This study introduces a modular, end-to-end framework for enhancing map-based decision-making. The proposal operates both as a standalone pipeline and as a complementary “glue layer” for existing Geographic Information Systems (GIS). The framework addresses key limitations in GIS, including accessibility, cost, and unconventional data integration such as digital map scans and IoT streams. It comprises three core phases: (1) automated data acquisition and preparation, leveraging advanced techniques to extract geospatial insights from map scans and contextual information enrichment with open data and IoT sensors; (2) flexible integration with diverse systems (e.g. GIS, or relational data warehouses); and (3) interactive map-based visualization enabling spatiotemporal analysis via layered meta-visualization. The framework aims to visually and functionally enrich maps by integrating embedded insights from multimedia data sources, and displaying temporal variations and dynamic geographic characteristics on the map. By unifying heterogenous data into dynamic, interactive maps, the framework reveals hidden spatiotemporal relationships and supports actionable insights through intuitive navigation. A proof-of-concept in precision agriculture demonstrates the utility of the proposal by integrating heterogeneous data—including soil maps, climate records, and real-time sensors—to generate actionable crop recommendations. The case study highlights the framework’s applicability and potential to improve decision-making accuracy and efficiency by bridging gaps between complex data and diverse user roles, from policymakers to field workers. Beyond Agriculture, the modular design enables seamless adaptation across domains and roles. By democratizing access to advanced geospatial analytics, the framework overcomes traditional GIS limitations, offering scalable, user-centric solutions for complex spatial challenges.
GoodchildMQuattrochiD.Scale in remote sensing and GIS. 2023. DOI: 10.1201/9780203740170.
2.
AndrienkoGAndrienkoNJankowskiP, et al. Geovisual analytics for spatial decision support: Setting the research agenda. Int J Geogr Inf Sci2007; 21(8): 839–857.
Sreevalsan-NairJ. 2023). On Metavisualization and Properties of Visualization. Graphics-Visualization-Computing Lab, International Institute of Information Technology Bangalore, 2023.
5.
SamiNMuftiTSohailSS, et al. Future Internet of Things (IOT) from Cloud Perspective: Aspects, Applications and Challenges. In: Internet of Things (IoT), 2020, pp.515–532. New York, NY: Springer.
6.
ElwoodSGoodchildMFSuiDZ.Researching volunteered geographic information: spatial data, geographic research, and new social practice. Ann Am Assoc Geogr2012; 102(3): 571–590.
7.
WuJGanWChaoH-C, et al. Geospatial big data: survey and challenges. IEEE J Sel Top Appl Earth Obs Remote Sens2024: 17007–17020.
8.
RothEMBisantzAMWangX, et al. A work-centered approach to system user-evaluation. J Cogn Eng Decis Mak2021; 15(4): 155–174.
9.
Fleta-AsínJMuñozFSáenz-RoyoC.A methodological approach for enhancing visualization of country data representation in the presence of significant spatial disparity. MethodsX2024; 13: 102833.
10.
WadaKWallnerGVosS.Studying the utilization of a map-based visualization with vitality datasets by domain experts. Geogr2022; 2(3): 379–396.
11.
FiratEEJoshiALarameeRS.Interactive visualization literacy: the state-of-the-art. Inf Vis2022; 21(3): 285–310.
12.
RehmanFUAfyouniILbathA, et al. Building socially-enabled event-enriched maps. GeoInformatica2020; 24: 371–409.
13.
DusseFSimões JúniorPTamires AlvesA, et al. Information visualization for emergency management: a systematic mapping study. Expert Syst Appl2016; 45: 424–437.
14.
ZastrowM.Data visualization: Science on the map. Nature2015; 519: 119–120.
15.
WalkikarPShiLTamaBA, et al. Discovery of multi-domain spatiotemporal associations. GeoInformatica2024; 28: 353–379.
16.
WisnubhadraI, et al. Open spatiotemporal data warehouse for agriculture production analytics. Int J Intell Eng Syst2020; 13: 419–431.
