OBJECTIVE: To explore the feasibility of producing small-area geospatial maps of chronic disease risk for use by clinical commissioning groups and public health teams. STUDY DESIGN: Cross-sectional geospatial analysis using routinely collected general practitioner electronic record data. SAMPLE AND SETTING: Tower Hamlets, an inner-city district of London, UK, characterised by high socioeconomic and ethnic diversity and high prevalence of non-communicable diseases. METHODS: The authors used type 2 diabetes as an example. The data set was drawn from electronic general practice records on all non-diabetic individuals aged 25-79 years in the district (n=163 275). The authors used a validated instrument, QDScore, to calculate 10-year risk of developing type 2 diabetes. Using specialist mapping software (ArcGIS), the authors produced visualisations of how these data varied by lower and middle super output area across the district. The authors enhanced these maps with information on examples of locality-based social determinants of health (population density, fast food outlets and green spaces). Data were piloted as three types of geospatial map (basic, heat and ring). The authors noted practical, technical and information governance challenges involved in producing the maps. RESULTS: Usable data were obtained on 96.2% of all records. One in 11 adults in our cohort was at 'high risk' of developing type 2 diabetes with a 20% or more 10-year risk. Small-area geospatial mapping illustrated 'hot spots' where up to 17.3% of all adults were at high risk of developing type 2 diabetes. Ring maps allowed visualisation of high risk for type 2 diabetes by locality alongside putative social determinants in the same locality. The task of downloading, cleaning and mapping data from electronic general practice records posed some technical challenges, and judgement was required to group data at an appropriate geographical level. Information governance issues were time consuming and required local and national consultation and agreement. CONCLUSIONS: Producing small-area geospatial maps of diabetes risk calculated from general practice electronic record data across a district-wide population was feasible but not straightforward. Geovisualisation of epidemiological and environmental data, made possible by interdisciplinary links between public health clinicians and human geographers, allows presentation of findings in a way that is both accessible and engaging, hence potentially of value to commissioners and policymakers. Impact studies are needed of how maps of chronic disease risk might be used in public health and urban planning.