{"id":"https://openalex.org/W2128959474","doi":null,"title":"Spatial Statistics and Geostatistics: Theory and Applications for Geographic Information Science and Technology","display_name":"Spatial Statistics and Geostatistics: Theory and Applications for Geographic Information Science and Technology","publication_year":2013,"publication_date":"2013-01-30","ids":{"openalex":"https://openalex.org/W2128959474","mag":"2128959474"},"language":"en","primary_location":{"id":"pmh:oai:aleph.bib-bvb.de:BVB01-024726957","doi":null,"is_oa":false,"landing_page_url":"https://bvbr.bib-bvb.de:443/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024726957&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5066484698","display_name":"Yongwan Chun","orcid":"https://orcid.org/0000-0002-4957-1379","observed_orcids":["https://orcid.org/0000-0002-4957-1379"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yongwan Chun","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-4957-1379","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5047557932","display_name":"Daniel A. Griffith","orcid":"https://orcid.org/0000-0001-5125-6450","observed_orcids":["https://orcid.org/0000-0001-5125-6450"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Daniel A. Griffith","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-5125-6450","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":18.1064,"has_fulltext":false,"cited_by_count":75,"citation_normalized_percentile":{"value":0.99020341,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":92,"max":99},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11911","display_name":"Spatial and Panel Data Analysis","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11911","display_name":"Spatial and Panel Data Analysis","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10770","display_name":"Soil Geostatistics and Mapping","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/1111","display_name":"Soil Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11830","display_name":"Point processes and geometric inequalities","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/spatial-statistics","display_name":"spatial statistics","score":0.9520000219345093},{"id":"https://openalex.org/keywords/geostatistics","display_name":"geostatistics","score":0.9980000257492065},{"id":"https://openalex.org/keywords/spatial-autocorrelation","display_name":"spatial autocorrelation","score":0.9929999709129333},{"id":"https://openalex.org/keywords/moran-s-i","display_name":"Moran's I","score":0.6759999990463257},{"id":"https://openalex.org/keywords/join-count-statistics","display_name":"join-count statistics","score":0.7739999890327454},{"id":"https://openalex.org/keywords/spatial-sampling-design","display_name":"spatial sampling design","score":0.7300000190734863},{"id":"https://openalex.org/keywords/spatial-heterogeneity","display_name":"spatial heterogeneity","score":0.6940000057220459},{"id":"https://openalex.org/keywords/anisotropy","display_name":"anisotropy","score":0.8420000076293945},{"id":"https://openalex.org/keywords/spatial-econometrics","display_name":"spatial econometrics","score":0.8560000061988831},{"id":"https://openalex.org/keywords/hot-spots","display_name":"hot spots","score":0.6240000128746033},{"id":"https://openalex.org/keywords/spatially-varying-coefficients","display_name":"spatially varying coefficients","score":0.7820000052452087},{"id":"https://openalex.org/keywords/kriging","display_name":"kriging","score":0.859000027179718},{"id":"https://openalex.org/keywords/spatial-interpolation","display_name":"spatial interpolation","score":0.5370000004768372},{"id":"https://openalex.org/keywords/spatial-autoregressive-model","display_name":"spatial autoregressive model","score":0.6060000061988831},{"id":"https://openalex.org/keywords/eigenvector-spatial-filtering","display_name":"eigenvector spatial filtering","score":0.9160000085830688},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.8939999938011169},{"id":"https://openalex.org/keywords/monte-carlo-simulation","display_name":"Monte Carlo simulation","score":0.781000018119812}],"concepts":[{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.7580608129501343},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.6953442692756653},{"id":"https://openalex.org/C125572338","wikidata":"https://www.wikidata.org/wiki/Q1440020","display_name":"Geostatistics","level":3,"score":0.6189558506011963},{"id":"https://openalex.org/C81692654","wikidata":"https://www.wikidata.org/wiki/Q225926","display_name":"Kriging","level":2,"score":0.5286393761634827},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5249823927879333},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4762601852416992},{"id":"https://openalex.org/C154881674","wikidata":"https://www.wikidata.org/wiki/Q2269270","display_name":"Variogram","level":3,"score":0.46912145614624023},{"id":"https://openalex.org/C138695830","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial dependence","level":2,"score":0.4512481093406677},{"id":"https://openalex.org/C94747663","wikidata":"https://www.wikidata.org/wiki/Q7574086","display_name":"Spatial variability","level":2,"score":0.3794565200805664},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.33838558197021484},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.301695853471756},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:aleph.bib-bvb.de:BVB01-024726957","doi":null,"is_oa":false,"landing_page_url":"https://bvbr.bib-bvb.de:443/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024726957&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:2128959474","doi":null,"is_oa":false,"landing_page_url":"https://ci.nii.ac.jp/ncid/BB12841035","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7114643921","https://openalex.org/W1634842813","https://openalex.org/W2004227744","https://openalex.org/W4244467391","https://openalex.org/W1498801991","https://openalex.org/W7095290494","https://openalex.org/W7095592163","https://openalex.org/W7125793679","https://openalex.org/W213793520","https://openalex.org/W7006036212"],"abstract_inverted_index":{"About":[0],"the":[1,38,69,134],"Authors":[2],"Preface":[3],"Introduction":[4],"Spatial":[5,11,15,43,47,57,60,101,112,116,156,169,171,188,226,239,263,270,282,294,297,301,304,316,321,347,363],"Statistics":[6,31,229,317],"and":[7,26,28,37,103,114,119],"Geostatistics":[8,244],"R":[9,52,107,178,206,233,275,307,369],"Basics":[10],"Autocorrelation":[12,44,48,102,172],"Indices":[13,224],"Measuring":[14],"Dependency":[16],"Important":[17],"Properties":[18,67],"of":[19,42,68,225,361],"MC":[20,23,27],"Relationships":[21],"Between":[22],"And":[24,186,214,241,245],"GR,":[25],"Join":[29],"Count":[30],"Graphic":[32],"Portrayals:":[33],"The":[34,97,99,291],"Moran":[35],"Scatterplot":[36],"Semi-variogram":[39,248],"Plot":[40],"Impacts":[41],"Testing":[45,122,143,218],"for":[46,54,109,123,144,161,166,180,208,277,309,320,371],"in":[49],"Regression":[50,185,191,193,195,198],"Residuals":[51],"Code":[53,108,179,207,234,276,308,370],"Concept":[55,110,181,209,236,278,310,372],"Implementations":[56,111,182,210,237,279,311,373],"Sampling":[58,61,71,76,84],"Selected":[59,70,328],"Designs":[62],"Puerto":[63,329],"Rico":[64,330],"DEM":[65,251],"Data":[66,96,222,259,268,322],"Designs:":[72],"Simulation":[73,77,85,335],"Experiment":[74,340,352],"Results":[75],"Experiments":[78,86,336],"On":[79,87],"A":[80,88,136,203,296,337,349,365],"Unit":[81],"Square":[82],"Landscape":[83,90,155],"Hexagonal":[89],"Structure":[91],"Resampling":[92],"Techniques:":[93],"Reusing":[94],"Sampled":[95],"Bootstrap":[98],"Jackknife":[100],"Effective":[104],"Sample":[105],"Size":[106],"Composition":[113],"Configuration":[115],"Heterogeneity:":[117,170],"Mean":[118],"Variance":[120,240],"ANOVA":[121],"Heterogeneity":[124,142,145],"Over":[125,146],"a":[126,131,147,153,254,261,346,357],"Plane:":[127,148],"Regional":[128,173],"Supra-Partitionings":[129,150],"Establishing":[130],"Relationship":[132],"to":[133,367],"Superpopulation":[135],"Null":[137],"Hypothesis":[138],"Rejection":[139],"Case":[140],"With":[141,243],"Directional":[149,175],"Covariates":[151],"Across":[152],"Geographic":[154,162,167,199,201,267],"Weights":[157,159,164],"Matrices":[158,160,165],"Distributions":[163,200],"Flows":[168],"Differences":[174],"Differences:":[176],"Anisotropy":[177],"Spatially":[183,230],"Adjusted":[184],"Related":[187,246],"Econometrics":[189],"Linear":[190,264],"Nonlinear":[192],"Binomial/Logistic":[194],"Poisson/Negative":[196],"Binomial":[197],"Flows:":[202],"Journey-To-Work":[204],"Example":[205],"Local":[211,223],"Statistics:":[212],"Hot":[213],"Cold":[215],"Spots":[216],"Multiple":[217],"with":[219],"Positively":[220],"Correlated":[221],"Association":[227],"Getis-Ord":[228],"Varying":[231],"Coefficients":[232],"For":[235,281],"Analyzing":[238],"Covariance":[242],"Techniques":[247,327],"Models":[249],"Co-kriging":[250],"Elevation":[252],"as":[253,260],"Covariate":[255,262],"Landsat":[256],"7":[257],"ETM+":[258],"Operators":[265],"Multivariate":[266],"Eigenvector":[269,300,342,354],"Filtering:":[271,302],"Correlation":[272],"Coefficient":[273],"Decomposition":[274],"Methods":[280,319],"Interpolation":[283],"In":[284,315],"Two":[285],"Dimensions":[286],"Kriging:":[287],"An":[288],"Algebraic":[289],"Basis":[290],"EM":[292,298,305],"Algorithm":[293,299,306],"Autoregression:":[295],"Another":[303],"More":[312],"Advanced":[313],"Topics":[314],"Bayesian":[318],"Markov":[323],"Chain":[324],"Monte":[325,333,338,350],"Carlo":[326,334,339,351],"Examples":[331],"Designing":[332],"Investigating":[341,353],"Selection":[343,355],"when":[344],"Constructing":[345],"Filter":[348],"from":[356],"Restricted":[358],"Candidate":[359],"Set":[360],"Vectors":[362],"Error:":[364],"Contributor":[366],"Uncertainty":[368],"References":[374],"Index":[375]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":6},{"year":2016,"cited_by_count":12},{"year":2015,"cited_by_count":6},{"year":2014,"cited_by_count":4},{"year":2013,"cited_by_count":3}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2016-06-24T00:00:00"}