17.
AndrienkoGAndrienkoNBudziakG, et al. Visual analysis of pressure in football. Data Min Knowl Discov2017; 31: 1793–1839.
18.
AndrienkoNAndrienkoG.A visual analytics framework for spatio-temporal analysis and modelling. Data Min Knowl Discov2013; 27: 55–83.
19.
SouthERodgersM.Data visualisation in scoping reviews and evidence maps on health topics: a cross-sectional analysis. Syst Rev2023; 12(2023): 142.
20.
GöbelFKieferPRaubalM.FeaturEyeTrack: automatic matching of eye tracking data with map features on interactive maps. GeoInformatica2019; 23: 663–687.
21.
SedrakyanGMannensEVerbertK.Guiding the choice of learning dashboard visualizations: linking dashboard design and data visualization concepts. J Comput Lang2019; 50: 19–38.
22.
HirveSet Pradeep ReddyCH. A survey on visualization techniques used for big data analytics. In: Advances in computer communication and computational sciences: proceedings of IC4S, 2018, pp.447–459. Singapore: Springer.
23.
StadlerJGDonlonKSiewertJD, et al. Improving the efficiency and ease of healthcare analysis through use of data visualization dashboards. Big Data2016; 4(2): 129–135.
24.
AbboudMTaherYZeitouniK, et al. How opportunistic mobile monitoring can enhance air quality assessment?GeoInformatica2024; 28: 679–710.
25.
BuchelOPenningtonDR.Geospatial analysis. In: Quan-Haase A and Sloan L (eds) The Sage handbook of social media research methods. London: SAGE Publications Ltd, 2022, pp.255–277.
26.
CarballadaAMBalsa-BarreiroJ.Geospatial analysis and mapping strategies for fine-grained and detailed COVID-19 data with GIS. ISPRS Int J Geo-Inform2021; 10(9): 602.
27.
OjoOIIlungaF. Geospatial analysis for irrigated land assessment, modeling and mapping. In: Rustamov RB, Hasanova S and Zeynalova MH (eds) Multi-purposeful application of geospatial data, vol. 9. London, UK: IntechOpen, 2018, pp.65–84.
28.
MurayamaY.Progress in geospatial analysis. Tokyo: Springer Science Business Media, 2012.
29.
HarrowerMFabrikantS. The role of map animation in geographic visualization. Zurich: University of Zurich, 2008.
30.
ShaoHMartinez-MaldonadoREcheverriaV, et al. Data storytelling in data visualisation: does it enhance the efficiency and effectiveness of information retrieval and insights comprehension?. In: Proceedings of the CHI conference on human factors in computing systems, 2024, pp.1–21.
31.
ZhaoZElmqvistN.The stories we tell about data: surveying data-driven storytelling using visualization. IEEE Comput Graph Appl2023; 43(4): 97–110.
32.
LundBD.The art of (data) storytelling: hip hop innovation and bringing a social justice mindset to data science and visualization. Int J Inf Divers Incl2022; 6(1/2): 1–11.
33.
ZhangYReynoldsMLugmayrA, et al. A visual data storytelling framework. In: Informatics. Vol. 9. Basel, Switzerland: MDPI, 2022, pp.73–95.
34.
ManovichL. Artistic visualization - A Companion to Digital Art, 2016, pp.426–444.
35.
RodríguezMTNunesSDevezasT.Telling stories with data visualization. In: Proceedings of the 2015 workshop on narrative & hypertext, 2015, pp.7–11.
36.
ViégasFBWattenbergM.Artistic data visualization: Beyond visual analytics. In: Online Communities and Social Computing: Second International Conference, OCSC 2007, Held as Part of HCI International 2007. Proceedings 2, Beijing, China, 22–27 July 2007, pp.182–191. Berlin Heidelberg: Springer.
37.
YinSLiHSunY, et al. Data visualization analysis based on explainable artificial intelligence: a survey. IJLAI Transactions on Science and Engineering2024; 2(2): 13–20.
38.
