scholarly journals Identifying Complex Junctions in a Road Network

2020 ◽  
Vol 10 (1) ◽  
pp. 4
Author(s):  
Jianting Yang ◽  
Kongyang Zhao ◽  
Muzi Li ◽  
Zhu Xu ◽  
Zhilin Li
Keyword(s):  
Spatial Data ◽  
Road Network ◽  
Structural Complexity ◽  
Network Data ◽  
Map Generalization ◽  
Test Results ◽  
Geometric Boundary ◽  
Road Data ◽  
Complex Junction ◽  
Topological Boundary

Automated generalization of road network data is of great concern to the map generalization community because of the importance of road data and the difficulty involved. Complex junctions are where roads meet and join in a complicated way and identifying them is a key issue in road network generalization. In addition to their structural complexity, complex junctions don’t have regular geometric boundary and their representation in spatial data is scale-dependent. All these together make them hard to identify. Existing methods use geometric and topological statistics to characterize and identify them, and are thus error-prone, scale-dependent and lack generality. More significantly, they cannot ensure the integrity of complex junctions. This study overcomes the obstacles by clarifying the topological boundary of a complex junction, which provides the basis for straightforward identification of them. Test results show the proposed method can find and isolate complex junctions in a road network with their integrity and is able to handle different road representations. The integral identification achieved can help to guarantee connectivity among roads when simplifying complex junctions, and greatly facilitate the geometric and semantic simplification of them.

scholarly journals Review of the Progress and Process of Satellite Imagery and Road Databases Conflation

2018 ◽  
Author(s):  
Mohamed Z.Jber ◽  
Dieter Fritsch
Keyword(s):  
Spatial Data ◽  
Road Network ◽  
Feature Matching ◽  
Primary Objective ◽  
Geospatial Data ◽  
Data Sources ◽  
Network Data ◽  
Incremental Updating ◽  
Heterogeneous Nature ◽  
State Of Art

Spatial data conflation plays a fundamental role in in many aspects of modern Geographic Information Systems (GIS) research and development such as geospatial data visualization, incremental updating of databases and disaster evaluation. The primary objective of conflation is to derive valuable information based on the comparison of multiple spatial data sources of homogeneous or heterogeneous nature. This paper reviews the state of art of the concept of conflation, feature matching, the most important progresses of conflation between image and road network data, and the complexity of spatial conflation.

scholarly journals SEMI-AUTOMATED APPROACH FOR MAPPING URBAN TREES FROM INTEGRATED AERIAL LiDAR POINT CLOUD AND DIGITAL IMAGERY DATASETS

2016 ◽  
Vol XLII-4/W1 ◽  
pp. 127-134 ◽  
Author(s):  
M. A. Dogon-Yaro ◽  
P. Kumar ◽  
A. Abdul Rahman ◽  
G. Buyuksalih
Keyword(s):  
Digital Image ◽  
Spatial Data ◽  
Urban Areas ◽  
Point Cloud ◽  
Structural Complexity ◽  
Urban Ecosystems ◽  
Field Work ◽  
Urban Trees ◽  
Decision Makers ◽  
Image Datasets

Mapping of trees plays an important role in modern urban spatial data management, as many benefits and applications inherit from this detailed up-to-date data sources. Timely and accurate acquisition of information on the condition of urban trees serves as a tool for decision makers to better appreciate urban ecosystems and their numerous values which are critical to building up strategies for sustainable development. The conventional techniques used for extracting trees include ground surveying and interpretation of the aerial photography. However, these techniques are associated with some constraints, such as labour intensive field work and a lot of financial requirement which can be overcome by means of integrated LiDAR and digital image datasets. Compared to predominant studies on trees extraction mainly in purely forested areas, this study concentrates on urban areas, which have a high structural complexity with a multitude of different objects. This paper presented a workflow about semi-automated approach for extracting urban trees from integrated processing of airborne based LiDAR point cloud and multispectral digital image datasets over Istanbul city of Turkey. The paper reveals that the integrated datasets is a suitable technology and viable source of information for urban trees management. As a conclusion, therefore, the extracted information provides a snapshot about location, composition and extent of trees in the study area useful to city planners and other decision makers in order to understand how much canopy cover exists, identify new planting, removal, or reforestation opportunities and what locations have the greatest need or potential to maximize benefits of return on investment. It can also help track trends or changes to the urban trees over time and inform future management decisions.

