking restrictions[6] and bicycle helmet legislation[7]. These observations are of particular concern given the potential existence of ‘deprivation amplification’[8], whereby the most socioeconomically disadvantaged experience poorer health and poorer access to resources[9]. A version of the ‘inverse care law’[10] may therefore operate for many interventions, whereby the provision of facilities that are spatial in nature may be poorest in areas with greatest need. One way of investigating such issues within evaluations is to explore the underlying geographical distribution of resources in relation to the context of the areas in which they are provided[11].

Achieving social equity in environmental planning and decision making processes is increasingly recognised as an important component of environmental policies in many countries[12]. Building on the concept of environmental inequity in the USA – defined as the apparent unequal geographical distribution of benefits or burdens among those in poverty or for minority groups[13] – early work focused on consideration of disamenities such as hazardous facilities[14, 15]. Subsequently, access to amenities has been considered, with studies examining the locations of parks[16], playgrounds[17, 18] and public health facilities[19], often suggesting that disadvantaged populations may have poorer access to given resources. A valuable component of environmental equity is therefore that of spatial equity.

An important current area of research is the exploration of the provision of resources that support physical activity. People who engage regularly in physical activity are less likely to experience a range of preventable chronic conditions including obesity[20]. Consequently, interventions have been developed in order to increase activity levels in target groups or populations[21] with mechanisms including the use of information, targeted behavioural and social programmes, and changing the physical environment and planning policy associated with it[22]. For example, interventions to encourage walking and cycling have included publicity campaigns to increase awareness, financial incentives, and improvements to footpaths and cycle routes[21]. Disadvantaged groups, such as those with low socio-economic status and ethnic minorities, often have higher levels of obesity and have sometimes been shown to live in areas with poorer access to facilities for physical activity or healthier eating[23]. This emphasises the importance of understanding how need and provision vary spatially so that interventions can be located to serve these high-need yet often overlooked populations. Nevertheless, decisions about where to locate infrastructure can be difficult to reconcile with the location of target populations due to issues of land availability and other context-specific circumstances[24]. There remains a paucity of evidence regarding the delivery of effective interventions[25] due to limited evaluation of interventions[26].

Methods using geographic information system (GIS) technology may be employed to assess the spatial equity of amenities, resources and infrastructure. Studies have calculated the distances between residential locations and amenities[27] and the number of facilities available per capita[28]. However, the utility of these findings is limited, as not all have considered the underlying geography of need, so it is not always known if a facility favours or disadvantages a certain type of population[17]. Some studies have addressed this; Nicholls[17, 29] evaluated the locations of disadvantaged populations (non-white populations, children, economically disadvantaged) with and without access to parks within 800m of home, finding that parks tended to be located in areas with greatest need. Such literature is generally concerned with the location of existing infrastructure rather than the evaluation of recent modifications to the built environment. Nevertheless, method and learning from these studies may be used to inform the evaluation of infrastructural interventions to target population health behaviours.

Process evaluation is central to determining whether interventions perform as intended[30] and can therefore aid understanding of how the context within which they are developed may affect their eventual effectiveness[31]. Spatial equity is the first step in a process towards reducing health inequality via structural or area-based interventions and should therefore be evaluated accordingly. If the intervention does not achieve a basic level of ‘availability’ through accurate targeting, then the next steps - uptake, efficacy, long-term compliance and health outcomes[1] - are unlikely to be achieved. It is thus important to examine the potential reach of interventions[32], and the implications of their presence[33] in order to give insight and possible explanation for the outcomes[34]. UK guidance recommends that components of planning, implementation and operation should be reviewed as a necessary precursor to a full evaluation of health interventions[35] to understand the local context, at whom a given intervention is aimed, and the components of an intervention[34]. It has been suggested that evaluations might be further strengthened by integration of geographic data[36] and we argue this is particularly so for spatial equity analysis. This may ultimately be used to give an early indication that an intervention might not eventually be effective because it is poorly targeted.

In this paper we develop and apply methods to evaluate how the spatial location of infrastructure relates to the underlying geography of population need, using the case study of a recent government-funded programme in England, ‘Healthy Towns’[37]. This government-funded programme aimed to provide interventions to encourage dietary and physical activity behaviour change to combat obesity. We investigate whether infrastructure developed from this was best located in relation to areas of need, according to the socio-demographic characteristics of neighbourhoods.


