{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T12:24:16Z","timestamp":1769257456087,"version":"3.49.0"},"reference-count":45,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T00:00:00Z","timestamp":1768953600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012645","name":"University of Texas at Dallas","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100012645","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Spatial data has distinctive properties that differentiate it from non-spatial data. One prominent characteristic is spatial autocorrelation (SA). When machine learning techniques are applied for spatial data modeling, they require spatially explicit consideration. If these inherent spatial structures are ignored, models may produce biased predictions. However, integrating this property into the model yields additional spatial insight, thereby enhancing learning and improving predictive accuracy. This study examines spatially explicit K-nearest neighbors (SE-KNN) by integrating SA as a spatially explicit property, \u03bb, into the learning algorithm. The innovation of SE-KNN lies in its alignment with the principle of spatial autocorrelation, as KNN\u2019s core learning assumption\u2014that similar observations tend to have similar outcomes\u2014naturally parallels spatial dependence. The proposed SE-KNN is applied to a house price prediction model using house sales data from Franklin County, Ohio to demonstrate a spatially dependent, data-rich, and real-world problem. The results show that SE-KNN achieved the best prediction accuracy compared to mean of absolute error (MAE) of three other machine learning approaches (i.e., standard KNN, linear regression, and artificial neural networks). The proposed method effectively captures the spatial structures in the housing market and leaves only a trace amount of SA in the residuals.<\/jats:p>","DOI":"10.3390\/ijgi15010046","type":"journal-article","created":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T11:11:17Z","timestamp":1768993877000},"page":"46","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Machine Learning Approach Using Spatially Explicit K-Nearest Neighbors for House Price Predictions"],"prefix":"10.3390","volume":"15","author":[{"given":"Meifang","family":"Chen","sequence":"first","affiliation":[{"name":"School of Economic, Political, and Policy Sciences, The University of Texas at Dallas, Richardson, TX 75080, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4602-0766","authenticated-orcid":false,"given":"Changho","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Economic, Political, and Policy Sciences, The University of Texas at Dallas, Richardson, TX 75080, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4957-1379","authenticated-orcid":false,"given":"Yongwan","family":"Chun","sequence":"additional","affiliation":[{"name":"School of Economic, Political, and Policy Sciences, The University of Texas at Dallas, Richardson, TX 75080, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1080\/13658816.2019.1684500","article-title":"GeoAI: Spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond","volume":"34","author":"Janowicz","year":"2020","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Liu, X., Kounadi, O., and Zurita-Milla, R. (2022). Incorporating spatial autocorrelation in machine learning models using spatial lag and eigenvector spatial filtering features. ISPRS Int. J. Geo-Inf., 11.","DOI":"10.3390\/ijgi11040242"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Nikparvar, B., and Thill, J.C. (2021). Machine learning of spatial data. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10090600"},{"key":"ref_4","first-page":"71","article-title":"GeoAI: Where machine learning and big data converge in GIScience","volume":"20","author":"Li","year":"2020","journal-title":"J. Spat. Inf. Sci."},{"key":"ref_5","first-page":"1887","article-title":"Tobler\u2019s First Law in GeoAI: A spatially explicit deep learning model for terrain feature detection under weak supervision","volume":"111","author":"Li","year":"2021","journal-title":"Ann. Am. Assoc. Geogr."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1111\/gean.12273","article-title":"Spatial models or random forest? Evaluating the use of spatially explicit machine learning methods to predict employment density around new transit stations in Los Angeles","volume":"54","author":"Credit","year":"2022","journal-title":"Geogr. Anal."