Spatial OLAP and Map Generalization

2012 ◽  
Vol 8 (1) ◽  
pp. 24-51 ◽  
Author(s):  
Sandro Bimonte ◽  
Michela Bertolotto ◽  
Jérôme Gensel ◽  
Omar Boussaid
Keyword(s):  
Data Warehouse ◽  
Spatial Data ◽  
Multidimensional Analysis ◽  
Central Component ◽  
Mental Process ◽  
Map Generalization ◽  
Spatial Decision Support ◽  
Spatial Decision Support Systems ◽  
Spatial Dimensions ◽  
And Algebra

Map generalization can be used as a central component of Spatial Decision Support Systems to provide a simplified and more readable cartographic visualization of geographic information. Indeed, it supports the user mental process for discovering important and unknown geospatial relations, trends and patterns. Spatial OLAP (SOLAP) integrates spatial data into OLAP and data warehouse systems. SOLAP models and tools are based on the concepts of spatial dimensions and measures that represent the axes and the subjects of the spatio-multidimensional analysis. Although powerful under some respect, current SOLAP models cannot support map generalization capabilities. This paper provides the first effort to integrate Map Generalization and OLAP. Firstly the authors define all modeling and querying requirements to do this integration, and then present a SOLAP model and algebra that support map generalization concepts. The approach extends SOLAP spatial hierarchies introducing multi-association relationships, supports imprecise measures, and it takes into account spatial dimensions constraints generated by multiple map generalization hierarchies.

scholarly journals Automated Conflation of Digital Elevation Model with Reference Hydrographic Lines

2020 ◽  
Vol 9 (5) ◽  
pp. 334
Author(s):  
Timofey E. Samsonov
Keyword(s):  
Spatial Analysis ◽  
Digital Elevation Model ◽  
Spatial Data ◽  
Drainage Network ◽  
Map Generalization ◽  
Data Types ◽  
Digital Elevation ◽  
Elevation Data ◽  
Elevation Model ◽  
Different Sources

Combining misaligned spatial data from different sources complicates spatial analysis and creation of maps. Conflation is a process that solves the misalignment problem through spatial adjustment or attribute transfer between similar features in two datasets. Even though a combination of digital elevation model (DEM) and vector hydrographic lines is a common practice in spatial analysis and mapping, no method for automated conflation between these spatial data types has been developed so far. The problem of DEM and hydrography misalignment arises not only in map compilation, but also during the production of generalized datasets. There is a lack of automated solutions which can ensure that the drainage network represented in the surface of generalized DEM is spatially adjusted with independently generalized vector hydrography. We propose a new method that performs the conflation of DEM with linear hydrographic data and is embeddable into DEM generalization process. Given a set of reference hydrographic lines, our method automatically recognizes the most similar paths on DEM surface called counterpart streams. The elevation data extracted from DEM is then rubbersheeted locally using the links between counterpart streams and reference lines, and the conflated DEM is reconstructed from the rubbersheeted elevation data. The algorithm developed for extraction of counterpart streams ensures that the resulting set of lines comprises the network similar to the network of ordered reference lines. We also show how our approach can be seamlessly integrated into a TIN-based structural DEM generalization process with spatial adjustment to pre-generalized hydrographic lines as additional requirement. The combination of the GEBCO_2019 DEM and the Natural Earth 10M vector dataset is used to illustrate the effectiveness of DEM conflation both in map compilation and map generalization workflows. Resulting maps are geographically correct and are aesthetically more pleasing in comparison to a straightforward combination of misaligned DEM and hydrographic lines without conflation.

