Poverty, inequality, and geographic targeting

"This paper applies small area estimation techniques to Mozambican data to develop high resolution (subdistrictlevel) poverty and inequality maps...The picture that emerges is one of considerable local-level economic heterogeneity, with the poor living alongside the nonpoor. Rather than finding stark pockets of intense poverty traps in one part of the country and a relative absence of poverty in other parts, the situation is much more nuanced. This suggests that targeting antipoverty efforts on purely geographic criteria is almost certain to be inefficient, with leakages to the nonpoor and under-coverage of the significant numbers of poor households in areas that are “less poor.”" From TextInequality ,Geographic targeting ,Small area estimation ,Poverty mapping ,

Estimation of forest variables using airborne laser scanning

Airborne laser scanning can provide three-dimensional measurements of the forest canopy with high efficiency and precision. There are presently a large number of airborne laser scanning instruments in operation. The aims of the studies reported in this thesis were, to develop and validate methods for estimation of forest variables using laser data, and to investigate the influence of laser system parameters on the estimates. All studies were carried out in hemi-boreal forest at a test area in southwestern Sweden (lat. 58°30’N, long. 13°40’ E). Forest variables were estimated using regression models. On plot level, the Root Mean Square Error (RMSE) for mean tree height estimations ranged between 6% and 11% of the average value for different datasets and methods. The RMSE for stem volume estimations ranged between 19% and 26% of the average value for different datasets and methods. On stand level (area 0.64 ha), the RMSE was 3% and 11% of the average value for mean tree height and stem volume estimations, respectively. A simulation model was used to investigate the effect of different scanning angles on laser measurement of tree height and canopy closure. The effect of different scanning angles was different within different simulated forest types, e.g., different tree species. High resolution laser data were used for detection of individual trees. In total, 71% of the field measurements were detected representing 91% of the total stem volume. Height and crown diameter of the detected trees could be estimated with a RMSE of 0.63 m and 0.61 m, respectively. The magnitude of the height estimation errors was similar to what is usually achieved using field inventory. Using different laser footprint diameters (0.26 to 3.68 m) gave similar estimation accuracies. The tree species Norway spruce (Picea abies L. Karst.) and Scots pine (Pinus sylvestris L.) were discriminated at individual tree level with an accuracy of 95%. The results in this thesis show that airborne laser scanners are useful as forest inventory tools. Forest variables can be estimated on tree level, plot level and stand level with similar accuracies as traditional field inventories

Performance and resource modeling for FPGAs using high-level synthesis tools

High-performance computing with FPGAs is gaining momentum with the advent of sophisticated High-Level Synthesis (HLS) tools. The performance of a design is impacted by the input-output bandwidth, the code optimizations and the resource consumption, making the performance estimation a challenge. This paper proposes a performance model which extends the roofline model to take into account the resource consumption and the parameters used in the HLS tools. A strategy is developed which maximizes the performance and the resource utilization within the area of the FPGA. The model is used to optimize the design exploration of a class of window-based image processing application

Estimation and dynamics of above ground biomass with very high resolution satellite images in Pinus pinaster stands

Biomass estimation is a tool for evaluating stands and forest dynamics. Traditional indirect methods use forest inventories and allometric functions at tree level to evaluate biomass at plot level, and an extrapolation method to assess an area. The goal of this study was the development of allometric functions for Pinus pinaster with crown horizontal projection derived from very high spatial resolution satellite images as an independent variable, as well as their application to the analysis of above ground biomass dynamics. The fitted functions show a good performance. The function used to estimate the above ground biomass per grid in 2004, 2007 and 2011 for the study area enable the evaluation of their temporal dynamics. From 2004 to 2007 it decreased in 90.5% of the study area, due to forest fires and cuts to control the pinewood nematode; from 2007 to 2011 increased in 45.6% and decreased in 51.6%, the latter corresponding to cuts to control the aforementioned disease. In 76.4% of the burnt areas, natural regeneration resulted in an increase of above ground biomass. The method's main advantages are the simultaneous evaluation of small or large areas and, when implemented in a GIS, it allows straightforward monitoring over a short period of time

SMALL AREA ESTIMATION OF LITERACY RATES ON SUB-DISTRICT LEVEL IN DISTRICT OF DONGGALA WITH HIERARCHICAL BAYES METHOD

Literacy Rate (LR) is defined as percentage of population aged over 15 with ability to read and write. LR, as one of people welfare indicators, is a measurement of educational development. The indicator, as a measurement of government performance on education, can be measured if all variables related is available. Statistics Indonesia (BPS) each year calculated LR based on National Socio-Economic Survey (SUSENAS) with estimation available only on provincial level and district level. Along with establishment of autonomous regional policy, where regional government had greater power to manage its own region, availability of LR on lower levels to monitor educational development is necessary. Due to sampling design of SUSENAS, accommodated only estimation on district level, will give high variance if used to estimate on lower sub-district level, although still unbiased. Modelling LR was done with Logit-Normal approach, because LR data followed Binomial Distribution. Good estimators from inadequate sample size can be obtained with method of Small Area Estimation (SAE). Hierarchical Bayes (HB) method is one of SAE methods which are proven to give good estimate on binomial distributed data as LR. Estimation on sub-district level in District of Donggala with HB method gave better result compared to the direct estimation with lower Mean Square Error (MSE).Key words : Small Area Estimation, Literacy Rate, Hierarchical Bayes, Logit-Normal Mode

Money and Risk Aversion in a DSGE Framework: A Bayesian Application to the Euro Zone

