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This is the case because of a concern for the long-term health of the earth and the obvious negative impacts of past and present human activities. Land use planning and management efforts have recognized this broader context and accordingly have devoted much care and attention to operational-level planning support. Spatial restrictions have long been recognized as central to limiting local impacts as well as ensuring landscape shape and structure irregularity. Unfortunately, planning to meet spatial restrictions may be disrupted, by fire, pests, or even on-the-ground conditions. For example, what if a fire destroys resources in a management unit that are adjacent to a unit(s) scheduled for harvest. In fact, this new opening\u002Fdisruption may prevent the planned activity of any of its neighboring units. Disruptions do occur, but have rarely been addressed in any meaningful way in planning optimization problems. This paper details spatial optimization approaches to support better understanding of the range of potential outcomes when disruption and uncertainty are taken into account in land use planning involving forest resources. Application results highlight the significance of handling disruption risk and spatial data uncertainty, indicating that identifying and selecting planning alternatives that are consistent with goals and intended outcomes are a difficult task. However, improved modeling approaches are possible that better support land use decision making.",{"EN":118},"Addressing risks and uncertainty in forest land use modeling",{"VOID":120},"[]",{"VOID":122},"California Board of Forestry and Fire Protection (2018) California Forest Practice Rules 2018. http:\u002F\u002Fbofdata.fire.ca.gov\u002F. Accessed 13 Dec 2018\nChurch RL, Murray AT (2009) Business site selection, location analysis, and GIS. Wiley, Hoboken, NJ\nChurch RL, Niblett MR, Gerrard RA (2015a) Modeling the potential for critical habitat. In: Eiselt HA, Marianov V (eds) Applications of location analysis. Springer, New York, pp 155–171\nChurch RL, Niblett MR, O’Hanley J, Middleton R, Barber K (2015b) Saving the forest by reducing fire severity: selective fuels treatment location and scheduling. In: Eiselt HA, Marianov V (eds) Applications of location analysis. Springer, New York, pp 173–190\nCohon JL (2004) Multiobjective programming and planning. Dover, New York\nDowns JA, Gates RJ, Murray AT (2008) Estimating carrying capacity for sandhill cranes using habitat suitability and spatial optimization models. Ecol Model 214(2):284–292\nErkut E, ReVelle C, Ulkusal Y (1996) Integer-friendly formulations for the r-separation problem. Eur J Oper Res 92(2):342–351\nGoycoolea M, Murray AT, Barahona F, Epstein R, Weintraub A (2005) Harvest scheduling subject to maximum area restrictions: exploring exact approaches. Oper Res 53(3):490–500\nGrubesic TH, Murray AT (2008) Sex offender residency and spatial equity. Appl Spat Anal Policy 1(3):175–192\nGrubesic TH, Murray AT, Pridemore WA, Tabb LP, Liu Y, Wei R (2012) Alcohol beverage control, privatization and the geographic distribution of alcohol outlets. BMC Public Health 12(1):1015\nHoward JL, Jones KC (2016) U.S. Timber Production, Trade, Consumption, and Price Statistics, 1965–2013. USDA Forest Service Research Paper. FPL–RP–679. U.S. Department of Agriculture, Forest Service, Forest Products Laboratory, Madison, WI\nJones JG, Meneghin BJ, Kirby MW (1991) Formulating adjacency constraints in linear optimization models for scheduling projects in tactical planning. For Sci 37(5):1283–1297\nKirby MW, Hager WA, Wong P (1986) Simultaneous planning of wildland management and transportation alternatives. TIMS Stud Manag Sci 21:371–387\nMoon ID, Chaudhry SS (1984) An analysis of network location problems with distance constraints. Manag Sci 30(3):290–307\nMurray AT (1999) Spatial restrictions in harvest scheduling. For Sci 45(1):45–52\nMurray AT, Church RL (1995) Heuristic solution approaches to operational forest planning problems. OR Spectr 17(2):193–203\nMurray AT, Church RL (1996) Analyzing cliques for imposing adjacency restrictions in forest models. For Sci 42(2):166–175\nMurray AT, Grubesic TH (2012) Spatial optimization and geographic uncertainty: implications for sex offender management strategies. In: Johnson M (ed) Community-based operations research. Springer, New York, pp 121–142\nMurray AT, Kim H (2008) Efficient identification of geographic restriction conditions in anti-covering location models using GIS. Lett Spat Resource Sci 1(2):159–169\nMurray AT, Weintraub A (2002) Scale and unit specification influences in harvest scheduling with maximum area restrictions. For Sci 48(4):779–789\nNaderializadeh N, Crowe KA (2018) Formulating the integrated forest harvest-scheduling model to reduce the cost of the road-networks. Oper Res Int J. