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Hassani, H., Marvian Mashhad, L., Stewart, S., & MacFeely, S. (2025). Integrating GIS and Official Statistics Using GISINTEGRATION. AppliedMath 5 (4) e166. 10.3390/appliedmath5040166.
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Abstract
Geospatial–statistical integration remains a persistent bottleneck for official statistics and applied spatial analysis. The GISINTEGRATION R package provides a modular, reproducible workflow for preprocessing, harmonizing, and linking heterogeneous GIS and non-GIS datasets, with export utilities that are compatible with common desktop GIS. This paper outlines the package architecture and demonstrates its use in two applications. The first integrates population statistics with newly introduced statistical output geographies for Northern Ireland, enabling rapid preparation of analysis-ready layers such as all usual residents and population density at Super Data Zones. The second links daily PM2.5 measurements from the U.S. EPA Air Quality System with county boundaries for California (July 2020) to produce policy-relevant indicators; spatial aggregation yielded valid monthly means for 46 of 58 counties (79.31%) and reduced variance from 40.716 (monitor level) to 5.777 (county means), improving signal stability and comparability. Across both cases, the workflow standardizes variable names, supports user-controlled overrides, identifies common keys, and performs quality checks, thereby reducing manual effort while increasing transparency and reproducibility. The results illustrate how standardized integration facilitates official statistical production, environmental monitoring, and evidence-based decision-making.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | geographic information systems (GIS); data integration; official statistics; applied statistics; R software; EPA air quality data |
| Research Programs: | Advancing Systems Analysis (ASA) Advancing Systems Analysis (ASA) > Cooperation and Transformative Governance (CAT) |
| Depositing User: | Luke Kirwan |
| Date Deposited: | 07 Jan 2026 09:12 |
| Last Modified: | 07 Jan 2026 09:12 |
| URI: | https://pure.iiasa.ac.at/21086 |
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