{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T18:30:44Z","timestamp":1771612244808,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T00:00:00Z","timestamp":1737590400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union","award":["PE0000001"],"award-info":[{"award-number":["PE0000001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Timely, region-based geo-maps like choropleths are essential for smart city applications like traffic monitoring and urban planning because they can reveal statistical patterns in geotagged data. However, because data overloading is brought on by the quick inflow of massive geospatial data, creating these visualizations in real time presents serious difficulties. This paper introduces ApproxGeoMap, a novel system designed to efficiently generate approximate geo-maps from fast-arriving georeferenced data streams. ApproxGeoMap employs a stratified spatial sampling method, leveraging geohash tessellation and Earth Mover\u2019s Distance (EMD) to maintain both accuracy and processing speed. We developed a prototype system and tested it on real-world smart city datasets, demonstrating that ApproxGeoMap meets time-based and accuracy-based quality of service (QoS) constraints. Results indicate that ApproxGeoMap significantly enhances efficiency in both running time and map accuracy, offering a reliable solution for high-speed data environments where traditional methods fall short.<\/jats:p>","DOI":"10.3390\/computers14020035","type":"journal-article","created":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T09:01:13Z","timestamp":1737622873000},"page":"35","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["ApproxGeoMap: An Efficient System for Generating Approximate Geo-Maps from Big Geospatial Data with Quality of Service Guarantees"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-9878-7929","authenticated-orcid":false,"given":"Reem Abdelaziz","family":"Alshamsi","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4796-2181","authenticated-orcid":false,"given":"Isam Mashhour","family":"Al Jawarneh","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9062-3647","authenticated-orcid":false,"given":"Luca","family":"Foschini","sequence":"additional","affiliation":[{"name":"Dipartimento di Informatica\u2014Scienza e Ingegneria, University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5107-1023","authenticated-orcid":false,"given":"Antonio","family":"Corradi","sequence":"additional","affiliation":[{"name":"Dipartimento di Informatica\u2014Scienza e Ingegneria, University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Al Jawarneh, I.M., Foschini, L., and Bellavista, P. (2023). Efficient Integration of Heterogeneous Mobility-Pollution Big Data for Joint Analytics at Scale with QoS Guarantees. Future Internet, 15.","DOI":"10.3390\/fi15080263"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Al Jawarneh, I.M., Bellavista, P., Corradi, A., Foschini, L., and Montanari, R. (2020, January 7\u201311). Locality-Preserving Spatial Partitioning for Geo Big Data Analytics in Main Memory Frameworks. Proceedings of the GLOBECOM 2020\u20132020 IEEE Global Communications Conference, Taipei, Taiwan.","DOI":"10.1109\/GLOBECOM42002.2020.9322544"},{"key":"ref_3","unstructured":"Hassan, A., and Vijayaraghavan, J. (2019). Geospatial Data Science Quick Start Guide: Effective Techniques for Performing Smarter Geospatial Analysis Using Location Intelligence, Packt Publishing Ltd."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Al Jawarneh, I.M., Foschini, L., and Bellavista, P. (2023). Polygon Simplification for the Efficient Approximate Analytics of Georeferenced Big Data. Sensors, 23.","DOI":"10.3390\/s23198178"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"990","DOI":"10.1007\/s10922-020-09549-6","article-title":"Big Spatial Data Management for the Internet of Things: A Survey","volume":"28","author":"Bellavista","year":"2020","journal-title":"J. Netw. Syst. Manag."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Al Jawarneh, I.M., Bellavista, P., Corradi, A., Foschini, L., and Montanari, R. (2021). QoS-Aware Approximate Query Processing for Smart Cities Spatial Data Streams. Sensors, 21.","DOI":"10.3390\/s21124160"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yu, J., Tahir, A., and Sarwat, M. (2019, January 8\u201311). GeoSparkViz in Action: A Data System with Built-in Support for Geospatial Visualization. Proceedings of the 2019 IEEE 35th International Conference on Data Engineering (ICDE), Macao, China.","DOI":"10.1109\/ICDE.2019.00222"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1145\/3178392.3178394","article-title":"SRC: Geospatial visual analytics belongs to database systems: The BABYLON approach","volume":"9","author":"Yu","year":"2018","journal-title":"SIGSPATIAL Spec."