{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T06:57:51Z","timestamp":1764053871677,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T00:00:00Z","timestamp":1692835200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The availability of spatial and spatiotemporal big data is increasing rapidly. Spatially and temporally high resolved data are especially gathered via the Internet of Things. This data can often be accessed as data streams that push new data tuples continuously and make the data available in real time. Such real-time spatiotemporal data have great potential for new analysis approaches based on modern data processing technologies. The ability to retrieve spatial big data in real time, as well as process it in real time, demands new analysis methodologies that catch up with the instantaneous and continuous character of today\u2019s spatiotemporal data. In this work, we present an evaluation of a high-frequent dynamic spatiotemporal autocorrelation approach. This approach allows for geostatistical analysis of streaming spatiotemporal data in real time and can provide insights into spatiotemporal processes while they are still ongoing. To evaluate this new approach, it was applied to mobility data from New York City. The results show that a high-frequent dynamic spatiotemporal autocorrelation approach provides comparable and meaningful results. In this way, high-frequent geostatistical analyses in real time can become an addition to retrospective analyses based on historical data.<\/jats:p>","DOI":"10.3390\/ijgi12090350","type":"journal-article","created":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T10:23:40Z","timestamp":1692872620000},"page":"350","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Geostatistics on Real-Time Geodata Streams\u2014High-Frequent Dynamic Autocorrelation with an Extended Spatiotemporal Moran\u2019s I Index"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3722-5284","authenticated-orcid":false,"given":"Thomas","family":"Lemmerz","sequence":"first","affiliation":[{"name":"Geodetic Institute and Chair for Computing in Civil Engineering & Geo Information Systems, RWTH Aachen University, 52074 Aachen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7995-8593","authenticated-orcid":false,"given":"Stefan","family":"Herl\u00e9","sequence":"additional","affiliation":[{"name":"Geodetic Institute and Chair for Computing in Civil Engineering & Geo Information Systems, RWTH Aachen University, 52074 Aachen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5700-8818","authenticated-orcid":false,"given":"J\u00f6rg","family":"Blankenbach","sequence":"additional","affiliation":[{"name":"Geodetic Institute and Chair for Computing in Civil Engineering & Geo Information Systems, RWTH Aachen University, 52074 Aachen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7069","DOI":"10.1007\/s12665-015-4243-8","article-title":"Design and development of an online geoinformation service delivery of geospatial models in the United Kingdom","volume":"74","author":"Shi","year":"2015","journal-title":"Environ. Earth Sci."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Gali\u0107, Z. (2016). Spatio-Temporal Data Streams, Springer.","DOI":"10.1007\/978-1-4939-6575-5"},{"key":"ref_3","unstructured":"Evans, M.R., Oliver, D., Zhou, X., and Shekhar, S. (2014). Spatial Big Data: Case Studies on Volume, Velocity, and Variety, CRC Press."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1111\/j.1538-4632.2008.00727.x","article-title":"A History of the Concept of Spatial Autocorrelation: A Geographer\u2019s Perspective","volume":"40","author":"Getis","year":"2008","journal-title":"Geogr. Anal."},{"key":"ref_5","first-page":"187","article-title":"The Saddlepoint Approximation of Moran\u2019s I\u2019s and Local Moran\u2019s I i\u2019s Reference Distributions and Their Numerical Evaluation","volume":"34","author":"Tiefelsdorf","year":"2002","journal-title":"Geogr. Anal."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Lemmerz, T., Herl\u00e9, S., and Blankenbach, J. (2023). Geostatistics on Real-Time Geodata Streams\u2014An Extended Spatiotemporal Moran\u2019s I Index with Distributed Stream Processing Technologies. ISPRS Int. J. Geo-Inf., 12.","DOI":"10.3390\/ijgi12030087"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1080\/10095020.2019.1643609","article-title":"Measuring spatio-temporal autocorrelation in time series data of collective human mobility","volume":"22","author":"Gao","year":"2019","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_8","unstructured":"New York City Taxi and Limousine Commission (2023, August 23). TLC Trip Record Data. 2018. New York City Taxi and Limousine Commission Website, Available online: https:\/\/www1.nyc.gov\/site\/tlc\/about\/tlc-trip-record-data.page."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1007\/s10109-011-0149-5","article-title":"Spatio-temporal autocorrelation of road network data","volume":"14","author":"Cheng","year":"2012","journal-title":"J. Geogr. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1080\/13658816.2019.1667501","article-title":"Spatio-temporal regression kriging for modelling urban NO 2 concentrations","volume":"34","author":"Osei","year":"2020","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.envint.2017.05.005","article-title":"Mapping urban air quality in near real-time using observations from low-cost sensors and model information","volume":"106","author":"Schneider","year":"2017","journal-title":"Environ. Int."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Lorkowski, P., and Brinkhoff, T. (2015, January 7\u20139). Environmental monitoring of continuous phenomena by sensor data streams: A system approach based on Kriging. Proceedings of the EnviroInfo and ICT for Sustainability 2015, Copenhagen, Denmark. Advances in Computer Science Research.","DOI":"10.2991\/ict4s-env-15.2015.4"},{"key":"ref_13","unstructured":"Kazemitabar, S.J. (2013, January 5). Spatiotemporal transformation of social media geostreams. Proceedings of the 4th ACM SIGSPATIAL International Workshop on GeoStreaming, Orlando, FL, USA."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Laska, M., Herle, S., Klamma, R., and Blankenbach, J. (2018). A Scalable Architecture for Real-Time Stream Processing of Spatiotemporal IoT Stream Data\u2014Performance Analysis on the Example of Map Matching. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7070238"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.cageo.2016.03.004","article-title":"Stream Kriging: Incremental and recursive ordinary Kriging over spatiotemporal data streams","volume":"90","author":"Zhong","year":"2016","journal-title":"Comput. Geosci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1093\/biomet\/37.1-2.17","article-title":"Notes on Continuous Stochastic Phenomena","volume":"37","author":"Moran","year":"1950","journal-title":"Biometrika"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1111\/j.1538-4632.1995.tb00338.x","article-title":"Local Indicators of Spatial Association-LISA","volume":"27","author":"Anselin","year":"1995","journal-title":"Geogr. Anal."},{"key":"ref_18","first-page":"115","article-title":"The Contiguity Ratio and Statistical Mapping","volume":"5","author":"Geary","year":"1954","journal-title":"Inc. Stat."},{"key":"ref_19","first-page":"137","article-title":"A Two-Variate Gamma Type Distribution","volume":"5","author":"Kibble","year":"1941","journal-title":"Sankhy\u0101 Indian J. Stat."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1111\/j.1538-4632.1992.tb00261.x","article-title":"The Analysis of Spatial Association by Use of Distance Statistics","volume":"24","author":"Getis","year":"1992","journal-title":"Geogr. Anal."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"255","DOI":"10.2307\/3212829","article-title":"The second-order analysis of stationary point processes","volume":"13","author":"Ripley","year":"1976","journal-title":"J. Appl. Probab."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1111\/0022-4146.00224","article-title":"Testing for Local Spatial Autocorrelation in the Presence of Global Autocorrelation","volume":"41","author":"Ord","year":"2001","journal-title":"J. Reg. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1246","DOI":"10.2113\/gsecongeo.58.8.1246","article-title":"Principles of geostatistics","volume":"58","author":"Matheron","year":"1963","journal-title":"Econ. Geol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1111\/gean.12069","article-title":"Measuring Spatial Autocorrelation of Vectors","volume":"47","author":"Liu","year":"2015","journal-title":"Geogr. Anal."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1111\/j.1435-5957.2011.00402.x","article-title":"A spatio-temporal measure of spatial dependence: An example using real estate data*","volume":"92","author":"Legros","year":"2013","journal-title":"Pap. Reg. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1111\/gean.12106","article-title":"Extending Moran\u2019s Index for Measuring Spatiotemporal Clustering of Geographic Events","volume":"49","author":"Lee","year":"2017","journal-title":"Geogr. Anal."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s12061-011-9065-9","article-title":"Two Phase Temporal-Spatial Autocorrelation of Urban Patterns: Revealing Focal Areas of Re-Urbanization in Tel Aviv-Yafo","volume":"5","author":"Porat","year":"2012","journal-title":"Appl. Spat. Anal. Policy"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s11634-006-0004-6","article-title":"Adaptive dissimilarity index for measuring time series proximity","volume":"1","author":"Douzal","year":"2007","journal-title":"Adv. Data Anal. Classif."},{"key":"ref_29","unstructured":"Anselin, L., Unwin, D.J., Scholten, H.J., and Fischer, M.M. (1996). Spatial Analytical Perspectives on GIS, Taylor & Francis."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/9\/350\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:38:04Z","timestamp":1760128684000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/9\/350"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,24]]},"references-count":29,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["ijgi12090350"],"URL":"https:\/\/doi.org\/10.3390\/ijgi12090350","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2023,8,24]]}}}