Fusing remote sensing with sparse demographic data for synthetic population generation: an algorithm and application to rural Afghanistan

We develop a new algorithm for population synthesis that fuses remote-sensing data with partial and sparse demographic surveys. The algorithm addresses non-binding constraints and complex sampling designs by translating population synthesis into a computationally efficient procedure for constrained network growth. As a case, we synthesize the rural population of Afghanistan, validate the algorithm with in-sample and out-of-sample tests, examine the variability of algorithm outputs over k-nearest neighbor manifolds, and show the responsiveness of our algorithm to additional data as a constraint on marginal population counts.

[1]  . Crime,et al.  Statistical annex to the note “the opium situation in Afghanistan” as of 29 August 2005 : draft / United Nations Office on Drugs and Crime. , 2005 .

[2]  K. Miyamoto.  AN AGENT BASED ESTIMATION METHOD OF HOUSEHOLD MICRO-DATA INCLUDING HOUSING INFORMATION FOR THE BASE YEAR IN LAND-USE MICROSIMULATION , 2010 .

[3]  Ta Theo Arentze,et al.  Creating Synthetic Household Populations , 2007 .

[4]  James Moffat,et al.  The Hybrid War Model: a complex adaptive model of complex urban conflict , 2011, J. Simulation.

[5]  Maciej M. Latek,et al.  Persistence in the Political Economy of Conflict: The Case of the Afghan Drug Industry , 2010, AAAI Fall Symposium: Complex Adaptive Systems.

[6]  Bruno Simeone,et al.  ON THE ITERATIVE PROPORTIONAL FITTING PROCEDURE : STRUCTURE OF ACCUMULATION POINTS AND L 1-ERROR ANALYSIS , 2009 .

[7]  P. Kanaroglou,et al.  Population Synthesis: Comparing the Major Techniques Using a Small, Complete Population of Firms , 2009 .

[8]  K. Moene,et al.  Opium for the Masses? Conflict-Induced Narcotics Production in Afghanistan , 2009, Review of Economics and Statistics.

[9]  P H Rees,et al.  The Estimation of Population Microdata by Using Data from Small Area Statistics and Samples of Anonymised Records , 1998, Environment & planning A.

[10]  Chandra R. Bhat,et al.  Population Synthesis for Microsimulating Travel Behavior , 2007 .

[11]  Alison J. Heppenstall,et al.  Using Hybrid Agent-Based Systems to Model Spatially-Influenced Retail Markets , 2006, J. Artif. Soc. Soc. Simul..

[12]  János Abonyi,et al.  Topology Representing Network Map - A New Tool for Visualization of High-Dimensional Data , 2008, Trans. Comput. Sci..

[13]  William Rand,et al.  Spatial process and data models: Toward integration of agent-based models and GIS , 2005, J. Geogr. Syst..

[14]  P. Dorosh,et al.  Wheat markets, food aid and food security in Afghanistan , 2007 .

[15]  David R. Pritchard,et al.  Advances in Agent Population Synthesis and Application in an Integrated Land Use and Transportation Model , 2009 .

[16]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[17]  Joshua Auld,et al.  Efficient Methodology for Generating Synthetic Populations with Multiple Control Levels , 2010 .

[18]  Joan L. Walker.  Making Household Microsimulation of Travel and Activities Accessible to Planners , 2005 .

[19]  D. Macdonald.  Drugs in Afghanistan: Opium, Outlaws and Scorpion Tales , 2007 .

[20]  Jeffrey Clemens,et al.  Opium in Afghanistan: Prospects for the Success of Source Country Drug Control Policies , 2008, The Journal of Law and Economics.

[21]  Graham Clarke,et al.  Improving the Synthetic Data Generation Process in Spatial Microsimulation Models , 2009 .

[22]  M. Piantanakulchai,et al.  Estimating Synthetic Baseline Population Distribution when Only Partial Marginal Information is Available , 2009 .

[23]  P. Waddell,et al.  Methodology to Match Distributions of Both Household and Person Attributes in Generation of Synthetic Populations , 2009 .

