Skip to main content

Advertisement

SpringerLink
Log in

When Spatial Analytics Meets Cyberinfrastructure: an Interoperable and Replicable Platform for Online Spatial-Statistical-Visual Analytics

  • Published:
Journal of Geovisualization and Spatial Analysis Aims and scope Submit manuscript

Abstract

Developing spatial analytical methods as open source libraries is an important endeavor to enable open and replicable science. However, despite the fact that large geospatial data and geospatial cyberinfrastructure (GeoCI) resources are becoming available, many libraries and toolkits are only initialized and designed for analytics in a desktop environment. Coupling spatial analytical functionality with big data and high-performance computing will result in immediate benefits for multidisciplinary research in terms of addressing challenging socioeconomic and environmental problems, as well as supporting remote collaboration between participants from physically distributed research groups, and assisting informed decision-making. In this article, we present the design and implementation of a general workflow to integrate state-of-the-art open source libraries with GeoCI resources. We also solve various interoperability and replicability issues that arise during the implementation process. The popular open source Python Spatial Analysis Library (PySAL) was selected to build the interoperable Web service, WebPySAL, which was then successfully integrated in GeoCI. With this integration between spatial analytics and cyberinfrastructure, the new GeoCI platform provides easy-to-use, efficient, and interactive exploratory spatial analysis functions to public users. The GeoCI capability is demonstrated through two regional economic case studies of (1) evaluating global spatial autocorrelation and identifying local clusters in the spatial pattern of median household incomes for US counties (with global and local Moran’s I statistics) and (2) modeling the space-time dynamics of per capita incomes at the state level (with spatial Markov statistics).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. Please see <http://cici.lab.asu.edu/gci2>

References

  • Allard S (2012) DataONE: facilitating eScience through collaboration. J eSci Librariansh 1(1):3

    Google Scholar 

  • Anselin L (1995) Local indicators of spatial association-LISA. Geogr Anal 27(2):93–115

    Google Scholar 

  • Anselin L, Rey SJ (2012) Spatial econometrics in an age of CyberGIScience. Int J Geograph Inform Sci IJGIS 26(12):2211–2226

    Google Scholar 

  • Anselin L & Rey SJ (2014) Modern spatial econometrics in practice: a guide to GeoDa, GeoDaSpace and PySAL. GeoDa Press

  • Anselin L, Rey SJ, Li W (2014) Metadata and provenance for spatial analysis: the case of spatial weights. Int J Geogr Information Sci 28(11):2261–2280

    Google Scholar 

  • Anselin L, Syabri I, Kho Y (2010) GeoDa: an introduction to spatial data analysis. Handbook of applied spatial analysis. pp 73–89

  • Astsatryan H, Hayrapetyan A, Narsisian W, Saribekyan A, Asmaryan S, Saghatelyan A, Muradyan V, Guigoz Y, Giuliani G, Ray N (2015) An interoperable Web portal for parallel geoprocessing of satellite image vegetation indices. Earth Sci Inf 8(2):453–460

    Google Scholar 

  • Bivand R, Anselin L, Berke O al Bernat A, Carvalho M, Chun Y et al (2011) spdep: spatial dependence: weighting schemes, statistics and models. R package version 0.5-31, URL http://CRAN.R-project.org/package=spdep. Retrieved from http://ftp.auckland.ac.nz/software/CRAN/src/contrib/Descriptions/spdep.html

  • Brandl G (2009) Sphinx: python documentation generator. URL: https://pypi.org/project/Sphinx/10.(last accessed on April 10, 2020)

  • Čepický J (2007) PyWPS 2.0.0: the presence and the future. Geoinformatics FCE CTU 2:61–64

    Google Scholar 

  • Cerón M, Fernández-Carmona M, Urdiales C, Sandoval F (2018) Smartphone-based vehicle emission estimation. In: Proceedings of the International Conference on Information Technology & Systems (ICITS 2018). Springer International Publishing, pp 284–293

  • Cliff AD, Ord JK (1981) Spatial processes, models & applications. Pion, London

    Google Scholar 

  • Delipetrev B, Jonoski A, Solomatine DP (2014) Development of a web application for water resources based on open source software. Comput Geosci 62:35–42

    Google Scholar 

  • Dubois G, Schulz M, Skøien J, Bastin L, Peedell S (2013) eHabitat, a multi-purpose Web processing service for ecological modeling. Environ Model Softw 41:123–133

    Google Scholar 

  • Harris R (2003) Building a GIScience community in cyberspace: reflections on GIScOnline. J Geogr High Educ 27(3):279–295

    Google Scholar 

  • Kang W, Rey SJ (2018) Conditional and joint tests for spatial effects in discrete Markov chain models of regional income distribution dynamics. Ann Reg Sci 61(1):73–93

    Google Scholar 

  • Keller GR (2003) GEON (GEOscience network): a first step in creating cyberinfrastructure for the geosciences. Seismol Res Lett 74(4):441–444

    Google Scholar 

  • Krishnan S, Crosby C, Nandigam V, Phan M, Cowart C, Baru C, Arrowsmith R (2011) OpenTopography: a services oriented architecture for community access to LIDAR topography. In: Proceedings of the 2nd International Conference on Computing for Geospatial Research & Applications. ACM, p 7