The healthy Towns programme in England

The Healthy Towns programme was funded from the £30 million ‘Healthy Community Challenge Fund’ (HCCF), set up by the UK government as part of the ‘Healthy Weight, Healthy Lives’ strategy for England[38]. This fund was allocated between nine local areas in England (Table 1), each of which had submitted a successful competitive bid for the provision of innovative interventions and community-driven programmes aimed at increasing physical activity and improving diet using a ‘whole-town’ approach[39]. The HCCF envisaged that part of the funding would enable each local area to provide a physical environment appropriate to encouraging healthy behaviours, using modifications to influence walkability, safety, vibrancy or supportiveness of the environment for active travel[40]. The areas responded by planning and implementing (building or improving) or mapping various pieces of physical infrastructure to modify the built environment and provide facilities to encourage behaviour change[39]. These interventions varied widely in nature from cycle signage and foot paths to allotment plots and green spaces.

Table 1 Characteristics of the local areas of the healthy towns programme

To assess the spatial equity of the delivery of these interventions, it was necessary to identify the spatial extent of the infrastructural developments in relation to the distribution of key population groups. This was examined to identify if particular groups were advantaged or disadvantaged in terms of local provision. In addition, funding allocations per town and by population diversity were examined to see if successful delivery of interventions varied according to the funding context. The evaluation considered the possible existence of trade-offs, whereby interventions aimed at more than one population group may have favoured one group at the expense of others in a given location.

This study is part of the wider national evaluation of the Healthy Towns programme carried out by a team including the authors of this paper. The team brings together academics with expertise in geography, public health and the evaluation of health interventions.

Spatial location of infrastructure

All spatial analyses were conducted using GIS software (ArcGIS 9.3™[42]). Each local area was defined by urban settlement area boundaries identified by the UK Ordnance Survey (OS), the national mapping agency of the UK. These boundaries were supplemented with a 400 m buffer in order to include populations within a short distance of the urban fringe.

Locations of physical infrastructure were identified and obtained from the Healthy Towns database developed as part of the overall programme evaluation. The database consisted of information extracted from reports and documentation provided by programme managers from each local area. This was augmented with further information from local area-specific websites, council maps, and planning applications. Updated maps and information were then reviewed by the relevant programme managers, and were amended where appropriate. Only infrastructure that was confirmed as complete or in progress was included.

Each item of infrastructure was classified according to four categories based on the primary function of that intervention: active travel (infrastructure to promote walking or cycling to access a location), food systems (outlets and facilities for growing and eating healthy food), healthy lifestyles (behaviours which positively influence health), and physical activity (movement and exercise). Classification by the type of facility provided was also undertaken. Interventions included advice/information, cafe/food co-ops, facilities for cycling/walking (e.g. cycle parking), food growing, green gym/dance studios, outdoor play area/green spaces, walking/cycling routes (e.g. paths and crossings), and walking/cycling mapping/signage. Each facility was mapped as a point, line or area as appropriate. Some components had multiple facilities that could be classified into different types or different categories. In such cases, each facility was considered separately.

Figure 1 illustrates the classification of infrastructure with the example of an area of Middlesbrough. This area is categorised according to socio-economic disadvantage relative to the rest of England; the percentage unemployed and in lower quality employment according to the National Statistics Socio-economic Classification[43].

Figure 1
figure 1

Example of healthy towns-funded physical infrastructure, Middlesbrough. Example of Healthy Towns-funded physical infrastructure, Middlesbrough, displayed on quintiles of socio-economic disadvantage (socio-economic classification according to NS-SEC[43]).

Defining populations

Two populations of need were identified for each intervention. Firstly, specific target populations were identified according to the group(s) specified for each intervention in each area’s project initiation document. These were varied and included, for example, black and minority ethnic (BME) groups, children (various ages specified), inactive/overweight individuals and retired households. Each intervention was aimed at between one and six target populations. In order to identify the geographical distributions of these populations, data were obtained from the 2001 Census of Population in England and mapped to the smallest possible spatial unit: the Output Area (each containing an average of 297 individuals). Estimates of obesity were only available at the coarser Middle Super Output Area level (each containing an average of 7,247 individuals)[44]. The data used to define the populations are detailed in Table 2.

