Development of an Adaptive Filter for Estimating Spatially  and Temporally Varying Parameters of Multivariate Models
{'Code': '04040400', 'Directorate': {'Abbreviation': 'SBE', 'LongName': 'Direct For Social, Behav & Economic Scie'}, 'Division': {'Abbreviation': 'BCS', 'LongName': 'Division Of Behavioral and Cognitive Sci'}}
{'SignBlockName': 'Bonney Sheahan', 'PO_EMAI': None, 'PO_PHON': None}
Spatial adaptive filters are techniques for mathematically              modelling changes in causal relationships among geographical variables          through time.  They modify original causal relationships by using               negative feedback, applied to subsequent of data observations.                  This project will further develop the modelling technique and its               applications to temporal data.  The case study used will be changes in          the locations of physicians among the 48 continguous states between             1963 and 1985.                                                                          Spatial adaptive filtering techniques offer the promise of              greatly improving our ability to analyze complex geographically                 distributed systems through time.  Robust techniques of this kind               would improve the effectiveness of geographic information systems.
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{'Name': 'Carnegie-Mellon University', 'CityName': 'PITTSBURGH', 'ZipCode': '152133815', 'PhoneNumber': '4122688746', 'StreetAddress': '5000 FORBES AVE', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Pennsylvania', 'StateCode': 'PA', 'CONGRESSDISTRICT': '18', 'CONGRESS_DISTRICT_ORG': 'PA18', 'ORG_UEI_NUM': 'U3NKNFLNQ613', 'ORG_LGL_BUS_NAME': 'CARNEGIE MELLON UNIVERSITY', 'ORG_PRNT_UEI_NUM': 'U3NKNFLNQ613'}
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{'Code': '1352', 'Text': 'Geography and Spatial Sciences'}
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