[24]  Michael Batty,et al.  Ucl Centre for Advanced Spatial Analysis Working Papers Series Key Challenges in Agent-based Modelling for Geo-spatial Simulation Paper 121 -sept 07 Key Challenges in Agent-based Modelling for Geo-spatial Simulation , 2022 .

[25]  W. K. Hastings,et al.  Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .

[26]  François Bousquet,et al.  Suitability of Multi-Agent Simulations to study irrigated system viability: application to case studies in the Senegal River Valley , 2004 .

[27]  Eric Bonabeau,et al.  Agent-based modeling: Methods and techniques for simulating human systems , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[28]  M. D. McKay,et al.  Creating synthetic baseline populations , 1996 .

[29]  Maciej M. Latek,et al.  How Corruption Blunts Counternarcotic Policies in Afghanistan: A Multiagent Investigation , 2011, SBP.

[30]  PRICES AND MARKET INTERACTIONS IN THE OPIUM ECONOMY , 2006 .

[31]  Mark Tranmer,et al.  Combining Sample and Census Data in Small Area Estimates: Iterative Proportional Fitting with Standard Software , 2005 .

[32]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[33]  Uskali Mäki,et al.  Fact and Fiction in Economics: Models, Realism and Social Construction , 2003 .

[34]  Zengyi Huang.  A COMPARISON OF SYNTHETIC RECONSTRUCTION AND COMBINATORIAL OPTIMISATION APPROACHES TO THE CREATION OF SMALL-AREA MICRODATA , 2002 .

[35]  Graham Clarke,et al.  The geography of smoking in Leeds: estimating individual smoking rates and the implications for the location of stop smoking services , 2008 .

[36]  Laxminarayana Ganapathi,et al.  Synthesized Population Databases: A US Geospatial Database for Agent-Based Models. , 2009, Methods report.

[37]  Robert L. Axtell,et al.  WHY AGENTS? ON THE VARIED MOTIVATIONS FOR AGENT COMPUTING IN THE SOCIAL SCIENCES , 2000 .

[38]  David Voas,et al.  Evaluating Goodness-of-Fit Measures for Synthetic Microdata , 2001 .

[39]  S Openshaw,et al.  Algorithms for Reengineering 1991 Census Geography , 1995, Environment & planning A.

[40]  Sandra Gottschalk.  Microdata disclosure by resampling – Empirical findings for business survey data , 2004 .

[41]  Mark Blaug,et al.  Fact and Fiction in Economics: Ugly currents in modern economics , 2002 .

[42]  Graham Clarke,et al.  A spatial micro-simulation analysis of methane emissions from Irish agriculture , 2009 .

[43]  Olufemi A. Omitaomu,et al.  Construction of Synthetic Populations with Key Attributes: Simulation Set-Up While Accommodating Multiple Approaches within a Flexible Simulation Platform , 2010, 2010 IEEE Second International Conference on Social Computing.

[44]  Kenneth Katzman.  Afghanistan: Politics, Elections, and Government Performance , 2010 .

[45]  Ann Harding,et al.  Assessing Poverty and Inequality at a Detailed Regional Level: New Advances in Spatial Microsimulation , 2004 .

[46]  S. Riley.  Coping without farm location data during a foot-and-mouth outbreak , 2010, Proceedings of the National Academy of Sciences.

[47]  Paul Williamson,et al.  An evaluation of the combinatorial optimisation approach to the creation of synthetic microdata , 2000 .

[48]  J. E. Dobson,et al.  LandScan: A Global Population Database for Estimating Populations at Risk , 2000 .

[49]  W. Byrd,et al.  Afghanistan's drug industry : structure, functioning, dynamics, and implications for counter-narcotics policy , 2006 .

[50]  Nicolas Becu,et al.  Participatory computer simulation to support collective decision-making: Potential and limits of stakeholder involvement , 2008 .

[51]  Afghan Livelihoods.  Afghanistan Research and Evaluation Unit , 2004 .