Table 2 Data used to define population

To enable a broader examination of equity in intervention provision we also evaluated each intervention in terms of the distribution of generally disadvantaged population groups. After reviewing evidence from the equity literature[23, 4749] and considering the target populations identified by each Healthy Town, these were determined to be four population groups comprising BME groups, retired households, all children (aged 0 – 18), and the socio-economically disadvantaged. These groups were also identified spatially and defined as the total count of people or households present in each of the four groups, using the relevant data listed in Table 2.

Equity analysis

Analysis was conducted to assess the equity of infrastructural provision from the Healthy Towns programme in relation to the locations of each population of need. Firstly, populations with ‘good’ access to infrastructure were spatially identified. Good access was defined as living within a ten minute walk (represented by a straight line distance of 800 m) of facilities, a distance used in previous analyses of accessibility based on how far people are willing to walk to access services[29, 5052]. For route infrastructure, a distance of 100 m either side of the centreline was used to capture characteristics of populations through which the route passed.

The number of people with good access in each of the populations of need (defined as the target populations and generally disadvantaged populations) was estimated by comparing census data boundaries containing population counts with the boundaries delineated around the infrastructure (the 800 m or 100 m distance as defined above). For each group, the number of individuals or households falling within the boundaries for all interventions was then estimated, and these groups were defined as having good access. In cases where a census area was only partially classified as having good access, populations were estimated based on the size of the area of overlap. This procedure was undertaken for each population of need group, as well as the remaining population group of the town. It allowed ‘targeting ratios’ to be computed using the formula in Equation 1 to assess whether interventions were well-located for the different groups. A targeting ratio above unity indicates that the population of need were more likely to live within areas classed as ‘good access’ compared to the rest of the population within the town, whereas a value below 1 means that the population of need were spatially disadvantaged. Associated 95% confidence intervals were calculated.

Targetting ratio = Population of need with good access Population of need in the town Rest of population with good access Rest of population in the town

Funding for each Healthy Town was examined to investigate resources allocated in relation to the number of people classified as having good access to interventions. Towns were grouped into three categories of population size based on natural breaks (small 17-22 K, medium 82 -132 K, large 195-500 K), and the average amount of funding for the towns in each category was calculated according to the total disadvantaged population and the disadvantaged population with good access.

The geographical diversity of disadvantaged populations was explored in order to investigate whether interventions were more equitably distributed in those towns with more clustered population groups. Within each town, the geographical distribution of each population of need was mapped, and a Global Moran’s I statistic was computed. This produced an index value on a scale of −1 to +1, where +1 indicates clustering of population groups and −1 indicates dispersion of population groups (0 indicates random distribution). Index scores were divided into tertiles and the mean targeting ratios were compared across tertiles by computing Kruskal-Wallis H statistics. The Kruskal-Wallis tests were chosen because the distribution of targeting ratios was positively skewed. In order to test for the potential presence of trade-offs in cases where the interventions were specifically targeted at more than one group, average targeting ratios were examined according to the number of population groups the intervention targeted, with the association again being tested using Kruskal-Wallis H statistics.


A total of 183 individual pieces of infrastructure that were either complete or in progress were identified across eight Healthy Towns. Of these, 80 (44%) were classified as ‘physical activity’ (e.g. green gyms and play areas), 59 (32%) as ‘active travel’ (e.g. walking maps and signed cycle routes), 39 (21%) as ‘food systems’ (e.g. community cafes and allotments) and 5 (3%) as ‘healthy lifestyle’ (e.g. advice centres and information trails). The most common types of intervention were outdoor play areas/green space (27%), walking/cycling mapping/signage (23%), food growing (18%) and walking/cycling routes (11%).

Table 3 shows the relationship between town size and per-capita funding according to the number of people in each town and those within a ten minute walk. Greater overall funding was associated with lower per-capita funding for the majority of population groups.

Table 3 Per capita funding from the healthy towns programme

Figure 2 shows the target population group of each town along with the associated average targeting ratio. For all population groups except BMEs and children aged 5–19, ratios were above unity suggesting that infrastructure tended to be positioned in areas where the associated target population group lived. However, few of the estimated targeting ratios were statistically significant. While the targeting ratio for interventions targeted at socio-economically disadvantaged populations showed the highest statistical significance (targeting ratio 1.27, 95% CI 1.17 to 1.37, p<0.001), the largest targeting ratios were observed for social housing households (2.16, 95% CI 1.26 to 3.05) and resident adults (1.48, 95% CI 0.97 to 2.00). When targeting ratios were examined in relation to the four identified generally disadvantaged population groups (Figure 3), the ratios were all above unity, suggesting that the locations of infrastructure tended to favour these groups even if they were not necessarily the target population. Indeed, a comparison with Figure 2 shows that BMEs were more favoured overall (1.45, 95% CI 1.10 to 1.81) than for infrastructure specifically targeted at them (0.77, 95% CI 0.40 to 1.14).

