BACKGROUND: In a spatially well-known and dispersed HIV epidemic, identifying geographic clusters with significantly higher HIV-prevalence is important for focusing interventions for people living with HIV (PLHIV). METHODS: We used Kulldorff spatial-scan Poisson model to identify clusters with high numbers of HIV-infected persons 15-64 years old. We classified PLHIV as belonging to either higher or lower prevalence (HP/LP) clusters, then assessed distributions of socio-demographic and bio-behavioral HIV risk factors and associations with clustering. RESULTS: About half of survey locations, 112/238 (47%) had high rates of HIV (HP clusters), with 1.1-4.6 times greater PLHIV adults observed than expected. Richer persons compared to respondents in lowest wealth index had higher odds of belonging to a HP cluster, adjusted odds ratio (aOR), 1.61(95% CI: 1.13-2.3), aOR 1.66(95% CI: 1.09-2.53), aOR 3.2(95% CI: 1.82-5.65), aOR 2.28(95% CI: 1.09-4.78) in second, middle, fourth and highest quintiles respectively. Respondents who perceived themselves to have greater HIV risk or were already HIV-infected had higher odds of belonging to a HP cluster, aOR 1.96(95% CI: 1.13-3.4) and aOR 5.51(95% CI: 2.42-12.55) respectively; compared to perceived low risk. Men who had ever been clients of FSW had higher odds of belonging to a HP cluster than those who had never been, aOR 1.47(95% CI: 1.04-2.08); and uncircumcised men vs circumcised, aOR 3.2, (95% CI: 1.74-5.8). CONCLUSION: HIV infection in Kenya exhibits localized geographic clustering associated with socio-demographic and behavioral factors, suggesting disproportionate exposure to higher HIV-risk. Identification of these clusters reveals the right places for targeting priority-tailored HIV interventions.