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e5518","DOI":"10.7717\/peerj.5518","article-title":"Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables","volume":"6","author":"Hengl","year":"2018","journal-title":"PeerJ"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2221","DOI":"10.1007\/s10618-021-00789-x","article-title":"Boosting house price predictions using geo-spatial network embedding","volume":"35","author":"Das","year":"2021","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"265","DOI":"10.3406\/spgeo.1992.3091","article-title":"What is spatial autocorrelation? Reflections on the past 25 years of spatial statistics","volume":"21","author":"Griffith","year":"1992","journal-title":"Espace G\u00e9ogr."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1186\/s40537-021-00516-9","article-title":"A survey on missing data in machine learning","volume":"8","author":"Emmanuel","year":"2021","journal-title":"J. Big Data"},{"key":"ref_11","unstructured":"Hastie, T., Tibshirani, R., James, G., and Witten, D. (2021). An Introduction to Statistical Learning, Springer. [2nd ed.]. Chapter 4."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1111\/tgis.12826","article-title":"Geospatial socio-economic\/demographic data: The existence of spatial autocorrelation mixtures in georeferenced data\u2014Part I","volume":"26","author":"Griffith","year":"2022","journal-title":"Trans. GIS"},{"key":"ref_13","unstructured":"Griffith, D.A. (2016). Spatial autocorrelation and art. Cybergeo, Available online: http:\/\/journals.openedition.org\/cybergeo\/27429."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"11985","DOI":"10.1002\/2017GL075710","article-title":"Estimating ground-level PM2.5 by fusing satellite and station observations: A geo-intelligent deep learning approach","volume":"44","author":"Li","year":"2017","journal-title":"Geophys. Res. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1002\/sam.11440","article-title":"The spatially conscious machine learning model","volume":"13","author":"Kiely","year":"2020","journal-title":"Stat. Anal. Data Min."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1111\/ejss.12687","article-title":"Spatial modelling with Euclidean distance fields and machine learning","volume":"69","author":"Behrens","year":"2018","journal-title":"Eur. J. Soil Sci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chen, L., Ren, C., Li, L., Wang, Y., Zhang, B., Wang, Z., and Li, L. (2019). A comparative assessment of geostatistical, machine learning, and hybrid approaches for mapping topsoil organic carbon content. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8040174"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Foresti, L., Pozdnoukhov, A., Tuia, D., and Kanevski, M. (2010). Extreme precipitation modelling using geostatistics and machine learning algorithms. geoENV VII\u2013Geostatistics for Environmental Applications, Springer.","DOI":"10.1007\/978-90-481-2322-3_4"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1080\/10106049.2019.1595177","article-title":"Geographical random forests: A spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling","volume":"36","author":"Georganos","year":"2021","journal-title":"Geocarto Int."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.eswa.2007.10.005","article-title":"Neural networks and statistical techniques: A review of applications","volume":"36","author":"Paliwal","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Baldominos, A., Blanco, I., Moreno, A.J., Iturrarte, R., Bern\u00e1rdez, \u00d3., and Afonso, C. (2018). Identifying real estate opportunities using machine learning. Appl. Sci., 8.","DOI":"10.20944\/preprints201810.0297.v1"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1080\/13658816.2019.1579333","article-title":"Assessing the performance of 38 machine learning models: The case of land consumption rates in Bavaria, Germany","volume":"33","author":"Hagenauer","year":"2019","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sanjar, K., Bekhzod, O., Kim, J., Paul, A., and Kim, J. (2020). Missing data imputation for geolocation-based price prediction using KNN-MCF method. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9040227"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1109\/TSMC.1976.5408784","article-title":"The Distance-Weighted k-Nearest-Neighbor Rule","volume":"6","author":"Dudani","year":"1976","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_25","first-page":"277","article-title":"Spatially weighted supervised classification for remote sensing","volume":"5","author":"Atkinson","year":"2004","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_26","unstructured":"Shen, X., Lin, Z., Brandt, J., Avidan, S., and Wu, Y. (2012, January 16\u201321). Object retrieval and localization with spatially-constrained similarity measure and k-NN re-ranking. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Providence, RI, USA."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Mohd, T., Jamil, N.S., Johari, N., Abdullah, L., and Masrom, S. (2020). An overview of real estate modelling techniques for house price prediction. Charting a Sustainable Future of ASEAN in Business and Social Sciences, Springer.","DOI":"10.1007\/978-981-15-3859-9_28"},{"key":"ref_28","unstructured":"IBM (2025, November 20). What Is the K-Nearest Neighbors Algorithm?. Available online: https:\/\/www.ibm.com\/think\/topics\/knn."