Figure 2
figure 2

Mean targeting ratios by target population group.

Figure 3
figure 3

Mean targeting ratios by disadvantaged population group.

Analysis of the mean targeting ratios according to intervention type and category showed that the majority of average ratios were above unity, suggesting that these populations were generally well-served by the items of infrastructure, although most did not reach statistical significance. For a full table of results, please see Additional files1 and2.

There was no evidence that overall population clustering was associated with targeting ratios (Table 4). However, when disaggregated by population group, there was evidence that targeting ratios were lowest in the most clustered populations for BME populations, whilst for socio-economically disadvantaged populations the highest ratio was observed in the most clustered tertile.

Table 4 Mean rank of targeting ratios for disadvantaged populations by tertiles of clustering

The average targeting ratio, according to mean rank, was significantly associated with the number of target groups for each intervention (p = 0.003), although the highest mean rank (139) was found for interventions targeted at the largest number of different groups (six), suggesting that multi-target population interventions tended to be better rather than more poorly targeted (full results not shown).


Summary of main findings

Our findings suggest that infrastructure developed within each Healthy Town generally met our criterion (the targeting ratio) for spatial equity and this was independent of the amount of funding received. This was most statistically significant for socio-economically disadvantaged populations, which is in keeping with the evidence that some of the towns specifically identified generally disadvantaged areas to be targeted in their project initiation documents. We suggest that certain types of intervention may be less easy to locate with respect to populations who may particularly need them due to the nature of the resources they require. An example is food growing and food systems, reflecting limitations caused by the need for suitable land for these types of infrastructure programme. Spatial clustering of population groups was not associated with the success of spatial targeting in general, although areas with the high concentrations of socio-economically disadvantaged groups did experience highest targeting success. This illustrates how infrastructure providers can face particular challenges in areas where populations of need are not concentrated in particular places. We found no evidence that interventions targeted at more than one population group were less well located.

Examining where interventions were located in relation to who they were aimed at and the local context, as recommended by UK guidance[34, 35], suggested that the Healthy Towns intervention was operating as it was initially intended in this respect. Thus, incorporating spatial equity analysis into the process evaluation of an environmental intervention allowed us to examine if the resources were directed to the most appropriate locations, a question which is appropriate to the current early stages of an intervention such as the Healthy Towns programme[31]. This is important in the context of evaluating environmental interventions, as health inequalities (and therefore population need) vary spatially[53] and therefore need careful spatial planning to ensure intervention success. The findings from this initial analysis may inform subsequent evaluation stages, providing explanation for outcomes, impacts and costs/benefits that may otherwise not be detected: our findings suggest that if the Healthy Towns programme is not successful and health inequalities are not reduced, it will be for reasons other than poor spatial planning. Indeed, qualitative evaluation of the implementation of the Healthy Towns programme has suggested that these reasons may include insufficient time, lack of evidence and poor alignment with national priorities[54]. We have generated new knowledge in the form of explicit, transparent and accurate information about the locating of infrastructure, thereby improving the evidence base for decision-making[30].

Strengths and limitations

This study is novel in that it has demonstrated one approach to incorporating spatial equity analysis into the process evaluation of a complex environmental intervention. Identifying the spatial distribution of newly-built infrastructure allowed us to examine how access to new facilities was patterned according to population need. A particular strength was having access to a complete, up-to-date and detailed database of the location and progress of the interventions, constructed by consulting a wide range of sources and relevant programme staff. This was important as some of the pieces of infrastructure considered differed from those described in the original plans in the implementation documentation. All information that was not in a geographically referenced digital format was manually digitised. The availability of national, detailed geographic data (OS mapping, census statistics) combined with information obtained from the local areas allowed accurate referencing of information. All geographical data was subsequently checked via discussions with programme staff in each of the local areas.

