{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T05:07:50Z","timestamp":1779167270571,"version":"3.51.4"},"reference-count":46,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T00:00:00Z","timestamp":1778544000000},"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>\u201cLaws\u201d of geography such as Tobler\u2019s First Law (spatial autocorrelation) and Zhu\u2019s Third Law (environmental similarity) offer fundamental principles for spatial prediction and mapping, yet their implications for digital soil mapping (DSM) are often opaque because the underlying principles and mechanisms of DSM models are rarely inspectable in typical DSM workflows. This study presents an interactive geovisualization portal that demystifies Tobler\u2019s Law, Zhu\u2019s Law, and a combined formulation in spatial prediction processes, using soil organic matter (SOM) concentration prediction in Xuancheng, China, as a case study. The portal integrates multiple DSM frameworks that operationalize two geographic laws\u2014inverse distance weighting (IDW), individual predictive soil mapping (iPSM), an iPSM-IDW hybrid, ordinary kriging (OK), and regression kriging (RK)\u2014and couples them with user-configurable parameters such as neighborhood size, distance-decay factor, and variogram model. The portal provides coordinated, interactive views that link SOM predictions to dynamic map and diagnostic statistical charts for explaining location-level predictions, visualizing the manifestation of geographic laws in constructing local predictions, examining weight allocation patterns, and assessing overall prediction accuracy. Additionally, a built-in sensitivity analysis enables users to investigate and understand the effects of varying the geographic law, modeling framework, and modeling parameters on prediction results. This geovisualization portal advances interpretable DSM by rendering its underlying geographic principles, model mechanics, and parameter influences visually inspectable.<\/jats:p>","DOI":"10.3390\/ijgi15050212","type":"journal-article","created":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:04:36Z","timestamp":1778601876000},"page":"212","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Demystifying Geographic \u201cLaws\u201d for Soil Mapping via Interactive Geovisualization"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7064-2138","authenticated-orcid":false,"given":"Guiming","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Geography & the Environment, University of Denver, Denver, CO 80210, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1016\/j.scitotenv.2019.02.420","article-title":"Assessing soil organic carbon stock of Wisconsin, USA and its fate under future land use and climate change","volume":"667","author":"Adhikari","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/B978-0-12-405942-9.00001-3","article-title":"Digital Mapping of Soil Carbon","volume":"Volume 118","author":"Minasny","year":"2013","journal-title":"Advances in Agronomy"},{"key":"ref_3","first-page":"81","article-title":"Global soil carbon: Understanding and managing the largest terrestrial carbon pool","volume":"5","author":"Scharlemann","year":"2014","journal-title":"Carbon Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5194\/essd-9-1-2017","article-title":"WoSIS: Providing standardised soil profile data for the world","volume":"9","author":"Batjes","year":"2017","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"299","DOI":"10.5194\/essd-12-299-2020","article-title":"Standardised soil profile data to support global mapping and modelling (WoSIS snapshot 2019)","volume":"12","author":"Batjes","year":"2020","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0016-7061(03)00223-4","article-title":"On digital soil mapping","volume":"117","author":"McBratney","year":"2003","journal-title":"Geoderma"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.geoderma.2015.07.017","article-title":"Digital soil mapping: A brief history and some lessons","volume":"264","author":"Minasny","year":"2016","journal-title":"Geoderma"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"103359","DOI":"10.1016\/j.earscirev.2020.103359","article-title":"Machine learning for digital soil mapping: Applications, challenges and suggested solutions","volume":"210","author":"Wadoux","year":"2020","journal-title":"Earth-Sci. Rev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1080\/19475683.2024.2324381","article-title":"iSoLIM: A similarity-based spatial prediction software for the big data