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Research Directions on GIS Database Design and Management

Affiliations

  • SRC, CSE, Sastra University, Kumbakonam – 612001, Tamil Nadu, India
  • School of Computing, SAP, CSE, Sastra University, Thanjavur - 613401, Tamil Nadu, India

Abstract


Objective: Geospatial Information System (GIS) is predominantly used in urban planning, and improves quality of people living in an area in many ways. Even though so many hardware and software systems are employed in GIS, the database gains the greatest significance. The intension is to progress a spatial database that should be used as a representation or model of the world, particularly to design for a very specific application. Analysis: The GIS data framework is the methodology proposed to promote an application specific spatial database. It comprises of integrating heterogeneous type of data, followed by constructing a semi-structured multi dimensional data model which directs to design a spatial database. Findings: The novelty in this GIS data framework is Building Information Model (BIM) integrated with the traditional data in support of answering indoor spatial queries. Moreover, this framework worked with BigData to support heterogeneous type of data and to automate decision support system. Enhancements: The views focused on the case studies in this paper help to travel in a new direction of GIS specification, utilization and research from the routine methodologies. This paper widens the scope of research directions in order to establish new techniques in each diverse field.

Keywords

Big Data, BIM, GIS, Spatial Database.

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References


  • Ran J, Nedovic-Budic Z. Integrating spatial planning and flood risk management: A new conceptual framework for the spatially integrated policy infrastructure. Computers, Environment and Urban Systems. 2016; 57:68–79.
  • Gouareh A, Settou N, Khalfi A, Recioui B, Negrou B, Rahmouni S, Dokkar B. GIS-based analysis of hydrogen production from geothermal electricity using CO2 as working fluid in Algeria. International Journal of Hydrogen Energy. 2015; 40:15244–15253.
  • Wang W, Ying Y, Wu Q, Zhang H, Ma D, Xiao W. A GIS-based spatial correlation analysis for ambient air pollution and AECOPD hospitalizations in Jinan, China. Respiratory Medicine. 2015; 109:372–378.
  • Xu J, Song X, Wu Y, Zeng Z. GIS-modelling based coal-fired power plant site identification and selection. Applied Energy. 2015; 159:520–39.
  • Camargo LR, Zink R, Dorner W, Stoeglehner G. Spatio-temporal modelling of roof-top photovoltaic panels for improved technical potential assessment and electricity peak load offsetting at the municipal scale Computers, Environment and Urban Systems. 2015; 52:58–69.
  • Kucuksari S, Khaleghi AM, Hamidi M, Zhang Y, Szidarovszky F, Bayraksan G, Son YJ. An integrated GIS, optimization and simulation framework for optimal PV size and location in campus area environments. Applied Energy. 2014; 113:1601–13.
  • Cavazzi S, Dutton AG. An Offshore Wind Energy Geographic Information System (OWE-GIS) for assessment of the UK's offshore wind energy potential. Renewable Energy. 2016; 87:212–28.
  • Agapiou A, Lysandrou V, Alexakis DD, Themistocleous K, Cuca B, Argyriou A, Sarris A, Hadjimitsis DG. Cultural heritage management and monitoring using remote sensing data and GIS: The case study of Paphos area, Cyprus. Computers, Environment and Urban Systems. 2015; 54:230–9.
  • Nelson JR, Grubesic TH, Sim L, Rose K, Graham J. Approach for assessing coastal vulnerability to oil spills for prevention and readiness using GIS and the blowout and spill occurrence model. Ocean and Coastal Management. 2015; 112:1–11.
  • Chan DV, Helfrich CA, Hursh NC, Rogers ES, Gopal S. Measuring community integration using Geographic Information Systems (GIS) and participatory mapping for people who were once homeless. Health and Place. 2014; 27:92–101.