Our work has some limitations. Defining the area of an intervention required a number of assumptions. The definition of a ten minute walking distance to approximate ‘good’ access was based on distances commonly used in the research literature, although some people will walk further to reach certain amenities[55], whilst others will be less mobile. In reality accessibility will vary by these individual characteristics. Because we did not have information on the locations of pedestrian only cut-throughs, common in urban areas, we used straight line distances rather than network distances when calculating accessibility. As just 183 new pieces of infrastructure were funded from the Healthy Towns programme, the sample size was limited for statistical analysis, particularly stratified analysis. In addition, we had no information regarding the quality of the interventions. Our population data were taken from the most recent (2001) Census of Population in England and Wales but this does mean that they reflect the local population almost ten years prior to the introduction of the Healthy Towns infrastructure. Finally, we did not attempt to evaluate the success of the interventions in terms of how and by whom they were used, or any resulting impact on behavioural or health outcomes.


Interpretation and contribution to existing knowledge

Building on existing research in the field of spatial equity analysis, we have presented the development and findings of a new method to help understand the implementation of interventions designed to change the built environment to promote healthier behaviours. We have contributed to the field of process evaluation, providing a robust means of initial evaluation of interventions compatible with UK[34, 35] and US[36] recommendations, that considers the locations of populations of need[17]. We have established that infrastructure provision from the Healthy Towns programme in England was generally spatially equitable in that it was located in areas of highest population need, suggesting that in contrast to the literature on environmental disamenities[14, 16, 56], disadvantaged populations do not necessarily lose out when environmental modifications are made. With careful planning and implementation of interventions, therefore, interventions need not necessarily result in deprivation amplification[11].

Implications for policy, practice and research

We propose that this form of spatial equity analysis should be incorporated as the first step in future process evaluations for spatially planned interventions in health, especially where it can answer questions at an early stage in the evaluation at which impact analysis may be premature. It may thus be a useful part of a formative evaluation. Nevertheless, the constantly changing policy landscape, coupled with often short-term fixed funding horizons for intervention delivery brings particular methodological and practical challenges, necessitating fast action to assemble required datasets whilst key personnel are still in post. Awareness of these challenges will be crucial to the success of similar analyses in future intervention studies.


  1. White M, Adams J, Heywood P: How and why do interventions that increase health overall widen inequalities within populations?. Health, inequality and society. Edited by: Babones S. 2009, Bristol: Policy Press, 65-81.

    Google Scholar 

  2. Mechanic D: Disadvantage, inequality, and social policy. Health Aff. 2002, 21: 48-59. 10.1377/hlthaff.21.2.48.

    Article  Google Scholar 

  3. Kirk T: Appraisal of the effectiveness of nutrition education in the context of infant feeding. J Hum Nutr. 1980, 34: 429-438.

    CAS  PubMed  Google Scholar 

  4. Colgan F, Gospel A, Petrie J, Adams J, Heywood P, White M: Does rear seat belt use vary according to socioeconomic status?. J Epidemiol Community Health. 2004, 58: 929-930. 10.1136/jech.2003.016972.

    Article  PubMed Central  PubMed  Google Scholar 

  5. Qi V, Philips SP, Hopman WM: Determinants of a healthy lifestyle and use of preventive screening in Canada. BMC Public Health. 2006, 6: 275-10.1186/1471-2458-6-275. Online 7 November 2006

    Article  PubMed Central  PubMed  Google Scholar 

  6. Townsend J, Roderick P, Cooper J: Cigarette smoking by socioeconomic group, sex, and age: effects of price, income, and health publicity. BMJ. 1994, 309: 923-927. 10.1136/bmj.309.6959.923.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  7. Macpherson AK, Macarthur C, To TM, Chipman ML, Wright JG, Parkin PC: Economic disparity in bicycle helmet use by children six years after the introduction of legislation. Inj Prev. 2006, 12: 231-235. 10.1136/ip.2005.011379.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  8. Macintyre S, Maciver S, Sooman A: Area, class and health: should we be focusing on places or people?. J Soc Policy. 1993, 22: 213-234. 10.1017/S0047279400019310.

    Article  Google Scholar 

  9. Pearce JR, Richardson EA, Mitchell RJ, Shortt NK: Environmental justice and health: the implications of the socio-spatial distribution of multiple environmental deprivation for health inequalities in the United Kingdom. Trans Inst Br Geogr. 2010, 35: 522-539. 10.1111/j.1475-5661.2010.00399.x.

