Leveraging spatial statistics in the development of an historical narrative for water resources in the Northeast United States

The complexity of water resource issues in the Northeast United States is engendered by multiple causal factors and interdependent relationships. Here, we present research that utilized spatial statistics to identify coincident areas of statistically high values (spatial autocorrelation) for biophysical variables such as nutrient loading, population growth, water withdrawals and others in the Northeast United States. The goals of this project were to identify sub-regions in the Northeastern United States that were spatially autocorrelated for multiple variables, and to relate these hotspots to social movements in an historical context. The data employed in this research were point (e.g., wastewater treatment plant location) and county level information for socioeconomic, hydrologic, and water usage variables. We used Local Indicators of Spatial Association, a spatial statistic, to identify county clusters of positive spatial autocorrelation for the region. These clusters were simultaneously overlaid onto a single map to identify areas of positive spatial autocorrelation among multiple variables. Preliminary spatial analysis results suggest that, between 1970 and 2000, positive spatial autocorrelation occurred among nutrient loads, wastewater treatment plant construction, population growth, and dam construction in the Chesapeake Bay area. The New York/New Jersey corridor also showed positive spatial autocorrelation among groundwater withdrawals, thermoelectric power generation, population growth, and wastewater treatment plant construction. Additionally, evaluation of these spatial clusters within their historical context suggests a regional linkage between surface water pollution, environmental regulation, and wastewater treatment plant construction. The results of this project indicate that spatial autocorrelation metrics can be employed in the creation of an historical narrative to more comprehensively understand the interplay between regional socioeconomic, hydrologic, and social phenomena.