  • Kullback S, Kupperman M, Ku HH (1962) Tests for contingency tables and Markov chains. Technometrics J Stat Phys Chem Eng Sci 4(4):573–608

    Google Scholar 

  • Laura J, Li W, Rey SJ, Anselin L (2015) Parallelization of a regionalization heuristic in distributed computing platforms – a case study of parallel-p-compact-regions problem. Int J Geograph Inform Sci IJGIS 29(4):536–555

    Google Scholar 

  • Li W (2018) Lowering the barriers for accessing distributed geospatial big data to advance spatial data science: the PolarHub solution. Ann Am Assoc Geogr 108(3):773–793

    Google Scholar 

  • Li W, Bhatia V, Cao K (2015a) Intelligent polar cyberinfrastructure: enabling semantic search in geospatial metadata catalogue to support polar data discovery. Earth Sci Inf 8(1):111–123

    Google Scholar 

  • Li W, Song M, Zhou B, Cao K, Gao S (2015b) Performance improvement techniques for geospatial Web services in a cyberinfrastructure environment–a case study with a disaster management portal. Comput Environ Urban Syst 54:314–325

    Google Scholar 

  • Li W, Shao H, Wang S, Zhou X, Wu S (2016a) A2CI: a cloud-based, service-oriented geospatial cyberinfrastructure to support atmospheric research. In: Cloud computing in ocean and atmospheric sciences. Academic Press, pp 137–161

  • Li W, Wang S, Bhatia V (2016b) PolarHub: a large-scale Web crawling engine for OGC service discovery in cyberinfrastructure. Comput Environ Urban Syst 59:195–207

    Google Scholar 

  • Li W, Wu S, Song M, Zhou X (2016c) A scalable cyberinfrastructure solution to support big data management and multivariate visualization of time-series sensor observation data. Earth Sci Inf 9(4):449–464

    Google Scholar 

  • Li W, Goodchild MF, Anselin L, Weber KT (2019a) A smart service-oriented CyberGIS framework for solving data-intensive geospatial problems. In: CyberGIS for geospatial discovery and innovation. Springer, Dordrecht, pp 189–211

    Google Scholar 

  • Li W, Song M, Tian Y (2019b) An ontology-driven cyberinfrastructure for intelligent spatiotemporal question answering and open knowledge discovery. ISPRS Int J Geo Inf 8(11):496

    Google Scholar 

  • Li X, Di L, Han W, Zhao P, Dadi U (2010) Sharing geoscience algorithms in a Web service-oriented environment (GRASS GIS example). Comput Geosci 36(8):1060–1068

    Google Scholar 

  • McMillen DP, McDonald JF (1991) A Markov chain model of zoning change. J Urban Econ 30(2):257–270

    Google Scholar 

  • Mihon D, Colceriu V, Bacu V, Gorgan D (2013) Grid based processing of satellite images in GreenLand Platform. Int J Adv Comput Sci Appl 3:41–49

    Google Scholar 

  • Neteler M, Bowman MH, Landa M, Metz M (2012) GRASS GIS: a multi-purpose open source GIS. Environ Model Softw 31:124–130

    Google Scholar 

  • Pebesma E (2012) spacetime: spatio-temporal data in R. J Stat Softw 51(7). https://doi.org/10.18637/jss.v051.i07

  • Quah DT (1993) Empirical cross-section dynamics in economic growth. Eur Econ Rev 37(2-3):426–434

    Google Scholar 

  • Rajib MA, Merwade V, Kim IL, Zhao L, Song C, Zhe S (2016) SWATShare – a Web platform for collaborative research and education through online sharing, simulation and visualization of SWAT models. Environ Model Softw 75:498–512

    Google Scholar 

  • Rey SJ (2014) Python Spatial Analysis Library (PySAL): an update and illustration. In: Brunsdon C, SIngleton A (eds) Geocomputation. Sage

  • Rey SJ (2016) Space–time patterns of rank concordance: local indicators of mobility association with application to spatial income inequality dynamics. Ann Am Assoc Geographers 106(4):788–803

    Google Scholar 

  • Rey SJ, Anselin L (2010) PySAL: a python library of spatial analytical methods. In: Fischer MM, Getis A (eds) Handbook of applied spatial analysis: software tools, methods and applications. Springer, Berlin, Heidelberg, pp 175–193

    Google Scholar 

  • Rey SJ, Anselin L, Li X, Pahle R, Laura J, Li W, Koschinsky J (2015) Open geospatial analytics with PySAL. ISPRS Int J Geo Inf 4(2):815–836

    Google Scholar 

  • Rey SJ, Janikas MV (2010) STARS: space-time analysis of regional systems, In Handbook of applied spatial analysis (pp. 91-112). Springer, Berlin, Heidelberg

    Google Scholar 

  • Rey SJ, Kang W, Wolf L (2016) The properties of tests for spatial effects in discrete Markov chain models of regional income distribution dynamics. J Geogr Syst 18(4):377–398

