Automated Delineation of New Enumeration Areas by Using Administrative Data and Hierarchical Split and Merge Approach in ESRI ArcGIS Pro: A Case Study of Abu Dhabi Emirate

As populations shift and urban landscapes evolve, enumeration area (EA) boundaries require regular updates to support accurate data collection, analysis, and dissemination, particularly for census operations. Existing approaches for creating or updating EAs lack automation and controls to prevent boundaries from intersecting building footprints. This often results in individual buildings being split across multiple EAs, leading to inaccuracies in household allocation and poor cartographic representation when overlaid with building and satellite data. To address these limitations, this study presents a fully automated tool developed in ArcGIS Pro using Python. The tool systematically subdivides lowest administrative units (community boundaries) into smaller geographic entities, termed primary units, by hierarchically intersecting road networks, legal parcel/plot boundaries, and building footprints as required. These primary units are then intelligently aggregated to generate EAs, ensuring that each building is entirely contained within a single EA. The resulting EAs are evaluated through both quantitative and visual validation. Household distributions are compared against legacy EAs, and spatial alignment is assessed through overlay with roads, parcels, building footprints, and satellite imagery. Applied to Abu Dhabi Emirate, the methodology generated 3298 EAs covering the emirate-wide household universe, representing a 57.6% increase in geographic granularity over the 2092 legacy EAs. The results demonstrate complete (100%) conformity with building boundaries, with 73.3% of populated EAs falling within the target household threshold of 75 - 200 and the maximum household count reduced from 87% above the 200-HH ceiling (legacy) to 63% above the ceiling (new). This approach significantly enhances pre-census mapping workflows in Abu Dhabi by reducing time and cost while improving spatial accuracy. The proposed automated tool is also applicable to other regions where EA boundaries are outdated or unavailable.