era","volume":"30","author":"Zhao","year":"2024","journal-title":"Ann. GIS"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.geoderma.2015.04.018","article-title":"CyberSoLIM: A cyber platform for digital soil mapping","volume":"263","author":"Jiang","year":"2016","journal-title":"Geoderma"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1080\/19475683.2022.2030943","article-title":"Commentary: General principles and analytical frameworks in geography and GIScience","volume":"28","author":"Goodchild","year":"2022","journal-title":"Ann. GIS"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1111\/j.1467-8306.2004.09402008.x","article-title":"The validity and usefulness of laws in geographic information science and geography","volume":"94","author":"Goodchild","year":"2004","journal-title":"Ann. Assoc. Am. Geogr."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"234","DOI":"10.2307\/143141","article-title":"A computer movie simulating urban growth in the Detroit region","volume":"46","author":"Tobler","year":"1970","journal-title":"Econ. Geogr."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Diggle, P.J., and Ribeiro, P.J. (2007). Model-Based Geostatistics, Springer.","DOI":"10.1007\/978-0-387-48536-2"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.geoderma.2003.08.018","article-title":"A generic framework for spatial prediction of soil variables based on regression-kriging","volume":"120","author":"Hengl","year":"2004","journal-title":"Geoderma"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation, Oxford University Press.","DOI":"10.1093\/oso\/9780195115383.001.0001"},{"key":"ref_17","unstructured":"Isaaks, E.H., and Srivastava, R.M. (1989). An Introduction to Applied Geostatistics, Oxford University Press."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1080\/19475683.2018.1534890","article-title":"Spatial prediction based on Third Law of Geography","volume":"24","author":"Zhu","year":"2018","journal-title":"Ann. GIS"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1080\/19475683.2022.2026467","article-title":"How is the Third Law of Geography different?","volume":"28","author":"Zhu","year":"2022","journal-title":"Ann. GIS"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1463","DOI":"10.2136\/sssaj2001.6551463x","article-title":"Soil mapping using GIS, expert knowledge, and fuzzy logic","volume":"65","author":"Zhu","year":"2001","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1111\/ejss.12244","article-title":"Predictive soil mapping with limited sample data","volume":"66","author":"Zhu","year":"2015","journal-title":"Eur. J. Soil Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"116683","DOI":"10.1016\/j.geoderma.2023.116683","article-title":"Large-area soil mapping based on environmental similarity with adaptive consideration of spatial distance to samples","volume":"439","author":"Fan","year":"2023","journal-title":"Geoderma"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"638","DOI":"10.1016\/S1002-0160(20)60016-9","article-title":"Soil property mapping by combining spatial distance information into the Soil Land Inference Model (SoLIM)","volume":"31","author":"Qin","year":"2021","journal-title":"Pedosphere"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Guo, P.-T., Li, W.-T., Li, M.-F., Yan, P.-S., Liu, Y., and Zhao, J. (2026). The iPSM-SD Framework: Enhancing Predictive Soil Mapping for Precision Agriculture Through Spatial Proximity Integration. Agronomy, 16.","DOI":"10.3390\/agronomy16020231"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/S0924-2716(02)00167-3","article-title":"Geovisualization illustrated","volume":"57","author":"Kraak","year":"2003","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1559\/152304001782173970","article-title":"Research Challenges in Geovisualization","volume":"28","author":"MacEachren","year":"2001","journal-title":"Cartogr. Geogr. Inf. Sci."},{"key":"ref_27","first-page":"115","article-title":"Persistent challenges in geovisualization\u2013a community perspective","volume":"3","author":"Bleisch","year":"2017","journal-title":"Int. J. Cartogr."},{"key":"ref_28","unstructured":"Geostokos (Ecosse) Limited (Kriging Game Teaching Software, 2019). Kriging Game Teaching Software, version 2009."},{"key":"ref_29","unstructured":"Walvoort, D. (2004). E{Z}-Kriging: Exploring the World of Ordinary Kriging, Wageningen University & Research."},{"key":"ref_30","first-page":"59","article-title":"Interactive maps: What we know and what we need to know","volume":"2013","author":"Roth","year":"2013","journal-title":"J. Spat. Inf. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1080\/15230406.2023.2293891","article-title":"A web-based geovisualization framework for exploratory analysis of individual VGI contributor\u2019s participation characteristics","volume":"52","author":"Zhang","year":"2025","journal-title":"Cartogr. Geogr. Inf. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1080\/19475683.2025.2497026","article-title":"Exploring social interaction patterns and drivers in VGI communities using a custom geovisual analytics tool","volume":"31","author":"Zhang","year":"2025","journal-title":"Ann. GIS"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Hengl, T., De Jesus, J.M., Heuvelink, G.B.M., Gonzalez, M.R., Kilibarda, M., Blagoti\u0107, A., Shangguan, W., Wright, M.N., Geng, X., and Bauer-Marschallinger, B. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0169748"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"217","DOI":"10.5194\/soil-7-217-2021","article-title":"SoilGrids 2.0: Producing soil information for the globe with quantified spatial uncertainty","volume":"7","author":"Poggio","year":"2021","journal-title":"Soil"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1016\/j.cageo.2007.05.001","article-title":"About regression-kriging: From equations to case studies","volume":"33","author":"Hengl","year":"2007","journal-title":"Comput. Geosci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.geoderma.2016.06.033","article-title":"Mapping soil organic matter concentration at different scales using a mixed geographically weighted regression method","volume":"281","author":"Zeng","year":"2016","journal-title":"Geoderma"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1007\/s10707-022-00461-6","article-title":"Spatial regression graph convolutional neural networks: A deep learning paradigm for spatial multivariate distributions","volume":"27","author":"Zhu","year":"2023","journal-title":"Geoinformatica"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.geoderma.2015.12.009","article-title":"An heuristic uncertainty directed field sampling design for digital soil mapping","volume":"267","author":"Zhang","year":"2016","journal-title":"Geoderma"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"637","DOI":"10.2136\/sssaj2015.08.0285","article-title":"Evaluation of Integrative Hierarchical Stepwise Sampling for Digital Soil Mapping","volume":"80","author":"Yang","year":"2016","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1016\/S1002-0160(17)60322-9","article-title":"Regional Soil Mapping Using Multi-Grade Representative Sampling and a Fuzzy Membership-Based Mapping Approach","volume":"27","author":"Yang","year":"2017","journal-title":"Pedosphere"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Cressie, N. (1993). Statistics for Spatial Data, John Wiley & Sons.","DOI":"10.1002\/9781119115151"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2505","DOI":"10.5194\/gmd-15-2505-2022","article-title":"SciKit-GStat 1.0: A SciPy-flavored geostatistical variogram estimation toolbox written in Python","volume":"15","year":"2022","journal-title":"Geosci. Model Dev."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1774","DOI":"10.1080\/10095020.2024.2439371","article-title":"Spatial distribution pattern analysis using variograms over geographic and feature space","volume":"28","author":"Zhao","year":"2025","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1396","DOI":"10.1111\/tgis.12730","article-title":"PyCLiPSM: Harnessing heterogeneous computing resources on CPUs and GPUs for accelerated digital soil mapping","volume":"25","author":"Zhang","year":"2021","journal-title":"Trans. GIS"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"e00627","DOI":"10.1016\/j.geodrs.2023.e00627","article-title":"Improving digital soil mapping in Bogor, Indonesia using parent material information","volume":"33","author":"Cahyana","year":"2023","journal-title":"Geoderma Reg."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1111\/ejss.12790","article-title":"Pedology and digital soil mapping (DSM)","volume":"70","author":"Ma","year":"2019","journal-title":"Eur. J. Soil Sci."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/15\/5\/212\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T04:17:47Z","timestamp":1779164267000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/15\/5\/212"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,12]]},"references-count":46,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["ijgi15050212"],"URL":"https:\/\/doi.org\/10.3390\/ijgi15050212","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,12]]}}}