Securing Spatial Data Infrastructures for Distributed Smart City applications and services

Chaturvedi, Kanishk; Matheus, Andreas; Nguyen, Son H.; Kolbe, Thomas H.

doi:10.1016/j.future.2019.07.002

Cited by 50 publications

(20 citation statements)

References 17 publications

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“…First, the government should continue to strengthen the construction of big data central systems in smart cities. By increasing the data integration of smart cities in smart transportation, smart fire, and social security, smart brains of cities covering a variety of disaster recovery information can be constructed, and big data and cloud computing can be used to improve the resilience capacity of smart cities [39].…”

Section: Discussionmentioning

confidence: 99%

Temporal and Spatial Differences in the Resilience of Smart Cities and Their Influencing Factors: Evidence from Non-Provincial Cities in China

et al. 2020

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Based on the sample data of 81 non-provincial smart cities in China in 2017, the comprehensive evaluation index of the resilience of sample cities is calculated by using the entropy method, and the spatial differences of different factors on the resiliency are analyzed by using the geographical weighted regression (GWR) model. The conclusions are as follows: Firstly, the comprehensive evaluation index of the resilience of smart cities presents a spatial distribution characteristic of decreasing from the east to the west. At the same time, the resilience comprehensive index, the public infrastructure resilience capacity index, the economic development resilience index, the social security resilience index, and the ecological environment resilience index of smart cities have obvious agglomeration effects on their geographical spaces. Secondly, the public infrastructure resilience capacity index and the ecological environment resilience index are both low with a discrete distribution, while the economic development resilience index and the social security resilience index are both high with a concentrated distribution. Thirdly, different factors have significantly positive effects on the resilience of smart cities. In particular, the public infrastructure capacity resilience index decreases from the north to the south with the spatial distribution pattern of concentration, the economic development resilience index and the ecological environment resilience index of smart cities decrease from the east to the west with a concentrated spatial distribution pattern, and the social security resilience index of smart cities decreases from the southwest to the northeast with a concentrated spatial distribution pattern. Therefore, it is necessary to enhance awareness of smart cities, strengthen the driving force of science and technology innovation, strengthen public infrastructure and service construction, and continuously improve the rapid resilience of smart cities.

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Section: Discussionmentioning

confidence: 99%

Temporal and Spatial Differences in the Resilience of Smart Cities and Their Influencing Factors: Evidence from Non-Provincial Cities in China

et al. 2020

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“… sustainable quality of life, sustainability of economic growth and development -this criterion is based on indicators of economic efficiency, quality of life assessment, and management effectiveness [2].…”

Section: Methodology and Datamentioning

confidence: 99%

International smart cities ratings and Russian cities IQ rating comparative study

Kamolov

Kornaukhova

2021

SHS Web of Conf.

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Research background: The global rating methods of smart cities rankings used in such international organizations as The Intelligent Community Forum, Center for Globalization and Strategy IESE and the United Nations Economic Commission for Europe. International ratings are considered to be a source of information that allows everyone to see and evaluate the scale of it. Along with this phenomenon, the Russian Federation develops national smart cities IQ index which creates the case in rating methodology to help researches assess and better understand smart cities revolution. Purpose of the article: Major international rankings of smart cities arise alongside one national rating through the prism of the following methodological objects: comparison criteria, scope and structure of the study, research goals and priorities, obtaining information methods, number of criteria, range of estimated values, frequency of ranking compilation and compilers. All mentioned comes up with the basics of global metarating and possibility of its creation. Methods: As for the main research methods, we turned to analysis and developed our own criteria in comparison, synthesis of metarating and approaches to the study of smart cities development. Findings & Value added: As a result of the study, the main criteria of the considered rating assessment methods and the basis of metarating were identified. Based on the study, substantiated conclusions are formulated, and scientifically based recommendations are given to provide the basis for further metarating creation and elaboration.

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“…Dynamizers have already been implemented as an Application Domain Extension (ADE) for CityGML 2.0 and are planned to become a part of the next version of CityGML (version 3.0). The conceptual UML model and XML Instance Schema are already made available for Dynamizer ADE (Chaturvedi, Kolbe, 2017). However, there have been a few recent additions in the Dynamizer UML model which is going to be proposed to the CityGML Working Group.…”

Section: Citygml Dynamizer Adementioning

confidence: 99%

“…Dynamizers have already been implemented as an Application Domain Extension (ADE) within the OGC Future City Pilot Phase 1 (Chaturvedi, Kolbe, 2017). The results are focused on two scenarios: (i) integrating real-time sensor stream with CityGML building models, and (ii) enriching 3D building wall and roof surfaces by time-dependent solar potential simulation results.…”

Section: Querying and Visualization Of Dynamic And Static Propertiesmentioning

confidence: 99%

See 1 more Smart Citation

Integrated Management and Visualization of Static and Dynamic Properties of Semantic 3d City Models

Chaturvedi

Yao²,

Kolbe

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. CityGML is an international standard issued by the Open Geospatial Consortium (OGC) for representing and exchanging Semantic 3D City Models. Due to their large scale and deeply nested structures, the management and visualization of CityGML based models require sophisticated solutions such as the 3D City Database (3DCityDB). The research work presented in this article proposes a high level architecture for extending the 3D City Database to store and manage dynamic properties encoded within a new Application Domain Extension (ADE) of CityGML called Dynamizer ADE. The implementation employs the 3DCityDB 4.2 ADE Plugin Manager, which provides an automatic way for dynamically extending the 3DCityDB to support the storage and management of CityGML models with ADEs. The paper introduces a relational database model for storing and managing the Dynamizer ADE within the 3DCityDB. Further, the research work includes the extension of the 3DCityDB Importer/Exporter in order to import and export CityGML documents including Dynamizer ADE data. 3DCityDB already comes with a Web Feature Service (WFS) interface allowing CityGML features to be requested in standardized ways. The proposed framework enables CityGML Viewers to access static data (using OGC WFS interface) and dynamic data (using the OGC SWE interfaces) in an integrated fashion.

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Securing Spatial Data Infrastructures for Distributed Smart City applications and services

Cited by 50 publications

References 17 publications

Temporal and Spatial Differences in the Resilience of Smart Cities and Their Influencing Factors: Evidence from Non-Provincial Cities in China

Temporal and Spatial Differences in the Resilience of Smart Cities and Their Influencing Factors: Evidence from Non-Provincial Cities in China

International smart cities ratings and Russian cities IQ rating comparative study

Integrated Management and Visualization of Static and Dynamic Properties of Semantic 3d City Models

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