In this paper, we set up and test a model of the Euro zone, with a special emphasis on the role of money. The model follows the New Keynesian DSGE framework, money being introduced in the utility function with a non-separability assumption. By using bayesian estimation techniques, we shed light on the determinants of output and inflation, but also of the interest rate, real money balances, flexible-price output and flexible-price real money balances variances. The role of money is investigated further. We find that its impact on output depends on the degree of agents’ risk aversion, increases with this degree, and becomes significant when risk aversion is high enough. The direct impact of the money variable on inflation variability is essentially minor whatever the risk aversion level, the interest rate (monetary policy) being the overwhelming explanatory factor.Bayesian Estimation; DSGE Model; Euro Area; Money

Small-Area Population Estimation: an Integration of Demographic and Geographic Techniques

Knowledge of detailed and accurate population information is essential to analyze and address a wide variety of socio-economic, political, and environmental issues and to support necessary planning practices for both public agencies and the private sector. However, such important data are generally only available once every decade through the National Census. Moreover, populations in some rapidly-developing areas may increase quickly, such that this ten-year frequency does not meet the needs of these areas. Therefore, a cost-effective method for population estimation is necessary. To address this issue, this research integrated geographic, sociological, and demographic theories and exploited remotely sensed imagery and geographic information system (GIS) datasets to derive better population estimates at the census block level, the finest level of the national census. Specifically, three new approaches have been proposed in this dissertation to assist in the improvement of small-area population estimation accuracy. First, existing remotely sensed and GIS data have been adopted to estimate two major components of a demographic framework, including the redistribution of newly built dwelling units from the aggregated geographic level to the census block level and the estimation of persons per household (PPH) at such a fine scale. Second, in addition to the use of existing data, new urban environmental indicators were also extracted and employed to improve population estimation. In particular, to implement the automatic enumeration for individual housing units, a new spectral index, biophysical composition index (BCI), has been proposed to derive impervious surface information, a desirable urban environmental parameter. Third, using the extracted high-resolution urban environmental information and GIS data, a new bottom-up method was developed for small-area population estimation at the census block level by incorporating these high-resolution data into the demographic framework. Analyses of the results suggest three major conclusions. First, existing GIS spatial factors, together with demographic information, can assist in improving the accuracy of small-area population estimation. Second, the BCI has a closer relationship with impervious surface area than do other popular indices. Moreover, it was shown to be the most effective index of the four evaluated for separating impervious surfaces and bare soil, which consequently might assist in more accurately deriving fractional land cover values. Third, the use of the new environmental indicators extracted from remote sensing imagery and GIS data and the integration of demographic and geographic approaches has significantly improved the estimation accuracy of housing unit (HU) numbers, PPH, and population counts at the census block level. Therefore, this research contributes to both the remote sensing and applied demography fields. The contribution to the remote sensing field lies in the development of a novel spectral index to characterize urban land for monitoring and analyzing urban environments. This index provided more significant separability between impervious surfaces and bare soil than did other existing indices. Moreover, three major contributions have been made in the field of applied demography: 1) the generation of accurate HU estimates using high-resolution remote sensing and GIS datasets, 2) the development of a model to derive an accurate PPH estimate, and 3) the improvement of small-area population estimation accuracy through the integration of geographic and demographic approaches

Industrial Location At the Intra-Metropolitan Level: A Negative Binomial Approach

The objective of this paper is to analyse the incidence of agglomeration economies on the new firms’ location decisions inside metropolitan areas. Following the literature we consider that agglomeration economies are related to the concentration of an industry (location economies) and/or the size of the city itself (urbanisation economies). We assume that those economies differ according the technological level of firms. So we use a sample of new firms belonging to high, intermediate and low technology levels. Our results confirm those sectoral differences and show some interesting location patterns of manufacturing firms Taking into account the renovated debate about the importance of the geography and distance in the location of economic activity, we introduce in the estimation the effect of the central city size as determinant for the location of new firms in the rest of the metropolitan area. This allows us to analyse if a suburbanisation effect exists and if that effect is the same depending on the industry and the central city size of the metropolitan area. Our main statistical source is the REI (Spanish Industrial Establishments Register), which has plant-level microdata for the creation and location of new industrial firms.

Automatic map-based FTTx access network design

Several mature and standardized optical access network technologies are available for network operators providing broadband services, being now in deployment phase; therefore cost estimation, business analysis, efficient deployment strategies, network and topology design issues for FTTx access networks play an increasingly important role regarding profitability and market success. In a competitive environment, techno-economic evaluation supports the optimal choice among available technologies. Even the tradeoff between future proof technical superiority and short term investment minimization requires a farseeing decision. In our point of view, cost estimation and techno-economic evaluation is strongly related to strategic network design: among others the uneven population density, irregular street system or infrastructure have significant impact on the network topology, thus the deployment costs as well. In order to deal with these aspects, a high-level, strategic network design is necessary that adapts to geospatial characteristics of the services area, providing accurate and detailed network information for the techno-economic evaluation [1]. We have developed a topology designer methodology that supprts the above requirements, providing (near) optimal topology of the fully or partially optical access network, based on the geospatial information about the service area: digital maps, existing infrastructure and subscriber database. Automatic topology design for large-scale service areas, with 10.000s of subsribers is a highly complex mathematical problem. The tough algorithms for a near optimal, yet efficient solution. The developed algorithms were evaluated regarding their speed and accuracy. Based on topology design results, a detailed and flexible techno-economic comparison is carried out, since the framework handles various broadband access network technologies, as presented in a case study. --Topology design,Strategic Design,Network planning,GIS,Map,Techno-economic,Cost estimation