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12351-018-0410-5\nNelson J, Brodie JD (1990) Comparison of a random search algorithm and mixed integer programming for solving area-based forest plans. Can J For Res 20(7):934–942\nNemhauser GL, Trotter LE (1975) Vertex packings: structural properties and algorithms. Math Program 8(1):232–248\nNiblett MR, Church RL (2015) The disruptive anti-covering location problem. Eur J Oper Res 247(3):764–773\nOregon Department of Forestry (2018) Oregon Forest Practices Act. http:\u002F\u002Fwww.oregon.gov\u002FODF\u002FWorking\u002FPages\u002FFPA.aspx. Accessed 12 Dec 2018\nPadberg MW (1973) On the facial structure of set packing polyhedra. Math Program 5(1):199–215\nRadoux J, Defourny P (2007) A quantitative assessment of boundaries in automated forest stand delineation using very high resolution imagery. Remote Sens Environ 110(4):468–475\nRonnqvist M, D’Amours S, Weintraub A, Jofre A, Gunn E, Haight RG, Martell D, Murray AT, Romero C (2015) Operations research challenges in forestry: 33 open problems. Ann Oper Res 232(1):11–40\nSustainable Forest Initiative (2018) SFI 2015-2019 Forest Management Standard. http:\u002F\u002Fwww.sfiprogram.org\u002Fsfi-standards\u002Fguide-to-2015-2019-standards\u002F. Accessed 13 Dec 18\nSynder S, ReVelle C (1996) The grid packing problem: selecting a harvesting pattern in an area with forbidden regions. For Sci 42(1):27–34\nThompson EF, Halterman BG, Lyon TJ, Miller RL (1973) Integrating timber and wildlife management planning. For Chron 49(6):247–250\nTurner BL, Janetos AC, Verburg PH, Murray AT (2013) Land system architecture: using land systems to adapt and mitigate global environmental change. Glob Environ Change 23(2):395–397\nWei R, Murray AT (2012) An integrated approach for addressing geographic uncertainty in spatial optimization. Int J Geogr Inf Sci 26(7):1231–1249\nWei R, Murray AT (2015) Spatial uncertainty in harvest scheduling. Ann Oper Res 232(1):275–289\nWilck JH, Mills SD, McDill ME (2014) Computational comparison of stand-centered versus cover-constraint formulations. J Sustain For 33(1):33–45\nYao J, Zhang X, Murray AT (2018) Spatial optimization for land-use allocation: accounting for sustainability concerns. Int Reg Sci Rev 41(6):579–600\nZeller RE, Achabal DD, Brown LA (1980) Market penetration and locational conflict in franchise systems. 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Am J Epidemiol 127:893–904",{"doi":518},"10.1093\u002Foxfordjournals.aje.a114892",{"id":20,"text":520,"url":20,"identifiers":521},"Rey S (2001) Spatial analysis of regional income inequality. REAL discussion paper 01-T9",{},{"id":20,"text":523,"url":20,"identifiers":524},"Richardson S (1992) Statistical methods for geographical correlation studies. In: Elliot P, Cuzick J, English D, Stern R (eds) Geographical and environmental epidemiology: methods for small area studies. Oxford University Press, New York, pp. 181–204",{},{"id":20,"text":526,"url":20,"identifiers":527},"Richardson S, Stucker L, Hemon D (1987) Comparison of relative risks obtained in ecological and individual studies: some methodological considerations. Int J Epidemiol 16:111–120",{"doi":528},"10.1093\u002Fije\u002F16.1.111",{"id":20,"text":530,"url":20,"identifiers":531},"Robinson WS (1950) Ecological correlations and the behavior of individuals. 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Geogr Anal 27(2):93–115",{"doi":669},{"id":665,"text":683,"url":667,"identifiers":684},"Anselin L, Can A (1986) Model comparison and model validation issues in empirical work on urban density functions. Geogr Anal 18:179–197",{"doi":669},{"id":686,"text":687,"url":688,"identifiers":689},"f359de5e-cc03-445f-b824-1db5d8f6e556","Asabere PK, Owusu-Banahene K (1983) Population density function for Ghanaian (African) cities: an empirical note. J Urban Econ 14(3):370–379","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002F0094119083900165",{"doi":690},"10.1016\u002F0094-1190(83)90016-5",{"id":665,"text":692,"url":667,"identifiers":693},"Batty M, Kwang SK (1992) Form follows function: reformulating urban population density functions. Urban Stud 29(7):1043–1070",{"doi":669},{"id":665,"text":695,"url":667,"identifiers":696},"Baumont C, Ertur C, Le Gallo J (2004) Spatial analysis of employment and population density: the case of the agglomeration of Dijon 1999. Geogr Anal 36:146–176",{"doi":669},{"id":665,"text":698,"url":667,"identifiers":699},"Bussière R, Snickars F (1970) Derivation of the negative exponential model by an entropy maximizing method. Environ Plann A 2:295–301",{"doi":669},{"id":665,"text":701,"url":667,"identifiers":702},"Cameron A, Windmeijer F (1996) R-Squared measures for count data regression models with applications to health care utilization. J Bus Econ Stat 14:209–220",{"doi":669},{"id":665,"text":704,"url":667,"identifiers":705},"Cameron A, Windmeijer F (1997) An R-Squared measure of goodness-of-fit for some common nonlinear-regression models. J Economet 77:329–342",{"doi":669},{"id":665,"text":707,"url":667,"identifiers":708},"Chen H-P (1997) Models of urban population and employment density: the spatial structure of monocentric and polycentric functions in greater taipei and a comparison to Los Angeles. Geogr Environ Modell 1:135–151",{"doi":669},{"id":665,"text":710,"url":667,"identifiers":711},"Clark C (1951) Urban population densities. J R Stat Soc Ser A 114:490–496",{"doi":669},{"id":665,"text":713,"url":667,"identifiers":714},"Crampton GR (1991) Residential density patterns in London—any