scholarly journals Spatial Disaggregation of Social Indicators: An Info-Metrics Approach

2020 ◽  
Vol 152 (2) ◽  
pp. 809-821
Author(s):  
Esteban Fernandez-Vazquez ◽  
Alberto Diaz Dapena ◽  
Fernando Rubiera-Morollon ◽  
Ana Viñuela
Keyword(s):  
Spatial Scale ◽  
Maximum Entropy ◽  
Numerical Experiments ◽  
Social Indicators ◽  
Small Scale ◽  
Practical Implementation ◽  
Sample Surveys ◽  
Small Areas ◽  
Generalized Maximum Entropy ◽  
Spatial Disaggregation

Abstract In this paper we propose a methodology to obtain social indicators at a detailed spatial scale by combining the information contained in census and sample surveys. Similarly to previous proposals, the method proposed here estimates a model at the sample level to later project it to the census scale. The main novelties of the technique presented are that (i) the small-scale mapping produced is perfectly consistent with the aggregates -regional or national- observed in the sample, and (ii) it does not require imposing strong distributional assumptions. The methodology suggested here follows the basics presented on Golan (2018) by adapting a cross-moment constrained Generalized Maximum Entropy (GME) estimator to the spatial disaggregation problem. This procedure is compared with the equivalent methodology of Tarozzi and Deaton (2009) by means of numerical experiments, providing a comparatively better performance. Additionally, the practical implementation of the methodology proposed is illustrated by estimating poverty rates for small areas for the region of Andalusia (Spain).

scholarly journals Maximum Entropy Analysis of Flow Networks: Theoretical Foundation and Applications

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 776 ◽  
Author(s):  
Robert K. Niven ◽  
Markus Abel ◽  
Michael Schlegel ◽  
Steven H. Waldrip
Keyword(s):  
Maximum Entropy ◽  
Joint Probability ◽  
Financial Economic ◽  
Flow Networks ◽  
Flow Network ◽  
Physical Constraints ◽  
Generalized Maximum Entropy ◽  
Deterministic Solution ◽  
Network Properties ◽  
The Cost

The concept of a “flow network”—a set of nodes and links which carries one or more flows—unites many different disciplines, including pipe flow, fluid flow, electrical, chemical reaction, ecological, epidemiological, neurological, communications, transportation, financial, economic and human social networks. This Feature Paper presents a generalized maximum entropy framework to infer the state of a flow network, including its flow rates and other properties, in probabilistic form. In this method, the network uncertainty is represented by a joint probability function over its unknowns, subject to all that is known. This gives a relative entropy function which is maximized, subject to the constraints, to determine the most probable or most representative state of the network. The constraints can include “observable” constraints on various parameters, “physical” constraints such as conservation laws and frictional properties, and “graphical” constraints arising from uncertainty in the network structure itself. Since the method is probabilistic, it enables the prediction of network properties when there is insufficient information to obtain a deterministic solution. The derived framework can incorporate nonlinear constraints or nonlinear interdependencies between variables, at the cost of requiring numerical solution. The theoretical foundations of the method are first presented, followed by its application to a variety of flow networks.

scholarly journals Development and comparative evaluation of a stochastic analog method to downscale daily GCM precipitation

2013 ◽  
Vol 17 (11) ◽  
pp. 4481-4502 ◽  
Author(s):  
S. Hwang ◽  
W. D. Graham
Keyword(s):  
Bias Correction ◽  
Daily Precipitation ◽  
Transition Probabilities ◽  
Small Scale ◽  
Analog Method ◽  
Dry Spell ◽  
Spatial Disaggregation ◽  
Dry Seasons ◽  
Stochastic Analog ◽  
Wet And Dry Seasons

Abstract. There are a number of statistical techniques that downscale coarse climate information from general circulation models (GCMs). However, many of them do not reproduce the small-scale spatial variability of precipitation exhibited by the observed meteorological data, which is an important factor for predicting hydrologic response to climatic forcing. In this study a new downscaling technique (Bias-Correction and Stochastic Analog method; BCSA) was developed to produce stochastic realizations of bias-corrected daily GCM precipitation fields that preserve both the spatial autocorrelation structure of observed daily precipitation sequences and the observed temporal frequency distribution of daily rainfall over space. We used the BCSA method to downscale 4 different daily GCM precipitation predictions from 1961 to 1999 over the state of Florida, and compared the skill of the method to results obtained with the commonly used bias-correction and spatial disaggregation (BCSD) approach, a modified version of BCSD which reverses the order of spatial disaggregation and bias-correction (SDBC), and the bias-correction and constructed analog (BCCA) method. Spatial and temporal statistics, transition probabilities, wet/dry spell lengths, spatial correlation indices, and variograms for wet (June through September) and dry (October through May) seasons were calculated for each method. Results showed that (1) BCCA underestimated mean daily precipitation for both wet and dry seasons while the BCSD, SDBC and BCSA methods accurately reproduced these characteristics, (2) the BCSD and BCCA methods underestimated temporal variability of daily precipitation and thus did not reproduce daily precipitation standard deviations, transition probabilities or wet/dry spell lengths as well as the SDBC and BCSA methods, and (3) the BCSD, BCCA and SDBC methods underestimated spatial variability in daily precipitation resulting in underprediction of spatial variance and overprediction of spatial correlation, whereas the new stochastic technique (BCSA) replicated observed spatial statistics for both the wet and dry seasons. This study underscores the need to carefully select a downscaling method that reproduces all precipitation characteristics important for the hydrologic system under consideration if local hydrologic impacts of climate variability and change are going to be reasonably predicted. For low-relief, rainfall-dominated watersheds, where reproducing small-scale spatiotemporal precipitation variability is important, the BCSA method is recommended for use over the BCSD, BCCA, or SDBC methods.