    Google Scholar 

  • Rey SJ, Murray AT, Grubesic TH, Mack E, Wei R, Anselin L, Griffin M (2014) Sex offender residential movement patterns: a Markov chain analysis. Prof Geogr J Assoc Am Geogr 66(1):102–111

    Google Scholar 

  • Rodero Castro I, Parashar M (2016) Architecting the cyberinfrastructure for National Science Foundation Ocean Observatories Initiative (OOI). In: Instrumentation viewpoint, vol 19. SARTI, pp 99–101

  • Rinner C, Keßler C, Andrulis S (2008) The use of Web 2.0 concepts to support deliberation in spatial decision-making. Comput Environ Urban Syst 32(5):386–395

    Google Scholar 

  • Steiniger S, Hunter AJS (2013) The 2012 free and open source GIS software map - a guide to facilitate research, development, and adoption. Comput Environ Urban Syst 39:136–150

    Google Scholar 

  • Sugumaran R, Meyer JC, Davis J (2009) A Web-based environmental decision support system for environmental planning and watershed management. In: Handbook of applied spatial analysis, pp 703–718

    Google Scholar 

  • Sun Y, Li S (2016) Real-time collaborative GIS: a technological review. ISPRS J Photogramm Remote Sens Off Publ Int Soc Photogramm Remote Sens 115:143–152

    Google Scholar 

  • Swain NR, Latu K, Christensen SD, Jones NL, Nelson EJ, Ames DP, Williams GP (2015) A review of open source software solutions for developing water resources Web applications. Environ Model Softw 67:108–117

    Google Scholar 

  • Song M, Li W, Zhou B, Lei T (2016) Spatiotemporal data representation and its effect on the performance of spatial analysis in a cyberinfrastructure environment–a case study with raster zonal analysis. Comput Geosci 87:11–21

    Google Scholar 

  • Shao H, Li W (2018) A comprehensive optimization strategy for real-time spatial feature sharing and visual analytics in cyberinfrastructure. Int J Digit Earth:1–20

  • Unsworth J (2008) Cyber infrastructure for the humanities and social sciences. Sydney University Press

  • Veenendaal B (2015) Using the geospatial Web to deliver and teach GIScience education programs. ISPRS - Int Arch Photogramm Remote Sens Spat Inform Sci XL-6/W1:17–21

    Google Scholar 

  • Wang FZ, Helian N, Wu S, Guo Y, Deng DY, Meng L et al (2009) Eight times acceleration of geospatial data archiving and distribution on the grids. IEEE Trans Geosci Remote Sens Publ IEEE Geosci Remote Sens Soc 47(5):1444–1453

    Google Scholar 

  • Wang S (2013) CyberGIS: blueprint for integrated and scalable geospatial software ecosystems. Int J Geograph Inform Sci IJGIS 27(11):2119–2121

    Google Scholar 

  • Wang S, Armstrong MP (2009) A theoretical approach to the use of cyberinfrastructure in geographical analysis. Int J Geograph Inform Sci IJGIS 23(2):169–193

    Google Scholar 

  • Wang S, Liu Y (2009) TeraGrid GIScience Gateway: bridging cyberinfrastructure and GIScience. Int J Geograph Inform Sci IJGIS 23(5):631–656

    Google Scholar 

  • Wang Y, Gui Z, Wu H, Peng D, Wu J, Cui Z (2020) Optimizing and accelerating space–time Ripley’s K function based on Apache Spark for distributed spatiotemporal point pattern analysis. Futur Gener Comput Syst 105:96–118

    Google Scholar 

  • Warmerdam F (2008) The geospatial data abstraction library. In: Open source approaches in spatial data handling. Springer, Berlin, Heidelberg, pp 87–104

    Google Scholar 

  • Wu A, Convertino G, Ganoe C, Carroll JM, Zhang X (luke) (2013) Supporting collaborative sense-making in emergency management through geo-visualization. Int J Hum Comput Stud 71(1):4–23

    Google Scholar 

  • Wu H, You L, Gui Z, Hu K, Shen P (2015) GeoSquare: collaborative geoprocessing models’ building, execution and sharing on Azure Cloud. Ann GIS 21(4):287–300

    Google Scholar 

Download references

Funding

This research was funded in part by National Science Foundation, grant number 1455349, 1831615, 1937908, 1936677.

Author information

Authors and Affiliations

  1. School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, 85287-5302, USA

    Hu Shao & Wenwen Li

  2. Center for Geospatial Sciences, University of California Riverside, Riverside, CA, 92521, USA

    Wei Kang & Sergio J. Rey

Authors
  1. Hu Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wenwen Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wei Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sergio J. Rey
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wenwen Li.

Ethics declarations

This paper is compliant with ethical standards.

Conflicts of Interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shao, H., Li, W., Kang, W. et al. When Spatial Analytics Meets Cyberinfrastructure: an Interoperable and Replicable Platform for Online Spatial-Statistical-Visual Analytics. J geovis spat anal 4, 17 (2020). https://doi.org/10.1007/s41651-020-00056-5

Download citation

  • Published:

  • DOI: https://doi.org/10.1007/s41651-020-00056-5

Keywords

Search

Navigation