This study investigates the usefulness of spatial autocorrelation analysis in construction sites. The objective is to provide construction managers with insights into the site's performance, identify potential areas for improvement, and ultimately lead to cost and time savings and improved project quality. To achieve this objective, the study identifies significant spatial autocorrelation variables for various applications in construction and analyzes their impact on the site's efficiency and potential problems.
The potential applications where spatial autocorrelation analysis could be useful were identified, including schedules, progress, earned value measures, safety incidents, workers' densities, equipment densities, energy expenditure, and quality non-conformity. A framework was developed to analyze construction sites spatially. The development of the framework involved identifying variables, different methods of assigning locations to objects and defining matrices and equations. Then, the framework was implemented on a sample model using hypothetical random data to analyze the insights it generates. Global and local Moran’s I were used to determine the construction site's overall spatial association and identify the hotspots, coldspots, and outliers.
The study results showed that maps of the zones or grids of the construction sites highlighting the hotspots, coldspots, and outliers could provide meaningful and useful insights that will help construction managers take corrective action and make informed decisions. These maps could act as early warning signs to highlight areas that require attention and areas that need further investigation to find underlying causes.
The framework was then tested on a case study to ensure the insights were useful in real life. A case study involving analysis of the Cairo Light Rail Transit Station’s site, using actual data collected from the site, was conducted. Three applications were analyzed: progress, scheduled activities, and quality non-conformity. The case study results and recommendations were presented to the project’s construction manager. As a result, corrective actions were taken to enhance the project’s performance.
The results were presented to 18 professionals, and a survey was conducted to validate the usefulness of the results. It indicated that spatial autocorrelation analysis could be useful in construction sites. Even though only a minority of the respondents were familiar with spatial autocorrelation; however, most recognized its relevance in construction sites. The respondents identified schedules and earned value measures as the most useful applications for spatial autocorrelation analysis in construction. At the same time, energy expenditure and quality non-conformity were perceived as the least useful.
The study has several implications for construction practitioners and researchers. Firstly, using spatial autocorrelation analysis in construction sites can help managers identify problem areas and take corrective action. Secondly, the findings of this study highlight the need for further research to identify the most suitable applications of spatial autocorrelation analysis in construction. Finally, the study provides a framework for integrating spatial autocorrelation analysis into existing construction management practices.
Overall, this study contributes to the existing literature on spatial autocorrelation analysis in construction. The study highlights the potential benefits of using this approach in construction sites and provides a framework for its integration into existing management practices. However, further research is needed to identify the most suitable applications and explore their potential benefits in more detail. The study's results indicate a perceived value and interest in using spatial autocorrelation analysis in construction projects. Integrating it into construction management practices could significantly improve project quality, cost savings, and time management.
School of Sciences and Engineering
Construction Engineering Department
MS in Construction Engineering
Committee Member 1
Committee Member 2
Institutional Review Board (IRB) Approval
Approval has been obtained for this item
(2023).Spatial Autocorrelation Analysis for Construction Sites [Master's Thesis, the American University in Cairo]. AUC Knowledge Fountain.
Moharram, Raghda. Spatial Autocorrelation Analysis for Construction Sites. 2023. American University in Cairo, Master's Thesis. AUC Knowledge Fountain.