Geo-environmental information is an important basis for geohazard analysis and the integration of geo-environmental data is crucial in the construction of urban emergency management systems. In existing urban spatial information systems, the integration of geo-environmental data is neither intuitive nor efficient enough to support the analysis of geohazards well. On the basis of Web virtual globe, this paper proposes a comprehensive framework for the integration and analysis of geo-environmental data. This framework can effectively integrate geological data with a 3D geological model as a carrier, seamlessly connect geographic data, dynamically load real-time monitoring data, and build 3D visualisation and analysis scenes of urban full-space temporal information in the browser. The application example shows that the proposed framework can provide good geo-environmental data and practical data analysis functions for geohazard early warning and decision making, and improve the efficiency of government departments’ response to geohazards.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Aksoy E, San BT (2019) Geographical information systems (GIS) and Multi-Criteria Decision Analysis (MCDA) integration for sustainable landfill site selection considering dynamic data source. Bull Eng Geol Env 78:779–791. https://doi.org/10.1007/s10064-017-1135-z
Blodgett D, Lucido J, Kreft J (2016) Progress on water data integration and distribution: a summary of select US Geological Survey data systems. J Hydroinf 18:226–237. https://doi.org/10.2166/hydro.2015.067
Carrino TA, Crosta AP, Toledo CLB, Silva AM (2018) Hyperspectral remote sensing applied to mineral exploration in southern Peru: a multiple data integration approach in the Chapi Chiara gold prospect. Int J Appl Earth Obs Geoinf 64:287–300. https://doi.org/10.1016/j.jag.2017.05.004
Chen QY, Liu G, Ma XG, Mariethoz G, He ZW, Tian YP, Weng ZP (2018a) Local curvature entropy-based 3D terrain representation using a comprehensive Quadtree. ISPRS J Photogramm Remote Sens 139:30–45. https://doi.org/10.1016/j.isprsjprs.2018.03.001
Chen QY, Liu G, Ma XG, Yao Z, Tian YP, Wang HL (2018b) A virtual globe-based integration and visualization framework for aboveground and underground 3D spatial objects. Earth Sci Inf 11:591–603. https://doi.org/10.1007/s12145-018-0350-x
Chen QY, Mariethoz G, Liu G, Comunian A, Ma XG (2018c) Locality-based 3-D multiple-point statistics reconstruction using 2-D geological cross sections. Hydrol Earth Syst Sci 22:6547–6566. https://doi.org/10.5194/hess-22-6547-2018
Chen QY, Liu G, Ma XG, Li XC, He ZW (2020) 3D stochastic modeling framework for Quaternary sediments using multiple-point statistics: a case study in Minjiang Estuary area, southeast China. Comput Geosci 136:14. https://doi.org/10.1016/j.cageo.2019.104404
Gray DJ (2016) Integration of historic groundwater data into the continent scale geochemistry initiative. Aust J Earth Sci 63:427–451. https://doi.org/10.1080/08120099.2016.1218932
Jiang X, Song JS, Lin YY, Gong YX (2018) A practical approach to constructing hierarchical networks for urban hazard mitigation planning using GIS: the case of Futian, Shenzhen. Int J Disaster Risk Reduct 28:629–639. https://doi.org/10.1016/j.ijdrr.2018.01.014
Kang TW, Hong CH (2015) A study on software architecture for effective BIM/GIS-based facility management data integration. Autom Constr 54:25–38. https://doi.org/10.1016/j.autcon.2015.03.019
Kim SA, Shin D, Choe Y, Seibert T, Walz SP (2012) Integrated energy monitoring and visualization system for Smart Green City development designing a spatial information integrated energy monitoring model in the context of massive data management on a web based platform. Autom Constr 22:51–59. https://doi.org/10.1016/j.autcon.2011.07.004
Kussul N, Shelestov A, Skakun S (2009) Grid and sensor web technologies for environmental monitoring. Earth Sci Inf 2:37–51. https://doi.org/10.1007/s12145-009-0024-9
Lee PC, Wang YH, Lo TP, Long DB (2018) An integrated system framework of building information modelling and geographical information system for utility tunnel maintenance management. Tunn Undergr Space Technol 79:263–273. https://doi.org/10.1016/j.tust.2018.05.010