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1109\/TPDS.2023.3330669","article-title":"SpatialSSJP: QoS-Aware Adaptive Approximate Stream-Static Spatial Join Processor","volume":"35","author":"Bellavista","year":"2024","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.1111\/tgis.12275","article-title":"An efficient data organization and scheduling strategy for accelerating large vector data rendering","volume":"21","author":"Guo","year":"2017","journal-title":"Trans. GIS"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1080\/13658816.2015.1032294","article-title":"A spatially adaptive decomposition approach for parallel vector data visualization of polylines and polygons","volume":"29","author":"Guo","year":"2015","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1137\/1129093","article-title":"The Monge\u2013Kantorovich Mass Transference Problem and Its Stochastic Applications","volume":"29","author":"Rachev","year":"1985","journal-title":"Theory Probab. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Al Jawarneh, I.M., Bellavista, P., Corradi, A., Foschini, L., and Montanari, R. (2020, January 14\u201316). Spatially Representative Online Big Data Sampling for Smart Cities. Proceedings of the 2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), Pisa, Italy.","DOI":"10.1109\/CAMAD50429.2020.9209294"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"104665","DOI":"10.1016\/j.cageo.2020.104665","article-title":"HiVision: Rapid visualization of large-scale spatial vector data","volume":"147","author":"Ma","year":"2021","journal-title":"Comput. Geosci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1683","DOI":"10.1109\/TVCG.2014.2346594","article-title":"Visual Abstraction and Exploration of Multi-class Scatterplots","volume":"20","author":"Chen","year":"2014","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1109\/TBDATA.2016.2586447","article-title":"Visual Analytics in Urban Computing: An Overview","volume":"2","author":"Zheng","year":"2016","journal-title":"IEEE Trans. Big Data"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1109\/MCG.2015.25","article-title":"Knowledge-Assisted Ranking: A Visual Analytic Application for Sports Event Data","volume":"36","author":"Chung","year":"2016","journal-title":"IEEE Comput. Graph. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2169","DOI":"10.1109\/TVCG.2013.193","article-title":"Space Transformation for Understanding Group Movement","volume":"19","author":"Andrienko","year":"2013","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1111\/cgf.12132","article-title":"TrajectoryLenses\u2014A Set-based Filtering and Exploration Technique for Long-term Trajectory Data","volume":"32","author":"Thom","year":"2013","journal-title":"Comput. Graph. Forum"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TVCG.2016.2598432","article-title":"SmartAdP: Visual Analytics of Large-scale Taxi Trajectories for Selecting Billboard Locations","volume":"23","author":"Liu","year":"2017","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liu, H., Gao, Y., Lu, L., Liu, S., Qu, H., and Ni, L.M. (2011, January 23\u201328). Visual analysis of route diversity. Proceedings of the 2011 IEEE Conference on Visual Analytics Science and Technology (VAST), Providence, RI, USA.","DOI":"10.1109\/VAST.2011.6102455"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1586","DOI":"10.1109\/TITS.2013.2263225","article-title":"VAIT: A Visual Analytics System for Metropolitan Transportation","volume":"14","author":"Liu","year":"2013","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1111\/cgf.12910","article-title":"There is More to Streamgraphs than Movies: Better Aesthetics via Ordering and Lassoing","volume":"35","author":"Hu","year":"2016","journal-title":"Comput. Graph. Forum"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1111\/j.0033-0124.1985.00075.x","article-title":"An Algorithm to Construct Continuous Area Cartograms*","volume":"37","author":"Dougenik","year":"1985","journal-title":"Prof. Geogr."