scholarly journals Effect of Actual and Perceived Violence on Internal Migration: Evidence from Mexico’s Drug War

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Amilcar Orlian Fernandez-Dominguez
Keyword(s):  
Maximum Entropy ◽  
Gravity Model ◽  
Internal Migration ◽  
Interstate Migration ◽  
Limited Evidence ◽  
Drug War ◽  
Homicide Rates ◽  
Oaxaca Decomposition ◽  
Generalized Maximum Entropy ◽  
Mexican Drug War

AbstractAccording to the Organisation for Economic Co-operation and Development (OECD), violence should be considered by examining both actual and perceived crime. However, the studies related to violence and internal migration under the Mexican drug war episode focus only on one aspect of violence (perception or actual), so their conclusions rely mostly on limited evidence. This article complements previous work by examining the effects of both perceived and actual violence on interstate migration through estimation of a gravity model along three 5-year periods spanning from 2000 to 2015. Using the methods of generalized maximum entropy (to account for endogeneity) and the Blinder–Oaxaca decomposition, the results show that actual violence (measured by homicide rates) does affect migration, but perceived violence explains a greater proportion of higher average migration after 2005. Since this proportion increased after 2010 and actual violence, the results suggest that there was some adaptation to the new levels of violence in the period 2010–2015.

Large-Scale Evaluation of Constrained Bearing Elements Made of Thermosetting Polyester Resin and Polyester Fabric Reinforcement

2006 ◽  
Vol 128 (4) ◽  
pp. 681-696 ◽  
Author(s):  
P. Samyn ◽  
W. Van Paepegem ◽  
J. S. Leendertz ◽  
A. Gerber ◽  
L. Van Schepdael ◽  
...  
Keyword(s):  
Plastic Deformation ◽  
Large Scale ◽  
Elastic Recovery ◽  
Small Scale ◽  
Practical Implementation ◽  
Fiber Failure ◽  
Stick Slip ◽  
Composite Surface ◽  
Pull Out ◽  
Stable Sliding

Polymer composites are increasingly used as sliding materials for high-loaded bearings, however, their tribological characteristics are most commonly determined from small-scale laboratory tests. The static strength and dynamic coefficients of friction for polyester/polyester composite elements are presently studied on large-scale test equipment for determination of its bearing capacity and failure mechanisms under overload conditions. Original test samples have a diameter of 250 mm and thickness of 40 mm, corresponding to the practical implementation in the sliding surfaces of a ball-joint, and are tested at various scales for simulation of edge effects and repeatability of test results. Static tests reveal complete elastic recovery after loading to 120 MPa, plastic deformation after loading at 150 MPa and overload at 200 MPa. This makes present composite favorable for use under high loads, compared to, e.g., glass-fibre reinforced materials. Sliding tests indicate stick-slip for pure bulk composites and more stable sliding when PTFE lubricants are added. Dynamic overload occurs above 120 MPa due to an expansion of the nonconstrained top surface. A molybdenum-disulphide coating on the steel counterface is an effective lubricant for lower dynamic friction, as it favorably impregnates the composite sliding surface, while it is not effective at high loads as the coating is removed after sliding and high initial static friction is observed. Also a zinc phosphate thermoplastic coating cannot be applied to the counterface as it adheres strongly to the composite surface with consequently high initial friction and coating wear. Most stable sliding is observed against steel counterfaces, with progressive formation of a lubricating transfer film at higher loads due to exposure of PTFE lubricant. Composite wear mechanisms are mainly governed by thermal degradation of the thermosetting matrix (max. 162°C) with shear and particle detachment by the brittle nature of polyester rather than plastic deformation. The formation of a sliding film protects against fiber failure up to 150 MPa, while overload results in interlaminar shear, debonding, and ductile fiber pull-out.

Maximum Entropy Method for Optimal Linkage Function Generation

1996 ◽  
Author(s):  
Xinsheng Hu ◽  
Ji Zhou ◽  
Jun Yu ◽  
Baochang Shi ◽  
Zhijian Zong
Keyword(s):  
Maximum Entropy ◽  
Numerical Experiments ◽  
Maximum Entropy Method ◽  
Entropy Method ◽  
Function Generation ◽  
Standard Software ◽  
Linkage Synthesis

Abstract A new algorithm for solving optimal linkage function generation is proposed. The algorithm is simple in form, easily used and has much reliability and accuracy than any other algorithms reported on linkage synthesis. The existing standard software of unconstrained differential optimization can directly be used in it. Numerical experiments indicate the effectiveness of the new algorithm.

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