000			01755nam a22002297a 4500
003			ZW-GwMSU
005			20221125154753.0
008			221125b |||||||| |||| 00| 0 eng d
040			_aMSU _cMSU _erda
100			_aWei , Ran _eauthor
245			_aAn integrated approach for addressing geographic uncertainty in spatial optimization _ccreated by Ran Wei &Alan T. Murray
264			_bTaylor and Francis _c2012
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
440			_vVolume , number ,
520			_aThere exist many facets of error and uncertainty in digital spatial information. As error or uncertainty will not likely ever be completely eliminated, a better understanding of its impacts is necessary. Spatial analytical approaches, in particular, must somehow address data-quality issues. This can range from evaluating impacts of potential data uncertainty in planning processes that make use of methods to devising methods that explicitly account for error/uncertainty. To date, little has been done to structure methods accounting for error. This article develops an integrated approach to address data uncertainty in spatial optimization. We demonstrate that it is possible to characterize uncertainty impacts by constructing and solving a new multi-objective model that explicitly incorporates facets of data uncertainty. Empirical findings indicate that the proposed approaches can be applied to evaluate the impacts of data uncertainty with statistical confidence, which moves beyond popular practices of simulating errors in data.
650			_aspatial uncertainty
650			_aspatial optimization
650			_aanti-covering location
942			_2lcc _cJA
999			_c160610 _d160610