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1109\/TVCG.2016.2598416","article-title":"SemanticTraj: A New Approach to Interacting with Massive Taxi Trajectories","volume":"23","author":"Wu","year":"2017","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1109\/TVCG.2015.2467771","article-title":"TrajGraph: A Graph-Based Visual Analytics Approach to Studying Urban Network Centralities Using Taxi Trajectory Data","volume":"22","author":"Huang","year":"2016","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1109\/TVCG.2015.2467112","article-title":"Visualization, Selection, and Analysis of Traffic Flows","volume":"22","author":"Scheepens","year":"2016","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1080\/136588199241247","article-title":"Interactive maps for visual data exploration","volume":"13","author":"Andrienko","year":"1999","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TVCG.2018.2865018","article-title":"TPFlow: Progressive Partition and Multidimensional Pattern Extraction for Large-Scale Spatio-Temporal Data Analysis","volume":"25","author":"Liu","year":"2019","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1660","DOI":"10.1080\/15481603.2022.2126375","article-title":"Modeling of spatial stratified heterogeneity","volume":"59","author":"Guo","year":"2022","journal-title":"GISci. Remote Sens."},{"key":"ref_31","unstructured":"Li, J., Chen, S., Andrienko, G., and Andrienko, N. (2018, January 4). Visual exploration of spatial and temporal variations of tweet topic popularity. Proceedings of the EuroVis Workshop on Visual Analytics, Brno, Czech Republic."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2232","DOI":"10.1109\/TITS.2017.2683539","article-title":"Visual Analytics of Mobility and Transportation: State of the Art and Further Research Directions","volume":"18","author":"Andrienko","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Sobral, T., Galv\u00e3o, T., and Borges, J. (2019). Visualization of Urban Mobility Data from Intelligent Transportation Systems. Sensors, 19.","DOI":"10.3390\/s19020332"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1080\/20964471.2020.1758537","article-title":"The visual analytics of big, open public transport data\u2014A framework and pipeline for monitoring system performance in Greater Sydney","volume":"5","author":"Lock","year":"2021","journal-title":"Big Earth Data"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1109\/TITS.2017.2727143","article-title":"An Interactive Visual Analytics Platform for Smart Intelligent Transportation Systems Management","volume":"19","author":"Kalamaras","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_36","unstructured":"Silva, C.T., Freire, J., Miranda, F., Lage, M., Doraiswamy, H., Hosseini, M., Tokuda, E., Ferreira, G., and Cesar, R.M. (2019). Integrated Analytics and Visualization for Multi-Modality Transportation Data, Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART)."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.cag.2018.09.008","article-title":"Real-time discovery of hot routes on trajectory data streams using interactive visualization based on GPU","volume":"76","author":"Gomes","year":"2018","journal-title":"Comput. Graph."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"He, J., Chen, H., Chen, Y., Tang, X., and Zou, Y. (2019). Diverse Visualization Techniques and Methods of Moving-Object-Trajectory Data: A Review. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8020063"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.jvlc.2018.08.009","article-title":"Visual exploration of urban functions via spatio-temporal taxi OD data","volume":"48","author":"Zhou","year":"2018","journal-title":"J. Vis. Lang. Comput."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"6298","DOI":"10.1109\/TITS.2021.3092036","article-title":"Intra-City Traffic Data Visualization: A Systematic Literature Review","volume":"23","author":"Clarinval","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Sinclair, C., and Das, S. (2021, January 21\u201323). Traffic Accidents Analytics in UK Urban Areas using k-means Clustering for Geospatial Mapping. Proceedings of the 2021 International Conference on Sustainable Energy and Future Electric Transportation (SEFET), Hyderabad, India.","DOI":"10.1109\/SeFet48154.2021.9375817"},{"key":"ref_42","first-page":"51","article-title":"\u0412\u0438\u0437\u0443\u0430\u043b\u044c\u043d\u044b\u0439 \u0430\u043d\u0430\u043b\u0438\u0437 \u0434\u0430\u043d\u043d\u044b\u0445 \u043f\u0430\u0441\u0441\u0430\u0436\u0438\u0440o\u043fo\u0442o\u043a\u0430 \u0436\u0435\u043b\u0435\u0437\u043do\u0434o\u0440o\u0436\u043do\u0433o \u0442\u0440\u0430\u043d\u0441\u043fo\u0440\u0442\u0430","volume":"9","year":"2021","journal-title":"Int. J. Open Inf. Technol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"63255","DOI":"10.1109\/ACCESS.2020.2983184","article-title":"Traffic Congestion Forecasting in Shanghai Based on Multi-Period Hotspot Clustering","volume":"8","author":"Xu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Ma, M., Wu, Y., Chen, L., Li, J., and Jing, N. (2019). Interactive and Online Buffer-Overlay Analytics of Large-Scale Spatial Data. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8010021"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Al Jawarneh, I.M., Bellavista, P., Foschini, L., and Montanari, R. (2019, January 9\u201313). Spatial-Aware Approximate Big Data Stream Processing. Proceedings of the 2019 IEEE Global Communications Conference (GLOBECOM), Big Island, HI, USA.","DOI":"10.1109\/GLOBECOM38437.2019.9014291"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Lohr, S.L. (2019). Sampling: Design and Analysis, Chapman and Hall\/CRC. [2nd ed.].","DOI":"10.1201\/9780429296284"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Stoehr, N., Meyer, J., Markl, V., Bai, Q., Kim, T., Chen, D.-Y., and Li, C. (2018, January 10\u201313). Heatflip: Temporal-Spatial Sampling for Progressive Heat Maps on Social Media Data. Proceedings of the 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA.","DOI":"10.1109\/BigData.2018.8621939"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Mitra, S., Khandelwal, P., Pallickara, S., and Pallickara, S.L. (2019, January 23\u201326). STASH: Fast Hierarchical Aggregation Queries for Effective Visual Spatiotemporal Explorations. Proceedings of the 2019 IEEE International Conference on Cluster Computing (CLUSTER), Albuquerque, NM, USA.","DOI":"10.1109\/CLUSTER.2019.8891029"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"352","DOI":"10.14778\/3157794.3157803","article-title":"GPU rasterization for real-time spatial aggregation over arbitrary polygons","volume":"11","author":"Zacharatou","year":"2017","journal-title":"Proc. VLDB Endow."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Bruhwiler, K., Buddhika, T., Pallickara, S., and Pallickara, S.L. (2020, January 7\u201310). Iris: Amortized, Resource Efficient Visualizations of Voluminous Spatiotemporal Datasets. Proceedings of the 2020 IEEE\/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT), Leicester, UK.","DOI":"10.1109\/BDCAT50828.2020.00003"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Bruhwiler, K., and Pallickara, S. (2019, January 2\u20135). Aperture: Fast Visualizations Over Spatiotemporal Datasets. Proceedings of the 12th IEEE\/ACM International Conference on Utility and Cloud Computing, Auckland, New Zealand.","DOI":"10.1145\/3344341.3368817"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Alsalama, A., Kubba, A., Alsmirat, M., and Al Jawarneh, I.M. (2024, January 17\u201320). A Novel Approximate Computing Method for Efficient Search in Satellite Remote Sensing Products. Proceedings of the 2024 International Conference on Multimedia Computing, Networking and Applications (MCNA), Valencia, Spain.","DOI":"10.1109\/MCNA63144.2024.10703889"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Al Jawarneh, I.M., Montanari, R., and Corradi, A. (2023, January 4\u20138). Cost-Effective Approximate Aggregation Queries on Geospatial Big Data. Proceedings of the 2023 IEEE Globecom Workshops (GC Wkshps), Kuala Lumpur, Malaysia.","DOI":"10.1109\/GCWkshps58843.2023.10465045"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2367728","DOI":"10.1080\/17538947.2024.2367728","article-title":"Real-time map rendering and interaction: A stylized hierarchical symbol model","volume":"17","author":"Huang","year":"2024","journal-title":"Int. J. Digit. Earth"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Huang, K., Wang, C., Wang, S., Liu, R., Chen, G., and Li, X. (2021). An Efficient, Platform-Independent Map Rendering Framework for Mobile Augmented Reality. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10090593"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Aljawarneh, I.M., Bellavista, P., Corradi, A., Montanari, R., Foschini, L., and Zanotti, A. (2017, January 3\u20136). Efficient spark-based framework for big geospatial data query processing and analysis. Proceedings of the 2017 IEEE Symposium on Computers and Communications (ISCC), Heraklion, Greece.","DOI":"10.1109\/ISCC.2017.8024633"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1111\/cgf.12932","article-title":"The State of the Art in Cartograms","volume":"35","author":"Nusrat","year":"2016","journal-title":"Comput. Graph. Forum"}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/2\/35\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:34:32Z","timestamp":1759919672000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/2\/35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,23]]},"references-count":57,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["computers14020035"],"URL":"https:\/\/doi.org\/10.3390\/computers14020035","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,23]]}}}