role left fro the exponential density gradient? Environ Plann A 23:1007–1024",{"doi":669},{"id":665,"text":716,"url":667,"identifiers":717},"Edmonston B, Goldberg MA, Mercer J (1985) Urban form in Canada and the United States: an examination of urban density gradients. Urban Stud 22:209–217",{"doi":669},{"id":665,"text":719,"url":667,"identifiers":720},"Feser E, Sweeney S, Renski H (2005) A descriptive analysis of discrete U.S. industrial complexes. J Reg Sci 45(2):395–419",{"doi":669},{"id":665,"text":722,"url":667,"identifiers":723},"Fonseca JW, Wong DWS (2000) Changing patterns of population density in the United States. Prof Geogr 52(3):504–517",{"doi":669},{"id":665,"text":725,"url":667,"identifiers":726},"Getis A, Ord JK (1992) The analysis of spatial association by use of distance statistics. Geogr Anal 24:189–206",{"doi":669},{"id":665,"text":728,"url":667,"identifiers":729},"Getis A, Anselin L, Lea A, Ferguson M, Miller H (2005) Spatial analysis and modeling in a GIS environment. In: McMaster R, Usery E (eds) A research agenda for geographic information science. CRC Press, Boca Raton, FL, pp 175–196",{"doi":669},{"id":665,"text":731,"url":667,"identifiers":732},"Gordon P, Richardson H, Wong HL (1986) The distribution of population and employment in a polycentric city: the case of Los Angeles. Environ Plann A 18:161–173",{"doi":669},{"id":734,"text":735,"url":736,"identifiers":737},"efbe7b82-26c3-4370-be3b-e8e90c057cb6","Griffith D (1981a) Modelling urban population density in a multi-centered city. J Urban Econ 9:298–310","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002F0094119081900292",{"doi":738},"10.1016\u002F0094-1190(81)90029-2",{"id":20,"text":740,"url":20,"identifiers":741},"Griffith D (1981b) Evaluating the transformation from a monocentric to a polycentric city. Prof Geog 33:189–196",{},{"id":743,"text":744,"url":745,"identifiers":746},"56990563-0c6f-4f0c-81c8-ff2e985dedf8","Griffith D (1999) Statistical and mathematical sources of regional science theory: map pattern analysis as an example. Pap Reg Sci 78:21–45","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fresolve\u002Fdoi?DOI=10.1007\u002Fs101100050010",{"doi":747},"10.1007\u002Fs101100050010",{"id":749,"text":750,"url":751,"identifiers":752},"319e2349-a1d8-41b6-93a4-c128cc926fd4","Griffith D (2004) Extreme eigenfunctions of adjacency matrices for planar graphs employed in spatial analyses. Linear Algebr Appl 204:201–219","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0024379503003689",{"doi":753},"10.1016\u002FS0024-3795(03)00368-9",{"id":665,"text":755,"url":667,"identifiers":756},"Griffith D, Can A (1995) Spatial statistical\u002Feconometric versions of simple urban population density models. In: Arlinghaus SL (ed) Practical handbook of spatial statistics. CRC Press, Boca Raton, FL, pp 231–249",{"doi":669},{"id":665,"text":758,"url":667,"identifiers":759},"Griffith D, Wong DWS, Whitfield T (2003) Exploring relationships between the global and regional measures of spatial autocorrelation. J Reg Sci 43(4):683–710",{"doi":669},{"id":665,"text":761,"url":667,"identifiers":762},"Han S (2005) Polycentric urban development and spatial clustering of condominium property values: Singapore in the 1990s. Environ Plann A 37:463–481",{"doi":669},{"id":665,"text":764,"url":667,"identifiers":765},"Heikkila E, Gordon P, Kim JI, Peiser RB, Richardson HW, Dale-Johnson D (1989) What happened to the CBD-distance gradient? Land values in a policentric city. Environ Plann A 21:221–232",{"doi":669},{"id":665,"text":767,"url":667,"identifiers":768},"Hill F (1973) Spatio-temporal trends in urban population density: a trend surface analysis. In: Bourne L, MacKinnon R, Simmons J (eds) The form of cities in central Canada. University of Toronto Press, ON, pp 103–119",{"doi":669},{"id":665,"text":770,"url":667,"identifiers":771},"Hoch I, Waddell P (1993) Apartment rents: another challenge to the monocentric model. Geogr Anal 25(1):20–34",{"doi":669},{"id":665,"text":773,"url":667,"identifiers":774},"Holzer H (1991) The spatial mismatch hypothesis: what has the evidence shown? Urban Stud 28:105–122",{"doi":669},{"id":665,"text":776,"url":667,"identifiers":777},"Horner MW (2002) Extensions to the concept of excess commuting. Environ Plann A 34(3):543–566",{"doi":669},{"id":665,"text":779,"url":667,"identifiers":780},"Houston D (2005) Methods to test the spatial mismatch hypothesis. Econ Geogr 81(4):407–434",{"doi":669},{"id":665,"text":782,"url":667,"identifiers":783},"Martori J, Suriñach J (2002) Urban population density functions: the case of the Barcelona region. Documents de Recerca, Universitat de Vic, Spain",{"doi":669},{"id":665,"text":785,"url":667,"identifiers":786},"McMillen DP (2003) Identifying sub-centers using contiguity matrices. Urban Stud 40(1):57–69",{"doi":669},{"id":665,"text":788,"url":667,"identifiers":789},"McMillen DP (2004) Employment densities, spatial autocorrelation, and subcenters in large metropolitan areas. J Reg Sci 44(2):225–243",{"doi":669},{"id":665,"text":791,"url":667,"identifiers":792},"Mills ES (1970) Urban density functions. Urban Stud 7:5–20",{"doi":669},{"id":665,"text":794,"url":667,"identifiers":795},"Mills ES, Tan JP (1980) A comparison of urban population density functions in developed and developing countries. Urban Stud 17:313–321",{"doi":669},{"id":665,"text":797,"url":667,"identifiers":798},"Mittlböck M, Schemper M (1999) Computing measures of explained variation for logistic regression models. Comput Methods Programs Biomed 58:17–24",{"doi":669},{"id":20,"text":800,"url":20,"identifiers":801},"Muth R (1969) Cities and housing: the spatial pattern of urban residential land use. University of Chicago Press, Chicago, IL",{},{"id":665,"text":803,"url":667,"identifiers":804},"Paez A, Uchida T, Miyamoto K (2001) Spatial association and heterogeneity issues in land price models. Urban Stud 38(9):1493–1508",{"doi":669},{"id":806,"text":807,"url":808,"identifiers":809},"5ab664fe-59df-4f68-ab6a-98691f31e1f6","Preston V, McLafferty S (1999) Spatial mismatch research in the 1990s: progress and potential. Pap Reg Sci 78:387–402","http:\u002F\u002Fdoi.wiley.com\u002F10.1007\u002Fs101100050033",{"doi":810},"10.1007\u002Fs101100050033",{"id":665,"text":812,"url":667,"identifiers":813},"Small K, Song S (1992) Wasteful commuting: a resolution. J Polit Econ 100:888–898",{"doi":669},{"id":665,"text":815,"url":667,"identifiers":816},"Thurston L, Yezer A (1991) Testing the monocentric urban model: evidence based on wasteful commuting. AREUEA J 19:41–51",{"doi":669},{"id":665,"text":818,"url":667,"identifiers":819},"Zheng X-P (1991) Metropolitan spatial structure and its determinants: a case-study of Tokyo. Urban Stud 28(1):87–104",{"doi":669},{"id":665,"text":821,"url":667,"identifiers":822},"Zielinski K (1979) Experimental analysis of eleven models of urban population density. Environ Plann A 11:629–641",{"doi":669},{"id":824,"createTime":825,"updateTime":826,"relativeEntities":827,"slug":828,"properties":829,"entityType":125,"verifyStatus":126,"verifyTime":838,"verifyNote":128,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":839,"fullTextUrl":20,"authors":840,"publicationType":189,"publisherRelationship":856,"citationCount":21,"citationInfo":912,"publishDate":915,"publishYear":913,"citationAnalyzeStatus":19,"lastCitationAnalyze":916,"indexDatabases":917,"openAccess":20,"references":20,"isForceReanalyzing":251},"538e3eec-3e28-46a0-803a-1efce57ef634","2024-01-19T17:23:31.898+00:00","2026-03-27T02:27:08.907+00:00",[],"Misspecifications-in-interaction-model-distance-decay-relations-A-spatial-structure-effect",{"abstract":830,"title":832,"gsPaper":834,"doi":836},{"EN":831}," An exclusively statistical approach is proposed to address the spatial structure effects of general interaction models. It is shown that the spatial heterogeneity in the estimated region-specific distance decay parameters may in part be due to the combination of two factors: (a) a functional mis-specification of the global distance decay relationship; and (b) the heterogeneity in the region-specific conditional distance distributions. A properly specified global distance decay function allows controlling for these spatially induced biases in the local distance decay parameters. However, inherent multicollinearities between the set of region specific distance decay parameters and other estimated model parameters prevent an unambiguous interpretation. A key conclusion is that a proper model specification, in particular, the specification of the global distance decay relationship, is of paramount importance in interaction modeling and for accessibility studies.",{"EN":833},"Misspecifications in interaction model distance decay relations: A spatial structure effect",{"VOID":835},"[\"8834724720942524124\"]",{"VOID":837},"10.1007\u002Fs101090300102","2024-05-02T04:53:13.303+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs101090300102",[841],{"id":842,"sortIndex":21,"researcher":20,"roles":843,"affiliations":844,"properties":853,"displayName":855,"givenName":20,"familyName":20},"6fad3cd6-a20d-4d3c-b2f3-3f974e7b378a",[134],[845],{"id":846,"sortIndex":21,"affiliation":847,"properties":20},"379b3662-d7e7-4466-a17c-da0934c107b4",{"id":846,"createTime":20,"updateTime":20,"relativeEntities":848,"slug":20,"properties":849,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":852,"statistic":20},[],{"title":850},{"VI":851},"Department of Geography, The Ohio State University, Columbus, Ohio 43210, USA (e-mail: tiefelsdorf.1@osu.edu), , US",[],{"title":854},{"VI":855},"M. 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IPUMS-Terra provides integrated spatiotemporal data to these scholars by simplifying access to thousands of raster and vector datasets, integrating them and providing them in formats that are useable to a broad array of research disciplines. IPUMS-Terra exemplifies a new class of National Spatial Data Infrastructure because it connects a large spatial data repository to advanced computational resources, allowing users to access the needle of information they need from the haystack of big spatial data. The project is trailblazing in its commitment to the open sharing of spatial data and spatial tool development, including describing its architecture, process development workflows, and openly sharing its products for the general use of the scientific community.