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1620","DOI":"10.1111\/2041-210X.13650","article-title":"Predicting into unknown space? Estimating the area of applicability of spatial prediction models","volume":"12","author":"Meyer","year":"2021","journal-title":"Methods Ecol. Evol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1883","DOI":"10.4249\/scholarpedia.1883","article-title":"K-Nearest Neighbor","volume":"4","author":"Peterson","year":"2009","journal-title":"Scholarpedia"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2311325","DOI":"10.1080\/17538947.2024.2311325","article-title":"Enhancing flood-prone area mapping: Fine-tuning the K-nearest neighbors (KNN) algorithm for spatial modelling","volume":"17","author":"Razavi","year":"2024","journal-title":"Int. J. Digit. Earth"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"10700","DOI":"10.1038\/s41467-024-55240-8","article-title":"Challenges in data-driven geospatial modeling for environmental research and practice","volume":"15","author":"Koldasbayeva","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"18","DOI":"10.56397\/JWE.2023.09.03","article-title":"Housing price prediction using machine learning algorithm","volume":"2","author":"Zhang","year":"2023","journal-title":"J. World Econ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"46","DOI":"10.5815\/ijmecs.2020.06.04","article-title":"House price prediction using a machine learning model: A survey of literature","volume":"12","author":"Zulkifley","year":"2020","journal-title":"Int. J. Mod. Educ. Comput. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"114590","DOI":"10.1016\/j.eswa.2021.114590","article-title":"Machine learning with explainability or spatial hedonics tools? An analysis of asking prices in Alicante, Spain","volume":"171","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.jeem.2012.12.001","article-title":"Does cleanup of hazardous waste sites raise housing values? Evidence of spatially localized benefits","volume":"65","author":"Timmins","year":"2013","journal-title":"J. Environ. Econ. Manag."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1002\/sam.11583","article-title":"Optimal ratio for data splitting","volume":"15","author":"Joseph","year":"2022","journal-title":"Stat. Anal. Data Min."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4540","DOI":"10.1038\/s41467-020-18321-y","article-title":"Spatial validation reveals poor predictive performance of large-scale ecological mapping models","volume":"11","author":"Ploton","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"109885","DOI":"10.1016\/j.petrol.2021.109885","article-title":"Fair train-test split in machine learning: Mitigating spatial autocorrelation for improved prediction accuracy","volume":"209","author":"Salazar","year":"2022","journal-title":"J. Pet. Sci. Eng."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2208","DOI":"10.1038\/s41467-022-29838-9","article-title":"Machine learning-based global maps of ecological variables and the challenge of assessing them","volume":"13","author":"Meyer","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1111\/gean.12423","article-title":"Geographical Gaussian process regression: A spatial machine-learning model based on spatial similarity","volume":"57","author":"Jiao","year":"2025","journal-title":"Geogr. Anal."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"193","DOI":"10.3844\/ajassp.2004.193.201","article-title":"House price prediction: Hedonic price model vs. artificial neural network","volume":"1","author":"Limsombunc","year":"2004","journal-title":"Am. J. Appl. Sci."},{"key":"ref_43","unstructured":"Yazdani, M. (2021). Machine learning, deep learning, and hedonic methods for real estate price prediction. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1007\/s00168-021-01101-x","article-title":"Spatial machine learning: New opportunities for regional science","volume":"68","author":"Kopczewska","year":"2022","journal-title":"Ann. Reg. Sci."},{"key":"ref_45","unstructured":"Bellman, R.E. (1957). Dynamic Programming, Princeton University Press. 6th Printing."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/15\/1\/46\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T05:12:41Z","timestamp":1769145161000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/15\/1\/46"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,21]]},"references-count":45,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["ijgi15010046"],"URL":"https:\/\/doi.org\/10.3390\/ijgi15010046","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,21]]}}}