scholarly journals Multisource Spatial Data Integration for Use Cases Applications

2021 ◽  
Author(s):  
Francesca Noardo
Keyword(s):  
Data Integration ◽  
Spatial Data ◽  
Open Data ◽  
Use Cases ◽  
Data Sets ◽  
Data Merging ◽  
Geometrical Complexity ◽  
Long Time ◽  
Spatial Data Integration ◽  
Definition Of

Big opportunities are given by the reuse and integration of data, which, nowadays, are more and more available, thanks to advances in acquisition and modelling technologies and the open data paradigm. Seamlessly integrating data from heterogenous data sources has been an interest of the geospatial community for long time. However, the higher semantic and geometrical complexity pose new challenges which have never been tackled in a comprehensive methodology. Building on the previous theories and studies, this paper proposes an overarching methodology for multisource spatial data integration. Starting from the definition of the use case-based requirements for the integrated data, it proposes a framework to analyse the involved datasets with respect to integrability and suggests actions to harmonise them towards the destination model. The overall workflow is explained, including the data merging phase and validation. The methodology is tested and exemplified on a case study. Considering the specific data sets’ features and parameters, this approach will allow the development of consistent, well documented and inclusive data integration workflows, for the sake of use cases processes automation and the production of Interoperable and Reusable data.

scholarly journals A redesign of OGC Symbology Encoding standard for sharing cartography

2017 ◽  
Author(s):  
Erwan Bocher ◽  
Olivier Ertz
Keyword(s):  
Spatial Data ◽  
Poor Quality ◽  
Use Cases ◽  
General Observation ◽  
Data Infrastructures ◽  
Open Standards ◽  
Definition Of ◽  
Data Requirements ◽  
Basic Level

Despite most Spatial Data Infrastructures are offering service-based visualization of geospatial data, requirements are often at a very basic level leading to poor quality of maps. This is a general observation for any geospatial architecture as soon as open standards as those of the Open Geospatial Consortium (OGC) shall be applied. To improve the situation, this paper does focus on improvements at the portrayal interoperability side by considering standardization aspects. We propose two major redesign recommendations. First to consolidate the cartographic theory at the core of the OGC Symbology Encoding standard. Secondly to build the standard in a modular way so as to be ready to be extended with upcoming future cartographic requirements. Thus, we start by defining portrayal interoperability by means of typical use cases that frame the concept of sharing cartography. Then we bring to light the strengths and limits of the relevant open standards to consider in this context. Finally we propose a set of recommendations to overcome the limits so as to make these use cases a true reality. Even if the definition of a cartographic-oriented standard is not able to act as a complete cartographic design framework by itself, we argue that pushing forward the standardization work dedicated to cartography is a way to share and disseminate good practices and finally to improve the quality of the visualizations.

Spatial Data Integration

2019 ◽  
pp. 1539-1546 ◽  
Author(s):  
Booma Sowkarthiga Balasubramani ◽  
Isabel F. Cruz
Keyword(s):  
Data Integration ◽  
Spatial Data ◽  
Spatial Data Integration

Optimized Spatial Query for Data Integration Based on Semantics

2012 ◽  
Vol 229-231 ◽  
pp. 1895-1899
Author(s):  
Shen Yi Qian ◽  
Hao Dong Zhu
Keyword(s):  
Data Integration ◽  
Query Optimization ◽  
Spatial Data ◽  
Hybrid Approach ◽  
Detailed Examination ◽  
Spatial Knowledge ◽  
Spatial Query ◽  
Spatial Queries ◽  
Spatial Data Integration ◽  
Spatial Semantics

Data integration of geospatial data in distributed, heterogeneous environment involves the use of semantic ontologies. In this kind of integration system, semantic technologies play an important role in improving performance and effectiveness of spatial queries. This paper focuses on methods of query optimization based on spatial semantics at the top level of semantic layer in central data integration systems. After analyzing the hybrid approach for spatial data integration, two categories of query optimization strategies are proposed based on detailed examination of special characteristics of spatial data. With spatial knowledge explicitly specified in ontologies and associated rules, spatial queries can be optimized intelligently.

scholarly journals Analysis of performance of selected geospatial analyses implemented on the basis of relational and NoSQL databases

2019 ◽  
Vol 51 (4) ◽  
pp. 167-179
Author(s):  
Marcin Pietroń
Keyword(s):  
Spatial Data ◽  
Large Data ◽  
Relational Model ◽  
Data Sets ◽  
Data Set ◽  
Nosql Databases ◽  
Long Time ◽  
The Right ◽  
Spatial Operators ◽  
Gis System

Abstract Databases are a basic component of every GIS system and many geoinformation applications. They also hold a prominent place in the tool kit of any cartographer. Solutions based on the relational model have been the standard for a long time, but there is a new increasingly popular technological trend – solutions based on the NoSQL database which have many advantages in the context of processing of large data sets. This paper compares the performance of selected spatial relational and NoSQL databases executing queries with selected spatial operators. It has been hypothesised that a non-relational solution will prove to be more effective, which was confirmed by the results of the study. The same spatial data set was loaded into PostGIS and MongoDB databases, which ensured standardisation of data for comparison purposes. Then, SQL queries and JavaScript commands were used to perform specific spatial analyses. The parameters necessary to compare the performance were measured at the same time. The study’s results have revealed which approach is faster and utilises less computer resources. However, it is difficult to clearly identify which technology is better because of a number of other factors which have to be considered when choosing the right tool.

scholarly journals A redesign of OGC Symbology Encoding standard for sharing cartography

2017 ◽  
Author(s):  
Erwan Bocher ◽  
Olivier Ertz
Keyword(s):  
Spatial Data ◽  
Poor Quality ◽  
Use Cases ◽  
General Observation ◽  
Data Infrastructures ◽  
Open Standards ◽  
Definition Of ◽  
Data Requirements ◽  
Basic Level

Despite most Spatial Data Infrastructures are offering service-based visualization of geospatial data, requirements are often at a very basic level leading to poor quality of maps. This is a general observation for any geospatial architecture as soon as open standards as those of the Open Geospatial Consortium (OGC) shall be applied. To improve the situation, this paper does focus on improvements at the portrayal interoperability side by considering standardization aspects. We propose two major redesign recommendations. First to consolidate the cartographic theory at the core of the OGC Symbology Encoding standard. Secondly to build the standard in a modular way so as to be ready to be extended with upcoming future cartographic requirements. Thus, we start by defining portrayal interoperability by means of typical use cases that frame the concept of sharing cartography. Then we bring to light the strengths and limits of the relevant open standards to consider in this context. Finally we propose a set of recommendations to overcome the limits so as to make these use cases a true reality. Even if the definition of a cartographic-oriented standard is not able to act as a complete cartographic design framework by itself, we argue that pushing forward the standardization work dedicated to cartography is a way to share and disseminate good practices and finally to improve the quality of the visualizations.

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