  Spatial point process models are widely applied to point pattern data from
various fields in the social and environmental sciences. However, a serious
hurdle in fitting point process models is the presence of duplicated points,
wherein multiple observations share identical spatial coordinates. This often
occurs because of decisions made in the geo-coding process, such as assigning
representative locations (e.g., aggregate-level centroids) to observations when
data producers lack exact location information. Because spatial point process
models like the Log-Gaussian Cox Process (LGCP) assume unique locations,
researchers often employ {\it ad hoc} solutions (e.g., jittering) to address
duplicated data before analysis. As an alternative, this study proposes a
Modified Minimum Contrast (MMC) method that adapts the inference procedure to
account for the effect of duplicates without needing to alter the data. The
proposed MMC method is applied to LGCP models, with simulation results
demonstrating the gains of our method relative to existing approaches in terms
of parameter estimation. Interestingly, simulation results also show the effect
of the geo-coding process on parameter estimates, which can be utilized in the
implementation of the MMC method. The MMC approach is then used to infer the
spatial clustering characteristics of conflict events in Afghanistan
(2008-2009).