    Article  Google Scholar 

  10. Tudor Hart J: The inverse care law. Lancet. 1971, 297: 405-412. 10.1016/S0140-6736(71)92410-X.

    Article  Google Scholar 

  11. Macintyre S: Deprivation amplification revisited; or, is it always true that poorer places have poorer access to resources for healthy diets and physical activity?. Int J Behav Nutr Phys Act. 2007, 4: 32-10.1186/1479-5868-4-32. [Online 7 August 2007]

    Article  PubMed Central  PubMed  Google Scholar 

  12. Kruize H, Driessen PPJ, Glasbergen P, van Egmond KND: Environmental equity and the role of public policy: experiences in the Rijnmond Region. Environ Manage. 2007, 40: 578-595. 10.1007/s00267-005-0378-9.

    Article  PubMed  Google Scholar 

  13. Mushak B: Environmental equity: a new coalition for justice. Environ Health Perspect. 1993, 101: 478-483.

    Article  PubMed Central  Google Scholar 

  14. Evans GW, Kantrowitz E: Socioeconomic status and health: the potential role of environmental risk exposure. Annu Rev Public Health. 2002, 23: 303-331. 10.1146/annurev.publhealth.23.112001.112349.

    Article  PubMed  Google Scholar 

  15. Oakes JM, Anderton DL, Anderson AB: A longitudinal analysis of environmental equity in communities with hazardous waste facilities. Soc Sci Res. 1996, 25: 125-148. 10.1006/ssre.1996.0006.

    Article  Google Scholar 

  16. Chang H-S, Liao C-H: Exploring an integrated method for measuring the relative spatial equity in public facilities in the context of urban parks. Cities. 2011, 28: 361-371. 10.1016/j.cities.2011.04.002.

    Article  Google Scholar 

  17. Smoyer-Tomic KE, Hewko JN, Hodgson MJ: Spatial accessibility and equity of playgrounds in Edmonton, Canada. Canadian Geographer. 2004, 48: 287-302. 10.1111/j.0008-3658.2004.00061.x.

    Article  Google Scholar 

  18. Talen E, Anselin L: Assessing spatial equity: an evaluation of measures of accessibility to public playgrounds. Environ Plan A. 1998, 30: 595-613. 10.1068/a300595.

    Article  Google Scholar 

  19. Rosero-Bixby L: Spatial access to health care in Costa Rica and its equity: a GIS-based study. Soc Sci Med. 2004, 58: 1271-1284. 10.1016/S0277-9536(03)00322-8.

    Article  PubMed  Google Scholar 

  20. World Health Organisation: Obesity and overweight. Fact sheet N°311. 2012, Geneva: WHO, Available: accessed 22 August 2012

    Google Scholar 

  21. Killoran A, Doyle N, Waller S, Wohlgemuth C, Crombie H: Transport interventions promoting safe cycling and walking: evidence briefing. 2006, London: National Institute for Health and Clinical Excellence

    Google Scholar 

  22. Kahn EB, Ramsey LT, Brownson RC, Heath GW, Howze EH, Powell KE, Stone EJ, Rajab MW, Corso P: The effectiveness of interventions to increase physical activity: a systematic review. Am J Prev Med. 2002, 22: 73-107.

    Article  PubMed  Google Scholar 

  23. Lovasi GS, Hutson MA, Guerra M, Neckerman KM: Built environments and obesity in disadvantaged populations. Epidemiol Rev. 2009, 31: 7-20. 10.1093/epirev/mxp005.

    Article  PubMed  Google Scholar 

  24. Tanser F: Methodology for optimising location of new primary health care facilities in rural communities: a case study in KwaZulu-Natal, South Africa. J Epidemiol Community Health. 2006, 60: 846-850. 10.1136/jech.2005.043265.

    Article  PubMed Central  PubMed  Google Scholar 

  25. Cummins S: Improving population health through area-based social interventions: generating evidence in a complex world. Evidence-Based Public Health: Effectiveness and Efficiency. Edited by: Killoran A, Kelly MP. 2010, Oxford: Oxford University Press, 287-297.

    Google Scholar 

  26. Ogilvie D, Bull F, Powell J, Cooper AR, Brand C, Mutrie N, Preston J, Rutter H: An applied ecological framework for evaluating infrastructure to promote walking and cycling: the iConnect study. Am J Public Health. 2011, 101: 473-481. 10.2105/AJPH.2010.198002.