\n",{"EN":928},"IPUMS-Terra: integrated big heterogeneous spatiotemporal data analysis 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R (2016) Introduction to geospatial information and communication technology (GeoICT). Springer",{},{"id":20,"text":1053,"url":1054,"identifiers":1055},"AgMIP (2017) Retrieved from https:\u002F\u002Fmygeohub.org\u002Fgroups\u002Fgabbs\u002Fproject_page. Accessed 6 Jan 2017","https:\u002F\u002Fmygeohub.org\u002Fgroups\u002Fgabbs\u002Fproject_page",{},{"id":665,"text":1057,"url":667,"identifiers":1058},"Armstrong MP (2000) Geography and computational science. Ann Assoc Am Geogr 90(1):146–156",{"doi":669},{"id":665,"text":1060,"url":667,"identifiers":1061},"Bédard Y, Merrett T, Han J (2001) Fundamentals of spatial data warehousing for geographic knowledge discovery. Geogr Data Min Knowl Discov 2:53–73",{"doi":669},{"id":665,"text":1063,"url":667,"identifiers":1064},"Butenuth M, Gösseln GV, Tiedge M, Heipke C, Lipeck U, Sester M (2007) Integration of heterogeneous geospatial data in a federated database. 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Cadernos de Saúde Pública 17:1153–1162. https:\u002F\u002Fdoi.org\u002F10.1590\u002FS0102-311X2001000500016\nLovelace R, Ellison R (2018) stplanr: a package for transport planning. R J 10(2):7–23. https:\u002F\u002Fdoi.org\u002F10.32614\u002FRJ-2018-053\nLovelace R, Nowosad J, Muenchow J (2019) Geocomputation with R. CRC, Boca Raton\nLovelace R, Ellison R, Morgan M (2020) stplanr: sustainable transport planning. https:\u002F\u002FCRAN.R-project.org\u002Fpackage=stplanr, r package version 0.6.2\nMajure JJ, Gebhardt A (2016) sgeostat: an object-oriented framework for geostatistical modeling in S+. https:\u002F\u002FCRAN.R-project.org\u002Fpackage=sgeostat, R package version 1.0-27\nMeyer D, Dimitriadou E, Hornik K, Weingessel A, Leisch F (2019) e1071: misc functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien. https:\u002F\u002FCRAN.R-project.org\u002Fpackage=e1071, r package version 1.7-3\nNeuwirth E (2014) RColorBrewer: ColorBrewer palettes. https:\u002F\u002FCRAN.R-project.org\u002Fpackage=RColorBrewer, R package version 1.1-2\nNowosad J (2019) ’CARTOColors’ palettes. https:\u002F\u002Fnowosad.github.io\u002Frcartocolor, R package version 2.0.0\nPebesma E (2012) spacetime: spatio-temporal data in R. J Stat Softw 51(7):1–30\nPebesma E (2018) Simple features for R: standardized support for spatial vector data. R J 10(1):439–446\nPebesma E (2020a) sf: simple features for R. https:\u002F\u002FCRAN.R-project.org\u002Fpackage=sf, R package version 0.9-5\nPebesma E (2020b) spacetime: classes and methods for spatio-temporal data. https:\u002F\u002FCRAN.R-project.org\u002Fpackage=spacetime, R package version 1.2-3\nPebesma E (2020c) stars: spatiotemporal arrays, raster and vector data cubes. https:\u002F\u002FCRAN.R-project.org\u002Fpackage=stars, R package version 0.4-3\nPebesma E, Bivand R (2005) Classes and methods for spatial data in R. R News 5(2):9–13\nPebesma E, Bivand R (2020) sp: classes and methods for spatial data. https:\u002F\u002FCRAN.R-project.org\u002Fpackage=sp, R package version 1.4-2\nPebesma E, Dunnington D (2020) In r-spatial, the Earth is no longer flat. https:\u002F\u002Fwww.r-spatial.org\u002Fr\u002F2020\u002F06\u002F17\u002Fs2.html. Accessed 25 Aug 2020\nPebesma EJ, Wesseling CG (1998) Gstat, a program for geostatistical modelling, prediction and simulation. Comput Geosci 24:17–31\nPebesma E, Bivand R, Ribeiro P (2015) Software for spatial statistics. J Stat Softw 63(1):1–8. https:\u002F\u002Fdoi.org\u002F10.18637\u002Fjss.v063.i01\nPebesma E, Mailund T, Hiebert J (2016) Measurement Units in R. R J 8(2):486–494. https:\u002F\u002Fdoi.org\u002F10.32614\u002FRJ-2016-061\nPebesma E, Mailund T, Kalinowski T (2020) units: measurement units for R vectors. https:\u002F\u002FCRAN.R-project.org\u002Fpackage=units, r package version 0.6-7\nRenka RJ, Gebhardt A (2020) tripack: triangulation of irregularly spaced data. https:\u002F\u002FCRAN.R-project.org\u002Fpackage=tripack, R package version 1.3-9.1\nRowlingson B, Diggle PJ (1993) Splancs: spatial point pattern analysis code in S-Plus. Comput Geosci 19:627–655\nRowlingson B, Diggle P (2017) splancs: spatial and space-time point pattern analysis. https:\u002F\u002FCRAN.R-project.org\u002Fpackage=splancs, R package version 2.01-40\nSawicka K, Heuvelink GB, Walvoort DJ (2018) Spatial uncertainty propagation analysis with the spup R package. R J 10(2):180–199. https:\u002F\u002Fdoi.org\u002F10.32614\u002FRJ-2018-047\nTennekes M (2018) tmap: thematic maps in R. J Stat Softw 84(6):1–39\nTennekes M (2020) tmap: thematic maps. https:\u002F\u002FCRAN.R-project.org\u002Fpackage=tmap, R package version 3.1\nVenables WN, Ripley BD (2002) Modern applied statistics with S, 4th edn. Springer, New York\nWarmerdam F (2008) The geospatial data abstraction library. In: Hall GB, Leahy M (eds) Open source approaches in spatial data handling. Springer, Berlin, pp 87–104\nWickham H (2014) Tidy data. J Stat Softw 59(10):1–23. https:\u002F\u002Fdoi.org\u002F10.18637\u002Fjss.v059.i10\nWickham H, Chang W, Henry L, Pedersen TL, Takahashi K, Wilke C, Woo K, Yutani H (2020) ggplot2: create elegant data visualisations using the grammar