Recent advancements in artificial intelligence (AI) and machine learning have begun to reshape the field of geospatial analysis, offering researchers powerful new tools for making sense of complex data. A recent study published on March 14, 2025, examined whether employing one comprehensive global model or multiple localized models would yield more accurate results when predicting outcomes within geospatial studies using the eXtreme Gradient Boosting (XGBoost) algorithm.
Given the challenges of spatial heterogeneity—changes in relationships across different geographic areas—traditional models often focus on local conditions to optimize analysis. This study builds on these insights, not only analyzing simulated datasets but also applying its findings to forecast daily COVID-19 infection cases across Germany.
According to researchers, when the relationships between independent and dependent variables are consistent—characterized by low spatial variation—Global modeling with XGBoost typically outperformed local models. Conversely, when faced with higher levels of spatial variation and inconsistency, local modeling yielded more stable and reliable predictions. “We show local modeling can be more advantageous under certain conditions; especially when the secondary data is involved,” noted the authors of the article.
The study's methodology involved the generation of synthetic data based on established mathematical functions to examine performance across varying conditions within simulated data. Operational characteristics were explored by assessing sample sizes, value ranges, and data distributions.
For the specific case of forecasting COVID-19 infections, the researchers analyzed infection data across Germany, which contains 400 regions at the NUTS 3 administrative level. Utilizing publicly available data compiled by the Robert Koch Institute (RKI), the models integrated various factors including human activity patterns and environmental impacts to strengthen their forecasts.
Interestingly, the findings indicate no significant differences between the global and local modeling approaches when applied to the task of forecasting COVID-19 infection rates. This consistency was somewhat surprising, leading investigators to suggest the approach taken for model training might be more significant than data partitioning.
The experiment also underscored the advantages of local modeling, particularly its capacity for parallel computing, allowing multiple models to be trained independently. “This enables time efficiency, especially important for urgent tasks,” the authors elaborated, highlighting how local models can be trained simultaneously to expedite findings.
Several spatial partitions were utilized for local modeling, which were classified based on economic indicators or the time series of infection cases. Local models, particularly those created from dynamic and comprehensive datasets, showcased improved performance metrics overall, surpassing the traditional NUTS 1-based approach.
Considerations were also made for the efficiency of modeling. The study noted significant discrepancies between the time taken for training global models versus local models due to the various data handling strategies inherent to each method. While global models required extensive computational resources for parameter tuning, local models could be more efficiently optimized through parallel processing, offering practical advantages under specific circumstances.
For practical applications, selecting the appropriate modeling strategy for different types of geospatial analyses continues to be fundamental. The authors concluded, "The selection of modeling methods must align with the specific characteristics of the data and the goals of the research project, balancing trade-offs between global comprehensiveness and local accuracy. This work highlights the need for future studies to explore the impacts of various modeling strategies on different types of relationships and datasets.”