Investigating Spatial Autocorrelation for Petroleum Projects

Author's Department

Construction Engineering Department

Second Author's Department

Construction Engineering Department

Third Author's Department

Construction Engineering Department

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https://doi.org/10.1007/978-3-031-97697-1_34

All Authors

Safinaz ElDawody Yasmeen Essawy Khaled Nassar

Document Type

Research Article

Publication Title

Lecture Notes in Civil Engineering

Publication Date

1-1-2025

doi

10.1007/978-3-031-97697-1_34

Abstract

Geospatial information involves the data that have an implicit or explicit association with its location according to a specific coordination system. This study uses Excel to study the spatial autocorrelation for multiple petroleum projects using specific variables in upstream, midstream, and downstream types of petroleum projects. The aim is to investigate the efficiency of employing this particular technique in the planning and monitoring phases of petroleum projects, while concurrently exploring the involved ways in which the chosen variables influence the ultimate project cost and duration. The reason is that recognizing the presence of spatial autocorrelation can significantly enhance the efficiency of the analysis by helping to identify correlated variables. This allows a more subtle understanding of the relationships between different spatial entities or phenomena. By detecting spatial autocorrelation for petroleum projects, decision-makers can better determine how variables are related across space, leading to more robust and insightful conclusions in their analyses. Global Moran’s I is the mathematical method used to examine the data of different locations and assess similar values in nearby areas across the entire region. Moran’s I is employed to determine whether the spatial pattern for the selected projects shows clustering or dispersion throughout the study area. The findings are shown on a map to demonstrate how the discussed variables relate in space visually. At the same time, Moran’s I plot helps determine if there is any pattern in the dataset, ranging from scattered to clustered values or no spatial correlation at all. Some limitations to this technique are addressed and conclusions and suggestions for future research work are discussed.

First Page

413

Last Page

423

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