of graphics. https:\u002F\u002FCRAN.R-project.org\u002Fpackage=ggplot2, R package version 3.3.2",{"VOID":1239},"10.1007\u002Fs10109-020-00336-0","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10109-020-00336-0",[1242],{"id":1243,"sortIndex":21,"researcher":20,"roles":1244,"affiliations":1245,"properties":1254,"displayName":1256,"givenName":20,"familyName":20},"6114946f-9d4b-46dd-af8f-b5aa144df73c",[134],[1246],{"id":1247,"sortIndex":21,"affiliation":1248,"properties":20},"bb97ebcd-ab74-4176-9ee3-3a8d4d5473a7",{"id":1247,"createTime":20,"updateTime":20,"relativeEntities":1249,"slug":20,"properties":1250,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1253,"statistic":20},[],{"title":1251},{"VI":1252},"Department of Economics, Norwegian School of Economics, Bergen, Norway",[],{"title":1255},{"VI":1256},"Roger S. Bivand",{"url":1240,"publisher":1258,"properties":1308},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1259,"slug":10,"properties":1260,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":1264,"manageAffiliations":1277,"indexDatabases":1288,"url":94,"thumbnailPath":20,"statistic":1303,"gsStatistic":20,"type":103,"analyzePriority":20},[],{"issn":1261,"title":1262,"eissn":1263},{"VOID":13},{"EN":15},{"VOID":17},[1265,1269,1273],{"id":24,"createTime":20,"updateTime":20,"relativeEntities":1266,"label":1267,"description":1268,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":27},{},{"id":30,"createTime":20,"updateTime":20,"relativeEntities":1270,"label":1271,"description":1272,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":33},{},{"id":36,"createTime":20,"updateTime":20,"relativeEntities":1274,"label":1275,"description":1276,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":39},{},[1278,1283],{"id":43,"createTime":20,"updateTime":20,"relativeEntities":1279,"slug":20,"properties":1280,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1282,"statistic":20},[],{"title":1281},{"EN":47},[49],{"id":51,"createTime":20,"updateTime":20,"relativeEntities":1284,"slug":20,"properties":1285,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1287,"statistic":20},[],{"title":1286},{"EN":55},[49],[1289,1296],{"id":59,"indexDatabase":1290,"url":70,"indexYears":71,"academicFieldIds":1295,"indexDatabaseRanking":76},{"id":61,"createTime":20,"updateTime":20,"relativeEntities":1291,"label":1292,"description":1293,"key":67,"publicationTags":1294,"standard":20},[],{"EN":64,"VI":64},{"EN":64,"VI":66},[69],[73,74,75],{"id":78,"indexDatabase":1297,"url":91,"indexYears":20,"academicFieldIds":1302,"indexDatabaseRanking":20},{"id":80,"createTime":20,"updateTime":20,"relativeEntities":1298,"label":1299,"description":1300,"key":87,"publicationTags":1301,"standard":20},[],{"EN":83,"VI":83},{"EN":85,"VI":86},[89,90],[93],{"impactFactor":21,"impactFactorByYear":1304,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":97,"totalPublicationByYear":1305,"totalCitation":21,"totalCitationByYear":1306,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":1307,"hindexLast5Year":21,"hindex":21},{},{"2009":99,"2017":100,"2019":100},{},{},{"pages":1309,"volume":1311},{"VOID":1310},"515-546",{"VOID":1312},"23","2020-10-16",2020,[89,76],{"id":1317,"createTime":1318,"updateTime":1319,"relativeEntities":1320,"slug":1321,"properties":1322,"entityType":125,"verifyStatus":126,"verifyTime":1319,"verifyNote":128,"languages":20,"translateLanguages":20,"viewCount":100,"primaryUrl":1329,"fullTextUrl":20,"authors":1330,"publicationType":189,"publisherRelationship":1372,"citationCount":20,"citationInfo":20,"publishDate":1428,"publishYear":1429,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":1430,"openAccess":20,"references":20,"isForceReanalyzing":251},"1a5305de-3d7d-4dfa-b52b-6ef36dcb4e86","2023-12-28T04:49:21.898+00:00","2025-02-26T18:12:08.803+00:00",[],"Web-based-analytical-tools-for-the-exploration-of-spatial-data",{"title":1323,"doi":1325,"abstract":1327},{"EN":1324},"Web-based analytical tools for the exploration of spatial data",{"VOID":1326},"10.1007\u002Fs10109-004-0132-5",{"EN":1328},"This paper deals with the extension of internet-based geographic information systems with functionality for exploratory spatial data analysis (esda). The specific focus is on methods to identify and visualize outliers in maps for rates or proportions. Three sets of methods are included: extreme value maps, smoothed rate maps and the Moran scatterplot. The implementation is carried out by means of a collection of Java classes to extend the Geotools open source mapping software toolkit. The web based spatial analysis tools are illustrated with applications to the study of homicide rates and cancer rates in U.S. counties.","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10109-004-0132-5",[1331,1346,1359],{"id":1332,"sortIndex":21,"researcher":20,"roles":1333,"affiliations":1334,"properties":1343,"displayName":1345,"givenName":20,"familyName":20},"11c7177d-63f7-4966-93ab-a5134b0926a9",[134],[1335],{"id":1336,"sortIndex":21,"affiliation":1337,"properties":20},"5e6ad6cd-dca2-4da1-88d7-60de350c92f2",{"id":1336,"createTime":20,"updateTime":20,"relativeEntities":1338,"slug":20,"properties":1339,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1342,"statistic":20},[],{"title":1340},{"VI":1341},"Spatial