  • Raghuvanshi TK, Negassa L, Kala PM. GIS based grid overlay method versus modelling approach – A comparative study for Landslide Hazard Zonation (LHZ) in Meta Robi District of West Showa Zone in Ethiopia. The Egyptian Journal of Remote Sensing and Space Sciences. 2015; 18:235–50.
  • Xu C. Preparation of earthquake-triggered landslide inventory maps using remote sensing and GIS technologies: Principles and case studies. Geoscience Frontiers. 2015; 6:825–36.
  • Mishra AK, Deep S, Choudhary A. Identification of suitable sites for organic farming using AHP and GIS. The Egyptian Journal of Remote Sensing and Space Sciences. 2015; 18:181–93.
  • Qaddah AA, Abdelwahed MF. GIS-based site-suitability modelling for seismic stations: Case study of the northern Rahat volcanic field, Saudi Arabia. Computers and Geosciences. 2015; 83:193–208.
  • Huang B, Pan X. GIS coupled with traffic simulation and optimization for incident response. Computers, Environment and Urban Systems. 2007; 31:116–32.
  • Zafar SM. Spatio-temporal analysis of land cover/land use changes using geoinformatics (A Case Study of Margallah Hills National Park). Indian Journal of Science and Technology. 2014 Jan; 7(11). DOI: 10.17485/ijst/2014/v7i11/47792.
  • Meena PK, Khare D, Shukla R, Mishra PK. Long term trend analysis of mega cities in Northern India using rainfall data. Indian Journal of Science and Technology. 2015 Feb; 8(3). DOI: 10.17485/ijst/2015/v8i3/59580.
  • Aybet J, Al-Saedy H, Farmer M. Watermarking spatial data in Geographic Information Systems. H. Jahankhani, A. G. Hessami and F. Hsu, editors. ICGS3 2009, CCIS. Berlin Heidelberg: Springer-Verlag. 2009; 45:18–26.
  • Al-sharif AAA, Pradhan B, Shafri HZM, Mansor S. Spatio-temporal analysis of urban and population growths in Tripoli using remotely sensed data and GIS. Indian Journal of Science and Technology. 2013 Aug; 6(8). DOI:10.17485/ijst/2013/v6i8/36357.
  • Lopez M, Couturier S, Barrera K. Design scheme for spatial database of climatic and environmental variables in Mexico integrating BigData Technology. Procedia Computer Science. 2015; 55:503–13.
  • Wilson MW. On the criticality of mapping practices: Geodesign as critical GIS? Landscape and Urban Planning. 2015 Oct; 142:226–34.
  • Vance TC. A primer on cloud computing. Cloud Computing in Ocean and Atmospheric Sciences; 2016. p. 1–13.
  • Hu M, Li C. Design smart city based on 3S, Internet of Things, grid computing and cloud computing technology. Y. Wang and X. Hang, editors. IOT Workshop, CCIS 312; 2012. p. 466–72.
  • Manjula KR, Keshari AK, Pahlazani A. An approach to perform uncertainity analysis on a spatial dataset using clustering and distance based outlier detection technique. Indian Journal of Science and Technology. 2015 Dec; 8(35). DOI: 10.17485/ijst/2015/v8i35/71972.
  • Correa FR. Is BIM big enough to take advantage of BigData analytics? Proceedings of the 32st ISARC; Oulu, Finland. 2015. p. 1–8.
  • Kang TW, Hong CH. A study on software architecture for effective BIM/GIS-based facility management data integration. Automation in Construction. 2015; 54:25–38.
  • Bedard Y. Principles of spatial database analysis and design. 1999. Available from: http://www.geos.ed.ac.uk/~gisteac/gis_book_abridged/files/ch29.pdf
  • Taniar D, Rahayu W. A taxonomy for region queries in spatial databases. Journal of Computer and System Sciences. 2015; 81:1508–31.
  • Luo C, Junlin L, Li G, Wei W, Li Y, Li J. Efficient reverse spatial and textual k-Nearest Neighbour queries on road networks. Knowledge-based Systems. 2016; 93:121–34.
  • Wei-Kleiner F. Tree decomposition-based indexing for efficient shortest path and Nearest Neighbors query answering on graphs. Journal of Computer and System Sciences. 2016; 82:23–44.
  • Worboys MF, Hearnshaw HM, Maguire DJ. Object-oriented data modelling for spatial databases. International Journal of Geographical Information Systems. 1990; 4(4):369–83.
  • Pinet F. Entity-relationship and object-oriented formalisms for modelling spatial environmental data. Environmental Modelling and Software. 2012; 33:80–91.

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