This ecological study identified an aggregation of urban neighbourhoods spatial patterns in the cumulative new case detection rate (NCDR) of leprosy in the municipality of Rondonópolis, central Brazil, as well as intra-urban socioeconomic differences underlying this distribution. Scan statistics of all leprosy cases reported in the area from 2011 to 2017 were used to investigate spatial and spatiotemporal clusters of the disease at the neighbourhood level. The associations between the log of the smoothed NCDR and demographic, socioeconomic, and structural characteristics were explored by comparing multivariate models based on ordinary least squares (OLS) regression, spatial lag, spatial error, and geographically weighted regression (GWR). Leprosy cases were observed in 84.1% of the neighbourhoods of Rondonópolis, where 848 new cases of leprosy were reported corresponding to a cumulative NCDR of 57.9 cases/100,000 inhabitants. Spatial and spatiotemporal high-risk clusters were identified in western and northern neighbourhoods, whereas central and southern areas comprised low-risk areas. The GWR model was selected as the most appropriate modelling strategy (adjusted R²: 0.305; AIC: 242.85). By mapping the GWR coefficients, we identified that low literacy rate and low mean monthly nominal income per household were associated with a high NCDR of leprosy, especially in the neighbourhoods located within high-risk areas. In conclusion, leprosy presented a heterogeneous and peripheral spatial distribution at the neighbourhood level, which seems to have been shaped by intra-urban differences related to deprivation and poor living conditions. This information should be considered by decision-makers while implementing surveillance measures aimed at leprosy control.