Spatio-temporal autocorrelation of road network data

2011 ◽  
Vol 14 (4) ◽  
pp. 389-413 ◽  
Author(s):  
Tao Cheng ◽  
James Haworth ◽  
Jiaqiu Wang
Keyword(s):  
Road Network ◽  
Network Data ◽  
Temporal Autocorrelation ◽  
Spatio Temporal

scholarly journals The importance of accurate road data for spatial applications in public health: customizing a road network

2009 ◽  
Vol 8 (1) ◽  
pp. 24 ◽  
Author(s):  
Brian G Frizzelle ◽  
Kelly R Evenson ◽  
Daniel A Rodriguez ◽  
Barbara A Laraia
Keyword(s):  
Public Health ◽  
Road Network ◽  
Road Data

User Preference Based Spatial Skyline Query Method

2014 ◽  
Vol 530-531 ◽  
pp. 832-838
Author(s):  
Yong Gui Zou ◽  
Zhi Wang
Keyword(s):  
Spatial Data ◽  
Road Network ◽  
User Preference ◽  
Skyline Query ◽  
Data Set ◽  
Second Stage ◽  
Processing Data ◽  
Data Volume ◽  
Definition Of ◽  
Query Algorithm

With the increasing of data volume and data dimensions in road network query, the response gets slow in searching services, which cannot satisfy users demand for preference-based searching. This paper proposes a user preference-based Skyline query algorithm. At the first stage, this method is based on the fact that the static property of data does not change during the query processes. Therefore, Skyline starts its calculation in the non-spatial data set to have the candidate results and dominance relation. Then it calculates the total costs of routine by defining user preference function. At the second stage, compare the data connections with the total costs of preference to minimize time for processing data and searching. The experiment result shows that the definition of user preference meets the users demand, and Skyline query algorithm benefits to have quick response.

Object-based flood risk modelling of the European road network: physical damage and mobility disruptions

2020 ◽  
Author(s):  
Ana Laura Costa ◽  
Elco Koks ◽  
Kees van Ginkel ◽  
Frederique de Groen ◽  
Lorenzo Alfieri ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Flood Risk ◽  
Road Network ◽  
Road Transport ◽  
Transport Infrastructure ◽  
Network Data ◽  
Physical Damage ◽  
Continental Scale ◽  
Object Based ◽  
Grid Based

<p>River flooding is among the most profound climate hazards in Europe and poses a threat to its road transport infrastructure. Traditional continental-scale flood risk studies do not accurately capture these disruptions because they are typically grid-based, whereas roads are relatively narrow line elements which are therefore omitted. Moreover, these grid-approaches disregard the network properties of roads, whereas the costs of reduced mobility could largely exceed the costs of the physical damage to the infrastructure.</p><p>We address these issues by proposing and applying an improved physical damage assessment coupled with the assessment of mobility disruption for a comprehensive risk assessment at a continental level.</p><p>In this study, we introduce an object-based, continental scale flood risk assessment of the European road network. We improve the estimates of direct, physical damage, by drawing road network data from OpenStreetMap, while making optimal use of the available metadata. We also introduce a set of road-specific flood damage functions, which are validated for an observed flood event in Germany. The results of this approach are compared to the traditional, grid-based approach to modelling road transport damage.</p><p>Next, we showcase how the object-based approach can be used to study potential mobility disruptions. In this study we present how the network data from OpenStreetMap and available metadata can be used to assess the flood impacts in terms of decreased connectivity, that is, increased distance, time and/or costs. The approach is flexible in physical scope, able to address national and continental resilience assessments and provide advice on tipping points of service performance. Furthermore, flexibility is also incorporated in terms of different resilience perspectives including decision-making by the asset owner or the national or trans-national supply chain disruption to a particular economic sector or company.</p><p>Finally, the risk assessment is discussed based on applications for the impacts of floods on European roads and the potential to extend to multi-hazard assessments (landslides, earthquakes, pluvial flooding) and other types of critical networks is discussed.  </p><p> </p><p>This paper has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 776479 for the project CO-designing the Assessment of Climate CHange costs. https://www.coacch.eu/</p><p> </p>

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