Analysis Laboratory, Department of Agricultural and Consumer Economics, USA",[],{"title":1344},{"VI":1345},"Luc 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local measures of spatial association for categorical data",{"VOID":1441},"10.1007\u002Fs10109-003-0110-3",{"EN":1443},"This paper describes a procedure for extending local statistics to categorical spatial data. The approach is based on the notion that there are two fundamental characteristics of categorical spatial data; composition and configuration. Further, it is argued that, when considered locally, the latter should be measured conditionally with respect to the former. These ideas are developed for binary, gridded data. Local composition is measured by counting the numbers of cells of a particular type, while local configuration is measured by join counts. The approach is illustrated using a small, empirical data set and an ad hoc procedure is developed to deal with the impact of global spatial autocorrelation on the local statistics.","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10109-003-0110-3",[1446],{"id":1447,"sortIndex":21,"researcher":20,"roles":1448,"affiliations":1449,"properties":1458,"displayName":1460,"givenName":20,"familyName":20},"a5558704-338c-446c-9561-9d7738cf3a9b",[134],[1450],{"id":1451,"sortIndex":21,"affiliation":1452,"properties":20},"d3f46360-8592-4441-9dfa-f4c2ed62b70e",{"id":1451,"createTime":20,"updateTime":20,"relativeEntities":1453,"slug":20,"properties":1454,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1457,"statistic":20},[],{"title":1455},{"VI":1456},"Department of Geography and Environmental Studies, Wilfrid Laurier University, Ontario, Canada",[],{"title":1459},{"VI":1460},"Barry Boots",{"url":1444,"publisher":1462,"properties":1512},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1463,"slug":10,"properties":1464,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":1468,"manageAffiliations":1481,"indexDatabases":1492,"url":94,"thumbnailPath":20,"statistic":1507,"gsStatistic":20,"type":103,"analyzePriority":20},[],{"issn":1465,"title":1466,"eissn":1467},{"VOID":13},{"EN":15},{"VOID":17},[1469,1473,1477],{"id":24,"createTime":20,"updateTime":20,"relativeEntities":1470,"label":1471,"description":1472,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":27},{},{"id":30,"createTime":20,"updateTime":20,"relativeEntities":1474,"label":1475,"description":1476,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":33},{},{"id":36,"createTime":20,"updateTime":20,"relativeEntities":1478,"label":1479,"description":1480,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":39},{},[1482,1487],{"id":43,"createTime":20,"updateTime":20,"relativeEntities":1483,"slug":20,"properties":1484,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1486,"statistic":20},[],{"title":1485},{"EN":47},[49],{"id":51,"createTime":20,"updateTime":20,"relativeEntities":1488,"slug":20,"properties":1489,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1491,"statistic":20},[],{"title":1490},{"EN":55},[49],[1493,1500],{"id":59,"indexDatabase":1494,"url":70,"indexYears":71,"academicFieldIds":1499,"indexDatabaseRanking":76},{"id":61,"createTime":20,"updateTime":20,"relativeEntities":1495,"label":1496,"description":1497,"key":67,"publicationTags":1498,"standard":20},[],{"EN":64,"VI":64},{"EN":64,"VI":66},[69],[73,74,75],{"id":78,"indexDatabase":1501,"url":91,"indexYears":20,"academicFieldIds":1506,"indexDatabaseRanking":20},{"id":80,"createTime":20,"updateTime":20,"relativeEntities":1502,"label":1503,"description":1504,"key":87,"publicationTags":1505,"standard":20},[],{"EN":83,"VI":83},{"EN":85,"VI":86},[89,90],[93],{"impactFactor":21,"impactFactorByYear":1508,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":97,"totalPublicationByYear":1509,"totalCitation":21,"totalCitationByYear":1510,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":1511,"hindexLast5Year":21,"hindex":21},{},{"2009":99,"2017":100,"2019":100},{},{},{"pages":1513,"volume":1515},{"VOID":1514},"139-160",{"VOID":911},"2003-08-01",[89,76],{"id":1519,"createTime":1520,"updateTime":1521,"relativeEntities":1522,"slug":1523,"properties":1524,"entityType":125,"verifyStatus":126,"verifyTime":1521,"verifyNote":128,"languages":20,"translateLanguages":20,"viewCount":100,"primaryUrl":1533,"fullTextUrl":20,"authors":1534,"publicationType":189,"publisherRelationship":1565,"citationCount":20,"citationInfo":20,"publishDate":1621,"publishYear":1622,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":1623,"openAccess":20,"references":20,"isForceReanalyzing":251},"2bfbffc0-0acd-4604-b512-d0882001f37a","2024-01-12T02:26:55.770+00:00","2025-02-26T15:31:17.825+00:00",[],"The-propagation-effect-of-commuting-to-work-in-the-spatial-transmission-of-COVID-19",{"abstract":1525,"title":1527,"references":1529,"doi":1531},{"EN":1526},"This work is concerned with the spatiotemporal dynamics of the coronavirus disease 2019 (COVID-19) in Germany. Our goal is twofold: first, we propose a novel spatial econometric model of the epidemic spread across NUTS-3 regions to identify the role played by commuting-to-work patterns for spatial disease