    Article  PubMed Central  PubMed  Google Scholar 

  27. Burdziej J: A Web-based spatial decision support system for accessibility analysis - concepts and methods. Appl Geomat. 2011, 4: 1-10.

    Google Scholar 

  28. McGrail MR, Humphreys JS: Measuring spatial accessibility to primary care in rural areas: improving the effectiveness of the two-step floating catchment area method. App Geography. 2009, 29: 533-541. 10.1016/j.apgeog.2008.12.003.

    Article  Google Scholar 

  29. Nicholls S: Measuring the accessibility and equity of public parks: a case study using GIS. Managing Leisure. 2001, 6: 201-219. 10.1080/13606710110084651.

    Article  Google Scholar 

  30. US Government Accountability Office: Performance measurement and evaluation: definitions and relationships. 2011, Washington, DC: GAO

    Google Scholar 

  31. Ogilvie D, Cummins S, Petticrew M, White M, Jones R, Wheeler K: Assessing the evaluability of complex public health interventions: five questions for researchers, funders, and policymakers. Milbank Q. 2011, 89: 206-225. 10.1111/j.1468-0009.2011.00626.x.

    Article  PubMed Central  PubMed  Google Scholar 

  32. Baranowski T, Stables G: Process evaluations of the 5-a-day projects. Health Educ Behav. 2000, 27: 157-166. 10.1177/109019810002700202.

    Article  CAS  PubMed  Google Scholar 

  33. Linnan L, Steckler A: Process evaluation for public health interventions and research: an overview. Process Evaluation for Public Health Interventions and Research. Edited by: Steckler A, Linnan L. 2002, San Francisco: Jossey Bass, 1-23.

    Google Scholar 

  34. Craig P, Dieppe P, Macintyre S, Michie S, Nazareth I, Petticrew M: Developing and evaluating complex interventions: new guidance. 2008, London: Medical Research Council

    Google Scholar 

  35. National Insitute for Health and Clinical Excellence: Obesity - working with local communities: consultation on the evidence (closed 22 July 2011). Review 1: Identifying the key elements and interactions of a whole system approach to obesity prevention. 2011, London: NICE

    Google Scholar 

  36. National Institute of Environmental Health Science: Partnerships for environmental public health: evaluation metrics manual. 2012, Durham, NC: National Institutes of Health

    Google Scholar 

  37. Department of Health: Healthy Community Challenge Fund: Healthy Community Challenge Fund: invitation to local areas. 2008, London: Department of Health, Available: accessed 12 January 2012,

    Google Scholar 

  38. Department of Health: Healthy Weight, Healthy Lives: a cross-government strategy for England. 2008, London: Department of Health

    Google Scholar 

  39. Department of Health: Healthy Towns early learning report. 2011, London: Department of Health

    Google Scholar 

  40. Department of Health: Healthy Towns workshop: summary of main points. 2008, London: Department of Health, Available: accessed 17 June 2013

    Google Scholar 

  41. Office for National Statistics: English Urban Areas. 2001, Available: accessed 17 June 2013, Census of Population in England and Wales: boundary derived datasets

    Google Scholar 

  42. ESRI: ArcGIS 9.3. 2009, California: ESRI

    Google Scholar 

  43. Office for National Statistics: Socio-economic classification (NS-SEC, UV31), 2001 Census of Population in England and Wales. 2001, Fareham: ONS, Available: accessed 19th October

    Google Scholar 

  44. Office for National Statistics: Population (UV01), 2001 Census of Population in England and Wales: Census Area Statistics. 2011, Population (UV01), 2001 Census of Population in England and Wales: Census Area Statistics,,

    Google Scholar 

  45. Office for National Statistics: Various datasets. 2001, accessed 25 July 2011, Census of Population in England and Wales: Census Area Statistics and Key Statistics

    Google Scholar 

  46. Office for National Statistics: Healthy lifestyle behaviours: model based estimates. 2003, accessed 25 July 2011, –2005

    Google Scholar 

  47. Villareal DT, Chode S, Parimi N, Sinacore DR, Hilton T, Armamento-Villareal R, Napoli N, Qualls C, Shah K: Weight loss, exercise, or both and physical function in obese older adults. N Eng J Med. 2011, 364: 1218-1229. 10.1056/NEJMoa1008234.

