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D. M. Smith, J. R. Pearce and K. Harland, “Can a Deterministic Spatial Microsimulation Model Provide Reliable Small-Area Estimates of Health Behaviours? An Example of Smoking Prevalence in New Zealand,” Health & Place, Vol. 17, No. 2, 2011, pp. 618-624. doi:10.1016/j.healthplace.2011.01.001

has been cited by the following article:

• TITLE: Geographical Analysis of Smoking in Leeds: A GIS-Based Location-Allocation Technique for the Optimal Location of Smoking Cessation Services

  AUTHORS: Mildred Oiza Ajebon, Monday Ohi Asikhia

  KEYWORDS: GIS; Location-Allocation; Optimization; Smoking Cessation Services

  JOURNAL NAME: Journal of Geographic Information System, Vol.5 No.3, June 19, 2013

  ABSTRACT: Smoking is associated with several illnesses in the UK. Smoking rate in Leeds is higher than the national average. Finding optimal locations for stop-smoking services will be a good place to start in reducing smoking rates. The study utilizes a GIS-Based location-allocation method for the optimal distribution of smoking cessation centres in relation to the spatial distribution of the smoking population in Leeds. The demand for the smoking cessation clinics was estimated based on the 2009 General Life Style (GLS) statistics on age and social class stratification of smoking rates for the UK. Leeds specific rates were then obtained from the 2001 census key statistics data on socioeconomic status and age structure for output areas via Census Area Statistics Website (CASWEB). The research findings show that spatial inequalities in smoking rate exist in output areas of Leeds. Poorer and non-skilled populations are demonstrated to have higher smoking rates compared with wealthier neighbourhoods. The study confirms the capability of GIS-Based location-allocation techniques to be useful modelling tools for determining the best locations for health facilities. The model allocates services in relation to the spatial patterns of demand in a fashion that minimises average travel distance.