Liu X, Hao LN, Yang WN (2019) BiGeo: a Foundational PaaS framework for efficient storage, visualization, management, analysis, service, and migration of geospatial big data-a case study of Sichuan Province, China. ISPRS Int J Geo-Inf 8:449. https://doi.org/10.3390/ijgi8100449
Ortolano G, Cirrincione R, Pezzino A, Tripodi V, Zappala L (2015) Petro-structural geology of the Eastern Aspromonte Massif crystalline basement (southern Italy-Calabria): an example of interoperable geo-data management from thin section - to field scale. J Maps 11:181–200. https://doi.org/10.1080/17445647.2014.948939
Peters SE, Husson JM, Czaplewski J (2018) Macrostrat: a platform for geological data integration and deep-time earth crust research. Geochem Geophys Geosyst 19:1393–1409. https://doi.org/10.1029/2018gc007467
Qi Y, Fang M, Zheng Z (2013) Webgis-based system of “monitoring and preventing geological disaster rely on the masses”. In: 2013 IEEE International Geoscience and Remote Sensing Symposium-IGARSS. IEEE, pp 664–667
Scarelli FM, Barboza EG, Cantelli L, Gabbianelli G (2017) Surface and subsurface data integration and geological modelling from the little ice age to the present, in the Ravenna coastal plain, northwest Adriatic Sea (Emilia-Romagna, Italy). Catena 151:1–15. https://doi.org/10.1016/j.catena.2016.12.005
Turganbaev E, Beldeubayeva Z, Rakhmetullina S, Krivykh V (2015) Information system of efficient data management of groundwater monitoring the Republic of Kazakhstan. In: 2015 9th International Conference on Application of Information and Communication Technologies. International Conference on Application of Information and Communication Technologies. IEEE, pp 72–75
Wang CB, Ma XG, Chen JG (2018) Ontology-driven data integration and visualization for exploring regional geologic time and paleontological information. Comput Geosci 115:12–19. https://doi.org/10.1016/j.cageo.2018.03.004
Wu ZN, Shen YX, Wang HL, Wu MM (2020) An ontology-based framework for heterogeneous data management and its application for urban flood disasters. Earth Sci Inf 13:377–390. https://doi.org/10.1007/s12145-019-00439-3
Xiong PC, Chi Y, Zhu SH, Moon HJ, Pu C, Hacigumus H (2011) Intelligent management of virtualized resources for database systems in cloud environment. In: Ieee 27th International Conference on Data Engineering. IEEE International Conference on Data Engineering. IEEE, pp 87–98
Xu Z, Zhang L, Li H, Lin YH, Yin S (2020) Combining IFC and 3D tiles to create 3D visualization for building information modeling. Autom Constr 109:102995. https://doi.org/10.1016/j.autcon.2019.102995
Yong-Peng F, Yi-Yong L, Lei W, Ling-Feng Z, Xue-Ping L (2018) Design and implementation of geological environment survey big data platform for Danjiangkou reservoir water source area based on WebGIS. In: 2018 4th Annual International Conference on Network and Information Systems for Computers (ICNISC). IEEE, pp 203–207
Yousif M, Sracek O (2016) Integration of geological investigations with multi-GIS data layers for water resources assessment in arid regions: El Ambagi Basin, Eastern Desert, Egypt. Environ Earth Sci 75:1–5. https://doi.org/10.1007/s12665-016-5456-1
Zhou CY et al (2019) Key technologies of a large-scale urban geological information management system based on a browser/server structure. IEEE Access 7:135582–135594. https://doi.org/10.1109/access.2019.2941348
Zhu LF, Chen XL, Li ZW (2019) Multiple-view geospatial comparison using web-based virtual globes. ISPRS J Photogramm Remote Sens 156:235–246. https://doi.org/10.1016/j.isprsjprs.2019.08.016
This research is funded by National Natural Science Foundation of China (U1711267), Natural Science Foundation of Hubei Province (2020CFB507), Fundamental Research Funds for the Central Universities (CUGL180823), Open Funding of Hubei Provincial Key Laboratory of Intelligent Geo-Information Processing (KLIGIP-2018B15). The authors thank the anonymous reviewers for their valuable comments.
Conflict of interest
The authors declare that there is no conflict of interest regarding the publication of this paper.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Communicated by: H. Babaie
About this article
Cite this article
Zhang, X., Zhang, J., Liu, G. et al. Comprehensive framework for the integration and analysis of geo-environmental data for urban geohazards. Earth Sci Inform (2021). https://doi.org/10.1007/s12145-021-00642-1
- Geo-environmental data
- Cesium virtual globe
- Data integration