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Philippine
Recent Conference Presentation
Recent Conference Presentation
Presented at the 11th International Conference on Integrative Disaster Risk Reduction and Management on 27-29 November 2024 at the Eastern Samar State University, Borongan City, Eastern Samar, Philippines.
Introduction
Introduction
Roads form a spatial network that can be abstracted by a graph G(V, E). In this graph, intersections and end roads (i.e., dead ends) serve as nodes or vertices in V, while road segments are represented as edges in E. The set of n nodes V includes elements {1, 2, ..., n}, and the set of m edges E includes elements {(i, j)∣ ∀ i,j ∈ V}. Each node i is associated with global coordinates (x, y) or (x, y, z), where x represents the longitude of i, y the latitude of i, and z the elevation of i above mean sea level if available. Each edge (i, j) has an associated value L, which represents the actual length of the road segment.
By representing road networks as data structures G, we can computationally analyze these networks to gain valuable insights. This analysis helps us understand, identify, visualize, and explore the road networks of various communities. To facilitate this understanding, it is crucial to have road network data readily available.
This website provides the road networks data of various Philippine municipalities and cities in multiple formats. These datasets include additional node and edge characteristics, such as node degree, betweenness centrality, and closeness centrality. Node degree measures the number of edges connected to a node, indicating its connectivity. Betweenness centrality quantifies the importance of a node in the shortest path between other nodes, reflecting its role in network flow. Closeness centrality measures how quickly information can spread from a node to all other nodes in the network.
These characteristics are essential for a comprehensive analysis of road networks, as they help us understand the structural properties and potential bottlenecks within the network. By examining these metrics, we can identify critical nodes and edges, optimize traffic flow, and improve infrastructure planning and management .
Purpose
Purpose
This website serves as a curated data repository for the road networks of all Philippine communities at the second lowest political and administrative hierarchy level, specifically municipalities and cities. If the political or administrative boundaries of the lowest community level, that of the Barangay, are available, the road network data for those communities will also be provided on this site. Additionally, the road networks of special communities will be included if their respective geographical boundaries are accessible.
In this repository, the data for each community are presented with road intersections and end roads abstractly represented as nodes in a graph G, while the road segments connecting two nodes are represented as edges in G. These data sets are available in various open data formats, facilitating ease of access and use.
By providing these comprehensive datasets, the website enables detailed analysis and exploration of road networks across different administrative levels. This information is invaluable for urban planning, infrastructure development, and traffic management. Users can analyze the connectivity and efficiency of road networks, identify potential improvements, and support decision-making processes for community development.
The availability of road network data in open formats ensures that researchers, planners, and policymakers can easily integrate this information into their analyses and applications. This open data approach promotes transparency, encourages collaborative efforts, and enhances the overall understanding of road network dynamics within Philippine communities.
By offering data at multiple administrative levels, the website ensures a holistic view of the road networks, from broad municipal connections to detailed local pathways. This comprehensive approach supports a wide range of applications, from high-level strategic planning to local community enhancements, ultimately contributing to the improvement of transportation infrastructure and urban development.
Brief Review
Brief Review
Several studies have underscored how network metrics like centrality, connectivity, and resilience support a detailed understanding of urban road networks. Through these metrics, urban planners can assess both the efficiency and resilience of road networks, enabling the design of cities that are navigable, robust, and responsive to growth. Employing these metrics is crucial for sustainable urban planning, as they reveal the underlying structure and performance of road systems.
Centrality and Connectivity in Urban Networks: Centrality metrics (e.g., betweenness, closeness) and connectivity are essential for identifying the most accessible and frequently traversed roads within an urban network. These metrics reveal high-impact nodes and links, offering a clearer understanding of the roles that specific roads and intersections play in supporting traffic flow and network-wide accessibility. Many studies have emphasized the importance of these metrics for strategic urban planning and managing congestion in high-density areas (Freeman, 1977; Crucitti, et al., 2006; Jiang & Claramunt, 2004; Xie & Levinson, 2007).
Structural Robustness and Resilience: Metrics focused on resilience, such as redundancy and robustness, help assess network stability and connectivity under adverse conditions, such as road closures or natural disasters. Cardillo, et al. (2006), Marshall (2005), D'Este & Taylor (2003), and Sohn (2006) provided frameworks for identifying critical links and assessing vulnerability, underscoring the value of redundancy in urban layouts for disaster preparedness and rapid recovery.
Efficiency and Traffic Flow Optimization: Analyzing traffic flow and efficiency with metrics like betweenness centrality helps in identifying bottlenecks and managing congestion. These insights support efficient route planning and distribution of traffic loads across the network. Studies indicate that road segments with high betweenness tend to experience higher traffic volumes, making them critical for targeted improvements to enhance traffic distribution and overall efficiency (Gao, et al., 2013; Saberi & Mahmassani, 2013; Xie & Levinson, 2009).
Network Evolution and Adaptability: Studies on network growth (Xie & Levinson, 2009; Barthélemy, 2011) explore how urban road networks adapt to increasing population and urban expansion over time. Network metrics enable planners to predict areas requiring expansion and design adaptable road systems that can handle future demands. This forward-looking approach helps in creating networks that are both scalable and resilient in the long term.
Citation
Citation
Please use the following citation for data obtained from this repository:
Pabico, JP. 2024. RoadNet-ph: A Database of Philippine Road Networks. http://sites.google.com/up.edu.ph/RoadNet-ph
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