Understanding the spatio-temporal characteristics or patterns of the 2019 novel coronavirus (2019-nCoV) epidemic is a benefit to effectively prevent and control this epidemic. Nevertheless, there isare only few researches to analyze the 2019-nCoV from the perspective of spatial dependency and temporal dynamic. As a resultTherefore, this research paper aims to detect spatio-temporal patterns of the 2019-nCoV epidemic by using spatio-temporal analysis technology at the county level in of Hubei province. The Mann-Kendall and Pettitt methods are used to identify the temporal trends and abrupt changes in time series of using the daily new confirmed cases, respectively. Besides, Local Moran’s I index is applied to uncover spatial patterns of the incident rates including spatial clusters and outliers. In the experimental analysis, Bbased on the 2019-nCoV data from January 25 to February 11, 2020, we found that there are 10 areas with different types of temporal patterns of the incidence rates, among which the pattern with increasing trend and abrupt change is mainly caused by the improvement of the ability of disease diagnosis. Spatial clusters with high incidence rates during the period are concentrated in Wuhan Metropolitan Area due to the great intensity of spatial interaction on population. Therefore, enhancing the disease diagnosis ability and managing population flow can be confirmed as effective measures to prevent and control the regional outbreak of the epidemic.