Most-Probable-State Analysis: A Macro Statistical Approach  to Testing Probabilistic Theories of Spatial Behavior
{'Code': '04040400', 'Directorate': {'LongName': 'Direct For Social, Behav & Economic Scie'}, 'Division': {'LongName': 'Division Of Behavioral and Cognitive Sci'}}
{'SignBlockName': 'Bonney Sheahan'}
Most-probable-state (MPS) analysis is a technique for estimating        general characteristics of large populations divided into regional and          local component groups.  MPS is a means of analyzing and forecasting            movements among regional and local units and therefore of making regional       population forecasts.  Professor Smith has been conducting research on          developing and refining MPS analysis with Foundation support.  This award       will support research on further extensions of the technique and on             elaboration of its applications.                                                        MPS offers the potential for unifying the theoretical basis for         modelling spatial interactions among regions and places.  The applications      of such a unification would result in improved techniques for measuring         and analyzing spatial interactions of all kinds, but especially those           that bear upon regional changes in population and employment.
{'FirstName': 'Tony', 'LastName': 'Smith', 'EmailAddress': 'tesmith@seas.upenn.edu', 'StartDate': '08/01/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'University of Pennsylvania', 'CityName': 'Philadelphia', 'ZipCode': '191046205', 'PhoneNumber': '2158987293', 'StreetAddress': 'Research Services', 'CountryName': 'United States', 'StateName': 'Pennsylvania', 'StateCode': 'PA'}
{'Code': '1352', 'Text': 'GEOGRAPHY AND SPATIAL SCIENCES'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1987&All=true', 'xml': '8709312.xml'}