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Purpose -The purpose of this research is to analyze and compares principal factors that contribute to successful public-private partnerships and public works projects. Design/methodology/approach -This study uses a qualitative analysis with in-depth interviews and observation. Interviews are conducted with key individuals of two projects. Success factors are derived from literature review and modified through in-depth interviews and comparative analysis. Findings -Ten success factors are presented and analyzed: specific plan/vision, commitment, open communication and trust, willingness to compromise/collaborate, respect, community outreach, political support, expert advice and review, risk awareness, and clear roles and responsibilities. High degrees of commitment and shared vision between the client, architect, and contractor are shown to be important aspects for construction success as well as pairing factors of open communication and trust with high levels of compromise or collaboration.Research limitations/implications -The major limitation of this research is that it is confined to two case studies. Thus, the generalizability can be utilized for future reference. Further research might review various types of partnerships, such as design-build relationships, construction manager at risk, or team-building partnering for projects that may be blocked from the public sector, or public works development.Originality/value -This study proposes two matrix frameworks to explore a more complete review of relationships of paired success factors. The frameworks view the interplay of how different combined levels or degrees of corresponding factors such as commitment and shared vision can help make a project more effective. These matrices create unique views of comparative factors developed as part of this research paper, which could be further investigated in future research.

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This paper develops a GIS-based integrated approach to risk assessment in natural hazards, with reference to bushfires. The challenges for undertaking this approach have three components: data integration, risk assessment tasks, and risk decision-making. First, data integration in GIS is a fundamental step for subsequent risk assessment tasks and risk decision-making. A series of spatial data integration issues within GIS such as geographical scales and data models are addressed. Particularly, the integration of both physical environmental data and socioeconomic data is examined with an example linking remotely sensed data and areal census data in GIS. Second, specific risk assessment tasks, such as hazard behavior simulation and vulnerability assessment, should be undertaken in order to understand complex hazard risks and provide support for risk decision-making. For risk assessment tasks involving heterogeneous data sources, the selection of spatial analysis units is important. Third, risk decision-making concerns spatial preferences and/or patterns, and a multicriteria evaluation (MCE)-GIS typology for risk decision-making is presented that incorporates three perspectives: spatial data types, data models, and methods development. Both conventional MCE methods and artificial intelligence-based methods with GIS are identified to facilitate spatial risk decision-making in a rational and interpretable way. Finally, the paper concludes that the integrated approach can be used to assist risk management of natural hazards, in theory and in practice.