transmission. Second, we explore if the imposed containment (lockdown) measures during the first pandemic wave in spring 2020 have affected the strength of this transmission channel. Our results from a spatial panel error correction model indicate that, without containment measures in place, commuting-to-work patterns were the first factor to significantly determine the spatial dynamics of daily COVID-19 cases in Germany. This indicates that job commuting, particularly during the initial phase of a pandemic wave, should be regarded and accordingly monitored as a relevant spatial transmission channel of COVID-19 in a system of economically interconnected regions. Our estimation results also provide evidence for the triggering role of local hot spots in disease transmission and point to the effectiveness of containment measures in mitigating the spread of the virus across German regions through reduced job commuting and other forms of mobility.",{"EN":1528},"The propagation effect of commuting to work in the spatial transmission of COVID-19",{"VOID":1530},"Atkeson A (2020) On using SIR models to model disease scenarios for COVID-19. Quart Rev Fed Reserve Bank Minneapolis 41(1):1–35\nBartik A, Cullen Z, Glaeser EL, Luca M, Stanton C (2020) The impact of COVID-19 on small business outcomes and expectations. Proc Natl Acad Sci 177(30):17656–17666\nBeenstock M, Felsenstein D (2010) Spatial error correction and cointegration in nonstationary panel data: regional house prices in Israel. J Geogr Syst 12(2):189–206\nBellégo C, Pape LD (2019) Dealing with the log of zero in regression models. Working Papers 2019–13. Center for Research in Economics and Statistics (CREST), for download at: http:\u002F\u002Fcrest.science\u002FRePEc\u002Fwpstorage\u002F2019-13.pdf. Accessed 02 Aug 2020\nBerlemann M, Haustein E (2020) Right and yet wrong: a spatio-temporal evaluation of Germany's COVID-19 containment policy, CESifo Working Paper Series 8446, CESifo\nBertozzi AL, Franco E, Mohler G, Short MB, Sledge D (2020) The challenges of modeling and forecasting the spread of COVID-19. Proc Natl Acad Sci 117(29):16732–16738\nBlundell R, Griffith R, Windmeijer F (2002) Individual effects and dynamics in count data models. J Econ 108(1):113–131\nChang S, Pierson E, Koh PW, Gerardin J, Redbird B, Grusky D, Leskovec J (2021) Mobility network models of COVID-19 explain inequities and inform reopening. Nature 589:82–87\nCharaudeau S, Pakdaman K, Boëlle PY (2014) Commuter mobility and the spread of infectious diseases: application to influenza in France. PLoS ONE 9(1):e83002\nChinazzi M, Davis JT, Ajelli M, Gioannini C, Litvinova M, Merler S, Pastore y Piontti A, Mu K, Rossi L, Sun K, Viboud C, Xiong X, Yu H, Halloran ME, Longini IM, Vespignani A (2020) The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science 368(6489):395–400\nCromley EK, McLafferty SL (2012) GIS and public health, 2nd edn. Guilford Press, New York\nDesjardins MR, Hohl A, Delmelle EM (2020) Rapid surveillance of COVID-19 in the United States using a prospective space-time scan statistic: detecting and evaluating emerging clusters. Appl Geogr 118(2020):102202\nDuan N (1983) Smearing estimate: a nonparametric retransformation method. J Am Stat Assoc 78(383):605–610\nElhorst JP (2014) Spatial econometrics. From cross-sectional data to spatial panels. SpringerBriefs in Regional Science. 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J Bus Econ Stats 15(4):419–431",{"VOID":1532},"10.1007\u002Fs10109-021-00349-3","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10109-021-00349-3",[1535,1550],{"id":1536,"sortIndex":21,"researcher":20,"roles":1537,"affiliations":1538,"properties":1547,"displayName":1549,"givenName":20,"familyName":20},"c4a5a6da-7105-42d0-9b71-453f6eb2fcf6",[134],[1539],{"id":1540,"sortIndex":21,"affiliation":1541,"properties":20},"3d2fdac2-4605-4f85-ab4f-edb6a726e2ae",{"id":1540,"createTime":20,"updateTime":20,"relativeEntities":1542,"slug":20,"properties":1543,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1546,"statistic":20},[],{"title":1544},{"VI":1545},"University of Southern Denmark, RWI and RCEA, Sønderborg, Denmark",[],{"title":1548},{"VI":1549},"Timo 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It then proceeds to open an investigation focused on child health—in the form of child growth failure, including (i) stunting; (ii) wasting; and (iii) underweight—that addresses the question. The main contribution of the work is to reconcile an array of data, collected across different spatial scales and over different timeframes, in a manner that enables some preliminary insight into the relationships explored. Evidence derived from the analysis suggests that the wave of urbanization breaking across Sub-Saharan Africa is associated with improvements in wellbeing, a finding that is qualified by need for further research.",{"EN":1634},"Urbanization and child growth failure in Sub-Saharan Africa: a geographical analysis",{"VOID":1636},"Angrist JD, Pischke SP (2015) Mastering metrics: the path from cause to effect. Princeton University Press, Princeton\nAnselin L (1988) Spatial econometrics: methods and models. 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