Spatial data analytics can detect patterns of clustering of events in small geographies across an urban region.
This study presents and demonstrates a robust research design to study the longitudinal stability of spatial clustering with small case numbers per census tract and assess the clustering changes over time across the urban environment to better inform public health policy making at the community level. The authors argue this analysis enables the greater efficiency of public health departments, while leveraging existing data and preserving citizen personal privacy.
Analysis at the census tract level is conducted in Mecklenburg County, North Carolina, on hypertension during pregnancy compiled from 2011–2014 birth certificates. Data were derived from per year and per multi-year moving counts by aggregating spatially to census tracts and then assessed for clustering using global Moran’s I. With evidence of clustering, local indicators of spatial association are calculated to pinpoint hot spots, while time series data identified hot spot changes. Knowledge regarding the geographical distribution of diseases is essential in public health to define strategies that improve the health of populations and quality of life.
The findings support that spatial aggregation at the census tract level contributes to identifying the location of at-risk “hot spot” communities to refine health programs, while temporal windowing reduces random noise effects on spatial clustering patterns. With tight state budgets limiting health departments’ funds, using geographic analytics provides for a targeted and efficient approach to health resource planning.