Route planning on GTFS using Neo4j

Vágner, Anikó

doi:10.33039/ami.2021.07.001

Cited by 3 publications

(2 citation statements)

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“…The basic structure includes nodes, relationships and properties. Nodes are connected via relationships to form a network structure [35].…”

Section: Construction Of the Knowledge Graphmentioning

confidence: 99%

Evaluating School Location Based on a Territorial Spatial Planning Knowledge Graph

Xu,

Hao,

Shen

2024

IJGI

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The reasonable spatial planning of primary and secondary schools is an important factor in education development. In spatial planning, there are many models for the locations of primary and secondary schools; however, few quantitative evaluation models are available. Therefore, based on the many factors affecting the layout planning of primary and secondary schools, a knowledge graph of territorial spatial planning that considers the topological relationship, direction relationship and metric relationship in spatial planning is designed and constructed. A school location evaluation model based on the knowledge graph of territorial spatial planning is proposed. The model combines many factors of the locations of schools, such as the service population, the impact of factories on schools, the adjacency and centrality of school plots, terrain and existing schools in the region, to quantitatively evaluate whether schools are reasonably located within a region. This study focuses on the Guangyang Island area in Chongqing, China, exploring the superiority and rationality of the planned land use for primary and secondary schools within the region. By analyzing the top three and bottom three ranked schools in conjunction with the actual conditions of the site, and comparing them with AHP hierarchical analysis and ArcGIS modelling research, the study concludes that the results of this model are highly reasonable within the scope of China’s territorial spatial planning.

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“…The basic structure includes nodes, relationships and properties. Nodes are connected via relationships to form a network structure [35].…”

Section: Construction Of the Knowledge Graphmentioning

confidence: 99%

Evaluating School Location Based on a Territorial Spatial Planning Knowledge Graph

Xu,

Hao,

Shen

2024

IJGI

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“…In summary, the query computes the nearest nodes to user-defined starting and ending points (start_node, end_node) within the graph. It uses Dijkstra's algorithm through the shortestPath function in Cypher to determine the shortest path between these nodes [44,45], which are connected by the hasNeighbor relation. The maximum distance allowed between the given starting and ending nodes is set to 15 hops.…”

Section: Computing Shortest Paths Based On Air Pollution Data With Gr...mentioning

confidence: 99%

SEDIA: A Platform for Semantically Enriched IoT Data Integration and Development of Smart City Applications

Lymperis,

Goumopoulos

2023

Future Internet

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The development of smart city applications often encounters a variety of challenges. These include the need to address complex requirements such as integrating diverse data sources and incorporating geographical data that reflect the physical urban environment. Platforms designed for smart cities hold a pivotal position in materializing these applications, given that they offer a suite of high-level services, which can be repurposed by developers. Although a variety of platforms are available to aid the creation of smart city applications, most fail to couple their services with geographical data, do not offer the ability to execute semantic queries on the available data, and possess restrictions that could impede the development process. This paper introduces SEDIA, a platform for developing smart applications based on diverse data sources, including geographical information, to support a semantically enriched data model for effective data analysis and integration. It also discusses the efficacy of SEDIA in a proof-of-concept smart city application related to air quality monitoring. The platform utilizes ontology classes and properties to semantically annotate collected data, and the Neo4j graph database facilitates the recognition of patterns and relationships within the data. This research also offers empirical data demonstrating the performance evaluation of SEDIA. These contributions collectively advance our understanding of semantically enriched data integration within the realm of smart city applications.

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