KarszniaIAdolfALeykS, et al. Using machine learning and data enrichment in the selection of roads for small-scale maps. Cartogr Geogr Inf Sci2024; 51: 60–78.
39.
RomanovP.Machine learning and artificial intelligence in data visualization. Thesis, 2023.
40.
WangQChenZWangY, et al. A survey on ML4VIS: applying machine learning advances to data visualization. IEEE Trans Vis Comput Graph2022; 28(12): 5134–5153.
41.
HarikumarSMannamVMahantaC, et al. Interactive map using data visualization and machine learning. In: 2020 6th IEEE congress on information science and technology (CiSt), Agadir - Essaouira, Morocco, 2020, pp.104-109.
42.
LebanGZupanBVidmarG, et al. VizRank: data visualization guided by machine learning. Data Min Knowl Discov2006; 13: 119–136.
43.
LabhaneSKeerthikaTDahiyaVT, et al. Virtual reality and augmented reality: future trend in technology and education. Acta Scientiae2024; 7(1): 1–8.
44.
Al-AnsiAMJaboobMGaradA, et al. Analyzing augmented reality (AR) and virtual reality (VR) recent development in education. Soc Sci Humanit Open2023; 8(1): 100532.
45.
LiberatoreMJWagnerWP.Virtual, mixed, and augmented reality: a systematic review for immersive systems research. Virtual Real2021; 25(3): 773–799.
46.
SutcliffeAde BruijnOThewS, et al. Developing visualization-based decision support tools for epidemiology. Inf Vis2014; 13(1): 3–17.
47.
BellatrecheLKerkadA.Query interaction based approach for horizontal data partitioning. Int J Data Warehousing Min2015; 11(2): 44–61.
48.
KerkadABellatrecheLRichardP, et al. A query beehive algorithm for data warehouse buffer management and query scheduling. Int J Data Warehousing Min2014; 10(3): 34–58.
49.
JarkeMLenzeriniMVassiliouY, et al. Fundamentals of data warehouses. Springer Science & Business Media,Lawrence Technological University, 2013.
50.
ChaudhuriSDayalUNarasayyaV.An overview of business intelligence technology. Communications of the ACM2011; 54(8): 88–98.
51.
PickJB.Data warehouses and GIS. In: ShekharSXiongH (eds) Encyclopedia of GIS. Boston, MA: Springer, 2008, pp.445–452.
52.
MacarthurR. Chapter 6 – Geographic Information Systems and their use for environmental monitoring. In: Environmental monitoring and characterization. Cambridge, MA: Academic Press, 2002, pp.85–100.
53.
MerryKBettingerPCrosbyM, et al. Geographic information system skills for foresters and natural resource managers. Amsterdam: Elsevier, 2023, pp.1–23.
54.
BimonteSPinetFMirallesA, et al. Special section on “Spatial data warehouses and SOLAP. GeoInformatica2014; 18: 269–272.
55.
KoubaZMatoušekKMikšovskýP. On data warehouse and GIS integration. Database and expert systems applications. In: DEXA. Lecture Notes in Computer Science, vol 1873, 2001. Berlin, Heidelberg: Springer.
56.
BorzaS.From GIS database to spatial data warehouse. Acad J Manuf Eng2015; 13(2): 136–141.
57.
MatousekKSvobodaL. 2000). Extending GIS by data warehouse. In: Proceedings of international carpathian control conference, TU Kosice.
58.
WangXYuSWenZ, et al. Application of modern GIS and remote sensing technology based on Big Data Analysis in Intelligent Agriculture. J Indian Soc Remote Sens2023; 51(9): 1891–1901.
59.
MaRSunEDZouJ.A spectral method for assessing and combining multiple data visualizations. Nat Commun2023; 14: 780.
60.
Vázquez-IngelmoAGarcía-PeñalvoFJTherónR.MetaViz – a graphical meta-model instantiator for generating information dashboards and visualizations. J King Saud Univ - Comput Inf Sci2022; 34(10): 9977–9990.
