Exploring Multi-Temporal Scale Co-Location of Childhood Respiratory Disease Incidents in Nanning City: A Guide to Geographically and Temporally Weighted Colocation Quotients

Background The incidence of disease data occurring in close spatial and temporal proximity are likely to exhibit unobserved effects. Investigating the spatial and temporal associations among various categories of childhood respiratory diseases is a crucial for modelling of demographic, environmental, and behavioral factors influencing these diseases. Traditional spatial statistical methods that do not account for associations among incident categories risk producing spurious findings. Methods This paper presents a practical approach for effectively handling spatio-temporal incident disease data, with a particular emphasis on optimizing sample size, addressing class imbalance, and examining temporal effects within the framework of Geographically and Temporally Weighted Co-Location Quotient (GTWCLQ) analysis. We apply this approach to investigate the patterns of childhood respiratory diseases in Nanning City, using data at both monthly and daily scales from December 2016. Results By utilizing datasets spanning different time scales, we discern the spatio-temporal association patterns of childhood respiratory diseases and compare disparities across these temporal scales. Our findings reveal a higher aggregation of childhood respiratory diseases in Nanning City on a daily scale, particularly on days with poor air quality, compared to days with good air quality. Moreover, the experimental results show that temporal resolution can affect the intensity of the co-occurrence pattern, while duration influences its frequency, and starting time affects both intensity and frequency. Conclusion Our findings demonstrate the utility of this practical guide in managing sample size and class imbalance within GTWCLQ analysis, establishing it as a valuable tool for exploring multi-scale spatio-temporal co-location patterns. Furthermore, this study enhances our understanding of the spatio-temporal distribution of childhood respiratory diseases, providing insights that can aid in identifying and mitigating potential underlying causes, which is of considerable significance for GIS-based health analysis and decision-making.