    Article  CAS  Google Scholar 

  48. Adler NE, Stewart J: Reducing obesity: motivating action while not blaming the victim. Milbank Q. 2009, 87: 49-70. 10.1111/j.1468-0009.2009.00547.x.

    Article  PubMed Central  PubMed  Google Scholar 

  49. Singh GK, Siahpush M, Kogan MD: Neighborhood socioeconomic conditions, built environments, and childhood obesity. Health Aff. 2010, 29: 503-512. 10.1377/hlthaff.2009.0730.

    Article  Google Scholar 

  50. Lovett A, Haynes R, Sunnenberga G, Galeb S: Car travel time and accessibility by bus to general practitioner services: a study using patient registers and GIS. Soc Sci Med. 2002, 55: 97-111. 10.1016/S0277-9536(01)00212-X.

    Article  PubMed  Google Scholar 

  51. Panter J, Jones A, Van Sluijs E, Griffin S: Neighborhood, route, and school environments and children’s active commuting. Am J Prev Med. 2010, 38: 268-278. 10.1016/j.amepre.2009.10.040.

    Article  PubMed  Google Scholar 

  52. Timperio A, Ball K, Salmon J, Roberts R, Giles-Corti B, Simmons D, Baur LA, Crawford D: Personal, family, social, and environmental correlates of active commuting to school. Am J Prev Med. 2006, 30: 45-51. 10.1016/j.amepre.2005.08.047.

    Article  PubMed  Google Scholar 

  53. House of Commons Health Committee: Health Inequalities: third report of session 2008–09. 2009, London: The Stationery Office Limited, Available: accessed 07 March 2012

    Google Scholar 

  54. Goodwin D, Cummins S, Sautkina E, Ogilvie D, Petticrew M, Jones A, Wheeler K, White M: The role and status of evidence and innovation in the healthy towns programme in England: a qualitative stakeholder interview study. J Epidemiol Community Health. 2013, 67: 106-112. 10.1136/jech-2012-201481.

    Article  PubMed Central  PubMed  Google Scholar 

  55. Reilly MK, Landis J: The influence of built form and land use on travel behaviour. 2002, Berkeley, CA: UC Transport Center

    Google Scholar 

  56. Braubach M, Fairburn J: Social inequities in environmental risks associated with housing and residential location—a review of evidence. Eur J Public Health. 2010, 20: 36-42. 10.1093/eurpub/ckp221.

    Article  PubMed  Google Scholar 

Download references


This is an independent report commissioned and funded by the Policy Research Programme in the Department of Health (Healthy Towns: Evaluation of the Healthy Community Challenge Fund) grant number 0620026. DO and AJ were also supported by the Centre for Diet and Activity Research (CEDAR), and MW was supported by Fuse, the Centre for Translational Research in Public Health. Both CEDAR and Fuse are UK Clinical Research Collaboration (UKCRC) Public Health Research Centres of Excellence, with funding from the British Heart Foundation, Cancer Research UK, Economic and Social Research Council, Medical Research Council, National Institute for Health Research (NIHR), and Wellcome Trust, under the auspices of the UKCRC, which is gratefully acknowledged. SC is also supported by a NIHR Senior Fellowship. The views expressed are not necessarily those of any of the above named funders. We thank Elena Sautkina and Denise Goodwin at Queen Mary, University of London for their inputs.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Alice M Dalton.

Additional information

Competing interests

The authors declare they have no actual or potential competing interests, financial or otherwise.

Authors’ contributions

AD acquired the data, carried out the analysis and drafted the manuscript. AJ made substantial contributions to conception and design of the analysis, the interpretation of data, and helped to draft the manuscript. DO, MP, MW and SC have provided advice regarding the design and analyses, and have been involved in revising the manuscript critically for important intellectual content. All authors read and approved the final manuscript.

Electronic supplementary material

Authors’ original submitted files for images

Below are the links to the authors’ original submitted files for images.

Authors’ original file for figure 1

Authors’ original file for figure 2

Authors’ original file for figure 3

Rights and permissions

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article

Dalton, A.M., Jones, A., Ogilvie, D. et al. Using spatial equity analysis in the process evaluation of environmental interventions to tackle obesity: the healthy towns programme in England. Int J Equity Health 12, 43 (2013).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: